A flow observation item whole-process collaborative management method

Abstract

The present application relates to the technical field of enterprise resource management, in particular to a kind of flow observation project whole-process collaborative management method, including by obtaining the multidimensional observation data and elevation model of the region to be observed, access inventory node to build digital instance library;Based on data construction collaborative constraint atlas, calculate grid node operation deployment resistance index and accessibility factor, generate access control matrix;To instance library executes traversal to obtain performance portrait, call element matching model to evaluate matching index and generate flow transfer work order;Receive stress accumulation log in logistics process, execute stress recovery evaluation to generate inventory frozen time window, dynamically update task scheduling time axis;Perform project compliance audit and evaluate business consistency index, generate performance qualified voucher, update the node in service state into resource allocation scheduling queue.The present application realizes the precise management of assets whole process by dynamic collaboration of multidimensional environmental constraints and equipment stress.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise resource management, in particular to a flow observation project whole-process collaborative management method. BACKGROUND

[0002] The flow observation project has the significant characteristics of long resource transfer link, complex delivery environment and high access threshold. In the collaborative management system of the project, the management end not only needs to maintain the static account of the assets, but also needs to perform precise business control on the whole life cycle of the observation resources from warehouse allocation, logistics transfer to on-site delivery, so as to ensure that the delivery state of each resource meets the preset task performance contract.

[0003] In the current digital management practice, the distribution of management instructions and the formulation of resource scheduling usually rely on standardized business logic. However, in the actual observation resource transfer chain, the business performance of the resources is often affected by the interlaced influence of multi-dimensional business constraints: first, the difference of geographical environment determines the accessibility and delivery difficulty after the resources arrive; second, the physical influence in the transportation process will cause the equipment to enter the performance recovery period, thereby dynamically limiting the subsequent operation task scheduling; finally, the background environmental interference of the operation site will directly increase the difficulty of business compliance audit.

[0004] The above-mentioned business variables of space, time and environment dimensions cause the information asymmetry between the actual delivery capacity of physical assets and the expected performance state of them in the management system. How to realize the consistency perception and real-time efficiency evaluation of multi-source heterogeneous physical sensor data in the large-scale transfer process under the unified management architecture in the multi-dimensional dynamic environment, the problem of large calculation deviation, low physical state restoration precision and resource scheduling response lag of the system in processing large-scale concurrent data flow.

[0005] Therefore, a flow observation project whole-process collaborative management method is proposed. SUMMARY

[0006] The application aims to provide a flow observation project whole-process collaborative management method, which realizes accurate asset whole-process management through dynamic collaboration of multi-dimensional environmental constraints and equipment stress.

[0007] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0008] A flow observation project whole-process collaborative management method comprises the following steps:

[0009] Obtain multi-dimensional observation data and an elevation model of a to-be-observed area, and access inventory observation nodes to construct a digital instance library; based on the multi-dimensional observation data, construct a collaborative constraint graph, calculate the job deployment resistance index of each grid node, generate an observation deployment accessibility factor using the elevation model, and generate an access control matrix in the collaborative constraint graph;

[0010] Iterate the digital instance library to obtain the performance portrait of the inventory observation nodes, call an element matching model, evaluate the matching evaluation index of the performance portrait and each grid node in the access control matrix, generate a flow work order according to the matching evaluation index, and the flow work order contains a unique device ID, a performance verification contract, and a logistics monitoring benchmark;

[0011] Receive stress accumulation logs uploaded based on the logistics monitoring benchmark, perform stress recovery evaluation, generate an inventory frozen time window, and use the inventory frozen time window as a dynamic constraint to update the task scheduling time axis of the flow work order;

[0012] Receive real-time response characteristics matched with the performance verification contract, perform project compliance auditing, evaluate the business consistency index between the real-time response characteristics and the theoretical benchmark of the corresponding node in the collaborative constraint graph, generate a performance qualified voucher, update the inventory observation nodes to an in-service state in the digital instance library, and include the inventory observation nodes in the resource allocation and scheduling queue.

[0013] Preferably, the process of constructing the digital instance library comprises: acquiring multispectral remote sensing images and road network vector data of the observation area to be observed, extracting surface medium texture features and road topology structure, and calculating the curvature and slope of the surface access path in combination with the elevation model; simultaneously acquiring historical background noise records of the observation area to be observed, calculating noise power spectral density reference values of different frequency bands, and uniformly packaging them as multidimensional observation data; reading the bottom firmware information of each inventory observation node through the Internet gateway, parsing the device serial number, sensor sensitivity calibration parameters and effective passband range; calling the object relationship mapping program, taking the device serial number as the primary key, packaging the sensor sensitivity calibration parameters and effective passband range as static attribute fields, and packaging the real-time power and idle state identification of the device as dynamic state fields, and storing them in the database to form the digital instance library.

[0014] Preferably, the process of constructing the collaborative constraint graph based on multidimensional observation data and calculating the job deployment resistance index of each grid node comprises: dividing the observation area to be observed into standardized geographic grids, taking the geometric center of each grid as the vertex of the collaborative constraint graph; calculating the noise cross-correlation coefficient between adjacent vertices according to the background noise records in the multidimensional observation data, establishing a directed edge between the vertices with noise cross-correlation coefficient exceeding the preset threshold, and constructing the collaborative constraint graph; extracting the remote sensing image texture features and elevation variance of the region corresponding to each grid, and constructing a complexity feature vector containing surface roughness and terrain relief; normalizing the complexity feature vector into a probability distribution form, calculating the uncertainty value of the probability distribution using the Shannon information entropy formula, and taking the uncertainty value as the job deployment resistance index.

[0015] Preferably, the process of generating the observation deployment accessibility factor using the elevation model and generating the access control matrix in the collaborative constraint graph comprises: based on the road network vector data in the multidimensional observation data, screening out backbone road sections with road grade attribute higher than the preset value, and constructing the job logistics topology structure; calling the Dijkstra shortest path algorithm to calculate the topological driving distance of each grid center point to the nearest access point along the actual direction of the backbone road section, and extracting the transportation loss coefficient of the driving path based on the elevation model; weighting and calculating the planned job distance and the transportation loss coefficient to obtain a comprehensive logistics cost value representing the difficulty of resource access, and taking the reciprocal of the comprehensive logistics cost value as the observation deployment accessibility factor; calculating the ratio of the job deployment resistance index and the observation deployment accessibility factor to obtain a comprehensive deployment coefficient weight, comparing the comprehensive deployment coefficient with the preset safety threshold, marking the nodes exceeding the threshold as unusable state, and generating the access control matrix.

[0016] Preferably, the traversing the digitalized instance library to obtain the asset performance portrait of the inventory observation node comprises: reading the equipment factory specification data stored in the digitalized instance library, extracting the instrument self-noise power spectral density curve and the mean time between failures; calling a data interface to read the system operation log of the current node, and parsing the cumulative running reference time length since the equipment was manufactured; calculating the ratio of the cumulative running reference time length to the mean time between failures, calling an exponential decay function to map and calculate the ratio, and generating a normalized equipment reliability coefficient; vectorizing and packaging the instrument self-noise power spectral density curve and the equipment reliability coefficient to generate the asset performance portrait.

[0017] Preferably, the calling the element matching model to evaluate the matching evaluation index and generating the asset flow transfer work order comprises: the element matching model comprises a frequency domain response analysis unit and a multi-dimensional performance weighting unit; inputting the instrument self-noise power spectral density curve in the asset performance portrait and the background noise spectrum of the target grid node in the access control matrix into the frequency domain response analysis unit, calculating the amplitude difference integral of the two curves within the effective bandwidth, and outputting the effective signal-to-noise ratio gain characteristic; constructing a feature vector containing the effective signal-to-noise ratio gain characteristic, the equipment reliability coefficient and the observation deployment reachability factor of the grid node, inputting the feature vector into the multi-dimensional performance weighting unit, performing linear transformation operation on the feature vector by using a preset weight matrix, and outputting the matching evaluation index; locking the target inventory observation node according to the matching evaluation index maximization principle, obtaining the equipment unique ID of the node; extracting the spatial topology model response spectrum from the target grid node of the collaborative constraint graph as a performance verification contract; generating a logistics monitoring benchmark containing a three-axis impact response spectrum envelope by using the anti-seismic threshold parameter of the node; packaging the equipment unique ID, the performance verification contract and the logistics monitoring benchmark into the asset flow transfer work order.

[0018] Preferably, the process of updating the task scheduling timeline comprises: receiving the stress accumulation log uploaded by the logistics monitoring terminal, analyzing the transient impact peak and the over-limit vibration duration in the transportation process, performing weighted accumulation operation to obtain the cumulative impact energy value representing the excitation degree of the equipment; calling the historical maintenance log of the equipment, extracting the excitation energy data and the subsequent zero drift recovery duration data in the previous transportation process, and constructing the mechanical stress-recovery efficiency correlation feature set exclusive to the current equipment; performing trend fitting operation on the mechanical stress-recovery efficiency correlation feature set to predict the dynamic static duration required by the equipment to recover the measurement accuracy under the current cumulative impact energy value; taking the time point when the equipment actually arrives at the observation node as the starting time, taking the dynamic static duration as the continuous span, generating the inventory freezing time window, and locking the state of the equipment in the inventory freezing time window as the calibration maintenance state in the system schedule table; reading the original estimated job start time of the transfer work order, judging whether the start time falls within the inventory freezing time window, if so, calculating the time difference between the original estimated job start time and the end time of the inventory freezing time window, and performing forward translation operation on the subsequent job task in the time axis to generate the updated task scheduling timeline.

[0019] Preferably, the execution project compliance audit process adopts a feature alignment model, which specifically comprises: a multi-source benchmark mapping unit: according to the communication protocol in the performance verification contract, real-time response features are called and measured response frequency spectrum is extracted; the standard reference frequency spectrum stored in the performance verification contract is read; the standard reference frequency spectrum and the measured response frequency spectrum are subjected to frequency domain discretization sampling, divided into N independent frequency band logical points, and a frequency domain feature space of the same dimension is constructed; the current environmental background noise benchmark value is read and converted into an environmental constraint vector corresponding to the frequency band logical point one by one to obtain a three-dimensional input tensor containing standard features, measured features and environmental constraints;

[0020] An environmental interference weighting unit: based on the three-dimensional input tensor, for each frequency band logical point, the amplitude of the corresponding environmental constraint vector and the amplitude of the measured response frequency spectrum are superimposed to obtain the total energy value of the current frequency band; the amplitude of the measured response frequency spectrum under the current frequency band logical point is divided by the total energy value to obtain the signal purity coefficient of the current frequency band; the signal purity coefficient is taken as the weight coefficient of the current frequency band, and the standard features and the measured features are subjected to element-by-element multiplication to output a weighted feature matrix;

[0021] A semantic alignment solving unit: for the weighted feature matrix, the weighted Euclidean distance between the standard feature vector and the measured feature vector is calculated; the weighted Euclidean distance is converted into a normalized value by using an inverse proportional mapping function as a business consistency index.

[0022] Preferably, the process of incorporating the resource allocation scheduling queue comprises: reading the preset minimum access threshold in the transfer order, comparing the service consistency index with the minimum access threshold; if the service consistency index is greater than or equal to the minimum access threshold, it is determined that the inventory observation node is verified to be in compliance; a data packet containing the unique ID of the device of the node, the service consistency index value of this verification and the timestamp of the determination of passing is constructed, and the data packet is executed with digital signature encapsulation to generate an unforgeable compliance qualified certificate; the compliance qualified certificate is written into the digital instance library as the compliance history record of the node for archiving; the state change transaction of the database is triggered, and the logical state field of the node is changed to in service; in response to the state change, a registration instruction is automatically sent to the observation task scheduling system to formally incorporate the node into the resource allocation scheduling queue.

[0023] Compared with the prior art, the present application has the following advantages:

[0024] 1. By constructing a collaborative constraint graph based on multi-source environmental background data and an elevation model, and deeply coupling the job impedance entropy and the accessibility factor into the access control matrix, the digital perception depth of the management system for the complexity of the field operation scene is significantly enhanced. This multi-dimensional space business modeling method enables the management end to make a fine prediction of the deployment difficulty of each observation node before asset allocation, effectively alleviating the blindness of resource allocation caused by geographical environmental uncertainty in the traditional static management mode, thereby improving the matching accuracy of asset transfer orders and actual operation environment at the source stage of resource flow.

[0025] 2. By using the logistics monitoring benchmark to capture stress accumulation logs during transportation, and calling state evaluation logic to convert physical excited energy into inventory freezing time window, the originally invisible and uncontrollable equipment transfer damage is converted into a dynamic time constraint that can be perceived by the management system, so that the task scheduling timeline can be adaptively shifted according to the real-time accuracy recovery needs of the physical entity, significantly reducing the business risk of assets being forced into operation in the performance deviation state, realizing the flexible cooperation between the management decision logic and the performance recovery period of physical assets, and ensuring the scientificity of the project execution plan.

[0026] 3. By introducing a dynamic quality weighting process in the feature alignment governance model, and generating a compliance confidence value based on the energy proportion of environmental constraints and measured responses, a quality governance means with environmental perception ability is provided for asset delivery in complex field environments. This reweighting mechanism based on signal purity can automatically weaken the negative impact of background interference on business consistency determination, so that the business consistency index generated by the management system can more truly reflect the degree of contract performance of the physical entity, effectively optimizing the audit accuracy at the asset delivery stage, and providing more reliable compliance data support for subsequent resource scheduling decisions.

[0027] 4. By encapsulating the digital asset instance library, the flow transfer work order, the compliance qualified certificate and the resource allocation scheduling queue with the whole-chain business logic, a whole-process collaborative management closed loop from asset allocation, risk monitoring to compliance access is constructed. By using the digital signature encapsulation of the digital certificate and the automatic triggering of the database state transaction, the real-time synchronization of the physical asset compliance result and the management system in-service state is realized, the intermediate transfer time delay of the resource from the field delivery to the scheduling queue is greatly reduced, and the resource response speed and the overall collaborative efficiency of the mobile observation project in processing large-scale and cross-regional tasks are significantly enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0028] Fig. 1 It is a flow observation project whole-process collaborative management method process schematic diagram of the application;

[0029] Fig. 2 It is an update task scheduling time axis process schematic diagram of the application;

[0030] Fig. 3 It is a project compliance audit process schematic diagram of the application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0032] Please refer to Figs. 1 to 3 The application provides a mobile observation project whole-process collaborative management method, and the technical solutions are as follows:

[0033] A mobile observation project whole-process collaborative management method, specifically as Fig. 1 shown, comprising:

[0034] Obtain multi-dimensional observation data and an elevation model of a to-be-observed area, and access the inventory observation nodes to construct a digital instance library; construct a collaborative constraint graph based on the multi-dimensional observation data, calculate the work deployment resistance index of each grid node, generate an observation deployment accessibility factor using the elevation model, and generate an access control matrix in the collaborative constraint graph;

[0035] Iterate the digital instance library to obtain the performance portrait of the inventory observation nodes, call a factor matching model, evaluate the matching evaluation index of the performance portrait and each grid node in the access control matrix, generate a flow transfer work order according to the matching evaluation index, and the flow transfer work order contains a device unique ID, a compliance verification contract and a logistics monitoring benchmark;

[0036] receiving stress accumulation logs uploaded based on logistics monitoring benchmarks, performing stress recovery assessment, generating inventory freeze time windows, injecting inventory freeze time windows as dynamic constraints into the flow order update task scheduling timeline;

[0037] receiving real-time response features matching the performance verification contract, performing project compliance audit, evaluating business consistency indicators between real-time response features and theoretical benchmarks of corresponding nodes in the collaborative constraint graph, generating performance qualified credentials, updating inventory observation nodes to in-service status in the digital instance library, and including them in the resource allocation scheduling queue.

[0038] Further, the process of constructing the digital instance library includes: acquiring multispectral remote sensing images and road network vector data of the to-be-observed area, extracting surface medium texture features and road topological structure, calculating the curvature and slope of the surface access path in combination with the elevation model; simultaneously acquiring historical background noise records of the to-be-observed area, calculating noise power spectral density benchmark values of different frequency bands, and uniformly packaging them as multidimensional observation data; reading the bottom firmware information of each inventory observation node through the Internet of Things gateway, parsing the device serial number, sensor sensitivity calibration parameters and effective passband range; calling the object relationship mapping program, taking the device serial number as the primary key, packaging the sensor sensitivity calibration parameters and effective passband range as static attribute fields, and packaging the real-time power and idle state identifier of the device as dynamic state fields, and storing them in the database to form the digital instance library.

[0039] Specifically, first, the Sentinel-2 multispectral remote sensing images of the to-be-observed area and the road network vector data in Shapefile format are called, the images are operated using the gray level co-occurrence matrix algorithm, the contrast and energy of the surface medium are extracted as texture features, and the types of the underlying surface such as gravel, soil or vegetation are identified, the extracted road topological structure and the elevation model are performed spatial superposition, and the curvature and slope of the surface access path are calculated by D8 algorithm. In this embodiment, the path with a slope greater than 15° and a curvature radius less than 10m is determined as a deployment restricted area.

[0040] Meanwhile, the historical background noise records of the region in the past three years are called from the historical data server, the Welch method (with Hanning window, 50% overlap sampling) is used to perform periodogram power spectrum estimation on the recorded data, the noise power spectrum density reference value in the frequency band of 0.1 Hz to 50 Hz is calculated, the above ground texture, path geometric features and noise reference value are mapped to a unified geographic grid, and encapsulated as multi-dimensional observation data indexed by grid ID; the Internet gateway establishes data connection with the inventory observation node through the MQTT communication protocol, the Internet gateway issues reading instructions to the node main control board, analyzes the binary configuration area in the firmware, extracts the unique serial number of the device, analyzes the sensitivity calibration parameters (2.5V / cm / s in this embodiment) of the sensor and the effective passband range (0.05Hz to 100Hz in this embodiment).

[0041] Subsequently, the embodiment calls the Django object relationship mapping program to perform data persistence operation, takes the device serial number as the primary key field, maps the parsed sensor sensitivity calibration parameters and effective passband range to the static attribute fields of the asset table, simultaneously, reads the power voltage value (12.6V in this embodiment) and the current task occupation state identifier of the node through the heartbeat packet of the Internet gateway, and encapsulates them as dynamic state fields, and stores them in the PostgreSQL database in real time, finally forms a digital instance library supporting subsequent business scheduling.

[0042] The specific data model and storage architecture of the digital instance library are as follows: taking the unique serial number of the device as the primary key, using variable-length character format storage, setting the sensitivity of the sensor to high-precision decimal format, unit: volt per centimeter per second, the upper and lower limit frequencies of the effective passband are stored in decimal format, unit: Hz, the self-noise power spectrum density curve is stored in floating-point array format, its dimension is fixed at 1024, used to record the noise values at different frequency points, the mean time between failures is stored in integer format, unit: hours, the state identifier field uses Boolean type, used to distinguish the idle or in-service state of the asset.

[0043] The grid vertex table stores the grid unique identifier, the grid center longitude and latitude coordinates, and the calculated deployment resistance index and accessibility factor. The graph edge table: through the foreign key connection of the starting vertex and the ending vertex in the grid vertex table, stores the noise cross-correlation coefficient and environmental interference weight between adjacent grids. This storage method of the adjacency list can significantly reduce the storage complexity under a large-scale grid compared with the adjacency matrix; vectorization encapsulation is performed on the asset performance portrait, the 1024-dimensional self-noise curve data and the 1-dimensional device reliability coefficient are connected in series to form a 1025-dimensional feature tensor, all floating point data are stored and operated in the single precision floating point format of the IEEE 754 standard to balance the calculation precision and system processing efficiency, and the digital instance library is constructed in full amount at system initialization, and then triggers database transactions for incremental update when the device returns to the inventory or performs on-site audit, so that the real-time synchronization of the logical state and the physical asset is ensured.

[0044] By integrating remote sensing images, road network topology and device firmware information, the environmental background and asset attributes are deeply mapped, and a digital base with environmental perception characteristics is constructed on the management end. This data encapsulation method provides a multi-dimensional reference benchmark for subsequent scheduling, so that the system can incorporate regional noise and terrain constraints into asset static attributes from the initial stage, and improves the incomplete asset performance portrait caused by the lack of environmental background information in the traditional management account.

[0045] Further, the multi-dimensional observation data is used to construct a collaborative constraint graph, and a job deployment resistance index of each grid node is calculated, including: dividing the to-be-observed region into standardized geographical grids, taking the geometric center of each grid as the vertex of the collaborative constraint graph; according to the background noise record in the multi-dimensional observation data, the noise cross-correlation coefficient between adjacent vertices is calculated, the undirected edge with environmental interference weight is established between the vertices whose noise cross-correlation coefficient exceeds a preset threshold, and the collaborative constraint graph is constructed; remote sensing image texture features and elevation variance of the region corresponding to each grid are extracted, and a complexity feature vector including surface roughness and terrain relief is constructed; the complexity feature vector is normalized into a probability distribution form, the uncertainty value of the probability distribution is calculated by using the Shannon information entropy formula, and the uncertainty value is used as the job deployment resistance index.

[0046] Specifically, first, the geographic information system plug-in is used to divide the to-be-observed region into standardized square geographic grids of 500 m x 500 m, the geometric center coordinates (longitude, latitude) of each grid are extracted, and the geometric center coordinates are defined as the vertices V of the collaborative constraint graph. For each grid vertex, the corresponding 24-hour continuous background noise data is called, and the normalized cross-correlation function is used to calculate the noise cross-correlation coefficient between adjacent vertices Vi and Vj in the 8-neighborhood. When the calculated cross-correlation coefficient exceeds a preset threshold (set to 0.65 in this embodiment), a connection relationship is established between the vertices Vi and Vj, a non-directed edge is formed, and the cross-correlation coefficient is taken as the environmental interference weight of the non-directed edge. The greater the weight, the higher the similarity of the background environment between the two grids, and the stronger the interference collaboration. Thus, the topological construction of the collaborative constraint graph is completed. Subsequently, for the remote sensing image of the region corresponding to each grid, the gray level co-occurrence matrix algorithm is used to extract texture features such as contrast and energy to represent the surface roughness. At the same time, the elevation model data is called to calculate the altitude variance of each sampling point in the grid to represent the terrain undulation. The eigenvalue corresponding to the surface roughness and the variance value corresponding to the terrain undulation are combined to construct a complexity feature vector. In this embodiment, the 8-neighborhood vertices of the adjacent vertices (i.e., the adjacent grids containing the up, down, left, right, and four diagonal directions of the current grid) are set. The purpose of setting the 8-neighborhood is to ensure the continuity and integrity of the environmental interference evolution identification. Since the propagation of background noise in geographic space has radial diffusion characteristics, its influence range is not limited to the orthogonal coordinate axis direction. If only 4-neighborhood is used for determination, the potential strong interference correlation in the diagonal direction will be ignored by the management system, resulting in misjudgment of interference islands in the collaborative constraint graph. By introducing the 8-neighborhood topological correlation in the diagonal direction, the environmental field collaboration features in the diagonal path can be more accurately captured, thereby providing spatial logic support without dead angles for risk assessment of subsequent flow orders.

[0047] In order to realize the quantification of the resistance index, the complexity feature vector is normalized, specifically: first, two components of the complexity feature vector are determined, which are the surface roughness feature value extracted based on remote sensing images and the terrain undulation variance value calculated based on the elevation model, and for each feature value, the range standardization processing is performed, specifically: the difference between the current value of the component and the historical minimum value of the region is calculated, and then divided by the difference between the historical maximum value and the historical minimum value, if the maximum value and the minimum value of a certain feature in the region are equal, in order to ensure the stability of the calculation, the normalized feature value is uniformly set to 0.5; the normalized feature values are summed up, and the ratio of each feature value in the sum is calculated, so as to convert the feature vector into a probability distribution vector whose sum of elements is equal to 1; the probability distribution vector is quantified by the Shannon information entropy formula, and the natural logarithm (i.e. with natural constant e as the base) is used for logarithmic operation in the calculation process, in order to avoid the undefined logarithmic operation when the probability value is 0, a small zero threshold value is set, which is 10 -6 When the probability value is less than the threshold value, the product result of this item is directly counted as 0 to ensure the continuity of the calculation process, and the finally calculated entropy value is the operation deployment resistance index, the numerical range of the index is strictly locked between 0 and ln2, the larger the entropy value, the more broken the topography of the grid region, the more intense the terrain, the higher the difficulty and uncertainty of the actual deployment of observation resources, and the index is stored as the inherent management attribute of the grid vertex in the database to provide a quantitative decision boundary for subsequent generation of flow table with environment adaptability.

[0048] The calculation of the noise cross-correlation coefficient between adjacent vertices includes: obtaining the background noise sequences of the adjacent two grid center points in the 8-neighborhood based on the unified time reference sampling in the same observation period, and calculating the numerical average of each background noise sequence; the sampling points in the background noise sequence are subtracted by the corresponding numerical average to obtain the first noise signal and the second noise signal after centralization processing; the first noise signal and the second noise signal are synchronized and aligned according to the time sequence step, and the product accumulation value of the same sampling time is calculated; the square sum of the first noise signal and the second noise signal sequence is calculated respectively, and the square root operation is performed on the square sum result to obtain the corresponding first amplitude feature constant and second amplitude feature constant; the product accumulation value is divided by the product of the first amplitude feature constant and the second amplitude feature constant, and the absolute value of the division result is taken, and the noise cross-correlation coefficient is output. This process quantifies the environmental interference cooperativity and suppresses the equipment deviation through 8-neighborhood sampling and normalization processing, provides a basis for identifying homogenization risk areas, and improves the rationality of resource scheduling and business cooperation depth.

[0049] By establishing the environmental interference correlation between the geographical grids and quantifying the operation resistance entropy, the abstract regional environment uncertainty is converted into a quantifiable management constraint index. This means enables the management system to identify the differences in the deployment difficulty of different operation nodes, providing spatial level quantitative support for resource allocation, helping to alleviate the deviation between the scheduling instructions and the actual situation on site due to the too general description of the space environment, and enhancing the prediction ability of the management scheme in the spatial dimension.

[0050] Further, the observation deployment reachability factor is generated by using the elevation model, and the access control matrix is generated in the collaborative constraint graph, including: based on the road network vector data in the multi-dimensional observation data, the backbone road sections with road grade attribute higher than the preset value are screened out, and the operation logistics topology structure is constructed; the Dijkstra shortest path algorithm is called to calculate the topological driving distance of each grid center point to the nearest access point along the actual direction of the backbone road section, and the transportation loss coefficient of the driving path is extracted based on the elevation model; the comprehensive logistics cost value representing the resource reach difficulty is obtained by weighting and accounting the planned operation mileage and the transportation loss coefficient, and the reciprocal of the comprehensive logistics cost value is taken as the observation deployment reachability factor; the ratio of the operation deployment resistance index to the observation deployment reachability factor is calculated to obtain the comprehensive deployment coefficient weight, the comprehensive deployment coefficient is compared with the preset safety threshold, the nodes exceeding the threshold are marked as unusable state, and the access control matrix is generated.

[0051] Specifically, first, the OSM format road network vector data of the to-be-observed region is extracted from the multi-dimensional observation data, and the backbone road sections such as expressways, national roads, provincial roads and county roads are selected by traversing the attribute labels of the roads. The dynamic nature of this step is that the system establishes a time-triggered task scheduler, which automatically obtains the latest road network connection state through a geographic information interface every preset period (such as 24 hours) or when receiving a natural disaster warning issued by the meteorological department. If the physical grade of a road section decreases or is closed due to landslides or snow, the logistics topology structure will be updated in real time by removing the invalid edges and reconstructing the topology, thereby ensuring the effectiveness of subsequent calculations. Subsequently, the logistics distribution center is taken as the starting access point, and the shortest path algorithm is used to search the optimal driving path to each geographical grid center point in the updated logistics topology, and the actual driving distance is calculated by accumulation. In this process, the system performs segmented calculation on each optimal path: the long-distance path is discretized into a series of continuous sampling segments with a step size of 50 meters. For each segment, the vertical height of the start point and the end point is extracted from the elevation model, and the height difference between the two points is calculated.

[0052] The loss determination logic for each micro-section is as follows: when the ratio of height difference to horizontal distance (i.e. slope) is between 0° and 5°, the transportation loss of the section is assigned a value of 1.0; when the slope is between 5° and 15°, the loss value increases with the increase of the slope, and is calculated by linear stepping (the loss value increases by 0.04 for every 1° increase); when the slope exceeds 15°, the loss of the section is directly assigned an upper limit value of 1.4. The loss values of all micro-sections in the entire path are averaged to obtain the transportation loss coefficient of the grid node. The actual driving distance calculated above is multiplied by the transportation loss coefficient to obtain a value representing the total logistics cost, and the reciprocal of the value is calculated to generate the observed deployment accessibility factor. The value of this factor fluctuates with the dynamic updating of the road network state. Then, the system calls the work deployment resistance index of each grid node calculated by the Shannon information entropy logic, and calculates the ratio of the index to the observed deployment accessibility factor. The ratio is the comprehensive deployment coefficient weight, which is dynamically derived from the dual feedback of geographical environment (resistance) and logistics state (accessibility): when the backbone road section leading to a grid is damaged, causing the accessibility factor to drop sharply, even if the grid has a flat terrain, the comprehensive deployment coefficient weight will increase rapidly due to the smaller denominator. In this embodiment, if the calculated comprehensive logistics cost value is 0 (e.g. when the access point is the target grid), the accessibility factor is set to a preset maximum gain constant to ensure the continuity of the calculation process.

[0053] A safety threshold of 0.85 is set, and before each cooperative scheduling instruction is initiated, a comparison program for all grid nodes is automatically triggered: if the comprehensive deployment coefficient weight of the grid exceeds 0.85, the index position corresponding to the grid in the memory is marked as a logical value of 0; if the weight is lower than the threshold, it is marked as a logical value of 1. These logical identifiers constitute a binary access control matrix corresponding to the geographical grid space structure, which serves as the bottom constraint mask of the asset allocation algorithm. When the scheduling logic is executed, it automatically masks all vertex coordinates with a state of 0, forcing the generated transfer work order to be limited within the area that meets the safety access conditions. This matrix updating mechanism based on environmental feedback realizes the dynamic avoidance of physical world risks by the management decision layer.

[0054] By combining road network topology and elevation model to calculate the travel cost and coupling it with the work impedance entropy to generate an access control matrix, the dual filtering of logistics accessibility and environmental suitability is realized. This mechanism excludes nodes that are difficult to perform due to excessive comprehensive deployment resistance in the transfer decision stage, reduces the possibility of resource misconfiguration, and makes the geographical target of the management instruction more practical, optimizing the matching degree of scheduling logic and physical environment from the access end.

[0055] Further, the traversing of the digital instance library to obtain the asset performance portrait of the inventory observation node comprises: reading the equipment factory specification data stored in the digital instance library, extracting the instrument self-noise power spectral density curve and the mean time between failures; calling a data interface to read the system running log of the current node, and parsing the cumulative running reference time length since the equipment was manufactured; calculating the ratio of the cumulative running reference time length to the mean time between failures, calling an exponential decay function to map and calculate the ratio, and generating a normalized equipment reliability coefficient; and vectorizing and packaging the instrument self-noise power spectral density curve and the equipment reliability coefficient to generate the asset performance portrait.

[0056] Specifically, first, the hardware specification database pre-stored in the digital instance library is accessed, and for each observation device in the inventory state, the instrument self-noise power spectral density curve and the mean time between failures at the time of factory shipment are extracted. The instrument self-noise power spectral density curve is not a simple factory parameter, but is generated by placing the device in a deep underground static force platform shielded from external mechanical vibration and electromagnetic interference before delivery, performing a 48-hour static background recording, and using the Welch method to perform segmented overlapping spectrum analysis processing on the collected original waveform, finally generating a series of numerical point sets reflecting the noise amplitude of the device at different frequency points, which is used to measure the inherent limit observation accuracy of the hardware circuit and mechanical structure. The mean time between failures is calculated by automatically retrieving the total running hours and the total number of non-human failures of all in-service nodes of the same type of device in the instance library in the past five years, calculating the ratio of the two and taking the statistical average to obtain the theoretical fault-free period reference.

[0057] Subsequently, the system running log of the current node is retrieved by calling the asset monitoring interface, the program scans the log sequence line by line, locks the physical time window of a single task using the start job marker and the stop job marker, and synchronously extracts the time series data of the sampling environment temperature, relative humidity and three-axis combined vibration acceleration in the window, and parses the cumulative running reference time length of the device in combination with the time series data of the environment temperature, relative humidity and three-axis combined vibration acceleration.

[0058] The cumulative running reference time length corrected by the environmental stress coupling is extracted, and the average failure interval time reference value corresponding to the device is retrieved from the digital instance library. The ratio between the two is calculated by performing a division operation, which accurately reflects the theoretical life ratio currently consumed by the observed resource in the physical layer. Subsequently, a nonlinear mapping is performed on the aforementioned ratio using a natural constant as the base of the negative exponential decay function, which converts it into a normalized device reliability coefficient strictly limited between 0 and 1. Under this mapping logic, when the cumulative running reference time length is much smaller than the average failure interval time, the coefficient tends to 1, indicating that the device is in the initial running period of high reliability. As the ratio increases, the coefficient presents a slow-to-fast accelerated downward trend, thereby mathematically simulating the nonlinear increase in failure rate of precision electronic components as the service time increases, achieving a conservative quantitative modeling of the current health status of the device. After obtaining the reliability coefficient, the instrument self-noise power spectral density curve pre-stored in the instance library is further retrieved. This curve is a high-dimensional feature sequence composed of numerical points reflecting the inherent noise level of the device at different frequency points, representing the limit observation accuracy of the resource. Then, a multi-dimensional feature vectorization packaging operation is performed, and the normalized device reliability coefficient generated by real-time calculation is appended as an independent dynamic feature dimension to the numerical sequence of the instrument self-noise power spectral density curve. This packaging method integrates the originally isolated hardware specification data and real-time running state data into a unified multi-dimensional feature tensor, which constitutes the asset performance portrait of the observation node. The finally generated asset performance portrait is stored in the job scheduling buffer of the system as the core input of the subsequent collaborative scheduling algorithm. By reading the composite features packaged in the portrait, the management system can automatically identify the fatigue degree and observation ability limit of each device, thereby automatically selecting the optimal asset combination according to the scientific value and environmental complexity of the task before initiating the collaborative scheduling instruction.

[0059] The cumulative running reference time length of the device is resolved, including: constructing a multi-dimensional environmental state vector and pre-setting a golden running state vector containing an ideal reference value; constructing an environmental stress coupling covariance matrix representing the interaction relationship between each environmental dimension, the diagonal elements of the matrix representing the independent damage weight of each single environmental factor, and the non-diagonal elements representing the cross-coupling damage coefficient between different environmental factors; for each sampling time in the running log, calculate the deviation vector of the measured environmental parameters and the golden running state vector, and calculate the inverse matrix of the environmental stress coupling covariance matrix; perform Mahalanobis distance quadratic form operation using the deviation vector and the inverse matrix to obtain the instantaneous comprehensive environmental stress intensity containing multi-physical field coupling effect; perform time domain integration operation on the instantaneous comprehensive environmental stress intensity within the physical time window to obtain the equivalent aging time length of a single task, and perform accumulation processing on all historical tasks.

[0060] Specifically, first, the micro-electro-mechanical system three-axis accelerometer and the digital temperature and humidity sensor built in the logistics monitoring terminal are used to collect high-frequency vibration sequences (sampling rate of 100 Hz) and low-frequency environmental sequences (sampling rate of 1 Hz) in the transportation process. In order to construct a multi-dimensional environmental state vector that is strictly synchronized in the time dimension, a time alignment operation is performed: linear interpolation processing is performed on the low-frequency temperature and humidity data based on the high-frequency vibration sampling time points to ensure that each millisecond-level sampling time point has accurate temperature, humidity, and three-axis combined vibration acceleration values. These three physical quantities are packaged as an ordered array, which constitutes the measured multi-dimensional environmental state vector at the current time.

[0061] A preset ideal golden running state vector is defined, which defines the best working condition point with the lowest device aging rate, and contains three specific dimension reference values: a standard constant temperature of 25 degrees Celsius, a standard suitable humidity of 45%, and a completely stationary state of 0 gravity acceleration. For the measured multi-dimensional environmental state vector at each sampling time in the operation log, a subtraction operation is performed with the golden running state vector to obtain a deviation vector.

[0062] The specific form and parameter setting of the environmental stress coupling covariance matrix are as follows: the matrix is a 3x3 symmetric positive definite matrix, corresponding to the temperature, humidity, and vibration three physical dimensions. In this embodiment, the standard deviation weight of temperature is preset to 5 (unit: degrees Celsius), the standard deviation weight of humidity is preset to 15 (unit: percentage relative humidity), and the standard deviation weight of vibration is preset to 0.1 (unit: gravity acceleration g). In the non-diagonal position of the matrix, the cross-influence of different environmental stresses is reflected through a preset coupling coefficient, wherein the coupling coefficient of temperature and humidity is set to 0.3; the coupling coefficient of temperature and vibration is set to 0.6, to represent the physical damage intensification effect (i.e. low-temperature brittleness effect) caused by material brittleness in a low-temperature environment; and the coupling coefficient of humidity and vibration is set to 0.4. When performing Mahalanobis distance quadratic form operation and inverting the covariance matrix, in order to avoid calculation divergence caused by poor matrix condition, a Tikhonov regularization processing mechanism is introduced, specifically: if the condition number of the matrix exceeds 1000, a unit matrix regularization parameter with a magnitude of 10 -6 of the matrix is superimposed to ensure the numerical stability of the inverse matrix solving process. Through the above precisely defined matrix parameters, the deviation vector of the measured environmental parameters deviating from the golden working condition can be converted into an instantaneous comprehensive environmental stress intensity containing multi-physical field coupling effects, thereby realizing the real restoration of the stress state of the internal microstructure of the device.

[0063] In the calculation process, firstly, the inverse matrix of the environmental stress coupling covariance matrix is calculated, and then, for each sampling time, Mahalanobis distance quadratic form operation is performed: that is, the continuous matrix multiplication operation of the bias vector (transpose) multiplied by the covariance inverse matrix and then multiplied by the bias vector is sequentially performed, and the square root processing is performed on the final scalar result. The output result of this operation is called instantaneous comprehensive environmental stress intensity. Unlike ordinary Euclidean distance, this intensity value not only reflects the distance of environmental deviation, but also automatically amplifies the stress weight in the high coupling risk area (such as low temperature and strong earthquake) through the transformation of the inverse matrix, thereby truly restoring the stress state of the microstructure inside the device.

[0064] Finally, the Riemann and algorithm is used to perform time domain integration operation on the instantaneous comprehensive environmental stress intensity within the physical time window of a single task. The physical meaning of the integration result is to convert the actual transportation process with violent fluctuations and complex working conditions into the equivalent aging time under the standard rated load. The above calculation process is repeated for all historical tasks of the device since its manufacture, and the equivalent aging time obtained each time is accumulated, and finally the cumulative running reference time length for asset performance portrait generation is obtained.

[0065] Finally, the cumulative running reference time length for asset performance portrait generation is obtained. The parameter setting of the environmental stress coupling covariance matrix is based on the empirical calibration results of a specific device type in a specific working environment range. In this embodiment, taking a wideband seismometer as an example, the parameter physical calibration and verification process is as follows: performing accelerated life test on the target device type, combining different gradients of temperature, humidity and vibration acceleration under controlled environment, recording the device performance attenuation sequence, using multi-factor variance analysis to establish the quantitative mapping of stress-aging, and deriving the matrix parameters exclusive to the device; setting every 12 months as a verification period, comparing the actual stress data in the historical maintenance log with the precision recovery data. If the actual deviation from the theoretical prediction value exceeds 15%, the matrix re-calibration process is automatically started; for different types of observed assets, maintain independent parameter matrix library in the digital instance library, and automatically call the corresponding matrix according to the actual device model; the quadratic form operation using Mahalanobis distance is essentially to transform and decouple the physical environmental deviation of each dimension in the multi-dimensional statistical space through the inverse matrix of the covariance matrix, so as to quantify the instantaneous comprehensive environmental stress intensity of the precision mechanical structure by the environmental factors.

[0066] The ratio of the cumulative running time of the computing device to the fault interval time is calculated, and the device reliability coefficient is generated using an exponential decay function, which realizes the digital characterization of the internal health status of the asset. This dynamic performance portrait method can reflect the reliability fluctuations of physical entities over time, enabling the management system to differentiate the dispatch based on the actual decay of the device, and alleviating the lag risk of resource scheduling based on a single factory specification.

[0067] Further, the calling element matching model evaluates the matching evaluation index and generates the asset flow transfer work order, including: the element matching model includes a frequency domain response analysis unit and a multi-dimensional performance weighting unit; input the instrument self-noise power spectrum density curve in the asset performance portrait and the background noise spectrum of the target grid node in the access control matrix into the frequency domain response analysis unit, calculate the amplitude difference integral of the two curves within the effective bandwidth, and output the effective signal-to-noise ratio gain characteristic; construct a feature vector containing the effective signal-to-noise ratio gain characteristic, the device reliability coefficient and the observation deployment accessibility factor of the grid node, input the multi-dimensional performance weighting unit, perform linear transformation operation on the feature vector using the preset weight matrix, and output the matching evaluation index; lock the target inventory observation node according to the matching evaluation index maximization principle, and obtain the device unique ID of the node; extract the spatial topology model response spectrum from the target grid node of the collaborative constraint graph as the performance verification contract; generate a logistics monitoring benchmark containing the three-axis impact response spectrum envelope using the anti-seismic threshold parameter of the node; encapsulate the device unique ID, the performance verification contract and the logistics monitoring benchmark into the asset flow transfer work order.

[0068] Specifically, start the element matching model, which internally integrates a frequency domain response analysis unit and a multi-dimensional performance weighting unit. When performing matching operations, first input the instrument self-noise power spectrum density curve in the asset performance portrait and the target grid node background noise spectrum extracted from the access control matrix into the frequency domain response analysis unit. The unit locks the effective working bandwidth of the device (0.1Hz to 50Hz in this embodiment), calculates the amplitude difference integral between the two spectrum curves within this bandwidth range, which represents the effective signal-to-noise ratio gain characteristic of the instrument in this specific environment in a physical sense, i.e., whether the instrument can distinguish extremely weak observation signals from complex environmental background. Subsequently, construct a feature vector containing multiple key dimensions, which is composed of the aforementioned effective signal-to-noise ratio gain characteristic, the real-time generated device reliability coefficient, and the observation deployment accessibility factor reflecting the geographic location accessibility of the grid node. Input the feature vector into the multi-dimensional performance weighting unit, perform linear transformation operation (i.e., weighted sum of multiple indicators) on the feature vector using the preset weight matrix, and output a matching evaluation index that can comprehensively reflect hardware performance, device health, and logistics difficulty.

[0069] Before performing the matching operation, the element matching model first performs preprocessing of dimension unification and score threshold unification on the extracted effective signal-to-noise ratio gain feature, the equipment reliability coefficient, and the observation deployment accessibility factor of the grid node through the range transformation algorithm, maps all index values to the interval of 0 to 1, so as to realize alignment of different physical characteristics under the standardized scale. In this embodiment, the preset weight matrix is determined in advance based on the analytic hierarchy process, a judgment matrix is constructed with observation accuracy, hardware health degree, and logistics accessibility as the criterion layer, and the importance of each other is compared to solve the corresponding weight vector [0.5, 0.3, 0.2]; the multi-dimensional performance weighting unit calls the weight vector and the standardized feature vector to perform weighted summation operation, and outputs a matching evaluation index that can comprehensively reflect hardware performance, equipment health degree, and logistics difficulty, which is used as the basis for locking the target inventory observation node and generating the asset flow work order.

[0070] Regarding the determination process of the weight vector in the element matching model, the embodiment performs the following quantitative derivation through the analytic hierarchy process: taking observation accuracy, hardware health degree, and logistics accessibility as three evaluation criteria, the importance ratio is determined through pairwise comparison, in this embodiment, the importance of observation accuracy is set to be 2 times that of hardware health degree, and 3 times that of logistics accessibility; the importance of hardware health degree is 2 times that of logistics accessibility. Based on the above ratio relationship, a 3x3 symmetric judgment matrix is constructed, and the judgment matrix is solved by using the eigenvector method, first the maximum eigenvalue of the matrix is calculated, and the corresponding eigenvector is solved, then the eigenvector is normalized (i.e. each component is divided by the sum of the components of the eigenvector), and the original weight vector [0.545, 0.276, 0.179] is obtained, which is rounded to one decimal place to finally determine [0.5, 0.3, 0.2], before performing the weighted operation, first the effective signal-to-noise ratio gain feature, the equipment reliability coefficient, and the observation deployment accessibility factor are normalized by using the range transformation algorithm, the values are all mapped to the interval of 0 to 1, the multi-dimensional performance weighting unit linearly weights and sums the above three standardized feature values and the corresponding weights, and the specific calculation logic is: the matching evaluation index is equal to 0.5 multiplied by the standardized effective signal-to-noise ratio gain feature, plus 0.3 multiplied by the standardized equipment reliability coefficient, plus 0.2 multiplied by the observation deployment accessibility factor.

[0071] According to the matching evaluation index maximization principle, the highest scoring observation device is automatically locked among all candidate inventory nodes, and the device unique ID of the node is obtained, in order to ensure that the observation quality after device deployment can be closed-loop verified, the spatial topology model response spectrum is extracted from the target grid node of the collaborative constraint graph, which records the theoretical vibration characteristics of the target node under certain excitation, and is encapsulated into the work order as the performance verification contract, which is used for automatic field acceptance after device installation, at the same time, the system uses the anti-seismic threshold parameter of the node in the factory calibration to generate the logistics monitoring benchmark containing the three-axis impact response spectrum envelope, which defines the safety boundary that the three-axis acceleration cannot exceed in the transportation process, finally, the device unique ID, the performance verification contract and the logistics monitoring benchmark containing the three-axis impact response spectrum envelope are structurally packaged to generate the final asset circulation work order and push it to the logistics scheduling terminal

[0072] Through frequency domain response analysis and multi-dimensional performance weighted operation, the asset performance is deeply coupled and compared with the background noise of the target node. This matching mechanism based on signal-to-noise ratio gain makes the generation process of the circulation work order not only depend on the asset location, but also depend on the compatibility of device performance and specific operation scene, thereby improving the scientificity of the work order contract content in the task issuing stage, and providing a clear and environment-specific benchmark for subsequent performance verification.

[0073] Further, the process of updating the task scheduling timeline includes: receiving the stress accumulation log uploaded by the accompanying logistics monitoring terminal, analyzing the transient impact peak value and out-of-limit vibration duration in the transportation process, performing weighted cumulative operation to obtain the cumulative impact energy value representing the excitation degree of the device; retrieving the historical maintenance log of the device, extracting the excitation energy data in the previous transportation process and the subsequent zero-point drift recovery duration data, and constructing the mechanical stress-recovery efficiency correlation feature set exclusive to the current device; performing trend fitting operation on the mechanical stress-recovery efficiency correlation feature set to predict the dynamic resting time required by the device to recover the measurement accuracy under the current cumulative impact energy value; taking the time point when the device actually arrives at the observation node as the starting time, and taking the dynamic resting time as the continuous span, to generate the inventory freezing time window, and lock the state of the device in the inventory freezing time window as the calibration maintenance state in the system scheduling table; reading the original estimated operation start time of the circulation work order, judging whether the start time falls within the inventory freezing time window, if yes, calculating the time difference between the original estimated operation start time and the end time of the inventory freezing time window, and performing forward translation operation on the subsequent operation task in the time axis to generate the updated task scheduling timeline, as shown in Fig. 2

[0074] ​Specifically, first, the stress accumulation log uploaded by the logistics monitoring terminal is received through the wireless communication module. The terminal is built-in with a 100Hz sampling rate three-axis acceleration sensor. Feature extraction is performed on the log data, and transient impact peaks with absolute values exceeding 3g (g of gravity) in the transportation process are identified. The total number of discrete extreme points is counted. At the same time, the duration of over-limit vibration with a root mean square value of acceleration continuously higher than 0.5g is analyzed. Then, weighted cumulative operation is performed. The count weight of the impact peak is preset to 2.0, and the weight of the over-limit vibration duration is 0.5. The weighted product items of the two are accumulated, and the cumulative impact energy value representing the excitation degree of the device is calculated. This numerical value quantifies the total amount of physical disturbance caused by mechanical vibration in the transportation process to the internal structural stability of the precision observation instrument (such as elastic element deformation or mechanical fastener slight loosening).

[0075] The electronic history maintenance log of the device is retrieved, and the excitation energy history data in the previous transportation tasks is extracted as the independent variable. The recovery duration data required until the device zero drift is completely zeroed is measured by a high-precision reference source and is used as the dependent variable. The two types of data are paired and associated to construct a mechanical stress-recovery efficiency correlation feature set exclusive to the device. Then, the least squares method is used to perform quadratic polynomial trend fitting operation on the feature set to establish a mathematical mapping model between stress and recovery time. The cumulative impact energy value calculated in the current task is input into the model to predict the dynamic resting time required for the device to recover to the calibration measurement accuracy under the current damage degree. The actual arrival time of the device at the observation node recorded by the global positioning system is taken as the starting point, and the predicted dynamic resting time is taken as the span to generate an inventory freezing time window on the time axis. The state of the device within the window is locked as a calibration maintenance state in the scheduling management database, and any task calling instruction is forcibly cut off.

[0076] The physical basis and boundary restrictions for using a quadratic polynomial for trend fitting are as follows: the selection of a quadratic polynomial fitting is based on the fact that, in the medium stress range (about 70% to 90% of the maximum expected transportation stress of the device), the zero drift recovery curve of the precision observation device exhibits an approximate parabolic characteristic in the initial stage, and the effective application range of the model is limited to the current cumulative impact energy value not exceeding 1.2 times the maximum historical value; if the ratio exceeds this, a conservative extrapolation mode will be automatically switched, and the prediction time will be limited to 1.3 times the maximum historical recovery time, and the theoretical maximum recovery time limit of the device in this embodiment is 72 hours (corresponding to the maximum allowed zero drift recovery period of the seismometer), if the dynamic static time predicted by the model exceeds 72 hours, it is determined that the device has irreversible physical damage or serious reliability degradation, and it is automatically isolated from the scheduling queue and prohibited from participating in subsequent observation tasks, before performing the fitting operation, the median and interquartile range of the historical recovery time data sequence are calculated, and abnormal data points exceeding 3 times the interquartile range are removed to eliminate noise interference in the historical maintenance records. At the same time, the determination coefficient in the fitting process is calculated, if the coefficient is less than 0.75, a data quality warning will be triggered, and a safety factor of 1.5 times will be applied to compensate for the final predicted static time.

[0077] The complete process of quadratic polynomial fitting using the least squares method and the boundary processing mechanism are as follows: before performing the fitting, the system first cleans up the recovery time data in the historical maintenance log, the specific method is: calculate the median and interquartile range of the historical recovery time sequence, and remove abnormal data points whose values exceed the median plus or minus 3 times the interquartile range, in order to eliminate the influence of dirty data caused by human operation interference or extreme environmental mutations on the model; after constructing the quadratic polynomial model, the least squares method is used to solve the model coefficients, and the determination coefficient (i.e. R2 value) of the fitting result is calculated simultaneously, in this embodiment, the qualified threshold of the determination coefficient is set to 0.75, if the calculated determination coefficient is less than 0.75, it means that the performance evolution law of the device does not meet the standard model, a safety warning will be automatically triggered, and a safety factor of 1.5 times will be multiplied on the basis of the prediction result, considering the physical limit of the precision observation instrument, the theoretical maximum recovery time limit of the device in this embodiment is 72 hours (corresponding to the maximum period of zero drift recovery of the seismometer), if the dynamic static time predicted by the model exceeds 72 hours, it is determined that the device has irreversible mechanical damage, and it is automatically marked as severely damaged in the logical state, and it is permanently isolated from the scheduling queue, if the cumulative impact energy value of the current task exceeds the maximum historical value, the system will adopt a conservative extrapolation strategy, and the predicted recovery time will be limited to 1.3 times the maximum historical recovery time, in order to prevent the mathematical model from producing unreasonable calculation values outside the data boundary.

[0078] After the completion of the time window locking, the preset original estimated job start time in the read flow order is read, and a logical judgment is performed to check whether the time point is within the closed interval range of the inventory freezing time window. If the result of the judgment is that it falls within the interval, it means that the original scheduling ignores the vibration recovery period, which may cause the observation data to be invalid. At this time, the time difference between the end time of the inventory freezing time window and the original estimated job start time is automatically calculated. The difference value is used as the reference step size. The current task and all subsequent related tasks in the scheduling chain are uniformly executed in the forward translation operation on the global time axis. The updated task scheduling time axis is regenerated and synchronized to the command and dispatch center and the logistics execution terminal. Through this dynamic correction, it is ensured that the precision observation task is always started in the stable state after the complete release of the equipment mechanical stress and the recovery of the zero point drift.

[0079] By converting the logistics transportation stress into an inventory freezing time window and performing forward translation of the scheduling time axis, dynamic time compensation for performance loss during the transfer process is achieved. This process enables the scheduling plan of the management system to actively adapt to the precision recovery period of the physical entity, reduces the probability of operating with a sick device, effectively improves the disconnection between the management system in the time dimension and the real state of the resources, and improves the flexibility and accuracy of the project execution plan.

[0080] Further, the execution project compliance audit process adopts a feature alignment model, specifically including: a multi-source reference mapping unit: according to the communication protocol in the performance verification contract, real-time response features are called and measured response frequency spectrum is extracted; a standard reference frequency spectrum stored in the performance verification contract is read; the standard reference frequency spectrum and the measured response frequency spectrum are executed for frequency domain discretization sampling, divided into N independent frequency band logical points, and a frequency domain feature space of the same dimension is constructed; the current environmental background noise reference value is read and converted into an environmental constraint vector corresponding to the frequency band logical point one by one to obtain a three-dimensional input tensor containing standard features, measured features and environmental constraints;

[0081] An environmental interference weighting unit: based on the three-dimensional input tensor, for each frequency band logical point, the amplitude of the corresponding environmental constraint vector and the amplitude of the measured response frequency spectrum are superimposed to obtain the total energy value of the current frequency band; the amplitude of the measured response frequency spectrum under the current frequency band logical point is divided by the total energy value to obtain the signal purity coefficient of the current frequency band; the signal purity coefficient is taken as the weight coefficient of the current frequency band, and the standard features and the measured features are multiplied element by element to output a weighted feature matrix;

[0082] A semantic alignment solution unit: for the weighted feature matrix, the weighted Euclidean distance between the standard feature vector and the measured feature vector is calculated; the weighted Euclidean distance is converted into a normalized value by using an inverse proportional mapping function as a business consistency index, and the specific process is as shown in Fig. 3 ​

[0083] Specifically, first, the multi-source reference mapping unit is started, the real-time response characteristics of the equipment are called through the asset monitoring interface according to the preset communication protocol in the performance verification contract, and the measured response spectrum reflecting the current actual working state of the equipment is extracted; at the same time, the standard reference spectrum generated by factory calibration or simulation is read from the storage field of the performance verification contract. In order to realize quantitative comparison of data from different sources in the same dimension, the standard reference spectrum and the measured response spectrum are subjected to frequency domain discretization sampling, the full-bandwidth frequency domain range is uniformly divided into N (for example, 1024) independent frequency band logical points, thereby constructing a unified dimension frequency domain feature space, then, the environment background noise benchmark value measured at the current observation site is read and converted into an environment constraint vector corresponding to the above-mentioned frequency band logical points one by one, and finally, the standard reference spectrum, the measured response spectrum and the environment constraint vector are stacked to generate a three-dimensional input tensor containing standard features, measured features and environment constraints.

[0084] Then, the environmental interference weighting unit is used to perform deep processing on the above-mentioned three-dimensional input tensor. For each frequency band logical point, first, the amplitude of the corresponding environment constraint vector is added to the amplitude of the measured response spectrum to obtain the total energy value in the frequency band, then, the calculation of the signal purity coefficient is performed: the amplitude of the measured response spectrum under the current frequency band logical point is divided by the total energy value. The physical meaning of this step is to evaluate the proportion of effective signals in the frequency band: if the background noise is extremely high, the purity coefficient tends to 0; if the environment is extremely clean, the purity coefficient tends to 1. The obtained signal purity coefficient is used as the dynamic weight coefficient of the frequency band, and the standard features and the measured features are executed. This processing method can automatically reduce the contribution of frequency bands severely disturbed by the environment in the compliance evaluation, thereby outputting a weighted feature matrix that eliminates environmental false interference.

[0085] Finally, the semantic alignment solving unit is called to perform final evaluation on the weighted feature matrix. First, the weighted Euclidean distance between the weighted standard feature vector and the measured feature vector is calculated. Since the distance is calculated in the space after signal purity weighting, it can truly reflect the performance degradation of the equipment due to its fatigue, wear and tear or failure, rather than the fluctuation caused by environmental noise. In order to make the audit results intuitive, the inverse proportional mapping function is used to convert the calculated weighted Euclidean distance into a normalized value with a numerical range of 0 to 1. The value is output as a business consistency indicator to the management terminal. If the indicator is close to 1, it is determined that the transfer and deployment of the asset fully comply with the requirements of the performance verification contract, the project passes the compliance audit and enters the formal operation phase; if the indicator is lower than the preset compliance threshold, the warning logic is triggered, requiring on-site calibration or secondary replacement of the equipment at the node.

[0086] In the embodiment, in order to ensure the stability and normalization of the calculation result, a preset audit sensitivity value is first defined to define the decay rate of the evaluation curve and eliminate singular values in the calculation process. In the embodiment, the preset audit sensitivity value is set to 25. When performing the mapping operation, the weighted Euclidean distance output by the previous step is first obtained, and the square value of the distance value is calculated to amplify the influence of the deviation on the final score through the power of 2 operation. Then, the preset audit sensitivity value 25 is summed with the calculated distance square value to construct a dynamic denominator that is always greater than 0. Finally, the preset audit sensitivity value 25 is taken as the numerator, and the dynamic denominator obtained by the summation is divided to output a business consistency index in the range of 0 to 1.

[0087] The logic of the process flow is that when the measured response completely matches the standard contract (i.e. the distance is 0), the numerator and denominator are both 25, and the output index is full score 1. As the deviation distance increases, the denominator rapidly increases with the square term, resulting in a non-linear downward trend of the output index that is first flat and then steep. This design ensures reasonable tolerance for small environmental fluctuations while maintaining high recognition for significant performance deviations, providing logical and quantitative support for the final compliance audit.

[0088] In the embodiment, the logic of the preset value 25 is based on establishing the half-value decay inflection point of the device performance. Since the weighted Euclidean distance d and the mapping index S satisfy a quadratic inverse proportion relationship, setting the constant 25 means that when the measured deviation distance d reaches the critical reference value 5, the business consistency index S is exactly attenuated to 0.5. This design not only conforms to the physical law that error energy increases with the square of the distance, but also amplifies the punishment of large size deviation through the power of 2 operation. In addition, the constant term provides a tolerance for small environmental disturbances, ensuring the highest recognition of the audit evaluation result near the critical deviation, thereby realizing the scientific quantification of the performance of the compliance contract.

[0089] The scope of application of the signal purity coefficient and the limitation of the physical model are as follows: the weighting mechanism is based on the physical assumption that the device response and the environmental noise are basically additive and phase-independent in the frequency domain. This assumption has higher reliability under the following conditions: first, the device installation location should be far away from the self-resonance frequency point of the factory calibration to avoid amplifying the environmental noise by self-resonance; second, the environmental noise spectrum should remain relatively stable within the sampling time window without sudden strong interference; finally, the correlation coefficient between the device measured response and the environmental noise should be below a low level of zero point three. If the above prerequisites cannot be met (for example, the device is forced to be installed near a strong interference source, or there is frequent instantaneous pulse interference in the environment), the simplified model may deviate, and at this time, more complex algorithms such as non-negative matrix factorization or blind source separation are recommended to further separate the effective signal. The signal purity coefficient essentially reflects the proportion of the measured response energy in the total energy of the current frequency band. This definition assumes that the amount of effective information contained in the measured response is proportional to the signal purity. When the measured response amplitude and the background noise amplitude in a certain frequency band are both zero (i.e., there is no energy input in that frequency band), the weight coefficient of that frequency band is automatically set to zero, and a data quality warning is issued simultaneously, prompting the management personnel to check the integrity of the device sensor or the background data acquisition link.

[0090] By introducing the signal purity-based weight allocation logic into the feature alignment model, a quality management means is provided for consistency determination in complex outdoor environments. The use of signal purity coefficients to adaptively weaken environmental interference enables the final generated business consistency indicators to more objectively reflect the performance level of physical entities, optimizing the audit accuracy during asset delivery stage and providing more reliable business status feedback to the management decision-making layer, solving the evaluation misalignment problem caused by environmental noise.

[0091] Further, the process of including the resource allocation and scheduling queue includes: reading the preset minimum access threshold in the flow transfer work order, comparing the business consistency indicator with the minimum access threshold; if the business consistency indicator is greater than or equal to the minimum access threshold, it is determined that the inventory observation node passes the performance verification; a data packet containing the unique ID of the device, the business consistency indicator value of this verification, and the timestamp of the determination passing is constructed, and the data packet is executed with digital signature encapsulation to generate an unforgeable performance qualified certificate; the performance qualified certificate is written into the digital instance library as the performance history record of the node for archiving; the state change transaction of the database is triggered, and the logical state field of the node is changed to in-service; in response to the state change, a registration instruction is automatically sent to the pending resource pool of the observation task scheduling system, and the node is formally included in the resource allocation and scheduling queue.

[0092] Specifically, first, the preset minimum access threshold (for example, set to 0.8) is read from the configuration field of the circulating work order, and the calculated service consistency index is logically compared with the threshold. If the service consistency index is greater than or equal to the minimum access threshold, it is determined that the inventory observation node passes the on-site performance verification. Then, a structured data packet is automatically constructed, which integrates the unique ID of the observation node, the accurate value of the service consistency index output by this verification, and the standard timestamp of the determination of passing. In order to ensure the authority and traceability of the audit results, the system calls the secure encryption module and uses the preset asymmetric encryption private key to perform digital signature packaging on the data packet, thereby generating a performance qualified certificate with tamper-proof characteristics. The certificate, as an electronic identity card of the qualified physical examination, is written into the digital instance library and associated with the performance history record file of the node, providing original data support for subsequent possible quality traceability.

[0093] At the same time of archiving the certificate, the backend triggers a database state change transaction, which changes the logical state field of the observation node in the digital instance library from transit or pending verification to in-service through an atomic database write operation. This state change operation can ensure that the node state in the distributed database remains consistent across the network. In response to the update of the state field, a registration instruction is automatically sent to the pending allocation resource pool of the observation task scheduling system, which contains the geographic coordinates, current portrait features, and latest reliability weight of the node, and the node is formally included in the resource allocation and scheduling queue.

[0094] Through the threshold determination of the service consistency index and the digital signature certificate packaging, the automatic connection of the performance results and the resource scheduling pool is realized. By using the atomic update logic of the database state transaction, the real-time synchronization of the physical asset in-service state and the digital instance library is ensured, the manual transfer delay from the on-site delivery to the formal inclusion in the scheduling sequence is reduced, and the collaborative efficiency of the observation project whole-process management and the closed-loop compliance of the data transfer are significantly enhanced.

[0095] By constructing a digital management architecture integrating environmental awareness, process monitoring and compliance auditing, an association mechanism between resource performance state observation and business decision logic is established under a unified management framework. In the resource access stage, the depth mapping of geographical environment and asset attributes is realized through collaborative constraint graph, improving the scene adaptation of configuration plan; in the resource flow stage, stress accumulation log and dynamic time window are used to update task scheduling, enhancing the adaptability of management instructions to the performance recovery period of physical entities; in the delivery stage, the feature alignment governance model is used to weaken the interference of environmental noise on consistency indicators, optimizing the objectivity of audit results. Through the collaborative control of the above dimensions, the information mismatch between the management end and the physical entity is improved, and the overall operation coordination efficiency and resource scheduling accuracy of the flow observation project under complex operation conditions are improved. Embodiments

[0096] In this scenario, the observation station plans to deploy a high-sensitivity broadband seismometer in a certain uninhabited area. During the 800-kilometer vehicle transportation process, the real-time data stream of extremely poor road conditions is recorded by the logistics monitoring terminal, including a continuous low temperature of-15 degrees Celsius and three-axis combined vibration acceleration caused by frozen soil road. Real-time retrieval of these data streams is performed, and the environmental stress coupling covariance matrix is used for operation. Since the low-temperature-vibration coupling coefficient is preset in the matrix, it is identified that the mechanical fatigue weight of the precision sensor is automatically amplified by 1.5 times in the extremely low-temperature environment. Finally, through Riemann sum algorithm time domain integration, the equivalent aging time of 124 hours caused by this transportation is analyzed, which is not the physical layer transportation time, and is recorded in the life cycle archive of the device.

[0097] Before the device arrives at the destination, the logistics terminal uploads a stress log containing a 4.5g transient impact. Then the historical maintenance log of the seismometer is automatically retrieved, and the mechanical stress-recovery efficiency correlation feature set is used for trend fitting. The prediction result shows that the mechanical suspension system inside the seismometer needs 18 hours of standing to completely recover the zero drift affected by this strong impact. An 18-hour inventory freezing time window is immediately generated on the scheduling time axis. Since the original operation time falls within this window, a positive translation operation is automatically performed to delay the installation and debugging task by 16 hours. From the logical level, the risk of the device outputting incorrect data due to stress not being released is avoided.

[0098] Subsequently, the element matching evaluation is performed, the environmental noise data of the target grid node is read, the background noise of the target grid node is extremely low near 1 Hz, about -145 decibels, which is compared with the self-noise curve of the device, and it is confirmed that it has excellent signal-to-noise ratio gain, the multi-dimensional performance weighting unit combines the current reliability coefficient of the device (the calculated value is 0.92) and the accessibility factor of the site, and outputs the matching evaluation index as 0.89. According to the high score result, the device is locked, and the spatial topology model response spectrum of the target node is extracted from the collaborative constraint graph, and the three-axis impact response spectrum envelope is packaged into the asset circulation work order.

[0099] After the installation of the device is completed, the on-site compliance audit process is entered, and the multi-source reference mapping unit collects the measured response spectrum of the installation point for 20 minutes. Due to the existence of weak wind power generator high-frequency interference near the installation site, the environmental interference weighting unit accurately identifies that the signal purity at the 40Hz frequency band is low, only 0.4, and automatically reduces the evaluation weight of this frequency band to prevent environmental interference from misleading the audit result. The semantic alignment calculation unit subsequently calculates the weighted Euclidean distance after removing noise interference as 1.84.

[0100] Finally, the inverse proportional mapping function with a preset constant of 25 is called, and the mapping operation is performed after squaring the distance, which is to take 25 as the numerator and the sum of 25 and the square of the distance (about 3.38) as the denominator. The business consistency index is calculated as 0.88. Since the index is higher than the access threshold of 0.8, it is determined that the node deployment is compliant, and a compliance qualified certificate containing a digital signature is immediately generated. The database triggers an atomic transaction to change the logical state of the device from to-be-inspected to in-service. With the registration instruction sent to the resource pool, the node is formally included in the scheduling sequence. In the collaborative observation task started that night, the scheduling algorithm assigns it as the observation node of the region according to its high sensitivity and high reliability characteristics in the portrait, completing the digital closed-loop circulation from inventory assets to in-service resources.

[0101] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative management of the entire process of mobile observation projects, characterized in that, include: Acquire multidimensional observation data and elevation models of the area to be observed, and connect them to the inventory observation nodes to build a digital instance library; Constructing a collaborative constraint map based on multidimensional observation data and calculating the operational deployment resistance index for each grid node includes: dividing the observation area into standardized geographic grids, with the geometric center of each grid as the vertex of the collaborative constraint map; calculating the noise cross-correlation coefficient between adjacent vertices based on background noise records in the multidimensional observation data, establishing undirected edges with environmental interference weights between vertices where the noise cross-correlation coefficient exceeds a preset threshold, and constructing the collaborative constraint map; extracting remote sensing image texture features and elevation variance of the corresponding area for each grid, constructing a complexity feature vector including surface roughness and terrain undulation; normalizing the complexity feature vector into a probability distribution form, calculating the uncertainty value of the probability distribution using the Shannon information entropy formula, and using the uncertainty value as the operational deployment resistance index; generating an observation deployment accessibility factor using an elevation model, and in the collaborative constraint map... The process of generating an access control matrix in a collaborative constraint graph, specifically by using an elevation model to generate an observation deployment access factor, includes: selecting backbone road segments with road grade attributes higher than preset values ​​based on road network vector data from the multi-dimensional observation data, and constructing an operational logistics topology; calling Dijkstra's shortest path algorithm to calculate the topological travel mileage from each grid center point along the actual route of the backbone road segment to the nearest access point, and extracting the transportation loss coefficient of the travel path based on the elevation model; weighting the planned operational mileage with the transportation loss coefficient to obtain a comprehensive logistics cost value characterizing the difficulty of resource access, and using the reciprocal of the comprehensive logistics cost value as the observation deployment access factor; calculating the ratio of the operational deployment resistance index to the observation deployment access factor to obtain the comprehensive deployment coefficient weight; comparing the comprehensive deployment coefficient with a preset safety threshold, marking nodes exceeding the threshold as unavailable, and generating an access control matrix. The digital instance library is traversed to obtain the asset performance profile of the inventory observation node. The element matching model is called to evaluate the matching evaluation index between the asset performance profile and each grid node in the access control matrix. Based on the matching evaluation index, an asset transfer work order is generated, which includes the unique equipment ID, performance verification contract and logistics monitoring benchmark. Receive stress accumulation logs uploaded based on logistics monitoring benchmarks, perform stress recovery assessments, generate inventory freeze time windows, and inject the inventory freeze time windows as dynamic constraints into the asset transfer work order update task scheduling time axis; Receive real-time response characteristics of the matching performance verification contract, perform project compliance audit, evaluate the business consistency index between the real-time response characteristics and the theoretical benchmark of the corresponding node in the collaborative constraint graph, generate performance qualification certificate, update the inventory observation node to the active status in the digital instance library, and include it in the resource allocation and scheduling queue.

2. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The process of constructing the digital instance library includes: acquiring multispectral remote sensing images and road network vector data of the area to be observed, extracting surface texture features and road topology, and calculating the curvature and slope of the surface access paths using an elevation model; simultaneously acquiring historical background noise records of the area to be observed, calculating the noise power spectral density benchmark values ​​for different frequency bands, and uniformly encapsulating them into multidimensional observation data; reading the underlying firmware information of each inventory observation node through an IoT gateway, parsing out the device serial number, sensor sensitivity calibration parameters, and effective bandwidth; calling an object-relational mapping program, using the device serial number as the primary key, encapsulating the sensor sensitivity calibration parameters and effective bandwidth as static attribute fields, and encapsulating the device's real-time power level and idle status identifier as dynamic status fields, and storing them in the database to form the digital instance library.

3. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The process of traversing the digital instance library to obtain the asset performance profile of the inventory observation nodes includes: reading the equipment manufacturing specification data stored in the digital instance library, extracting the instrument's self-noise power spectral density curve and mean time between failures; calling the data interface to read the system operation log of the current node, parsing out the cumulative operating reference time of the instrument since its manufacture; calculating the ratio of the cumulative operating reference time to the mean time between failures, calling the exponential decay function to map the ratio, and generating a normalized equipment reliability coefficient; and vectorizing and encapsulating the instrument's self-noise power spectral density curve and the equipment reliability coefficient to generate an asset performance profile.

4. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The process of calling the element matching model to evaluate matching indicators and generate an asset transfer work order includes: the element matching model includes a frequency domain response analysis unit and a multi-dimensional performance weighting unit; inputting the instrument self-noise power spectral density curve in the asset performance profile and the background noise spectrum of the target grid node in the access control matrix into the frequency domain response analysis unit, calculating the integral of the amplitude difference between the two curves within the effective bandwidth, and outputting the effective signal-to-noise ratio gain feature; constructing a feature vector containing the effective signal-to-noise ratio gain feature, equipment reliability coefficient, and observation deployment accessibility factor of the grid node, inputting it into the multi-dimensional performance weighting unit, performing a linear transformation operation on the feature vector using a preset weight matrix, and outputting the matching evaluation indicator; locking the target inventory observation node according to the principle of maximizing the matching evaluation indicator, and obtaining the unique equipment ID of the node; extracting the spatial topology model response spectrum from the target grid node of the collaborative constraint graph as the performance verification contract; generating a logistics monitoring benchmark containing a triaxial impact response spectrum envelope using the seismic threshold parameter of the node; and encapsulating the unique equipment ID, the performance verification contract, and the logistics monitoring benchmark into the asset transfer work order.

5. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The process of updating the task scheduling timeline includes: receiving the stress accumulation log uploaded by the accompanying logistics monitoring terminal, parsing the transient impact peak and the duration of excessive vibration during transportation, performing weighted accumulation calculations to obtain the cumulative impact energy value characterizing the degree of equipment excitation; retrieving the equipment's historical maintenance logs, extracting the excitation energy data during transportation and the subsequent zero-point drift recovery time data, and constructing a mechanical stress-recovery efficiency correlation feature set specific to the current equipment; performing trend fitting calculations on the mechanical stress-recovery efficiency correlation feature set to predict the equipment recovery measurement accuracy under the current cumulative impact energy value. The required dynamic settling time; taking the actual arrival time of the equipment at the observation node as the starting time and the dynamic settling time as the continuous span, the inventory freeze time window is generated, and the status of the equipment within the inventory freeze time window is locked as calibration and maintenance status in the system scheduling table; the original estimated start time of the asset transfer work order is read, and it is determined whether the start time falls within the inventory freeze time window. If so, the time difference between the original estimated start time of the work and the end time of the inventory freeze time window is calculated, and the subsequent work tasks are shifted forward on the time axis to generate an updated task scheduling time axis.

6. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The compliance audit process for the project implementation adopts a feature alignment model, specifically including: a multi-source benchmark mapping unit: based on the communication protocol in the performance verification contract, it retrieves real-time response features and extracts the measured response spectrum; it reads the standard reference spectrum stored in the performance verification contract; it performs frequency domain discretization sampling on the standard reference spectrum and the measured response spectrum, dividing them into N independent frequency band logical points, and constructs a frequency domain feature space of the same dimension; it reads the current environmental background noise benchmark value, converts it into an environmental constraint vector corresponding one-to-one with the frequency band logical points, and obtains a three-dimensional input tensor containing standard features, measured features, and environmental constraints; Environmental interference weighting unit: Based on the three-dimensional input tensor, for each frequency band logic point, the magnitude of the corresponding environmental constraint vector is superimposed with the magnitude of the measured response spectrum to obtain the total energy value of the current frequency band; the magnitude of the measured response spectrum under the current frequency band logic point is divided by the total energy value to obtain the signal purity coefficient of the current frequency band; the signal purity coefficient is used as the weighting coefficient of the current frequency band, and the standard features and measured features are multiplied element-wise to output the weighted feature matrix; Semantic alignment calculation unit: For the weighted feature matrix, calculate the weighted Euclidean distance between the standard feature vector and the measured feature vector; use the inverse proportional mapping function to convert the weighted Euclidean distance into a normalized value, which serves as a business consistency indicator.

7. The method for collaborative management of the entire process of a mobile observation project according to claim 1, characterized in that, The process of including the node in the resource allocation and scheduling queue includes: reading the preset minimum admission threshold in the asset transfer work order and comparing the business consistency index with the minimum admission threshold; if the business consistency index is greater than or equal to the minimum admission threshold, the inventory observation node is deemed to have passed the performance verification; constructing a data packet containing the node's unique device ID, the business consistency index value of this verification, and the timestamp of the passing determination, and performing digital signature encapsulation on the data packet to generate an immutable performance qualification certificate; writing the performance qualification certificate into the digital instance library and archiving it as the node's performance history; triggering a database state change transaction to make the node's logical state field active; and responding to the state change, automatically sending a registration instruction to the resource pool to be allocated in the observation task scheduling system to formally include the node in the resource allocation and scheduling queue.

Images