Flowing observation project whole-process collaborative management method

Abstract

The invention relates to the technical field of enterprise resource management, in particular to a mobile observation project full-process collaborative management method, which comprises the following steps: acquiring multi-dimensional observation data and an elevation model of a to-be-observed area, and accessing an inventory node to construct a digital instance library; constructing a collaborative constraint graph based on the data, calculating a grid node operation deployment resistance index and an accessibility factor, and generating an admission control matrix; traversing the instance library to obtain an efficiency portrait, and calling an element matching model to evaluate a matching index and generate a circulation work order; receiving a stress accumulation log in the logistics process, executing stress recovery evaluation to generate an inventory freezing time window, and dynamically updating a task scheduling time axis; and executing project compliance auditing and evaluating a business consistency index, generating a performance qualification voucher, and incorporating the nodes updated to be in the in-service state into a resource allocation scheduling queue. According to the invention, through dynamic cooperation of the multi-dimensional environment constraint and the equipment stress, the whole-process accurate management of assets is realized.

Description

Technical Field

[0001] This invention relates to the field of enterprise resource management technology, specifically a collaborative management method for the entire process of mobile observation projects. Background Technology

[0002] Mobile observation projects are characterized by long resource transfer chains, complex delivery environments, and high entry barriers. In the collaborative management system of these projects, the management team not only needs to maintain static asset ledgers, but also needs to implement precise business control over the entire lifecycle of observation resources, from warehouse allocation and logistics transfer to on-site delivery, to ensure that the delivery status of each resource conforms to the pre-set task performance contract.

[0003] In current digital management practices, the distribution of management instructions and the formulation of resource scheduling typically rely on standardized business logic. However, in the actual observation of the resource flow chain, the business performance of resources is often affected by the intertwining of multiple business constraints: First, the differences in geographical environment determine the accessibility and delivery difficulty of resources after arrival; second, physical impacts during transportation can cause equipment to enter a performance recovery period, thereby dynamically limiting the scheduling of subsequent work tasks; finally, background environmental interference at the work site directly increases the difficulty of business compliance audits.

[0004] These spatial, temporal, and environmental business variables lead to information asymmetry between the actual delivery capability of physical assets and their expected performance status within the management system. The challenge lies in achieving consistent perception and real-time performance evaluation of multi-source heterogeneous physical sensor data during large-scale data transfer within a unified management architecture and a multi-dimensional dynamic environment. This results in problems such as large computational biases, low accuracy in physical state reconstruction, and delayed resource scheduling responses when processing large-scale concurrent data streams.

[0005] To address this, a collaborative management method for the entire process of mobile observation projects is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a collaborative management method for the entire process of mobile observation projects. Through multi-dimensional environmental constraints and dynamic coordination of equipment stress, it achieves precise asset management throughout the entire process. This includes: acquiring multi-dimensional observation data and elevation models of the area to be observed; constructing a digital instance library by connecting inventory nodes; building a collaborative constraint graph based on the data; calculating the grid node deployment resistance index and accessibility factor to generate an access control matrix; traversing the instance library to obtain performance profiles; calling an element matching model to evaluate matching indicators and generate transfer work orders; receiving stress accumulation logs during the logistics process; performing stress recovery assessments to generate inventory freeze time windows; dynamically updating the task scheduling timeline; performing project compliance audits and evaluating business consistency indicators; generating performance qualification certificates; and adding nodes updated to an active status to the resource allocation and scheduling queue.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A collaborative management method for the entire process of mobile observation projects includes: 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; construct a collaborative constraint map based on the multidimensional observation data, calculate the operation deployment resistance index of each grid node, generate the observation deployment access factor using the elevation model, and generate the access control matrix in the collaborative constraint map; The digital instance library is traversed to obtain the performance profile of the inventory observation node. The element matching model is called to evaluate the matching evaluation index between the performance profile and each grid node in the access control matrix. Based on the matching evaluation index, a transfer work order is generated, which includes the unique equipment ID, the performance verification contract and the logistics monitoring benchmark. Receive stress accumulation logs uploaded based on logistics monitoring benchmarks, perform stress recovery assessment, generate inventory freeze time windows, and inject the inventory freeze time windows as dynamic constraints into the task scheduling time axis of the workflow order update. 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.

[0008] Preferably, 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 medium texture features and road topology, and calculating the curvature and slope of the surface access path 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 a digital instance library.

[0009] Preferably, the step of constructing a collaborative constraint map based on multidimensional observation data and calculating the operational deployment resistance index for each grid node includes: dividing the area to be observed 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 the background noise records in the multidimensional observation data, establishing undirected edges with environmental interference weights between vertices whose noise cross-correlation coefficients exceed a preset threshold, and constructing the collaborative constraint map; extracting the remote sensing image texture features and elevation variance of the area corresponding to each grid, and constructing a complexity feature vector containing 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.

[0010] Preferably, the step of generating an observation deployment accessibility factor using an elevation model and generating an access control matrix in a collaborative constraint map includes: based on the road network vector data in the multidimensional observation data, selecting backbone road sections with road grade attributes higher than preset values, and constructing an operational logistics topology; calling Dijkstra's shortest path algorithm to calculate the topological travel mileage from each grid center point to the nearest access point along the actual route of the backbone road section, and extracting the transportation loss coefficient of the travel path based on the elevation model; performing a weighted calculation on the planned operational mileage and 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 accessibility factor; calculating the ratio of the operational deployment resistance index to the observation deployment accessibility 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.

[0011] Preferably, the step of traversing the digital instance library to obtain the asset performance profile of the inventory observation node includes: reading the equipment manufacturing specification data stored in the digital instance library, extracting the instrument 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 equipment 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 self-noise power spectral density curve and the equipment reliability coefficient to generate an asset performance profile.

[0012] Preferably, the step 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 index; locking the target inventory observation node according to the principle of maximizing the matching evaluation index, 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.

[0013] Preferably, 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 and subsequent zero-point drift recovery time data during each transportation process, 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 recovery efficiency of the equipment under the current cumulative impact energy value. The dynamic settling time required for measurement accuracy; 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 workflow 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 workflow 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.

[0014] Preferably, the compliance audit process for the project adopts a feature alignment model, specifically including: a multi-source benchmark mapping unit: based on the communication protocol in the performance verification contract, retrieving real-time response features and extracting the measured response spectrum; reading the standard reference spectrum stored in the performance verification contract; performing frequency domain discretization sampling on the standard reference spectrum and the measured response spectrum, dividing them into N independent frequency band logical points, and constructing a frequency domain feature space of the same dimension; reading the current environmental background noise benchmark value, converting it into an environmental constraint vector corresponding one-to-one with the frequency band logical points, and obtaining 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.

[0015] Preferably, the process of including the node in the resource allocation and scheduling queue includes: reading the preset minimum admission threshold in the workflow 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 determined 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 passed 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 record; 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.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By constructing a collaborative constraint map based on multi-source environmental background data and elevation models, and deeply coupling the operational impedance entropy and accessibility factors into the access control matrix, the management system's digital perception depth of the complexity of field operation scenarios is significantly enhanced. This multi-dimensional business modeling approach enables the management end to make a refined prediction of the deployment difficulty of each observation node before asset allocation, effectively alleviating the blindness of resource allocation caused by the uncertainty of the geographical environment under the traditional static management model, thereby improving the matching accuracy between asset transfer work orders and the actual operation environment at the source stage of resource transfer.

[0017] 2. By capturing stress accumulation logs during transportation using logistics monitoring benchmarks and invoking state assessment logic to convert physical stress energy into inventory freeze time windows, the previously invisible and uncontrollable equipment flow damage is transformed into dynamic time constraints that the management system can perceive. This allows the task scheduling timeline to adaptively shift according to the real-time accuracy recovery requirements of physical entities, significantly reducing the business risk of assets being forced into operation under performance deviation conditions. It also achieves flexible collaboration between management decision-making logic and the physical asset performance recovery cycle, ensuring the scientific nature of the project execution plan.

[0018] 3. By introducing a dynamic quality weighting process into the feature alignment governance model and generating performance confidence values ​​based on the energy ratio of environmental constraints and measured responses, a quality governance method with environmental awareness 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 judgment, enabling the business consistency indicators generated by the management system to more accurately reflect the contract performance of physical entities. This effectively optimizes the audit accuracy during the asset delivery stage and provides more reliable compliance data support for subsequent resource scheduling decisions.

[0019] 4. By encapsulating the entire business logic of the digital asset instance library, transfer work orders, performance qualification certificates, and resource allocation and scheduling queue, a closed-loop collaborative management system covering the entire process from asset allocation and risk monitoring to compliance access was constructed. Utilizing the digital signature encapsulation of digital certificates and the automatic triggering of database status transactions, real-time synchronization between physical asset performance results and the operational status of the management system was achieved. This significantly reduced the intermediate transfer latency of resources from on-site delivery to entry into the scheduling queue, and significantly enhanced the resource response speed and overall collaborative efficiency of mobile observation projects when handling large-scale, cross-regional tasks. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a collaborative management method for the entire process of a mobile observation project according to the present invention; Figure 2 This is a schematic diagram of the update task scheduling timeline process of the present invention; Figure 3 This is a schematic diagram of the project compliance audit process of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figures 1 to 3 This invention provides a method for collaborative management of the entire process of mobile observation projects, the technical solution of which is as follows: A collaborative management method for the entire process of mobile observation projects, specifically as follows: Figure 1 As shown, it includes: 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; construct a collaborative constraint map based on the multidimensional observation data, calculate the operation deployment resistance index of each grid node, generate the observation deployment access factor using the elevation model, and generate the access control matrix in the collaborative constraint map; The digital instance library is traversed to obtain the performance profile of the inventory observation node. The element matching model is called to evaluate the matching evaluation index between the performance profile and each grid node in the access control matrix. Based on the matching evaluation index, a transfer work order is generated, which includes the unique equipment ID, the performance verification contract and the logistics monitoring benchmark. Receive stress accumulation logs uploaded based on logistics monitoring benchmarks, perform stress recovery assessment, generate inventory freeze time windows, and inject the inventory freeze time windows as dynamic constraints into the task scheduling time axis of the workflow order update. 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.

[0023] Furthermore, 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 medium texture features and road topology, and calculating the curvature and slope of the surface access path 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 range; calling an object-relational mapping program, using the device serial number as the primary key, encapsulating the sensor sensitivity calibration parameters and effective bandwidth range into static attribute fields, and encapsulating the device's real-time power level and idle status identifier into dynamic status fields, storing them in the database to form a digital instance library.

[0024] Specifically, firstly, Sentinel-2 multispectral remote sensing images and road network vector data in Shapefile format of the area to be observed are retrieved. The gray-level co-occurrence matrix algorithm is used to process the images to extract texture features such as contrast and energy of the surface medium, which are used to identify the underlying surface type such as gravel, soil or vegetation. The extracted road topology structure is spatially superimposed with the elevation model. The curvature and slope of the surface access path are calculated segment by segment using the D8 algorithm. In this embodiment, paths with a slope greater than 15° and a curvature radius less than 10m are identified as deployment restricted areas.

[0025] Simultaneously, historical background noise records for the region over the past three years were retrieved from the historical data server. The Welch method (with Hanning window and 50% overlap sampling) was used to perform periodogram power spectrum estimation on the recorded data, calculating the noise power spectral density baseline value in the frequency band from 0.1Hz to 50Hz. The aforementioned surface texture, path geometry features, and noise baseline values ​​were mapped to a unified geographic grid and encapsulated as multidimensional observation data indexed by grid ID. A data connection was established with the inventory observation node via the MQTT communication protocol using an IoT gateway. The IoT gateway issued a read command to the node's main control board, parsed the binary configuration area in the firmware, extracted the device's unique serial number, and parsed the sensor's sensitivity calibration parameters (2.5V / cm / s in this embodiment) and effective bandwidth range (0.05Hz to 100Hz in this embodiment).

[0026] Subsequently, this embodiment calls the Django object-relational mapping program to perform data persistence operations. Using the device serial number as the primary key field, the parsed sensor sensitivity calibration parameters and effective bandwidth range are mapped to the static attribute fields of the asset table. At the same time, the power voltage value of the node (12.6V in this embodiment) and the current task occupancy status identifier are read in real time through the heartbeat packet of the IoT gateway, and encapsulated as a dynamic status field and stored in the PostgreSQL database in real time, ultimately forming a digital instance library to support subsequent business scheduling.

[0027] The specific data model and storage architecture of the digital instance library are as follows: the unique serial number of the device is used as the primary key and stored in a variable-length character format; the sensor sensitivity is set to a high-precision decimal format in volts per centimeter per second; the upper and lower limits of the effective passband are stored in decimal format in Hz; the self-noise power spectral density curve is stored in a floating-point array format with a fixed dimension of 1024, used to record the noise floor values ​​at different frequency points; the mean time between failures is stored in integer format in hours; and the status identifier field is of Boolean type, used to distinguish the idle or active status of the asset.

[0028] The grid vertex table stores the unique identifier of the grid, the latitude and longitude coordinates of the grid center, and the calculated deployment resistance index and accessibility factor. The graph edge table, linked by foreign keys, stores the noise cross-correlation coefficients and environmental interference weights between adjacent grids. This adjacency list storage method significantly reduces the storage complexity of large-scale grids compared to adjacency matrices. Asset performance profiles are vectorized and encapsulated, concatenating 1024-dimensional self-noise curve data with 1-dimensional device reliability coefficients to form a 1025-dimensional feature tensor. All floating-point data is stored and computed using the IEEE 754 standard single-precision floating-point format to balance computational precision and system processing efficiency. The digital instance library is fully constructed during system initialization, and then incrementally updated via database transactions when equipment is returned to inventory or when on-site audits are performed, ensuring real-time synchronization between logical state and physical assets.

[0029] By integrating remote sensing imagery, road network topology, and equipment firmware information, a deep mapping between environmental background and asset attributes is achieved, constructing a digital foundation with environmental awareness at the management end. This data encapsulation method provides a multi-dimensional reference benchmark for subsequent scheduling, enabling the system to incorporate regional noise and terrain constraints into the static attributes of assets from the initial stage, thus improving the incomplete asset performance profile caused by the lack of environmental background information in traditional management ledgers.

[0030] Furthermore, the step of constructing a collaborative constraint map based on multidimensional observation data and calculating the operational deployment resistance index for each grid node includes: dividing the area to be observed 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 the background noise records in the multidimensional observation data, establishing undirected edges with environmental interference weights between vertices whose noise cross-correlation coefficients exceed a preset threshold, and constructing the collaborative constraint map; extracting the remote sensing image texture features and elevation variance of the area corresponding to each grid, and constructing a complexity feature vector containing 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.

[0031] Specifically, firstly, the observation area is divided into a standardized 500m × 500m square geographic grid using a geographic information system plugin. The geometric center coordinates (longitude and latitude) of each grid are extracted and defined as vertices V of the collaborative constraint map. For each grid vertex, its corresponding 24-hour continuous background noise data is retrieved. The noise cross-correlation coefficient between adjacent vertices Vi and Vj in the 8-neighborhood is calculated using a normalized cross-correlation function. When the calculated cross-correlation coefficient exceeds a preset threshold (set to 0.65 in this embodiment), a connection is established between vertices Vi and Vj, forming an undirected connection. The cross-correlation coefficient is used as the environmental interference weight for undirected edges. A larger weight indicates a higher similarity in the background environment between two grids and a stronger interference cooperation, thus completing the topological construction of the cooperative constraint map. Subsequently, for the remote sensing image of the region corresponding to each grid, texture features such as contrast and energy are extracted using the gray-level co-occurrence matrix algorithm to characterize the surface roughness. At the same time, the elevation model data is retrieved to calculate the elevation variance of each sampling point in the grid to characterize the terrain undulation. The feature value corresponding to the surface roughness is combined with the variance value corresponding to the terrain undulation to construct a complexity feature vector. In this embodiment, the 8-neighborhood vertices (i.e., adjacent grids including the current grid in the top, bottom, left, right and four diagonal directions) are set to ensure the continuity and integrity of environmental interference evolution identification. Since background noise propagates radially in geospatial space, its influence is not limited to the orthogonal coordinate axis direction. If only 4-neighborhood judgment is used, the management system will ignore the potential strong interference correlation in the diagonal direction, resulting in misjudgment of interference islands in the collaborative constraint map. By introducing 8-neighborhood topological correlation in the diagonal direction, the environmental field collaborative characteristics on the diagonal path can be captured more accurately, thereby providing comprehensive spatial logic support for the risk assessment of subsequent work orders.

[0032] To quantify the resistance index, the complexity feature vector is normalized. Specifically, the two components of the complexity feature vector are first determined: the surface roughness feature value extracted from remote sensing images and the topographic relief variance value calculated from the elevation model. For each feature value, range standardization is performed. Specifically, the difference between the current value of the component and the historical minimum value for the region is calculated, and then divided by the difference between the historical maximum and the historical minimum value. If the maximum and minimum values ​​of a feature in the region are equal, the standardized feature value is uniformly set to 0.5 to ensure calculation stability. The standardized feature values ​​are then summed, and the proportion of each feature value in the sum is calculated, thus transforming the feature vector into a probability distribution vector where the sum of its elements equals 1. The uncertainty of the probability distribution vector is quantified using the Shannon information entropy formula. During the calculation, the natural logarithm (i.e., with the natural constant e as the base) is used for logarithmic operations. To avoid undefined logarithmic operations when the probability value is 0, a very small zero-value truncation threshold of 10 is set. -6 When the probability value is less than the threshold, the product result of this item is directly counted as 0 to ensure the continuity of the calculation process. The entropy value calculated in the end is the operation deployment resistance index. The value range of this index is strictly locked between 0 and ln2. The larger the entropy value, the more fragmented the terrain and the more severe the topography of the grid area, which means that the difficulty and uncertainty of actually deploying observation resources are higher. This index is stored in the database as an inherent management attribute of the grid vertices to provide a quantitative decision boundary for the subsequent generation of flow work orders with environmental adaptability.

[0033] The calculation of the noise cross-correlation coefficient between adjacent vertices includes: acquiring background noise sequences sampled from two adjacent grid center points within an 8-neighborhood during the same observation period based on a unified time reference, and calculating the average value of each background noise sequence; subtracting the corresponding average value from the sampled points in the background noise sequence to obtain a centered first noise signal and a second noise signal; synchronizing the first noise signal and the second noise signal according to the time sequence step size, and calculating the cumulative sum of their multiplication at the same sampling time; calculating the sum of squares of the first noise signal and the second noise signal sequences respectively, and performing a square root operation on the sum of squares to obtain the corresponding first amplitude characteristic constant and second amplitude characteristic constant; dividing the cumulative sum of their multiplication by the product of the first amplitude characteristic constant and the second amplitude characteristic constant, and taking the absolute value of the division result to output the noise cross-correlation coefficient. This process, through 8-neighborhood sampling and normalization processing, quantifies the synergy of environmental interference and smooths out equipment deviations, providing a basis for identifying homogeneous risk areas and improving the rationality of resource scheduling and the depth of business collaboration.

[0034] By establishing environmental disturbance correlations across geographic grids and quantifying operational impedance entropy, abstract regional environmental uncertainties are transformed into quantifiable management constraints. This approach enables the management system to identify differences in deployment difficulty among different operational nodes, providing spatial-level quantitative support for resource allocation. It helps mitigate deviations between scheduling instructions and actual on-site conditions caused by overly general spatial environmental descriptions, enhancing the predictive capabilities of management solutions in the spatial dimension.

[0035] Furthermore, the step of generating an observation deployment accessibility factor using an elevation model and generating an access control matrix in the collaborative constraint map includes: based on the road network vector data in the multidimensional observation data, selecting backbone road sections with road grade attributes higher than preset values, and constructing an operational logistics topology; calling Dijkstra's shortest path algorithm to calculate the topological travel mileage from each grid center point to the nearest access point along the actual route of the backbone road section, and extracting the transportation loss coefficient of the travel path based on the elevation model; performing a weighted calculation on the planned operational mileage and 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 accessibility factor; calculating the ratio of the operational deployment resistance index to the observation deployment accessibility 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.

[0036] Specifically, the system first extracts OSM-format road network vector data of the area to be observed from multidimensional observation data. By traversing the attribute labels of roads, it filters out backbone road sections such as highways, national roads, provincial roads, and county roads. The dynamism of this step is reflected in the system's establishment of a timed task scheduler. Every preset period (e.g., 24 hours) or when a natural disaster warning is received from the meteorological department, the system automatically obtains the latest road network connectivity status through the geographic information interface. If a road section's physical level is lowered or closed due to landslides or snow accumulation, the logistics topology will be removed from the invalid edges and reconstructed in real time, thus ensuring the effectiveness of subsequent calculations. Subsequently, using the logistics distribution center as the starting access point, the system uses the shortest path algorithm to search for the optimal driving path to the center point of each geographic grid in the updated logistics topology and accumulates the actual driving mileage. During this process, the system performs segmented calculations for each optimal path: long-distance paths are discretized into a series of continuous sampling micro-segments with a step size of 50 meters. For each micro-segment, the elevation model is retrieved to extract the vertical height between its starting and ending points, and the height difference between the two points is calculated.

[0037] The loss determination logic for each micro-segment is as follows: when the ratio of the elevation difference to the horizontal distance (i.e., the slope) is between 0° and 5°, the transportation loss for that segment is assigned a value of 1.0; when the slope is between 5° and 15°, the loss value increases synchronously with the increase of the slope, calculated using a linear step method (the loss value increases by 0.04 for every 1° increase); when the slope exceeds 15°, the loss for that segment is directly assigned an upper limit value of 1.4. The loss values ​​of all micro-segments along the entire path are averaged to obtain the transportation loss coefficient for that grid node. The actual mileage calculated above is multiplied by the transportation loss coefficient to obtain a value representing the total logistics cost, and the reciprocal of this value is calculated to generate the observation deployment accessibility factor. The value of this factor will fluctuate with the dynamic updates of the road network status. Next, the system retrieves the operational deployment resistance index of each grid node previously calculated using Shannon information entropy logic and calculates the ratio of this index to the observation deployment accessibility factor. This ratio is the weight of the comprehensive deployment coefficient. Its dynamism stems from the dual feedback of changes in the geographical environment (resistance) and logistics status (accessibility). When the backbone road leading to a grid is damaged, causing the accessibility factor to drop sharply, even if the surface of the grid is flat, the weight of the comprehensive deployment coefficient will increase rapidly due to the decrease in the denominator. In this embodiment, if the calculated comprehensive logistics cost value is 0 (such as when the access point is the target grid), the accessibility factor is set to the preset maximum gain constant to ensure the continuity of the calculation process.

[0038] A safety threshold of 0.85 is set. Before each coordinated 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 is marked with a logical value of 0 in memory; if the weight is lower than the threshold, it is marked with a logical value of 1. These logical identifiers constitute a binary access control matrix that is completely corresponding to the geographic grid spatial structure. This matrix serves as the underlying constraint mask for the asset allocation algorithm. When the scheduling logic is executed, it automatically masks the coordinates of all vertices with a state of 0, forcibly restricting the generated work orders to areas with safe access conditions. This matrix update mechanism based on environmental feedback enables the management decision-making level to dynamically avoid physical risks.

[0039] By combining road network topology and elevation models to calculate passage costs, and coupling these costs with operational impedance entropy to generate an access control matrix, a dual filtering of logistics accessibility and environmental suitability is achieved. This mechanism eliminates nodes that are difficult to fulfill due to excessive comprehensive deployment resistance during the flow decision-making stage, reduces the possibility of resource misallocation, makes the geographical objectives issued by management instructions more practical, and optimizes the matching degree between scheduling logic and physical environment from the access point.

[0040] Furthermore, the step of traversing the digital instance library to obtain the asset performance profile of the inventory observation node includes: reading the equipment manufacturing specification data stored in the digital instance library, extracting the instrument 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 equipment 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 self-noise power spectral density curve and the equipment reliability coefficient to generate an asset performance profile.

[0041] Specifically, the system first accesses the pre-stored hardware specification database in the digital instance library. For each observation device in stock, it extracts the instrument's self-noise power spectral density curve and mean time between failures (MTBF), which were calibrated at the time of manufacture. The self-noise power spectral density curve is not simply a factory parameter; rather, it is generated by placing the device in a deeply buried underground static platform shielded from external mechanical vibration and electromagnetic interference before delivery, performing continuous 48-hour static background recording, and then using the Welch method to perform segmented overlapping spectrum analysis on the acquired raw waveforms. This generates a set of numerical points reflecting the device's noise floor amplitude at different frequency points, used to measure the inherent limits of the hardware circuitry and mechanical structure's observation accuracy. The MTBF is calculated by automatically retrieving the total operating hours and the total number of non-human-caused failures of all in-service nodes of the same model of equipment in the instance library over the past five years, calculating the ratio between the two, and taking the statistical average to obtain the theoretical fault-free cycle benchmark.

[0042] Subsequently, by calling the asset monitoring interface to retrieve the system operation log of the current node, the program scans the log sequence line by line, uses the start operation marker and stop operation marker to lock the physical time window of a single task, and simultaneously extracts the time series data of the sampled ambient temperature, relative humidity and triaxial vibration acceleration within the window. Combining the time series data of ambient temperature, relative humidity and triaxial vibration acceleration, the cumulative reference operating time of the equipment is parsed out.

[0043] The cumulative operating reference time, corrected for environmental stress coupling, is extracted, and the corresponding mean time between failures (MTBF) benchmark value for the device is retrieved from the digital instance library. The ratio between the two is calculated by performing a division operation. This ratio accurately reflects, at the physical level, the theoretical lifetime multiplier currently consumed by the observed resource. Subsequently, a negative exponential decay function with a natural constant base is used to perform a nonlinear mapping on the aforementioned ratio, transforming it into a normalized device reliability coefficient with a value strictly limited to between 0 and 1. Under this mapping logic, when the cumulative operating reference time is much smaller than the MTBF, the coefficient approaches 1, indicating that the device is in the initial high-reliability operating period. As the ratio increases, the coefficient exhibits an accelerating downward trend, first slowly and then rapidly, thus mathematically simulating the nonlinear increase in failure rate of precision electronic components due to increased service time. This achieves a conservative quantitative modeling of the current health state of the device. After obtaining the reliability coefficient, the values ​​pre-stored in the instance library are further retrieved. The instrument self-noise power spectral density curve, a high-dimensional feature sequence composed of numerical points reflecting the inherent noise level of the equipment at different frequency points, characterizes the limit of the resource's observation accuracy. Next, a multi-dimensional feature vectorization encapsulation operation is performed, adding the real-time calculated normalized equipment reliability coefficient as an independent dynamic feature dimension to the numerical sequence of the instrument self-noise power spectral density curve. This encapsulation method integrates the originally isolated hardware specification data and real-time operating status data into a unified multi-dimensional feature tensor, thus forming the asset performance profile of the observation node. The final generated asset performance profile is stored in the system's job scheduling buffer as the core input for subsequent collaborative scheduling algorithms. By reading the composite features encapsulated in the profile, the management system can automatically identify the fatigue level and observation capability limits of each piece of equipment, thereby automatically selecting the optimal asset combination based on the scientific value of the task and the complexity of the environment before initiating collaborative scheduling instructions.

[0044] The process of resolving the cumulative operating reference time of the equipment includes: constructing a multi-dimensional environmental state vector and pre-setting a golden operating state vector containing ideal benchmark values; constructing an environmental stress coupling covariance matrix representing the interaction between various environmental dimensions, where the diagonal elements of the matrix represent the independent damage weight of each single environmental factor, and the off-diagonal elements represent the cross-coupling damage coefficient between different environmental factors; for each sampling moment in the operating log, calculating the deviation vector between the measured environmental parameters and the golden operating state vector, and calculating the inverse matrix of the environmental stress coupling covariance matrix; performing a quadratic Mahalanobis distance operation using the deviation vector and the inverse matrix to obtain the instantaneous comprehensive environmental stress intensity containing multi-physics coupling effects; performing a time-domain integration operation on the instantaneous comprehensive environmental stress intensity within a physical time window to obtain the equivalent aging time of a single task, and performing cumulative processing on all historical tasks.

[0045] Specifically, firstly, using the microelectromechanical system triaxial accelerometer and digital temperature and humidity sensor built into the accompanying logistics monitoring terminal, high-frequency vibration sequences (sampling rate 100Hz) and low-frequency environmental sequences (sampling rate 1Hz) during transportation are collected respectively. In order to construct a multi-dimensional environmental state vector that is strictly synchronized in the time dimension, a time alignment operation is performed: taking the high-frequency vibration sampling time point as the reference, linear interpolation is performed on the low-frequency temperature and humidity data to ensure that every millisecond-level sampling moment has accurate temperature, humidity and triaxial composite vibration acceleration values ​​at the same time. These three physical quantities are encapsulated into an ordered array, which constitutes the measured multi-dimensional environmental state vector at the current moment.

[0046] An ideal golden operating state vector is preset, which defines the optimal operating point with the lowest equipment aging rate. It includes three specific dimensional benchmark values: a standard constant temperature of 25 degrees Celsius, a standard suitable humidity of 45%, and a completely static state with zero gravity acceleration. For the measured multidimensional environmental state vector at each sampling moment in the operation log, the deviation vector is obtained by subtracting it from the golden operating state vector.

[0047] The specific form and parameter settings of the environmental stress coupling covariance matrix are as follows: the matrix is ​​a 3x3 symmetric positive definite matrix, corresponding to the three physical dimensions of temperature, humidity, and vibration. In this embodiment, the standard deviation weight of temperature is preset to 5 (in degrees Celsius), the standard deviation weight of humidity is preset to 15 (in percentage relative humidity), and the standard deviation weight of vibration is preset to 0.1 (in gravitational acceleration g). At the off-diagonal positions of the matrix, preset coupling coefficients are used to reflect the cross-influence of different environmental stresses. Specifically, the coupling coefficient between temperature and humidity is set to 0.3; the coupling coefficient between temperature and vibration is set to 0.6 to characterize the physical damage aggravation effect caused by material embrittlement at low temperatures (i.e., low-temperature embrittlement effect); and the coupling coefficient between humidity and vibration is set to 0.4. When performing the Mahalanobis distance quadratic form operation and inverting the covariance matrix, a Tikhonov regularization mechanism is introduced to avoid computational divergence due to poor matrix behavior. Specifically, if the condition number of the matrix exceeds 1000, a coefficient of magnitude 10 is superimposed on the main diagonal of the matrix. -6 The identity matrix regularization parameter is used to ensure the numerical stability of the inverse matrix solution process. Through the above precisely defined matrix parameters, the deviation vector of measured environmental parameters from the golden working condition can be transformed into the instantaneous comprehensive environmental stress intensity containing the multi-physics coupling effect, thereby realizing the true restoration of the stress state of the internal microstructure of the equipment.

[0048] During the calculation process, the inverse matrix of the environmental stress coupling covariance matrix is ​​first calculated. Then, for each sampling time, a quadratic Mahalanobis distance operation is performed: that is, a continuous matrix multiplication operation is performed, in which the deviation vector (transpose) is multiplied by the inverse covariance matrix and then multiplied by the deviation vector. The final scalar result is then square-rooted. The result of this operation is called the 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 weights of those in high coupling risk areas (such as low temperature and strong earthquake) through the transformation of the inverse matrix, thereby truly restoring the stress state of the internal microstructure of the equipment.

[0049] Finally, the Riemann summation algorithm is used to perform time-domain integration 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 operating conditions into the equivalent aging time under standard rated load. The above calculation process is repeated for all historical tasks of the equipment since it left the factory, and the equivalent aging time obtained each time is accumulated to finally obtain the cumulative operating reference time used for asset performance profile generation.

[0050] The final cumulative operating reference time for generating asset performance profiles is obtained. The parameter settings of the environmental stress coupling covariance matrix are based on empirical calibration results of specific equipment types within a specific working environment. In this embodiment, taking a broadband seismometer as an example, the physical calibration and verification process of its parameters is as follows: accelerated life tests are performed on the target equipment type, and different gradients of temperature, humidity, and vibration acceleration are applied in a controlled environment. The equipment performance degradation sequence is recorded, and a quantitative mapping of stress-aging is established using multifactor variance analysis to derive the specific matrix parameters for the equipment. A verification cycle is set at 12 months, and the actual stress data and accuracy recovery data in the historical maintenance log are compared. If the actual deviation from the theoretical prediction value exceeds 15%, the matrix recalibration process is automatically initiated. For different types of observation assets, an independent parameter matrix library is maintained in the digital instance library, and the corresponding matrix is ​​automatically called according to the actual equipment model. The quadratic operation using Mahalanobis distance is essentially a spatial transformation and decoupling of the physical environmental deviations of each dimension in a multivariate statistical space through the inverse matrix of the covariance matrix, thereby quantifying the instantaneous comprehensive environmental stress intensity of environmental factors on precision mechanical structures.

[0051] By calculating the ratio of cumulative equipment runtime to failure interval, and using an exponential decay function to generate equipment reliability coefficients, a digital representation of the internal health status of assets is achieved. This dynamic performance profiling method can reflect the reliability fluctuations of physical entities over time, enabling the management system to differentiate resource allocation based on the actual degradation of equipment, thus mitigating the lag risk of resource scheduling based on a single factory specification.

[0052] Further, the step 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 index; locking the target inventory observation node according to the principle of maximizing the matching evaluation index, 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.

[0053] Specifically, the element matching model is initiated. This model integrates a frequency domain response analysis unit and a multi-dimensional performance weighting unit. When performing matching operations, the instrument's self-noise power spectral density curve from the asset performance profile and the background noise spectrum of the target grid node extracted from the access control matrix are first input into the frequency domain response analysis unit. This unit locks the effective operating bandwidth of the equipment (0.1Hz to 50Hz in this embodiment) and calculates the integral of the amplitude difference between the two spectral curves within this bandwidth range. This integration process physically represents the effective signal-to-noise ratio gain characteristic of the instrument in this specific environment, that is, assessing whether the instrument can distinguish extremely weak observation signals from complex environmental backgrounds. Subsequently, a feature vector containing multiple key dimensions is constructed. This vector is composed of the aforementioned effective signal-to-noise ratio gain characteristic, the real-time generated equipment reliability coefficient, and the observation deployment accessibility factor reflecting the geographical accessibility of the grid node. This feature vector is input into the multi-dimensional performance weighting unit, and a linear transformation operation (i.e., weighted summation of multiple indicators) is performed on the feature vector using a preset weight matrix, thereby outputting a matching evaluation index that comprehensively reflects hardware performance, equipment health, and logistics difficulty.

[0054] Before performing the matching operation, the element matching model first uses a range transformation algorithm to preprocess the extracted effective signal-to-noise ratio gain features, equipment reliability coefficients, and observation deployment accessibility factors of grid nodes to unify the dimensions and scoring thresholds. This maps all indicator values ​​to the range of 0 to 1, achieving alignment of different physical features under a standardized scale. In this embodiment, the preset weight matrix is ​​predetermined based on the analytic hierarchy process. By constructing a judgment matrix with observation accuracy, hardware health, and logistics accessibility as the criteria layers, and performing pairwise importance comparisons, the corresponding weight vector is calculated as [0.5, 0.3, 0.2]. The multidimensional efficiency weighting unit retrieves this weight vector and performs a weighted summation operation with the standardized feature vector, outputting a matching evaluation index that comprehensively reflects hardware performance, equipment health, and logistics difficulty. This index serves as the basis for locking target inventory observation nodes and generating asset transfer work orders.

[0055] Regarding the determination process of the weight vector in the element matching model, this embodiment performs the following quantitative derivation using the analytic hierarchy process: using observation accuracy, hardware health, and logistics accessibility as three evaluation criteria, their importance ratio is determined by pairwise comparison. In this embodiment, the importance of observation accuracy is set to be twice that of hardware health and three times that of logistics accessibility; the importance of hardware health is twice that of logistics accessibility. Based on the above proportional relationship, a 3×3 symmetric judgment matrix is ​​constructed. The judgment matrix is ​​solved using the eigenvector method. First, the largest eigenvalue of the matrix is ​​calculated, and its corresponding eigenvector is obtained. Then, the eigenvector is normalized (i.e., each component is divided by the sum of the eigenvector components) to obtain the original weight vector [0.545, 0.276, 0.179]. It is rounded to one decimal place and finally determined to be [0.5, 0.3, 0.2]. Before performing the weighting operation, the effective signal-to-noise ratio gain feature, equipment reliability coefficient, and observation deployment accessibility factor are normalized using the range transformation algorithm to map all values ​​to the interval between 0 and 1. The multidimensional performance weighting unit linearly weights and sums the above three standardized eigenvalues ​​with their corresponding weights. 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.

[0056] Based on the principle of maximizing matching evaluation indicators, the observation device with the highest score is automatically locked from all candidate inventory nodes, and its unique ID is obtained. To ensure closed-loop verification of the observation quality after equipment deployment, the spatial topology model response spectrum is extracted from the target grid node of the collaborative constraint graph. This response spectrum records the theoretical vibration characteristics of the target node under specific excitations and is encapsulated into a work order as a performance verification contract for automated on-site acceptance after equipment installation. Simultaneously, the system uses the seismic threshold parameters calibrated at the factory for this node to generate a logistics monitoring benchmark containing a triaxial impact response spectrum envelope. This envelope defines the safety boundary that the triaxial acceleration must not be exceeded during transportation. Finally, the unique device ID, the performance verification contract, and the logistics monitoring benchmark containing the triaxial impact response spectrum envelope are structurally encapsulated to generate the final asset transfer work order and pushed to the logistics scheduling terminal. By combining frequency domain response analysis with multi-dimensional performance weighting, 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 ensures that the generation of work orders depends not only on asset location but also on the compatibility of equipment performance with specific operating scenarios. This enhances the scientific rigor of work order contract content during the task assignment phase and provides a clear and environmentally specific benchmark for subsequent performance verification.

[0057] Furthermore, 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 and subsequent zero-point drift recovery time data during each transportation process, 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 recovery time of the equipment under the current cumulative impact energy value. The required dynamic settling time for measurement accuracy; 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 equipment's status 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 workflow 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 and the end time of the inventory freeze time window is calculated, and subsequent tasks are shifted forward on the time axis to generate an updated task scheduling time axis, specifically as follows: Figure 2 As shown.

[0058] Specifically, the system first receives stress accumulation logs uploaded by the accompanying logistics monitoring terminal via a wireless communication module. This terminal has a built-in triaxial accelerometer with a sampling rate of 100Hz. It performs feature extraction on the log data to identify transient impact peaks with an absolute value of transient acceleration exceeding 3g (gravitational acceleration) during transportation and counts the total number of discrete extreme points. Simultaneously, it analyzes the duration of over-limit vibrations where the root mean square value of acceleration is consistently higher than 0.5g. Then, it performs a weighted accumulation calculation, with the weight of the impact peak count set at 2.0 and the weight of the over-limit vibration duration set at 0.5. The weighted product terms of the two are summed to calculate the cumulative impact energy value, which characterizes the degree of excitation of the equipment. This value quantifies the total physical disturbance caused by mechanical vibration during transportation to the internal structural stability of precision observation instruments (such as deformation of elastic elements or slight loosening of mechanical fasteners).

[0059] The electronic historical maintenance logs of the equipment were retrieved, and historical data on the excitation energy from each transportation mission were extracted as independent variables. The recovery time data required until the equipment's zero-point drift was completely returned to zero, measured by a high-precision reference source, was then used as the dependent variable. These two types of data were paired and correlated to construct a mechanical stress-recovery efficiency correlation feature set specific to the equipment. Subsequently, a quadratic polynomial trend fitting operation was performed on this feature set using the least squares method to establish a mathematical mapping model between stress and recovery time. The cumulative impact energy value calculated from the current mission was input into this model to predict the dynamic settling time required for the equipment to recover to the calibration measurement accuracy under the current damage level. Starting from the time when the equipment actually arrived at the observation node as recorded by the Global Positioning System, and spanning the predicted dynamic settling time, an inventory freeze time window was generated on the time axis. The status of the equipment within the window was locked as calibration and maintenance status in the scheduling management database, forcibly cutting off any mission call commands.

[0060] The physical basis and boundary constraints for using quadratic polynomials for trend fitting are as follows: The selection of quadratic polynomial fitting is based on the fact that, within a medium stress range (approximately 70% to 90% of the equipment's expected maximum transport stress), the zero-point drift recovery curve of the precision observation equipment exhibits an approximate parabolic characteristic in the initial stage. The effective application range of this model is limited to the current cumulative impact energy value not exceeding 1.2 times the historical maximum recorded value. If this ratio is exceeded, the model will automatically switch to a conservative extrapolation mode, limiting the prediction duration to 1.3 times the historical maximum recovery duration. In this embodiment, the theoretical maximum recovery time of the equipment is preset to 72 hours (corresponding to the seismometer's maximum allowable zero-point drift recovery period). If the dynamic static duration predicted by the model exceeds 72 hours, it will be determined that the equipment has suffered irreversible physical damage or severe reliability degradation, and it will be automatically isolated from the scheduling queue, prohibiting it from participating in subsequent observation tasks. Before performing the fitting calculation, the median and interquartile range of the historical recovery duration data sequence are calculated first, 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 coefficient of determination 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 will be applied to compensate for the final predicted settling time.

[0061] The complete process and boundary handling mechanism for quadratic polynomial fitting using the least squares method are as follows: Before performing the fitting, the system first cleans the recovery time data in the historical maintenance log. Specifically, the median and interquartile range of the historical recovery time sequence are calculated, and abnormal data points with values ​​exceeding the median plus or minus three times the interquartile range are removed to eliminate the influence of dirty data caused by human operation interference or extreme environmental changes on the model. After constructing the quadratic polynomial model, the model coefficients are solved using the least squares method, and the coefficient of determination (i.e., R² value) of the fitting result is calculated simultaneously. In this embodiment, the qualified threshold for the coefficient of determination is set to 0.75. If the calculated coefficient of determination is lower than 0.75, it indicates that the performance evolution law of the device does not conform to the expected value. If the model conforms to the standard, a safety warning will be automatically triggered, and the prediction result will be multiplied by a safety factor of 1.5. Considering the physical limits of precision observation instruments, this embodiment presets the theoretical maximum recovery time of the equipment to be 72 hours (corresponding to the longest period of zero-point drift recovery of the seismometer). If the dynamic static time predicted by the model exceeds 72 hours, it will be determined that the equipment has suffered irreversible mechanical damage, its logical state will be automatically marked as severe damage, and it will be permanently isolated from the scheduling queue. If the cumulative impact energy value generated by the current task exceeds the maximum value in the historical records, the system will adopt a conservative extrapolation strategy to limit the predicted recovery time to 1.3 times the maximum historical recovery time to prevent the mathematical model from generating unreasonable calculation values ​​outside the data boundary.

[0062] After locking the time window, the preset original estimated start time of the operation in the workflow order is read, and a logical judgment is performed to check whether the time point is within the closed interval of the inventory freeze time window. If the judgment result is within the interval, it means that the original schedule ignored 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 freeze time window and the original estimated start time of the operation is automatically calculated. Using this difference as the benchmark step size, a forward translation operation is uniformly performed on the global time axis for the affected current task and all subsequent related tasks in the scheduling chain, and 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 a stable state after the mechanical stress of the equipment is fully released and the zero drift is recovered.

[0063] By transforming logistics and transportation stress into an inventory freeze time window and performing a forward shift of the scheduling time axis, dynamic time compensation for performance loss during the circulation process is achieved. This process enables the management system's scheduling plan to proactively adapt to the accuracy recovery cycle of physical entities, reducing the probability of equipment operating with defects, effectively improving the disconnect between the management system and the actual state of resources in the time dimension, and enhancing the flexibility and accuracy of project execution plans.

[0064] Furthermore, the compliance audit process for the execution project 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 transform the weighted Euclidean distance into a normalized value, which serves as a business consistency indicator. The specific process is as follows: Figure 3 As shown.

[0065] Specifically, the multi-source reference mapping unit is first activated. Based on the communication protocol preset in the performance verification contract, the real-time response characteristics of the equipment are retrieved through the asset monitoring interface, and the measured response spectrum reflecting the current actual working status of the equipment is extracted. At the same time, the standard reference spectrum, either factory-calibrated or simulated, is read from the storage field of the performance verification contract. In order to achieve quantitative comparison of data from different sources in the same dimension, frequency domain discretization sampling is performed on the standard reference spectrum and the measured response spectrum. The full bandwidth frequency domain range is uniformly divided into N (e.g., 1024) independent frequency band logical points, thereby constructing a frequency domain feature space with a unified dimension. Subsequently, the measured environmental background noise reference value at the current observation location is read and converted into an environmental constraint vector corresponding one-to-one with the above frequency band logical points. Finally, the standard reference spectrum, the measured response spectrum, and the environmental constraint vector are stacked as tensors to generate a three-dimensional input tensor containing standard features, measured features, and environmental constraints.

[0066] Next, the three-dimensional input tensor is processed using an environmental interference weighting unit. For each frequency band logic point, the magnitude of the corresponding environmental constraint vector is first superimposed with the measured response spectrum magnitude to obtain the total energy value for that frequency band. Then, the signal purity coefficient is calculated: the measured response spectrum magnitude for the current frequency band logic point is divided by the total energy value. The physical meaning of this step is to evaluate the proportion of effective signals in that frequency band: if the background noise is extremely high, the purity coefficient approaches 0; if the environment is extremely clean, the purity coefficient approaches 1. The obtained signal purity coefficient is used as the dynamic weighting coefficient for that frequency band and applied to the standard features and measured features. This processing method can automatically reduce the contribution of frequency bands severely affected by environmental interference in the compliance evaluation, thereby outputting a weighted feature matrix that eliminates environmental artifact interference.

[0067] Finally, the semantic alignment solution unit is invoked to perform a final evaluation of the weighted feature matrix. First, the weighted Euclidean distance between the weighted standard feature vector and the measured feature vector is calculated. Since this distance is calculated within the space after signal purity weighting, it accurately reflects the performance degradation caused by the equipment's own fatigue, wear, or failure, rather than fluctuations caused by environmental noise. To make the audit results more intuitive, an inverse proportional mapping function is used to convert the calculated weighted Euclidean distance into a normalized value between 0 and 1. This value is then output to the management terminal as a business consistency indicator. If the indicator is close to 1, the transfer and deployment of the asset are deemed to fully comply with the performance verification contract requirements, and the project passes the compliance audit and enters the formal operation phase. If the indicator is below the preset compliance threshold, an early warning logic is triggered, requiring on-site calibration or replacement of the equipment at that node.

[0068] In this embodiment, to ensure the stability and normalization characteristics of the calculation results, an audit sensitivity value is first preset to define the decay rate of the evaluation curve and eliminate singular values ​​in the calculation process. In this 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 of the distance value is calculated to amplify the influence of the deviation on the final score through the power operation. Then, the preset audit sensitivity value 25 is summed with the calculated square distance value to construct a dynamic denominator that is always greater than 0. Finally, the preset audit sensitivity value 25 is used as the numerator and divided by the dynamic denominator obtained by the above summation to output a business consistency index with a value range between 0 and 1.

[0069] The logic of this process is as follows: when the measured response perfectly matches the standard contract (i.e., the distance is 0), both the numerator and denominator are 25, and the output index is a perfect score of 1. As the deviation distance increases, the denominator increases rapidly with the square term, resulting in a non-linear decreasing trend of the output index that is first flat and then steep. This design ensures reasonable tolerance for small environmental fluctuations, while maintaining a very high degree of identification for significant performance deviations, providing logically rigorous quantitative support for the final compliance audit.

[0070] In this embodiment, the logic of setting the preset value of 25 is based on establishing the half-value decay inflection point of equipment performance. Since the weighted Euclidean distance d and the mapping index S satisfy a quadratic inverse proportional relationship, setting the constant 25 means that when the measured deviation distance d reaches the critical benchmark value of 5, the business consistency index S decays 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 penalty of large deviations through the quadratic power operation. It also uses the smoothing effect of the constant term to provide the system with tolerance for small environmental disturbances, ensuring that the audit evaluation results have the highest recognition near the critical deviation, thereby realizing the scientific quantification of the performance of the contract.

[0071] The applicable scope and physical model limitations of the aforementioned signal purity coefficient are as follows: This weighting mechanism is based on the physical assumption that the device response and environmental noise are essentially additive and phase-independent in the frequency domain. This assumption has high reliability when the following conditions are met: First, the device installation location must be far from its factory-calibrated self-resonant frequency point to avoid self-resonance amplification of environmental noise; second, the environmental noise spectrum should remain relatively stable within the sampling time window, without sudden strong interference; finally, the correlation coefficient between the measured device response and environmental noise should be at a low level below 0.3. If the above prerequisites cannot be met (e.g., the device is forced to be installed near a strong interference source, or the environment has frequency...), the following conditions apply. (Due to frequent instantaneous pulse interference), this simplified model may produce biases. In such cases, it is recommended to use more complex algorithms such as non-negative matrix factorization or blind source separation 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 background noise amplitude of a certain frequency band are both zero (i.e., there is no energy input in the frequency band), the weight coefficient of the frequency band will be automatically set to zero, and a data quality warning will be issued simultaneously to prompt the management personnel to check the integrity of the equipment sensors or background data acquisition link.

[0072] By introducing signal purity-based weight allocation logic into the feature alignment model, a quality governance approach is provided for consistency determination in complex field environments. The signal purity coefficient is used to adaptively mitigate environmental interference, enabling the final generated business consistency indicators to more objectively reflect the performance level of physical entities. This optimizes the audit accuracy during asset delivery, provides more reliable business status feedback to management decision-makers, and resolves the assessment inaccuracies caused by environmental noise.

[0073] Furthermore, the process of including the node in the resource allocation and scheduling queue includes: reading the preset minimum admission threshold in the workflow 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 record; 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.

[0074] Specifically, the system first reads the preset minimum access threshold (e.g., set to 0.8) from the configuration fields of the workflow order, and then logically compares the calculated business consistency index with this threshold. If the business consistency index is greater than or equal to the minimum access threshold, the inventory observation node is determined to have passed the on-site performance verification. Subsequently, a structured data packet is automatically constructed. This data packet integrates the observation node's unique device ID, the precise value of the business consistency index output from this verification, and the standard timestamp indicating that the verification was successful. To ensure the authority and traceability of the audit results, the system calls the security encryption module and uses a preset asymmetric encryption private key to perform digital signature encapsulation on the data packet, thereby generating a tamper-proof performance qualification certificate. This certificate, as an electronic ID card for the device's successful inspection, is synchronously written into the digital instance library and associated with the node's performance history archive, providing original data support for possible subsequent quality traceability.

[0075] While the credentials are being archived, the backend triggers a database state change transaction. This transaction performs an atomic database write operation to formally change the logical state field of the observation node in the digital instance database from transit or pending verification to active. This state change operation ensures that the node state in the distributed database remains consistent across the entire network. In response to the update of the state field, a registration instruction is automatically sent to the resource pool to be allocated in the observation task scheduling system. This instruction includes the node's geographical coordinates, current profile features, and the latest reliability weight, formally adding the node to the resource allocation scheduling queue.

[0076] By using threshold determination of business consistency indicators and encapsulating digital signature credentials, automated connection between performance results and resource scheduling pools was achieved. Utilizing the atomic update logic of database state transactions, real-time synchronization between the operational status of physical assets and the digital instance library was ensured, reducing the manual workflow latency from on-site delivery to formal inclusion in the scheduling sequence. This significantly enhanced the collaborative efficiency of the entire project management process and the closed-loop compliance of data flow.

[0077] By constructing a digital management architecture integrating environmental background awareness, process monitoring, and compliance auditing, a mechanism linking the observed resource performance status with business decision-making logic is established within a unified management framework. In the resource access phase, a deep mapping between the geographical environment and asset attributes is achieved through a collaborative constraint graph, improving the scenario adaptability of configuration plans. In the resource transfer phase, stress accumulation logs and dynamic time windows are used to update task schedules, enhancing the responsiveness of management instructions to the physical entity's performance recovery cycle. In the delivery phase, a feature alignment governance model is used to mitigate the interference of environmental noise on consistency indicators, optimizing the objectivity of audit results. Through collaborative control across these dimensions, this application improves the information mismatch between the management end and physical entities, comprehensively enhancing the overall operational efficiency and resource scheduling accuracy of mobile observation projects under complex operating conditions. Example

[0078] In this scenario, the observation station plans to deploy a high-sensitivity broadband seismometer in an uninhabited area. During the 800-kilometer vehicle transport, the accompanying logistics monitoring terminal recorded extremely harsh road conditions, including a continuous low temperature of -15 degrees Celsius and triaxial composite vibration acceleration caused by frozen soil. These data streams were retrieved in real time and calculated using the environmental stress coupling covariance matrix. Since the matrix has a preset low temperature-vibration coupling coefficient, it was identified that the mechanical fatigue weight of the precision sensor was automatically amplified by 1.5 times in the extremely low temperature environment. Finally, through time-domain integration using the Riemann sum algorithm, the equivalent aging time generated by this transport was determined to be 124 hours, rather than the physical transport time, and this was included in the life cycle file of the device.

[0079] Just before the equipment arrived at its destination, the logistics terminal uploaded a stress log containing a 4.5g transient impact. It then automatically retrieved the historical maintenance log of the seismometer and used the mechanical stress-recovery efficiency correlation feature set to perform trend fitting. The prediction results showed that, affected by this strong impact, the mechanical suspension system inside the seismometer needed 18 hours of rest to allow the zero-point drift to fully recover. An 18-hour inventory freeze time window was immediately generated on the scheduling timeline. Since the original operation time happened to fall within this window, a forward translation operation was automatically executed, postponing the installation and commissioning task by 16 hours. This logically avoided the risk of the equipment outputting incorrect data due to unreleased stress.

[0080] Subsequently, an element matching evaluation was performed. The environmental noise data of the target grid node was read. The background noise of the node was extremely low around 1Hz, approximately -145 dB. This was compared with the self-noise curve of the equipment, confirming that it had excellent signal-to-noise ratio gain. The multi-dimensional performance weighted unit combined the current reliability coefficient of the equipment (calculated to be 0.92) and the accessibility factor of the location to output a matching evaluation index of 0.89. Based on this high score, the equipment was located, and the spatial topology model response spectrum of the target node was extracted from the collaborative constraint graph. Together with the three-axis impact response spectrum envelope, it was encapsulated into the asset transfer work order.

[0081] After equipment installation, the on-site compliance audit process commenced. The multi-source reference mapping unit collected 20 minutes of measured response spectrum data from the installation site. Due to weak high-frequency interference from wind turbines near the installation location, the environmental interference weighting unit accurately identified a low signal purity of only 0.4 in the 40Hz band and automatically lowered the evaluation weight of this band to prevent environmental interference from misleading the audit results. The semantic alignment solution unit then calculated the weighted Euclidean distance after noise interference removal to be 1.84.

[0082] Finally, an inverse proportional mapping function with a preset constant of 25 is invoked to perform a mapping operation after squaring the distance. Specifically, 25 is used as the numerator, and the sum of 25 and the squared distance (approximately 3.38) is used as the denominator. The calculated business consistency index is 0.88. Since this index is higher than the admission threshold of 0.8, the node deployment is deemed compliant, and a fulfillment certificate containing a digital signature is immediately generated. The database triggers an atomic transaction, changing the device's logical status from pending verification to active. With the registration instruction sent to the resource pool, the node is officially included in the scheduling queue. In the collaborative observation task that started that evening, the scheduling algorithm, based on its high sensitivity and high reliability characteristics in its profile, assigned it as an observation node for the region, completing the digital closed-loop flow from inventory assets to active resources.

[0083] Although embodiments of the invention 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 to these embodiments without departing from the principles and spirit of the invention, 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; A collaborative constraint map is constructed based on multidimensional observation data. The operational deployment resistance index of each grid node is calculated. An observation deployment accessibility factor is generated using an elevation model. An access control matrix is ​​then generated in the collaborative constraint map. The digital instance library is traversed to obtain the performance profile of the inventory observation node. The element matching model is called to evaluate the matching evaluation index between the performance profile and each grid node in the access control matrix. Based on the matching evaluation index, a transfer work order is generated, which includes the unique equipment ID, the performance verification contract and the logistics monitoring benchmark. Receive stress accumulation logs uploaded based on logistics monitoring benchmarks, perform stress recovery assessment, generate inventory freeze time windows, and inject the inventory freeze time windows as dynamic constraints into the task scheduling time axis of the workflow order update. 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 constructing a collaborative constraint map based on multidimensional observation data and calculating the operational deployment resistance index for each grid node includes: dividing the area to be observed 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 the background noise records in the multidimensional observation data, establishing undirected edges with environmental interference weights between vertices whose noise cross-correlation coefficients exceed a preset threshold, and constructing the collaborative constraint map; extracting the remote sensing image texture features and elevation variance of the area corresponding to each grid, and constructing a complexity feature vector containing 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.

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 generating an observation deployment accessibility factor using an elevation model and generating an access control matrix in a collaborative constraint map includes: based on the road network vector data in the multi-dimensional observation data, selecting backbone road sections with road grade attributes higher than preset values, and constructing an operational logistics topology; calling Dijkstra's shortest path algorithm to calculate the topological travel mileage from each grid center point to the nearest access point along the actual route of the backbone road section, and extracting the transportation loss coefficient of the travel path based on the elevation model; performing a weighted calculation on the planned operational mileage and 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 accessibility factor; calculating the ratio of the operational deployment resistance index to the observation deployment accessibility 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.

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 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.

6. 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.

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 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 workflow 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 workflow and the end time of the inventory freeze time window is calculated, and the subsequent work tasks are forward shifted on the time axis to generate an updated task scheduling time axis.

8. 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.

9. 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 a node in the resource allocation and scheduling queue includes: reading the preset minimum admission threshold in the workflow 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 as the node's performance history for archiving; 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.

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