A pollution source monitoring device self-maintenance system

Abstract

The present application belongs to the technical field of environmental monitoring, and discloses a self-maintenance system of a pollution source monitoring device, which comprises a data acquisition and preprocessing module, a device value and redundancy evaluation module, a maintenance strategy simulation module, a multi-objective optimization decision module, and an execution and verification feedback module. From the perspective of the global data deducibility of the monitoring network, objective and quantitative evaluation of the importance of device data contribution and its repeatability is realized, thereby fundamentally solving the technical problems of isolated maintenance decision and unscientific resource allocation caused by the lack of global perspective in the traditional maintenance mode, providing a core basis for accurate division of maintenance priority, simulating the candidate maintenance strategy as repair of specific device data in a space-time data matrix, reconstructing the global network data by using a matrix completion algorithm, and then calculating the reconstruction error to quantize the pre-evaluation strategy performance, so that the system can be put into actual maintenance resources in advance.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to a self-maintenance system for pollution source monitoring equipment. Background Technology

[0002] Online pollution source monitoring networks are the core infrastructure for environmental regulation. Their long-term, stable, and high-quality operation is the foundation for accurately assessing pollution status and implementing regulatory responsibilities. As the monitoring network expands and equipment service life increases, the traditional operation and maintenance model that relies on fixed-cycle inspections and post-failure repairs is increasingly showing its limitations. This model is essentially a passive response model, and its decision-making is often based on isolated equipment alarms or simple time-series thresholds. It lacks a forward-looking assessment and global optimization of the robustness of the overall data quality of the monitoring network. Therefore, there is an urgent need for an intelligent self-maintenance system that can take a global network perspective, proactively assess the importance of equipment, predict maintenance effects, and dynamically optimize decisions.

[0003] However, existing related technologies have the following problems and cannot yet meet the above requirements:

[0004] (1) Existing research focuses on using data-driven methods to provide status warnings or fault diagnosis for a single device. The optimization goal is the reliability of a single device. These methods fail to assess the global impact of a device’s performance degradation or data loss on the integrity and inference capabilities of the entire monitoring network from the perspective of information contribution. This results in a lack of network-level scientific basis for the allocation of maintenance resources, which may lead to resource mismatch problems such as “insufficient maintenance of critical equipment and excessive maintenance of secondary equipment”.

[0005] (2) Although some studies have applied information theory (such as value information and mutual information) and matrix completion algorithms to the optimization layout and performance evaluation of sensor networks, their application scenarios are limited to the spatial layout design in the early stage of network construction or the offline evaluation of the static performance of the deployed network. These methods have not innovatively applied the dynamic evaluation concept of information value and data redundancy, as well as the pre-evaluation idea of ​​network performance simulation, to the time dimension problem of continuous dynamic optimization in network operation maintenance decision-making. Therefore, the current maintenance strategy formulation still relies heavily on historical experience and cannot conduct quantitative simulation and comparison of the network-level effects of different strategies before implementation. It lacks the key technical support for upgrading from experience-driven to model and simulation-driven decision-making. Summary of the Invention

[0006] To address the shortcomings of the existing technology, this invention provides a self-maintenance system for pollution source monitoring equipment. This invention aims to solve the problems of isolated decision-making, unclear effects, and lack of predictability in existing maintenance methods.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] The pollution source monitoring equipment self-maintenance system provided by the present invention includes a data acquisition and preprocessing module, an equipment value and redundancy assessment module, a maintenance strategy simulation module, a multi-objective optimization decision-making module, and an execution and verification feedback module.

[0009] Furthermore, the data acquisition and preprocessing module is used to connect to pollution source monitoring equipment in the monitoring network, collect the operating data of the monitoring equipment and environmental monitoring data, perform preprocessing, construct an original spatiotemporal data matrix, and provide the original spatiotemporal data matrix to the equipment value and redundancy assessment module. In the data acquisition and preprocessing module, the preprocessing includes data standardization and coarse noise filtering. Data standardization uses min-max standardization to map the operating data of the monitoring equipment and environmental monitoring data to... Standardized operation and monitoring data are obtained from the interval, and coarse noise filtering is adopted. The criteria eliminate abnormal data from standardized operation and monitoring data.

[0010] Furthermore, the equipment value and redundancy assessment module, connected to the data acquisition and preprocessing module, calculates a data value index for each monitoring device and a data redundancy for each pair of monitoring devices using the original spatiotemporal data matrix based on information theory methods. The data value index and data redundancy are simultaneously provided to the maintenance strategy simulation module and the multi-objective optimization decision module. Multiple candidate maintenance strategies are generated based on the data value index and data redundancy. The maintenance strategy simulation module, connected to the equipment value and redundancy assessment module, receives at least one candidate maintenance strategy. Based on the original spatiotemporal data matrix, it maps each candidate maintenance strategy to a data region to be repaired in the original spatiotemporal data matrix and uses a matrix completion algorithm to simulate the network data reconstruction effect after maintenance, obtaining a completed complete data matrix. By calculating the error between the completed complete data matrix and the original spatiotemporal data matrix, the performance pre-assessment results of each candidate maintenance strategy are obtained.

[0011] Furthermore, the maintenance strategy simulation module receives candidate maintenance strategies, each of which specifies a set of monitoring devices to be maintained and their corresponding future maintenance period. Each candidate maintenance strategy is mapped to an original spatiotemporal data matrix, where matrix elements represent the standardized data of the corresponding monitoring device at the corresponding time. Data points corresponding to the monitoring devices to be maintained in the candidate maintenance strategy within the future maintenance period are marked as high-quality known data points, and a known data region is constructed. Based on the high-quality known data points in the known data region, a non-negative matrix factorization algorithm is used to complete and reconstruct the original spatiotemporal data matrix. By calculating the root mean square error between the complete data matrix obtained after completion and the original spatiotemporal data matrix at the unknown data locations, the effectiveness of the candidate maintenance strategy is quantitatively pre-evaluated.

[0012] Furthermore, the multi-objective optimization decision module is connected to both the maintenance strategy simulation module and the equipment value and redundancy assessment module. Based on the data value index, data redundancy between equipment, and performance pre-assessment results, it establishes and solves a multi-objective optimization model. The model sets binary decision variables to represent whether the monitored equipment is included in the maintenance plan. It constructs a three-objective function that maximizes the sum of the data value indices of the monitored equipment, minimizes the sum of the data redundancy between the monitored equipment, and minimizes the simulation prediction error. At the same time, it sets a constraint that the total maintenance cost does not exceed the preset budget. The model is solved using a multi-objective evolutionary algorithm to obtain the Pareto optimal solution set. Then, based on the current preset budget, the optimal maintenance plan is selected from the solution set and provided to the execution and verification feedback module.

[0013] Furthermore, the execution and verification feedback module connects the multi-objective optimization decision module and the data acquisition and preprocessing module. It receives and sends out the optimal maintenance plan. After maintenance, it collects actual monitoring network data as verification data. Using the verification data of the maintained monitoring equipment as known points, it infers the data of the unmaintained monitoring equipment again through the matrix completion algorithm to obtain the inferred data matrix. It calculates the root mean square error between the matrix and the actual verification data of the unmaintained monitoring equipment as the actual effect score after maintenance. It compares the actual effect score with the predicted effect score of the pre-evaluation to obtain deviation information. If the deviation continues to exceed the preset threshold, the deviation information is fed back to the equipment value and redundancy assessment module and the multi-objective optimization decision module. The equipment value and redundancy assessment module adjusts the smoothing parameters of the probability distribution estimation algorithm for calculating the data value index and redundancy according to the deviation information. The multi-objective optimization decision module adjusts the weight coefficients of the objective function in the optimization model according to the deviation information to realize the closed-loop adaptive learning of the system.

[0014] This invention also provides a self-maintenance method for pollution source monitoring equipment based on information value and matrix completion. This method is applied to the above-mentioned system and includes the following steps:

[0015] Step S1: Monitoring network spatiotemporal data acquisition and construction;

[0016] Step S2: Evaluation of equipment data value and redundancy;

[0017] Step S3: Maintenance strategy simulation and effect pre-evaluation;

[0018] Step S4: Generation of multi-objective optimization maintenance decisions;

[0019] Step S5: Maintenance execution and closed-loop verification.

[0020] The beneficial effects achieved by adopting the above solution are as follows:

[0021] (1) By calculating the data value index (VOI) of each monitoring device and the data redundancy (TE) between devices, from the perspective of the inferability of global data in the monitoring network, an objective and quantitative assessment of the importance and repeatability of device data contribution is realized. This fundamentally solves the technical problems of isolated maintenance decisions and unscientific resource allocation caused by the lack of a global network perspective in the traditional maintenance mode, and provides a core basis for the accurate division of maintenance priorities.

[0022] (2) By simulating the candidate maintenance strategy as the repair of specific device data in the spatiotemporal data matrix, and using the matrix completion algorithm to reconstruct the network data, and then calculating the reconstruction error to quantify the effectiveness of the pre-evaluation strategy, the system can virtually simulate and compare the network-level repair effects of different schemes before actually investing maintenance resources. This solves the technical problem that maintenance decisions have long relied on experience and the effects are difficult to predict, and realizes the paradigm shift of maintenance decisions from experience-driven to simulation optimization-driven.

[0023] (3) By constructing a complete closed-loop architecture of “evaluation, simulation, decision-making, execution and verification”, and introducing a parameter adaptive adjustment mechanism based on effect deviation feedback, the internal parameters of the information theory evaluation model and the multi-objective optimization decision model are dynamically corrected by utilizing the difference between the actual effect data after maintenance and the pre-evaluation results. This endows the system with continuous self-learning and adaptive capabilities, solves the technical problem that static models are difficult to cope with dynamic factors such as equipment aging and environmental changes, which leads to the gradual failure of decision-making strategies, and ensures the accuracy and robustness of decision-making in the long-term operation of the system. Attached Figure Description

[0024] The accompanying drawings are provided to further understand the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the invention and do not constitute a limitation thereof.

[0025] Figure 1 This is an overall module diagram of a self-maintenance system for pollution source monitoring equipment proposed in this invention;

[0026] Figure 2 This is a flowchart illustrating the steps of the self-maintenance method for pollution source monitoring equipment based on information value and matrix completion proposed in this invention.

[0027] Figure 3 This is a detailed flowchart of the maintenance strategy simulation and effect pre-evaluation proposed in this invention. Detailed Implementation

[0028] Example 1, see Figures 1-3 The pollution source monitoring equipment self-maintenance system provided by the present invention includes a data acquisition and preprocessing module, an equipment value and redundancy assessment module, a maintenance strategy simulation module, a multi-objective optimization decision-making module, and an execution and verification feedback module.

[0029] The data acquisition and preprocessing module connects to all pollution source monitoring devices in the monitoring network and is used to collect device operation data and environmental monitoring data to construct the original spatiotemporal data matrix.

[0030] The equipment value and redundancy assessment module is connected to the data acquisition and preprocessing module. It is used to calculate the data value index of the monitoring equipment and the data redundancy between equipment based on information theory methods, and to generate multiple candidate maintenance strategies based on the data value index and data redundancy.

[0031] The maintenance strategy simulation module is connected to the equipment value and redundancy assessment module, receives at least one candidate maintenance strategy, and uses a matrix completion algorithm to simulate the received candidate maintenance strategy and pre-evaluate its effect.

[0032] The multi-objective optimization decision module connects the maintenance strategy simulation module and the equipment value and redundancy assessment module, and is used to generate the optimal maintenance plan through multi-objective optimization.

[0033] The execution and verification feedback module connects the multi-objective optimization decision-making module and the data acquisition and preprocessing module, and is used to execute the maintenance plan and verify the effect, realizing closed-loop feedback learning.

[0034] Example 2, based on the above examples, involves the data acquisition and preprocessing module acquiring historical and real-time operational data and environmental monitoring data from all devices in the pollution source monitoring network. Preprocessing and feature extraction are performed to construct an original spatiotemporal data matrix arranged by time and device nodes. The device operational data includes calibration history deviation, sensor signal drift rate, and fault alarm frequency. The environmental monitoring data includes pollutant concentration, flow rate, temperature, and humidity. The preprocessing includes data standardization and coarse noise filtering. Data standardization uses minimum-maximum standardization to map all features to... Interval, eliminating dimensional differences, and coarse noise filtering are employed. The criteria remove obviously abnormal data.

[0035] Example 3, based on the above examples, describes an equipment value and redundancy assessment module that uses information theory to calculate the data value index of each monitoring device in the monitoring network. Data redundancy between each pair of monitoring devices Specifically, it includes:

[0036] Equipment Data Value Index Calculation: Based on the value information method, for any first monitoring device in the monitoring network. Calculate the first monitoring device With each of the second monitoring devices in the monitoring network, except for the first monitoring device. The amount of shared information between data states determines the data redundancy between devices. Finally, the contribution values ​​of all other devices in the network are integrated to obtain the first monitoring device. Global data value index Calculate the first monitoring device For each second monitoring device The formula for calculating value contribution is:

[0037] ;

[0038] in, For information collection, For device data status set, Indicates a specific state, Indicates action, For cost matrix elements, For prior probability, For posterior probability, the device Global data value index: ;in, Represents the set of all devices in the network;

[0039] Data redundancy between devices Calculation: Based on the mutual information method, calculate the first monitoring device. Except for the first monitoring equipment Each of the second monitoring devices outside The amount of shared information between data states is calculated using the following formula: ;

[0040] in, For computing devices In state And other equipment In state The joint probability, and This represents the marginal probability.

[0041] Example 4, based on the above examples, involves the maintenance strategy simulation module receiving at least one candidate maintenance strategy and using a matrix completion algorithm to quantify and pre-evaluate the effectiveness of each strategy. Specifically, this includes:

[0042] Candidate maintenance strategy definition: Map each candidate maintenance strategy to the original spatiotemporal data matrix. In the original spatiotemporal data matrix, the number of time points is used as the rows and the number of monitoring device nodes is used as the columns. The matrix elements represent the standardized data of the corresponding monitoring device at the corresponding time. The data points corresponding to the monitoring devices to be maintained specified in the candidate maintenance strategy during the future maintenance period are marked as high-quality known data points. ,in For the number of time points, Number of device nodes Matrix elements represent devices At any moment Standardized data, for a candidate maintenance strategy The set of equipment it plans to maintain is denoted as Construct the known data region corresponding to the candidate maintenance strategy and denote it as... ;

[0043] Matrix completion simulation reconstruction: using known data regions Based on high-quality known data points, a nonnegative matrix factorization algorithm is used to process the original spatiotemporal data matrix. The process of completing and reconstructing the system is transformed into solving the following optimization problem: ;in, and It is a non-negative decomposition matrix. Given the preset matrix rank, the objective function is... Defined as: ;in, The regularization coefficient is . The Frobenius norm of the matrix is ​​used to calculate the loss only for unknown locations. Solving this optimization problem yields the complete data matrix after completion. Nonnegative matrix factorization is achieved by solving an optimization problem that minimizes an objective function, which is only applicable to the known data region. The reconstruction error is calculated based on the location of unknown data other than the decomposition matrix, and includes a regularization term for the decomposition matrix.

[0044] Quantitative Pre-evaluation of Strategy Effectiveness: By Calculating the Reconstruction Matrix With the original spacetime matrix Evaluating candidate maintenance strategies based on errors at unknown data locations. The effect is assessed using root mean square error as a pre-evaluation metric: ;in, Represents a set of unknown locations. The number of elements in the set of unknown locations. The smaller the value, the stronger the candidate maintenance strategy. The better the prediction of the overall network data quality, the better.

[0045] Example 5, based on the above examples, describes a multi-objective optimization decision module that establishes and solves a multi-objective optimization model to generate an optimal maintenance plan, specifically including:

[0046] A multi-objective optimization model is constructed with the goal of minimizing maintenance costs and maximizing the overall data quality of the network.

[0047] Let binary decision variables be used. This indicates whether the corresponding monitoring equipment has been included in the maintenance plan. =1 indicates the device It has been included in the maintenance plan, and the objective function is as follows:

[0048] First objective function: Maximize the total value of the data. ;

[0049] Second objective function: Minimize the total redundancy within the maintenance set. ;

[0050] Third objective function: Minimize simulation prediction error. ;

[0051] in, This indicates that the above binary decision variables The constructed decision vector Pre-assessment of the corresponding maintenance strategy The value, constrained by the total maintenance cost. ,in To maintain equipment The cost, For the total budget;

[0052] Pareto optimal solution set: The non-dominated sorting genetic algorithm with elitist strategy is used to solve the optimization problems of the first objective function, the second objective function and the third objective function to obtain the Pareto optimal solution set;

[0053] Optimal decision generation: based on the current total budget The most suitable solution is selected from the Pareto optimal solution set as the optimal maintenance plan.

[0054] Example 6, based on the above examples, describes an execution and verification feedback module that executes the maintenance plan and verifies its effectiveness to achieve closed-loop feedback. Specifically, it includes:

[0055] Maintenance instruction execution: Issue and execute the optimal maintenance plan;

[0056] Post-maintenance effect verification: After maintenance, actual data was collected to obtain a verification data matrix. The verification data matrix Using the data of the maintained equipment as known points, the matrix completion algorithm is used again to infer the data of the unmaintained equipment, thus obtaining the inference matrix. Calculate the actual effect score:

[0057]

[0058] in, This is the set of data locations corresponding to unmaintained equipment.

[0059] Feedback and Adaptive Learning: Scoring Actual Results Compared with the pre-assessed value (The root mean square error of the preliminary evaluation of the candidate maintenance strategy in Example 4) By comparing the two, we can obtain the deviation information between them; if the deviation... Continuously exceeding the threshold The deviation information will then be fed back to the equipment value and redundancy assessment module and the multi-objective optimization decision-making module.

[0060] The equipment value and redundancy assessment module adjusts the smoothing parameters of the probability distribution estimation algorithm used when calculating the data value index (VOI) and data redundancy (TE) based on the deviation information.

[0061] The multi-objective optimization decision module adjusts the objective function in its multi-objective optimization model based on the deviation information. (i.e., the weighting coefficients that minimize the prediction error term);

[0062] Through the above process, the system achieves closed-loop adaptive learning.

[0063] Example 7 describes a self-maintenance method for pollution source monitoring equipment based on information value and matrix completion, using the system described above. This method is applied to the system and includes the following steps:

[0064] Step S1: Monitoring network spatiotemporal data acquisition and construction;

[0065] Step S2: Evaluation of equipment data value and redundancy;

[0066] Step S3: Maintenance strategy simulation and effect pre-evaluation;

[0067] Step S4: Generation of multi-objective optimization maintenance decisions;

[0068] Step S5: Maintenance execution and closed-loop verification.

[0069] Through the above embodiments, the present invention has achieved a fundamental transformation in the maintenance of pollution source monitoring equipment, from passive response to proactive prediction, from local optimization to global balance, and from open-loop execution to closed-loop learning, significantly improving the overall data reliability and operation and maintenance management efficiency of the monitoring network.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-maintenance system for pollution source monitoring equipment, characterized in that: It includes a data acquisition and preprocessing module, an equipment value and redundancy assessment module, a maintenance strategy simulation module, a multi-objective optimization decision-making module, and an execution and verification feedback module; The data acquisition and preprocessing module acquires operational data from the monitoring equipment and environmental monitoring data, performs preprocessing, and constructs an original spatiotemporal data matrix. The equipment value and redundancy assessment module, based on information theory, uses the original spatiotemporal data matrix to calculate the data value index and data redundancy between monitoring devices for each monitoring device, and generates multiple candidate maintenance strategies. The method for calculating the data value index for each monitoring device includes: based on the value information method, for any first monitoring device in the monitoring network, calculating the value contribution of the data of the first monitoring device to assess the data status of each second monitoring device in the monitoring network other than the first monitoring device; then summing the value contribution values ​​of the first monitoring device to all second monitoring devices to obtain the data value index of the first monitoring device; The method for calculating the data redundancy between monitoring devices is as follows: based on the mutual information method, for any first monitoring device in the monitoring network, the amount of shared information between the data status of the first monitoring device and each second monitoring device in the monitoring network other than the first monitoring device is calculated to obtain the data redundancy between devices; The maintenance strategy simulation module receives candidate maintenance strategies, maps them to the data regions to be repaired in the original spatiotemporal data matrix, and uses a matrix completion algorithm to simulate the network data reconstruction effect after maintenance, thereby obtaining the complete data matrix after completion; and calculates the performance pre-evaluation results. The multi-objective optimization decision module establishes and solves the multi-objective optimization model based on the data value index, data redundancy between devices, and performance pre-assessment results, generates the optimal maintenance plan, and provides the plan to the execution and verification feedback module. The execution and verification feedback module is used to execute the optimal maintenance plan, verify the recovery effect of network data quality after maintenance, and obtain the verification results.

2. The self-maintenance system for pollution source monitoring equipment according to claim 1, characterized in that: In the data acquisition and preprocessing module, the preprocessing includes data standardization and coarse noise filtering.

3. The self-maintenance system for pollution source monitoring equipment according to claim 2, characterized in that: The maintenance strategy simulation module is specifically used to perform the following operations: Receive candidate maintenance strategies, each of which specifies the set of monitoring devices to be maintained and their corresponding future maintenance period; Each of the candidate maintenance strategies is mapped to the original spatiotemporal data matrix, where the matrix elements represent the standardized data of the corresponding monitoring device at the corresponding time. The data points corresponding to the monitoring equipment to be maintained specified in the candidate maintenance strategy during the future maintenance period are marked as high-quality known data points, forming a known data area. Data outside the known data area is unknown data. Based on the high-quality known data points in the known data region, the original spatiotemporal data matrix is ​​completed and reconstructed using a non-negative matrix factorization algorithm; The effectiveness of the candidate maintenance strategy is quantitatively pre-evaluated by calculating the root mean square error between the complete data matrix obtained after completion and reconstruction and the original spatiotemporal data matrix at the unknown data locations.

4. The self-maintenance system for pollution source monitoring equipment according to claim 3, characterized in that: The multi-objective optimization decision module is specifically used for: Establish a multi-objective optimization model with the goal of minimizing maintenance costs and maximizing the overall data quality of the network; Define binary decision variables and the objective function of the model. Define the constraints of the multi-objective optimization model, wherein the total maintenance cost of all monitoring devices selected for the maintenance plan does not exceed the preset budget; A multi-objective evolutionary algorithm was used to solve the multi-objective optimization model, and a set of Pareto optimal solutions were obtained. Based on the current preset budget, the final optimal maintenance plan is selected from the Pareto optimal solutions.

5. A self-maintenance system for pollution source monitoring equipment according to claim 4, characterized in that: The execution and verification feedback module is specifically used for: The optimal maintenance plan generated by the multi-objective optimization decision module is then deployed and executed. After the maintenance task is completed, the actual data of the monitoring network is collected as verification data. The data of the maintained monitoring equipment in the verification data is used as known data points. The matrix completion algorithm is used again to infer the data of the unmaintained equipment to obtain the inferred data matrix. The actual root mean square error between the inferred data matrix and the data corresponding to the unmaintained equipment in the verification data is calculated and used as the score of the actual effect after maintenance. The actual performance score is compared with the predicted performance score obtained by the maintenance strategy simulation module in its pre-evaluation of the optimal maintenance plan to obtain the deviation information between the two. If the deviation between the actual effect score and the predicted effect score continues to exceed a preset threshold, the deviation information will be fed back to the equipment value and redundancy assessment module and the multi-objective optimization decision module. The equipment value and redundancy assessment module adjusts the smoothing parameters of the probability distribution estimation algorithm when calculating the data value index and redundancy based on the deviation information. The multi-objective optimization decision module adjusts the weight coefficients of the objective function in the optimization model based on the deviation information.

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