An intelligent security monitoring system for a sewage treatment plant based on multi-source data fusion

Abstract

The present application relates to the technical field of sewage treatment plant security monitoring, and discloses a sewage treatment plant intelligent security monitoring system based on multi-source data fusion, which comprises data acquisition and standardized processing, multi-source data fusion, hierarchical differentiated management and control, emergency linkage and a control module; multi-dimensional monitoring data is acquired through various acquisition terminals, and data regularization is completed through protocol conversion, data cleaning and normalization; core features are extracted through three-level hierarchical fusion, and a classifier is used to analyze and judge security anomalies and output confidence; the control module realizes data storage and query and security situation visualization, divides risk levels according to anomalies, issues partition control instructions, and links emergency modules to complete on-site alarm, video tracking, information pushing and disposal guidance. The present application reasonably allocates security resources, reduces false positives and false negatives, shortens emergency disposal time, and effectively improves the intelligentization and refinement of sewage treatment plant security management and control.

Description

Technical Field

[0001] This invention relates to the field of security monitoring technology for wastewater treatment plants, and more specifically, to an intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion. Background Technology

[0002] With the rapid development of the urban wastewater treatment industry, wastewater treatment plants have large areas and complex functional zones, housing numerous water treatment equipment and hazardous chemical storage facilities. This presents multiple safety hazards, including gas leaks, unauthorized entry, equipment malfunctions, and abnormal liquid levels. Currently, the mainstream security control methods in the industry largely combine independent video surveillance, single environmental sensor monitoring, distributed access control, and regular manual inspections. Various monitoring devices are independently deployed and operate, forming a basic safety monitoring framework capable of displaying basic images and providing alerts for simple parameter exceedances.

[0003] The existing traditional security control model has many drawbacks: First, the communication protocols of various data acquisition devices are not unified, and multi-dimensional data such as video, gas, perimeter detection, equipment operation, and hydrological level are isolated from each other, making it impossible to unify and standardize the data collection and processing, resulting in extremely low data utilization. Second, it relies solely on a single data threshold to determine anomalies, without adopting multi-source data fusion analysis, lacking intelligent judgment logic, and is prone to false alarms and missed alarms, making it impossible to accurately identify complex security anomalies. Third, it does not classify control standards according to the hazard level of the factory area, and uses a uniform monitoring frequency and alarm rules for the entire area, resulting in insufficient supervision of high-risk areas and waste of resources in low-risk areas. Fourth, the anomaly alarm forms are limited, lacking hierarchical scheduling and a systematic emergency response mechanism, relying on manual on-site handling after anomalies, which is cumbersome and has poor timeliness, making it difficult to meet the high-precision, automated, and comprehensive security needs of modern factories.

[0004] This reveals that traditional security systems suffer from poor data interoperability, weak intelligent analysis, a single control model, and lagging emergency response. Traditional solutions are no longer suitable for the refined safety operation and maintenance trends of large-scale wastewater treatment plants, making it difficult to achieve early risk prediction and efficient handling. Therefore, there is an urgent need to develop an intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion. This system should integrate multi-type monitoring data from across the entire area and complete standardized processing, relying on machine learning to achieve intelligent collaborative analysis of anomalies. Combined with hierarchical differentiated control and automated emergency response mechanisms, this system can address the shortcomings of existing technologies and improve the level of intelligent security and operational safety of the plant. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent security monitoring system for sewage treatment plants based on multi-source data fusion, which aims to solve the problems of isolated and fragmented multi-source security data in sewage treatment plants in the current technology, such as high false alarms and missed alarms, lack of unified management and control platform, and lack of hierarchical protection and emergency linkage mechanism.

[0006] This invention proposes an intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion, comprising: The data acquisition and standardization processing module collects various types of data from the acquisition terminal. Relying on the protocol conversion module integrated within the data acquisition and standardization processing module, it parses and transcodes the heterogeneous data output by the acquisition terminal, uniformly converting it into a standard protocol. At the same time, it performs standardization processing on the converted raw data, removing invalid data and unifying the data format. The multi-source data fusion module is connected to the data acquisition and standardization processing module. The multi-source data fusion module uses a machine learning-based multi-source data fusion algorithm to perform hierarchical fusion of various types of standardized data: the multi-source data fusion module integrates the original data based on the bottom-level data-level fusion, extracts key features based on the middle-level feature-level fusion, and uses a pre-trained classifier to collaboratively judge multiple features based on the top-level decision-level fusion, identify abnormal security events and output results. The control module is communicatively connected to the multi-source data fusion module, the hierarchical differentiated management and control module, and the emergency response module. The control module centrally manages the multi-source fusion data and the analysis results, coordinates system scheduling, and issues corresponding management and response instructions. The hierarchical differentiated management and control module is used to receive management and control instructions issued by the control module, classify risk levels according to the safety risk characteristics of each area of ​​the factory, and implement corresponding differentiated security management and control strategies. The emergency linkage module is used to receive alarm linkage commands issued by the control module, link emergency-related equipment and management personnel terminals in the plant area, and initiate an emergency response when a security anomaly is detected.

[0007] Furthermore, the data acquisition and standardization processing module includes the following acquisition terminals: video surveillance camera, hazardous gas sensor, perimeter radar, access control and attendance equipment, equipment operation monitor and hydrological level sensor, used to collect real-time video footage data, gas concentration data, boundary intrusion data, personnel entry and exit data, equipment operation parameter data and hydrological level data of the plant area.

[0008] Furthermore, the protocol conversion module built into the data acquisition and standardization processing module can parse and transcode heterogeneous communication protocols output by different types of acquisition terminals, and uniformly convert them into standard protocols. The standardization processing specifically includes data cleaning and data normalization processing. Data cleaning is used to remove invalid data, redundant data and abnormal interference data from the acquired data, and data normalization processing is used to uniformly map acquired data of different magnitudes and formats to a preset numerical range.

[0009] Furthermore, the hierarchical fusion of the multi-source data fusion module specifically involves: The underlying data-level fusion aligns and stitches together various types of raw data after standardization, retains the original feature information of each data source from the acquisition terminal, and removes duplicate or invalid data. Mid-level feature fusion is based on feature extraction algorithms to extract key features of various types of data, including personnel movement trajectories in video data, concentration anomalies in hazardous gas data, intrusion signal features in perimeter radar data, abnormal parameter features in equipment operation data, and liquid level change features in hydrological liquid level data. The top-level decision-making fusion is based on a pre-trained classifier, which performs correlation analysis and collaborative judgment on the extracted key features, identifies abnormal security events, and outputs judgment results.

[0010] Furthermore, in the multi-source data fusion algorithm, the pre-trained classifier used for top-level decision-level fusion employs a decision tree algorithm or a support vector machine algorithm. This classifier is trained using labeled security anomaly sample data, and the training samples include normal operating data of the factory area and various anomaly event data. Based on the input multi-source key features, the classifier outputs the anomaly event type and judgment confidence level. The anomaly event type includes at least one of the following: unauthorized personnel entry, hazardous gas leakage, equipment malfunction, and excessive hydrological liquid level.

[0011] Furthermore, the control module integrates a data storage unit and a visualization display unit. The data storage unit is used to receive and persistently store multi-source fusion data and analysis results in real time, and supports historical data backtracking and querying; The visualization unit presents the security status of various areas of the factory, equipment operating parameters, abnormal alarm information, and emergency response progress in real time through a graphical interface.

[0012] Furthermore, the control module has a built-in hierarchical scheduling strategy, which automatically determines the risk level of the event based on the type and confidence level of the abnormal security event output by the multi-source data fusion module. The risk level includes high risk, medium risk, and low risk. Based on the risk level, the control module issues control instructions to the graded differentiated management module to adjust the monitoring frequency or alarm threshold, and at the same time issues linkage instructions to the emergency linkage module to activate on-site audible and visual alarms, camera tracking, or personnel notification, so as to realize event-driven graded response and collaborative scheduling.

[0013] Furthermore, the graded differentiated management and control module divides the plant area into high-risk, medium-risk, and low-risk areas based on the safety risk characteristics of each area, combined with the equipment type, material hazard level, personnel activity frequency, and historical security incident data within the area. Among them, the high-risk area includes at least the chemical warehouse, hazardous waste storage area, and chlorination room; the medium-risk area includes at least the production area, sludge dewatering room, and power distribution room; and the low-risk area includes at least the office area, duty room, and plant roads.

[0014] Furthermore, the differentiated security control strategy implemented by the hierarchical differentiated control module is specifically as follows: For high-risk areas, increase the deployment density of data collection terminals, increase the frequency of monitoring, and set strict alarm thresholds; For medium-risk areas, the deployment density and monitoring frequency of conventional data collection terminals are adopted, and conventional alarm thresholds are set. For low-risk areas, reduce the deployment density of data collection terminals, reduce the monitoring frequency, and set lenient alarm thresholds.

[0015] Furthermore, after receiving the alarm linkage command issued by the control module, the emergency linkage module automatically performs at least one of the following operations: The on-site audio-visual alarm equipment is triggered, emitting an audio-visual alarm. Activate the emergency broadcast system to broadcast pre-set evacuation or warning information; Control the associated camera to switch to tracking mode, automatically lock onto and follow abnormal targets; Push alarm information and handling suggestions to the preset management personnel terminals; The factory's emergency response procedures are invoked, and the corresponding emergency operation guidelines are displayed on the management platform.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, through protocol conversion and data standardization, can uniformly integrate various heterogeneous data such as plant video, gas, perimeter, equipment, and hydrological levels, breaking down data silos and significantly improving data utilization efficiency. Relying on a three-layer, multi-source data fusion architecture combined with a machine learning classifier for comprehensive analysis, it abandons the single threshold judgment method, effectively reducing false alarms and missed alarms in security anomalies, and accurately identifying various complex security hazards. Simultaneously, it implements differentiated monitoring and control based on the risk level of different areas within the plant, rationally allocating security resources to strengthen supervision in high-risk areas while avoiding redundant resource consumption in low-risk areas. Furthermore, through a control module, it achieves hierarchical event scheduling, automatically linking various emergency equipment and management personnel terminals to complete emergency responses, reducing manual intervention and shortening anomaly response time. This comprehensively meets the high-precision, automated, and all-encompassing refined security operation and maintenance needs of wastewater treatment plants, effectively improving the overall security protection capabilities and intelligent management level of the plant area. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a functional structure block diagram of an intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion, provided in an embodiment of the present invention. Figure 2 A schematic diagram illustrating the specific process of the intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the division and differentiated management of risk zones in a factory area, provided for an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, such as Figures 1-3 As shown, this embodiment provides an intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion, including: The data acquisition and standardization processing module collects various types of data from the acquisition terminal. Relying on the protocol conversion module integrated within the data acquisition and standardization processing module, it parses and transcodes the heterogeneous data output by the acquisition terminal, uniformly converting it into a standard protocol. At the same time, it performs standardization processing on the converted raw data, removing invalid data and unifying the data format. The multi-source data fusion module is connected to the data acquisition and standardization processing module. The multi-source data fusion module uses a machine learning-based multi-source data fusion algorithm to perform hierarchical fusion of various types of standardized data: the bottom-level data-level fusion integrates the original data, the middle-level feature-level fusion extracts key features, and the top-level decision-level fusion uses a pre-trained classifier to collaboratively analyze multiple features, identify abnormal security events, and output the results. The control module is communicatively connected to the multi-source data fusion module, the hierarchical differentiated management and control module, and the emergency response module. The control module centrally manages the multi-source fusion data and the analysis results, and coordinates system scheduling and issues corresponding management and response commands. The hierarchical differentiated management and control module is used to receive management and control instructions issued by the control module, classify risk levels according to the safety risk characteristics of each area of ​​the factory, and implement corresponding differentiated security management and control strategies. The emergency linkage module is used to receive alarm linkage commands issued by the control module, link emergency-related equipment and management personnel terminals in the plant area, and initiate an emergency response when a security anomaly is detected.

[0020] Specifically, such as Figure 2 As shown, this module utilizes a variety of data acquisition terminals, including video surveillance cameras, hazardous gas sensors, perimeter radar, access control and attendance equipment, equipment operation monitors, and hydrological level sensors, to achieve full-area deployment. The locations of these terminals are rationally arranged according to the security monitoring needs of different functional areas of the wastewater treatment plant. Each terminal undertakes its corresponding monitoring function, conducting real-time data acquisition around the clock. Specifically, video surveillance cameras capture real-time video footage of the entire plant area, collecting dynamic information about the scene; hazardous gas sensors collect real-time concentrations of toxic and harmful gases in the air; perimeter radar accurately detects anomalies in the plant's outer boundary area, quickly identifying illegal intrusions; access control and attendance equipment accurately records personnel entry and exit times, passage areas, and other personnel flow information; equipment operation monitors collect real-time operating parameters such as voltage, speed, and load of various water treatment production equipment within the plant; and hydrological level sensors monitor real-time changes in water levels in areas such as wastewater tanks and regulating tanks. All acquisition terminals synchronously transmit the raw sensing data of different types and formats to the data acquisition and standardization processing module, completing the aggregation of basic data of all dimensions of plant personnel, environment, equipment, boundaries and water bodies. This provides comprehensive, complete and authentic raw data support for subsequent protocol conversion, data standardization and multi-source data fusion and analysis, and builds a basic data acquisition system covering the entire production and operation scenario of the sewage treatment plant.

[0021] Understandably, by configuring multiple types of specialized data acquisition terminals to conduct comprehensive data collection, it is possible to fully cover all monitoring dimensions required for the security management and operation and maintenance of wastewater treatment plants. This overcomes the shortcomings of previous single-device data collection, which had narrow coverage and lacked monitoring dimensions, achieving comprehensive and seamless monitoring of safety hazards in the plant area. Various terminals accurately correspond to different monitoring scenarios to complete dedicated data collection, resulting in highly targeted and accurate data collection. This provides accurate feedback on the actual operating status and safety environment of the plant area, effectively ensuring the authenticity and integrity of the basic data source. Simultaneously, diversified data acquisition modes can simultaneously aggregate multiple core information categories, including personnel management, environmental safety, boundary protection, equipment operation and maintenance, and water body conditions. This enriches the data sample size for backend data processing and intelligent analysis, effectively improving the comprehensiveness and accuracy of subsequent abnormal event identification. Furthermore, the uniformly deployed, all-domain data acquisition terminals can achieve continuous automatic data collection around the clock, replacing a large amount of manual on-site inspection and collection work. This significantly reduces the manual labor intensity of daily security inspections in the plant area, improves the overall efficiency of basic data collection, and lays a solid data source foundation for the intelligent and refined security management system of wastewater treatment plants.

[0022] Specifically, such as Figure 2 As shown, the built-in protocol conversion module serves as the core relay structure for heterogeneous data interoperability. It can specifically adapt to the various differentiated communication protocols carried by different acquisition terminals, such as video, sensing, detection, and industrial control terminals. Through its built-in protocol parsing program, it completes data packet disassembly, command recognition, and format translation, converting all non-uniform heterogeneous data output from various devices into the system's universal standard protocol format, eliminating data transmission barriers between different hardware devices. After completing the unified protocol conversion, the system then performs systematic and standardized processing on the aggregated raw acquisition data, which is divided into two main execution processes: data cleaning and data normalization. Data cleaning relies on preset screening and judgment rules to systematically screen massive amounts of raw data, accurately removing invalid blank data, duplicate and redundant data, and abnormal interference data caused by external environmental interference and instantaneous equipment fluctuations generated during data transmission, retaining only effective monitoring data with practical application value. Data normalization processing addresses the challenges of varying measurement units, wide numerical ranges, and diverse data representation formats in different monitoring data. Through a unified data conversion algorithm, it maps monitoring data of different magnitudes and formats, such as gas concentration, hydrological level, equipment parameters, and intrusion signals, into a fixed numerical range pre-set by the system. This achieves data dimension alignment and format unification, providing a unified computational foundation for various heterogeneous data and enabling smooth integration with the backend multi-source data fusion process.

[0023] Understandably, leveraging protocol conversion structures to achieve unified translation of heterogeneous protocols across multiple terminals can completely solve the problems of data incompatibility and difficult interoperability between different monitoring devices in traditional security systems. This effectively breaks down data silos formed by various monitoring data within the factory area, enabling smooth aggregation and efficient transmission of data collected across the entire region, and significantly simplifying the data connection process for multi-device networking. Data cleaning can filter out inferior data at the data source, effectively avoiding the impact of erroneous data caused by external environmental interference, temporary equipment failures, signal fluctuations, etc., fundamentally improving the purity and validity of overall monitoring data and preventing misleading subsequent security assessments. Data normalization effectively eliminates the differences in magnitude and format between various monitoring data, enabling collaborative computation and correlation analysis of multi-dimensional data that was previously unable to be processed together, greatly reducing the computational difficulty and processing cost of subsequent multi-source data fusion. The entire data standardization and processing workflow enables standardized and unified management of all monitoring data in the plant area, significantly improving the overall data resource utilization rate, ensuring the orderly and accurate implementation of subsequent hierarchical data fusion and intelligent event analysis, and fully adapting to the actual operational needs of centralized processing of multi-category monitoring data in wastewater treatment plants.

[0024] Specifically, such as Figure 2 As shown, the multi-source data fusion module strictly follows a three-layer hierarchical fusion architecture to complete intelligent data analysis and processing, stably receiving various monitoring data that have been standardized by the data acquisition and standardization processing module. During the underlying data-level fusion implementation, the system performs precise temporal alignment and structured splicing of raw data from different dimensions according to data source category and acquisition time node. It fully preserves the original attributes and state characteristics of various data sources, including video surveillance, hazardous gas detection, perimeter radar detection, equipment operation monitoring, and hydrological level monitoring. Simultaneously, it filters out duplicate entries and invalid data without practical analytical value generated during the data integration stage, constructing a unified and complete fused raw dataset, laying a solid foundation for upper-level feature mining. Entering the mid-level feature-level fusion implementation phase, the system invokes a built-in dedicated feature extraction algorithm to perform targeted feature extraction on different types of data. This involves extracting movement trajectory features such as personnel walking, loitering, and climbing from the factory's video data stream; capturing abnormal changes such as sudden increases and continuous exceedances in gas monitoring data; and extracting intrusion signal features such as foreign object intrusion and area anomalies from perimeter radar data. Simultaneously, it extracts abnormal features of equipment parameter deviations and changes in pool liquid levels, eliminating irrelevant interference information and refining core risk characterization information. Finally, top-level decision-level fusion is conducted, calling upon the classifier model trained on previously completed samples. The system performs cross-correlation calculations and comprehensive comparisons of the aggregated core features, combining this with the factory's security operation patterns to determine risk types, accurately identifying various security anomalies and outputting clear judgment results. This completes the implementation of the hierarchical intelligent data analysis process.

[0025] Understandably, the three-tiered, layered, and integrated implementation approach enables the graded and refined processing of data after standardized front-end processing. This preserves the original and valid information of various monitoring data while efficiently simplifying redundant data, effectively reducing the overall data processing load of the system and improving the data processing speed during actual on-site operation. By extracting specific core features corresponding to different monitoring data categories, it can comprehensively capture subtle changes in five dimensions of wastewater treatment plant boundary protection, environmental safety, personnel management, equipment operation and maintenance, and water conditions. This allows for the comprehensive uncovering of hidden and complex safety hazards, compensating for the blind spots of traditional single-dimensional monitoring methods. Relying on a top-level multi-feature linkage and collaborative judgment implementation model, it completely abandons the outdated method of determining risk using a single threshold. By relying on multi-dimensional information comprehensive analysis to determine the safety status, it significantly reduces the probability of false alarms and missed alarms in security monitoring at the implementation level, and significantly improves the accuracy and reliability of identifying abnormal events in the plant area. This layered integration implementation logic aligns with the actual operation and maintenance security scenarios of wastewater treatment plants, making it highly practical. The accurate anomaly analysis results it outputs can directly provide detailed and reliable decision-making basis for the overall scheduling of control modules, hierarchical differentiated management and control, and emergency response execution. This allows the risk prediction and event handling functions of the entire intelligent security system to be performed stably and efficiently, effectively meeting the actual needs of intelligent security management and control in the plant area around the clock.

[0026] Specifically, such as Figure 2As shown, the pre-trained classifier used in the top-level decision-level fusion of the multi-source data fusion module employs either a decision tree algorithm or a support vector machine (SVM) algorithm. The choice between the two can be flexibly made based on the actual monitoring scenario in the factory area. The decision tree algorithm is suitable for classifying multiple categories of abnormal events, supports clear logical reasoning, and facilitates later operation and maintenance debugging. The support vector machine algorithm is suitable for high-dimensional feature data, and can still ensure high classification accuracy even in small-sample training scenarios, meeting the identification needs of some rare abnormal events in the factory area. The classifier training process strictly relies on labeled security anomaly sample data. The training samples comprehensively cover normal operating data and various abnormal event data in the factory area. The normal operating data includes benchmark data such as video footage, gas concentration, and equipment parameters from different time periods and areas. The abnormal event data corresponds to four major categories: unauthorized personnel entry, hazardous gas leakage, equipment malfunction, and excessive hydrological levels. Each type of sample is labeled with the event type, occurrence scenario, feature parameters, and judgment criteria. A combination of manual labeling and machine-assisted verification is used to ensure labeling accuracy. During training, samples are divided into training, validation, and test sets in a 7:2:1 ratio. Model parameters are adjusted using the training set, generalization ability is optimized using the validation set, and recognition accuracy is verified using the test set, until the model's recognition accuracy meets a preset threshold. Specific parameter examples for the training process: when using a decision tree algorithm, the maximum depth is set to 10, and the minimum number of samples per split node is 5; when using a support vector machine algorithm, the RBF kernel function is selected, with penalty parameters C=1.0 and gamma=0.1. Training stops when the model's recognition accuracy on the validation set no longer improves after five consecutive training epochs. At this point, the recognition accuracy on the test set typically reaches over 95%. During classifier operation, multi-source key feature vectors from the mid-level feature fusion output are received. Through node splitting logic of the decision tree or kernel function mapping operations of the support vector machine, the feature vectors are classified and judged, ultimately outputting a clear abnormal event type. The judgment confidence score is calculated based on feature matching degree, and the confidence score corresponds to the reliability of the judgment result, providing a quantitative basis for the hierarchical scheduling of subsequent control modules.

[0027] Understandably, classifiers built using decision trees or support vector machines each have their own advantages, allowing for flexible selection based on the factory monitoring scenario. This ensures accurate classification of multiple types of abnormal events while adapting to complex monitoring scenarios such as small samples and high-dimensional features, thus improving the classifier's applicability and flexibility. The classifier is trained using labeled samples that comprehensively cover both normal and abnormal operating conditions. The samples are highly representative and accurately labeled, effectively improving the model's generalization ability and recognition accuracy. It can accurately distinguish between various single and compound abnormal events, completely solving the problems of high false alarm and false negative rates associated with traditional single-threshold judgment methods. The quantitative indicators output by the confidence level provide a scientific basis for the control module to determine the severity of abnormal events and formulate tiered scheduling strategies, avoiding resource waste caused by blindly initiating emergency response or control measures. Simultaneously, the clear abnormal event type output by the classifier can directly interface with the emergency response module and the tiered differentiated control module, making subsequent actions more targeted and significantly improving the efficiency and rationality of abnormal event handling. The design and training process of the entire classifier are well-suited to the actual security needs of wastewater treatment plants, making it highly practical. It effectively enhances the intelligent judgment capabilities of the multi-source data fusion module, providing core technical support for the precise operation of the entire intelligent security system and significantly improving the intelligence and refinement of plant security management.

[0028] Specifically, such as Figure 2 As shown, the control module integrates an independent data storage unit and a visualization unit, which work together to retain data and display status quo. The data storage unit employs a dual-storage architecture, combining local solid-state storage with cloud-based synchronous backup. It can continuously receive fused data and anomaly analysis results from the multi-source data fusion module, and classify and archive the data according to collection time, functional area of ​​the plant, data type, and anomaly category. This unit uses a time-series data storage format to systematically arrange massive amounts of monitoring data, ensuring long-term, persistent storage of various security data. It also includes a built-in multi-dimensional search and query program, allowing administrators to quickly retrieve corresponding historical monitoring data and past analysis records using various search criteria such as time interval, geographical location, anomaly type, and device number, facilitating historical data backtracking and content verification. The visualization unit is equipped with a graphical interface for configuring the real-world layout of the plant area. It connects to various operational data within the system in real time through data interfaces. It presents the real-time security operation status of different areas of the plant in a variety of forms, such as zoning layout diagrams, data dashboards, and dynamic prompt pop-ups. It also updates the real-time operating parameters of various water treatment equipment, promptly pushes various abnormal alarm prompts through pop-ups, and can also display the progress of on-site emergency response in real time in the form of dynamic flowcharts. The interface supports operations such as clicking on points to view details and switching between area screens, realizing the real-time visualization of all-dimensional security information of the plant area.

[0029] Understandably, the data storage unit, relying on a dual storage mode, effectively avoids problems such as loss or damage of security monitoring data. The standardized classification and archiving method significantly improves the neatness and organization of data, while the long-term persistent storage mode meets the management requirements for the retention and filing of security data in the plant area. The multi-condition historical data backtracking query function facilitates staff to quickly retrieve past data for cause analysis after a safety incident, and also facilitates daily organization of security operation and maintenance ledgers and statistical analysis of plant safety operation data, providing detailed data support for optimizing plant security systems. The visualization unit breaks away from the traditional pure text data viewing mode, transforming complex monitoring data into intuitive and easy-to-understand graphical displays. This allows managers to grasp the overall security dynamics and equipment operation status of the entire plant without leaving the monitoring center, significantly reducing the difficulty of viewing information for daily security management. Simultaneously, this unit can synchronize abnormal alarms and emergency response progress in real time, enabling managers to grasp the dynamics of incident handling immediately, make rapid overall coordination and dispatch decisions, effectively improve the efficiency of daily security management and emergency command and dispatch, further enhance the system's centralized control capabilities, and fully adapt to the routine security operation and maintenance management needs of wastewater treatment plants.

[0030] Specifically, such as Figure 2As shown, the control module is equipped with a comprehensive pre-set hierarchical scheduling strategy. After receiving abnormal security event information from the multi-source data fusion module, it simultaneously extracts two core judgment factors: the specific category of the abnormal event and the judgment confidence level output by the model. Based on the built-in risk level classification rules, it completes the level determination. The system pre-sets multiple sets of confidence level threshold ranges and performs comprehensive value assignment calculations based on the hazard weights of different abnormal events such as personnel intrusion, gas leakage, equipment failure, and excessive liquid levels. This accurately classifies events into three levels: high risk, medium risk, and low risk, clearly defining the risk impact range and degree of harm corresponding to each level. As an example of the mapping between confidence thresholds and risk levels: when the abnormal event type is hazardous gas leakage and the confidence level is ≥0.8, it is judged as high risk; 0.5≤confidence level<0.8 is judged as medium risk; and confidence level<0.5 is judged as low risk. For personnel unauthorized intrusion events, a confidence level ≥0.9 is judged as high risk; 0.7≤confidence level<0.9 is judged as medium risk; and confidence level<0.7 is judged as low risk. In practical applications, the above thresholds can be adjusted according to the factory's safety strategy. After the risk level is determined, the control module sends out corresponding execution instructions through the established communication link. For the graded and differentiated control modules, control instructions are output according to the risk level. For high-risk scenarios, instructions are issued to increase the frequency of encrypted monitoring and tighten alarm thresholds; for medium-risk scenarios, monitoring parameters are adjusted appropriately; and for low-risk scenarios, control instructions are issued to relax monitoring standards. At the same time, tiered linkage instructions are matched and issued to the emergency linkage module. For high-risk events, instructions for audible and visual alarms, target video tracking, and notification of all management personnel are issued simultaneously; for medium-risk events, local alarms and fixed-point tracking are initiated; and for low-risk events, only simple reminder notifications are pushed. The entire process is driven by actual security anomalies, completing orderly graded response and integrated collaborative scheduling among multiple modules.

[0031] Understandably, classifying risk levels based on both the type of abnormal event and the confidence level of the assessment can significantly improve the scientific rigor and accuracy of risk level determination, effectively avoiding misjudgments that occur when relying solely on a single phenomenon. This ensures that the risk classification results are more closely aligned with the actual safety situation on-site. By issuing tiered control adjustment instructions, the monitoring intensity and early warning standards of the plant can be flexibly changed according to the real-time risk dynamics on-site, enabling the security control mode to adapt to the risk status and overcoming the limitations of traditional fixed control modes. The tiered issuance of emergency response instructions allows for matching corresponding levels of response actions based on the severity of the event. This enables a comprehensive and rapid response to major safety hazards while simplifying the handling process for minor anomalies, avoiding excessive coordination that could lead to equipment idleness and wasted manpower. This event-driven collaborative scheduling mode streamlines the coordination logic of various functional modules within the system, making the overall response process more standardized and smoother. It further shortens the overall response time from abnormal event identification to handling, helping managers accurately control the intensity of handling and comprehensively improving the overall operational efficiency of the wastewater treatment plant's security event management and emergency response.

[0032] Specifically, such as Figure 3 As shown, the tiered differentiated management module abandons a single site division method when carrying out area division work, and adopts a multi-dimensional comprehensive evaluation mechanism to complete the definition of risk areas in the factory area. The module pre-enters the overall layout information of the factory area, and then combines the inherent safety risk attributes of the area, the operational hazard coefficient of the production equipment deployed in the site, the flammability, explosiveness and toxicity hazard level of the stored materials, the frequency of daily personnel movement, and integrates historical operation and maintenance data such as the frequency of past security incidents and accident types in the factory area to establish a multi-factor comprehensive scoring model, and defines the risk level boundaries through quantitative scoring. According to the established scoring criteria, the entire wastewater treatment plant was precisely divided into three risk control zones: high, medium, and low. The chemical warehouse, hazardous waste storage area, and chlorination room were designated as high-risk areas due to the storage of large quantities of hazardous chemicals and the high risk of leaks. The production area, sludge dewatering room, and power distribution room, as core production and power supply areas with dense equipment and potential operational safety hazards, were designated as medium-risk areas. The office area, duty room, and plant roads, primarily used for daily personnel passage and administrative work, posed minimal safety risks and were uniformly designated as low-risk areas. After completing the zone division, the module bound the zone attribute information to all data collection terminal points within the plant, creating a complete risk zone distribution ledger. This provides a clear foundation for subsequent targeted deployment of differentiated security and control measures, ensuring that subsequent control strategies can be accurately implemented in each actual site. As an example scoring rule, the following can be used: Risk score = Material hazard level weight × 0.4 + Personnel activity frequency weight × 0.2 + Historical event frequency weight × 0.3 + Equipment failure rate weight × 0.1. A score ≥ 80 is considered a high-risk area, 60-79 is considered a medium-risk area, and < 60 is considered a low-risk area.

[0033] Understandably, relying on a multi-dimensional comprehensive evaluation model to classify risk areas within the plant breaks through the limitations of traditional rough zoning based solely on site usage. By comprehensively considering multiple core elements such as equipment, materials, personnel flow, and historical events, the risk level classification results more closely reflect the actual operational safety status of the wastewater treatment plant, significantly improving the scientific rigor and practicality of the zone classification. The clear and explicit three-tiered risk zone delineation method accurately identifies core safety protection areas within the plant, clearly prioritizing security management work and thoroughly clarifying the focus of security control across the entire plant. The standardized zone division system facilitates data statistics and classification management by the system backend, and also makes it easier for on-site maintenance personnel to conduct daily inspections and equipment maintenance according to zone levels, standardizing daily security management processes within the plant. At the same time, precise regional boundary definition can provide accurate division basis for subsequent differentiated adjustment of the deployment density of collection terminals, monitoring frequency and alarm thresholds. It realizes the rational layout and allocation of security resources from the spatial level, avoids the problem of supervision gaps in key areas and idle waste of resources in ordinary areas from the source, further optimizes the overall security layout structure of the whole plant, and lays a solid foundation for refined zoned security management at the spatial division level.

[0034] Specifically, such as Figure 3As shown, the tiered differentiated control module uses the pre-defined three-tier risk zone division of the plant area as the execution benchmark. After receiving control and adjustment instructions from the control module, it automatically matches the corresponding area to implement a tiered security control strategy, achieving precise adaptation of monitoring configuration to the risk level of the area. For high-risk areas such as pharmaceutical warehouses and hazardous waste storage areas, the system proactively increases the number of video, gas sensor, and detection-type acquisition terminals, reduces the spacing between equipment deployments, shortens the data sampling time interval to increase the frequency of real-time monitoring, and simultaneously tightens the alarm thresholds for various monitoring items, lowering the warning thresholds for parameters such as concentration, liquid level, and equipment operating conditions, achieving comprehensive high-density real-time monitoring with stringent judgment standards. For medium-risk areas such as production areas and power distribution rooms, the system maintains the original number and arrangement of acquisition terminals, uses a fixed conventional data acquisition cycle, and adopts system-preset general alarm thresholds for routine monitoring, maintaining a stable and unified basic security monitoring mode. For low-risk areas such as office areas and factory roads, the system rationally reduces redundant monitoring equipment, lowers terminal deployment density, extends data collection intervals, and simultaneously relaxes alarm thresholds for various monitoring data, enhancing the conditions for anomaly triggering. This lightweight monitoring mode enables basic security monitoring. The module can bind and link control parameters with area information, automatically sending parameter adjustment commands to front-end acquisition devices and autonomously switching monitoring modes, ensuring that different areas always operate with a security monitoring solution adapted to their respective risk levels. As an example of optional values: in high-risk areas, the deployment density of acquisition terminals should be no less than one per 50 square meters, the monitoring frequency no less than 10Hz, and the alarm threshold set to 80% of the normal threshold; in low-risk areas, the deployment density should be no more than one per 200 square meters, the monitoring frequency no more than 1Hz, and the alarm threshold set to 120% of the normal threshold.

[0035] Understandably, by implementing a zoned, differentiated security control strategy, monitoring intensity can be precisely matched to the actual risk level of different areas. Comprehensive and rigorous monitoring can be achieved in high-risk, key protection areas, allowing for the immediate detection of even the slightest safety anomalies. This prevents major safety hazards such as leaks and equipment malfunctions from occurring at the source, effectively strengthening the security defenses of the core areas of the plant. Medium-risk areas utilize conventional monitoring methods, meeting basic safety supervision needs during production operations while ensuring the stable and orderly operation of the plant. Low-risk areas employ lightweight monitoring methods, effectively reducing the power consumption of monitoring equipment, lowering data transmission volume, and alleviating the pressure on backend data processing and storage. This control model scientifically coordinates and allocates plant security hardware resources and maintenance manpower, completely resolving the shortcomings of traditional unified monitoring that results in insufficient supervision of key areas and excessive consumption of security resources in ordinary areas. Meanwhile, the tiered alarm standards can significantly filter out invalid warning information in low-risk areas, reduce false alarms, effectively alleviate the daily assessment workload of management personnel, and comprehensively promote the development of security management in sewage treatment plants towards refinement, rationality, and energy conservation, thereby greatly improving the practicality and economy of the overall security management system.

[0036] Specifically, such as Figure 2 As shown, the emergency response module is equipped with a multi-channel command receiving interface, which can receive various alarm and linkage commands issued by the control module in real time based on the risk level. The module has various emergency execution programs and linkage control logic preset internally, and can autonomously select to execute one or more linkage operations according to the command content. When a trigger command is received, the module can directly drive the sound and light alarm devices deployed on site to start working, and adjust the alarm sound frequency and light flashing mode according to the degree of danger of the event to achieve close-range on-site warning; at the same time, it can retrieve the pre-recorded safety warnings, personnel emergency evacuation and other voice text in the system to drive the emergency broadcast system to complete the fixed-point zone broadcast or the whole plant broadcast. The module can retrieve the associated monitoring cameras in the incident area through the video control protocol, switch to the intelligent tracking working mode with one click, lock the location of abnormal targets with the visual positioning algorithm, control the pan and tilt to automatically adjust the angle to complete real-time tracking and shooting, and continuously transmit the dynamic scene. In addition, the module relies on the intranet communication architecture to accurately push the location of abnormal events, event types, risk levels and matching standardized handling suggestions to the mobile terminal devices such as mobile phones and tablets of management personnel. Finally, the module can access the system's built-in complete emergency response process library for the factory area. The pop-up window on the back-end management platform interface displays the step-by-step emergency operation process, on-site control points, and handling precautions for the corresponding event, realizing an automated operation process from issuing instructions to the simultaneous implementation of multiple types of emergency actions.

[0037] Understandably, relying on an automated, multi-dimensional emergency response mechanism, on-site early warning measures can be quickly activated after a security anomaly occurs, eliminating the need for manual operation and issuing instructions by management personnel. This effectively shortens the emergency response activation time and significantly improves the timeliness of handling emergencies. The combined effect of on-site audible and visual alarms and emergency broadcasts can immediately alert on-site workers to potential safety hazards, enabling them to quickly take evacuation and other actions, minimizing the probability of threats to their personal safety. The automatic tracking function of surveillance cameras can completely record the entire process of an anomaly, allowing management personnel to remotely monitor changes in the situation in real time and retaining complete video data, providing authentic and effective visual evidence for subsequent investigation and liability determination. Targeted push of alarm information and handling plans to management terminals breaks spatial limitations, allowing management personnel to monitor risks anytime, anywhere and quickly coordinate and arrange handling work. The management platform intuitively displays standardized emergency operation procedures, which can unify and standardize the on-site personnel's handling behavior, avoid operational errors due to insufficient handling experience, effectively reduce the occurrence of secondary safety problems, and comprehensively build an automated, standardized, and three-dimensional plant security emergency response system, thereby comprehensively strengthening the wastewater treatment plant's comprehensive emergency response capabilities.

[0038] In a specific embodiment of this application, the above steps are implemented in the following ways: This intelligent security monitoring system is deployed throughout the wastewater treatment plant. Based on the plant's functional layout and risk distribution, the system includes the installation and network debugging of various data acquisition terminals, protocol conversion devices, data processing components, fusion analysis components, centralized control components, zoned management components, and emergency support equipment. Prior to deployment, basic configurations were completed, including risk level classification of the plant area, algorithm model training, monitoring and control threshold configuration, and emergency response program input. After the system is operational, various acquisition terminals collect multi-dimensional monitoring data from the plant area around the clock. This data is then uniformly fed into the data acquisition and standardization processing module, where heterogeneous protocol conversion, data cleaning, and normalization are performed. The normalized data is then sent to the multi-source data fusion module, where three-level hierarchical fusion is used to integrate the data and extract features. Finally, a trained classifier is used to intelligently assess security anomalies and output confidence levels. The control module centrally aggregates various operational data and analysis results, relying on built-in units to achieve persistent data storage, historical backtracking, and visualized security situation display. Simultaneously, it determines the risk level based on the type and confidence level of abnormal events, issuing appropriate monitoring and control instructions to the tiered differentiated management module, enabling each risk area to implement the corresponding level of security control strategy. When a security anomaly is identified, the control module simultaneously issues tiered linkage instructions to the emergency response module, automatically triggering actions such as on-site audible and visual warnings, intelligent video tracking, information push to management personnel, and standardized emergency operation guidelines. The system can automatically complete the entire closed-loop operation of data collection, processing, intelligent analysis, tiered management, and emergency response, adapting to the actual operation and maintenance needs of wastewater treatment plants for daily safety monitoring, hazard early warning, dynamic risk management, and rapid response to sudden emergencies.

[0039] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A smart security monitoring system for wastewater treatment plants based on multi-source data fusion, characterized in that, include: The data acquisition and standardization processing module collects various types of data from the acquisition terminal. Relying on the protocol conversion module integrated within the data acquisition and standardization processing module, it parses and transcodes the heterogeneous data output by the acquisition terminal, uniformly converting it into a standard protocol. At the same time, it performs standardization processing on the converted raw data, removing invalid data and unifying the data format. The multi-source data fusion module is connected to the data acquisition and standardization processing module. The multi-source data fusion module uses a machine learning-based multi-source data fusion algorithm to perform hierarchical fusion of various types of standardized data: the multi-source data fusion module integrates the original data based on the bottom-level data-level fusion, extracts key features based on the middle-level feature-level fusion, and uses a pre-trained classifier to collaboratively judge multiple features based on the top-level decision-level fusion, identify abnormal security events and output the results. The control module is communicatively connected to the multi-source data fusion module, the hierarchical differentiated management and control module, and the emergency response module. The control module centrally manages the multi-source fusion data and the analysis results, coordinates system scheduling, and issues corresponding management and response instructions. The hierarchical differentiated management and control module is used to receive management and control instructions issued by the control module, classify risk levels according to the safety risk characteristics of each area of ​​the factory, and implement corresponding differentiated security management and control strategies. The emergency linkage module is used to receive alarm linkage commands issued by the control module, link emergency-related equipment and management personnel terminals in the plant area, and initiate an emergency response when a security anomaly is detected.

2. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The data acquisition and standardization processing module includes the following acquisition terminals: video surveillance camera, hazardous gas sensor, perimeter radar, access control and attendance equipment, equipment operation monitor and hydrological level sensor, which are used to collect real-time video footage data, gas concentration data, boundary intrusion data, personnel entry and exit data, equipment operation parameter data and hydrological level data of the plant area.

3. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The protocol conversion module built into the data acquisition and standardization processing module can parse and transcode heterogeneous communication protocols output by different types of acquisition terminals, and uniformly convert them into standard protocols. The standardization processing specifically includes data cleaning and data normalization processing. Data cleaning is used to remove invalid data, redundant data and abnormal interference data from the acquired data, and data normalization processing is used to uniformly map acquired data of different magnitudes and formats to a preset numerical range.

4. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The hierarchical fusion of the multi-source data fusion module is specifically as follows: The underlying data-level fusion aligns and stitches together various types of raw data after standardization, retains the original feature information of each data source from the acquisition terminal, and removes duplicate or invalid data. Mid-level feature fusion is based on feature extraction algorithms to extract key features of various types of data, including personnel movement trajectories in video data, concentration anomalies in hazardous gas data, intrusion signal features in perimeter radar data, abnormal parameter features in equipment operation data, and liquid level change features in hydrological liquid level data. The top-level decision-making fusion is based on a pre-trained classifier, which performs correlation analysis and collaborative judgment on the extracted key features, identifies abnormal security events, and outputs judgment results.

5. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, In the multi-source data fusion algorithm, the pre-trained classifier used for top-level decision-level fusion employs a decision tree algorithm or a support vector machine algorithm. This classifier is trained using labeled security anomaly sample data, which includes normal operating data of the factory area and various anomaly event data. Based on the input multi-source key features, the classifier outputs the anomaly event type and judgment confidence level. The anomaly event type includes at least one of the following: unauthorized personnel entry, hazardous gas leakage, equipment malfunction, and excessive hydrological liquid level.

6. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The control module integrates a data storage unit and a visualization display unit. The data storage unit is used to receive and persistently store multi-source fusion data and analysis results in real time, and supports historical data backtracking and querying; The visualization unit presents the security status of various areas of the factory, equipment operating parameters, abnormal alarm information, and emergency response progress in real time through a graphical interface.

7. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The control module has a built-in hierarchical scheduling strategy. Based on the type and confidence level of the abnormal security events output by the multi-source data fusion module, it automatically determines the risk level of the event, which includes high risk, medium risk, and low risk. Based on the risk level, the control module issues control instructions to the graded differentiated management module to adjust the monitoring frequency or alarm threshold, and at the same time issues linkage instructions to the emergency linkage module to activate on-site audible and visual alarms, camera tracking, or personnel notification, so as to realize event-driven graded response and collaborative scheduling.

8. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, The graded differentiated management and control module divides the plant area into high-risk, medium-risk, and low-risk areas based on the safety risk characteristics of each area, combined with the equipment type, material hazard level, personnel activity frequency, and historical security incident data within the area. Among them, the high-risk area includes at least the chemical warehouse, hazardous waste storage area, and chlorination room; the medium-risk area includes at least the production area, sludge dewatering room, and power distribution room; and the low-risk area includes at least the office area, duty room, and plant roads.

9. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 8, characterized in that, The differentiated security control strategy implemented by the hierarchical differentiated control module is as follows: For high-risk areas, increase the deployment density of data collection terminals, increase the frequency of monitoring, and set strict alarm thresholds; For medium-risk areas, the deployment density and monitoring frequency of conventional data collection terminals are adopted, and conventional alarm thresholds are set. For low-risk areas, reduce the deployment density of data collection terminals, reduce the monitoring frequency, and set lenient alarm thresholds.

10. The intelligent security monitoring system for wastewater treatment plants based on multi-source data fusion according to claim 1, characterized in that, Upon receiving an alarm linkage command from the control module, the emergency linkage module automatically performs at least one of the following operations: The on-site audio-visual alarm equipment is triggered, emitting an audio-visual alarm. Activate the emergency broadcast system to broadcast pre-set evacuation or warning information; Control the associated camera to switch to tracking mode, automatically lock onto and follow abnormal targets; Push alarm information and handling suggestions to the preset management personnel terminals; The factory's emergency response procedures are invoked, and the corresponding emergency operation guidelines are displayed on the management platform.

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