Water affair informatization safety monitoring system, method and equipment based on Internet of Things and machine learning

Abstract

The invention provides a water affair informatization safety monitoring system, method and device based on the Internet of Things and machine learning, and belongs to the technical field of water affair monitoring. Through real-time data collection, intelligent early warning of abnormal operation of a water affair system, device health assessment and water volume scheduling optimization are carried out; through combination of multi-source data fusion analysis and a machine learning model, water resource optimization scheduling and equipment energy efficiency management and control are realized. Through a multi-level early warning threshold setting mechanism, an intelligent matching emergency plan library, a multi-mode notification unit and an emergency command decision support unit, and in combination with a historical early warning event tracing analysis interface, identification and response of abnormal events of a water affair system are realized. Classification statistics according to departments, personnel or equipment is supported to generate a visual chart; a machine learning algorithm is utilized to predict future trend change, and hydrological data distribution conditions are displayed on a map based on GIS integration. And the level and efficiency of water affair management are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water service monitoring, and particularly relates to a water service informatization security monitoring system, method and device based on the Internet of Things and machine learning. Background Art

[0002] Water service information is a technical system involved in a series of links such as information collection, transmission, processing, storage, analysis and application in the water service field.

[0003] Water service information is based on sensor technology to monitor information such as pH value, dissolved oxygen, turbidity, and flow rate of water flow in real time. By laying optical fiber cables, each water service monitoring station, control center, etc. are connected to achieve high-speed data transmission and meet the real-time transmission requirements of water service information.

[0004] In related technologies, data collection is often scattered and incomplete. Different types of data may come from different devices and systems, making it difficult to effectively integrate and uniformly manage, resulting in low data utilization rate and inability to provide comprehensive and accurate information support for water service management. Data may face security risks such as being stolen and tampered with during storage and transmission. At the same time, the access authority management for different users of the system is not strict, easily leading to problems such as data leakage and misoperation. In traditional water service systems, the data presentation method is single, and it is difficult for managers to quickly obtain valuable information from a large amount of data, thus affecting the accurate application of water service information. Summary of the Invention

[0005] The present invention provides a water service informatization security monitoring system based on the Internet of Things and machine learning. The system functions cover multiple aspects such as security monitoring, intelligent analysis, early warning and emergency response, operation and maintenance management, and report generation, meeting diverse needs from daily management to emergency handling.

[0006] The system includes: a security monitoring module, an intelligent analysis module, an early warning and emergency module, an operation and maintenance management module, and a report and statistics module; The security monitoring module is used to realize intelligent early warning of abnormal operation of the water service system, equipment health assessment, and water volume scheduling optimization by collecting and analyzing water quality parameters, water supply pipe network pressure and flow rate, reservoir water level and water volume data in real time, and combining equipment operation status monitoring and network situation awareness methods; The intelligent analysis module is used to realize early warning of water quality anomalies, location of pipeline leakage, prediction of water consumption demand, optimization of water resource scheduling, and control of equipment energy efficiency through multi-source data fusion analysis combined with machine learning models; The early warning and emergency module is used to identify, respond to, and handle abnormal events in the water service system by means of a multi-level early warning threshold setting mechanism, intelligent matching of the emergency plan library, multi-modal notification unit, and emergency command decision support unit, and in combination with the historical early warning event traceability analysis interface, so as to form a full-cycle intelligent control display covering pre-event prevention, in-event handling, and post-event review; The operation and maintenance management module is used to record, add, edit, and delete equipment asset information, track equipment status, and manage the asset life cycle; it is also used to create and assign inspection tasks, track task completion, and generate inspection reports; it is also used to create maintenance work orders, record fault details, track work order status, and provide maintenance history queries; it is also used to remotely monitor and control key equipment to achieve equipment parameter adjustment and abnormal handling; and it is used to formulate long-term maintenance plans, set maintenance cycles according to equipment usage, and track the implementation of the plans; The report and statistics module sets the time range, data dimension, and report style according to user requirements to generate customized reports; supports exporting report data; automatically fills data according to a fixed template to generate standardized supervision reports; supports generating visual charts by classifying statistics according to departments, personnel, or equipment; and analyzes and displays the change trends of water quality, flow, and energy consumption data items within a preset time period, and displays historical data trend information.

[0007] Preferably, it further includes: a security and permission management module; The security and permission management module is used to encrypt data storage and transmission using encryption algorithms, detect encryption protocol compliance, and display the data encryption storage and transmission function of the encryption status; based on the role-based access control mechanism, it realizes creating and editing roles, binding users, and dynamically adjusting permissions; records the time, type, and target information of user operation behaviors, supports conditional filtering and log export; has regular automatic backup and manual backup; and hides sensitive information in the exported or shared data by defining desensitization rules.

[0008] Preferably, it further includes: a storage and management module and a backup and recovery module; The storage and management module is used to provide an operation interface for historical data query and retrieval, obtain user login information, and provide an operation interface for data storage and management; through the historical data query option, combined with the time range, site number, and sensor type for query settings, it returns data records that meet the conditions, and also exports the query results as a preset format file; The backup and recovery module supports manual and automatic scheduled backup modes, performs quality checks on hydrological data through outlier detection, duplicate data elimination, and data correction operations; creates a distributed database index, and provides real-time display of storage space.

[0009] Preferably, it further includes: an analysis and visualization module; The analysis and visualization module is used to calculate the mean, variance, extreme values, and generate statistical result tables and charts; use machine learning algorithms to model historical hydrological data and predict future trend changes; based on the correlation relationships of different hydrological variables and detect abnormal data points to display the correlations with a heat map and mark the abnormal points; add data sources, configure layouts and output formats according to requirements, create personalized data reports and generate custom reports; and display the distribution of hydrological data on a map based on GIS integration.

[0010] Preferably, it further includes: a source code screenshot module; The source code screenshot module collects data based on multiple sensors by configuring the IP address, port number, collection frequency, and parameters; supports creating and editing tasks, setting timed or real-time collection modes, and monitoring and displaying task progress; identifies abnormal data by setting abnormal thresholds, provides operations such as ignoring, correcting, and alarming, and records abnormal logs; supports the access of multiple sensors and collection devices, configures device information, communication parameters, tests the connection, and binds tasks or sites; It also verifies the timestamps, formats, and logical relationships of the collected data, provides repair suggestions, and sets the backup frequency and path, supporting remote saving of backup files.

[0011] Preferably, it further includes: a security management and auditing module; The security management and auditing module is used to build a security protection framework covering user identity management, data transmission security, behavior traceability, attack defense, and permission governance by integrating dynamic identity authentication, end-to-end encrypted transmission, full-link operation auditing, real-time threat response, and adaptive access control policies.

[0012] Preferably, it further includes: a management and operation and maintenance module; The management and operation and maintenance module is used to provide an operation interface for registering, deleting, and modifying hydrological monitoring devices and configure relevant parameters; Real-time monitor the performance indicators of the system CPU usage rate and memory occupancy rate, alarm for indicators exceeding the thresholds, and support data export; Responsible for optimizing resource allocation, providing an operation interface for automatically generating or manually adjusting scheduling, migrating tasks of high-load nodes; centrally manage and maintain system configuration files, record version history, compare differences, and support rollback.

[0013] Preferably, it further includes: a monitoring and alarm module and a historical data backtracking and comparison module; The monitoring and alarm module is used to build an integrated monitoring platform covering data visualization, device operation and maintenance, risk warning, and emergency response through a visual dashboard, a dynamic configuration unit, a device status perception and abnormal marking mechanism, a programmable alarm rule engine, and an event full-process processing unit, and realize the identification and hierarchical disposal of abnormal events in the water service system; The historical data backtracking and comparison module is used to provide an operation interface for historical data backtracking analysis, display the historical data curve of the selected time period in the form of a chart, and overlay the current real-time data, that is, display the historical data curve of the selected time period in the form of a chart and overlay the current real-time data; support marking and commenting on abnormal fluctuations.

[0014] The present application also provides a water service informatization security monitoring method based on the Internet of Things and machine learning. The method includes: Step S101: Real-time collect water quality parameters, water supply pipe network pressure and flow rate, reservoir water level and water volume data of the water service system through the Internet of Things sensor network, and synchronously obtain device operation status information and network situation awareness data to form an original water service operation data set; Step S102: Clean and extract features from the original water service operation data set collected in step S101, combine multi-source data fusion analysis with a machine learning model, and perform water quality anomaly warning, pipe network leakage location, water demand prediction, water resource optimal scheduling, and device energy efficiency control; Step S103: Based on the output result of step S102, set an early warning threshold, match an emergency plan library to generate a disposal plan, trigger an early warning through sound and light, text messages, and platform push, and call the historical event database for correlation analysis and decision support; Step S104: Construct a full life cycle management system for device assets, perform device status tracking, inspection task distribution and acceptance, closed-loop management of maintenance work orders, remote parameter regulation, and preventive maintenance plan formulation to achieve traceable management of the operation and maintenance process; Step S105: Generate visual reports on water quality trends, energy consumption statistics, and device failure distributions according to a preset template, support custom time periods, data dimensions, and export formats, and use time series analysis algorithms to output the long-term change rules of water quality parameters, flow fluctuations, and energy consumption efficiency.

[0015] According to another embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the water service informatization security monitoring system based on the Internet of Things and machine learning is implemented.

[0016] From the above technical solutions, the present invention has the following advantages: The present invention provides a water service informatization security monitoring system based on the Internet of Things and machine learning. Through the source code screenshot module, IP addresses, port numbers, etc. are configured based on multiple sensors for data collection, supporting the access of multiple sensors and collection devices, and performing timestamp, format, and logical relationship verification on the collected data. At the same time, the modules cooperate with each other to realize the fusion analysis of multi-source data such as water quality parameters, water supply pipe network pressure and flow, reservoir water level and water volume. The security monitoring module realizes intelligent early warning of abnormal operation of the water service system by collecting and analyzing various data in real time, combining device operation status monitoring and network situation awareness methods, and using technologies such as machine learning. The operation and maintenance management module records, adds, edits, deletes device asset information, tracks device status and conducts asset life cycle management, creates and assigns inspection tasks, tracks task completion status and generates inspection reports, creates maintenance work orders, records fault details, tracks work order status and provides maintenance history queries, and can also remotely monitor and control key devices to realize device parameter adjustment and abnormal handling.

[0017] The security and permission management module uses encryption algorithms to encrypt data storage and transmission, detects the compliance of encryption protocols, realizes the creation and editing of roles, binding of users and dynamic adjustment of permissions based on role-based access control mechanisms, records user operation behaviors, has regular automatic backups and manual backups, and hides sensitive information in exported or shared data. The report and statistics module generates custom reports and standardized supervision reports according to user needs, supports data export and classification statistics to generate visual charts, the analysis and visualization module calculates means, variances, etc. and generates statistical result tables and charts, uses machine learning algorithms to predict future trend changes, and displays the distribution of hydrological data on the map based on GIS integration, etc. It improves the level and efficiency of water service management. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the present invention, the drawings required to be used in the description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic diagram of a water service informatization security monitoring system based on the Internet of Things and machine learning; Figure 2 It is an example diagram of real-time monitoring of water quality parameters of the security monitoring module; Figure 3 It is an example diagram of intelligent detection of pipe network leakage; Figure 4 It is an example diagram of optimal water resource allocation; Figure 5 It is an example diagram of multi-level early warning mechanism configuration; Figure 6 It is an example diagram for generating a custom report in the report and statistics module; Figure 7 It is an example diagram for statistical analysis of hydrological data in the analysis and visualization module; Figure 8 It is an example diagram for resource scheduling and load balancing; Figure 9 It is a schematic diagram of an electronic device. Detailed implementation manners

[0020] The water service informatization security monitoring system based on the Internet of Things and machine learning involved in this application is developed based on programming languages such as Python and JavaScript, and combines modern technology frameworks such as Django and Vue.js to ensure efficient data processing capabilities and a friendly user interaction experience. The core target users of this system include water supply enterprises, sewage treatment plants, flood control and drought relief command centers, and water environment protection agencies, etc. Whether it is real-time monitoring of water quality parameters, optimizing water resource scheduling, or predicting pipe network leakage, the system can significantly improve operation efficiency and reduce potential risks. Its main functions cover multiple aspects such as security monitoring, intelligent analysis, early warning and emergency response, operation and maintenance management, and report generation, meeting diverse needs from daily management to emergency handling. In the field of water service security monitoring, the system realizes real-time monitoring of key indicators such as water quality, pipe network pressure, and reservoir water level through the access of Internet of Things devices and the integration of GIS maps. At the same time, with the help of big data analysis and artificial intelligence technologies, the system can accurately warn of abnormal situations and provide scientific decision-making support. For example, through trend analysis of historical data, the system can help users predict future water consumption, so as to formulate a more reasonable scheduling plan.

[0021] The functions and modules of the water service informatization security monitoring system based on the Internet of Things and machine learning involved in this application will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details.

[0022] It should be understood that when used in the specification of this application, the term "including" indicates the existence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections. The terms "including", "comprising", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0023] Statements such as "an embodiment" or "some embodiments" described in this application mean that the specific features, structures, or characteristics described in the embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different parts of this application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0024] The water affairs informatization security monitoring system can acquire and process associated data based on artificial intelligence technology. Among them, the water affairs informatization security monitoring system can utilize the machine simulation, extension, and expansion of human intelligence by digital computers, and is a theory, method, technology, and application device that can perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0025] The water affairs informatization security monitoring system has both hardware-level technologies and software-level technologies. The basic technologies of the intelligent diagnosis method for numerically controlled machine tools generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of the intelligent diagnosis method for numerically controlled machine tools mainly include computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc. It also has a machine learning function, and the machine learning and deep learning in the method of the present invention generally include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and rote learning.

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 Shown is a schematic diagram of a water affairs informatization security monitoring system based on the Internet of Things and machine learning in a specific embodiment. The system includes: a security monitoring module, an intelligent analysis module, an early warning and emergency module, an operation and maintenance management module, a report and statistics module, a security and permission management module, a storage and management module, a backup and recovery module, an analysis and visualization module, a source code screenshot module, a security management and audit module, a management and operation and maintenance module, a monitoring and alarm module, and a historical data backtracking and comparison module.

[0028] The safety monitoring module of this embodiment is used to realize intelligent early warning of abnormal operation of water system, equipment health assessment and water volume scheduling optimization through real-time collection and analysis of water quality parameters, water supply network pressure and flow, reservoir water level and water volume data, and combined with equipment operation status monitoring and network situation awareness.

[0029] like Figure 2 Specifically, the safety monitoring module can monitor water quality parameters in the water system in real time, including pH, turbidity, residual chlorine, heavy metals, etc. Users can view specific data for each monitoring point through the GIS map interface, and support chart display and historical data comparison.

[0030] The safety monitoring module monitors water supply network pressure and flow. Dynamic tracking of pressure and flow in the water supply network helps users promptly identify anomalies such as leaks or blockages. The system supports multi-dimensional data display, including tables, graphs, and heat maps.

[0031] The safety monitoring module also monitors reservoir water levels and flow. Users can continuously monitor changes in reservoir water levels and flow to ensure they operate within safe limits. The system also provides predictive analysis capabilities to assist users in developing scheduling plans.

[0032] This embodiment also involves equipment status monitoring. Real-time status monitoring is performed on key equipment in the water system (such as pumps, valves, and sensors) to ensure proper operation. Users can access the health of the equipment through an intuitive dashboard.

[0033] The Security Monitoring Module provides cybersecurity situational awareness, identifying cyber threats within the water system and ensuring information security. By analyzing network traffic and system logs, the system can quickly detect intrusions or other unusual activity.

[0034] The intelligent analysis module of this embodiment is used to achieve water quality anomaly warning, pipeline leakage location, water demand prediction, water resource optimization scheduling and equipment energy efficiency management through multi-source data fusion analysis combined with machine learning models.

[0035] The intelligent analysis module conducts multi-dimensional analysis of real-time water quality monitoring data (such as pH value, turbidity, residual chlorine, heavy metal concentration, etc.), combines historical data and machine learning models, automatically identifies potential water quality anomalies, and issues early warning prompts.

[0036] like Figure 3 As shown in the figure, during the intelligent detection of pipeline leakage, the data collected by pressure sensors and flow meters can be used, combined with the pipeline topology and machine learning algorithms, to quickly locate potential leakage areas and provide repair suggestions.

[0037] For water consumption prediction and analysis, based on factors such as historical water consumption data, weather forecasts, and holidays, time series analysis can be used to predict water demand for a period in the future.

[0038] As Figure 4 shown, the intelligent analysis module is also involved in the optimal scheduling of water resources. According to factors such as reservoir water levels, pipe network loads, and user demands, a scientific water resource allocation plan is formulated to ensure the efficient use of limited resources.

[0039] This embodiment can perform energy consumption analysis and optimization. Analyze the energy consumption data of equipment such as water pumps and booster stations, identify inefficient operation links, and propose suggestions for energy conservation and emission reduction.

[0040] The early warning and emergency module of this embodiment is used to realize the identification, response, and disposal of abnormal events in the water service system through a multi-level early warning threshold setting mechanism, intelligent matching of the emergency plan library, multi-modal notification unit, and emergency command decision support unit, and combined with the historical early warning event traceability analysis interface, forming a full-cycle intelligent control display covering pre-event prevention, in-event handling, and post-event review.

[0041] Specifically, the multi-level early warning mechanism configuration is based on the administrator's ability to set multi-level early warning thresholds for different types of abnormal events. For example, changes in pH value in water quality monitoring, pressure drops in water supply pipe networks, or abnormal fluctuations in reservoir water levels can all set multiple early warning levels according to actual needs. Such as level 1 early warning, level 2 early warning, and level 3 early warning.

[0042] As Figure 5 shown, the specific implementation method is: start the early warning and emergency module, and select the parameter types to be monitored (such as pH value, pressure, water level, etc.) in the configuration interface. Enter the specific numerical range of each early warning level according to the actual situation (such as a pH value below 6.5 triggers a level 1 early warning, and below 6.0 triggers a level 2 early warning). After setting, the system will automatically verify the validity of the configuration and prompt whether it is successful.

[0043] During the automatic push of the emergency plan, when the system detects an abnormal event, it will automatically match the corresponding emergency plan according to the preset rules and push it to relevant personnel through text messages, emails, or App notifications. In addition, users can also manually view and download the emergency plan documents for reference.

[0044] Specifically, various emergency plan documents (supporting PDF and Word formats) can be pre-uploaded and maintained in the early warning and emergency module. When an abnormal event is triggered, the system will automatically pop up a prompt window to display the relevant emergency plan link. Users can choose to send it to the designated personnel through text messages, emails, or the App. After receiving the notification, the recipient can directly click on the link to access the detailed emergency plan content.

[0045] This embodiment also relates to the push of early warning information. Based on user-defined multiple notification methods (such as text messages, emails, App notifications, etc.), relevant personnel can be notified in a timely manner when abnormal situations occur. At the same time, group management and priority setting are supported to ensure that important information can be quickly transmitted to key decision-makers.

[0046] Here is based on the notification settings interface. Add recipient information, including mobile phone numbers, email addresses, and App-bound accounts. Select the notification channels to be enabled (text messages, emails, or Apps). Configure the message template, supporting custom titles and body contents. Test the notification function to ensure correct configuration. When an abnormal event is triggered, the system will automatically send notifications according to the set rules.

[0047] When an emergency occurs, this embodiment provides real-time data analysis and visualization support to help commanders quickly formulate response measures. For example, display information such as the affected area, equipment status, and surrounding resource distribution based on the GIS map. In addition, a simulation exercise function is also provided to evaluate the effects of different solutions.

[0048] An optional implementation method is as follows: Based on the emergency command center interface, load the latest GIS map data. Mark the location of abnormal events and related affected areas. View the list of available nearby resources (such as repair vehicles, spare equipment, etc.) and allocate and dispatch them. Use the simulation exercise tool to test the effects of different response strategies. Generate a report on the finally determined solution and distribute it to relevant personnel for implementation.

[0049] The early warning and emergency module of this embodiment has a method for querying historical early warning records. Users can conveniently query past early warning events and their handling processes, so as to summarize experience and lessons and optimize future work processes. Support filtering by multiple conditions such as time range, event type, handling status, etc., and export the results as archived materials.

[0050] The operation and maintenance management module of this embodiment is used to record, add, edit, and delete equipment asset information, track equipment status, and manage the asset life cycle; it is also used to create and assign inspection tasks, track task completion status, and generate inspection reports; it is also used to create repair work orders, record fault details, track work order status, and provide repair history queries; it is also used for remote monitoring and control of key equipment to achieve equipment parameter adjustment and abnormal handling; and it is used to formulate long-term maintenance plans, set maintenance cycles according to equipment usage, and track the implementation of the plans.

[0051] Specifically, the operation and maintenance management module is used to record and manage all device asset information in water facilities. Users can add, edit, delete devices through this function and view the detailed information of the devices (such as device models, installation locations, maintenance histories, etc.). In addition, the system supports device status tracking (normal operation, under repair, scrapped, etc.) and asset lifecycle management.

[0052] The operation and maintenance management module also has a patrol task assignment and execution method. This method can help administrators create and assign patrol tasks and track the completion status of tasks. By setting periodic or ad-hoc patrol plans, operation and maintenance personnel can promptly discover potential problems and take measures. After the patrol is completed, the system will generate a patrol report for management analysis and reference.

[0053] When a device fails, users can create a repair work order through this function, record the details of the failure, and dispatch it to a professional repair team. The system supports work order status tracking (such as pending, in progress, completed) and provides a repair history query function.

[0054] The operation and maintenance management module supports remote device control. Key devices (such as pumps, valves, etc.) are remotely monitored and controlled through the system. For example, adjusting the pressure set value of the pumping station or closing a valve operating abnormally, thereby reducing the need for on-site intervention and improving response efficiency.

[0055] To prevent device failures, the system supports formulating long-term maintenance plans (such as quarterly maintenance, annual overhaul). Administrators can set the maintenance cycle according to the device usage frequency and historical data and track the implementation status of the plan.

[0056] In some embodiments, the report and statistics module sets the time range, data dimensions, and report styles according to user requirements to generate custom reports; supports exporting report data; automatically fills data according to a fixed template to generate standardized supervision reports; supports generating visual charts by classifying statistics according to departments, personnel, or devices; and analyzes and displays the change trends of water quality, flow, and energy consumption data items within a preset time period and displays historical data trend information.

[0057] As Figure 6 shown, specifically, users can generate custom reports according to their needs through this function. It supports setting the time range, data dimensions (such as water quality parameters, flow, pressure, etc.), and report styles (tables, bar charts, line charts, etc.).

[0058] This embodiment has a data export function. Users can export the generated report data in multiple formats (Excel, PDF, CSV, etc.) for offline analysis or sharing. It supports batch exporting of multiple reports and retains the original data accuracy.

[0059] This embodiment can automatically generate standardized reports according to a fixed template. Users only need to select the time range, and the system will automatically fill in the relevant data and generate a report file that meets the specifications.

[0060] For the statistics of performance appraisal indicators, the system can quickly count the performance appraisal indicators of teams or equipment, such as equipment operation efficiency, water consumption prediction accuracy, leakage detection success rate, etc. It supports classification statistics by department, personnel, or equipment and generates visual charts.

[0061] This embodiment also involves the analysis of historical data trends. It can analyze the change trend of a certain data item within a specific time period to help users discover potential problems or optimization opportunities. It supports long-term trend comparison of multiple data sources (such as water quality, flow rate, energy consumption, etc.).

[0062] The security and permission management module is used to encrypt data storage and transmission using encryption algorithms, detect the compliance of encryption protocols, and display the data encryption storage and transmission function of the encryption status; based on the role-based access control mechanism, it realizes creating and editing roles, binding users, and dynamically adjusting permissions; records the time, type, and target information of user operation behaviors, supports conditional filtering and log export; has regular automatic backup and manual backup; hides sensitive information in the exported or shared data by defining desensitization rules.

[0063] This embodiment can be used to ensure that all sensitive data in the system is protected using high-strength encryption algorithms during storage and transmission. Specifically, according to the control instruction, enable the encryption setting sub-item in data security. Configure the parameters of storage encryption (such as AES-256) and transmission encryption (such as TLS 1.3). The system will automatically detect whether the current encryption protocol meets the requirements for the protection of national critical information infrastructure and prompt upgrade suggestions. The encryption status can be viewed on the system health check interface to ensure that all data streams are encrypted. In this way, data leakage and illegal access can be prevented, and the security of the water affairs informatization system can be guaranteed.

[0064] This embodiment can provide a role-based access control (RBAC) mechanism that supports assigning different operation permissions to different users. The specific implementation method is as follows: obtain the control instruction and enter the role management of permission management. Create or edit a role and define the modules, functions, and data ranges that can be accessed (for example, a "water quality monitor" can only view real-time water quality data). Bind the user to the corresponding role or configure additional permissions for specific users separately. Support dynamic adjustment of permissions, and the administrator can change the user's access scope at any time and it will take effect immediately. This function ensures that only authorized users can access sensitive data or perform critical operations, reducing the risks of misoperations and malicious attacks.

[0065] This embodiment can record the complete process of operation logs. It records the operation behaviors of all users in the system for subsequent auditing and problem tracking. The main steps include: Under the log management interface, select the operation log tab. View the log list, which contains detailed information such as time, user, operation type, target module, and result. Support filtering log records by conditions such as date range, user name, operation type, etc. The logs can be exported as CSV files for offline analysis. Through this function, the activities within the system can be comprehensively monitored, and abnormal behaviors can be discovered in a timely manner and measures can be taken.

[0066] The backup and recovery module of this embodiment supports manual and automatic scheduled backup modes. It performs quality checks on hydrological data through outlier detection, duplicate data elimination, and data correction operations; creates a distributed database index, and provides real-time display of storage space.

[0067] For the data backup and recovery mechanism, it provides the ability to perform regular automatic backups and manually triggered backups, and at the same time supports fast recovery. Configure backup policies, including frequency (such as daily / weekly), storage location, and retention period. When performing a manual backup, start the immediate backup to generate the latest snapshot. When data needs to be restored, select the target version from the backup list and confirm the recovery operation. This function ensures that in the event of data loss caused by hardware failures or human errors, the normal operating state can be quickly restored.

[0068] The backup and recovery module of this embodiment can perform data desensitization. Automatically hide sensitive information when exporting or sharing data to protect privacy and compliance. The usage method is as follows: Enter the data desensitization option in the data management interface. Define desensitization rules, such as partially hiding fields such as ID card numbers and mobile phone numbers (such as only showing the first few digits). Apply the rules to the specified data set or report. The exported data has automatically applied the desensitization rules without manual intervention. This function is particularly suitable for scenarios where data needs to be provided to external institutions but sensitive information still needs to be protected.

[0069] This embodiment can also automatically collect multi-source hydrological data. Automatically obtain hydrological data from various sensors and devices. Users can configure and start data collection tasks through the following steps: Enable the data collection management interface. Add new sensors or devices in the data source configuration option, and fill in the IP address, port number, and communication protocol of the device (such as Modbus, OPC, etc.). Configure the collection frequency and collection parameters (such as water level, flow rate, rainfall, etc.). Based on the start instruction, the system will automatically collect data from the specified device according to the set rules. After the data collection is completed, the task status and collection results can be viewed in the collection log. The data collection task scheduling and management method in this embodiment is for users to create, edit, and manage data collection tasks, supporting timed and real-time collection modes. The specific operation method is as follows: Enter the task scheduling interface and execute the creation of a new task. Fill in the task name, target device, collection parameters, and execution time (either a one-time task or a periodic task can be selected). Set the priority and resource allocation strategy to optimize the performance of multi-task concurrent operation. After starting and saving, the task will be added to the scheduling queue. Users can view the task execution progress and status in real time through the task monitoring interface.

[0070] The method for detecting and processing abnormal data in this embodiment can be used to identify and process abnormal data that appears during the collection process to ensure data quality. The execution method is as follows: According to the control instruction, start the abnormal detection interface and set the abnormal threshold (such as upper and lower temperature limits, humidity range, etc.). Compare the collected data with the preset threshold and mark the data that exceeds the range as abnormal. For abnormal data, operations such as "ignore", "correct", or "alarm" can be started. If "correct" is selected, it can be based on entering the correct value or using the system-recommended value. The abnormal records will be saved in the "historical log" for subsequent analysis.

[0071] In this embodiment, the access management method for sensors and collection devices can support the access of multiple sensors and collection devices and provide flexible device management tools. The specific execution method is as follows: Enable the addition of a new device in the device management interface. Enter the basic information of the device, including device type, brand model, MAC address, etc. Configure the communication parameters (baud rate, parity mode, etc.) according to the device manual. Test the device connection to ensure normal communication. Bind the device to a specific collection task or site.

[0072] The data integrity verification and backup in this embodiment are to ensure the integrity and consistency of the collected data and provide a regular backup mechanism. The specific execution method is as follows: Enter the data integrity verification interface and select the data set to be checked. The system will automatically verify the timestamp, format, and logical relationship of the data. If data loss or errors are found, the system will generate repair suggestions. In the data backup interface, set the backup frequency and storage path. The backup files can be remotely saved through FTP or cloud storage. The storage and management module in this embodiment is used to provide an operation interface for querying and retrieving historical data, obtain user login information, and provide an operation interface for data storage and management; through the historical data query option, query settings are combined with the time range, site number, and sensor type to return data records that meet the conditions, and the query results are also exported as files in a preset format.

[0073] In this embodiment, during the historical data query and retrieval process, this function can be used to query and retrieve the stored historical hydrological data. It supports multi-dimensional conditional filtering based on time range, station number, sensor type, etc., to meet the refined data requirements. The operation method is as follows: Enter the storage and management module. Enable the historical data query option. Set the query conditions in the query interface, such as start and end times, station number, or sensor type. The system will return the data records that meet the conditions. Optionally, export the query results as a CSV or Excel format file for subsequent use.

[0074] This embodiment also relates to historical data query and retrieval. It is used to regularly back up hydrological data and perform data recovery when necessary to ensure the security and integrity of the data. It supports two modes: manual backup and automatic scheduled backup.

[0075] Specifically, enter the storage and management module and start the data backup option. Select the time range or specific station data to be backed up in the backup interface. A backup file will be generated and stored in the specified location. If data recovery is required, enable data recovery, upload the backup file, and confirm the recovery target.

[0076] The data quality control in this embodiment can perform quality checks on the collected hydrological data, including operations such as outlier detection, duplicate data elimination, and data correction, to ensure the accuracy and consistency of the data.

[0077] For the data index optimization method, it can be used to improve the data query efficiency. By creating and maintaining distributed database indexes, it accelerates the data retrieval process, especially suitable for large-scale time series data scenarios. The specific implementation method is to enter the storage and management module. View the current index status. If it is found that the query performance has decreased, the index can be rebuilt. Select the table or field to be optimized and perform the index optimization. After the index reconstruction task is completed, refresh the interface to take effect.

[0078] This embodiment can also monitor and expand the storage capacity. It provides real-time monitoring and dynamic expansion capabilities for the storage space, helping administrators understand the current storage usage situation and adjust resource configuration in a timely manner to avoid the problem of insufficient storage space.

[0079] The analysis and visualization module of this embodiment is used to calculate the mean, variance, extreme values, and generate statistical result tables and charts; use machine learning algorithms to model historical hydrological data and predict future trend changes; based on the correlation relationships of different hydrological variables and detect abnormal data points to display the correlation with a heat map and mark the abnormal points; add data sources, configure the layout and output format according to requirements, create personalized data reports and generate custom reports; and display the distribution of hydrological data on the map based on GIS integration.

[0080] The analysis and visualization module of this embodiment supports multi-dimensional statistical analysis of hydrological data, including but not limited to the calculation of indicators such as mean, variance, and extreme values. It can be automatically executed through the following steps: Enable the analysis and visualization module in the navigation bar to enter hydrological data statistical analysis. In the pop-up interface, select the dataset to be analyzed (such as river flow, precipitation, evaporation, etc.). Set the time range (such as the past year, a specific month) and statistical dimensions (such as daily, monthly, or yearly). Automatically calculate and generate statistical result tables and charts. Users can download the generated statistical reports in PDF or Excel format.

[0081] For trend prediction and model analysis, through machine learning algorithms, historical hydrological data can be modeled and future trend changes can be predicted. The specific implementation method is as follows: Enter the trend prediction and model analysis function. Load the target dataset, such as the historical water level records of a certain monitoring station. Configure the prediction parameters, including the prediction time period (such as the next 30 days), model type (such as linear regression, ARIMA model, etc.). Generate prediction curves and related statistical indicators based on the selected model. The results are displayed in the form of a line chart, and users can further adjust the model parameters to optimize the prediction effect.

[0082] In this embodiment, correlation analysis and anomaly detection can help users discover the correlation relationships between different hydrological variables and detect potential abnormal data points.

[0083] This embodiment can also customize report generation. Users can create personalized data reports according to their needs. Add data sources and specify the fields to be included (such as date, station name, observed value, etc.). Configure the report layout, supporting the addition of elements such as tables, charts, and text descriptions. Set the output format (such as PDF, Word, Excel), and select whether to export it as a template for subsequent reuse. After completion of the configuration, the generated file can be previewed and saved.

[0084] This embodiment can also be based on geographic information visualization. With the help of GIS integration technology, it supports intuitively displaying the distribution of hydrological data on the map. Load the base map (such as administrative division map or satellite image). Overlay hydrological monitoring station or regional data on the map, supporting color grading to represent the size of data values. Users can view detailed information of specific locations by dragging and zooming the map. Support exporting the current view as a static picture or a dynamic interactive HTML interface for easy sharing and demonstration.

[0085] The security management and auditing module of this embodiment is used to build a security protection framework covering user identity management, data transmission security, behavior traceability, attack defense, and permission governance by integrating dynamic identity authentication, end-to-end encrypted transmission, full-link operation auditing, real-time threat response, and adaptive access control policies.

[0086] This embodiment can provide a user login verification and permission allocation mechanism to ensure that only authorized users can access system resources. Users access the login interface of the system's security management console through a browser or mobile device. After entering the username and password, the system will call the backend authentication service for identity verification. If the password is incorrect, the system will prompt "Username or password error". The system supports two-factor authentication (such as SMS verification code or Google Authenticator) to further enhance security. Users need to enter the second-level verification information after logging in. According to the user's account type (administrator, ordinary user, etc.), the system will load the corresponding permission list to restrict their access to specific functions. After the user completes the operation, they can safely end the session by clicking the "Logout" button in the upper right corner.

[0087] The security management and auditing module encrypts network communications to prevent sensitive data from being stolen or tampered with during transmission. Specifically, the HTTPS protocol can be used, and all communications between the client and the server are encrypted by SSL / TLS. During the deployment phase, the administrator needs to upload a valid SSL certificate to the server and ensure that it is correctly bound to the domain name. When a user first accesses the system, the browser and the server will automatically complete the key exchange and establish an encrypted connection. The system will add a MAC (Message Authentication Code) to each HTTP request to ensure that the data has not been modified during transmission. Each successful or failed encryption handshake will be logged in the system log for subsequent auditing.

[0088] In terms of operation log auditing, all operation behaviors of users are recorded and detailed log files are generated for security event tracking and problem troubleshooting. Each time a user performs a critical operation (such as logging in, deleting data, modifying configuration, etc.), the system will generate a log record containing the timestamp, operation content, and operator. The logs are divided into three categories: system logs, operation logs, and security logs, which are convenient for administrators to query as needed. The system downloads the log files within a specified time period through the security management console, and the formats supported are CSV and JSON. The system provides built-in log analysis tools that can quickly count the users with frequent operations or abnormal activity patterns.

[0089] The security event monitoring and response of this embodiment can monitor security events in the system in real time and trigger corresponding alarm or automatic response measures.

[0090] Exemplarily, abnormal behaviors can be captured by the built-in security engine, such as multiple login failures, illegal IP access, etc. When a security event is detected, the system will immediately send an email or SMS alert notification to the administrator. For some predefined security events (such as brute force attack attempts), the system can automatically block the relevant IP address or lock the user account. According to the severity level of the event (low, medium, high), the system will classify and process the alerts, and give priority to handling high-risk events.

[0091] This embodiment also relates to the management of data access control policies. Define and manage data access control policies to ensure that sensitive data can only be accessed by authorized personnel. Support dynamic adjustment of access rights according to time, geographical location or device type. For example, external networks are prohibited from accessing internal sensitive data. Before the policy takes effect, the administrator can simulate and test the policy effect to ensure that it will not affect normal business operations. After the policy is created, the system will automatically apply it to all relevant data objects and record the change log. Regularly review and optimize the existing policies to ensure that they comply with the latest security requirements and regulatory standards.

[0092] In some embodiments, the source code screenshot module collects data based on multiple sensors by configuring the IP address, port number, collection frequency and parameters; supports creating and editing tasks, setting timed or real-time collection modes, and monitoring and displaying task progress; identifies abnormal data by setting abnormal thresholds, provides ignore, correction, alarm operations and records abnormal logs; supports the access of multiple sensors and collection devices, configures device information, communication parameters, tests the connection and binds tasks or sites; also performs timestamp, format and logical relationship verification on the collected data, provides repair suggestions, and sets the backup frequency and path, and supports remote saving of backup files.

[0093] The management and operation and maintenance module of this embodiment is used to provide operation interfaces for registering, deleting and modifying hydrological monitoring devices, and configure relevant parameters; monitor the system CPU usage rate, memory occupancy rate and other performance indicators in real time, alarm for indicators exceeding the threshold and support data export; be responsible for optimizing resource allocation, provide an operation interface for automatic generation or manual adjustment of scheduling, and migrate tasks of high-load nodes; centrally manage and maintain system configuration files, record version history, compare differences and support rollback.

[0094] The monitoring and alarm module is used to build an integrated monitoring platform covering data visualization, equipment operation and maintenance, risk warning and emergency response through a visual dashboard dynamic configuration unit, a device status perception and anomaly marking mechanism, a programmable alarm rule engine, and an event full-process processing unit, so as to identify and classify and dispose of abnormal events in the water service system; the historical data backtracking and comparison module is used to provide an operation interface for historical data backtracking analysis, display the historical data curve of the selected time period in the form of a chart, and overlay the current real-time data, and will display the historical data curve of the selected time period in the form of a chart and overlay the current real-time data; support marking and making remarks on abnormal fluctuations.

[0095] In some specific embodiments, the registration, deletion, and modification operations of the hydrological monitoring equipment are completed through the interface. Based on parameters such as the device unique identifier (such as MAC address or SN number), type, and location information. Select the device protocol and configure the communication port and data format. After confirmation, save the device information, and the device will automatically connect to the system and start collecting data. If it is necessary to delete or modify the device, the target device can be found in the list and the corresponding operation can be executed.

[0096] This embodiment can monitor the overall performance of the system in real time, including CPU usage rate, memory occupancy rate, disk I / O situation, etc. Display the performance index chart of the most recent 1 minute, and the viewing range can be adjusted through the time slider (such as the past 1 hour, 1 day). If it is found that a certain index exceeds the preset threshold, the system will automatically trigger an alarm and prompt in red on the interface. Click on the alarm message to view the details. Support exporting the performance monitoring data as an Excel file for subsequent analysis and archiving.

[0097] As Figure 8 shown, for resource scheduling and load balancing, the system resource allocation can be optimized, the load balance between nodes can be ensured, and the overall performance can be improved. Automatically generate a recommended scheduling plan according to the algorithm, or manually adjust the task distribution strategy. For high-load nodes, part of the tasks can be selected to be migrated to low-load nodes and the migration is confirmed. After the scheduling is completed, the system will recalculate the load distribution and update the interface display to ensure that the new plan takes effect.

[0098] This embodiment can also configure management and version control. Help the administrator centrally manage and maintain the system configuration files and version history. When adding or modifying the configuration, fill in the parameter name, value, and description information. This embodiment can view the historical versions, compare the differences between different versions, and quickly locate the changed content. Support rolling back to a specified version to avoid problems caused by incorrect configuration.

[0099] In the online upgrade and dynamic expansion mode, the system realizes non-stop upgrade and dynamic expansion capabilities, ensuring business continuity. The system automatically detects version compatibility and lists the upcoming updates. When performing an upgrade, the system restarts each node one by one to ensure that the normal service is not affected during the whole process. For newly added hydrological monitoring points, deployment can be completed by simply entering relevant information in the "Dynamic Expansion" interface without manual intervention.

[0100] For the monitoring and alarm module, this function can be used to customize the monitoring dashboard and select the hydrological data indicators to be displayed (such as flow rate, water level, rainfall, etc.). The hydrological parameters that can be monitored (multiple selections are supported). Drag and adjust the position and size of the dashboard components, and set the display style of each component (line chart, bar chart or digital display).

[0101] When monitoring the device status, the running status of the devices connected to the system (such as sensor battery level, communication connection status, etc.) can be viewed in real time, and the abnormal devices can be marked. The system will display an overview of the status of all devices, including online / offline status, remaining battery power, signal strength and other information. If an abnormal device (such as offline or low battery) is found, the device details button can be used to further troubleshoot the problem. Support for locating the device position through the map view, which is convenient for on-site maintenance personnel to find the target device. In addition, the alarm threshold for the device status can be set, and a notification will be automatically triggered when the device status exceeds the range.

[0102] The alarm rule settings in this embodiment can define various alarm conditions and notification methods in this function to ensure timely response to abnormal situations. Alarm conditions can also be set (such as water level exceeding a certain threshold, flow rate changing too fast, etc.), and single conditions or combinations of multiple conditions can be selected. Specify the alarm notification methods (text message, email, system message, etc.) and the recipient group. When the system detects an abnormality, an alarm event will be generated and managed and processed through this function. For important alarms, it can be directly jumped to the relevant monitoring interface for in-depth analysis. The system will record the processing status of each alarm, which is convenient for subsequent auditing and summarizing experience.

[0103] Regarding the historical data backtracking and comparison method, this function can be used to backtrack the hydrological data for a period of time in the past and compare it with the current data for analysis. The system will display the historical data curve for the selected time period in the form of a chart and overlay the current real-time data. Support for marking and commenting on abnormal fluctuations, which is convenient for subsequent research and decision-making reference. The comparison results can also be exported in PDF or Excel format for external sharing or archiving.

[0104] This application also provides a water service informatization security monitoring method based on the Internet of Things and machine learning. The method includes: Step S101: Real-time collect the water quality parameters, water supply pipe network pressure and flow rate, reservoir water level and water volume data of the water service system through the Internet of Things sensor network, synchronously obtain the equipment operation status information and network situation awareness data, and form the original water service operation dataset.

[0105] Step S102: Clean and extract features from the original water service operation dataset collected in Step S101, combine multi-source data fusion analysis with machine learning models, and perform water quality anomaly warning, pipe network leakage location, water consumption demand prediction, water resource optimal scheduling, and equipment energy efficiency control.

[0106] Step S103: Based on the output result of Step S102, set the warning threshold, match the emergency plan library to generate a disposal plan, trigger the warning through sound and light, text messages, and platform push, and call the historical event database for correlation analysis and decision support.

[0107] Step S104: Construct a full life cycle management system for equipment assets, perform equipment status tracking, inspection task dispatch and acceptance, closed-loop management of maintenance work orders, remote parameter regulation, and formulation of preventive maintenance plans, and realize traceable management of the operation and maintenance process.

[0108] Step S105: Generate visual reports on water quality trends, energy consumption statistics, and equipment failure distributions according to a preset template, support custom time periods, data dimensions, and export formats, and use time series analysis algorithms to output the long-term change laws of water quality parameters, flow fluctuations, and energy consumption efficiency.

[0109] As Figure 9 shown, the present application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored on the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of the power transmission project GIM model parsing and loading method.

[0110] In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0111] In the embodiments of the present application, the processor 101 may be implemented by using at least one of an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented in a controller. For a software implementation, an implementation of a process or function may be implemented with a separate software module that allows performing at least one function or operation. The software code may be implemented by a software application (or program) written in any suitable programming language. The software code may be stored in a memory and executed by the controller.

[0112] The display module 103 is configured to display information input by a user or information provided to the user. The display module 103 may include a display panel, and the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0113] The memory 102 may be used to store software programs and various data. The memory 102 may include a high-speed random access memory, and may further include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0114] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information security monitoring system for water utilities based on the Internet of Things and machine learning, characterized in that, Including: A safety monitoring module, an intelligent analysis module, an early warning and emergency module, an operation and maintenance management module, and a report and statistics module; The safety monitoring module is used to realize intelligent early warning of abnormal operation of the water service system, equipment health assessment, and water volume scheduling optimization by collecting and analyzing water quality parameters, water supply network pressure and flow, reservoir water level and water volume data in real time, and combining equipment operation status monitoring and network situation awareness methods; The intelligent analysis module is used to realize early warning of water quality anomalies, location of pipeline leakage, prediction of water use demand, optimization of water resource scheduling, and control of equipment energy efficiency through multi-source data fusion analysis combined with machine learning models; The early warning and emergency module is used to realize the identification, response, and disposal of abnormal events in the water service system through a multi-level early warning threshold setting mechanism, intelligent matching of emergency plan libraries, multi-modal notification units, and emergency command decision support units, and combining the historical early warning event traceability analysis interface, forming a full-cycle intelligent control display covering pre-event prevention, in-event disposal, and post-event review; The operation and maintenance management module is used to record, add, edit, and delete equipment asset information, track equipment status and conduct asset life cycle management; it is also used to create and assign inspection tasks, track task completion status and generate inspection reports; it is also used to create maintenance work orders, record fault details, track work order status and provide maintenance history queries; it is also used to remotely monitor and control key equipment to realize equipment parameter adjustment and abnormal handling; and it is used to formulate long-term maintenance plans, set maintenance cycles according to equipment usage and track the implementation of the plans; The report and statistics module sets the time range, data dimension, and report style according to user needs to generate custom reports; supports exporting report data; automatically fills data according to a fixed template to generate standardized supervision reports; supports generating visual charts by classifying and statistics according to departments, personnel, or equipment; And analyze and display the change trends of water quality, flow, and energy consumption data items within a preset time period, and display historical data trend information.

2. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: A safety and permission management module; The safety and permission management module is used to encrypt and store and transmit data using encryption algorithms, detect the compliance of encryption protocols, and display the data encryption storage and transmission function of the encryption status; based on a role-based access control mechanism, it realizes creating and editing roles, binding users, and dynamically adjusting permissions; records the time, type, and target information of user operation behaviors, supports conditional filtering and log export; has regular automatic backup and manual backup; hides sensitive information in exported or shared data by defining desensitization rules.

3. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that It also includes: A storage and management module and a backup and recovery module; The storage and management module is used to provide an operation interface for historical data query and retrieval, obtain user login information, and provide an operation interface for data storage and management; through the historical data query option, combined with the time range, site number, and sensor type for query settings, return data records that meet the conditions, and export the query results to a preset format file; The backup and recovery module supports manual and automatic scheduled backup modes, performs quality checks on hydrological data through outlier detection, deduplication, and data correction operations; creates distributed database indexes, and provides real-time display of storage space.

4. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: The analysis and visualization module; The analysis and visualization module is used to calculate the mean, variance, and extreme values, and generate statistical result tables and charts; Use machine learning algorithms to model historical hydrological data and predict future trend changes; based on the correlation relationships of different hydrological variables, detect abnormal data points, and display the correlations and mark the abnormal points in a heat map; Add data sources, configure layouts and output formats according to requirements, create personalized data reports and generate custom reports; and display the distribution of hydrological data on the map based on GIS integration.

5. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: The source code screenshot module; The source code screenshot module collects data based on multiple sensors by configuring the IP address, port number, collection frequency, and parameters; supports creating and editing tasks, setting scheduled or real-time collection modes, and monitoring and displaying task progress; Identify abnormal data by setting abnormal thresholds, provide operations such as ignoring, correcting, and alarming, and record abnormal logs; support the access of multiple sensors and collection devices, configure device information, communication parameters, test connections, and bind tasks or sites; It also performs timestamp, format, and logical relationship verification on the collected data, provides repair suggestions, and sets the backup frequency and path, and supports remote saving of backup files.

6. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: The security management and auditing module; The security management and auditing module is used to build a security protection framework covering user identity management, data transmission security, behavior traceability, attack defense, and permission governance by integrating dynamic identity authentication, end-to-end encrypted transmission, full-link operation auditing, real-time threat response, and adaptive access control policies.

7. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: The management and operation and maintenance module; The management and operation and maintenance module is used to provide operation interfaces for registering, deleting, and modifying hydrological monitoring devices, and configure relevant parameters; Real-time monitor the CPU usage rate and memory occupancy performance indicators of the system, alarm for indicators exceeding the threshold, and support data export; Responsible for optimizing resource allocation, providing operation interfaces for automatic generation or manual adjustment of scheduling, migrating tasks of high-load nodes; centrally manage and maintain system configuration files, record version history, compare differences, and support rollback.

8. The water service informatization security monitoring system based on the Internet of Things and machine learning according to claim 1, characterized in that, It also includes: The monitoring and alarm module and the historical data backtracking and comparison module; The monitoring and alarm module is used to build an integrated monitoring platform covering data visualization, device operation and maintenance, risk warning, and emergency response through a visual dashboard, dynamic configuration unit, device status perception and abnormal marking mechanism, programmable alarm rule engine, and event full-process processing unit, and realize the identification and hierarchical disposal of abnormal events in the water service system; The historical data backtracking and comparison module is used to provide an operation interface for historical data backtracking analysis, display the historical data curve of the selected time period in the form of a chart, and overlay the current real-time data, and will display the historical data curve of the selected time period in the form of a chart and overlay the current real-time data; support marking and commenting on abnormal fluctuations.

9. A water service informatization security monitoring method based on the Internet of Things and machine learning, characterized in that, A method is used to implement the water service informatization security monitoring system based on the Internet of Things and machine learning as described in any one of claims 1 to 8; The method includes: Step S101: Real-time collect water quality parameters, water supply pipe network pressure and flow, reservoir water level and water volume data of the water service system through the Internet of Things sensor network, synchronously obtain device operation status information and network situation awareness data, and form an original water service operation data set; Step S102: Clean and extract features from the original water service operation data set collected in step S101, combine multi-source data fusion analysis with a machine learning model, and perform water quality anomaly early warning, pipe network leakage location, water use demand prediction, water resource optimal scheduling, and device energy efficiency control; Step S103: Based on the output result of step S102, set an early warning threshold, match the emergency plan library to generate a disposal plan, trigger an early warning through sound and light, text messages, and platform push, and call the historical event database for correlation analysis and decision support; Step S104: Construct a full life cycle management system for device assets, perform device status tracking, inspection task dispatch and acceptance, closed-loop management of maintenance work orders, remote parameter regulation, and preventive maintenance plan formulation, and realize traceability management of the operation and maintenance process; Step S105: Generate visual reports on water quality trends, energy consumption statistics, and device failure distributions according to a preset template, support custom time periods, data dimensions, and export formats, and use time series analysis algorithms to output the long-term change laws of water quality parameters, flow fluctuations, and energy consumption efficiency.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, it implements the water service informatization security monitoring method based on the Internet of Things and machine learning as described in any one of claim 9.

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