A test equipment quantity value traceability prediction method

Abstract

The application provides a test equipment value traceability prediction method. In the method, attribute data of a target test equipment is collected in real time, and an equipment working condition perception space representing a space-time running track of the target test equipment is constructed based on the attribute data; a time-domain state deduction track of the target test equipment is obtained by performing multi-level state recursive deduction of the equipment working condition in the equipment working condition perception space, and an objectified running degradation feature representing a task load drift state of a value of the target test equipment is generated accordingly; an evolution gradient of the objectified running degradation feature is counted to obtain a value offset evolution trend distribution, a periodic extrapolation operation of a metrological accuracy degradation risk is performed based on the value offset evolution trend distribution to generate an adaptive traceability time node sequence of the target test equipment; a metrological period arrangement of the value traceability is determined based on the adaptive traceability time node sequence, and a preset traceability task plan of the target test equipment is adjusted based on the metrological period arrangement.

Description

Technical Field

[0001] This application relates to the field of test equipment management technology, and more specifically, to a method for predicting the traceability of test equipment measurements. Background Technology

[0002] In a comprehensive test data management system, test equipment is the core component ensuring data accuracy. Because equipment consumes energy and experiences physical wear during test operations, its metrological accuracy dynamically drifts with the operating load. Existing comprehensive test data management systems primarily maintain the calibration status of equipment and record calibration times and verification results through a "metrology cycle management" module.

[0003] Existing management solutions typically employ a plan model based on fixed validity periods. This approach first involves entering the equipment's metering validity period and maintenance cycle into the system; then, the system automatically generates reminders based on preset time nodes; finally, managers assign measurement traceability tasks based on these reminders.

[0004] However, this fixed-cycle-based traceability scheme has significant technical drawbacks. Due to the varying intensity of test tasks, the fixed cycle cannot detect the immediate accuracy degradation caused by high-frequency operation or abnormal energy consumption. At the same time, the static plan ignores the cumulative impact of failure frequency and depreciation costs recorded in the "maintenance management log" on metrological performance, which can easily lead to equipment accuracy deviations under heavy load conditions, while causing unnecessary traceability cost waste under low-frequency use, making it difficult to meet the adaptive requirements for more accurate management of test equipment. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method for predicting the traceability of measurement values ​​for experimental equipment, thereby at least alleviating the aforementioned technical problems.

[0006] A method for traceability and prediction of measurement values ​​for testing equipment, comprising: Step 1: Collect attribute data of the target test equipment in real time. The attribute data includes the unit energy consumption of the equipment, operating frequency, maintenance management ledger records and depreciation unit cost. Based on the attribute data, construct an equipment condition perception space that represents the spatiotemporal operating trajectory of the target test equipment. Step 2: Call the preset task load simulation model to perform multi-level state recursive simulation of the equipment condition in the equipment condition perception space to obtain the time domain state simulation trajectory of the target experimental equipment, and generate the physical operation degradation characteristics that characterize the magnitude of the target experimental equipment as the task load drifts. Step 3: Statistically calculate the evolution gradient of the physical operation degradation characteristics to obtain the distribution of the value offset evolution trend. Based on the distribution of the value offset evolution trend, perform periodic extrapolation calculation of the measurement accuracy degradation risk to generate the adaptive traceability time node sequence of the target test equipment. Step 4: Determine the measurement cycle arrangement for measurement value traceability based on the adaptive traceability time node sequence, and then adjust the preset traceability task plan of the target experimental equipment based on the measurement cycle arrangement.

[0007] Optionally, in step 1, constructing an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment based on the attribute data includes: performing multi-source data dimension alignment processing on the attribute data based on the constructed multi-dimensional state mapping matrix to generate a multi-dimensional aligned feature tensor; performing spatiotemporal correlation coupling calculation processing on the multi-dimensional aligned feature tensor to obtain a spatiotemporal associated feature manifold; performing topological semantic reconstruction processing on the spatiotemporal associated feature manifold to determine the equipment condition topology network; and performing graph trajectory dynamic mapping processing on the equipment condition topology network to generate an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment.

[0008] Optionally, the step of performing spatiotemporal correlation coupling calculation on the multidimensional aligned feature tensor to obtain a spatiotemporally correlated feature manifold includes: performing cross-dimensional temporal convolution processing on the multidimensional aligned feature tensor to extract the temporal dynamic dependency features of device operation and generate a temporal feature increment set; performing local manifold embedding processing on the temporal feature increment set to construct a low-dimensional manifold structure representing the local geometric structure of the data; and performing high-dimensional space expansion processing on the low-dimensional manifold structure using a nonlinear dimensionality reduction operator to obtain the spatiotemporally correlated feature manifold.

[0009] Optionally, the equipment operating condition topology network is determined by performing topological semantic reconstruction processing on the spatiotemporally related feature manifold, including: spatially discretizing and sampling the spatiotemporally related feature manifold to determine multiple logical nodes representing typical equipment states; calculating the geodesic distance between each logical node to obtain a node spacing metric matrix, and performing adjacency probability calculation based on the node spacing metric matrix to generate an initial operating condition topology graph; injecting preset equipment maintenance management semantics and depreciation cost weights into the initial operating condition topology graph, and performing dynamic evolution calculation processing of edge weights to determine the equipment operating condition topology network.

[0010] Optionally, the equipment operating condition topology network is subjected to graph trajectory dynamic mapping processing to generate an equipment operating condition perception space representing the spatiotemporal operating trajectory of the target test equipment. This includes: performing state transformation on the real-time collected equipment operating status data to obtain the current state; performing logical node matching processing on the current state to lock the matching nodes in the equipment operating condition topology network, and performing relocation processing on the matching nodes to obtain a dynamic operating trajectory node sequence; performing temporal smoothing calculation processing on the dynamic operating trajectory node sequence to generate a smooth trajectory sequence, and performing perception attribute encapsulation processing on the smooth trajectory sequence to generate an equipment operating condition perception space representing the spatiotemporal operating trajectory of the target test equipment.

[0011] Optionally, the task load inference model includes the following structural layers: a quantity spatiotemporal semantic encoding layer, an accuracy recursive evolution layer, and a source trajectory reconstruction layer. In step 2, the preset task load inference model is invoked to perform multi-level recursive inference of the equipment operating conditions in the equipment operating condition perception space to obtain the time-domain state inference trajectory of the target experimental equipment. This includes: performing quantity neighborhood association feature aggregation processing on the equipment operating condition perception space through the quantity spatiotemporal semantic encoding layer to generate a perception state code representing the instantaneous quantity state of the equipment; performing nonlinear accuracy degradation recursive evolution processing on the perception state code through the accuracy recursive evolution layer to obtain a multi-level hidden state evolution sequence recording the quantity drift trend; performing quantity deviation space mapping processing on the multi-level hidden state evolution sequence through the source trajectory reconstruction layer to obtain a discrete trajectory coordinate set recording the quantity prediction value, and performing continuous source trajectory interpolation fitting processing on the discrete trajectory coordinate set to generate the time-domain state inference trajectory of the target experimental equipment.

[0012] Optionally, the spatiotemporal semantic coding layer includes a neighborhood quantity scanning module, a source semantic modeling module, and a quantity feature compression module. The spatiotemporal semantic coding layer performs neighborhood correlation feature aggregation processing on the equipment operating condition perception space to generate a perception state code. This includes: using the neighborhood quantity scanning module to scan the neighborhood quantity attributes of the equipment operating condition perception space to obtain a local topological correlation matrix reflecting the correlation degree between adjacent operating conditions; using the source semantic modeling module to perform source semantic modeling processing on the local topological correlation matrix to obtain node interaction semantic features characterizing the influence of environment and load on equipment quantities; and using the quantity feature compression module to perform quantity dimension compression processing on the node interaction semantic features to generate a perception state code.

[0013] Optionally, the accuracy recursive evolution layer includes an accuracy gating calculation module, a magnitude drift mapping module, and a degradation feature encapsulation module. The accuracy recursive evolution layer performs nonlinear accuracy degradation recursive evolution processing on the perceived state code to obtain a multi-level hidden state evolution sequence, including: using the accuracy gating calculation module to perform time-series accuracy gating cyclic recursive processing on the perceived state code to obtain intermediate state evolution components reflecting the trend of equipment accuracy changes; using the magnitude drift mapping module to perform multi-dimensional nonlinear magnitude drift mapping processing on the intermediate state evolution components to obtain multi-scale evolution features covering different time dimensions; and using the degradation feature encapsulation module to encapsulate the multi-scale evolution features with degradation trajectory features to generate the multi-level hidden state evolution sequence.

[0014] Optionally, the source trajectory reconstruction layer includes a quantity parameter projection module, a source trajectory smoothing module, and a continuous error reconstruction module. The source trajectory reconstruction layer performs quantity deviation spatial mapping processing on the multi-level hidden state evolution sequence to obtain a discrete trajectory coordinate set, and then performs continuous source trajectory interpolation fitting processing on the discrete trajectory coordinate set to generate the time-domain state projection trajectory of the target experimental equipment. This includes: using the quantity parameter projection module to perform quantity parameter spatial projection processing on the multi-level hidden state evolution sequence to obtain a time-domain quantity prediction point set representing the quantity deviation at future time points; using the source trajectory smoothing module to perform source trajectory spline smoothing processing on the time-domain quantity prediction point set to obtain a preliminary fitted trajectory conforming to the physical degradation law; and using the continuous error reconstruction module to perform continuous quantity error reconstruction processing on the preliminary fitted trajectory to generate the time-domain state projection trajectory of the target experimental equipment.

[0015] Optionally, generating materialized operational degradation features characterizing the drift state of the target test equipment's quantities with the task load includes: performing multi-scale time-frequency analysis processing on the time-domain state projection trajectory to extract high-frequency components of quantity drift that reflect the instantaneous characteristics of quantity shift; performing degradation-related semantic mapping processing on the high-frequency components of quantity drift to obtain a materialized operational degradation description characterizing the physical performance degradation state of the equipment; and performing feature dimensionality encapsulation processing based on the materialized operational degradation description to generate materialized operational degradation features characterizing the drift state of the target test equipment's quantities with the task load.

[0016] Optionally, step 3, which involves statistically analyzing the evolution gradient of the physical operation degradation features to obtain the distribution of the magnitude offset evolution trend, includes: performing time-series sliding window segmentation on the physical operation degradation features to generate a set of degradation feature time-series components that record the feature evolution sequence; statistically analyzing the evolution gradient of the physical operation degradation features based on the feature differences between adjacent time windows in the set of degradation feature time-series components; and performing non-parametric kernel density estimation on the evolution gradient to obtain the distribution of the magnitude offset evolution trend.

[0017] Optionally, step 3 involves performing periodic extrapolation of the measurement accuracy degradation risk based on the distribution of the measurement value offset evolution trend to generate an adaptive traceability time node sequence for the target test equipment. This includes: performing risk probability density mapping on the distribution of the measurement value offset evolution trend to obtain a measurement value deviation risk probability field characterizing the probability of the equipment's measurement values ​​exceeding the tolerance; calling the constructed degradation clock extrapolation model to perform future time domain risk boundary search processing on the measurement value deviation risk probability field to generate a set of risk boundary time points predicting risk boundary; and performing node offset adaptive correction processing on the equipment task scheduling constraints based on the risk boundary time point set to generate the adaptive traceability time node sequence for the target test equipment.

[0018] Optionally, the degradation clock extrapolation model includes: a risk trend extrapolation layer and a time-domain threshold optimization layer; the constructed degradation clock extrapolation model is invoked to perform future time-domain risk boundary search processing on the probability field of the magnitude deviation risk to generate a set of risk boundary time points for predicting risk boundary, including: performing time-series risk cumulative integration processing on the probability field of the magnitude deviation risk through the risk trend extrapolation layer to obtain a dynamic risk evolution surface characterizing the risk evolution over time; and performing risk boundary intersection solution processing on the dynamic risk evolution surface through the time-domain threshold optimization layer to generate a set of risk boundary time points for predicting risk boundary.

[0019] Optionally, in step 4, determining the metering cycle arrangement for traceability based on the adaptive traceability time node sequence includes: performing time-domain interval density clustering on the adaptive traceability time node sequence to obtain a set of periodic distribution cluster centers that reflects the urgency of the traceability task; and performing optimal adaptation search on the preset metering resource availability matrix based on the set of periodic distribution cluster centers to determine the metering cycle arrangement for traceability.

[0020] Optionally, in step 4, adjusting the preset traceability task plan of the target experimental equipment based on the metering cycle arrangement includes: performing original plan deviation calculation on the metering cycle arrangement to obtain the plan adjustment deviation feature characterizing the magnitude of plan changes; and generating a dynamic rescheduling strategy for task flow to guide task priority changes based on the plan adjustment deviation feature.

[0021] The technical advantages of the technical solution provided in this application are: The experimental equipment measurement traceability prediction method proposed in this application addresses the technical shortcomings of traditional fixed-cycle management, such as lagging state perception and poor cycle adaptability. It constructs a perception space by real-time collection of equipment unit energy consumption, operating frequency, maintenance records, and depreciation costs, solving the problems of single data dimensions and inability to quantify the impact of complex operating conditions in traditional solutions. Compared to traditional methods relying solely on static calendars, this solution, through multi-source data alignment and correlation coupling calculation, can more meticulously reconstruct the physical wear trajectory of equipment in actual testing tasks, providing more accurate data support for the prediction of measurement drift.

[0022] Based on the perception space, this method utilizes a task load extrapolation model to generate materialized operational degradation characteristics, solving the problem that traditional methods cannot characterize the nonlinear drift of measurement values ​​with load. Traditional methods can only passively respond when the validity period expires, while this method, through multi-level hidden state evolution and continuous trajectory reconstruction, can extrapolate the future accuracy status of the equipment in real time. Compared with simple clock counting, the generated degradation characteristics better reflect the logical relationship between the internal physical state of the equipment and external measurement results, effectively improving the perception depth of the trend of measurement accuracy decay.

[0023] By statistically evolving gradients and performing periodic extrapolation, an adaptive tracing node sequence is generated, solving the problem of insufficient response to sudden high-load tasks in traditional schemes. This scheme quantifies the risk probability field through kernel density estimation and searches for risk thresholds using a degraded clock model, achieving a shift from "periodic tracing" to "on-demand tracing." Compared to traditional fixed-cycle tracing, this adaptive mechanism can more flexibly handle the differentiated losses between different devices, effectively reducing the resource costs of redundant tracing while ensuring measurement accuracy.

[0024] Finally, by optimizing the cyclical arrangement through density clustering and implementing dynamic rescheduling, a closed-loop management system was formed, solving the problem of the disconnect between management plans and actual needs. This solution ensures that high-risk, high-value equipment receives priority metrological resource support by calculating deviations from the plan and implementing priority changes. Overall, it improves the efficiency of metrological value transfer and the reliability of test data for the test equipment cluster, and is better suited to the dynamic support requirements of modern integrated testing environments. Attached Figure Description

[0025] Figure 1 This application provides an embodiment of a method for predicting the traceability of measurement values ​​for experimental equipment. Figure 2 This application provides an embodiment of a test equipment measurement traceability and prediction device. Figure 3 This is an electronic device according to an embodiment of the present application. Detailed Implementation

[0026] like Figure 1 As shown, this embodiment of the present application provides a method for predicting the traceability of measurement values ​​for experimental equipment, comprising: Step 1: Collect attribute data of the target test equipment in real time. The attribute data includes the unit energy consumption of the equipment, operating frequency, maintenance management ledger records and depreciation unit cost. Based on the attribute data, construct an equipment condition perception space that represents the spatiotemporal operating trajectory of the target test equipment. Step 2: Call the preset task load simulation model to perform multi-level state recursive simulation of the equipment condition in the equipment condition perception space to obtain the time domain state simulation trajectory of the target experimental equipment, and generate the physical operation degradation characteristics that characterize the magnitude of the target experimental equipment as the task load drifts. Step 3: Statistically calculate the evolution gradient of the physical operation degradation characteristics to obtain the distribution of the value offset evolution trend. Based on the distribution of the value offset evolution trend, perform periodic extrapolation calculation of the measurement accuracy degradation risk to generate the adaptive traceability time node sequence of the target test equipment. Step 4: Determine the measurement cycle arrangement for measurement value traceability based on the adaptive traceability time node sequence, and then adjust the preset traceability task plan of the target experimental equipment based on the measurement cycle arrangement.

[0027] Optionally, in step 1, constructing an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment based on the attribute data includes: performing multi-source data dimension alignment processing on the attribute data based on the constructed multi-dimensional state mapping matrix to generate a multi-dimensional aligned feature tensor; performing spatiotemporal correlation coupling calculation processing on the multi-dimensional aligned feature tensor to obtain a spatiotemporal associated feature manifold; performing topological semantic reconstruction processing on the spatiotemporal associated feature manifold to determine the equipment condition topology network; and performing graph trajectory dynamic mapping processing on the equipment condition topology network to generate an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment.

[0028] Preferably, in the specific implementation of step 1, a multi-dimensional state mapping matrix is ​​first constructed for the attribute data of the target test equipment. Then, based on the multi-dimensional state mapping matrix, the attribute data is processed to align multiple data dimensions to generate a multi-dimensional aligned feature tensor. The attribute data of the target test equipment originates from the comprehensive test data management system, which records full-dimensional equipment operation and management data. This includes data on unit energy consumption, unit depreciation cost, maintenance cycle, and traceability cycle recorded in the basic information of the test equipment; statistical data on operating frequency during equipment operation; data on equipment failure frequency, downtime, and failure handling details recorded in the maintenance management ledger; data on pre- and post-maintenance indicators and maintenance execution records recorded in the maintenance management system; and data on equipment calibration time and verification results recorded in the metrology cycle management system. In the constructed multi-dimensional state mapping matrix, rows represent different attribute data dimensions, columns represent continuous time sampling nodes, and the elements at the intersection of rows and columns represent the collected values ​​of the corresponding attribute dimension at the corresponding time sampling node. Because attribute data from different sources have issues such as inconsistent time sampling granularity and inconsistent numerical units—for example, maintenance management ledger data is recorded in an event-triggered manner, while equipment unit energy consumption data is collected continuously at fixed periods—we perform multi-source data dimension alignment processing on attribute data based on a multi-dimensional state mapping matrix. First, we perform time dimension normalization interpolation processing on all attribute data with a unified time granularity, so that all attribute data achieve a one-to-one correspondence of sampling nodes in the time dimension. Then, we standardize attribute values ​​with different units to eliminate the interference of unit differences on subsequent feature processing, and finally generate a multi-dimensional aligned feature tensor with unified dimensions and time alignment.

[0029] Preferably, after obtaining the multidimensional aligned feature tensor, cross-dimensional temporal convolution processing is performed on the multidimensional aligned feature tensor to extract the temporal dynamic dependency features of device operation and generate a temporal feature increment set. The multidimensional aligned feature tensor contains the complete values ​​of multidimensional attribute data in a continuous time series. There are temporal causal relationships between different attribute dimensions. For example, a continuous increase in device operating frequency will be accompanied by a synchronous increase in device unit energy consumption, and an abnormal surge in device unit energy consumption will increase the probability of subsequent fault maintenance events. The accumulation rate of device depreciation unit cost is directly related to operating time and fault frequency. Based on the above-mentioned correlation characteristics, cross-dimensional temporal convolution processing is performed on the multidimensional aligned feature tensor. A time window of a set length is slid along the time dimension, and correlation features are extracted from the full-dimensional attribute data within each time window to capture the mutual influence and dependency relationships of different attribute dimensions in time, thereby obtaining temporal dynamic dependency features that can reflect the changing law of device operating status over time. Then, according to the sliding order of the time window, the time-series dynamic dependency features extracted in different time windows are incrementally integrated to fully retain the incremental information of the changes in the device's operating status over time, and finally generate a time-series feature increment set.

[0030] Preferably, after obtaining the time-series feature increment set, local manifold embedding processing is performed on the time-series feature increment set to construct a low-dimensional manifold structure representing the local geometric structure of the data. Then, high-dimensional space expansion processing is performed on the low-dimensional manifold structure to obtain the spatiotemporal correlation feature manifold. The time-series feature increment set is a high-dimensional feature set, which contains a large amount of redundant information that is unrelated to the core changes in the device's operating state. Directly using it for subsequent processing will increase computational overhead and also mask the core laws of device performance degradation. Therefore, local manifold embedding processing is first performed on the time-series feature increment set. Within each local time neighborhood, the local geometric structure relationship between feature data is preserved, and the high-dimensional time-series feature increment set is mapped to a low-dimensional feature space. While eliminating redundant information, the core features of the device's operating state changes are fully preserved, and a low-dimensional manifold structure representing the local geometric structure of the data is constructed. Then, a nonlinear dimension reduction operator is used to perform a high-dimensional space expansion on the low-dimensional manifold structure. The nonlinear dimension reduction operator can restore the coupling and correlation characteristics of the feature data in the time dimension and attribute dimension while preserving the core geometric relationship of the low-dimensional manifold structure. Finally, a spatiotemporal correlation feature manifold that can simultaneously represent the dynamic correlation of device attribute data in the time dimension and the spatial coupling relationship of attribute dimension is obtained.

[0031] Preferably, after obtaining the spatiotemporal correlation feature manifold, spatial discretization sampling is performed on the spatiotemporal correlation feature manifold to determine multiple logical nodes representing typical equipment states. Then, an initial topology map of operating conditions is generated based on these logical nodes. The spatiotemporal correlation feature manifold completely represents the full range of changes in the equipment's operating states. To achieve a structured representation of the equipment's operating states, spatial discretization sampling is first performed on the spatiotemporal correlation feature manifold. Sampling points are selected according to typical operating condition intervals during the actual operation of the equipment. Typical operating condition intervals include the equipment's no-load operating state, rated load operating state, heavy load operating state, shutdown pending repair state, post-maintenance calibration state, and post-metrization qualified state. Each sampling point corresponds to a logical node representing a typical equipment state, and each logical node is associated with a feature vector of full-dimensional attribute data under the corresponding operating condition. Then, the geodesic distance between each logical node is calculated. The geodesic distance is the shortest path length between two logical nodes on the spatiotemporal correlation feature manifold, accurately reflecting the similarity between two typical equipment states. Based on the pairwise geodesic distances between all logical nodes, a node spacing metric matrix is ​​generated. Subsequently, adjacency probability calculation is performed based on the node spacing metric matrix. For each pair of logical nodes, the adjacency probability between the two nodes is calculated according to the corresponding geodesic distance value in the node spacing metric matrix. The adjacency probability is negatively correlated with the geodesic distance, that is, the closer the geodesic distance between two logical nodes, the higher the similarity of the device states and the higher the adjacency probability. When the adjacency probability exceeds the set threshold, a topological connection edge is established between the two logical nodes, and finally an initial topology map of the working conditions that can reflect the correlation between the typical states of the device is generated.

[0032] Preferably, after obtaining the initial topology diagram, equipment maintenance management semantics and depreciation cost weights are injected into the initial topology diagram. Then, dynamic evolution calculation of edge weights is performed on the injected initial topology diagram to determine the equipment operating condition topology network. The initial topology diagram only reflects the geometric similarity relationship between the typical states of the equipment and does not incorporate the business rules and cost influencing factors in the equipment's full life cycle management process. These factors are directly related to the change law of equipment measurement performance. Therefore, the preset equipment maintenance management semantics and depreciation cost weights are first injected into the initial topology diagram. The equipment maintenance management semantics include the degree of impact of equipment fault repair on metrological performance, the restorative effect of maintenance operations on the equipment's operating status, and the resetting effect of metrological calibration on the accuracy of equipment measurement values. The depreciation cost weights include the weight coefficients corresponding to the equipment's unit time depreciation cost, single repair cost, and single metrological traceability cost. The above semantic and weight data are all derived from the full equipment management records in the comprehensive test data management. Next, for the initial topology graph of the operating conditions after injecting equipment maintenance management semantics and depreciation cost weights, dynamic evolution calculation of edge weights is performed. For each topological connection edge connecting two logical nodes in the initial topology graph, the dynamic weight value of the topological connection edge is calculated based on the degree of maintenance management semantic association and the difference in depreciation cost weights between the equipment states corresponding to the two logical nodes. The higher the degree of maintenance management semantic association and the smaller the difference in depreciation cost weights between the two logical nodes, the higher the dynamic weight value of the topological connection edge, which represents the higher probability of transition between the typical states of the two devices. Finally, by updating and fixing the dynamic weight values ​​of all topological connection edges in the initial topology graph of the operating conditions, an equipment operating condition topology network that can simultaneously represent the physical association of equipment states, maintenance management semantics, and the impact of cost weights is obtained.

[0033] Preferably, after obtaining the equipment operating condition topology network, the real-time collected equipment operating status data is processed by state transformation to obtain the current state feature vector. Then, logical node matching and relocation processing are performed on the current state feature vector to obtain the dynamic operating trajectory node sequence. After the equipment operating condition topology network is constructed, the real-time operating status of the target test equipment needs to be mapped into the topology network to reconstruct the spatiotemporal operating trajectory of the equipment. Therefore, the real-time collected operating status data of the target test equipment is first processed by state transformation, converting the real-time collected multi-dimensional attribute data into a current state feature vector that is completely consistent with the feature dimensions of the logical nodes in the equipment operating condition topology network. The real-time collected operating status data includes real-time updated equipment unit energy consumption, operating frequency, maintenance event records, and accumulated depreciation cost data. Then, logical node matching processing is performed on the current state feature vector, calculating the similarity between the current state feature vector and the feature vector corresponding to each logical node in the equipment operating condition topology network. The logical node with the highest similarity is locked as the matching node corresponding to the current time sampling node, and the position information of this matching node in the equipment operating condition topology network is recorded. According to the order of time sampling, the matching nodes corresponding to each time sampling node in the continuous time series are relocated. All matching nodes are sorted and integrated according to the time sequence to obtain a dynamic operation trajectory node sequence that can completely reflect the continuous change of the target test equipment's operating status over time.

[0034] Preferably, after obtaining the dynamic operating trajectory node sequence, a time-series smoothing calculation is performed on the dynamic operating trajectory node sequence to generate a smooth trajectory sequence. Then, a perception attribute encapsulation process is performed on the smooth trajectory sequence to generate an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment. The change in the equipment operating state is a continuous physical process and will not have irregular jumps. However, there may be abnormal jumps in the nodes in the dynamic operating trajectory node sequence due to data acquisition noise and instantaneous operating condition fluctuations. Therefore, a time-series smoothing calculation is first performed on the dynamic operating trajectory node sequence to eliminate the interference of abnormal node jumps and generate a smooth trajectory sequence that conforms to the physical operating law of the equipment. Next, the smooth trajectory sequence is encapsulated with perception attributes. The equipment attribute data, state association information, maintenance management semantics, and cost weight information corresponding to each trajectory node in the smooth trajectory sequence are integrated and encapsulated. Each trajectory node in the smooth trajectory sequence is given complete equipment condition perception attributes, so that the encapsulated smooth trajectory sequence can fully characterize the operating status changes, operating condition transition process, and performance degradation trend of the target test equipment in the continuous time dimension. Finally, an equipment condition perception space that can provide complete data and feature support for subsequent quantitative source tracing and prediction and characterize the spatiotemporal operating trajectory of the target test equipment is generated.

[0035] Optionally, the step of performing spatiotemporal correlation coupling calculation on the multidimensional aligned feature tensor to obtain a spatiotemporally correlated feature manifold includes: performing cross-dimensional temporal convolution processing on the multidimensional aligned feature tensor to extract the temporal dynamic dependency features of device operation and generate a temporal feature increment set; performing local manifold embedding processing on the temporal feature increment set to construct a low-dimensional manifold structure representing the local geometric structure of the data; and performing high-dimensional space expansion processing on the low-dimensional manifold structure using a nonlinear dimensionality reduction operator to obtain the spatiotemporally correlated feature manifold.

[0036] Preferably, in this application, the core processing object of the spatiotemporal correlation coupling calculation is the multidimensional alignment feature tensor. The multidimensional alignment feature tensor originates from the full-dimensional management and operation data of the target test equipment in the integrated test data management system. It is formed after multi-source data dimension alignment processing. The dimensional data contained in the tensor correspond to the unit energy consumption, unit depreciation cost, maintenance cycle, and traceability cycle of the test equipment recorded in the basic information of the test equipment; the operating frequency collected during the real-time operation of the equipment; the fault frequency, downtime, and fault handling details recorded in the maintenance management ledger; the equipment pre-maintenance indicators, post-maintenance indicators, and maintenance execution records retained in the maintenance management; and the equipment calibration time, verification result description, and measurement validity period data stored in the measurement cycle management. All dimensional data in the tensor are aligned with sampling nodes on a unified time series, eliminating the time granularity and dimensional differences of different data sources, and providing a unified data foundation for the subsequent extraction of cross-dimensional time series correlation features.

[0037] Preferably, cross-dimensional temporal convolution processing is performed on the multidimensional aligned feature tensor to extract the temporal dynamic dependency features of equipment operation. During the execution of the cross-dimensional temporal convolution processing, a fixed-length sliding time window is first set along a unified time series direction. The length of the sliding time window can be adapted and adjusted according to the operating characteristics of the target test equipment and the test task cycle. Then, according to the set sliding step size, the sliding time window moves continuously along the time series. For the multidimensional aligned feature tensor data within the coverage of each sliding time window, full-dimensional correlation convolution calculation is performed to capture the temporal mutual influence and causal dependency relationships of data from different attribute dimensions. For example, the driving effect of the continuous increase in equipment operating frequency on the unit energy consumption of the equipment, the impact of abnormal fluctuations in the unit energy consumption of the equipment on the probability of subsequent fault maintenance events, the repair effect of equipment maintenance operations on metrological performance, and the correlation between the accumulation rate of equipment depreciation costs and operating time and fault frequency. Finally, temporal dynamic dependency features reflecting the changing pattern of equipment operating status over time are extracted from each sliding time window.

[0038] Preferably, a time-series feature increment set is generated based on the extracted time-series dynamic dependency features. During the continuous sliding of the sliding time window along the time series, a set of time-series dynamic dependency features is extracted for each sliding time window. There is partial overlap in time coverage between adjacent sliding time windows to ensure the continuity of the equipment's operating state changes is not interrupted. Then, according to the sliding order of the time windows, the time-series dynamic dependency features corresponding to all sliding time windows are incrementally integrated, completely recording the incremental changes in time-series dynamic dependency features between adjacent time windows. Simultaneously, the time node and full-dimensional attribute association information corresponding to each time-series dynamic dependency feature are retained, avoiding the loss of instantaneous state change information related to the degradation of metering performance during equipment operation. Finally, the time-series feature increment set is formed.

[0039] Preferably, local manifold embedding is performed on the time-series feature increment set to construct a low-dimensional manifold structure representing the local geometry of the data. The time-series feature increment set is a high-dimensional feature set, which contains a large amount of redundant information unrelated to the degradation of equipment metrological performance. Directly using this information for subsequent feature processing would increase computational overhead and also obscure the core change patterns of equipment measurement drift. Therefore, when performing local manifold embedding, a corresponding local neighborhood range is first determined for each feature point in the time-series feature increment set. The size of the local neighborhood range can be adapted and adjusted according to the operating condition change characteristics of the target test equipment. Then, for each feature point, the feature distance between it and all adjacent feature points within its corresponding local neighborhood range is calculated to represent the similarity between different equipment operating states. Subsequently, the time-series feature increment set in the high-dimensional space is mapped to the low-dimensional feature space through local linear embedding. During the mapping process, the geometric structure relationship between each feature point and its adjacent feature points in its local neighborhood is fully preserved. This eliminates redundant information while fully retaining the core features related to changes in equipment operating state and degradation of metrological performance, ultimately constructing a low-dimensional manifold structure representing the local geometry of the data.

[0040] Preferably, during the execution of local manifold embedding processing, core feature constraint rules are set for the construction process of the low-dimensional manifold structure. The content of the core feature constraint rules comes from the management data directly related to equipment measurement traceability in the integrated test data management system, including the qualified and out-of-tolerance status of equipment metrological verification results, the changes in metrological performance before and after equipment maintenance events, the repair effect of equipment maintenance operations on the operating status, and the correlation between equipment heavy-load operation conditions and metrological accuracy drift. During the mapping process from high-dimensional features to low-dimensional space, the core feature constraint rules ensure that the core feature information related to the degradation of equipment metrological performance is not lost or distorted due to dimensionality reduction processing. At the same time, in the low-dimensional manifold structure, feature points corresponding to operating states with similar equipment metrological performance form a clustered distribution, while feature points corresponding to operating states with large differences in equipment metrological performance form a separated distribution. This strengthens the ability of the low-dimensional manifold structure to represent changes in equipment metrological performance and provides a reliable low-dimensional feature foundation for the subsequent reconstruction of spatiotemporal correlation features.

[0041] Preferably, a nonlinear dimension reduction operator is used to perform high-dimensional space expansion processing on the low-dimensional manifold structure to obtain a spatiotemporally correlated feature manifold. The nonlinear dimension reduction operator is not the inverse operation of the local manifold embedding process, but a feature restoration operator designed for the scenario requirements of traceability prediction of test equipment values. Its core function is to restore the coupling relationship between the full-dimensional attribute data and the time dimension corresponding to the low-dimensional features while preserving the local geometric relationship of the equipment operating state in the low-dimensional manifold structure. When performing high-dimensional space expansion processing, the nonlinear dimension reduction operator is first used to map each feature point in the low-dimensional manifold structure back to a high-dimensional feature space with the same dimension as the original multidimensional aligned feature tensor. Then, for the mapped high-dimensional features, the coupling relationship enhancement processing of the time dimension and attribute dimension is performed to highlight the spatiotemporal coupling relationship between core attributes such as equipment operating time, task load intensity, energy consumption accumulation level, maintenance records, and depreciation cost changes and equipment measurement performance changes. Finally, a spatiotemporally correlated feature manifold that can simultaneously represent the dynamic correlation of equipment attribute data in the time dimension and the spatial coupling relationship of attribute dimensions is obtained.

[0042] Preferably, during the high-dimensional space expansion process using nonlinear dimensionality reduction operators, appropriate weight coefficients are set for different attribute dimensions. The values ​​of the weight coefficients are derived from the historical operation and measurement data of the target test equipment in the integrated test data management system. By statistically analyzing the correlation between equipment measurement deviation events and each attribute dimension in the historical data, higher weight coefficients are set for attribute dimensions with a high degree of correlation with equipment measurement performance degradation, and lower weight coefficients are set for attribute dimensions with a low degree of correlation with equipment measurement performance degradation. In this way, during the high-dimensional space expansion process, the feature expression of the core attributes that have a significant impact on equipment value drift is further strengthened, and the interference of irrelevant redundant information is weakened. This makes the final spatiotemporal correlation feature manifold more closely match the actual operating characteristics of the target test equipment, and more accurately represent the intrinsic correlation between equipment operating conditions and measurement performance changes. At the same time, it provides a high-quality feature foundation for subsequent topological semantic reconstruction processing, realizing the functional connection and effect support of the preceding and following technical processing links.

[0043] Optionally, the equipment operating condition topology network is determined by performing topological semantic reconstruction processing on the spatiotemporally related feature manifold, including: spatially discretizing and sampling the spatiotemporally related feature manifold to determine multiple logical nodes representing typical equipment states; calculating the geodesic distance between each logical node to obtain a node spacing metric matrix, and performing adjacency probability calculation based on the node spacing metric matrix to generate an initial operating condition topology graph; injecting preset equipment maintenance management semantics and depreciation cost weights into the initial operating condition topology graph, and performing dynamic evolution calculation processing of edge weights to determine the equipment operating condition topology network.

[0044] Preferably, the core processing object of the topological semantic reconstruction process is the spatiotemporal correlated feature manifold. This manifold originates from the output of spatiotemporal correlation coupling calculations performed on multidimensional aligned feature tensors. Internally, it fully carries the comprehensive operational and management data of the target test equipment over a continuous time series. This includes data such as unit energy consumption, operating frequency, fault frequency and fault handling details recorded in the integrated test data management system, pre- and post-maintenance indicators and maintenance execution records in maintenance management, calibration time and verification results in metering cycle management, and depreciation unit cost and traceability cycle data in the equipment's basic information. Simultaneously, it fully preserves the dynamic temporal correlation and spatial coupling relationships between the data across all dimensions. The core objective of the topological semantic reconstruction process is to transform the continuous high-dimensional spatiotemporal correlated feature manifold into a graph network structure capable of structured operations and state mapping, providing a structured state foundation for subsequent mapping of equipment operating trajectories and the construction of the equipment condition perception space.

[0045] Preferably, the spatiotemporal correlation feature manifold is spatially discretized and sampled to determine multiple logical nodes representing typical equipment states. During the execution of spatial discretization sampling, the historical full life cycle data of the target test equipment in the integrated test data management system is first combined to divide the typical operating condition intervals of the equipment. The typical operating condition intervals include the equipment's no-load operation state, rated load operation state, heavy load operation state, shutdown and repair state, maintenance completed and repaired state, qualified state after metrological calibration, and metrological performance out-of-tolerance warning state. Each typical operating condition interval has a corresponding set of clear feature threshold ranges. The feature threshold ranges are determined based on the historical statistical results of the equipment's operating frequency, unit energy consumption, metrological deviation amplitude, and failure probability under the corresponding operating conditions. For each defined typical operating condition interval, uniform sampling is performed in the spatiotemporal correlation feature manifold. At least one sampling point is selected in each typical operating condition interval. Each sampling point corresponds to a logical node representing the typical state of the equipment. Each logical node is associated with a feature vector that is completely consistent with the dimension of the spatiotemporal correlation feature manifold. The feature vector contains full-dimensional operation and management attribute data of the corresponding equipment under the typical state. This ensures that the sampled logical nodes can completely cover all possible operating states of the target test equipment and avoid omissions of key states related to the degradation of metrological performance.

[0046] Preferably, the geodesic distance between each logical node is calculated to obtain the node spacing metric matrix. Unlike conventional Euclidean distance, which calculates straight-line distance in high-dimensional space, geodesic distance represents the shortest path length between two logical nodes on a spatiotemporally related manifold. This better reflects the physical laws governing device state changes and more accurately represents the true similarity between typical states of two devices. In the calculation of the geodesic distance, all logical nodes are paired. For each pair of logical nodes, the shortest path length between them is calculated on the spatiotemporally related manifold, and this shortest path length is used as the geodesic distance between the two logical nodes. Based on the pairwise geodesic distances of all logical nodes, a node spacing metric matrix is ​​constructed. The rows and columns of the node spacing metric matrix correspond to all sampled logical nodes. The element at the intersection of a row and a column represents the geodesic distance between the logical node in that row and the logical node in that column. The node spacing metric matrix fully quantifies the similarity between all typical states of devices, providing a quantitative basis for subsequent adjacency probability calculations.

[0047] Preferably, adjacency probability calculation is performed based on the node spacing metric matrix to generate the initial topology map for the operating conditions. The core rule of adjacency probability calculation is that the adjacency probability between two logical nodes is negatively correlated with the geodesic distance between the two nodes. That is, the closer the geodesic distance between two logical nodes, the higher the similarity of the typical states of the corresponding equipment, the greater the probability of state transitions, and the higher the corresponding adjacency probability. During the execution of adjacency probability calculation, the adjacency probability is first calculated for each group of logical nodes based on the geodesic distance recorded in the node spacing metric matrix. Then, combined with the historical state transition data of the target test equipment in the integrated test data management system, an adjacency probability threshold is set. The historical state transition data includes the actual transition frequency and transition probability of the equipment between different operating conditions. The adjacency probability threshold is set to cover all possible state transition paths during normal operation of the equipment. When the adjacency probability between two logical nodes exceeds the set adjacency probability threshold, a topological connection edge is established between the two logical nodes. Finally, an initial topological graph is generated with all logical nodes as graph nodes and all topological connection edges that meet the threshold requirements as graph edges. The initial topological graph initially realizes the structured representation of the equipment's operating state space, fully reflecting the geometric similarity and basic transformation possibilities between various typical states of the equipment, and providing a topological structure carrier for the subsequent injection of equipment maintenance management semantics and depreciation cost weights.

[0048] Preferably, preset equipment maintenance management semantics and depreciation cost weights are injected into the initial topology diagram of the operating conditions. The preset equipment maintenance management semantics are entirely derived from the equipment lifecycle management rules and historical data recorded in the integrated test data management system. This includes the impact of equipment failure repair events recorded in the maintenance management log on metrological performance, the restorative effect of equipment maintenance operations recorded in maintenance management on operating status, the resetting effect of metrological calibration operations recorded in metrological cycle management on the accuracy of equipment measurements, and the rules governing the accelerating impact of equipment failure frequency on the deterioration process of metrological performance. The preset depreciation cost weights are derived from the equipment lifecycle cost data recorded in the integrated test data management system, including weight coefficients corresponding to equipment unit time depreciation cost, single maintenance operation cost, single metrological traceability operation cost, and equipment unit operating energy consumption cost. The value of each weight coefficient is positively correlated with the degree of correlation between the corresponding cost item and the deterioration of equipment metrological performance; that is, the cost item with a greater impact on metrological performance deterioration has a higher corresponding weight coefficient. During the injection process, the semantics of equipment maintenance and management are assigned to each logical node in the initial topology graph of the operating condition, and each logical node is given a corresponding equipment status maintenance and management attribute. The weight of depreciation cost is assigned to each logical node and topology connection edge in the initial topology graph of the operating condition, and the nodes and edges are given corresponding cost impact attributes. In this way, the initial topology graph of the operating condition, which originally only reflected the geometric similarity of the state, has completed the integration with the actual operation and maintenance management business rules of the equipment, and provides business rules and quantitative basis for the subsequent dynamic evolution calculation of edge weights.

[0049] Preferably, dynamic evolution calculation of edge weights is performed on the initial topology graph of the operating conditions after semantic and weight injection to determine the equipment operating condition topology network. The core objective of dynamic evolution calculation of edge weights is to dynamically update the weights of each topology connection edge in the initial topology graph of the operating conditions by combining the injected equipment maintenance and management semantics and depreciation cost weights, so that the updated edge weights can accurately reflect the true transition probability between the equipment states of two corresponding logical nodes, the degree of impact on metering performance degradation, and the corresponding life cycle cost impact. During the dynamic evolution calculation of edge weights, for each topological connection edge in the initial topology graph of the operating conditions, the semantic association coefficient between the two logical nodes connected by the edge is first calculated based on the equipment maintenance and management semantics. The value of the semantic association coefficient is positively correlated with the degree of business association between the two equipment states. For example, the semantic association coefficient between the repair state after maintenance and the rated load operation state is relatively high, and the semantic association coefficient between the normal operation state and the metering performance deviation warning state is positively correlated with the historical failure frequency of the equipment. Then, the cost impact coefficient between the two logical nodes connected by the edge is calculated based on the depreciation cost weights. The value of the cost impact coefficient is positively correlated with the magnitude of maintenance, metering, and depreciation costs generated during the transition between the two states. Finally, the initial weight of the topological connection edge is dynamically updated using the semantic association coefficient and the cost impact coefficient to obtain the final dynamic weight value of the edge. After completing the dynamic update of the weights of all topological connection edges in the initial topology graph of the operating conditions, the equipment operating condition topology network is determined.

[0050] Preferably, the constructed equipment condition topology network provides complete structured state space support for subsequent dynamic mapping of graph trajectories. Each logical node in the equipment condition topology network fully carries the operational attributes, metrological performance attributes, maintenance and management semantic attributes, and cost weight attributes of the corresponding typical equipment state. The dynamic weight of each topological connection edge in the network accurately quantifies the possibility of transition between two corresponding equipment states, the degree of impact on metrological performance degradation, and the impact on the entire life cycle cost. Unlike the initial topology graph that only reflects the geometric relationship between states, the equipment condition topology network achieves a deep integration of the physical operation law of the equipment, the law of metrological performance degradation, and the business rules of operation and maintenance management, which can better meet the actual needs of equipment management in the context of comprehensive test data management. When performing dynamic mapping of graph trajectories in the subsequent process, the real-time operating status data of the equipment can be directly mapped to the equipment condition topology network, the corresponding logical nodes can be locked, and the continuous operating trajectory of the equipment can be restored. Based on this, the equipment condition perception space is constructed, ensuring that the equipment condition perception space can completely and accurately represent the spatiotemporal operating trajectory and metrological performance degradation trend of the target test equipment.

[0051] Optionally, the equipment operating condition topology network is subjected to graph trajectory dynamic mapping processing to generate an equipment operating condition perception space representing the spatiotemporal operating trajectory of the target test equipment. This includes: performing state transformation on the real-time collected equipment operating status data to obtain the current state; performing logical node matching processing on the current state to lock the matching nodes in the equipment operating condition topology network, and performing relocation processing on the matching nodes to obtain a dynamic operating trajectory node sequence; performing temporal smoothing calculation processing on the dynamic operating trajectory node sequence to generate a smooth trajectory sequence, and performing perception attribute encapsulation processing on the smooth trajectory sequence to generate an equipment operating condition perception space representing the spatiotemporal operating trajectory of the target test equipment.

[0052] Preferably, the core processing object of the graph trajectory dynamic mapping processing is the equipment operating condition topology network. This network originates from the output of topological semantic reconstruction processing of the spatiotemporally correlated feature manifold. Internally, it comprehensively covers all possible operating conditions of the target test equipment in the form of logical nodes. This includes the equipment's no-load operating state, rated load operating state, heavy load operating state, shutdown pending repair state, repair state after maintenance, qualified state after metrological calibration, and metrological performance deviation warning state recorded in the integrated test data management system. Each logical node is associated with full-dimensional equipment attribute data, maintenance management semantics, and depreciation cost weights for the corresponding operating condition. The topological connection edges between logical nodes quantify the conversion probability and influence degree between different operating conditions. The core purpose of the graph trajectory dynamic mapping processing is to map the real-time operating status data of the target test equipment onto the structured state space of the equipment operating condition topology network, reconstructing the trajectory of the equipment's operating status changes over continuous time, and providing complete trajectory and attribute support for the construction of the equipment operating condition perception space.

[0053] Preferably, the real-time acquired equipment operating status data undergoes state transformation processing to obtain the current state feature vector. The real-time acquired equipment operating status data originates entirely from the comprehensive test data management system, synchronously updating the target test equipment's full-dimensional operation and management data. This includes real-time equipment operating frequency, real-time unit energy consumption, newly added fault records in the maintenance management ledger, newly added maintenance execution records in maintenance management, the latest verification results in metering cycle management, and real-time accumulated data on equipment depreciation costs. All of this data is completely consistent with the attribute data dimensions used when constructing the equipment operating condition topology network, thus ensuring feature dimension matching before and after the state transformation. During the state transformation process, the real-time acquired equipment operating status data first undergoes standardization processing, consistent with the previous multi-source data dimension alignment processing, eliminating differences in data units and time granularity. Then, the standardized multi-dimensional data is integrated into a vector structure completely consistent with the feature vector dimensions of the logical nodes in the equipment operating condition topology network, ultimately obtaining the current state feature vector representing the target test equipment's current operating state.

[0054] Preferably, logical node matching processing is performed on the current state feature vector to lock the matching node in the equipment operating condition topology network. The core logic of logical node matching processing is to find the logical node that best matches the current operating state of the target test equipment by calculating the similarity between the current state feature vector and the corresponding feature vector of each logical node in the equipment operating condition topology network. During the execution of the matching processing, for each logical node in the equipment operating condition topology network, the feature similarity between the feature vector corresponding to that node and the current state feature vector is calculated. The method of calculating feature similarity is in line with the representation requirements of the test equipment operating state, and can simultaneously consider the matching degree of equipment operating attributes, metrological performance attributes, and maintenance and management attributes, rather than only considering the numerical difference of a single dimension. After completing the similarity calculation of all logical nodes, the logical node with the highest similarity value is locked as the matching node corresponding to the current moment. The matching node fully carries all attribute information, semantic information, and weight information corresponding to the current equipment operating state, providing a node foundation for subsequent trajectory reconstruction.

[0055] Preferably, relocation processing is performed on the matching nodes to obtain a dynamic running trajectory node sequence. During the relocation processing, the matching nodes corresponding to each time sampling node in the continuous time series are integrated in a temporal sequence according to the order of time sampling. Each time sampling node corresponds to one matching node, and each matching node records the corresponding timestamp information, equipment status characteristic information, and position information in the equipment operating condition topology network. Then, according to the order of timestamps, all matching nodes are sorted and concatenated to form a node sequence. The connection relationship between two adjacent matching nodes in the node sequence corresponds to the topological connection edge between two logical nodes in the equipment operating condition topology network. This completely restores the complete process of the target test equipment transitioning from one operating condition state to another during continuous operation, and finally obtains a dynamic running trajectory node sequence that can characterize the continuous change of the target test equipment's operating state over time.

[0056] Preferably, a time-series smoothing calculation is performed on the dynamic operating trajectory node sequence to generate a smooth trajectory sequence. The dynamic operating trajectory node sequence is formed by concatenating matching nodes corresponding to discrete-time sampling points. Affected by instantaneous operating condition fluctuations, data acquisition noise, and abnormal data jumps during equipment operation, node jumps that do not conform to the physical operating laws of the equipment may occur in the dynamic operating trajectory node sequence. Such jumps affect the trajectory's ability to represent the true operating state of the equipment. During the execution of the time-series smoothing calculation, firstly, based on the historical state transition data of the target test equipment in the integrated test data management system, smoothing constraint rules for equipment state transitions are set. These rules clarify the reasonable path and time span requirements for transitions between different operating conditions of the equipment. Then, based on the smoothing constraint rules, abnormal jump nodes in the dynamic operating trajectory node sequence are corrected to eliminate trajectory distortion caused by data acquisition noise and instantaneous fluctuations. Finally, continuous interpolation processing is performed on the corrected node sequence to convert the discrete node sequence into a smooth trajectory sequence that conforms to the physical operating laws of the equipment and can represent the continuous change process of the equipment state.

[0057] Preferably, the smooth trajectory sequence is subjected to perception attribute encapsulation processing to generate an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment. The core purpose of perception attribute encapsulation processing is to assign complete equipment condition perception attributes to each trajectory point in the smooth trajectory sequence, so that the final generated equipment condition perception space can fully carry the full-dimensional information of equipment operation, providing complete data support for subsequent task load extrapolation and measurement source prediction. During the execution of perception attribute encapsulation processing, firstly, each trajectory point in the smooth trajectory sequence is associated with the full-dimensional attribute data of the target test equipment at the corresponding time node, including equipment unit energy consumption, operating frequency, maintenance management ledger records, depreciation unit cost, maintenance execution records, and measurement verification results; then, each trajectory point is associated with the corresponding equipment maintenance management semantics and depreciation cost weight, clarifying the degree of influence of the equipment status corresponding to the trajectory point on the degradation of measurement performance and the corresponding full life cycle cost impact; finally, the smooth trajectory sequence with completed attribute associations is structurally integrated to form a structured space that can fully represent the changes in operating status of the target test equipment in the time dimension, the process of operating condition transformation in the spatial dimension, and the performance degradation trend in the attribute dimension, i.e., the equipment condition perception space.

[0058] Preferably, the constructed equipment condition awareness space provides a complete input foundation for subsequent task load simulation models. Unlike traditional solutions that rely solely on fixed time points and static parameters for equipment status management, the equipment condition awareness space fully integrates real-time operating data, full lifecycle management data, and metrological performance change patterns of the target test equipment. It can dynamically and continuously characterize the equipment's spatiotemporal operating trajectory and performance degradation trends. When subsequently calling the task load simulation model, the system can directly extract comprehensive operating status information and condition transition trajectories from the equipment condition awareness space, performing multi-level recursive state simulations of the equipment condition. This ensures that the simulation results closely match the actual operating state of the equipment, more accurately predicting the drift of equipment values ​​with task loads, and providing a reliable predictive basis for generating adaptive traceability time points.

[0059] Optionally, the task load inference model includes the following structural layers: a quantity spatiotemporal semantic encoding layer, an accuracy recursive evolution layer, and a source trajectory reconstruction layer. In step 2, the preset task load inference model is invoked to perform multi-level recursive inference of the equipment operating conditions in the equipment operating condition perception space to obtain the time-domain state inference trajectory of the target experimental equipment. This includes: performing quantity neighborhood association feature aggregation processing on the equipment operating condition perception space through the quantity spatiotemporal semantic encoding layer to generate a perception state code representing the instantaneous quantity state of the equipment; performing nonlinear accuracy degradation recursive evolution processing on the perception state code through the accuracy recursive evolution layer to obtain a multi-level hidden state evolution sequence recording the quantity drift trend; performing quantity deviation space mapping processing on the multi-level hidden state evolution sequence through the source trajectory reconstruction layer to obtain a discrete trajectory coordinate set recording the quantity prediction value, and performing continuous source trajectory interpolation fitting processing on the discrete trajectory coordinate set to generate the time-domain state inference trajectory of the target experimental equipment.

[0060] Preferably, in the specific technical implementation of step 2, the pre-set task load inference model is a multi-level time-series inference structure designed for the scenario of traceability prediction of test equipment values. The model sequentially sets up three serially connected structural layers: a value spatiotemporal semantic encoding layer, an accuracy recursive evolution layer, and a traceability trajectory reconstruction layer. The output of the previous structural layer is directly used as the input processing object of the next structural layer, thus forming a complete equipment operating condition inference link. The core input of the model is the equipment operating condition perception space constructed in step 1. The equipment operating condition perception space fully carries the spatiotemporal operation trajectory of the target test equipment, including all-dimensional information such as equipment unit energy consumption, operating frequency, maintenance management ledger records, depreciation unit cost, maintenance execution records, metering cycle calibration data, and equipment operating condition transition trajectory recorded in the comprehensive test data management system, providing complete basic data and feature support for the multi-level state recursive inference of the model.

[0061] Preferably, a spatial-temporal semantic coding layer is used to aggregate the neighborhood association features of the equipment operating condition perception space to generate a perception state code representing the real-time measurement state of the equipment. The core function of the spatial-temporal semantic coding layer is to extract core association features directly related to the measurement state of the equipment from the high-dimensional equipment operating condition perception space, eliminate redundant information unrelated to measurement drift, and provide concise and highly correlated feature inputs for subsequent accuracy evolution deduction. In the process of the spatiotemporal semantic coding layer of measurement values, the neighborhood measurement value attribute scanning process is first performed on the equipment operating condition perception space. Along the time dimension of the equipment running trajectory, a corresponding neighborhood time range is defined for each trajectory node. The full-dimensional operating condition attribute data of each trajectory node and its neighborhood range are scanned. The magnitude of attribute changes and the degree of correlation between measurement value states between adjacent operating condition nodes are statistically analyzed. Based on this, a local topological correlation matrix is ​​constructed. The rows and columns of the local topological correlation matrix correspond to continuous trajectory nodes in the equipment operating condition perception space. The element at the intersection of the row and column of the matrix is ​​the value of the correlation between the measurement value states of the corresponding two trajectory nodes. The correlation value is positively correlated with the similarity of the operating condition attributes of the two nodes and the continuity of the change in equipment measurement performance.

[0062] Preferably, after constructing the local topological correlation matrix, the measurement spatiotemporal semantic coding layer performs source semantic modeling on the local topological correlation matrix to obtain node interaction semantic features that characterize the influence of environment and load on equipment measurements. During the source semantic modeling process, the equipment lifecycle management rules and historical data recorded in the integrated test data management system are combined to inject corresponding business semantic constraints into the local topological correlation matrix. The business semantic constraints include the restorative effect of equipment maintenance events on metrological performance, the stabilizing effect of maintenance operations on equipment operating status, the deteriorating effect of heavy-load operating conditions on measurement accuracy, and the resetting effect of metrological calibration operations on measurement deviations. Based on the injected business semantic constraints, the node correlation degree in the local topological correlation matrix is ​​semantically weighted and corrected to extract node interaction semantic features that can reflect the intrinsic relationship between operating condition changes, business operations, and equipment measurement status. The semantic features of node interaction are then compressed in terms of magnitude. While fully preserving the core feature information related to the device's magnitude status, the dimensional redundancy of the features is reduced. Finally, a perception state code with uniform dimensions that can accurately represent the device's real-time magnitude status is generated. The perception state code fully condenses the magnitude accuracy status of the device under the current operating condition, the cumulative impact of historical operating condition changes on the magnitude, and the effect of business management operations on the magnitude status, providing high-quality feature input for the subsequent accuracy recursive evolution layer.

[0063] Preferably, the accuracy recursive evolution layer takes the perceived state code output by the measurement spatiotemporal semantic coding layer as input and performs nonlinear accuracy degradation recursive evolution processing on the perceived state code to obtain a multi-level hidden state evolution sequence that records the measurement drift trend. The core function of the accuracy recursive evolution layer is to deduce the nonlinear degradation process of the equipment's measurement accuracy in the future time domain based on the equipment's historical and current measurement state characteristics, and to fully record the temporal evolution trend of the equipment's measurement drift. In the process of accuracy recursive evolution layer, the time-series accuracy gating recursive processing is first used to recursively calculate the perception state code on the continuous time series. During the recursive calculation, a gating mechanism is set to filter the input perception state code, focusing on retaining the feature components that can reflect the trend of equipment measurement accuracy changes, filtering out non-substantial feature interference caused by instantaneous operating condition fluctuations, and accumulating and recording the long-term deterioration effect of historical operating conditions on equipment accuracy. In this way, intermediate state evolution components reflecting the trend of equipment accuracy change are obtained. The intermediate state evolution components are distributed according to the time series, and the intermediate state evolution component corresponding to each time node fully carries the predicted state of equipment accuracy at that node and the historical cumulative impact.

[0064] Preferably, after obtaining the intermediate state evolution component, the accuracy recursive evolution layer performs multi-dimensional nonlinear value drift mapping processing on the intermediate state evolution component to obtain multi-scale evolution features covering different time dimensions. Then, the multi-scale evolution features are encapsulated with degradation trajectory features to generate a multi-level hidden state evolution sequence. During the multi-dimensional nonlinear value drift mapping process, based on the historical measurement data, fault maintenance data, and heavy-load operation records of the target test equipment recorded in the integrated test data management system, a nonlinear mapping relationship for equipment value drift is constructed. This mapping relationship can characterize the nonlinear correlation between equipment operating load, energy consumption accumulation, maintenance operations, and the magnitude of measurement accuracy drift. Based on this mapping relationship, the intermediate state evolution component is mapped to three different time dimensions: short-term, medium-term, and long-term. This yields the instantaneous change characteristics of equipment accuracy in the short-term time domain, the trend change characteristics in the medium-term time domain, and the cumulative degradation characteristics in the long-term time domain, which are then integrated to form multi-scale evolution features covering different time dimensions. The multi-scale evolutionary features are then encapsulated as degradation trajectory features. The evolutionary features of different time dimensions are structurally integrated according to time series and hierarchical relationships to form a multi-level feature sequence, namely a multi-level hidden state evolution sequence. Each level of the multi-level hidden state evolution sequence corresponds to the accuracy evolution feature of a time scale, and each sequence node corresponds to the hidden state of equipment accuracy at a time node. This fully records the continuous degradation trend and value drift law of equipment measurement accuracy in the future time domain, providing a complete evolutionary feature foundation for the subsequent source tracing trajectory reconstruction layer.

[0065] Preferably, the source trajectory reconstruction layer takes the multi-level hidden state evolution sequence output by the accuracy recursive evolution layer as input, and performs spatial mapping processing on the multi-level hidden state evolution sequence to obtain a discrete trajectory coordinate set recording the estimated quantity values. The core function of the source trajectory reconstruction layer is to convert the abstract hidden state evolution features into quantifiable time-domain trajectories of equipment quantity deviations, providing directly analyzable trajectory data for the subsequent generation of physical operation degradation features. In the process of spatial mapping of measurement deviations, a measurement parameter space is first constructed. The dimensions of the measurement parameter space correspond to the equipment measurement performance evaluation parameters recorded in the comprehensive test data management system, including core measurement parameters such as the magnitude of equipment measurement deviation, measurement accuracy level, probability of exceeding tolerance, and validity period of measurement traceability. Then, each hidden state node in the multi-level hidden state evolution sequence is projected onto the constructed measurement parameter space, and the abstract hidden state characteristics are converted into specific measurement parameter estimates. Each hidden state node corresponds to a coordinate point in the measurement parameter space, and the value of each dimension of the coordinate point corresponds to the estimated value of the measurement parameter at the next time node. The coordinate points corresponding to all time nodes are integrated to form a discrete trajectory coordinate set. The discrete trajectory coordinate set completely records the discrete estimated values ​​of the measurement deviation of the target test equipment at future continuous time nodes.

[0066] Preferably, after obtaining the discrete trajectory coordinate set, the source trajectory reconstruction layer performs continuous source trajectory interpolation fitting on the discrete trajectory coordinate set to obtain a preliminary fitted trajectory that conforms to the physical degradation law. Then, continuous measurement error reconstruction processing is performed on the preliminary fitted trajectory to generate the time-domain state projection trajectory of the target test equipment. During the continuous source trajectory interpolation fitting process, a spline interpolation method conforming to the physical degradation law of the equipment is used to continuously interpolate and fit the discrete coordinate points in the discrete trajectory coordinate set, eliminating trajectory jumps between discrete points. This ensures that the fitted trajectory conforms to the physical law of gradual degradation of equipment measurement accuracy with accumulated operating load and time, thus obtaining the preliminary fitted trajectory. The preliminary fitted trajectory is then subjected to continuous measurement error reconstruction processing. Based on the historical metrological verification data of the target test equipment and the statistical results of measurement deviation prediction errors recorded in the integrated test data management system, an error correction model is constructed. The error correction model is used to correct the measurement deviation prediction values ​​at each continuous time point on the preliminary fitted trajectory, compensating for the systematic and random errors generated during the prediction process. Finally, a time-domain state projection trajectory is generated that can accurately characterize the continuous change process of metrological accuracy and the continuous evolution law of measurement deviation of the target test equipment in the future time domain. The time-domain state projection trajectory fully quantifies the drift state of the equipment's measurement values ​​with the task load, providing direct trajectory data support for the subsequent extraction and generation of physical operation degradation characteristics.

[0067] Preferably, the three structural layers of the task load extrapolation model are deeply adapted to the equipment lifecycle management data in the integrated test data management system throughout the processing, forming a complete functional support and logical connection relationship between the structural layers. The spatial-temporal semantic encoding layer of the measurement values ​​realizes the dimensionality reduction and condensation from the high-dimensional operating condition space to the low-dimensional measurement value features, filtering redundant information for the extrapolation calculation of the accuracy recursive evolution layer and improving the correlation between the extrapolation results and the equipment measurement value status; the accuracy recursive evolution layer realizes the nonlinear extrapolation from the historical current state to the future evolution trend, completely restoring the degradation law of equipment measurement accuracy, and providing full-time-scale feature support for the trajectory generation of the traceability trajectory reconstruction layer; the traceability trajectory reconstruction layer realizes the transformation and correction from the abstract hidden state to the concrete measurement value trajectory, and the final output time-domain state extrapolation trajectory can be directly used for the extraction of equipment measurement value degradation features and the prediction of traceability time nodes. This model differs from the traditional fixed-cycle scheme that relies solely on static time nodes. Through multi-level serial processing, it can dynamically adapt to the actual operating conditions of the equipment, maintenance operations, and the impact of changes in task load on metering performance. The time-domain state projection trajectory obtained is more consistent with the actual operating state of the equipment, providing a more reliable predictive basis for the subsequent generation of adaptive traceability time nodes.

[0068] Optionally, the spatiotemporal semantic coding layer includes a neighborhood quantity scanning module, a source semantic modeling module, and a quantity feature compression module. The spatiotemporal semantic coding layer performs neighborhood correlation feature aggregation processing on the equipment operating condition perception space to generate a perception state code. This includes: using the neighborhood quantity scanning module to scan the neighborhood quantity attributes of the equipment operating condition perception space to obtain a local topological correlation matrix reflecting the correlation degree between adjacent operating conditions; using the source semantic modeling module to perform source semantic modeling processing on the local topological correlation matrix to obtain node interaction semantic features characterizing the influence of environment and load on equipment quantities; and using the quantity feature compression module to perform quantity dimension compression processing on the node interaction semantic features to generate a perception state code.

[0069] Preferably, the spatiotemporal semantic coding layer is the first-layer processing structure of the task load inference model. Its core input is the equipment condition perception space constructed in step 1, and its core output is the perception state code that can characterize the real-time quantitative state of the equipment. The coding layer is equipped with three serially connected functional modules: a neighborhood quantitative scanning module, a source semantic modeling module, and a quantitative feature compression module. The output of the previous module is directly used as the input processing object of the next module, forming a complete quantitative neighborhood association feature aggregation processing link. The equipment condition perception space fully carries the spatiotemporal operation trajectory of the target test equipment, including the basic information of the test equipment, maintenance management ledger, maintenance management data, metering cycle management data, real-time operating frequency of the equipment, unit energy consumption of the equipment, depreciation unit cost, and other full-dimensional condition attribute information recorded in the comprehensive test data management system, providing complete basic data support for the entire process of the coding layer.

[0070] Preferably, the neighborhood measurement attribute scanning module performs neighborhood measurement attribute scanning processing on the equipment operating condition perception space to obtain a local topological correlation matrix reflecting the correlation between adjacent operating conditions. During the execution of the neighborhood measurement attribute scanning processing, firstly, along the time dimension of the equipment's operating trajectory within the equipment operating condition perception space, a corresponding neighborhood time range is defined for each operating condition node on the trajectory. The definition of the neighborhood time range is adapted and adjusted based on the test task cycle, maintenance cycle, and measurement cycle of the target test equipment recorded in the integrated test data management system to ensure that the neighborhood range of each operating condition node can fully cover the impact cycle corresponding to the change in operating condition of that node. Then, for each operating condition node, the full-dimensional operating condition attribute data of that node and its neighborhood time range are scanned. The magnitude of attribute changes between adjacent operating condition nodes, the continuity of equipment measurement value status, and the impact of operating condition transitions on measurement accuracy are statistically analyzed to quantify the correlation between the measurement value statuses of adjacent operating condition nodes.

[0071] Preferably, after the neighborhood measurement value scanning module completes the scanning and correlation quantification of all operating condition nodes, it constructs a local topological correlation matrix based on the quantified correlation degree of the measurement value states of adjacent operating condition nodes. The rows and columns of the local topological correlation matrix correspond to continuous operating condition nodes arranged in a time sequence within the equipment operating condition perception space. The element at the intersection of the row and column represents the correlation degree of the measurement value states between the operating condition node corresponding to that row and the operating condition node corresponding to that column. The correlation degree is positively correlated with the similarity of the attributes of the two operating condition nodes, the continuity of changes in equipment measurement performance, and the stability of the impact of operating condition transitions on the measurement value states. For two operating condition nodes that are adjacent in the time dimension, have small changes in operating condition attributes, and have continuous and stable measurement value states, the corresponding correlation degree is high; for two operating condition nodes that are far apart in the time dimension, have abrupt changes in operating condition attributes, or have jumps in measurement value states, the corresponding correlation degree is low. The constructed local topological correlation matrix fully quantifies the correlation relationships of measurement value states between different operating condition nodes throughout the entire operating cycle of the equipment, providing a structured topological data foundation for the subsequent processing of the source tracing semantic modeling module.

[0072] Preferably, the local topological association matrix is ​​processed using a source-tracing semantic modeling module to obtain the node interaction semantic features characterizing the influence of environment and load on equipment measurements. The core function of source-tracing semantic modeling is to inject the business semantic constraints of the equipment lifecycle operation and maintenance management in the integrated test data management system into the local topological association matrix, which only quantifies numerical correlation, so that the node association relationships within the matrix can conform to the actual business logic of test equipment measurement source-tracing management. In the processing, a business semantic constraint system is first constructed based on the historical operation and maintenance data and measurement data of the target test equipment recorded in the integrated test data management system. The business semantic constraint system includes the restorative effect of equipment fault repair events recorded in the maintenance management ledger on measurement performance, the stabilizing effect of equipment maintenance operations recorded in maintenance management on operating status, the deteriorating effect of heavy-load operation conditions on measurement accuracy, the resetting effect of measurement calibration operations recorded in measurement cycle management on measurement deviation, and the long-term impact rules of equipment operating frequency and energy consumption accumulation on measurement performance.

[0073] Preferably, the source tracing semantic modeling module, based on the constructed business semantic constraint system, performs semantic weighting correction on the node correlation degree values ​​in the local topology correlation matrix to generate node interaction semantic features. During the semantic weighting correction process, for each set of working condition nodes in the local topology correlation matrix, the correlation degree value is adjusted by combining the operation and maintenance business events, working condition load changes, and metering status changes that occur between the two nodes, matching the corresponding semantic weights in the business semantic constraint system, and correcting the original correlation degree value. For scenarios where business events such as metering calibration and maintenance operations occur between the two nodes that can repair the measurement status, the semantic correlation weight between the two nodes is increased accordingly, reflecting the positive intervention effect of operation and maintenance operations on the measurement status. For scenarios where events such as fault repair and heavy load operation occur between the two nodes that may degrade the measurement status, the semantic correlation weight between the two nodes is adjusted accordingly, reflecting the negative impact of abnormal working conditions and fault events on the measurement status. After the correction is completed, the semantic information of the association of all nodes in the local topology association matrix is ​​structured and integrated to extract the node interaction semantic features that can reflect the inherent relationship between changes in working conditions, business operations, load changes and equipment value status. The node interaction semantic features fully carry the inherent laws of the equipment value status being affected by the external environment, task load and operation and maintenance, providing a highly correlated semantic feature foundation for the subsequent value feature compression module.

[0074] Preferably, the semantic features of node interactions are compressed in terms of their dimensions using a value compression module to generate a perceptual state code representing the real-time value status of the device. The core purpose of the value compression is to reduce the dimensional redundancy of the semantic features of node interactions while fully preserving the core feature information related to the device's value status and measurement accuracy degradation. This provides a unified and focused feature input for the subsequent accuracy recursive evolution layer. During the processing, the importance of each dimension of the node interaction semantic features is first ranked based on its correlation with the device's value status. The higher the correlation between a feature and changes in device measurement accuracy or value drift trends, the higher its importance ranking. Then, based on the dimensional requirements of the input features from the task load inference model, the target dimension for feature compression is set. Following the feature importance ranking, the core feature components with the highest correlation to the device's value status are retained, while redundant feature components and noise interference components unrelated to value status changes are filtered out.

[0075] Preferably, the measurement feature compression module incorporates a core feature protection mechanism during feature compression to ensure that key feature components related to metrological cycle management, maintenance management, and upkeep management within the integrated test data management system are not filtered out. The core feature protection mechanism covers feature components corresponding to equipment metrological verification results, fault repair events, maintenance operations, equipment operating load and energy consumption accumulation, and equipment depreciation costs. These feature components are all directly related to the changing trends in equipment measurement accuracy and are fully preserved during the compression process. After feature screening and dimensional compression, the retained core feature components are standardized to unify the numerical range of all feature components, ultimately generating a perception state code with fixed dimensions that can accurately characterize the real-time measurement status of the target test equipment. The perception state coding fully condenses the measurement accuracy status of the equipment under the current operating conditions, the cumulative impact of historical operating condition changes on the measurement values, the effect of operation and maintenance management on the measurement status, and the driving law of task load on measurement drift. It can directly adapt to the input requirements of the subsequent accuracy recursive evolution layer and provide a high-quality feature foundation for the recursive deduction of the equipment measurement accuracy degradation trend.

[0076] Preferably, the three functional modules of the quantity spatiotemporal semantic coding layer form a complete functional support and logical connection relationship. The neighborhood quantity scanning module realizes the transformation from the high-dimensional equipment operating condition perception space to the structured topological association matrix, completes the basic quantification of the quantity correlation degree between operating condition nodes, and provides a numerical topological foundation for the source tracing semantic modeling module. The source tracing semantic modeling module realizes the upgrade from numerical correlation to business semantic correlation, injects business semantics that fits the actual management of test equipment quantity values ​​into the topological association matrix, and enables the extracted node interaction semantic features to truly reflect the inherent driving law of equipment quantity value state changes, providing high-value semantic features for the quantity value feature compression module. The quantity value feature compression module realizes the simplification from high-dimensional semantic features to low-dimensional focused coding, eliminates redundant information while fully retaining the core quantity value related features, and the final output perception state code can directly adapt to the processing needs of the subsequent inference layer. This coding layer differs from traditional solutions that simply extract equipment operating parameters. It deeply integrates multi-dimensional operation and maintenance management data and business rules from the comprehensive test data management system, enabling the generated perception state code to more accurately represent the true quantitative state of the equipment and providing reliable feature support for more accurate subsequent inference of quantitative drift trends.

[0077] Optionally, the accuracy recursive evolution layer includes an accuracy gating calculation module, a magnitude drift mapping module, and a degradation feature encapsulation module. The accuracy recursive evolution layer performs nonlinear accuracy degradation recursive evolution processing on the perceived state code to obtain a multi-level hidden state evolution sequence, including: using the accuracy gating calculation module to perform time-series accuracy gating cyclic recursive processing on the perceived state code to obtain intermediate state evolution components reflecting the trend of equipment accuracy changes; using the magnitude drift mapping module to perform multi-dimensional nonlinear magnitude drift mapping processing on the intermediate state evolution components to obtain multi-scale evolution features covering different time dimensions; and using the degradation feature encapsulation module to encapsulate the multi-scale evolution features with degradation trajectory features to generate the multi-level hidden state evolution sequence.

[0078] Preferably, the accuracy recursive evolution layer is the second-layer processing structure of the task load extrapolation model. Its core input is the perceptual state encoding output from the quantity spatiotemporal semantic encoding layer, and its core output is a multi-level hidden state evolution sequence recording the equipment quantity drift trend. The evolution layer internally sets up three serially connected functional modules: an accuracy gating calculation module, a quantity drift mapping module, and a degradation feature encapsulation module. The output of the previous module directly serves as the input processing object of the next module, forming a complete nonlinear accuracy degradation recursive evolution processing link. The perceptual state encoding fully condenses the quantity accuracy status of the target test equipment under the current operating condition, the cumulative impact of historical operating condition changes on the quantity, and the effect of operation and maintenance management operations on the quantity status. Its data foundation comes entirely from the test equipment basic information, maintenance management ledger, maintenance management data, metering cycle management data, equipment operating frequency, equipment unit energy consumption, depreciation unit cost, and other full-dimensional information recorded in the integrated test data management system, providing a highly correlated feature foundation for the full-process recursive evolution processing of the evolution layer.

[0079] Preferably, the perceived state code is subjected to time-series accuracy-gated recursive processing through an accuracy-gated calculation module to obtain intermediate evolution components reflecting the trend of equipment accuracy changes. The core function of the time-series accuracy-gated recursive processing is to perform recursive calculation on the perceived state code over a continuous time series. While filtering out instantaneous operating condition fluctuations and noise interference, it fully accumulates the long-term impact of historical operating conditions, maintenance operations, and task loads on the equipment's measurement accuracy, thus accurately capturing the continuous trend of equipment accuracy changes. During the processing, a gating mechanism is first set for the recursive process. The gating mechanism includes an update gating unit and a reset gating unit. The update gating unit controls the update magnitude of the perceived state code input at the current moment on the equipment accuracy evolution state, while the reset gating unit controls the degree of influence of historically accumulated accuracy degradation information on the current evolution state. The weight parameters of the two gating units are pre-trained and adapted based on the historical measurement data and maintenance data of the target test equipment in the integrated test data management system to ensure that the gating mechanism can fit the actual operating characteristics of the target equipment.

[0080] Preferably, during the execution of the time-series accuracy gating recursive processing, the accuracy gating calculation module dynamically adjusts the weights of the gating units in conjunction with the equipment's full lifecycle maintenance events recorded in the integrated test data management system to optimize the representation accuracy of intermediate evolution components. When the equipment undergoes a calibration operation at the corresponding time node, the reset gating unit automatically increases the reset weight, resetting historically accumulated accuracy degradation information and matching the actual physical laws governing the restoration of the equipment's measurement accuracy to a qualified state after calibration. When the equipment experiences a fault repair event at the corresponding time node, the update gating unit and the reset gating unit synchronously adjust their weights, resetting the sudden accuracy degradation component caused by the fault while retaining the cumulative degradation impact from long-term equipment operation. When the equipment performs maintenance operations at the corresponding time node, the update gating unit reduces the update magnitude of the degradation trend, matching the stabilizing effect of maintenance operations on the equipment's operating status and the repair effect on metrological performance. When the equipment is under heavy-load, high-frequency operating conditions, the update gating unit increases the update magnitude of the degradation trend, matching the accelerated degradation impact of high-load conditions on the equipment's metrological accuracy. After completing the gated recursive calculation of the entire time series, intermediate state evolution components are generated and continuously distributed according to the time series. The intermediate state evolution component corresponding to each time node fully carries the predicted state of the device's measurement accuracy at that node, the cumulative impact of historical operating conditions, and the intervention effect of operation and maintenance, providing a continuous accuracy evolution basis for the subsequent value drift mapping module.

[0081] Preferably, the intermediate state evolution components are processed using a multidimensional nonlinear magnitude drift mapping module to obtain multi-scale evolutionary characteristics covering different time dimensions. The core function of this multidimensional nonlinear magnitude drift mapping is to map the continuously distributed intermediate state evolution components to a measurement parameter space at different time scales, thereby uncovering the nonlinear degradation patterns of equipment measurement accuracy in short-term, medium-term, and long-term time dimensions, and solving the challenge of characterizing the nonlinear changes in equipment magnitude drift with task load and runtime. During the processing, a nonlinear mapping relationship library for equipment magnitude drift is first constructed based on historical measurement verification data, fault repair records, heavy-load operation records, and maintenance execution records of the target test equipment recorded in the integrated test data management system. This library comprehensively records the nonlinear correlation rules between equipment operating load, energy consumption accumulation, fault frequency, maintenance operations, measurement calibration, and the magnitude of equipment magnitude accuracy drift. These correlation rules can accurately characterize the different patterns of magnitude accuracy change under different operating conditions, such as accelerated degradation, repair and reset, and steady decay with runtime.

[0082] Preferably, the measurement drift mapping module, based on the constructed nonlinear mapping relation library, maps the intermediate evolution components to three different time dimensions: short-term, medium-term, and long-term, generating evolutionary features corresponding to the time dimensions and integrating them to form multi-scale evolutionary features covering different time dimensions. Specifically, the short-term time dimension range matches the single test task cycle of the target test equipment recorded in the integrated test data management system, and the generated short-term evolutionary features can characterize the instantaneous change trend of measurement accuracy and the risk of sudden deterioration within the upcoming test task cycle; the medium-term time dimension range matches the equipment's maintenance management cycle, and the generated medium-term evolutionary features can characterize the continuous decay trend of measurement accuracy and the repair effect of maintenance intervention between two adjacent maintenance operations; the long-term time dimension range matches the validity period of the equipment's metrological cycle management, and the generated long-term evolutionary features can characterize the cumulative deterioration trend of measurement accuracy and the evolution law of out-of-tolerance risk within the complete metrological cycle. During the mapping process, the module matches the corresponding association rules in the nonlinear mapping relationship library according to the characteristics of different time dimensions. For the short-term dimension, it strengthens the impact of instantaneous load fluctuations on the drift of measurement values; for the medium-term dimension, it strengthens the intervention impact of maintenance operations and fault repair on the measurement value status; and for the long-term dimension, it strengthens the cumulative impact of running time, energy consumption accumulation, and depreciation costs on the measurement value performance. The resulting multi-scale evolution features can fully cover all the evolution laws of equipment measurement accuracy under different time dimensions, providing full-time-scale feature support for the subsequent deterioration feature encapsulation module.

[0083] Preferably, the degradation trajectory features of multi-scale evolution features are encapsulated using a degradation feature encapsulation module to generate a multi-level hidden state evolution sequence. The core function of degradation trajectory feature encapsulation is to structurally and hierarchically integrate and encapsulate the multi-scale evolution features scattered across different time dimensions, forming a standardized sequence structure that can completely characterize the entire trajectory of equipment accuracy degradation and can be directly processed by the subsequent traceability trajectory reconstruction layer. During the processing, the multi-scale evolution features are first time-synchronized, aligning the short-term, medium-term, and long-term evolution features to the continuous time axis corresponding to the equipment operating condition perception space, ensuring that the evolution features of different time dimensions are completely matched at the time nodes and eliminating the time-series deviation between features of different dimensions. Then, the time-synchronized multi-scale evolution features are hierarchically divided, with short-term evolution features divided into the first level of the sequence, medium-term evolution features into the second level, and long-term evolution features into the third level. Each level corresponds to the accuracy evolution law of a time scale, and the levels form a complete hierarchical structure through feature association at time nodes.

[0084] Preferably, after completing the temporal synchronization and hierarchical division of multi-scale evolutionary features, the degradation feature encapsulation module structurally integrates the multi-level evolutionary features of each time node according to the time series, generating a multi-level hidden state evolution sequence. Each sequence node in the multi-level hidden state evolution sequence corresponds to a discrete time point on a continuous time axis. Each sequence node encapsulates the short-term, medium-term, and long-term evolutionary features of the corresponding time point, fully carrying the core information such as the predicted value of the equipment measurement accuracy, the magnitude of the value drift, the degradation rate, and the probability of exceeding tolerance at that time point. During the encapsulation process, the module sets up a core feature protection mechanism to ensure that the core degradation features related to the measurement cycle nodes, maintenance plan nodes, test task scheduling nodes, and fault repair records in the integrated test data management system are not lost. At the same time, the feature components in the sequence are standardized to make the numerical range of all feature components uniform, so that they can directly adapt to the input requirements of the subsequent traceability trajectory reconstruction layer. The resulting multi-level hidden state evolution sequence fully records the nonlinear degradation process of measurement accuracy and the continuous evolution trend of measurement drift of the target test equipment in the future time domain. It realizes the complete deduction from the current operating characteristics of the equipment to the future accuracy evolution law, and provides a complete and reliable feature basis for the subsequent generation of the equipment time domain state deduction trajectory and the extraction of physical operation degradation characteristics.

[0085] Preferably, the three functional modules of the accuracy recursive evolution layer form a complete functional support and logical connection relationship. The accuracy gating calculation module realizes the conversion from discrete perception state encoding to continuous accuracy evolution components, completes the basic characterization of the equipment accuracy change trend, filters irrelevant noise interference, retains the cumulative influence of historical operating conditions, and provides a continuous temporal evolution basis for the quantity drift mapping module. The quantity drift mapping module realizes the upgrade from a single temporal evolution component to multi-timescale evolution features, explores the nonlinear change law of equipment quantity drift in different time dimensions, fits the management needs of test tasks, maintenance, and measurement in different cycles in test equipment management, and provides full-time-scale degradation feature support for the degradation feature encapsulation module. The degradation feature encapsulation module realizes the integration from multi-dimensional dispersed features to structured hierarchical sequences, forming a standardized and directly reusable multi-level hidden state evolution sequence, and completes the whole process of equipment accuracy degradation recursive evolution. This evolution layer differs from traditional solutions that rely solely on linear degradation prediction based on fixed time periods. It deeply integrates multi-dimensional operation and maintenance data from the comprehensive test data management system with the actual operating patterns of the equipment. Through gated recursive loops and nonlinear mapping, it can more accurately characterize the nonlinear drift of equipment measurement accuracy with task load, operation and maintenance, and runtime. This provides more accurate projection results for adaptive prediction of subsequent measurement traceability time nodes, which are more closely aligned with the actual state of the equipment.

[0086] Optionally, the source trajectory reconstruction layer includes a quantity parameter projection module, a source trajectory smoothing module, and a continuous error reconstruction module. The source trajectory reconstruction layer performs quantity deviation spatial mapping processing on the multi-level hidden state evolution sequence to obtain a discrete trajectory coordinate set, and then performs continuous source trajectory interpolation fitting processing on the discrete trajectory coordinate set to generate the time-domain state projection trajectory of the target experimental equipment. This includes: using the quantity parameter projection module to perform quantity parameter spatial projection processing on the multi-level hidden state evolution sequence to obtain a time-domain quantity prediction point set representing the quantity deviation at future time points; using the source trajectory smoothing module to perform source trajectory spline smoothing processing on the time-domain quantity prediction point set to obtain a preliminary fitted trajectory conforming to the physical degradation law; and using the continuous error reconstruction module to perform continuous quantity error reconstruction processing on the preliminary fitted trajectory to generate the time-domain state projection trajectory of the target experimental equipment.

[0087] Preferably, the source trajectory reconstruction layer is the third processing layer of the task load extrapolation model. Its core input is the multi-level hidden state evolution sequence output by the accuracy recursive evolution layer, and its core output is the time-domain state extrapolation trajectory that can accurately characterize the continuous change process of the measurement accuracy of the target test equipment in the future time domain. The reconstruction layer is equipped with three serially connected functional modules: a measurement parameter projection module, a source trajectory smoothing module, and a continuous error reconstruction module. The output of the previous module is directly used as the input processing object of the next module, forming a complete measurement deviation space mapping and trajectory fitting processing link. The multi-level hidden state evolution sequence completely encapsulates the measurement accuracy evolution characteristics of the target test equipment in different time dimensions such as short-term, medium-term, and long-term. Its data foundation comes entirely from the comprehensive test data management system, which records the basic information of the test equipment, maintenance management ledger, maintenance management data, measurement cycle management data, equipment operating frequency, equipment unit energy consumption, depreciation unit cost, and other full-dimensional operation and maintenance information, providing full-time-scale feature support for the full-process trajectory generation and processing of the reconstruction layer.

[0088] Preferably, the multi-level latent state evolution sequence is processed by a measurement parameter space projection module to obtain a time-domain measurement value prediction point set representing the measurement value deviation at future time points. The core function of the measurement parameter space projection processing is to transform the abstract multi-level latent state evolution characteristics into quantifiable and analyzable equipment measurement performance parameters, realizing the dimensional transformation from latent state characteristics to concrete measurement value deviation data. In the processing, a measurement parameter space that is fully adapted to the measurement value traceability management scenario of the test equipment is first constructed. The dimensions of the measurement parameter space correspond to the core evaluation parameters of equipment measurement performance recorded in the comprehensive test data management system, including six core dimensions: equipment measurement value deviation amplitude, measurement accuracy level, measurement value out-of-tolerance risk probability, remaining validity period of measurement value traceability, influence coefficient of equipment operating load on measurement value, and repair coefficient of operation and maintenance on measurement value. The parameter thresholds and value ranges of each dimension are adapted and set based on the historical measurement verification data of the target test equipment and the requirements of national measurement technical specifications to ensure that the measurement parameter space can fit the actual measurement management needs of the equipment.

[0089] Preferably, after constructing the measurement parameter space, the measurement parameter projection module projects each sequence node in the multi-level hidden state evolution sequence sequentially into the measurement parameter space in chronological order, generating a time-domain measurement value prediction point set. During the projection process, the module sets appropriate projection weights for the evolution characteristics of different time dimensions based on the hierarchical characteristics of the multi-level hidden state evolution sequence. For short-term evolution characteristics, it strengthens the influence weight of instantaneous task load and operating condition fluctuations on measurement value deviation prediction, matching the measurement value change characteristics within a single test task. For medium-term evolution characteristics, it strengthens the influence weight of maintenance operations and fault repair events on measurement value deviation prediction, matching the measurement value decay law within the equipment maintenance cycle. For long-term evolution characteristics, it strengthens the influence weight of equipment running time, energy consumption accumulation, and depreciation costs on measurement value deviation prediction, matching the measurement value deterioration trend within the complete measurement cycle of the equipment. Simultaneously, the module combines the historical calibration nodes, maintenance plan nodes, and test task scheduling nodes recorded in the integrated test data management system to correct the projected parameters at the corresponding time points. This ensures that the predicted deviation of the measurement values ​​at the calibration nodes can match the accuracy reset effect after calibration, and that the predicted deviation of the measurement values ​​at the maintenance nodes can match the stabilizing effect of maintenance operations on measurement performance. The resulting time-domain predicted value point set is distributed along a continuous time axis, with each predicted point corresponding to the predicted value of the equipment measurement parameters at a future time node. This fully records the discrete change pattern of the equipment measurement value deviation in the future time domain, providing a discrete predicted data foundation for the subsequent traceability trajectory smoothing module.

[0090] Preferably, the source trajectory smoothing module performs source trajectory spline smoothing on the time-domain value prediction point set to obtain a preliminary fitted trajectory that conforms to the physical degradation law. The core function of the source trajectory spline smoothing is to transform the discretely distributed time-domain value prediction point set into a continuous value deviation change trajectory, while eliminating abnormal jumps between discrete prediction points, so that the fitted trajectory fully conforms to the objective physical law that the measurement accuracy of the test equipment gradually deteriorates with physical wear and operating load. During the processing, a cubic spline interpolation method adapted to the equipment's value degradation characteristics is selected to perform continuous interpolation fitting on the time-domain value prediction point set. The cubic spline interpolation method can ensure that the first and second derivatives of the fitted trajectory are continuous at each time node, perfectly matching the physical characteristics of the equipment's measurement performance changing continuously and steadily with the operating time, avoiding situations such as abrupt trajectory changes and broken line jumps that do not conform to the actual operating law of the equipment.

[0091] Preferably, during the spline smoothing process, the trajectory smoothing module combines the equipment's full lifecycle operation and maintenance events recorded in the integrated test data management system to set trajectory smoothing constraint rules, and performs boundary constraints and trend corrections on the fitted trajectory. The trajectory smoothing constraint rules are clearly defined: when the fitted trajectory covers scheduled operation and maintenance event nodes such as metering calibration, maintenance operations, and fault repair, trajectory inflection point constraints must be set at the corresponding nodes to match the intervention effect of the operation and maintenance events on the equipment's measurement status. For example, at the metering calibration node, the trajectory should show an inflection point where the measurement deviation is reset to the acceptable range; at the fault repair node, the trajectory should show a trend where the sudden change in measurement deviation caused by the fault is repaired; within the heavy-load test task range, the trajectory should show a trend of accelerated deterioration of the measurement deviation. This ensures that the fitted trajectory not only meets the mathematically continuous smoothness characteristics but also closely matches the changes in the measurement status during the actual operation and maintenance of the equipment. After completing the interpolation fitting and constraint correction, a preliminary fitting trajectory that conforms to the physical degradation law of the equipment is generated. The preliminary fitting trajectory is a continuous curve covering the entire prediction time domain. Each continuous time point on the curve corresponds to a continuous estimated value of the equipment's quantity deviation, providing a continuous trajectory basis for the subsequent continuous error reconstruction module.

[0092] Preferably, the preliminary fitted trajectory is processed by a continuous error reconstruction module to generate a time-domain state projection trajectory for the target test equipment. The core function of the continuous error reconstruction process is to compensate and correct the systematic and random errors in the preliminary fitted trajectory, improve the accuracy of the trajectory in predicting the future state of the equipment, and ultimately generate a time-domain state projection trajectory that can be directly used for extracting physical operation degradation features. In the process, based on the historical metrological verification data of the target test equipment, the error statistics of historical measurement deviation prediction results and actual verification results recorded in the integrated test data management system, a measurement prediction error correction model is first constructed. The error correction model can quantify the systematic and random errors generated during the prediction process. The systematic errors come from the inherent characteristics of the equipment and the fixed deviations of the prediction model, while the random errors come from the non-fixed deviations caused by operating condition fluctuations, data acquisition noise, and sudden changes in test tasks.

[0093] Preferably, the continuous error reconstruction module, based on the constructed error correction model, performs point-by-point error correction on the estimated deviation of the quantity values ​​at each continuous time point on the initially fitted trajectory, completing the continuous quantity value error reconstruction across the entire time domain. During the error correction process, the module sets appropriate error correction weights for prediction intervals in different time dimensions. For short-term prediction intervals, it focuses on correcting trajectory deviations caused by random errors, matching the uncertainty brought about by short-term operating condition fluctuations. For medium- and long-term prediction intervals, it focuses on correcting trajectory trend deviations caused by systematic errors, matching the fixed degradation deviations caused by inherent wear and tear of equipment during long-term operation. Simultaneously, the module combines historical measurement data and quantity prediction error statistics of similar equipment in the integrated test data management system to generalize and adapt the error correction model, avoiding correction deviations caused by insufficient historical data for the target equipment. After completing the error correction and reconstruction in the entire time domain, the final time domain state projection trajectory is generated. The time domain state projection trajectory completely and accurately represents the continuous change process of measurement accuracy, the continuous evolution law of measurement deviation, and the continuous change trend of out-of-tolerance risk of the target test equipment in the future prediction time domain. It realizes the complete transformation from the implicit state evolution characteristics of the equipment to the concrete measurement change trajectory, and provides direct and reliable trajectory data support for the subsequent extraction of physical and chemical operation degradation characteristics and adaptive prediction of measurement traceability time nodes.

[0094] Preferably, the three functional modules of the source trajectory reconstruction layer form a complete functional support and logical connection relationship. The quantity parameter projection module realizes the transformation from abstract hidden state sequence to concrete discrete prediction point set, and completes the dimensional mapping from feature space to measurement parameter space, providing a discrete prediction data foundation for the source trajectory smoothing module. The source trajectory smoothing module realizes the transformation from discrete prediction point set to continuous fitting trajectory, eliminates data jumps, conforms to the physical law of equipment quantity degradation, and provides a continuous trajectory foundation for the continuous error reconstruction module. The continuous error reconstruction module realizes the upgrade from preliminary fitting trajectory to more accurate time domain state deduction trajectory, compensates for prediction error, improves trajectory prediction accuracy, and completes the entire process of source trajectory reconstruction. This reconstruction layer differs from traditional solutions that rely solely on fixed-cycle linear value decay prediction. It deeply integrates multi-dimensional operation and maintenance data, equipment metrology management rules, and equipment physical degradation patterns from the comprehensive test data management system. Through serial processing of spatial projection, smooth fitting, and error reconstruction, the generated time-domain state projection trajectory can more accurately match the nonlinear change patterns of values ​​during actual equipment operation. This provides a more accurate and realistic prediction basis for the adaptive adjustment of the subsequent equipment value traceability cycle.

[0095] Optionally, generating materialized operational degradation features characterizing the drift state of the target test equipment's quantities with the task load includes: performing multi-scale time-frequency analysis processing on the time-domain state projection trajectory to extract high-frequency components of quantity drift that reflect the instantaneous characteristics of quantity shift; performing degradation-related semantic mapping processing on the high-frequency components of quantity drift to obtain a materialized operational degradation description characterizing the physical performance degradation state of the equipment; and performing feature dimensionality encapsulation processing based on the materialized operational degradation description to generate materialized operational degradation features characterizing the drift state of the target test equipment's quantities with the task load.

[0096] Preferably, the core input to the physical operation degradation feature generation stage is the time-domain state projection trajectory output by the task load extrapolation model, and the core output is the physical operation degradation feature that can accurately characterize the drift state of the target test equipment's values ​​with the task load. This stage consists of three sequentially connected processing steps: multi-scale time-frequency analysis processing, degradation correlation semantic mapping processing, and feature dimensional encapsulation processing. The output of the previous step directly serves as the input for the next step, forming a complete degradation feature extraction and generation chain. The time-domain state projection trajectory fully quantifies the continuous change process of the target test equipment's measurement accuracy, the evolution law of measurement deviation, and the changing trend of out-of-tolerance risk in the future prediction time domain. Its data foundation comes entirely from the comprehensive test data management system, which records basic information of the test equipment, maintenance management ledgers, maintenance management data, measurement cycle management data, equipment operating frequency, equipment unit energy consumption, depreciation unit cost, test task scheduling, and other full-dimensional operation and maintenance information, providing complete trajectory and data support for the entire process of degradation feature generation.

[0097] Preferably, the time-domain state projection trajectory is subjected to multi-scale time-frequency analysis processing to extract high-frequency components of the magnitude drift that reflect the instantaneous characteristics of the magnitude deviation. The core function of multi-scale time-frequency analysis processing is to transform the continuous magnitude deviation change trajectory in the time domain dimension into a multi-scale decomposition in the time-frequency domain dimension, separating different frequency components in the magnitude deviation change of the equipment, extracting high-frequency components that can reflect the instantaneous change characteristics of the magnitude deviation, and filtering out low-frequency stationary trend components and noise interference components that are unrelated to the physical performance degradation of the equipment. In the processing, based on the test task cycle, maintenance management cycle, and metrology management cycle of the target test equipment recorded in the integrated test data management system, a multi-scale decomposition level of time-frequency analysis is set. The decomposition level corresponds to the short-term scale of a single test task, the medium-term scale of adjacent maintenance operations, and the long-term scale of the complete metrology cycle, respectively, to ensure that the time-frequency analysis can cover the magnitude change characteristics of different management dimensions throughout the entire life cycle of the equipment.

[0098] Preferably, after completing the multi-scale decomposition of the time-domain state extrapolation trajectory, the multi-scale time-frequency analytical processing performs feature screening on the time-frequency components at different decomposition levels to extract high-frequency components of magnitude drift. During feature screening, for short-term decomposition results, the focus is on extracting high-frequency components related to instantaneous task load fluctuations and sudden changes in operating conditions. These components can reflect the instantaneous degradation impact of heavy-load operation and high-frequency start-stop operations on the accuracy of equipment magnitudes during a single test task. For medium-term decomposition results, the focus is on extracting high-frequency components related to equipment maintenance operations and fault repair events. These components can reflect the abrupt changes in the magnitude state of the equipment before and after maintenance intervention and the repair effect. For long-term decomposition results, the focus is on extracting high-frequency components related to long-term wear, energy consumption accumulation, and depreciation losses. These components can reflect the abrupt trend of magnitude deviation caused by the long-term degradation of the equipment's physical performance. After extracting high-frequency components at different scales, all effective high-frequency components at all levels are time-series synchronized and integrated to form complete high-frequency components with magnitude drift. These components fully preserve the instantaneous change characteristics of all physical performance degradation, operating load effects, and maintenance intervention effects in the changes of equipment magnitude deviation, providing a relatively accurate feature basis for subsequent degradation-related semantic mapping processing.

[0099] Preferably, the high-frequency components of the magnitude drift are subjected to degradation-related semantic mapping processing to obtain a materialized operational degradation description characterizing the physical performance degradation state of the equipment. The core function of degradation-related semantic mapping processing is to deeply correlate the numerical high-frequency components of magnitude drift with the equipment operation and maintenance management business semantics and physical performance degradation laws in the integrated test data management system. This transforms abstract numerical features into a structured description that can concretely characterize the physical performance degradation state of the equipment, solving the problem that numerical features cannot directly reflect the essence of equipment degradation. In the processing, a degradation-related semantic mapping rule base is first constructed based on the historical metrological verification data, fault repair records, maintenance execution data, heavy-load operation records, and equipment physical performance parameters of the target test equipment recorded in the integrated test data management system. The mapping rule base fully defines the correspondence between high-frequency components of magnitude drift with different characteristics and the physical performance degradation state of the equipment, the impact of operation and maintenance events, and the degradation effect of operating loads. This correspondence perfectly matches the actual operating characteristics of the test equipment and the metrological management business logic.

[0100] Preferably, the degradation-related semantic mapping processing is based on the constructed mapping rule library. It performs segment-by-segment semantic matching and mapping on high-frequency components of measurement drift to generate a structured physical description of operational degradation. During semantic mapping, for high-frequency components related to instantaneous load fluctuations, the corresponding operational degradation semantics in the mapping rule library are matched to clarify the task load type, runtime, physical wear degree of the equipment's metering components, and degradation magnitude of measurement accuracy. For high-frequency components related to fault repair events, the corresponding fault repair semantics in the mapping rule library are matched to clarify the degree of damage to the equipment's physical performance caused by the fault type, the repair effect of maintenance operations on the measurement status, and the degradation trend of equipment performance after repair. For high-frequency components related to maintenance operations, the corresponding maintenance stabilization semantics in the mapping rule library are matched to clarify the optimization effect of maintenance operations on the equipment's operating status and the delaying effect on metering performance degradation. For high-frequency components related to long-term operational wear, the corresponding performance degradation semantics in the mapping rule library are matched to clarify the physical performance aging degree corresponding to the equipment's cumulative runtime, energy consumption accumulation, and depreciation losses, and the long-term evolution trend of measurement deviation. After completing the semantic mapping of all components, all matched semantic information is structurally integrated with degradation type in chronological order to form a materialized operational degradation description. The materialized operational degradation description fully and concretely reveals the essence of physical performance degradation, driving factors and evolution law behind the changes in equipment value deviation, providing a structured semantic foundation for subsequent feature dimensional encapsulation processing.

[0101] Preferably, the physical operational degradation description is encapsulated using feature dimensionality to generate physical operational degradation features that characterize the drift state of the target test equipment's values ​​with the task load. The core function of feature dimensionality encapsulation is to convert the structured physical operational degradation description into a standardized, computable feature vector that can be directly used for subsequent degradation risk extrapolation, thus completing the final generation of physical operational degradation features. During the process, based on the processing requirements of evolutionary gradient statistics and risk cycle extrapolation calculation in subsequent step 3, core dimensions for feature encapsulation are set. These core dimensions include five categories: operating condition load degradation dimension, fault damage repair dimension, maintenance intervention effect dimension, long-term performance degradation dimension, and value deviation risk dimension. Each dimension corresponds to a type of semantic information in the physical operational degradation description, ensuring that the encapsulated features can completely cover all driving factors and degradation characteristics of equipment value drift.

[0102] Preferably, after the core dimensions are divided, the feature dimensionalization encapsulation process quantifies and integrates the semantic information in the description of physical operational degradation to generate the final physical operational degradation features. During the quantization and encoding process, the semantic information of each core dimension is combined with historical equipment data and metrological specifications recorded in the integrated test data management system to perform standardized numerical encoding. This ensures that the feature values ​​of each dimension can quantify the degree of degradation, the magnitude of impact, and the evolution trend of the corresponding type. Simultaneously, a core feature protection mechanism is set up to ensure that core degradation features related to equipment metrological cycle management, maintenance management, upkeep management, and test task scheduling are not lost, and to fully retain key information directly related to equipment measurement traceability management. After quantization and encoding, the feature values ​​of all dimensions are integrated and encapsulated according to a preset structure to form a unified and standardized physical operational degradation feature. This feature fully integrates the numerical change characteristics of equipment measurement deviation, the essential laws of physical performance degradation, and the intervention effects of operation and maintenance management, enabling a relatively accurate and comprehensive characterization of the full-dimensional characteristics of the target test equipment's measurement values ​​drifting with the task load.

[0103] Preferably, the three processing steps in the physical operation degradation feature generation process form a complete functional support and logical connection relationship. Multi-scale time-frequency analysis processing realizes the extraction of relatively accurate high-frequency feature components from continuous time-domain trajectories, filters irrelevant interference information, and locks the quantitative change characteristics directly related to equipment degradation, providing a relatively accurate numerical feature basis for degradation correlation semantic mapping processing. Degradation correlation semantic mapping processing realizes the transformation from abstract numerical features to concrete degradation semantic description, reveals the essence of physical performance degradation behind quantitative changes, fits the equipment operation and maintenance management business logic of the integrated test data management system, and provides a structured semantic basis for feature dimensional encapsulation processing. Feature dimensional encapsulation processing realizes the transformation from structured semantic description to standardized degradation features, and the generated physical operation degradation features can directly adapt to the processing requirements of evolution gradient statistics and quantitative deviation risk extrapolation calculation in subsequent step 3. This generation process differs from traditional methods that rely solely on simple statistical analysis of numerical deviations. It deeply integrates the physical performance degradation patterns of the equipment with comprehensive operational and maintenance information from the integrated test data management system. The generated physical degradation features not only characterize the surface phenomena of equipment value drift but also reveal the underlying degradation essence, providing comprehensive and reliable feature support for more accurate prediction of subsequent adaptive tracing time nodes.

[0104] Optionally, step 3, which involves statistically analyzing the evolution gradient of the physical operation degradation features to obtain the distribution of the magnitude offset evolution trend, includes: performing time-series sliding window segmentation on the physical operation degradation features to generate a set of degradation feature time-series components that record the feature evolution sequence; statistically analyzing the evolution gradient of the physical operation degradation features based on the feature differences between adjacent time windows in the set of degradation feature time-series components; and performing non-parametric kernel density estimation on the evolution gradient to obtain the distribution of the magnitude offset evolution trend.

[0105] Preferably, the core input of the evolutionary gradient statistics and magnitude offset evolution trend distribution generation step in step 3 is the physical operation degradation feature generated in step 2, and the core output is the magnitude offset evolution trend distribution that can fully characterize the temporal evolution law of equipment magnitude deviation. This step is set up with three sequentially connected processing steps: temporal sliding window segmentation processing, evolutionary gradient statistics processing, and nonparametric kernel density estimation processing. The output of the previous step is directly used as the input processing object of the next step, forming a complete degradation trend quantification and distribution generation link. The physical operation degradation feature fully integrates the numerical change characteristics of equipment magnitude deviation, the essential law of physical performance degradation, and the intervention effect of operation and maintenance management. Its data foundation comes entirely from the comprehensive test data management system, which records basic information of test equipment, maintenance management ledger, maintenance management data, metering cycle management data, equipment operating frequency, equipment unit energy consumption, depreciation unit cost, test task scheduling, and other full-dimensional operation and maintenance information, providing complete feature and data support for the entire process of this step.

[0106] Preferably, the physical degradation characteristics are segmented using a time-series sliding window process to generate a set of time-series components of degradation characteristics that record the evolution sequence of features. The core function of the time-series sliding window segmentation process is to segment and structurally integrate the continuous physical degradation characteristics across the entire time domain according to a preset time window, converting the continuous feature sequence into discrete time-series components that can be used for difference calculation and gradient statistics, while fully preserving the evolution characteristics of degradation features within different time windows. During the processing, a multi-scale sliding window system is first set up based on the test task cycle, maintenance management cycle, and metrology management cycle of the target test equipment recorded in the integrated test data management system. The sliding window system includes three types: short-term windows corresponding to a single test task, medium-term windows corresponding to adjacent maintenance operations, and long-term windows corresponding to the complete metrology cycle, ensuring that the window segmentation can fully match the degradation evolution characteristics of different management dimensions throughout the entire life cycle of the equipment.

[0107] Preferably, after setting up the multi-scale sliding window system, the time-series sliding window segmentation process adaptively adjusts the window sliding step size and window overlap rate, and completes feature segmentation across the entire time domain, generating a set of time-series components of deteriorated features. The adjustment rules for the window sliding step size and overlap rate perfectly match the equipment operation and maintenance events in the integrated test data management system. When the equipment is in a continuous and stable operating condition, a fixed step size and a fixed overlap rate are used for sliding segmentation to ensure the continuity of the feature sequence. When the time axis covers key event nodes such as equipment calibration, maintenance operations, fault repair, and heavy-load test tasks, the sliding step size is automatically reduced and the window overlap rate is increased to enhance the capture of details of changes in deteriorated features before and after key events, and to prevent feature abrupt changes caused by key events from being smoothly filtered out. After completing the sliding segmentation of the entire time domain, the physical operation degradation features within each time window are structurally encapsulated to form degradation feature time series components arranged continuously in time order. All time series components are integrated to form a degradation feature time series component set. Each component in this set fully retains the physical operation degradation characteristics of the device in each dimension within the corresponding time window, and the components maintain strict temporal continuity, providing a standardized time series data foundation for subsequent statistical calculation of evolution gradients.

[0108] Preferably, the evolution gradient of the physical operation degradation characteristics is statistically calculated based on the feature differences between adjacent time windows in the set of degradation feature time series components. The physical meaning of the evolution gradient is to quantitatively characterize the rate and direction of change of the physical operation degradation of the equipment. The positive or negative value of the gradient corresponds to the aggravation or mitigation of the equipment degradation degree, and the absolute value of the gradient corresponds to the rate and magnitude of the equipment degradation change. This gradient can reveal the inherent driving law of the equipment's value deviation relatively accurately. In the statistical processing, for each set of adjacent time windows in the set of degradation feature time series components, feature differences are calculated for the five core dimensions of the physical operation degradation characteristics: working condition load degradation dimension, fault damage repair dimension, maintenance intervention effect dimension, long-term performance degradation dimension, and value deviation risk dimension, to obtain the degradation feature change amount of each dimension. Then, combined with the duration of the corresponding time window, the equipment operating condition, and the type of operation and maintenance event, the feature change amount of each dimension is normalized to calculate the degradation change rate per unit time for each dimension, i.e., the single-dimensional evolution gradient.

[0109] Preferably, after calculating the single-dimensional evolutionary gradients for each dimension, the evolutionary gradients across all dimensions are time-series calibrated and validated to generate a standardized evolutionary gradient time-series sequence. During the time-series calibration process, the equipment calibration nodes, maintenance plan nodes, test task scheduling nodes, and fault repair records recorded in the integrated test data management system are used as time benchmarks to align and calibrate the time axes of the evolutionary gradients for each dimension. This eliminates time deviations caused by window segmentation, ensuring that the time-series nodes of the evolutionary gradients perfectly match the time nodes of actual equipment operation and maintenance events. During the validity validation process, gradient threshold constraints are set based on the objective laws of equipment physical performance degradation and historical degradation data in the integrated test data management system. Abnormal gradient values ​​exceeding the threshold constraints are traced and validated. If the abnormal gradient value corresponds to degradation mitigation caused by normal operation and maintenance events such as calibration and repair, the gradient value is retained and marked with an operation and maintenance event identifier. If the abnormal gradient value originates from data acquisition noise or non-substantial operating condition fluctuations, the gradient value is smoothed and corrected to eliminate invalid interference. After calibration and verification, the evolution gradients of each dimension are integrated in chronological order to form a standardized evolution gradient time series. This series records the rate of change and evolution trend of equipment physical operation degradation in the entire time domain in a complete and relatively accurate manner, providing a high-quality input basis for subsequent nonparametric kernel density estimation processing.

[0110] Preferably, the evolution gradient is subjected to nonparametric kernel density estimation to obtain the distribution of the magnitude offset evolution trend. The core advantage of nonparametric kernel density estimation is that it does not require prior assumptions that the equipment degradation trend and magnitude offset follow a fixed parameter distribution. It can fit the magnitude offset evolution law that closely matches the actual operating state of the equipment based entirely on the actual data distribution characteristics of the evolution gradient, thus solving the trend fitting bias problem caused by the mismatch between the distribution assumptions and the actual degradation characteristics in traditional parameter estimation methods. In the processing, based on the historical metrological verification data of the target test equipment, the historical magnitude offset data of similar equipment, and the physical performance degradation characteristics of the equipment recorded in the integrated test data management system, a kernel function that is suitable for the metrological characteristics of the test equipment is selected. The selection of the kernel function prioritizes matching the continuous and smooth evolution characteristics of the equipment magnitude offset with the running time and load accumulation, ensuring that the kernel density estimation can accurately capture the evolution law of the magnitude offset.

[0111] Preferably, after selecting the kernel function, the nonparametric kernel density estimation process performs adaptive bandwidth optimization and point-by-point density value calculation to complete the transformation from the evolutionary gradient distribution to the distribution of the magnitude shift evolution trend. During the adaptive bandwidth optimization process, the bandwidth of the kernel function is adaptively adjusted based on the data dispersion of the evolutionary gradient time series, the predicted time domain length of the equipment metrology cycle, and the historical data sample size in the integrated test data management system. This bandwidth adjustment simultaneously considers the smoothness of the distribution fit and the ability to capture details, avoiding excessive smoothing of deterioration and abrupt changes due to excessive bandwidth, or excessive noise interference in the distribution fit due to insufficient bandwidth. After bandwidth optimization, the kernel density value is calculated point-by-point across the entire time domain for the evolutionary gradient time series to obtain the probability density distribution of the evolutionary gradient. Then, based on the inherent correlation between the evolutionary gradient and the magnitude shift, the probability density distribution of the evolutionary gradient is converted into the temporal evolution distribution of the magnitude shift. Simultaneously, considering national metrological technical specifications and equipment metrological qualification thresholds, a confidence interval for the distribution is set, ultimately generating the magnitude shift evolution trend distribution. This distribution fully quantifies the probability distribution of the magnitude of the deviation, the evolution trend of the degradation rate, and the probability distribution of the risk of deviation at different time points in the prediction time domain. It can comprehensively and relatively accurately characterize the future evolution law of the deviation of the equipment's magnitude.

[0112] Preferably, the three processing steps in this stage form a complete functional support and logical connection relationship. The time-series sliding window segmentation process realizes the conversion from continuous degradation features to discrete time-series components, fully preserving the time-series evolution details of degradation features and providing a standardized time-series data foundation for evolutionary gradient statistics. The evolutionary gradient statistics process realizes the quantification from degradation feature differences to degradation change rates, accurately revealing the intrinsic driving law of equipment value offset and providing high-quality sequence input for nonparametric kernel density estimation. The nonparametric kernel density estimation process realizes the fitting from discrete gradient sequences to continuous value offset trend distributions. The generated value offset evolution trend distribution can directly adapt to the processing needs of subsequent value deviation risk probability field construction and degradation clock inference model invocation. This step differs from the traditional approach that relies solely on linear degradation prediction based on a fixed period. It deeply integrates multi-dimensional operation and maintenance data from the comprehensive test data management system. Through a full-process processing involving multi-scale window segmentation, multi-dimensional gradient statistics, and non-parametric distribution fitting, the generated value offset evolution trend distribution perfectly matches the non-linear degradation characteristics of the equipment during actual operation. This provides comprehensive and reliable trend data support for more accurate prediction of subsequent adaptive source tracing time nodes.

[0113] Optionally, step 3 involves performing periodic extrapolation of the measurement accuracy degradation risk based on the distribution of the measurement value offset evolution trend to generate an adaptive traceability time node sequence for the target test equipment. This includes: performing risk probability density mapping on the distribution of the measurement value offset evolution trend to obtain a measurement value deviation risk probability field characterizing the probability of the equipment's measurement values ​​exceeding the tolerance; calling the constructed degradation clock extrapolation model to perform future time domain risk boundary search processing on the measurement value deviation risk probability field to generate a set of risk boundary time points predicting risk boundary; and performing node offset adaptive correction processing on the equipment task scheduling constraints based on the risk boundary time point set to generate the adaptive traceability time node sequence for the target test equipment.

[0114] Preferably, in step 3, the core input to the extrapolation calculation of the measurement accuracy deterioration risk cycle and the generation of the adaptive traceability time node sequence is the previously generated distribution of the evolution trend of measurement value offset, and the core output is an adaptive traceability time node sequence that can match the actual deterioration state of the equipment and the needs of operation and maintenance management. This step is set up with three sequentially connected processing steps: risk probability density mapping processing, risk boundary search processing, and node offset adaptive correction processing. The output of the previous step is directly used as the input processing object of the next step, forming a complete link for deterioration risk extrapolation and traceability node generation. The distribution of the evolution trend of measurement value offset fully quantifies the probability distribution of the magnitude of measurement value offset, the evolution trend of the deterioration rate, and the probability distribution of measurement value deviation risk at different time nodes in the prediction time domain. Its data foundation comes entirely from the comprehensive test data management system, which records the basic information of the test equipment, maintenance management ledger, maintenance management data, measurement cycle management data, equipment operating frequency, equipment unit energy consumption, depreciation unit cost, test task scheduling, and other full-dimensional operation and maintenance information, providing complete trend and data support for the full-process extrapolation calculation of this step.

[0115] Preferably, the distribution of the trend of measurement deviation is subjected to risk probability density mapping to obtain a probability field of measurement deviation risk, which characterizes the probability of equipment measurement deviation. The core function of risk probability density mapping is to transform the amplitude distribution of equipment measurement deviation in the trend distribution of measurement deviation into the probability distribution of measurement deviation risk at the corresponding time node, constructing a continuous risk probability field across the entire time domain, and realizing the quantitative conversion from measurement deviation trend to deviation risk. In the process, based on the requirements of national metrological technical specifications, the equipment's factory metrological performance indicators, and the historical metrological verification qualification thresholds recorded in the comprehensive test data management system, a judgment benchmark for equipment measurement deviation is first set. This benchmark clarifies the qualified range boundary of equipment metrological accuracy. When the measurement deviation amplitude of the equipment exceeds this boundary, it is judged as metrological performance deviation, which cannot meet the metrological accuracy requirements of the test task.

[0116] Preferably, after setting the tolerance judgment benchmark, the risk probability density mapping process performs a point-by-point mapping transformation from the tolerance deviation amplitude to the tolerance risk probability for each time node in the full-time domain of the tolerance deviation evolution trend distribution, thus completing the construction of the tolerance risk probability field. During the mapping transformation, for each time node, based on the probability distribution of the tolerance deviation amplitude of that node in the tolerance deviation evolution trend distribution, the cumulative probability of the tolerance deviation amplitude exceeding the tolerance judgment benchmark is statistically calculated. This cumulative probability is the equipment tolerance risk probability for that time node. At the same time, combined with the equipment physical operation degradation characteristics recorded in the integrated test data management system, the tolerance risk probability is corrected by multi-dimensional weighting. Corresponding risk correction weights are set for the dimensions of working condition load degradation, fault damage repair, maintenance intervention effect, and long-term performance degradation. The weights corresponding to heavy load operation, fault not repaired, and long-term performance aging are positively increased, amplifying the tolerance risk probability in the corresponding scenarios. The weights corresponding to metering calibration, maintenance repair, and stable light load operation are negatively decreased, reflecting the suppression effect of operation and maintenance on tolerance risk. After completing the full-time domain mapping transformation and weighted correction, a continuous magnitude deviation risk probability field covering the entire prediction time domain is constructed. This probability field fully quantifies the magnitude deviation risk probability, risk evolution rate, and risk accumulation trend of the equipment at different time nodes in the future, providing a continuous risk data foundation for the risk boundary search and processing of the subsequent degradation clock extrapolation model.

[0117] Preferably, the constructed degradation clock extrapolation model is invoked to perform future time-domain risk boundary search processing on the probability field of measurement deviation risk, thereby generating a set of risk boundary time points for predicting risk thresholds. The degradation clock extrapolation model is a two-layer extrapolation structure designed for the evolution characteristics of metrological degradation risk in test equipment. It sequentially sets a risk trend extrapolation layer and a time-domain threshold optimization layer. The core input of the model is the probability field of measurement deviation risk, and the core output is the set of risk boundary time points where the equipment's measurement deviation risk reaches the warning threshold. First, the risk trend extrapolation layer performs time-series risk accumulation integration processing on the probability field of measurement deviation risk, obtaining a dynamic risk evolution surface that characterizes the risk evolution over time. The core function of time-series risk accumulation integration processing is to transform the instantaneous deviation risk probability at a single time point into a comprehensive degradation risk that accumulates over time, fully characterizing the cumulative effect of equipment metrological performance degradation, and avoiding prediction bias caused by judging the source node solely based on instantaneous risk.

[0118] Preferably, after constructing the dynamic risk evolution surface, the degradation clock prediction model uses a time-domain threshold optimization layer to perform risk boundary intersection solving on the dynamic risk evolution surface, generating a set of risk boundary time points. During this process, based on historical out-of-tolerance event data recorded in the integrated test data management system, metrological risk control experience with similar equipment, and the test task's requirement level for equipment metrological accuracy, a risk warning threshold for equipment measurement traceability is set. This threshold serves as the critical warning line for equipment metrological performance degradation. When the accumulated degradation risk of the equipment reaches this threshold, a measurement traceability operation must be performed to restore the equipment's metrological accuracy. Using the risk warning threshold as a benchmark, the dynamic risk evolution surface is intersected across the entire time domain to obtain the time node where the dynamic risk evolution surface intersects with the risk warning threshold. This node is the boundary time point of equipment metering degradation risk. Simultaneously, in conjunction with equipment metering cycle management requirements and maintenance management cycles, multiple levels of risk warning thresholds are set, including early warning thresholds, critical execution thresholds, and mandatory traceability thresholds. The boundary time points of the corresponding thresholds are solved for each. All boundary time points of all levels are integrated and deduplicated in chronological order to form a risk boundary time point set. This set fully covers the key time nodes of the entire process from risk warning to mandatory traceability, providing a core time benchmark for the subsequent correction and generation of adaptive traceability time nodes.

[0119] Preferably, based on the set of risk boundary time points, adaptive node offset correction processing is performed on the equipment task scheduling constraints to generate an adaptive traceability time node sequence for the target test equipment. The core function of the adaptive node offset correction processing is to adaptively adjust the traceability time nodes based on the risk boundary time points, combined with the actual equipment operation task schedule, maintenance plan, and resource constraints. This ensures that the generated traceability time nodes meet both the risk control requirements of equipment metrology performance and align with the actual operation and management plan of the equipment, avoiding conflicts between traceability operations and test tasks and maintenance plans. During the processing, constraints such as the future test task scheduling plan, equipment maintenance plan, equipment downtime window, and metrology resource availability plan for the target test equipment are first extracted from the integrated test data management system. This clarifies the effective time window for the equipment to perform traceability operations and the task operation window during which traceability operations are prohibited.

[0120] Preferably, after extracting the constraints of equipment operation and management, the node offset adaptive correction process performs adaptive node offset adjustment for each boundary time point in the risk boundary time point set, generating the final adaptive traceability time node sequence. The node offset adjustment rules are as follows: when the risk boundary time point is within the effective time window for the equipment to perform traceability operations, the boundary time point is directly determined as the basic traceability time node; when the risk boundary time point is within the prohibited window period for the equipment test task, the traceability time node is shifted forward to the nearest effective downtime window before the task operation, according to the principle of "advance rather than delay", to ensure that the traceability operation is completed before the equipment metrological performance reaches the risk threshold, avoiding metrological deviation problems during the test task; when the risk boundary time point coincides with the scheduled maintenance plan time of the equipment, the traceability time node and the maintenance plan time are merged to achieve coordinated execution of equipment maintenance and metrological traceability, reducing the number of equipment downtimes and operation and maintenance costs. After completing the adaptive offset adjustment for all boundary time points, all adjusted traceability time nodes are sorted chronologically. Simultaneously, considering the mandatory requirements of national metrological technical specifications for the maximum traceability period of equipment, an upper limit constraint on the traceability period is set. If the interval between two adjacent traceability time nodes exceeds the maximum mandatory period requirement, mandatory traceability time nodes are added within the interval, ultimately forming a standardized adaptive traceability time node sequence. This sequence perfectly matches the actual degradation trend of equipment metrological performance, test task scheduling requirements, and operation and maintenance management plans, realizing the transformation from fixed-cycle traceability to adaptive on-demand traceability based on the actual degradation state of the equipment. This provides a direct time node basis for the optimization of metrological cycle arrangement and adjustment of the traceability plan in subsequent step 4.

[0121] Optionally, the degradation clock extrapolation model includes: a risk trend extrapolation layer and a time-domain threshold optimization layer; the constructed degradation clock extrapolation model is invoked to perform future time-domain risk boundary search processing on the probability field of the magnitude deviation risk to generate a set of risk boundary time points for predicting risk boundary, including: performing time-series risk cumulative integration processing on the probability field of the magnitude deviation risk through the risk trend extrapolation layer to obtain a dynamic risk evolution surface characterizing the risk evolution over time; and performing risk boundary intersection solution processing on the dynamic risk evolution surface through the time-domain threshold optimization layer to generate a set of risk boundary time points for predicting risk boundary.

[0122] Preferably, the degradation clock extrapolation model is a dedicated two-layer extrapolation structure designed for the irreversible degradation of the metrological performance of test equipment and the nonlinear evolution law of measurement drift. It is the core processing unit connecting the quantification of measurement deviation risk and the prediction of traceability time nodes. The core input of the model is the probability field of measurement deviation risk constructed in the early stage, and the core output is the set of risk threshold time points when the equipment metrological degradation risk reaches the warning threshold. The model internally sets two functional layers in sequence: a risk trend extrapolation layer and a time domain threshold optimization layer. The output of the previous layer directly serves as the input processing object of the next layer, forming a complete processing link from continuous risk probability distribution to discrete risk threshold time points. Its underlying data foundation comes entirely from the basic information of test equipment, maintenance management ledger, maintenance management data, metrological cycle management data, historical operation and degradation data of equipment, and metrological risk control experience of similar equipment recorded in the integrated test data management system, ensuring that the extrapolation logic of the model is fully consistent with the actual business scenario of test equipment measurement traceability management.

[0123] Preferably, before calling the deterioration clock extrapolation model to perform risk boundary search processing, the input quantity value excess risk probability field is preprocessed through the risk trend extrapolation layer to complete time series standardization alignment and data validity verification. The probability field for measurement deviation risk fully quantifies the instantaneous probability of measurement deviation risk at each time node within the prediction time domain. However, its time series axis may have issues such as mismatch with equipment management cycle, uneven distribution of data points, and noise interference. The preprocessing operation first uses the equipment metrology cycle, maintenance management cycle, and test task scheduling cycle recorded in the integrated test data management system as a unified time benchmark to resample and standardize the time series axis of the probability field for measurement deviation risk, ensuring that the time nodes of the probability field are fully matched with the time benchmark of actual equipment operation and maintenance management. Then, based on the requirements of national metrological technical specifications for equipment metrological performance and the reasonable value range of historical metrological verification data, the probability values ​​of deviation risk in the probability field are validated to remove invalid outliers caused by operating condition fluctuations and data acquisition noise. The missing data points after validation are smoothed and supplemented based on the risk evolution trend of adjacent nodes, ultimately forming a standardized probability field for measurement deviation risk with time series standard and valid data, laying a compliant and reliable data foundation for subsequent time series risk accumulation integral processing.

[0124] Preferably, the standardized measurement deviation risk probability field is processed by time-series risk accumulation integration through a risk trend extrapolation layer to obtain a dynamic risk evolution surface characterizing the risk evolution over time. The core function of time-series risk accumulation integration is to transform the instantaneous deviation risk probability at a single time node into a comprehensive degradation risk that accumulates continuously over time, fully characterizing the irreversible cumulative effect of the degradation of the metrological performance of the test equipment, and solving the problem of prediction lag and excessive deviation caused by judging the source node solely based on instantaneous risk. In the processing, a multi-dimensional risk accumulation weight system is first constructed based on the physical operation degradation characteristics of the equipment recorded in the comprehensive test data management system. For the five core dimensions of working condition load degradation, fault damage repair, maintenance intervention effect, long-term performance decay, and measurement deviation risk, corresponding accumulation weight coefficients are set. The weight coefficients are pre-trained and adaptively adjusted based on the equipment's historical degradation data and the risk evolution law of similar equipment. The weights corresponding to heavy load operation, fault not repaired, and long-term performance aging are positively increased to amplify the risk accumulation effect in the corresponding scenarios, while the weights corresponding to metrological calibration, maintenance repair, and stable light load operation are negatively decreased to reflect the operation and maintenance. To suppress the accumulation of risk, the starting point of the prediction time domain is used as the starting point for integration. Based on the instantaneous out-of-range risk probability at each time node and combined with the cumulative weight coefficient of the corresponding dimension, the time-series risk accumulation integration is performed node by node to obtain the comprehensive cumulative degradation risk value corresponding to each time node. Finally, a three-dimensional dynamic risk evolution surface covering the entire prediction time domain is constructed with time as the horizontal axis, degradation dimension as the vertical axis, and cumulative degradation risk value as the vertical axis. This surface completely and continuously represents the dynamic evolution law of equipment metering degradation risk with time, operating conditions, and maintenance events, providing a continuous quantitative basis for solving the risk boundary of the subsequent time domain threshold optimization layer.

[0125] Preferably, a multi-level risk warning threshold system is constructed through a time-domain threshold optimization layer to adapt to equipment management needs and metrological specifications, providing a judgment benchmark for solving the intersection of risk boundaries. The construction of the multi-level risk warning threshold system is entirely based on the equipment management business needs in the integrated test data management system, the mandatory requirements of national metrological technical specifications, and the level requirements of test tasks for equipment metrological accuracy, avoiding the problem that a single threshold cannot adapt to the management needs of the entire equipment lifecycle and all scenarios. This threshold system is divided into three levels: early warning threshold, critical execution threshold, and mandatory traceability threshold. The early warning threshold is set based on the early warning cycle of historical out-of-tolerance events and the early preparation cycle of test tasks. It corresponds to the warning level where the cumulative degradation risk of the equipment reaches a point where traceability operations can be prepared in advance, allowing sufficient preparation time for the traceability schedule. The critical execution threshold is set based on the critical boundary of the equipment's metrological performance qualification range and the out-of-tolerance critical value of historical metrological verification data. It corresponds to the critical level where the equipment's metrological performance is about to exceed the qualification range, representing the optimal time to perform metrological traceability operations. The mandatory traceability threshold is set based on the maximum permissible error boundary of the equipment as stipulated in the national metrological technical specifications and the minimum metrological accuracy requirements of the test task. It corresponds to the mandatory level where the equipment's metrological performance has exceeded the qualification range and cannot meet the test requirements, representing the latest time when metrological traceability operations must be performed. Furthermore, the threshold system can be adaptively adjusted based on the equipment's importance level, the precision level of the test task, and the equipment's historical degradation rate. For high-value, high-precision, and core test equipment, the thresholds at each level can be tightened to provide early warning of risks and ensure that the equipment's metrological performance remains under control.

[0126] Preferably, the dynamic risk evolution surface is processed by risk boundary intersection solving through a time-domain threshold optimization layer to generate a set of risk boundary time points for prediction. The core function of the risk boundary intersection solving process is to match the continuous dynamic risk evolution surface with multi-level risk warning thresholds, so as to accurately locate the corresponding time nodes when the equipment degradation risk reaches each level threshold, and realize the transformation from risk quantification to source tracing and time node prediction. During the processing, the established multi-level risk warning threshold system is used as a benchmark. The dynamic risk evolution surface is scanned point by point across the entire time domain. For each level of warning threshold, the intersection point between the dynamic risk evolution surface and the threshold plane is calculated. The time axis value corresponding to the intersection point is the risk threshold time point corresponding to that risk level. Then, combined with the equipment scheduled operation and maintenance plans recorded in the integrated test data management system, the validity of the calculated threshold time points is verified and adjusted. If a threshold time point coincides with or is within the preset interval of the scheduled measurement calibration or comprehensive maintenance operation time node of the equipment, the threshold time point is merged with the scheduled operation and maintenance node to avoid repeated equipment downtime operations. If a threshold time point is within the operating window of the equipment continuous test task, the time point is marked, and the corresponding risk level and early warning time are output simultaneously to provide a basis for task scheduling adjustment. After solving, verifying and adjusting the risk threshold time points for all levels, all valid risk threshold time points are sorted and deduplicated in chronological order. Each time point is labeled with its corresponding risk level, triggering reason and risk value, ultimately forming a standardized set of risk threshold time points. This set fully covers the key time nodes of the entire process from early risk warning to mandatory traceability of the equipment, providing a core time benchmark for the subsequent correction and generation of adaptive traceability time nodes.

[0127] Preferably, a complete functional support and logical closed loop is formed between the risk trend extrapolation layer and the time-domain threshold optimization layer of the degradation clock extrapolation model. Simultaneously, a model iteration optimization mechanism is set up to continuously improve the accuracy of risk boundary prediction. Specifically, the risk trend extrapolation layer realizes the transformation from instantaneous risk probability to continuous cumulative risk, fully restoring the irreversible cumulative effect of equipment metering degradation. This solves the problem that traditional linear prediction methods cannot match the nonlinear degradation law of equipment, providing a more accurate and continuous quantitative basis for risk evolution for the time-domain threshold optimization layer. The time-domain threshold optimization layer realizes the transformation from continuous risk evolution data to discrete executable boundary time points, deeply integrating the quantified risk data with the actual operation and maintenance management business needs of equipment and the requirements of national metrological standards. The output set of risk boundary time points can be directly adapted to the needs of subsequent traceability planning, completing the entire closed loop of degradation risk extrapolation. Meanwhile, the model is equipped with an online iterative optimization mechanism. Based on the real-time updated equipment measurement and verification results, operation and maintenance execution records, actual operating condition data, and measurement deviation event records in the integrated test data management system, the model continuously iterates and optimizes the cumulative weight system of the risk trend extrapolation layer and the threshold system of the time domain threshold optimization layer, and performs parameter self-correction. This continuously reduces the deviation between the risk boundary prediction time and the actual risk occurrence time, enabling the model to continuously adapt to the changes in the degradation characteristics of the equipment throughout its entire life cycle and maintain high prediction accuracy throughout the long-term use of the equipment.

[0128] Preferably, the degradation clock extrapolation model has a dedicated adaptation logic for abnormal operating conditions of the equipment, ensuring the applicability and predictive reliability of the model under all operating conditions. When the integrated test data management system records abnormal operating conditions such as heavy-load high-frequency continuous operation, sudden fault repair, extreme environmental changes, and operation exceeding rated parameters, the risk trend extrapolation layer will automatically adjust the calculation step size and weight coefficient of the time-series risk cumulative integral, reducing the integral step size to improve the risk calculation accuracy of the abnormal operating condition interval, and simultaneously amplifying the cumulative weight of the corresponding degradation dimension of the abnormal operating condition, fully reflecting the accelerated degradation effect of the abnormal operating condition on the equipment's metrological performance; the time-domain threshold optimization layer will simultaneously tighten the risk warning threshold for the abnormal operating condition interval, triggering risk warnings in advance, and correcting and predicting the risk evolution trend after the abnormal operating condition, accurately locating the risk threshold time point brought about by the abnormal operating condition, avoiding the problem of sudden out-of-tolerance of equipment metrological performance due to abnormal conditions without prior prediction, ensuring that the model can adapt to the operating status of the equipment in all scenarios and under all operating conditions, and providing a full-cycle, highly reliable risk prediction basis for the formulation of equipment measurement traceability plans.

[0129] Optionally, in step 4, determining the metering cycle arrangement for traceability based on the adaptive traceability time node sequence includes: performing time-domain interval density clustering on the adaptive traceability time node sequence to obtain a set of periodic distribution cluster centers that reflects the urgency of the traceability task; and performing optimal adaptation search on the preset metering resource availability matrix based on the set of periodic distribution cluster centers to determine the metering cycle arrangement for traceability.

[0130] Preferably, the core input to the metering cycle arrangement generation step in step 4 is the adaptive traceability time node sequence output in step 3, and the core output is a standardized metrological cycle arrangement that is executable and adaptable to equipment management needs and metrological resource constraints. This step consists of two sequentially connected core steps: time-domain interval density clustering processing and cycle node optimal adaptation search processing. The output of the previous step directly serves as the input for the next step, forming a complete processing link from discrete traceability time nodes to continuous, structured metering cycle arrangements. The adaptive traceability time node sequence fully covers the key time nodes of the entire process from early risk warning to mandatory traceability of the equipment. Its underlying data foundation is entirely derived from the basic information of the test equipment, maintenance management ledgers, maintenance management data, metering cycle management data, historical equipment operation and deterioration data, and test task scheduling plans recorded in the integrated test data management system, ensuring that the generation logic of the cycle arrangement fully conforms to the actual business scenario and execution requirements of the test equipment metrological traceability management.

[0131] Preferably, before performing time-domain interval density clustering, the input adaptive source time node sequence is first subjected to time-series standardization preprocessing and multi-dimensional feature annotation operations to lay a compliant and reliable data foundation for subsequent clustering analysis. The time-series standardization preprocessing first uses the equipment calendar time, work calendar, and equipment downtime window period recorded in the integrated test data management system as a unified time benchmark. It standardizes the format and aligns all time points in the adaptive traceability time node sequence, removing duplicate, invalid, and abnormal time nodes that exceed the maximum mandatory period of national metrological standards. For missing nodes caused by adjustments to the equipment operation and maintenance plan, it completes the sequence based on the risk evolution trend of adjacent nodes. The multi-dimensional feature annotation operation labels each time node in the sequence with five core features: risk level, triggering reason, equipment importance level, test task relevance, and mandatory traceability time limit. The risk level includes three categories: early warning, critical execution, and mandatory traceability. The triggering reasons include four categories: natural performance degradation, accelerated degradation under heavy load conditions, calibration after fault repair, and periodic mandatory calibration. The annotated time node sequence fully reflects the business attributes and execution priority of each traceability node, providing multi-dimensional classification basis for subsequent density clustering.

[0132] Preferably, the preprocessed and feature-annotated adaptive tracing time node sequence is subjected to temporal interval density clustering to obtain a periodic distribution cluster center set reflecting the urgency of the tracing task. The temporal interval density clustering uses a density clustering algorithm adapted to the temporal characteristics of equipment metering management. It uses time intervals as the core clustering dimension and the risk level and execution priority of node annotations as clustering weight constraints. It eliminates the need for pre-setting the number of clusters and can adaptively match the temporal distribution characteristics of the tracing time nodes, solving the problem that traditional algorithms with fixed cluster numbers cannot adapt to the non-uniform node distribution caused by equipment nonlinear degradation. During the clustering process, each time node in the sequence is first used as the core, and a preset temporal neighborhood radius is used as the search range. The number of time nodes and the weighted density value within the neighborhood are counted. The neighborhood radius is set based on the minimum maintenance cycle and the shortest test task cycle of the equipment recorded in the integrated test data management system. The weighted density value is calculated based on the risk level and execution priority of the nodes, with critical execution and mandatory tracing nodes having significantly higher weights than early warning nodes. Then, based on the weighted density values, core nodes that can represent the core distribution of the tracing task are selected, and adjacent high-density core nodes are grouped together. Clustering is performed into the same cluster, while low-density isolated noise nodes are removed. Finally, the centroid of the temporal distribution of each cluster is solved to obtain the cluster center point corresponding to each cluster. This center point is the optimal benchmark execution time point for the traceability task of that cluster. After sorting the cluster center points of all clusters in temporal order and removing duplicates, a standardized set of periodic distribution cluster center points is formed. This set of center points fully reflects the urgency distribution, execution priority and temporal arrangement of traceability tasks in the entire prediction time domain of the equipment, providing a core time benchmark for subsequent metering resource adaptation and periodic arrangement optimization.

[0133] Preferably, based on the comprehensive operation and maintenance and resource data of the integrated test data management system, a preset metrology resource availability matrix is ​​constructed to provide resource constraints and adaptation benchmarks for optimal adaptation search processing of periodic nodes. The metrology resource availability matrix is ​​a two-dimensional structured matrix with time as the horizontal axis and metrology resource dimension as the vertical axis. Each element in the matrix corresponds to the availability status, carrying capacity, and priority matching rules of the corresponding metrology resource within a specified time window, and its data comes entirely from the real-time updated data of the integrated test data management system. The matrix's horizontal axis time window is fully aligned with the equipment's work calendar, downtime windows, and test task scheduling plans. The granularity of the time window is adaptively adjusted based on the equipment management precision requirements, with a significantly reduced granularity for core traceability nodes, improving adaptation accuracy. The matrix's vertical axis metrology resource dimension includes four categories: availability of metrology standards, qualifications and scheduling of metrology technicians, metrology laboratory site and environmental conditions, and equipment-supporting operation and maintenance resources. Each category of resources is labeled with its corresponding carrying capacity, qualification matching requirements, and available time periods. The matrix also incorporates priority matching rules, setting resource priority matching permissions for high-value, high-precision, and core test equipment, as well as high-priority traceability tasks such as mandatory traceability and critical execution tasks, ensuring that high-priority tasks can occupy high-quality metrology resources first. The matrix is ​​dynamically updated based on real-time data from the integrated test data management system, ensuring the accuracy and timeliness of resource availability status and preventing the failure of periodic scheduling due to changes in resource status.

[0134] Preferably, based on the periodically distributed cluster center point set, an optimal adaptation search process for periodic nodes is performed on the preset metering resource availability matrix to ultimately determine the metering cycle arrangement for traceability. The optimal adaptation search process for periodic nodes uses the cluster center point as the baseline execution time, metering resource availability as a constraint, and aims to minimize traceability task execution priority, equipment downtime costs, and metering resource utilization. It employs a multi-objective optimal search algorithm to match the optimal execution time window and resource allocation scheme for each cluster center point. During the search process, firstly, for each cluster center in the periodically distributed cluster center set, using that center point as a benchmark, within its corresponding mandatory traceability time limit, all available time windows that meet the resource requirements are searched based on the metrological resource availability matrix. Then, for each available time window, a fit score is calculated based on a preset multi-objective optimization function. The fit score comprehensively considers the deviation between the time window and the cluster center point, the degree of conflict between equipment downtime and the test task, the carrying capacity and matching degree of metrological resources, and the execution priority of the traceability task. Among these, the smaller the deviation from the benchmark center point, the less conflict with the test task, the higher the resource matching degree, and the higher the task priority, the better the fit score. The optimal execution time window is selected based on its fit score. Then, the optimal execution time window with the highest fit score for each cluster center is selected. A corresponding metrology resource configuration scheme is matched to this time window. Simultaneously, the traceability task is merged and coordinated with maintenance tasks within the same time period, based on the equipment maintenance plan recorded in the integrated test data management system, to reduce unnecessary equipment downtime. Finally, all optimal execution time windows are sorted chronologically, and each time window is labeled with its corresponding traceability task level, triggering reason, resource configuration scheme, collaborative maintenance content, and latest execution deadline, forming a complete, structured, and implementable metrology traceability cycle arrangement.

[0135] Preferably, after generating the metrological cycle arrangement for traceability, a mandatory compliance verification and dynamic adaptation adjustment mechanism is implemented to ensure that the cycle arrangement fully complies with national metrological technical specifications and the actual management needs of the equipment. Mandatory compliance verification first verifies the time interval between two adjacent traceability nodes in the metrological cycle arrangement based on the maximum permissible traceability cycle and mandatory verification requirements for the corresponding type of test equipment in the national metrological technical specifications. If the interval exceeds the maximum permissible cycle requirement, a mandatory traceability node is immediately added within the interval to ensure that the cycle arrangement fully meets the statutory metrological requirements. Then, based on the historical metrological compliance records of the equipment in the integrated test data management system and the metrological management requirements of the equipment's usage location, the traceability level and implementation standards of the cycle arrangement are verified to ensure the compliance and effectiveness of traceability activities. The dynamic adaptation and adjustment mechanism sets dynamic update trigger conditions for the periodic arrangement. When critical events occur in the integrated test data management system, such as sudden equipment failure repair, heavy load test task plan adjustment, change of metrology resource status, or sudden deterioration of equipment metrology performance, the periodic arrangement is immediately recalculated and optimized to ensure that the periodic arrangement can adapt to the actual operating status of the equipment, management plan and resource conditions in real time, and always maintain optimal executability and effectiveness.

[0136] Preferably, the temporal interval density clustering processing and the optimal adaptation search processing for cycle nodes form a complete functional support and business closed loop. Together, they achieve a full-process transformation from risk-driven traceability time nodes to executable metering cycle arrangements. Specifically, the temporal interval density clustering processing transforms discrete, multi-level traceability time nodes into structured, priority-based cluster center point sets, fully restoring the temporal distribution characteristics and task urgency of equipment metering degradation risks. This solves the core problem that traditional fixed-cycle arrangements cannot adapt to the nonlinear degradation characteristics of equipment, providing a more accurate time benchmark and priority basis for subsequent resource adaptation. The optimal adaptation search processing for cycle nodes transforms theoretically optimal benchmark times into practically executable cycle arrangements, deeply integrating equipment degradation risk management requirements with metering resource constraints, test task scheduling, and equipment operation and maintenance plans. This solves the problem of traditional traceability plans being disconnected from actual business scenarios and resource conditions, leading to implementation difficulties and execution conflicts. The final generated measurement cycle arrangement for traceability can achieve on-demand traceability based on the actual degradation state of equipment measurement performance, avoid the risk of equipment measurement deviation in advance, and maximize the adaptation to existing measurement resources and business plans, reduce equipment downtime costs and traceability management costs, thus achieving dual optimization of equipment measurement performance risk control and operation and maintenance management efficiency.

[0137] Optionally, in step 4, adjusting the preset traceability task plan of the target experimental equipment based on the metering cycle arrangement includes: performing original plan deviation calculation on the metering cycle arrangement to obtain the plan adjustment deviation feature characterizing the magnitude of plan changes; and generating a dynamic rescheduling strategy for task flow to guide task priority changes based on the plan adjustment deviation feature.

[0138] Preferably, the core input of the traceability task plan adjustment step in step 4 is the previously generated traceability measurement cycle arrangement, and the core output is the adjusted traceability task plan adapted to the actual measurement degradation status of the equipment, legal measurement requirements, and business management needs, as well as the dynamic rescheduling strategy of the task flow to guide the entire task execution process. This step uses the integrated test data management system as the core data carrier and business execution carrier. Before executing the core processing flow, the benchmark data extraction and standardization preprocessing are completed: the preset traceability task plan of the target test equipment is retrieved from the integrated test data management system, and the full-dimensional benchmark information such as the original plan's traceability time nodes, task level, execution standards, resource configuration scheme, related operation and maintenance tasks, legally mandatory cycle requirements, and approval process rules are extracted; at the same time, the preset traceability task plan is verified for compliance and validity, and expired, invalid, or non-compliant plan items are removed, and content that does not conform to the current operating status and management requirements of the equipment is corrected, finally forming a standardized benchmark traceability task plan dataset, providing a unified, compliant, and relatively accurate comparison benchmark for subsequent plan deviation calculation.

[0139] Preferably, the generated traceability measurement cycle arrangement and benchmark traceability task plan are processed to calculate the deviation from the original plan, resulting in a plan adjustment deviation characteristic that can comprehensively characterize the magnitude of plan changes. First, a multi-dimensional deviation calculation index system adapted to the traceability task management dimension of the integrated test data management system is constructed. This system covers five core calculation dimensions: time node deviation, task level deviation, resource allocation deviation, operation and maintenance collaboration deviation, and compliance deviation. Each dimension has corresponding quantitative calculation rules and value ranges. The time node deviation is used to calculate the time deviation, direction, and impact on the statutory mandatory cycle and test task scheduling of the optimal execution time window in the metering cycle arrangement. The task level deviation is used to calculate the difference between the risk level and execution priority of the traceability tasks marked in the metering cycle arrangement and the original planned task level. The resource allocation deviation is used to calculate the difference between the matching metering resource requirements in the metering cycle arrangement and the original planned resource allocation scheme. The operation and maintenance collaboration deviation is used to calculate the matching degree between the collaborative maintenance and repair tasks in the metering cycle arrangement and the original planned operation and maintenance arrangements. The compliance deviation is used to calculate the difference between the metering cycle arrangement and the requirements of statutory metering standards and internal metering management system. During the accounting process, the baseline traceability task plan is used as the comparison benchmark and the metering cycle arrangement is used as the adjustment target. After completing the quantitative accounting in each dimension, the accounting weights of each dimension are set based on the importance level of the equipment, the urgency of the task, and the legal compliance requirements. The deviation of each dimension is weighted and integrated to finally obtain the deviation characteristics of the plan adjustment. At the same time, the corresponding change triggering reason, compliance impact, and execution priority are marked for each deviation characteristic, so as to provide a more accurate quantitative basis for the subsequent adjustment of the traceability task plan.

[0140] Preferably, based on the magnitude, type, and execution priority of the planned adjustment deviation characteristics, the benchmark traceability task plan is refined item by item according to the hierarchical adjustment rules. The adjustment rules are fully compatible with the traceability task lifecycle management requirements of the integrated test data management system and the statutory requirements of national metrological technical specifications. First, a four-level hierarchical adjustment threshold is set, classifying the planned adjustment deviation into four levels: minor adjustment, routine adjustment, major adjustment, and mandatory adjustment. Minor adjustment corresponds to deviations within the preset threshold that do not affect the compliance of the task and the core execution requirements; routine adjustment corresponds to deviations exceeding the threshold but not involving changes to the statutory mandatory cycle or the core requirements of the task; major adjustment corresponds to significant changes involving the task level, core time nodes, and resource allocation; and mandatory adjustment corresponds to hard changes involving the statutory mandatory traceability cycle and the critical value of equipment metrological deviation risk. Differentiated adjustment strategies are implemented for deviations of different levels: minor adjustments simply update the corresponding items based on the original plan without changing the overall execution framework; routine adjustments simultaneously update the task's time nodes, resource configuration, and collaborative operation and maintenance content, as well as the task's execution reminder rules and approval process nodes; major adjustments involve re-compiling the full-dimensional execution plan for the corresponding traceability task, simultaneously updating the task's execution standards, resource configuration plan, risk control requirements, and collaborative business arrangements; mandatory adjustments prioritize locking the latest execution deadline for tasks and simultaneously triggering high-priority task alerts to ensure that task execution fully complies with legal metrology requirements and equipment risk control bottom lines. After completing each adjustment, a preliminary adjusted traceability task plan is formed, and each adjusted task item is labeled with the basis for adjustment, changes, execution requirements, and risk level, achieving full traceability of the plan adjustment process.

[0141] Preferably, the initially adjusted traceability task plan undergoes comprehensive compliance verification and business synergy optimization to ensure that the adjusted plan not only complies with legal metrological standards but also achieves deep synergy with equipment testing schedules, operation and maintenance management plans, and metrological resource allocation, avoiding execution conflicts. Compliance verification is divided into two levels. The first level is legal compliance verification, which verifies the traceability cycle interval, task execution level, traceability standard basis, and metrological transfer system requirements of the corresponding type of testing equipment in national metrological technical specifications, ensuring that the plan fully meets legal metrological compliance requirements. The second level is management compliance verification, which verifies the plan's approval process, execution record requirements, archiving standards, and responsible person system based on the metrological management system documents of the equipment's owner and the metrological control rules of the equipment's usage location recorded in the integrated test data management system, ensuring that the plan complies with internal full-process management requirements. Business collaboration optimization is based on real-time updated business data in the integrated test data management system. It further optimizes the execution time window of the adjusted plan based on equipment test task scheduling, equipment maintenance plans, and equipment downtime windows. This merges traceability tasks with equipment maintenance, performance verification, and downtime repair tasks within the same timeframe, reducing unnecessary equipment downtime and business interruptions. Simultaneously, based on the metrology resource availability matrix, the resource allocation plan for tasks is optimized to ensure that the adjusted task resource requirements fully match the availability and capacity of core resources such as metrology standards, metrology technicians, and laboratory facilities, avoiding resource conflicts that could prevent task execution. After verification and optimization, the final adjusted traceability task plan for the target test equipment is formed. This plan enables on-demand traceability management based on the actual metrology performance degradation of the equipment, while also possessing full compliance and feasibility.

[0142] Preferably, based on the characteristics of planned adjustment deviations and the adjusted traceability task plan, a multi-dimensional traceability task priority classification system is constructed to provide the core priority determination basis and scheduling rule foundation for the generation of dynamic rescheduling strategies for task flows. The priority classification system strictly sets four priority levels according to legal compliance requirements, equipment risk level, and business importance: emergency priority, high priority, medium priority, and low priority. Among them, emergency priority corresponds to traceability tasks of mandatory adjustment, including traceability tasks of key equipment whose equipment metrological performance is about to exceed the qualified range, whose legally mandated traceability period is about to expire, and those involving core high-precision testing tasks. These tasks have the highest resource allocation authority, simplified approval process authority, and execution priority. High priority corresponds to traceability tasks of major adjustment, including traceability tasks of core production testing equipment with significantly increased equipment metrological degradation risk and significant deviations from the original plan. Medium priority corresponds to traceability tasks of routine adjustment, including routine plan adjustments caused by normal equipment degradation and general equipment traceability tasks without compliance risks or execution conflicts. Low priority corresponds to traceability tasks of minor adjustment, including traceability tasks of auxiliary equipment that only involve minor adjustments to execution details and do not affect the core execution requirements of the task. At the same time, corresponding rescheduling basic rules are set for each priority level, including resource priority matching permissions, task execution early warning time limits, approval process rules, and conflict handling priorities, to ensure that tasks of different priorities can be executed in a standardized dynamic scheduling manner according to the corresponding rules, laying the framework foundation for the construction of subsequent full-process rescheduling strategies.

[0143] Preferably, based on the established priority classification system for traceability tasks, and combined with the task scheduling management rules and dynamic management mechanism of metrology resources in the integrated test data management system, a dynamic rescheduling strategy covering the entire lifecycle of traceability tasks is generated. This strategy can guide the dynamic changes in task priorities, the dynamic allocation of resources, and the dynamic adjustment of execution plans, achieving closed-loop control of the entire traceability task process. The core of the dynamic rescheduling strategy includes four major modules: static basic scheduling rules, dynamic triggering adjustment mechanism, conflict handling scheduling rules, and execution closed-loop feedback mechanism. The static basic scheduling rules are based on a task priority hierarchy, clarifying the basic execution order, resource allocation ratio, approval process requirements, and tiered early warning rules for tasks of different priorities, providing a standardized basic execution framework for task scheduling. The dynamic trigger adjustment mechanism clarifies the triggering conditions and execution process for task rescheduling. When critical events occur in the integrated test data management system, such as sudden degradation of equipment metrological performance, significant adjustments to test task plans, changes in metrological resource status, sudden equipment failure repairs, updates to national metrological standards, or significant changes in equipment usage scenarios, the mechanism immediately triggers a reassessment of task priorities and a dynamic adjustment of the scheduling plan, ensuring that the scheduling strategy can adapt to the dynamic changes in equipment status and business needs in real time. The conflict handling scheduling rules clarify the handling of multiple tasks and multiple devices. The system now employs standardized handling rules for time conflicts and metering resource conflicts, strictly allocating resources and scheduling execution order according to task priority levels. Priority is given to tasks with high priority and high compliance requirements, while the execution plans for low-priority tasks are collaboratively optimized to avoid task stagnation and resource waste without affecting the execution of core tasks. The execution closed-loop feedback mechanism clarifies the full-process feedback requirements after task execution, synchronously feeding back the actual task execution status, equipment metering performance verification results, resource usage, and deviation handling records to the integrated test data management system. This provides real business data support for subsequent adjustments to traceability task plans, optimization of equipment degradation prediction models, and iterative updates to scheduling rules, forming a full-process data closed loop for traceability task management.

[0144] Preferably, the generated task flow dynamic rescheduling strategy also possesses global scheduling optimization capabilities at the multi-device cluster level, adapting to the clustered management needs of multi-device, multi-batch traceability tasks in the integrated test data management system. This achieves globally optimal scheduling of traceability tasks across the entire laboratory and maximizes the utilization of metrological resources. For the adjusted traceability task plans of multiple test devices under unified management in the integrated test data management system, this strategy can perform global task flow integration and resource scheduling optimization based on the priority level, time window requirements, resource configuration requirements, and device type attributes of all tasks. It batches and centrally schedules traceability tasks of the same type, time period, and resource requirements, maximizing the utilization efficiency of core metrological resources such as metrological standards, metrological technicians, and laboratory environment facilities, while reducing overall traceability management costs. Simultaneously, for urgent and high-priority tasks within the cluster, it prioritizes and guarantees cross-device and cross-group allocation of global metrological resources, ensuring that the execution of core equipment and urgent tasks is not limited by local resources. In addition, this strategy can generate a global task scheduling and control dashboard and hierarchical early warning information based on the traceability task execution status and metrology resource status of the entire laboratory. This information is then pushed to the corresponding management module of the integrated test data management system, providing metrology management personnel with a full-dimensional task control view and decision-making data support. This enables an upgrade in metrology management capabilities from single-device on-demand traceability to multi-device clustered and intelligent metrology control.

[0145] like Figure 2 The image shows a measurement traceability and prediction device for experimental equipment, comprising: The perception space construction module 21 is used to collect attribute data of the target test equipment in real time. The attribute data includes equipment unit energy consumption, operating frequency, maintenance management ledger records and depreciation unit cost. Based on the attribute data, an equipment condition perception space representing the spatiotemporal operating trajectory of the target test equipment is constructed. The deduction feature generation module 22 is used to call the preset task load deduction model to perform multi-level state recursive deduction of the equipment condition in the equipment condition perception space to obtain the time domain state deduction trajectory of the target experimental equipment, and generate the physical operation degradation features characterizing the magnitude of the target experimental equipment as the task load drifts. The risk sequence generation module 23 is used to statistically analyze the evolution gradient of the physical operation degradation characteristics to obtain the distribution of the magnitude offset evolution trend, and to perform periodic extrapolation calculation of the measurement accuracy degradation risk based on the magnitude offset evolution trend distribution to generate the adaptive traceability time node sequence of the target test equipment. The dynamic adjustment module 24 is used to determine the measurement cycle arrangement of the measurement value traceability based on the adaptive traceability time node sequence, and then adjust the preset traceability task plan of the target experimental equipment based on the measurement cycle arrangement.

[0146] like Figure 3 As shown, an electronic device includes a processor 301 and a memory 302; The memory 301 is used to store computer programs; When the processor 302 executes the program stored in the memory, it implements the above-mentioned method for tracing and predicting the measurement values ​​of the test equipment.

[0147] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive prediction method for traceability cycle of test equipment as described above.

[0148] The above Figures 2-3 The exemplary descriptions in the embodiments are similar to those described above. Figure 1 Explanation.

Claims

1. A method for traceability and prediction of measurement values ​​of experimental equipment, characterized in that, include: Step 1: Collect attribute data of the target test equipment in real time. The attribute data includes the unit energy consumption of the equipment, operating frequency, maintenance management ledger records and depreciation unit cost. Based on the attribute data, construct an equipment condition perception space that represents the spatiotemporal operating trajectory of the target test equipment. Step 2: Call the preset task load simulation model to perform multi-level state recursive simulation of the equipment condition in the equipment condition perception space to obtain the time domain state simulation trajectory of the target test equipment, and generate the physical operation degradation characteristics that characterize the drift state of the target test equipment with the task load. Step 3: Statistically calculate the evolution gradient of the physical operation degradation characteristics to obtain the distribution of the value offset evolution trend. Based on the distribution of the value offset evolution trend, perform periodic extrapolation calculation of the measurement accuracy degradation risk to generate the adaptive traceability time node sequence of the target test equipment. Step 4: Determine the measurement cycle arrangement for traceability based on the adaptive traceability time node sequence, and then adjust the preset traceability task plan for the target test equipment based on the measurement cycle arrangement; In step 2, a pre-set task load simulation model is invoked to perform multi-level recursive state simulation of the equipment operating conditions in the equipment operating condition perception space to obtain the time-domain state simulation trajectory of the target test equipment, including: The device operating condition perception space is processed by a value-value neighborhood association feature aggregation layer to generate a perception state code that represents the instantaneous value state of the device. The perception state encoding is subjected to nonlinear accuracy degradation recursive evolution processing through an accuracy recursive evolution layer to obtain a multi-level hidden state evolution sequence that records the magnitude drift trend. The multi-level hidden state evolution sequence is processed by the trace trajectory reconstruction layer to obtain a discrete trajectory coordinate set with recorded quantity prediction values ​​by performing quantity deviation spatial mapping processing on the multi-level hidden state evolution sequence. The discrete trajectory coordinate set is then subjected to continuous trace trajectory interpolation fitting processing to generate the time domain state inference trajectory of the target test equipment. The accuracy recursive evolution layer includes an accuracy gating calculation module, a magnitude drift mapping module, and a degradation feature encapsulation module. The perceptual state encoding is subjected to nonlinear accuracy degradation recursive evolution processing through the accuracy recursive evolution layer to obtain a multi-level hidden state evolution sequence, including: The accuracy gating calculation module performs time-series accuracy gating recursive processing on the sensing state code to obtain intermediate state evolution components that reflect the trend of equipment accuracy change. The intermediate state evolution component is processed by multidimensional nonlinear magnitude drift mapping through the magnitude drift mapping module to obtain multi-scale evolution features covering different time dimensions. The degradation feature encapsulation module performs degradation trajectory feature encapsulation processing on the multi-scale evolution features to generate the multi-level hidden state evolution sequence.

2. The method according to claim 1, characterized in that, In step 1, constructing an equipment condition perception space characterizing the spatiotemporal operational trajectory of the target test equipment based on the attribute data includes: Based on the constructed multidimensional state mapping matrix, the attribute data is subjected to multi-source data dimension alignment processing to generate a multidimensional aligned feature tensor. Spatiotemporal correlation coupling calculation is performed on the multidimensional aligned feature tensor to obtain the spatiotemporally correlated feature manifold; The topological network of equipment operating conditions is determined by performing topological semantic reconstruction on the spatiotemporally correlated feature manifold. The equipment operating condition topology network is subjected to graph trajectory dynamic mapping processing to generate an equipment operating condition perception space that represents the spatiotemporal operating trajectory of the target test equipment.

3. The method according to claim 2, characterized in that, The process of performing spatiotemporal correlation coupling calculation on the multidimensional aligned feature tensor to obtain the spatiotemporally correlated feature manifold includes: Perform cross-dimensional temporal convolution processing on the multidimensional aligned feature tensor to extract the temporal dynamic dependency features of device operation and generate a temporal feature increment set; Local manifold embedding processing is performed on the time-series feature increment set to construct a low-dimensional manifold structure representing the local geometry of the data; The low-dimensional manifold structure is subjected to high-dimensional space expansion using a nonlinear dimension reduction operator to obtain a spatiotemporally correlated feature manifold.

4. The method according to claim 2, characterized in that, By performing topological semantic reconstruction on the spatiotemporally correlated feature manifold, the topological network of equipment operating conditions is determined, including: Spatial discretization sampling is performed on the spatiotemporal correlated feature manifold to determine multiple logical nodes representing typical states of devices; Calculate the geodesic distance between each of the logical nodes to obtain the node spacing metric matrix, and perform adjacency probability calculation based on the node spacing metric matrix to generate the initial topology map for the working condition. The preset equipment maintenance management semantics and depreciation cost weights are injected into the initial topology graph of the operating conditions, and the edge weight dynamic evolution calculation process is performed to determine the equipment operating condition topology network.

5. The method according to claim 2, characterized in that, The equipment operating condition topology network is subjected to graph trajectory dynamic mapping processing to generate an equipment operating condition perception space representing the spatiotemporal operating trajectory of the target test equipment, including: Perform a state transformation on the real-time collected equipment operating status data to obtain the current state; Logical node matching is performed on the current state to lock the matching node in the device operating condition topology network, and relocation processing is performed on the matching node to obtain a dynamic running trajectory node sequence; The dynamic running trajectory node sequence is subjected to time-series smoothing calculation to generate a smooth trajectory sequence, and the smooth trajectory sequence is subjected to perception attribute encapsulation to generate an equipment condition perception space that characterizes the spatiotemporal running trajectory of the target test equipment.

6. The method according to claim 1, characterized in that, The spatiotemporal semantic coding layer for quantities includes a neighborhood quantity scanning module, a source semantic modeling module, and a quantity feature compression module. The measurement-value spatiotemporal semantic coding layer performs measurement-value neighborhood association feature aggregation processing on the equipment operating condition perception space to generate a perception state code, including: The neighborhood value scanning module performs neighborhood value attribute scanning processing on the equipment operating condition perception space to obtain a local topological correlation matrix that reflects the correlation degree between adjacent operating conditions. The source semantic modeling module is used to perform source semantic modeling on the local topological association matrix to obtain node interaction semantic features that characterize the influence of the equipment quantity on the environment and load. The quantity feature compression module performs quantity dimension compression processing on the node interaction semantic features to generate a perceptual state code.

7. The method according to claim 1, characterized in that, The source trajectory reconstruction layer includes a magnitude parameter projection module, a source trajectory smoothing module, and a continuous error reconstruction module. The source trajectory reconstruction layer performs magnitude deviation spatial mapping processing on the multi-level hidden state evolution sequence to obtain a discrete trajectory coordinate set, and then performs continuous source trajectory interpolation fitting processing on the discrete trajectory coordinate set to generate the time-domain state deduction trajectory of the target experimental equipment, including: The measurement parameter projection module is used to perform measurement parameter spatial projection processing on the multi-level hidden state evolution sequence to obtain a time-domain measurement prediction point set that characterizes the measurement deviation at future time points. The source trajectory smoothing module is used to perform source trajectory spline smoothing on the time domain value prediction point set to obtain a preliminary fitted trajectory that conforms to the physical degradation law. The continuous error reconstruction module performs continuous magnitude error reconstruction processing on the preliminary fitted trajectory to generate the time-domain state deduction trajectory of the target test equipment.

8. The method according to claim 1, characterized in that, Generate physical operational degradation characteristics that characterize the drift state of the target test equipment as a function of the mission load, including: Multi-scale time-frequency analysis processing is performed on the time-domain state deduction trajectory to extract the high-frequency component of the magnitude drift that reflects the instantaneous characteristics of the magnitude shift. The high-frequency components of the magnitude drift are subjected to degradation-related semantic mapping processing to obtain a physical operational degradation description that characterizes the physical performance degradation state of the equipment. Based on the physical operational degradation description, feature dimensional encapsulation processing is performed to generate physical operational degradation features that characterize the drift state of the target test equipment values ​​as the task load changes.

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