A full-process digital operation and maintenance management system of a water and gas pollution source online monitoring system

Abstract

The application discloses a kind of water, gas pollution source on-line monitoring system's whole-process digital operation and maintenance management system, it is related to environmental monitoring digital operation and maintenance technical field, the system includes: local data processing module, federal aggregation training module, fault intelligent research and judgment module, work order process control module and model iteration update module;The application is by processing multi-source monitoring operation and maintenance data and retaining station original data, generate global fault identification model by means of federal aggregation training, eliminate data leakage hazard, combine site attribute self-adaptive adjustment model weight, rely on feature matching accurate identification fault, reduce false alarm and miss alarm;Multi-dimensional matching operation and maintenance personnel, automatically compare working condition and verify work order closed loop;Effective fault sample is automatically collected and triggered model iteration in layer, increase training weight for the fault with poor identification effect, form whole-process closed loop, continuously optimize fault identification effect, reduce operation and maintenance labor cost.

Description

Technical Field

[0001] This invention relates to the field of digital operation and maintenance technology for environmental monitoring, specifically a full-process digital operation and maintenance management system for online monitoring systems of water and air pollution sources. Background Technology

[0002] The deployment of online monitoring stations for water and air pollution sources continues to expand, with polluting enterprises installing online monitoring equipment for water quality and flue gas. Ecological and environmental departments rely on this monitoring data to conduct pollution discharge supervision, trace sources of pollution exceeding standards, and calculate total emissions. However, these monitoring devices operate outdoors under high loads for extended periods, making them prone to problems such as sensor drift, pipeline blockage, and transmission failures. The stable operation of these devices directly determines the validity of the monitoring data. Currently, pollution source maintenance relies heavily on regular manual inspections, and maintenance records are kept on paper or simple forms. Data on monitoring conditions, fault handling, and equipment calibration are scattered across multiple independent systems belonging to enterprises, third-party maintenance units, and regulatory platforms. This lack of standardized data processing results in issues such as inconsistent time-series information and inconsistent measurement units, hindering the automated identification of equipment faults and the digital management of the entire maintenance process. The industry urgently needs an integrated maintenance management solution covering data acquisition, fault analysis, work order processing, and model iteration.

[0003] Existing pollution source monitoring and maintenance management platforms have many shortcomings: Traditional fault identification relies on fixed threshold comparison rules, failing to adjust the judgment logic based on the differences in site location, equipment model, and operating years. A uniform judgment standard easily leads to frequent false alarms or missed alarms in mountainous areas and with older equipment. Furthermore, fault identification models can only be trained centrally using a centralized database, requiring all original monitoring data from each site to be uploaded to the cloud, posing a risk of leakage of sensitive enterprise pollution discharge monitoring data and insufficient data privacy compliance. Maintenance work orders are allocated based solely on simple regional divisions, without considering fault priority or the workload of maintenance personnel for intelligent matching. The entire work order processing process lacks an automatic verification mechanism, relying solely on manual uploading of records to confirm closure. Data before and after handling cannot be automatically compared, making it difficult to identify false closures and inadequate handling. The fault sample update and model training are disconnected. The effective fault data generated by the closed-loop work order cannot be automatically fed back into the sample library. The model can only rely on periodic manual batch updates, which cannot achieve local fine-tuning at the site and global federated aggregation iteration. After long-term use, the accuracy of fault identification continues to decline. It is also unable to adaptively increase the model learning weight for difficult-to-handle faults, and the intelligent operation and maintenance iteration capability is lacking.

[0004] In summary, existing online monitoring and maintenance technologies for pollution sources suffer from several core technical deficiencies, including low data standardization, significant data security risks associated with centralized model training, poor adaptability in fault diagnosis, lack of automated verification for work order management, and the inability of models to autonomously iterate and update. To address these shortcomings, this invention proposes a full-process digital operation and maintenance management system for online monitoring systems of water and air pollution sources. This system relies on five core modules: local data standardization processing, federated distributed model training, adaptive fault diagnosis, intelligent closed-loop management of work orders, and sample-driven model iteration. It achieves accurate fault identification, automated management of operation and maintenance processes, and continuous optimization of the global model without requiring raw monitoring data to leave the site. This addresses the industry's technical challenges of insufficient intelligence, data insecurity, weak process management, and poor model generalization capabilities in existing operation and maintenance systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a full-process digital operation and maintenance management system for online monitoring systems of water and air pollution sources. It can unify and organize multi-source monitoring and operation and maintenance data; aggregate only encrypted parameters of each site, and retain the original data locally; adaptively adjust parameters based on site attributes; determine faults and classify priorities based on feature vector matching; intelligently allocate personnel and automatically verify closed loops; collect and process data to expand the sample library; trigger model updates in two levels; increase the sample weight for faults with poor identification performance; and continuously optimize the global fault identification model.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a full-process digital operation and maintenance management system for an online monitoring system for water and air pollution sources, the system comprising: Local data processing module: Collects and retains the monitoring equipment operation data and maintenance record data of each water and air pollution source online monitoring station. After completing the data standardization and time sequence alignment, it outputs real-time operation data to the fault intelligent judgment module and provides standardized samples to the local model fine-tuning. Federated Aggregation Training Module: Receives encrypted model parameter increments uploaded by online monitoring stations of various water and air pollution sources, performs distributed aggregation operations to generate a global fault identification model, and distributes the global fault identification model to the fault intelligent judgment module; The intelligent fault assessment module loads a global fault identification model, identifies abnormal states of online monitoring equipment for water and air pollution sources based on real-time operational data, determines the fault type and handling priority, and pushes the fault information to the work order process management module. Work order process control module: After receiving fault information, it matches maintenance personnel and generates standardized work orders, tracks the entire handling process, and synchronizes the handling data to the model iteration and update module after completing the work order closed-loop verification. Model Iteration Update Module: Collects the processing data of closed-loop work orders and updates the fault sample library. When the set trigger conditions are met, it sends a training instruction to the federated aggregation training module to trigger a new round of model aggregation iteration, thus completing the continuous iteration of the global fault identification model.

[0007] Furthermore, the local data processing module connects to the main control unit of the water and air pollution source monitoring equipment through a standard communication interface to collect the monitoring equipment operation data. The monitoring equipment operation data includes operating condition data and status data. It also connects to the handheld terminal for operation and maintenance to collect operation and maintenance record data, which includes on-site handling records, component replacement records, and equipment calibration records. All collected multi-source data are processed for dimensional unification and time sequence alignment to generate a standardized dataset and store it in the local storage unit.

[0008] Furthermore, the federated aggregation training module generates global model parameters through a weighted average aggregation method, calculated using the following formula: Among them, w g The updated global model parameters; k is the total number of sites participating in this training round; n i w represents the total number of local fault samples at the i-th site. i The local model weight parameters are obtained by fine-tuning the i-th station based on local samples. This formula is used to aggregate the encrypted model parameter increments uploaded by each online monitoring station of water and air pollution sources by weighted average according to the number of local samples, and automatically determine the contribution weight of each station based on the proportion of sample size, so as to aggregate and generate global fault identification model parameters without leaving the station with the original data.

[0009] Furthermore, after loading the global fault identification model, the fault intelligent judgment module automatically adjusts the feature weights within the global fault identification model based on the attribute characteristics of each online monitoring station for water and air pollution sources. The attribute characteristics of the online monitoring station include at least one of the following: geographical environment, equipment model, and years of operation. The fault intelligent judgment module matches corresponding weight allocation rules for stations with different attribute combinations and adjusts the contribution ratio of different operating characteristics in the fault judgment process.

[0010] Furthermore, the intelligent fault assessment module determines the fault type through feature vector matching, using the following formula: Where sim is the feature matching degree between real-time operating conditions and fault types; A is a multi-dimensional feature vector extracted from real-time equipment operation data, which is automatically extracted from the original operation data by the global fault identification model; B is the standard feature vector of the corresponding fault type in the fault feature library; when the feature matching degree reaches the set threshold, the fault intelligent judgment module outputs the judgment result of the corresponding fault type.

[0011] Furthermore, after receiving fault information reported by online monitoring stations of water and air pollution sources, the work order process control module filters the corresponding maintenance personnel according to multi-dimensional matching rules. The matching dimensions include fault handling priority, the service area to which the maintenance personnel belong, and the number of tasks currently in transit. The standardized work order generated by the work order process control module includes at least the location of the faulty site, the fault type, and the handling priority. The work order information is pushed to the matched maintenance personnel through the maintenance terminal, and the current status of the work order is updated to the maintenance management ledger simultaneously.

[0012] Furthermore, the work order process control module continuously tracks the status of all nodes of the maintenance work order. After the maintenance personnel complete the on-site handling, they upload the on-site handling record and equipment recovery status information through a handheld terminal. After receiving the feedback information, the work order process control module retrieves the monitoring equipment operation data of the corresponding water and air pollution source online monitoring stations for verification. It compares the equipment operating data before and after the handling, and completes the work order closure after confirming that the equipment operating status has returned to the normal range. At the same time, it archives all the data of this handling to the maintenance record database.

[0013] Furthermore, the model iteration update module collects fault feature data and handling effect data corresponding to all closed-loop work orders, verifies the validity of the handling results, and synchronously updates the fault sample library of the corresponding water and air pollution source online monitoring stations to the fault sample library of the verified and handled fault samples. When the number of new fault samples at a single station reaches a first set number, the local model fine-tuning operation on the station side is triggered and new model parameter increments are generated and uploaded to the federated aggregation training module. When the total number of new fault samples at all stations in the entire region reaches a second set threshold, a formal training instruction is sent to the federated aggregation training module to trigger a new round of aggregation calculation of the global fault identification model. After the calculation is completed, the updated global fault identification model is distributed to all connected water and air pollution source online monitoring stations, and the version information of this update and the corresponding training sample coverage are recorded synchronously.

[0014] Furthermore, after each global fault identification model update, the model iteration update module statistically analyzes the identification and matching of fault types at each online monitoring station for water and air pollution sources. For fault types where the treatment effect is not as expected, it automatically increases the weight ratio of the corresponding fault sample in the next round of training and adjusts the learning weight coefficient of the global fault identification model for the corresponding fault features in subsequent training processes.

[0015] Compared with existing technologies, this fully digital operation and maintenance management system for online monitoring of water and air pollution sources has the following advantages: I. This invention uses a local data processing module to uniformly collect monitoring equipment operating conditions, status information, and all types of maintenance records. After unifying the dimensions and aligning the time series of multi-source data, it generates standardized local samples. The entire process eliminates the need to transmit original monitoring data from individual sites. A federated aggregation training module incrementally weights and aggregates encrypted model parameters from each site to generate a global fault identification model, fundamentally avoiding the information leakage risks associated with centralized uploading of sensitive pollution monitoring data. Simultaneously, a fault intelligent judgment module adaptively adjusts the internal feature weights of the model based on attributes such as site location, equipment model, and years of operation. A feature vector matching mechanism is used to determine faults, overcoming the limitations of traditional fixed threshold discrimination modes. This allows for adaptation to the differentiated operating patterns of monitoring equipment in various usage scenarios, significantly reducing the frequency of missed and false alarms. Based on a local preprocessing and distributed model training architecture, it balances data security and fault identification accuracy, providing reliable and refined fault judgment criteria for backend maintenance work order scheduling.

[0016] Second, this invention automatically allocates maintenance personnel through a work order process control module based on multi-dimensional matching rules, synchronously records and tracks the entire workflow status of work orders, and automatically retrieves site operation data for comparison and verification after maintenance personnel provide on-site feedback on handling information, completing standardized closed-loop archiving of work orders. This avoids problems such as falsification of handling records and missing verification caused by manual review of closed loops, achieving full traceability and verifiability of the maintenance handling process. Relying on the model iteration update module, it automatically collects effective fault data within the closed-loop work orders to expand the local sample library. Based on the dual-layer triggering mechanism of site sample increment and total sample volume, it links the federated aggregation training module to update the global model, and simultaneously increases the training weight of corresponding samples for fault types with substandard handling effects, forming a complete closed-loop link of data collection, fault identification, work order handling, sample return, and model update. This continuously improves the fault identification and adaptation capabilities of the entire domain monitoring equipment and reduces the cost of manual verification and model maintenance in the later stages.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1A diagram showing the module composition of a fully digital operation and maintenance management system for an online monitoring system of water and air pollution sources; Figure 2 This is a flowchart of the training and iteration sub-process of the federated aggregation model of the present invention; Figure 3 This is a flowchart of the fault assessment and work order closed-loop processing sub-process of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below. Example

[0021] The local data processing module connects to the main control units of monitoring equipment at various online monitoring stations for water and air pollution sources via a standard communication interface. It continuously collects operational data from the monitoring equipment, including both operational and status data. Operational data covers the entire process of equipment sampling, analysis, and data output, while status data covers core status information such as power supply, communication, and self-testing of core components. The combination of these two types of data provides a complete picture of the equipment's real-time operational performance and internal health status. Simultaneously, it connects to the maintenance handheld terminal to collect maintenance records, including on-site handling records, component replacement records, and equipment calibration records. All collected multi-source data undergoes dimensional unification and time-series alignment processing to generate standardized datasets stored locally. Simultaneously, it outputs real-time operational data to the fault intelligent judgment module and provides standardized samples for local model fine-tuning. Directly connecting to the equipment's main control unit via a standard communication interface allows for the acquisition of native operational data, avoiding data loss and bias caused by intermediate conversion processes. Covering both operational and status data dimensions provides sufficient data support for subsequent anomaly identification, reducing missed or incorrect judgments due to incomplete data. Synchronous integration with handheld maintenance terminals collects maintenance records, enabling all offline maintenance operations to be incorporated into a digital management system. This breaks down the disconnect between equipment operation data and maintenance operation data, ensuring traceability and correlation for every maintenance operation. Multi-source data undergoes unified dimensionality and time-series alignment, unifying data from different sampling frequencies, measurement standards, and timestamps to the same time dimension and measurement system. This eliminates comparison barriers caused by data caliber differences, ensuring consistency and reliability of data input during subsequent fault assessment. The generated standardized datasets are stored locally, eliminating the need for external data transmission and guaranteeing data security at the site. The output real-time operational data supports continuous anomaly monitoring by the intelligent fault assessment module, and the provided standardized samples can be directly used for local model fine-tuning, reducing resource consumption in the data preprocessing stage.

[0022] The federated aggregation training module receives encrypted model parameter increments uploaded from online monitoring stations of various water and air pollution sources, and performs distributed aggregation operations using weighted average aggregation calculations to generate a global fault identification model. The formula is as follows: Among them, w g The updated global model parameters; k is the total number of sites participating in this training round; n i w represents the total number of local fault samples at the i-th site. i The local model weight parameters are obtained by fine-tuning the i-th site based on local samples. After the calculation is completed, the global fault identification model is distributed to the fault intelligent judgment module. Each site only uploads the encrypted model parameter increments, and the original operating data and maintenance data are stored locally at the site throughout the process, which can meet the data security management requirements and avoid the compliance risks caused by the cross-regional transmission of sensitive monitoring data. The weighted average aggregation calculation uses the number of local fault samples at each site as the weight basis, and automatically matches the contribution ratio of each site's model parameters in the global model. Sites with more samples correspond to higher weight ratios, making the generated global fault identification model more in line with the actual operating characteristics of most sites, and avoiding excessive interference of small sample sites on the accuracy of the global model under the average aggregation method. The distributed aggregation operation relies on the collaboration of multiple sites, eliminating the need to centrally collect all raw data, which can significantly reduce the data transmission cost and storage pressure required for centralized training. The generated global fault identification model uniformly covers the common fault characteristics of all access sites, which can replace the mode of training models individually at each site, reducing the waste of resources caused by repeated training, while ensuring that the fault identification capabilities of all sites maintain a uniform benchmark. The generated global fault identification model is distributed to the fault intelligent analysis module, which can provide unified model support for front-end anomaly identification, ensure that the fault judgment standards of each site are consistent, and avoid the problem of inconsistent judgment scales between different sites.

[0023] After loading the global fault identification model, the intelligent fault assessment module automatically adjusts the feature weights within the global fault identification model based on the unique attributes of each online monitoring station for water and air pollution sources. These attributes include at least one of the following: geographical environment, equipment model, and years of operation. For stations with 5 years or more of operation, a weight allocation rule is applied that increases the weight of aging-related operational features to 65%. For mass-produced equipment of the same model, a standardized feature weight ratio is uniformly matched, adjusting the contribution ratio of different operational features in the fault determination process. Subsequently, feature vector matching is used to calculate and identify abnormal states of online monitoring equipment for water and air pollution sources. The formula is: Here, sim represents the feature matching degree between real-time operating conditions and fault types; A is a multi-dimensional feature vector extracted from real-time equipment operation data; and B is the standard feature vector corresponding to the fault type in the fault feature library. When the feature matching degree between real-time operating conditions and fault types reaches 0.82, the corresponding fault type judgment result is output, the fault handling priority is determined simultaneously, and the fault information is pushed to the work order process management module. By adjusting the feature weights within the model in conjunction with the site's own attribute characteristics, the general global fault identification model can be adapted to the differentiated operating scenarios of different sites, making up for the shortcomings of the general model in the recognition accuracy of specific scenarios. For equipment with a long service life, increasing the weights of aging-related features can identify potential faults caused by equipment aging in advance, improving the foresight of early warnings. Feature vector matching calculation can automatically extract multi-dimensional feature vectors from the original operating data and compare them with the standard feature vectors corresponding to the fault types in the fault feature library. The entire process does not require manual feature extraction and comparison, which can greatly improve the efficiency and objectivity of fault identification and reduce the time cost and human error caused by manual investigation. Simultaneously determining the priority of fault handling can differentiate the urgency and scope of impact of faults, providing a clear decision-making basis for subsequent operation and maintenance resource scheduling. Pushing fault information to the work order process management module can trigger subsequent automated work order generation and scheduling processes, achieving seamless connection from fault discovery to handling and scheduling, reducing the time delay of manual information transmission, and improving fault response speed.

[0024] After receiving fault information reported by online monitoring stations of water and air pollution sources, the work order process control module prioritizes the dispatch of the three highest priority levels for fault handling, selects the corresponding maintenance personnel based on a multi-dimensional matching rule that the service area of ​​the maintenance personnel is within a 5-kilometer radius, and the number of currently en route tasks does not exceed 2. It generates a standard work order that includes at least the location of the faulty site, the fault type, and the handling priority. The work order information is pushed to the matched maintenance personnel through the maintenance terminal, and the current status of the work order is updated to the maintenance management ledger simultaneously. The work order process control module continuously tracks the status of all nodes of the maintenance work order. After the on-site handling is completed, it receives the on-site handling record and equipment recovery status information uploaded by the handheld terminal, retrieves the monitoring equipment operation data of the corresponding online monitoring stations of water and air pollution sources for verification, compares the equipment operating data before and after the handling, and completes the work order closure after confirming that the fluctuation range of the core operating parameters of the equipment has fallen back to within ±8%. At the same time, all data of this handling is archived to the maintenance record database. Selecting maintenance personnel based on multi-dimensional matching rules comprehensively considers the urgency of fault handling, the service coverage of maintenance personnel, and their current workload. This ensures that high-priority faults receive priority response and handling, preventing serious faults from escalating their impact. It also balances the workload of different maintenance personnel, improving the overall utilization efficiency of maintenance resources and avoiding situations where some personnel are overloaded while others are idle. The generated standardized work orders uniformly include core handling information, ensuring that maintenance personnel receive complete and accurate fault information, avoiding omissions or insufficient preparation due to information transmission errors, and reducing the number of trips to the site for handling. Work order information is pushed in real time through maintenance terminals, allowing maintenance personnel to obtain task information immediately, shortening response time. Synchronously updating the work order status to the maintenance management ledger allows managers to monitor the progress of all maintenance work orders in real time, achieving transparent control of maintenance progress without the need for manual summarization and reporting. Continuous tracking of the full-node status of maintenance work orders completely records the entire process from work order generation to closure, facilitating subsequent maintenance process review and accountability. After the incident is resolved, equipment operation data is retrieved for verification. Objective comparison of operating data confirms whether the equipment's operating status has returned to normal, avoiding subjective biases from manual feedback and ensuring the authenticity and effectiveness of the work order closure, preventing residual fault issues caused by false closures. All incident data is archived in the maintenance record database, accumulating comprehensive maintenance experience and providing a data foundation for subsequent fault analysis and model optimization. It can also serve as a basis for maintenance performance evaluation and process optimization.

[0025] The model iteration and update module collects fault feature data and handling effect data corresponding to all closed-loop work orders, verifies the effectiveness of the handling results, and synchronously updates the fault sample library of the corresponding water and air pollution source online monitoring stations to the fault sample library of the verified and effective handling. When a single station adds 30 fault samples, it triggers the local model fine-tuning operation on the station side and generates new model parameter increments, which are then uploaded to the federated aggregation training module. When the total number of new fault samples of all stations in the entire region reaches 200, it sends a formal training instruction to the federated aggregation training module, triggering a new round of aggregation calculation of the global fault identification model. After the calculation is completed, the updated global fault identification model is distributed to all connected water and air pollution source online monitoring stations, and the version information of this update and the corresponding training sample coverage are recorded synchronously. After each global fault identification model update, the model iteration and update module counts the identification and matching of the corresponding fault types of each water and air pollution source online monitoring station. For fault types whose handling effect does not meet expectations, it automatically increases the weight ratio of the corresponding fault samples in the next round of training and adjusts the learning weight coefficient of the corresponding fault features of the global fault identification model in the subsequent training process. By collecting fault characteristic data and handling effect data from closed-loop work orders, actual operation and maintenance experience can be transformed into sample resources that can be used for model training. This enables the linkage between operation and maintenance practice and model optimization, allowing model capabilities to improve synchronously with the accumulation of operation and maintenance experience. Verifying the effectiveness of handling results filters out truly valuable training samples and excludes invalid samples due to improper handling or misjudgment of faults, preventing invalid samples from interfering with model training and ensuring the quality of the fault sample library. A two-level trigger mechanism is set up: when the number of new samples added to a single site reaches a set limit, local model fine-tuning is triggered, which can quickly adapt to the new fault characteristics of a single site and improve the personalized recognition capability of a single site; when the number of new samples added across the entire domain reaches a set threshold, global aggregation iteration is triggered, which can synchronize the new fault characteristics of each site to the global model, improving the overall recognition capability of all sites. This two-level trigger mechanism balances the flexibility of single-site adaptation with the stability of global model updates, avoiding the resource consumption and system fluctuations caused by frequent global updates. After updating the model, version information and sample coverage are recorded synchronously, enabling traceability of model versions. This facilitates subsequent comparison of the recognition effects of different versions of the model and allows for quick location of the corresponding training sample range when recognition deviations occur. By statistically analyzing the identification and matching of fault types at each site, weaknesses in the model's identification capabilities can be accurately identified. For fault types where the handling effect is not as expected, the training weights of the corresponding samples can be increased. This allows the model to focus on learning the characteristic patterns of these faults during subsequent training, gradually improving the identification accuracy of weak fault types. This enables continuous autonomous optimization of the model's capabilities, completing capability iterations without manual adjustment of model parameters, thus reducing the manual cost of model maintenance. Figure 1 As shown. Example

[0026] The local data processing module adapts the communication interfaces of newly added online monitoring stations for water and air pollution sources. It connects to the main control unit of the corresponding monitoring equipment to collect operational and status-based monitoring data. Operational data covers operational parameters for each stage of equipment sampling, reagent delivery, analysis and testing, and data output. Status data covers basic status information such as power supply status, communication link status, and self-test results of core components. Simultaneously, it connects to the handheld maintenance terminal to collect corresponding maintenance records, including on-site handling records, component replacement records, and equipment calibration records. It performs dimensional unification and time-series alignment processing on all multi-source data from the new stations, generating corresponding standardized datasets and storing them in the local storage unit. Simultaneously, it outputs real-time operational data of the new stations to the intelligent fault analysis module and organizes historical maintenance data from the new stations into standardized samples for local model fine-tuning. Completing the communication interface adaptation for new stations allows new monitoring equipment of different models and communication protocols to quickly connect to the existing data acquisition system without the need to develop separate acquisition programs for each device, significantly shortening the data access cycle for new stations and reducing development costs during the access phase. By connecting to the main control unit of the monitoring equipment to collect operational and status-based data, the system can comprehensively acquire operational status information of newly added equipment, providing comprehensive basic data support for subsequent fault analysis and avoiding missed anomalies due to insufficient data dimensions. Simultaneously connecting to the handheld maintenance terminal to collect maintenance record data allows for the digital management of maintenance operations at new sites from the initial integration stage, avoiding management blind spots caused by missing maintenance data at new sites and ensuring that all maintenance operations have traceable digital records. Performing dimensional unification and time-series alignment processing on multi-source data from new sites ensures that the data format of new sites is completely consistent with existing sites, ensuring that new site data can be directly integrated into the existing data system without additional conversion processing. This guarantees that subsequent fault analysis and model training can directly call upon new site data, eliminating data barriers between new and old sites. The generated standardized dataset is stored in a local storage unit, ensuring data security for new sites and meeting the management requirements for local data retention. The output real-time operational data allows the intelligent fault analysis module to cover anomaly monitoring at new sites immediately, enabling new sites to have intelligent monitoring capabilities from the moment they are connected. Organizing historical operation and maintenance data of new sites into standardized samples can quickly accumulate initial training samples for new sites, accelerate the local model adaptation process, and shorten the break-in period for new site models.

[0027] The federated aggregation training module receives encrypted model parameter increments uploaded by all access sites, including newly added sites. It performs distributed aggregation calculations using weighted average aggregation to generate a global fault identification model covering the operational characteristics of the new site. The updated global fault identification model is then distributed to the fault intelligent assessment module. Including new sites in the federated aggregation training system allows their operational characteristics to be quickly integrated into the global model, eliminating the need for separate model training for each new site, reducing redundant construction costs, and enabling rapid reuse of operational capabilities. Each site only uploads encrypted model parameter increments; the original monitoring and operational data of the new site do not need to leave the local machine, meeting the data security management requirements of the new site and avoiding compliance risks associated with cross-domain data transmission. This is particularly suitable for data-sensitive monitoring scenarios. Weighted average aggregation calculation uses the number of local fault samples at each site as the weight, automatically calculating the contribution ratio of each site's model parameters. When a new site initially has a small sample size, its weight ratio is relatively low, minimizing its impact on the stability of the existing global model. As the sample size of new sites gradually accumulates, its weight ratio automatically increases, allowing the global model to gradually adapt to the operational characteristics of the new sites, achieving a smooth transition in model capabilities and avoiding significant fluctuations in recognition accuracy during the initial integration phase. Distributed aggregation computation eliminates the need for centralized collection of raw data from all sites, significantly reducing the data transmission and storage pressure brought by new site integration. It supports continuous expansion of site scale without requiring expansion of central storage resources. After generating a global fault recognition model covering the operational characteristics of new sites, it is distributed to the fault intelligent judgment module, enabling the front-end judgment module to quickly acquire fault recognition capabilities for new sites. This ensures that new sites receive consistent intelligent judgment support with existing sites immediately upon integration, without waiting for a lengthy model training cycle. Figure 2 As shown.

[0028] The intelligent fault assessment module loads the updated global fault identification model and automatically matches corresponding weight allocation rules to the characteristics of newly added sites. It adjusts the feature weight ratios within the global fault identification model to adapt to the equipment operating characteristics of the new sites. The online monitoring of site characteristics includes at least one of the following: geographical environment, equipment model, and years of operation. For sites in coastal high-humidity and high-salt environments, a weight allocation rule is implemented that adjusts the weight ratio of corrosion and moisture-related operating features to 70%. Differentiated feature weight ratios are matched separately for niche customized equipment models. Subsequently, feature vector matching calculations are used to identify abnormal states in the real-time operating data of the new sites. When the feature matching degree between the real-time operating condition and the fault type reaches 0.78, the corresponding fault type judgment result is output, the corresponding fault handling priority is determined simultaneously, and the fault information is pushed to the work order process management module. Matching corresponding weight allocation rules to the characteristics of newly added sites allows the global model to quickly adapt to the geographical environment, equipment model, and years of operation of the new sites, avoiding the problem of low recognition accuracy of general models in new site scenarios. This significantly shortens the model adaptation cycle for new sites and eliminates the need for manual adjustment of model parameters. After adjusting the feature weight ratios, the contribution ratio of sensitive operating features in new site scenarios can be amplified in a targeted manner. For sites in high-humidity environments, the weight of corrosion and moisture-related features is increased, which can identify potential faults caused by environmental factors in advance and improve the foresight of anomaly warnings. Feature vector matching calculation is used to identify anomalies in the real-time operating data of new sites. Multi-dimensional feature vectors can be automatically extracted from the raw operating data and compared with standard feature vectors in the fault feature library. The entire process does not require manual setting of judgment rules for new equipment, which significantly reduces the configuration cost of operation and maintenance rules for new sites and reduces rule deviations caused by manual configuration. Simultaneously determining the fault type and handling priority allows faults at new sites to also have clear handling levels, providing a clear basis for subsequent work order scheduling. Pushing fault information to the work order process management module can trigger automated operation and maintenance scheduling for faults at new sites, enabling new sites to be included in the fully automated operation and maintenance system from the initial access stage without the need to build a separate operation and maintenance scheduling process.

[0029] After receiving fault information reported by a newly added site, the work order process control module selects the corresponding maintenance personnel according to multi-dimensional matching rules such as fault handling priority, service area coverage radius within 20 kilometers of the maintenance personnel's area, and the number of currently en route tasks not exceeding one. It generates a standardized work order containing the location of the faulty site, fault type, and handling priority. The work order information is pushed to the matched maintenance personnel through the maintenance terminal. At the same time, the work order status of the newly added site is included in the maintenance management ledger for unified management. The work order process control module continuously tracks the full node status of the maintenance work order of the newly added site, receives the on-site handling record and equipment recovery status information uploaded by the handheld terminal after the on-site handling is completed, retrieves the monitoring equipment operation data of the corresponding newly added site for verification, compares the equipment operating data before and after the handling, and completes the work order closure after confirming that the deviation of the core operating parameters of the equipment has fallen back to within ±10%. At the same time, the full data of the handling of the newly added site is archived to the maintenance record database. By using multi-dimensional matching rules to screen maintenance personnel for faults at new sites, existing maintenance resource scheduling systems can be directly reused at new sites without the need for separate maintenance scheduling rules. This significantly reduces maintenance management costs for new sites and enables the coordinated use of maintenance resources. The generated standardized work orders contain complete handling information, ensuring maintenance personnel can quickly grasp the fault status of new sites, prepare for handling in advance, improve the first-time handling success rate, and reduce the handling time for faults at new sites. Work order information is pushed in real time through maintenance terminals, ensuring timely response to faults at new sites and avoiding delays in fault handling due to incomplete maintenance systems. Simultaneously, work orders for new sites are included in the unified maintenance management ledger for integrated management of maintenance progress across all sites, eliminating the need for separate statistics on the maintenance status of new sites and improving management efficiency. Continuous tracking of the full-node status of maintenance work orders for new sites completely records every maintenance handling process, providing data support for the accumulation of maintenance experience for subsequent new sites. After the handling is completed, the operational data of the newly added site is retrieved for verification. By comparing objective operating data, the effectiveness of the handling is confirmed, ensuring that the fault at the new site is truly resolved and avoiding residual faults caused by human error feedback, thus guaranteeing the operational stability of the new site. All handling data from the new site is archived to the operation and maintenance record database, gradually enriching the operational and maintenance data accumulation of the new site. This provides a data foundation for subsequent model iterations and operational and maintenance process optimization, forming a positive cycle from operational and maintenance practice to data accumulation and capability improvement. Figure 3 As shown.

[0030] The model iteration update module collects fault feature data and handling effect data corresponding to all closed-loop work orders of newly added sites, verifies the effectiveness of the handling results, and synchronously updates the fault sample library of the corresponding newly added site to the valid handling samples. When the cumulative number of fault samples of a single newly added site reaches 25, the local model fine-tuning operation on the newly added site side is triggered and new model parameter increments are generated and uploaded to the federated aggregation training module. When the total number of new fault samples of all sites, including the newly added site, reaches 180, a formal training instruction is sent to the federated aggregation training module to trigger a new round of aggregation iteration of the global fault identification model. After the calculation is completed, the global fault identification model adapted to the characteristics of the newly added site is distributed to all connected water and air pollution source online monitoring sites, and the version information of this update and the corresponding training sample coverage are recorded synchronously. After the global fault identification model is updated, the model iteration update module counts the identification and matching of the corresponding fault types of the newly added sites. For fault types in the newly added sites where the handling effect is not as expected, the weight ratio of the corresponding fault samples in the next round of training is automatically increased, and the learning weight coefficient of the corresponding fault features of the global fault identification model is adjusted in the subsequent training process. By collecting fault characteristics and handling effect data from closed-loop work orders of newly added sites, the operation and maintenance practices of these sites can be quickly transformed into model training samples. This enables the rapid accumulation and reuse of operation and maintenance experience for new sites, allowing for direct reuse of relevant experience when similar new sites are connected. Verifying the effectiveness of handling results allows for the selection of valid fault samples applicable to new sites, ensuring the quality of the fault sample library and preventing invalid samples from affecting the local model fine-tuning. Local fine-tuning is triggered when the number of samples for a single new site reaches a set threshold, allowing new sites to quickly complete personalized model adaptation, improving their fault identification accuracy and gaining more tailored identification capabilities without waiting for global model updates. Global aggregation iteration is triggered when the total number of new samples across the entire domain reaches a set threshold, synchronizing typical fault characteristics of new sites to the global model. This allows all connected sites to obtain the corresponding fault identification capabilities, achieving a synchronized improvement in the overall system's operation and maintenance capabilities. Recording version information and sample coverage after model updates clearly traces the process of integrating new site features into the global model, facilitating subsequent effect evaluation and problem localization. By statistically analyzing the fault identification and matching of newly added sites, we can accurately pinpoint the weak links of the global model in the new site scenario. For fault types where the handling effect is not as expected, we can increase the training weights, allowing the model to focus on learning the corresponding fault characteristics in subsequent iterations. This will gradually improve the identification accuracy in the new site scenario, promote the continuous adaptation of the global model to the ever-expanding site scale and equipment types, ensure that the capabilities of the entire operation and maintenance system improve in sync with the site expansion, and support the large-scale and stable expansion of the operation and maintenance system.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A fully digital operation and maintenance management system for an online monitoring system of water and air pollution sources, characterized in that, The system includes: Local data processing module: Collects and retains the monitoring equipment operation data and maintenance record data of each water and air pollution source online monitoring station. After completing the data standardization and time sequence alignment, it outputs real-time operation data to the fault intelligent judgment module and provides standardized samples to the local model fine-tuning. Federated Aggregation Training Module: Receives encrypted model parameter increments uploaded by online monitoring stations of various water and air pollution sources, performs distributed aggregation operations to generate a global fault identification model, and distributes the global fault identification model to the fault intelligent judgment module; The intelligent fault assessment module loads a global fault identification model, identifies abnormal states of online monitoring equipment for water and air pollution sources based on real-time operational data, determines the fault type and handling priority, and pushes the fault information to the work order process management module. Work order process control module: After receiving fault information, it matches maintenance personnel and generates standardized work orders, tracks the entire handling process, and synchronizes the handling data to the model iteration and update module after completing the work order closed-loop verification. Model Iteration Update Module: Collects the processing data of closed-loop work orders and updates the fault sample library. When the set trigger conditions are met, it sends a training instruction to the federated aggregation training module to trigger a new round of model aggregation iteration, thus completing the continuous iteration of the global fault identification model.

2. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, The local data processing module connects to the main control unit of the water and air pollution source monitoring equipment through a standard communication interface to collect the monitoring equipment operation data. The monitoring equipment operation data includes operating condition data and status data. It also connects to the handheld maintenance terminal to collect maintenance record data, which includes on-site handling records, component replacement records, and equipment calibration records. All collected multi-source data are processed for dimensional unification and time sequence alignment to generate a standardized dataset and store it in the local storage unit.

3. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, The federated aggregation training module generates global model parameters through a weighted average aggregation method, calculated using the following formula: , where w g The updated global model parameters; k is the total number of sites participating in this training round; n i w represents the total number of local fault samples at the i-th site. i The local model weight parameters are obtained by fine-tuning the i-th site based on local samples.

4. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, After loading the global fault identification model, the fault intelligent judgment module automatically adjusts the feature weights within the global fault identification model based on the attribute characteristics of each online monitoring station for water and air pollution sources. The attribute characteristics of the online monitoring station include at least one of the following: geographical environment, equipment model, and years of operation. The fault intelligent judgment module matches corresponding weight allocation rules for stations with different attribute combinations and adjusts the contribution ratio of different operating characteristics in the fault judgment process.

5. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, The intelligent fault assessment module determines the fault type through feature vector matching, and the calculation formula is as follows: Where sim is the feature matching degree between real-time operating conditions and fault types; A is a multi-dimensional feature vector extracted from real-time equipment operation data; B is the standard feature vector of the corresponding fault type in the fault feature library; when the feature matching degree reaches the set threshold, the fault intelligent judgment module outputs the judgment result of the corresponding fault type.

6. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, After receiving fault information reported by online monitoring stations of water and air pollution sources, the work order process control module filters the corresponding maintenance personnel according to multi-dimensional matching rules. The matching dimensions include fault handling priority, the service area to which the maintenance personnel belong, and the number of tasks currently in transit. The standardized work order generated by the work order process control module includes at least the location of the faulty site, the fault type, and the handling priority. The work order information is pushed to the matched maintenance personnel through the maintenance terminal, and the current status of the work order is updated to the maintenance management ledger simultaneously.

7. The full-process digital operation and maintenance management system for an online monitoring system for water and air pollution sources according to claim 1, characterized in that, The work order process control module continuously tracks the status of all nodes of the maintenance work order. After the maintenance personnel complete the on-site handling, they upload the on-site handling record and equipment recovery status information through a handheld terminal. After receiving the feedback information, the work order process control module retrieves the monitoring equipment operation data of the corresponding water and air pollution source online monitoring stations for verification. It compares the equipment operating data before and after the handling, and completes the work order closure after confirming that the equipment operating status has returned to the normal range. At the same time, it archives all the data of this handling to the maintenance record database.

8. The full-process digital operation and maintenance management system for an online monitoring system for water and air pollution sources according to claim 1, characterized in that, The model iteration and update module collects fault feature data and handling effect data corresponding to all closed-loop work orders, verifies the validity of the handling results, and synchronously updates the fault sample library of the corresponding water and air pollution source online monitoring stations to the fault sample library of the verified and handled fault samples. When the number of new fault samples at a single station reaches a first set number, the local model fine-tuning operation on the station side is triggered and new model parameter increments are generated and uploaded to the federated aggregation training module. When the total number of new fault samples at all stations in the entire region reaches a second set threshold, a formal training instruction is sent to the federated aggregation training module to trigger a new round of aggregation calculation of the global fault identification model. After the calculation is completed, the updated global fault identification model is distributed to all connected water and air pollution source online monitoring stations, and the version information of this update and the corresponding training sample coverage are recorded synchronously.

9. The full-process digital operation and maintenance management system for an online monitoring system of water and air pollution sources according to claim 1, characterized in that, After each global fault identification model update, the model iteration update module statistically analyzes the identification and matching of fault types at each online monitoring station for water and air pollution sources. For fault types where the treatment effect is not as expected, it automatically increases the weight ratio of the corresponding fault samples in the next round of training and adjusts the learning weight coefficients of the corresponding fault features of the global fault identification model in subsequent training processes.

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