Water quality monitoring data management method and system

Abstract

The invention relates to a water quality monitoring data management method and system, and the method comprises the steps: obtaining system configuration information and initialization parameters of water quality monitoring equipment, and carrying out the communication connection with the water quality monitoring equipment, and obtaining an equipment connection state; acquiring a preprocessing control instruction according to the equipment connection state, and executing a water sample collection preprocessing operation to obtain preprocessing state data; obtaining water sample measurement data collected by water quality monitoring equipment, and performing correlation analysis on the water sample measurement data and the pre-processing state data to obtain a water quality monitoring result; acquiring operation environment parameters of the water quality monitoring equipment, and performing equipment analysis in combination with the equipment connection state to obtain an equipment operation state; comprehensively judging the water quality monitoring result based on the equipment connection state, the equipment operation state and the preprocessing state to obtain identification data; and performing scheme construction according to the equipment connection state and the equipment operation state to obtain a comprehensive data management scheme. According to the invention, the whole-process monitoring of the water sample collection process can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a water quality monitoring data management method and system. Background Art

[0002] As an important part of environmental protection, water quality monitoring plays a key role in ensuring water environment quality and public health. With the continuous development of water quality monitoring technology, automated online monitoring systems have gradually become the main way of water quality monitoring. How to achieve efficient management of monitoring equipment and accurate data collection has become one of the key issues in current research. Existing online water quality monitoring systems usually adopt a combination of PLC and central control system. This separate control architecture has limitations in many aspects. First, the modules of the system are independent of each other, and information interaction depends on multiple communication operations, which not only increases the communication load, but also affects the real-time and stability of data collection. Secondly, the existing system has limited control capabilities for analytical instruments, and can often only achieve basic data collection, and cannot deeply set and adjust instrument parameters. In addition, due to the lack of a unified data management platform, the operating data and status information of each device are stored in a decentralized manner, making it difficult to achieve centralized management and comprehensive analysis of data. These problems seriously restrict the monitoring efficiency and data quality of the water quality monitoring system, and there is an urgent need for an intelligent monitoring solution that can achieve unified equipment management and centralized data processing. Summary of the invention

[0003] The main purpose of the present invention is to provide a water quality monitoring data management method and system, which can realize the whole process monitoring of water sample collection and ensure the accuracy and traceability of monitoring data.

[0004] To achieve the above object, the present invention provides a water quality monitoring data management method, comprising: Obtaining system configuration information and initialization parameters of a water quality monitoring device, and establishing a communication connection with the water quality monitoring device to obtain a device connection status; Obtaining preprocessing control instructions according to the device connection status, and performing water sample collection preprocessing operations to obtain preprocessing status data; Acquire the water sample measurement data collected by the water quality monitoring device, and perform correlation analysis with the pre-processing state data to obtain the water quality monitoring result; Obtaining the operating environment parameters of the water quality monitoring device, and performing device analysis in combination with the device connection status to obtain the device operating status; Comprehensively determine the water quality monitoring result based on the device connection status, device operation status and pre-treatment status to obtain identification data; A plan is constructed according to the device connection status and the device operation status to obtain a comprehensive data management plan.

[0005] Furthermore, the obtaining of system configuration information and initialization parameters of the water quality monitoring device, and establishing a communication connection with the water quality monitoring device to obtain a device connection status includes: Identify the communication interface of the water quality monitoring equipment to obtain a list of RS232 interface devices and a list of RS485 interface devices; Perform communication parameter configuration according to the RS232 interface device list and the RS485 interface device list to obtain the system configuration information; Parsing the water quality monitoring equipment using the Modbus-RTU communication protocol to obtain a mapping table of equipment function codes and register addresses; Perform a communication test according to the system configuration information and the register address mapping table to obtain device communication response information; Performing hierarchical management processing on the device communication response information and initializing communication parameters to obtain the initialization parameters; Perform communication connection analysis with the water quality monitoring device according to the initialization parameters to obtain the device connection status.

[0006] Furthermore, the preprocessing control instruction is obtained according to the device connection status, and the water sample collection preprocessing operation is performed to obtain the preprocessing status data, including: Performing communication quality evaluation on the device connection status to obtain communication quality parameters; Performing a preprocessing mode determination on the water quality monitoring device according to the communication quality parameters to obtain a preprocessing working mode and parameters; Performing control instruction parsing and combining on the preprocessing working mode and parameters to obtain a valve control sequence and a pump group control sequence; Generate an execution timing table according to the valve control sequence and the pump group control sequence to obtain a preprocessing execution step; Performing timing execution parameter analysis on the preprocessing execution steps to obtain step operation parameters; The preprocessing process data is obtained according to the step operation parameters, and multi-dimensional analysis is performed to obtain the preprocessing state data.

[0007] Furthermore, the obtaining of the water sample measurement data collected by the water quality monitoring device and the correlation analysis with the pre-processing state data to obtain the water quality monitoring result includes: Performing standard conversion on the original measurement data collected by the water quality monitoring equipment to obtain the water sample measurement data; Performing validity detection on the water sample measurement data according to preset standard measurement parameters to obtain valid measurement data; Classifying and counting the effective measurement data to obtain measurement index data; Performing data matching processing on the measurement index data and the preprocessing state data to obtain matching relationship data; Performing data association on the measurement results according to the matching relationship data to obtain association analysis data; A comprehensive data evaluation is performed on the correlation analysis data to obtain a water quality monitoring evaluation result.

[0008] Furthermore, the acquisition of the operating environment parameters of the water quality monitoring device and the device analysis in combination with the device connection status to obtain the device operating status include: Classify and split the operating environment parameters to obtain power supply parameters, temperature and humidity parameters, and equipment operating parameters; Performing voltage fluctuation monitoring on the power supply parameters to obtain power supply fluctuation data; Performing stability analysis on the power supply parameters according to the power supply fluctuation data to obtain power supply operation status information; Performing environmental adaptability matching on the device connection state according to the temperature and humidity parameters to obtain a temperature and humidity influence coefficient; Performing fault diagnosis and performance evaluation on the working parameters of the equipment to obtain working operation parameters; A multi-dimensional fusion analysis is performed based on the power supply operation status information, the temperature and humidity influence coefficients and the working operation parameters to obtain the equipment operation status.

[0009] Furthermore, the fault diagnosis and performance evaluation of the equipment operating parameters to obtain the operating parameters include: Analyzing the communication status and signal strength of the device operating parameters to obtain a device communication quality characteristic value; Performing equipment operation analysis on the equipment operating parameters to obtain equipment operation characteristic values ​​including power supply status, equipment status and environmental status; Performing fault alarm analysis on the operating parameters of the equipment to obtain fault characteristic values ​​including communication fault, equipment fault and environmental fault; Performing performance attenuation calculation on the equipment operating parameters according to the equipment operation characteristic values ​​to obtain an equipment performance attenuation curve; Performing correlation analysis on the fault characteristic value according to the equipment communication quality characteristic value and the equipment operation characteristic value to obtain fault diagnosis results and fault type distribution data; Performing piecewise fitting calculation on the equipment performance attenuation curve according to the fault type distribution data to obtain equipment performance evaluation data; Thresholding the equipment performance evaluation data to obtain equipment warning level data; The fault diagnosis result, the equipment performance evaluation data and the equipment warning level data are weightedly calculated to obtain the working operation parameters.

[0010] Furthermore, the water quality monitoring result is comprehensively determined based on the device connection status, device operation status and pre-treatment status to obtain identification data, including: Combining the water quality monitoring result with the power failure status to obtain a power data identifier; Combining the water quality monitoring result with the instrument status to obtain an instrument fault data identifier; The water quality monitoring results are combined with the key parameters of the instrument to obtain the upper limit and lower limit data identification; Combining the water quality monitoring result with the device connection status to obtain a device offline data identifier; The water quality monitoring result is combined with the pretreatment state to obtain a no water sample and manual start data identification; Performing temperature compensation correction on the water quality monitoring result to obtain a temperature-corrected water quality monitoring result; Performing spike recovery processing according to the water quality monitoring results to obtain a spike recovery rate; Performing parallel sample measurements on water samples according to the water quality monitoring results to obtain parallel sample data; The power supply data identifier, the instrument fault data identifier, the over-upper limit and over-lower limit data identifiers, the equipment offline data identifier, the spiked recovery rate and the parallel sample data are integrated and corrected to obtain the data identifier.

[0011] Furthermore, the scheme is constructed according to the device connection status and the device operation status to obtain a comprehensive data management scheme, including: Extracting parameters of the device connection state and the device operation state to obtain a device state parameter matrix; Classifying the device state parameter matrix hierarchically to obtain a multi-level data structure; Performing operation mode judgment on the multi-level data structure to obtain an operation mode judgment result; Performing equipment pre-control construction according to the operation mode judgment result to obtain a pre-processing control plan; Performing electrode calibration on the multi-level data structure according to the preprocessing control scheme to obtain an electrode calibration scheme; Performing environmental compensation analysis on the electrode calibration scheme to obtain an environmental compensation coefficient; Performing data management on the multi-level data structure according to the operation mode judgment result to obtain an operation data management plan; The pre-processing control scheme, the electrode calibration scheme and the operation data management scheme are integrated according to the environmental compensation coefficient to obtain the comprehensive data management scheme.

[0012] The present invention also provides a water quality monitoring data management system, which is applied to any one of the water quality monitoring data management methods described above, comprising: A collection module, the collection module is used to obtain system configuration information and initialization parameters of the water quality monitoring device, and communicate with the water quality monitoring device to obtain the device connection status; An analysis module, the analysis module is used to obtain a pre-processing control instruction according to the device connection status, and perform a water sample collection pre-processing operation to obtain pre-processing status data; A correlation module, the correlation module is used to obtain the water sample measurement data collected by the water quality monitoring equipment, and perform correlation analysis with the pre-processing state data to obtain a water quality monitoring result; A processing module, the processing module is used to obtain the operating environment parameters of the water quality monitoring device, and perform device analysis in combination with the device connection status to obtain the device operating status; A control module, the control module is used to make a comprehensive judgment on the water quality monitoring result based on the device connection state, device operation state and pre-processing state to obtain identification data; An execution module is used to construct a solution according to the device connection status and the device operation status to obtain a comprehensive data management solution.

[0013] The present invention provides a water quality monitoring data management method and system, which has the following beneficial effects: Through the unified communication connection management mechanism, the system can grasp the connection status of each device in real time, discover and handle communication anomalies in time, and improve the stability and reliability of system operation. Based on the correlation analysis of pre-processing state data and measurement data, the whole process of water sample collection is monitored, ensuring the accuracy and traceability of monitoring data. By integrating the sensor calibration function into the central control system, it not only simplifies the system structure, but also expands the sensor access capability, breaks through the channel limitation of the traditional electrode controller, and improves the scalability of the system. Combined with the real-time monitoring of operating environment parameters, the system can identify equipment anomalies and make adjustments in time to ensure the normal operation of the monitoring equipment. Based on a unified data management solution, the centralized storage and management of information such as measurement data, operating status, and operation logs are realized, which facilitates the comprehensive analysis and historical tracing of data. By establishing a complete data management system, the system can flexibly adapt to different types of water quality monitoring needs and provide reliable technical support for water environment monitoring. This integrated management solution not only improves the operating efficiency of the system, but also reduces maintenance costs, providing a new technical path for the intelligent development of water quality monitoring systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a water quality monitoring data management method is provided for the present invention; Figure 2 It is a device data management block diagram of a water quality monitoring data management method provided by the present invention; Figure 3 It is a structural diagram of a water quality monitoring data management system provided by the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1-Figure 2 As shown, the present invention provides a water quality monitoring data management method, comprising: Step S1: Obtain system configuration information and initialization parameters of the water quality monitoring device, and establish a communication connection with the water quality monitoring device to obtain the device connection status; Step S2: obtaining a preprocessing control instruction according to the device connection status, and performing a water sample collection preprocessing operation to obtain preprocessing status data; Step S3: Obtain water sample measurement data collected by the water quality monitoring equipment, perform correlation analysis with the pre-processing state data, and obtain water quality monitoring results; Step S4: performing sensor comprehensive calibration on the water quality monitoring result based on the device connection status to obtain the device connection status; Step S5: Obtain the operating environment parameters of the water quality monitoring equipment, and perform equipment analysis in combination with the equipment connection status to obtain the equipment operating status; Step S6: construct a plan based on the device connection status and the device operation status to obtain a comprehensive data management plan.

[0019] Based on the above steps, the detailed process is as follows: Step S1: The central control system collects and stores the system configuration information of each device, including basic configuration data such as device address, communication parameters, and operation mode, and obtains the initialization parameters of the device. An independent communication connection is established with 4 analyzers and 4 quality controllers through the RS232 physical interface. The analyzer transmits data to the analyzer interface through the analyzer motherboard, and the quality controller transmits data to the quality controller interface through the quality controller motherboard. Both the analyzer interface and the quality controller interface are controlled by the central control system.

[0020] Six electrodes, a general input / output control board, UPS, air conditioners, access control and other equipment are connected through an RS485 bus. The system adopts the standard Modbus-RTU communication protocol, with the central control system as the master station and each device module as a slave station. The camera is connected to the central control system after connecting to the hard disk recorder through the network interface. During the communication establishment process, the system performs communication tests on each device to verify the reliability and stability of data transmission. The RS232 interface ensures that the large amount of data transmission of the analyzer and quality control instrument is not interfered with, and the RS485 bus realizes efficient connection management of multiple devices. The system records the online status, communication quality, response time and other parameters of each device, and forms complete device connection status information based on the communication connection status, which serves as the basic basis for subsequent data acquisition and control. The device connection status information includes detailed parameters such as communication interface type, connection quality assessment, and data transmission rate, providing a comprehensive connection status reference for system operation.

[0021] Step S2: The central control system generates a pre-processing control instruction according to the equipment connection status, and performs the water sample collection pre-processing operation through the general input / output control board. The control board operates the switch status of the water inlet valve and the drain valve according to the control instruction, adjusts the operating parameters of the water pump, and completes the pre-processing process such as water sample collection, filtration, and quantification. During the pre-processing process, the working status information is collected through water immersion sensors, smoke sensors, liquid level sensors, etc. The pre-processing control instruction contains specific parameters such as valve switch timing, water pump start and stop control, and flow regulation to ensure the accuracy of water sample pre-processing. The control board monitors the working status of the pre-processing equipment in real time, including operating parameters such as valve position feedback, water pump operating current, and pipeline pressure. At the same time, it records abnormal conditions during the pre-processing process, such as pipeline blockage, pump body failure, liquid level abnormality and other status information. The central control system integrates the equipment operation data, sensor status, abnormal records and other information in the pre-processing stage to form complete pre-processing status data, realize the full monitoring and data traceability of the pre-processing process, and provide reliable data support for subsequent analysis and measurement.

[0022] Step S3: The central control system sends a measurement command to the analyzer and the quality control instrument through the RS232 interface to trigger the water sample analysis. The analyzer collects water sample measurement data, including measurement data, standard solution verification data, zero point verification data, span verification data, spike recovery data, parallel sample data and other multi-dimensional information. The quality control instrument executes the quality control process to ensure the accuracy and reliability of the measurement data. The electrode sensor transmits water quality parameter data in real time through the RS485 bus, including basic indicators such as pH value, dissolved oxygen, and conductivity. The system performs digital filtering and signal processing on the collected raw data to eliminate random interference and system errors. During the measurement process, the influencing factors such as ambient temperature and humidity are synchronously recorded for later data correction. The central control system performs multi-dimensional correlation analysis on the processed water sample measurement data and the pre-processing state data, establishes a logical relationship between the data, identifies abnormal data points, and evaluates the credibility of the data. Through the data compensation algorithm, the influence of the fluctuation of the pre-processing conditions on the measurement results is eliminated, and finally accurate and reliable water quality monitoring results are generated, realizing the accurate evaluation and effective analysis of water quality data.

[0023] Step S4: The central control system performs a comprehensive calibration of the water quality monitoring results of various sensors and analytical instruments based on the equipment connection status. The electrode sensor connected through the RS485 bus performs the calibration operation. The system integrates the calibration function of the electrode control, replaces the independent electrode controller, and improves the system integration. The calibration process includes various methods such as electrode zero point calibration, slope calibration and multi-point calibration to ensure the measurement accuracy of the sensor. The analyzer sets and adjusts the calibration parameters through the RS232 interface, including range calibration, linear calibration, etc. The quality control instrument supervises and verifies the calibration process and evaluates the calibration effect through standard solution testing. The system records various parameters of the calibration process, including calibration coefficients, temperature compensation values, response time and other information. The calibration data is associated with the equipment connection status to ensure the traceability of the data. The central control system verifies and analyzes the calibrated data, evaluates the effectiveness of the calibration, generates the equipment connection status, and ensures the accuracy and reliability of the measurement data.

[0024] Step S5: The central control system obtains operating environment parameters through the integrated environmental monitoring module. The UPS module provides power supply status information, including key parameters such as the mains power supply status, battery remaining, and working status. The air conditioning system provides ambient temperature and humidity data for evaluating the suitability of the equipment operating environment. The access control system records personnel entry and exit information and combines camera monitoring data to achieve security management of the equipment operating environment. The system conducts a comprehensive analysis of these environmental parameters and the equipment connection status to evaluate the impact of environmental factors on the measurement results. Abnormal operating conditions are identified through correlation analysis, such as data drift caused by excessive temperature and measurement interference caused by power supply fluctuations. Based on the analysis results, the system generates an equipment operation status report, including information such as equipment working status, fault diagnosis, maintenance recommendations, etc., to achieve intelligent monitoring and management of equipment operation.

[0025] Step S6: The central control system builds a complete data management solution based on the equipment connection status and operation status information. The system classifies and organizes the collected data to form data sets of multiple time dimensions such as real-time data, minute data, hourly data, daily data, monthly data, and annual data. According to the measurement type, the data is divided into measurement data, standard liquid verification data, zero point verification data, span verification data, spike recovery data, parallel sample data, etc. The system generates various types of operation logs, including system status logs, alarm logs, operation logs, operation logs, etc., to record and trace the entire process of equipment operation. The data management solution supports remote data transmission, uploads data to the remote server platform via Ethernet, and realizes dynamic management and control of big data. The system manages the security of the data, sets access rights, and ensures the confidentiality and integrity of the data. The comprehensive data management solution realizes the full life cycle management of water quality monitoring data and provides data support for the efficient operation of the water quality monitoring system.

[0026] The present invention provides a water quality monitoring data management method, which can grasp the connection status of each device in real time, timely discover and handle communication anomalies through a unified communication connection management mechanism, and improve the stability and reliability of system operation. Based on the correlation analysis of pre-processing state data and measurement data, the whole process monitoring of the water sample collection process is realized, and the accuracy and traceability of the monitoring data are ensured. By integrating the sensor calibration function into the central control system, not only the system structure is simplified, but also the sensor access capability is expanded, breaking through the channel limitation of the traditional electrode controller, and improving the scalability of the system. Combined with the real-time monitoring of the operating environment parameters, the system can timely identify equipment anomalies and make adjustments, ensuring the normal operation of the monitoring equipment. Based on a unified data management solution, the centralized storage and management of information such as measurement data, operating status, and operation logs are realized, which is convenient for comprehensive analysis and historical tracing of data. By establishing a complete data management system, the system can flexibly adapt to different types of water quality monitoring needs, and provide reliable technical support for water environment monitoring. This integrated management solution not only improves the operating efficiency of the system, but also reduces the maintenance cost, and provides a new technical path for the intelligent development of water quality monitoring systems.

[0027] Reference Figure 2 As shown, in one embodiment, the present invention provides a water quality monitoring data management method, comprising: It consists of multiple modules, including analyzer module, quality control module, sensor electrode, bottom controller, UPS, electrode, air conditioner and other equipment. The central control system is the core, responsible for data collection, processing, control and transmission between modules to ensure efficient operation of the system and accuracy of data.

[0028] During the system initialization phase, the central control system obtains the configuration information and initialization parameters of each module. The device address and communication parameters (such as baud rate, data bit, check bit, etc.) of the analyzer module and the quality control module are entered into the system. The device address and communication parameters of each sensor electrode, the configuration information of the universal input / output controller, the communication parameters and monitoring port of the UPS, the control parameters and operation mode of the air conditioner, the configuration information of the access control system, etc. are also obtained and stored. Through the standard Modbus-RTU protocol, the central control system establishes communication connections with each device one by one and confirms the device connection status. As the master station, the central control system sends initialization commands to each slave device and obtains the status response of each device. The analyzer module, the quality control module, the sensor electrode, the universal input / output controller, the UPS, the air conditioner and the access control system respond to the master station's request in turn and return the device connection status. If all devices are connected normally, the system enters the next step.

[0029] According to the connection status of the equipment, the central control system generates and sends water sample collection preprocessing control instructions. The central control system sends control commands to the general input / output controller to start the bottom valves and pumps to collect water samples. At the same time, the central control system sends preprocessing commands to the analyzer module to enable the preprocessing function and perform necessary preprocessing operations such as filtering and mixing on the collected water samples. During the preprocessing process, the central control system periodically sends read commands to obtain the status data of each sensor, including the data of the liquid level sensor, the water immersion sensor data, the smoke sensor and the temperature and humidity sensor data. The preprocessing status data includes the current water sample parameters such as the liquid level, temperature, and humidity.

[0030] After the pretreatment is completed, the central control system sends a sampling measurement command to the analyzer module and the quality control module to start water quality monitoring data collection. The analyzer module and the quality control module collect various parameters of the water sample, such as pH value, dissolved oxygen, turbidity, conductivity, etc. The central control system sends a read command at regular intervals to obtain the measurement data of the analyzer and the quality control instrument. These measurement data are correlated and analyzed with the pretreatment status data, and the influencing factors such as the liquid level, temperature, and humidity of the water sample are comprehensively considered to obtain accurate water quality monitoring results.

[0031] To ensure the accuracy of the monitoring data, the central control system performs sensor comprehensive calibration on the water quality monitoring results. The central control system sends calibration commands to each sensor electrode according to the pre-set calibration parameters. Each sensor electrode automatically performs the calibration operation and feeds back the calibrated data to the central control system. The central control system compares and analyzes the calibration data with the monitoring results, corrects the errors, and obtains the connection status data of the calibration equipment.

[0032] The central control system continuously monitors the operating environment parameters of the equipment, including the power supply status of the UPS (mains power supply, battery remaining, working status, etc.), the operating status of the air conditioner (current operating mode, temperature and humidity parameters, fault status, etc.), and the switch records of the access control system (entry and exit records of operation and maintenance personnel, fingerprint unlocking status, etc.). Combined with the equipment connection status and calibration equipment connection status data, the central control system performs equipment analysis and evaluates the operating status of each device. The central control system comprehensively analyzes the data of the UPS, air conditioner, and access control system to determine whether the current power supply status is normal and whether the equipment is operating under appropriate environmental conditions. Combined with the water quality monitoring results, it is determined whether there are any abnormalities in the analyzer and quality control instrument and whether further calibration or maintenance is required.

[0033] According to the connection status and operation status of the equipment, the central control system builds a comprehensive data management plan. The plan includes a data collection and processing plan, which optimizes the data collection frequency and processing method based on the status of each sensor and analyzer to ensure the accuracy and reliability of the data. The equipment maintenance and calibration plan, according to the equipment operation status, formulates a regular maintenance and calibration plan to ensure the long-term stable operation of the equipment. The environmental control and emergency response plan optimizes the operation mode of the air conditioner by monitoring environmental parameters to ensure that the equipment works in a suitable environment. In response to power supply anomalies or equipment failures, emergency response measures are formulated to ensure the continuous operation of the system.

[0034] Through the above operations, the intelligent water quality monitoring system realizes data collection, processing, transmission and control of each module, ensuring the accuracy and reliability of water quality monitoring data. In the stage of building a comprehensive data management solution, the central control system formulates data collection, processing and storage strategies according to the operating status of each module. The system transmits monitoring data to the remote platform through a router to achieve remote backup and sharing of data. The hard disk recorder saves environmental monitoring video to provide auxiliary support for data analysis. The access control system's entry and exit records are associated with the operation log to ensure the security and traceability of data management.

[0035] This embodiment uses the central control system as the core for unified management, realizes seamless collaboration between various functional modules, and significantly improves the automation level and operation efficiency of water quality monitoring. The system uses the standard Modbus-RTU protocol to communicate with each device, which greatly enhances the compatibility and scalability of the system and facilitates the upgrade and maintenance of later equipment. By pre-processing water samples and combining the correlation analysis of various sensor data, the accuracy and reliability of water quality monitoring data are effectively improved. The system has an automatic calibration function, which can promptly detect and correct measurement errors to ensure the scientific nature of the monitoring results. By real-time monitoring of equipment operating environment parameters, combined with UPS power supply status, air conditioning operating status and other data, the system can promptly detect potential problems and take preventive measures, significantly improving the operating stability and service life of the equipment. In addition, the system has established a complete data management solution, realized remote backup and sharing of data, and ensured data security and traceability through the records of the access control system.

[0036] In one embodiment, the system configuration information and initialization parameters of the water quality monitoring device are obtained, and the communication connection with the water quality monitoring device is established to obtain the device connection status, including: The central control system identifies the communication interface of the water quality monitoring equipment to achieve unified management and control of the monitoring equipment. The central control system identifies all connected RS232 interface devices and RS485 interface devices by scanning the connected physical ports. The RS232 interface devices include 4 analyzers and 4 quality control instruments, each of which occupies an independent physical interface; the RS485 interface devices include 6 electrodes, 1 universal input / output control board, UPS, air conditioner and access control, and these devices share an RS485 bus. The central control system generates a list of RS232 interface devices and a list of RS485 interface devices based on the identified device information to achieve classified management of the equipment.

[0037] The central control system configures differentiated communication parameters for different types of equipment based on the RS232 interface device list and the RS485 interface device list. For the analyzers and quality control instruments in the RS232 interface device list, since these devices have large data communication volume and high real-time requirements, the central control system sets the communication baud rate to 9600bps, 8 data bits, 1 stop bit, and no parity bit to ensure the stability and real-time performance of data transmission. For the electrodes, general control panels, UPS, air conditioners, access control and other equipment in the RS485 interface device list, since their data communication volume is relatively small, the central control system sets the communication baud rate to 4800bps, 8 data bits, 1 stop bit, and no parity bit. Through differentiated communication parameter configuration, the central control system generates system configuration information to ensure the communication quality of various types of equipment.

[0038] The central control system uses the standard Modbus-RTU communication protocol to parse the water quality monitoring equipment. Through protocol parsing, the central control system extracts the function codes supported by the equipment, including function code 03H for reading holding registers, function code 04H for reading input registers, function code 06H for writing single holding registers, function code 10H for writing multiple holding registers, etc. At the same time, the central control system parses the register address mapping table of each device, establishes the data storage address 0x0000-0x00FF for storing measurement data, the status storage address 0x0100-0x01FF for storing device operating status, the parameter storage address 0x0200-0x02FF for storing device configuration parameters, and the control command storage address 0x0300-0x03FF for storing device control instructions. Through the parsing of the device function code and the register address mapping table, the central control system achieves accurate access and control of device data.

[0039] The central control system performs device communication tests based on the system configuration information and register address mapping table. The central control system sends communication test commands to each device and reads basic device information, including device ID, firmware version number, device type identification, device status code, etc. By sending test commands and receiving responses, the central control system obtains device communication response information for evaluating the quality of the communication link. For the acquired device communication response information, the central control system establishes a four-level communication quality management mechanism: response time less than 100ms is defined as high-quality communication, which is used for real-time transmission of critical data; response time 100-500ms is defined as good communication, which is used for transmission of regular data; response time 500-1000ms is defined as general communication, which is used for transmission of non-critical data; response time greater than 1000ms or no response is defined as faulty communication, which triggers the communication exception handling mechanism.

[0040] The central control system initializes the communication parameters of each device according to the communication quality classification results. For high-quality communication equipment, the communication timeout is set to 200ms and the number of retries is 1; for good communication equipment, the communication timeout is set to 1000ms and the number of retries is 2; for general communication equipment, the communication timeout is set to 2000ms and the number of retries is 3; for faulty communication equipment, the communication timeout is set to 5000ms and the number of retries is 5. Through differentiated communication parameter initialization, the central control system establishes a fault-tolerant mechanism for device communication and improves the stability of the system.

[0041] The central control system establishes a communication connection with the water quality monitoring equipment based on the initialization parameters to achieve dynamic monitoring of the equipment connection status. The central control system reads the status of high-quality communication equipment every 100ms to achieve real-time monitoring of key equipment; reads the status of good communication equipment every 500ms to achieve periodic monitoring of conventional equipment; reads the status of general communication equipment every 1000ms to achieve low-frequency monitoring of non-critical equipment; and attempts to re-establish the connection with faulty communication equipment every 5000ms to achieve automatic recovery of communication failures. The central control system monitors the communication quality of each device in real time and dynamically adjusts the communication parameters to ensure the stability and reliability of the system operation. At the same time, the central control system records changes in the communication status of the equipment and generates communication logs to provide data support for system maintenance and fault diagnosis.

[0042] This embodiment uses a central control system to classify and manage water quality monitoring equipment and perform differentiated communication configurations, thereby achieving the optimal communication solution for different types of equipment and improving the overall communication efficiency of the system. A high baud rate configuration is used for RS232 interface equipment to ensure the real-time transmission of large amounts of data; a low baud rate configuration is used for RS485 interface equipment to ensure the communication stability when multiple devices are on the same line. By establishing a four-level communication quality management mechanism, the system can dynamically adjust communication parameters according to the communication quality of the equipment, achieve reasonable allocation of communication resources and automatic recovery of faults. The use of standard Modbus-RTU protocol and unified register address mapping simplifies the device access process and improves the scalability of the system. At the same time, differentiated communication parameter initialization and dynamic monitoring strategies have established a complete communication fault-tolerant mechanism, significantly improving the system's operational reliability and providing a strong guarantee for the stable operation of water quality monitoring equipment.

[0043] In one embodiment, a preprocessing control instruction is obtained according to the device connection status, and a water sample collection preprocessing operation is performed to obtain preprocessing status data, including: After the central control system establishes a communication connection with the water quality monitoring equipment through the RS232 and RS485 interfaces, the central control system conducts a communication quality assessment on the equipment connection status. The communication quality assessment includes a comprehensive analysis of indicators such as communication response time, data transmission stability, and communication error rate to generate communication quality parameters. The communication quality parameters reflect the health of the communication link between the current system and each device, and provide a basis for the selection of the preprocessing mode.

[0044] The central control system determines the pre-processing mode of the water quality monitoring equipment based on the communication quality parameters. When the communication quality parameters show that the communication status is good, the system selects the standard pre-processing working mode and parameters; when the communication quality parameters show that the communication is fluctuating, the system selects the backup pre-processing working mode and parameters; when the communication quality parameters show that the communication quality is poor, the system selects the emergency pre-processing working mode and parameters. Through the pre-processing mode determination, the system determines the most suitable pre-processing working mode and parameters at present.

[0045] The central control system parses and combines the control instructions for the pre-processing working mode and parameters, and converts the pre-processing working mode and parameters into a specific control sequence. For the standard pre-processing working mode and parameters, the system generates a standard valve control sequence and a standard pump group control sequence; for the standby pre-processing working mode and parameters, the system generates a simplified valve control sequence and a simplified pump group control sequence; for the emergency pre-processing working mode and parameters, the system generates an emergency valve control sequence and an emergency pump group control sequence. Through the control instruction parsing, the system converts the abstract working mode into a specific execution instruction sequence.

[0046] The central control system generates a pre-processing execution time table based on the valve control sequence and the pump group control sequence. The execution time table records the changes in the valve switch state and the pump group start and stop state in chronological order to form the pre-processing execution steps. The pre-processing execution steps include specific execution time points, execution action types, execution parameters and other information, providing clear operation guidance for the subsequent execution process.

[0047] The central control system performs sequential execution parameter analysis on the preprocessing execution steps, and generates step operation parameters by analyzing the time interval, execution sequence, parameter settings and other information of each execution step. The step operation parameters include specific execution details such as execution duration, switching conditions, parameter thresholds, etc., providing a detailed control basis for the actual execution of the preprocessing process.

[0048] The central control system executes the pretreatment process according to the step operation parameters, and collects the operation data of each execution step during the execution process, including valve position feedback, pump group operation status, flow pressure data and other information. The system performs multi-dimensional analysis on the collected pretreatment process data, analyzes the execution progress from the time dimension, analyzes the execution effect from the space dimension, and analyzes the execution quality from the state dimension, and finally generates pretreatment status data. The pretreatment status data fully reflects the execution status of the pretreatment process and provides an important reference for the subsequent water quality monitoring process.

[0049] The pretreatment status data is a comprehensive summary of the pretreatment process, including data from multiple dimensions such as equipment status information, process parameter information, and execution quality information. These data reflect the integrity, effectiveness, and reliability of the pretreatment process, and provide necessary support for the subsequent analysis of water quality monitoring results. Through the pretreatment status data, the system can determine whether the pretreatment process meets the requirements of water quality monitoring and whether optimization and adjustment of the pretreatment process is required.

[0050] This embodiment performs communication quality assessment on the device connection status and dynamically selects the preprocessing working mode and parameters based on the communication quality parameters, so that the system can flexibly adjust the working strategy according to the actual communication environment to ensure stable operation under different communication conditions. By converting the preprocessing working mode and parameters into a specific control sequence and generating a detailed execution timing table, the preprocessing process is more standardized and standardized, effectively avoiding human operational errors. By analyzing the timing execution parameters of the preprocessing execution steps and performing multi-dimensional process data analysis, the system can fully grasp the execution of the preprocessing process, promptly discover and handle abnormal situations, and improve the accuracy and reliability of water quality monitoring. This method realizes the intelligent management of the preprocessing process, reduces the degree of manual intervention, and improves the system operation efficiency.

[0051] In one embodiment, water sample measurement data collected by water quality monitoring equipment is obtained, and correlation analysis is performed with pre-processing state data to obtain water quality monitoring results, including: The central control system receives the original water quality measurement data collected by the analyzer mainboard and the quality control data of the quality control instrument mainboard. At the same time, it obtains the real-time parameter data and fault status collected by the 6 electrode sensors through the Modbus protocol and the electrode calibration protocol, and converts these original data into water sample measurement data in a standard format through the data standard conversion module. The data standard conversion module performs unit conversion and numerical standardization on the original data according to the measurement units and numerical ranges of different measurement parameters. For example, the mg / L measured by the analyzer is converted to μg / L, and the ppm measured by the electrode is converted to mg / L, etc., to generate water sample measurement data in a unified format.

[0052] The central control system performs validity detection on water sample measurement data based on preset standard measurement parameters. Standard measurement parameters include measurement value range, measurement time interval, signal stability and other indicators. The system sets the upper and lower limits of the value range, sampling time interval requirements, signal fluctuation allowable range and other conditions, combined with environmental data such as water immersion, smoke, temperature and humidity collected by the general input / output control board, as well as the power supply status of the UPS, the temperature control parameters of the air conditioner and other operating conditions, to screen out valid measurement data that meets the requirements. For abnormal data that exceeds the preset range, the system will mark and remove it to ensure the validity of the data.

[0053] The central control system classifies and compiles the effective measurement data collected by the analyzer mainboard, quality control instrument mainboard and electrode sensor according to different measurement indicators. The system classifies the pH value, dissolved oxygen, COD and other indicator data collected by each analyzer and electrode, calculates the statistical parameters such as the mean value, standard deviation, coefficient of variation of each indicator, and generates measurement indicator data. The statistical results include information such as the numerical distribution characteristics and change trends of each indicator.

[0054] The central control system performs data matching processing on the measurement index data and the pre-processing status data of the bottom valves, pumps, air compressors, liquid level sensors, etc. fed back by the general input / output control board. The system matches the measurement index data with the pre-processing status data of the corresponding time period, establishes the association between the measurement data and the pre-processing conditions, and generates matching relationship data. During the matching process, the system records the correspondence between the working condition parameters such as water sample pre-processing temperature, pressure, flow rate, etc. and the measurement results.

[0055] The central control system performs data association analysis on the measurement results based on the matching relationship data. The system analyzes the influence of preprocessing conditions on the measurement results, evaluates the stability and reliability of the measurement data under different preprocessing parameters, and generates association analysis data based on the door opening and closing status and door opening and closing records recorded by the access control system, as well as the real-time video stream of the camera connected through the router and the historical video stream recorded by the hard disk recorder. The association analysis results reflect the correlation between the preprocessing conditions and the measurement accuracy.

[0056] The central control system conducts a comprehensive data evaluation on the correlation analysis data. The system comprehensively considers multiple evaluation indicators such as the accuracy, stability, and reliability of the measurement data, conducts an overall evaluation of the quality of the measurement results, and generates water quality monitoring and evaluation results. The evaluation results include information such as the comprehensive credibility of the measurement data and the compliance status of various indicators. The evaluation results are uploaded from the router to the remote platform through the TCP / IP protocol to achieve remote monitoring and data management.

[0057] This embodiment can effectively improve the accuracy and reliability of water quality monitoring data through the integration and standardized data processing process of the central control system. The system converts a variety of raw data into water sample measurement data in a unified format through a data standard conversion module to ensure the consistency and comparability of the data. Validity detection is performed based on preset standard measurement parameters, and abnormal data is eliminated to ensure the validity of the measurement data. The classification statistics and data matching processing steps further refine the data analysis and clarify the correlation between the measurement indicators and the pre-processing status. The data association analysis and comprehensive evaluation module provide detailed water quality monitoring and evaluation results through multi-dimensional data analysis. The automated processing of the entire process reduces manual intervention, improves work efficiency, and realizes real-time data transmission and remote monitoring through a variety of communication protocols, enhancing the practicality and flexibility of the system. At the same time, the system records and analyzes a variety of auxiliary information such as environmental data, operating status, and on-site video streams, providing comprehensive background support for water quality monitoring, which helps to improve the credibility and scientificity of the monitoring results.

[0058] In one embodiment, the operating environment parameters of the water quality monitoring device are obtained, and the device is analyzed in combination with the device connection status to obtain the device operating status, including: The central control system collects data from UPS, air conditioners, environmental sensors and other equipment through the RS485 bus to obtain environmental operating parameters. The system classifies and splits the collected operating environment parameters and divides the parameters into three categories: power supply parameters, temperature and humidity parameters, and equipment operating parameters. Power supply parameters include UPS power supply status, battery remaining and other information; temperature and humidity parameters include air conditioner operating status, environmental temperature and humidity values ​​and other information; equipment operating parameters include water immersion status, smoke sensor status, liquid level status and other information. The power supply parameters, temperature and humidity parameters and equipment operating parameters are obtained through classification and splitting.

[0059] The central control system monitors the UPS power supply parameters in real time and records the voltage fluctuations. The system records the voltage fluctuations when they exceed ±5% according to the preset voltage fluctuation monitoring rules and generates power supply fluctuation data. The data contains information such as voltage fluctuation time, fluctuation amplitude, and duration. The central control system performs stability analysis on the power supply parameters based on the power supply fluctuation data. According to the preset power supply stability judgment rules, the system determines that the power supply status is unstable when the number of voltage fluctuations exceeds 3 times within 1 hour, or the duration of a single fluctuation exceeds 10 minutes. The power supply operation status information including power supply status, battery status, power supply stability rating and other information is obtained through analysis.

[0060] The central control system performs environmental adaptability matching analysis on the equipment connection status according to the temperature and humidity parameters. According to the preset environmental adaptability matching rules, the system determines that the environmental adaptability is good when the ambient temperature is in the range of 15-35°C and the relative humidity is in the range of 45-85%; when it exceeds this range, the system calculates the degree of influence of temperature and humidity on the monitoring data and generates the temperature and humidity influence coefficient. The central control system performs fault diagnosis and performance evaluation on the equipment working parameters. According to the preset fault diagnosis rules, the system monitors the status of sensors such as water immersion and smoke in real time; according to the preset performance evaluation rules, the system performs statistical analysis on the equipment operation time, maintenance cycle, etc. Through diagnosis and evaluation, the working operation parameters including equipment status, fault information, and performance level are obtained.

[0061] The central control system performs a multi-dimensional fusion analysis of the power supply operation status information, temperature and humidity influence coefficients, and working operation parameters. The system uses a preset multi-dimensional evaluation model to comprehensively calculate the three types of information according to the weights. When the power supply is stable, the environmental adaptability is good, and the equipment is operating normally, the equipment operation status is judged to be excellent; when any abnormality occurs, the operation status level is determined according to the degree of abnormality. The equipment operation status including operation level, abnormal warning, and maintenance suggestions is obtained through analysis.

[0062] The central control system establishes a data processing model based on the device connection status. The system divides the connection status into three levels: normal connection, abnormal connection, and disconnection according to the preset data classification rules. For devices with normal connection, the system enables the standard data processing process; for devices with abnormal connection, the system enables the backup data processing strategy; for disconnected devices, the system records the disconnection time and issues an alarm. The system builds a management strategy model based on the device operation status. For devices with excellent operation status, the system adopts conventional management strategies; for devices with abnormal operation status, the system matches the corresponding processing strategy according to the degree and type of abnormality.

[0063] The central control system conducts comprehensive analysis and processing of equipment operating parameters. The system associates pre-processing status data, equipment connection status, equipment operating status and other information in time series to form a complete equipment operating status. The system conducts multi-dimensional analysis of the data chain, forming real-time data, minute data, hourly data, daily data, monthly data, annual data, etc. in the time dimension; generates measurement data, standard liquid verification data, zero point verification data, span verification data, etc. in the monitoring dimension; and organizes status logs, alarm logs, operation logs, etc. in the management dimension.

[0064] This embodiment collects and classifies various environmental parameters through the RS485 bus, establishes a unified data collection and processing mechanism, and avoids the problems of data dispersion and inconsistent formats in traditional systems. The power supply status, environmental adaptability, etc. are monitored and evaluated in real time through preset multi-level judgment rules, which improves the stability and reliability of system operation. The weighted multi-dimensional fusion analysis method is adopted to comprehensively evaluate the power supply status, environmental adaptability and equipment working status, so that the determination of the system operation status is more accurate and comprehensive. Through the hierarchical data processing model, the system can automatically select the corresponding processing strategy according to the device connection status, enhancing the adaptability and fault tolerance of the system. Multi-dimensional analysis and cascade processing of data are realized, and a complete data management system is established to facilitate data storage, analysis and application. Remote transmission and management of data are realized through standard communication protocols, which provides strong support for the intelligent operation and maintenance of water quality monitoring systems.

[0065] In one embodiment, fault diagnosis and performance evaluation are performed on the equipment operating parameters to obtain operating parameters, including: The central control system analyzes the communication status with each device through the Modbus protocol. The communication status analysis includes statistics on indicators such as communication response time, communication success rate, and data transmission stability. The system performs a comprehensive calculation on these indicators to generate a device communication quality characteristic value. This characteristic value reflects the reliability and efficiency of device communication.

[0066] The central control system analyzes the operating status of the equipment's operating parameters. In terms of power supply status, the UPS module collects data such as the mains power supply status, battery remaining capacity, and power usage alarms; in terms of equipment status, the operating parameters of the analyzer and quality control instrument, valve pump status, and air compressor operating status are collected; in terms of environmental status, environmental monitoring data such as temperature and humidity, water immersion, and smoke are collected. The system normalizes these data to generate equipment operating characteristic values.

[0067] For fault alarm analysis, the central control system counts three types of faults: communication faults, equipment faults, and environmental faults. Communication faults include communication interruption, data anomalies, etc.; equipment faults include analyzer faults, quality control instrument faults, electrode faults, etc.; environmental faults include temperature over-limit, humidity over-limit, water immersion alarms, etc. The system classifies and summarizes these fault data to generate fault characteristic values.

[0068] Based on the equipment operation characteristic values, the central control system establishes an equipment performance attenuation model. This model calculates the equipment performance attenuation curve by analyzing the historical change trend of equipment performance indicators and combining the expected life of the equipment. The performance attenuation curve reflects the law of equipment performance changes over time.

[0069] The calculation of the equipment performance degradation curve is based on multi-dimensional performance indicator data, which mainly includes the following calculation steps: The equipment operation characteristic values ​​include multiple key performance indicators, such as analyzer response time, measurement accuracy, electrode sensitivity, signal strength, etc. The system sets standard reference values ​​for these indicators and uses the deviation rate between the actual measurement value and the reference value as a quantitative indicator of performance degradation.

[0070] For each performance indicator, the central control system records the historical data sequence of its changes over time. By normalizing the data sequence, the indicators of different dimensions are converted to a unified evaluation standard. The normalized data reflects the relative change trend of each performance indicator.

[0071] In time series analysis, the system uses weighted moving average method to smooth the performance index data to eliminate the impact of short-term fluctuations. The smoothed data can better reflect the long-term trend of equipment performance. For the smoothed data sequence, the system uses nonlinear regression method to fit the performance attenuation curve.

[0072] The performance decay curve is calculated using an exponential decay model: ; P(t) represents the performance value at time t, It represents the initial performance value, λ is the basic attenuation coefficient, α is the periodic fluctuation amplitude coefficient (reflecting the periodic impact of the environment), ω is the periodic fluctuation frequency (can be set according to daily / monthly / seasonal changes), β is the performance limit attenuation coefficient (reflecting the maximum performance loss ratio of the equipment), and γ is the limit approach rate coefficient. The basic attenuation coefficient λ is obtained by fitting historical data and reflects the speed at which the equipment performance decays over time.

[0073] To improve the accuracy of curve fitting, the system sets a weight coefficient for each performance indicator. The weight coefficient is determined based on the degree of influence of the indicator on the overall performance of the device. By weighted combination of the attenuation curves of multiple performance indicators, the overall performance attenuation curve of the device is obtained.

[0074] The system establishes a performance degradation benchmark model based on the device type and working environment. The actual calculated performance degradation curve is compared with the benchmark model to evaluate whether the device performance degradation is within the normal range. When the degradation rate exceeds the preset threshold, the system will issue a warning signal.

[0075] The calculation results of the performance decay curve are used to predict the remaining service life of the equipment and provide a basis for decision-making on equipment maintenance and replacement. By continuously monitoring and updating the performance decay curve, the system can promptly detect abnormal changes in equipment performance and take corresponding maintenance measures.

[0076] The central control system correlates and analyzes the equipment communication quality characteristic values, equipment operation characteristic values, and fault characteristic values. By analyzing the correlation between these characteristic values, the system identifies the typical mode and propagation path of the fault and generates the fault diagnosis results. At the same time, the fault type is statistically analyzed to generate fault type distribution data.

[0077] For the equipment performance attenuation curve, the central control system determines the target node based on the fault type distribution data and divides the curve into multiple stages for segmented fitting. By calculating the performance attenuation rate and trend of each stage, the system generates equipment performance evaluation data. This data reflects the performance status of the equipment at different stages.

[0078] The central control system sets multi-level warning thresholds based on equipment performance evaluation data. The system divides the performance status into normal, mild warning, moderate warning and severe warning levels, and generates equipment warning level data. The warning level division is determined based on the degree of deviation of equipment performance indicators.

[0079] Finally, the central control system performs weighted calculations on the fault diagnosis results, equipment performance evaluation data, and equipment warning level data. The system assigns weights according to the importance of each indicator and obtains the working parameters through weighted summation. This parameter fully reflects the overall operating status of the equipment.

[0080] This embodiment uses an innovative composite exponential decay model to evaluate equipment performance. The system achieves multiple technical effects in water quality monitoring data management: by establishing a multi-dimensional performance indicator system and combining the correlation analysis of equipment communication quality characteristic values, operation characteristic values, and fault characteristic values, an accurate characterization of the equipment performance decay law is achieved. The composite model of periodic fluctuation terms and limit characteristic terms is introduced to accurately reflect the periodic impact of environmental factors on equipment performance and the limit characteristics of performance decay, thereby improving the accuracy of performance evaluation. The multi-level early warning mechanism established based on performance evaluation data can detect equipment anomalies in a timely manner and provide graded early warnings, effectively reducing the risk of equipment failure. Through weighted analysis of fault diagnosis results, performance evaluation data, and early warning level data, a comprehensive working operation parameter evaluation system is formed, which provides a reliable basis for equipment maintenance decisions and significantly improves the operating reliability and maintenance efficiency of water quality monitoring equipment.

[0081] In one embodiment, the water quality monitoring result is comprehensively determined based on the device connection status, device operation status and pre-processing status to obtain identification data, including: The central control system establishes communication connection with the analyzer and quality control instrument through RS232, and establishes communication connection with the electrode through RS485 to ensure the stability and real-time performance of data transmission.

[0082] When the water quality monitoring results are combined with the power supply fault status to obtain the power supply data identification, the central control system collects the measurement value of the zero-point standard solution through the analyzer, compares it with the theoretical zero-point value, calculates the zero-point deviation value, and performs zero-point calibration compensation on the measurement result. When abnormal conditions such as mains power outage, UPS battery power below 20%, or UPS output voltage instability are detected, the system automatically marks the water quality monitoring data for the corresponding period and generates a power supply data identification containing the fault type, occurrence time, and duration.

[0083] When the water quality monitoring results are combined with the instrument status to obtain the instrument fault data identification, the central control system uses the standard solution to measure the range point, and calculates the range calibration coefficient by comparing the proportional relationship between the measured value and the standard value. By analyzing the internal temperature, pressure, flow and other operating parameters of the instrument, when problems such as instrument component failure, excessive measurement deviation or abnormal response are detected, the system generates an instrument fault data identification containing the fault equipment number, fault type, and fault level.

[0084] When the water quality monitoring results are combined with the instrument's key parameters to obtain data identification of exceeding the upper and lower limits, the system collects the measurement values ​​of high-concentration standard solutions and calculates the span calibration parameters in combination with the zero-point calibration data. The upper and lower limit warning values ​​and alarm values ​​are set for parameters such as pH value, dissolved oxygen, turbidity, and conductivity. When the monitoring data exceeds the warning value or alarm value range, the system generates a data identification containing the exceeding parameter, degree of exceeding, and duration.

[0085] When the water quality monitoring results are combined with the device connection status to obtain the device offline data identification, the system uses the zero calibration data and span calibration parameters to establish a linear relationship between the measured value and the actual concentration, and obtains the water quality monitoring results through least squares fitting. When the device response times out, communication is interrupted, or data transmission is abnormal, the system generates a device offline data identification containing the offline device type, offline time point, and offline reason.

[0086] When the water quality monitoring results are combined with the pre-processing status to obtain the data mark of no water sample and manual start, the system obtains the current water sample temperature value and performs temperature compensation on the water quality monitoring results according to the relationship curve between temperature and measurement value. The liquid level sensor detects the existence of water samples. When the liquid level is lower than the preset value or manual intervention is detected, the system generates a data mark containing the pre-processing abnormality type, occurrence time, and impact range.

[0087] When the temperature-corrected water quality monitoring results are corrected by temperature compensation, the central control system uses a temperature compensation algorithm to correct the measurement data based on the difference between the water sample temperature and the standard temperature. The system calculates the measurement deviation caused by temperature changes based on the temperature coefficients of different parameters and generates water quality monitoring results after temperature compensation.

[0088] When the spike recovery process is performed based on the water quality monitoring results to obtain the spike recovery rate, the central control system regularly performs spike recovery tests. Standard substances of known concentrations are added to water samples, the changes in water quality parameters before and after the spike are measured, the recovery rate of the standard substances is calculated, and test data including spike substances, spike concentrations, and recovery rates are generated.

[0089] When parallel sample data is obtained by performing parallel sample determination based on the water quality monitoring results, the central control system repeats the measurement of the same water sample. Collect multiple sets of parallel sample measurement data, calculate the relative deviation of the measurement results, and generate parallel sample data including the number of parallel sample groups, measurement values, and relative deviations. The system calculates statistical parameters such as relative standard deviation and coefficient of variation based on the parallel sample data to evaluate the precision of the measurement method.

[0090] When the power supply data identification, instrument failure data identification, over-upper and over-lower limit data identification, equipment offline data identification, spike recovery rate and parallel sample data are integrated and corrected to obtain data identification, the central control system comprehensively analyzes various data identifications. By evaluating the influence of various abnormal factors on the measurement results, a data reliability evaluation system is established to generate the final data identification, which comprehensively reflects the quality status of water quality monitoring data.

[0091] This embodiment achieves all-round quality control of water quality monitoring data by setting up multiple data identification and calibration processing mechanisms in the central control system. Based on the multi-level data identification system, it can accurately identify and trace influencing factors such as power failure, instrument abnormality, and equipment offline, ensuring the reliability and integrity of the monitoring data. Through multiple calibration processes such as zero drift compensation, range calibration, and span calibration, the system error of the measurement system is effectively eliminated and the accuracy of the data is improved. Combined with quality control methods such as temperature compensation correction, spiked recovery calculation, and parallel sample determination, a complete data quality evaluation system has been established to enhance the scientificity and credibility of the monitoring results. The use of RS232 and RS485 communication methods ensures the stability and real-time nature of data transmission and improves the operating efficiency of the entire monitoring system.

[0092] In one embodiment, a scheme is constructed based on the device connection status and the device operation status to obtain a comprehensive data management scheme, including: The central control system classifies and processes the status identification information in the equipment status parameter matrix according to dimensions such as equipment type, functional attributes, and importance, and generates a multi-level data structure that includes multiple layers such as the communication connection layer, equipment control layer, data acquisition layer, and environmental monitoring layer.

[0093] Based on the status information of each level in the multi-level data structure, the central control system determines whether the current system is in normal operation mode, fault handling mode or maintenance calibration mode, and obtains the operation mode judgment result. The judgment rules include multiple judgment conditions such as whether the communication connection is normal, whether there is a fault in the equipment, and whether maintenance calibration is required.

[0094] Based on the results of the operation mode judgment, the central control system builds a corresponding pre-treatment control plan. In the normal operation mode, a standard water sample pre-treatment process is built; in the fault handling mode, a fault diagnosis and handling process is built; in the maintenance and calibration mode, an equipment maintenance and calibration process is built to form a pre-treatment control plan.

[0095] The central control system calibrates the electrode data in the multi-level data structure according to the pre-processing control scheme. Through zero point calibration, range calibration, linear calibration and other methods, the electrode response curve is established, the calibration coefficient is calculated, and the electrode calibration scheme is generated. During the calibration process, environmental parameters such as temperature and humidity are also recorded.

[0096] The central control system performs environmental compensation analysis on the electrode calibration scheme, calculates the influence coefficient of the environment on the measurement results based on environmental parameters such as temperature and humidity, and obtains the environmental compensation coefficient. The compensation analysis process takes into account the influence of environmental parameters on the electrode response characteristics.

[0097] The central control system classifies and manages various types of data in the multi-level data structure based on the results of the operation mode judgment. It performs statistical analysis on the measurement data by minute, hour, day, month, and year; performs classification storage such as standard liquid verification, zero point verification, and span verification on the calibration data; and records the operation data in the form of status logs, alarm logs, operation logs, and operation logs to form an operation data management plan.

[0098] The central control system integrates and optimizes the pre-treatment control scheme, electrode calibration scheme and operation data management scheme based on the environmental compensation coefficient. The environmental compensation coefficient is applied to measurement data correction, calibration parameter adjustment, control parameter optimization, etc., and finally forms a comprehensive data management scheme with complete functions including pre-treatment control, electrode calibration, data management, etc. This scheme realizes the intelligent management of the whole process of water quality monitoring.

[0099] This embodiment realizes the intelligent management of the whole process of the water quality monitoring system by constructing a hierarchical and partitioned comprehensive data management solution in the central control system. The solution converts the equipment status information into a multi-dimensional parameter matrix and classifies it in a hierarchical manner, so that the system can accurately grasp the operating status of various types of equipment and improve the reliability of system operation. Through the intelligent judgment of the operating mode and the dynamic construction of the preprocessing control scheme, the system can adaptively adjust the control strategy according to different working scenarios, significantly improving the adaptability and control accuracy of the system. Combined with the environmental compensation analysis and electrode calibration scheme, the system realizes real-time correction of the measurement data and effectively eliminates the influence of environmental factors on the measurement results. In terms of data management, the system has established a classification management system including measurement data, calibration data, and operation data, which provides comprehensive data support for the water quality monitoring process, and at the same time improves the overall coordination efficiency of the system through scheme integration and optimization.

[0100] Reference Figure 3 As shown, the present invention also provides a water quality monitoring data management system, which is applied to any of the above water quality monitoring data management methods, including: The acquisition module is used to obtain the system configuration information and initialization parameters of the water quality monitoring equipment, and to communicate with the water quality monitoring equipment to obtain the equipment connection status; The analysis module is used to obtain pre-processing control instructions according to the device connection status, and perform water sample collection pre-processing operations to obtain pre-processing status data; The correlation module is used to obtain the water sample measurement data collected by the water quality monitoring equipment, and perform correlation analysis with the pre-processing state data to obtain the water quality monitoring results; The processing module is used to obtain the operating environment parameters of the water quality monitoring equipment, and perform equipment analysis in combination with the equipment connection status to obtain the equipment operating status; A control module, the control module is used to make a comprehensive judgment on the water quality monitoring results based on the equipment connection status, equipment operation status and pre-treatment status to obtain identification data; The execution module is used to construct a plan based on the device connection status and the device operation status to obtain a comprehensive data management plan.

[0101] The present invention provides a water quality monitoring data management system. Through a unified communication connection management mechanism, the system can grasp the connection status of each device in real time, timely discover and handle communication anomalies, and improve the stability and reliability of system operation. Based on the correlation analysis of pre-processing state data and measurement data, the whole process monitoring of the water sample collection process is realized, and the accuracy and traceability of the monitoring data are ensured. By integrating the sensor calibration function into the central control system, not only the system structure is simplified, but also the sensor access capability is expanded, breaking through the channel limitation of the traditional electrode controller, and improving the scalability of the system. Combined with the real-time monitoring of the operating environment parameters, the system can timely identify equipment anomalies and make adjustments, ensuring the normal operation of the monitoring equipment. Based on a unified data management solution, the centralized storage and management of information such as measurement data, operating status, and operation logs are realized, which is convenient for comprehensive analysis and historical tracing of data. By establishing a complete data management system, the system can flexibly adapt to different types of water quality monitoring needs and provide reliable technical support for water environment monitoring. This integrated management solution not only improves the operating efficiency of the system, but also reduces maintenance costs, and provides a new technical path for the intelligent development of water quality monitoring systems.

[0102] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0103] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0104] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A water quality monitoring data management method, characterized in that: include: Obtaining system configuration information and initialization parameters of a water quality monitoring device, and establishing a communication connection with the water quality monitoring device to obtain a device connection status; Obtaining preprocessing control instructions according to the device connection status, and performing water sample collection preprocessing operations to obtain preprocessing status data; Acquire the water sample measurement data collected by the water quality monitoring device, and perform correlation analysis with the pre-processing state data to obtain the water quality monitoring result; Obtaining the operating environment parameters of the water quality monitoring device, and performing device analysis in combination with the device connection status to obtain the device operating status; Comprehensively determine the water quality monitoring result based on the device connection status, device operation status and pre-treatment status to obtain identification data; A plan is constructed according to the device connection status and the device operation status to obtain a comprehensive data management plan.

2. The water quality monitoring data management method according to claim 1, characterized in that: The step of obtaining system configuration information and initialization parameters of the water quality monitoring device, and communicating with the water quality monitoring device to obtain the device connection status includes: Identify the communication interface of the water quality monitoring equipment to obtain a list of RS232 interface devices and a list of RS485 interface devices; Perform communication parameter configuration according to the RS232 interface device list and the RS485 interface device list to obtain the system configuration information; Parsing the water quality monitoring equipment using the Modbus-RTU communication protocol to obtain a mapping table of equipment function codes and register addresses; Perform a communication test according to the system configuration information and the register address mapping table to obtain device communication response information; Performing hierarchical management processing on the device communication response information and initializing communication parameters to obtain the initialization parameters; Perform communication connection analysis with the water quality monitoring device according to the initialization parameters to obtain the device connection status.

3. The water quality monitoring data management method according to claim 1, characterized in that: The method of obtaining a preprocessing control instruction according to the device connection status and performing a water sample collection preprocessing operation to obtain preprocessing status data includes: Performing communication quality evaluation on the device connection status to obtain communication quality parameters; Performing a preprocessing mode determination on the water quality monitoring device according to the communication quality parameters to obtain a preprocessing working mode and parameters; Performing control instruction parsing and combining on the preprocessing working mode and parameters to obtain a valve control sequence and a pump group control sequence; Generate an execution timing table according to the valve control sequence and the pump group control sequence to obtain a preprocessing execution step; Performing timing execution parameter analysis on the preprocessing execution steps to obtain step operation parameters; The preprocessing process data is obtained according to the step operation parameters, and multi-dimensional analysis is performed to obtain the preprocessing state data.

4. The water quality monitoring data management method according to claim 1, characterized in that: The obtaining of the water sample measurement data collected by the water quality monitoring device and the correlation analysis with the pre-processing state data to obtain the water quality monitoring result includes: Performing standard conversion on the original measurement data collected by the water quality monitoring equipment to obtain the water sample measurement data; Performing validity detection on the water sample measurement data according to preset standard measurement parameters to obtain valid measurement data; Classifying and counting the effective measurement data to obtain measurement index data; Performing data matching processing on the measurement index data and the preprocessing state data to obtain matching relationship data; Performing data association on the measurement results according to the matching relationship data to obtain association analysis data; A comprehensive data evaluation is performed on the correlation analysis data to obtain a water quality monitoring evaluation result.

5. The water quality monitoring data management method according to claim 1, characterized in that: The obtaining of the operating environment parameters of the water quality monitoring device and performing device analysis in combination with the device connection status to obtain the device operating status includes: Classify and split the operating environment parameters to obtain power supply parameters, temperature and humidity parameters, and equipment operating parameters; Performing voltage fluctuation monitoring on the power supply parameters to obtain power supply fluctuation data; Performing stability analysis on the power supply parameters according to the power supply fluctuation data to obtain power supply operation status information; Performing environmental adaptability matching on the device connection state according to the temperature and humidity parameters to obtain a temperature and humidity influence coefficient; Performing fault diagnosis and performance evaluation on the working parameters of the equipment to obtain working operation parameters; A multi-dimensional fusion analysis is performed based on the power supply operation status information, the temperature and humidity influence coefficients and the working operation parameters to obtain the equipment operation status.

6. The water quality monitoring data management method according to claim 5, characterized in that: The fault diagnosis and performance evaluation of the equipment working parameters to obtain working parameters include: Analyzing the communication status and signal strength of the device operating parameters to obtain a device communication quality characteristic value; Performing equipment operation analysis on the equipment operating parameters to obtain equipment operation characteristic values ​​including power supply status, equipment status and environmental status; Performing fault alarm analysis on the operating parameters of the equipment to obtain fault characteristic values ​​including communication fault, equipment fault and environmental fault; Performing performance attenuation calculation on the equipment operating parameters according to the equipment operation characteristic values ​​to obtain an equipment performance attenuation curve; Performing correlation analysis on the fault characteristic value according to the equipment communication quality characteristic value and the equipment operation characteristic value to obtain fault diagnosis results and fault type distribution data; Performing piecewise fitting calculation on the equipment performance attenuation curve according to the fault type distribution data to obtain equipment performance evaluation data; Thresholding the equipment performance evaluation data to obtain equipment warning level data; The fault diagnosis result, the equipment performance evaluation data and the equipment warning level data are weightedly calculated to obtain the working operation parameters.

7. The water quality monitoring data management method according to claim 1, characterized in that: The water quality monitoring result is comprehensively determined based on the device connection status, device operation status and pre-treatment status to obtain identification data, including: Combining the water quality monitoring result with the power failure status to obtain a power data identifier; Combining the water quality monitoring result with the instrument status to obtain an instrument fault data identifier; The water quality monitoring results are combined with the key parameters of the instrument to obtain the upper limit and lower limit data identification; Combining the water quality monitoring result with the device connection status to obtain a device offline data identifier; The water quality monitoring result is combined with the pretreatment state to obtain a no water sample and manual start data identification; Performing temperature compensation correction on the water quality monitoring result to obtain a temperature-corrected water quality monitoring result; Performing spike recovery processing according to the water quality monitoring results to obtain a spike recovery rate; Performing parallel sample measurements on water samples according to the water quality monitoring results to obtain parallel sample data; The power supply data identifier, the instrument fault data identifier, the over-upper limit and over-lower limit data identifiers, the equipment offline data identifier, the spiked recovery rate and the parallel sample data are integrated and corrected to obtain the data identifier.

8. The water quality monitoring data management method according to claim 1, characterized in that: The scheme is constructed according to the device connection status and the device operation status to obtain a comprehensive data management scheme, including: Extracting parameters of the device connection state and the device operation state to obtain a device state parameter matrix; Classifying the device state parameter matrix hierarchically to obtain a multi-level data structure; Performing operation mode judgment on the multi-level data structure to obtain an operation mode judgment result; Performing equipment pre-control construction according to the operation mode judgment result to obtain a pre-processing control plan; Performing electrode calibration on the multi-level data structure according to the preprocessing control scheme to obtain an electrode calibration scheme; Performing environmental compensation analysis on the electrode calibration scheme to obtain an environmental compensation coefficient; Performing data management on the multi-level data structure according to the operation mode judgment result to obtain an operation data management plan; The pre-processing control scheme, the electrode calibration scheme and the operation data management scheme are integrated according to the environmental compensation coefficient to obtain the comprehensive data management scheme.

9. A water quality monitoring data management system, characterized in that: The water quality monitoring data management method applied to any one of claims 1 to 8 above comprises: A collection module, the collection module is used to obtain system configuration information and initialization parameters of the water quality monitoring device, and communicate with the water quality monitoring device to obtain the device connection status; An analysis module, the analysis module is used to obtain a pre-processing control instruction according to the device connection status, and perform a water sample collection pre-processing operation to obtain pre-processing status data; A correlation module, the correlation module is used to obtain the water sample measurement data collected by the water quality monitoring equipment, and perform correlation analysis with the pre-processing state data to obtain a water quality monitoring result; A processing module, the processing module is used to obtain the operating environment parameters of the water quality monitoring device, and perform device analysis in combination with the device connection status to obtain the device operating status; A control module, the control module is used to make a comprehensive judgment on the water quality monitoring result based on the device connection state, device operation state and pre-processing state to obtain identification data; An execution module is used to construct a solution according to the device connection status and the device operation status to obtain a comprehensive data management solution.

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