Real-time quality control and visualization system based on environmental monitoring data

Abstract

The invention discloses a real-time quality control and visualization system based on environmental monitoring data, and belongs to the technical field of environmental monitoring, and the system comprises an environmental perception module which obtains the temperature and humidity parameters of a monitoring substation and the switching state of a UPS power supply of the monitoring substation in real time; the intelligent quality control module is used for generating a calibration result based on the temperature and humidity parameters, and dynamically adjusting a preset equipment heat dissipation strategy in the monitoring substation according to the switching state of the UPS; the central decision module is used for loading a calibration result, a heat dissipation strategy execution state and a positioning coordinate and generating an equipment cooling alarm signal, a pollutant standard exceeding alarm signal and a pollution source position mark, and the visual platform is used for generating an emergency disposal instruction set and an equipment maintenance instruction set based on the pollution source position mark. Through the environment sensing module, the intelligent quality control module, the central decision-making module and the visual platform, the overheating risk of monitoring equipment parts is eliminated, and cross-department collaborative scheduling of pollution event disposal and equipment fault response is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, in particular to a real-time quality control and visualization system based on environmental monitoring data. BACKGROUND

[0002] In today's era of globalization, environmental protection has become an urgent task universally recognized by the international community, and its strategic position is becoming increasingly prominent. Environmental monitoring, as a key link for assessing environmental quality, tracking pollution sources and formulating environmental protection policies, is self-evident in its importance. However, with the rapid expansion of environmental monitoring sites, how to implement an efficient and comprehensive supervision strategy for the operation and maintenance of monitoring sites has become an increasingly prominent problem.

[0003] At present, the sampling device in the substation of the monitoring site collects air data, and the monitoring instrument performs real-time analysis. The quality control tasks (such as zero point calibration and filter replacement) are operated by engineers in the quality assurance laboratory on site. The original data is processed by the center computer room, and finally the system support laboratory maintains the operation of the equipment.

[0004] In the existing environmental monitoring technology, the substation of the monitoring site lacks real-time environmental perception ability and relies on passive intervention of artificial station temperature and humidity control, resulting in a decrease in instrument measurement accuracy under extreme temperature and a risk of equipment overheating. At the same time, the quality control process highly depends on periodic tasks operated by engineers on site, resulting in low operation and maintenance efficiency and significant delay in fault response of remote sites.

[0005] Therefore, it is urgent to provide a real-time quality control and visualization system based on environmental monitoring data to solve the above problems. SUMMARY

[0006] The technical problem to be solved by the present application is to overcome the shortcomings of the existing technology, i.e., the substation of the monitoring site lacks real-time environmental perception ability and relies on passive intervention of artificial station temperature and humidity control, resulting in a decrease in instrument measurement accuracy under extreme temperature and a risk of equipment overheating. At the same time, the quality control process highly depends on periodic tasks operated by engineers on site, resulting in low operation and maintenance efficiency and significant delay in fault response of remote sites. The present application provides a real-time quality control and visualization system based on environmental monitoring data.

[0007] To solve the above technical problems, one technical solution adopted by the present application is to provide a real-time quality control and visualization system based on environmental monitoring data, comprising an environmental perception module, an intelligent quality control module, a central decision module and a visualization platform. An environmental perception module comprises a collection unit and a power monitoring unit deployed in a preset monitoring substation, the collection unit acquires a temperature and humidity parameter of the monitoring substation in real time, the power monitoring unit is used for regulating and controlling a UPS power switching state of the monitoring substation, and an environmental report is generated by integrating the temperature and humidity parameter and the UPS power switching state and transmitted to the intelligent quality control module; An intelligent quality control module triggers a preset automatic calibration task based on the temperature and humidity parameter in the environmental report, generates a calibration result, dynamically adjusts a preset equipment cooling strategy in the monitoring substation according to the UPS power switching state in the environmental report, records a cooling strategy execution state, and uploads the calibration result, the cooling strategy execution state and a positioning coordinate of the monitoring substation to the central decision module after binding them; A central decision module loads the calibration result, the cooling strategy execution state and the positioning coordinate, generates a device cooling alarm signal when the execution state of the cooling strategy is abnormal, generates a pollutant overproof alarm signal by analyzing the calibration result when the execution state of the cooling strategy is normal, generates a pollution source position mark according to the positioning coordinate, associates the device cooling alarm signal and the pollutant overproof alarm signal to the pollution source position mark, and pushes them to a visualization platform; The visualization platform renders a preset double-color alarm icon comprising red and blue in a preset map based on the pollution source position mark, wherein the red alarm icon is associated with the pollutant overproof alarm signal and the pollution source position mark, the blue alarm icon is associated with the device cooling alarm signal, an emergency disposal instruction set and a device maintenance instruction set are generated according to the double-color alarm icon, and are merged and issued to a preset terminal device.

[0008] The application further provides that the collection unit comprises a heat-sensitive sensing matrix attached to the inner wall of the instrument cabin of the monitoring substation, a humidity-sensitive sensing array suspended in the cabin space, a multi-channel signal converter electrically connected to the heat-sensitive sensing matrix and the humidity-sensitive sensing array, and a self-checking module for implementing time domain stability checking on the converted signals of the multi-channel signal converter.

[0009] The application further provides that the UPS power switching state of the monitoring substation in the environmental perception module comprises automatic switching to a battery power supply mode when a mains power supply mode is interrupted, forced switching to a bypass power supply mode when a UPS device is overloaded or fails, and manual switching to a maintenance bypass mode under maintenance demand.

[0010] The application further provides that the calibration result in the intelligent quality control module is generated by the following steps: S1, extract the temperature and humidity parameters in the environment report, compare and analyze the extracted temperature and humidity parameter values with a preset first threshold range, generate a comparison and analysis result, identify abnormal temperature and humidity parameter values that exceed the preset first threshold range according to the comparison and analysis result, and associate the abnormal temperature and humidity parameter values with the monitoring device components in the monitoring substation; S2, based on the identification information of the associated monitoring device components, retrieve the pre-stored calibration database to obtain the calibration program and standard parameter value of the monitoring device components, drive the preset calibration execution device to implement calibration operation on the associated monitoring device components according to the obtained calibration program and standard parameter value, and generate feedback information; S3, receive the feedback information, and generate a calibration result after analyzing and processing the original data in the feedback information.

[0011] The application further provides that: the intelligent quality control module dynamically adjusts the preset device cooling strategy in the monitoring substation, and the specific steps of recording the cooling strategy execution state are as follows: S101, identify the UPS power switching state in the environment report, enter the normal cooling strategy when the UPS power switching state is the mains power supply mode, set the preset cooling device reference speed and execute the continuous cooling action; when the UPS power switching state is the battery power supply mode, enter the energy-saving cooling strategy, limit the maximum power consumption of the cooling device and execute the intermittent cooling action; when the UPS power switching state is the bypass power supply mode, enter the compensation cooling strategy, increase the operating frequency of the cooling device and execute the high-density cooling action; when the UPS power switching state is the maintenance bypass mode, enter the manual cooling strategy, turn off the cooling device and switch to the manual start-stop mode; S102, record the power consumption parameter of the cooling device at a preset period under the normal cooling strategy, record the speed drop data of the cooling device at the cooling interval under the energy-saving cooling strategy, and record the frequency adjustment data of the cooling device under the action density of the compensation cooling strategy; S103, based on the action execution result under the current device cooling strategy, associate the identification information of the cooling device to generate a device cooling strategy execution state record.

[0012] The application further provides that: the device cooling strategy execution state record contains the timestamp and value sequence of the power consumption parameter, the speed drop data or the frequency adjustment data; The generation steps of the device cooling alarm signal in the central decision module are as follows: Q1, load the timestamp and value sequence data in the device cooling strategy execution state record, and perform deviation calculation on the power consumption parameter, the speed drop data or the frequency adjustment data; Q2, analyze the state mark of the monitoring device component in the calibration result and the measured value of the pollutant monitored by the monitoring device component, and determine the spatial position of the monitoring substation by correlating the positioning coordinates of the monitoring substation; Q3, when the result of the deviation calculation continuously exceeds the set deviation and the state mark of the monitoring device component in the calibration result is abnormal, a cooling alarm signal containing the spatial position and the identification information of the monitoring device component is generated.

[0013] The application further provides that: the generation of the pollution source position mark in the central decision module is as follows: Q101, the central decision module loads the measured value of the pollutant and the identification information of the monitoring device component in the calibration result, performs continuous frequency statistics on the measured value of the pollutant exceeding the preset limit value, assigns a pollution mark to the measured value of the pollutant exceeding the set range in the statistical result, and draws the center point of the pollution mark according to the monitoring range of the monitoring device component, extends to the surrounding according to the center point of the pollution mark in a mesh structure, sets a plurality of sub-pollution marks at a preset monitoring distance, indexes the plurality of sub-pollution marks and the pollution mark, generates a pollutant over-limit set, and generates a pollutant over-limit alarm signal after processing the pollutant over-limit set according to a preset processing unit; Q102, according to the spatial topological relationship of the positioning coordinates of the monitoring substation, extract the coordinate data of all the sub-pollution marks and the pollution mark in the pollutant over-limit set, construct a multi-level spatial grid with a preset dynamic expansion radius, automatically fuse the area where the density of the pollution mark or the density of the sub-pollution mark in the adjacent spatial grid reaches a set second threshold value into a composite pollution block, and bind the identification information of all monitoring device components in the composite pollution block to generate a pollution source position mark.

[0014] The application further provides that: after the central decision module generates the pollution source position mark according to the positioning coordinates, the central decision module converts the longitude and latitude data of the positioning coordinates into a geographic grid, fills the device cooling alarm signal, the pollutant over-limit alarm signal and the pollution source position mark into the geographic grid, and respectively attaches the code of the geographic grid, generates a data packet and pushes it to the visualization platform.

[0015] The application is further provided that: the generation step of the emergency disposal instruction set in the visualization platform comprises: based on the pollution exceeding alarm signal associated with the red icon and the pollution source position marker, extracting the spatial coordinate sequence of the composite pollution block and the identification information of the monitoring equipment component; performing adjacent area clustering analysis on the composite pollution block according to the spatial topological relationship to generate a pollution diffusion path prediction model; dynamically dividing the emergency disposal priority level according to the binding relationship between the pollution diffusion path prediction model and the identification information of the monitoring equipment component; based on the priority level matching the pre-stored emergency resource scheduling scheme template, generating an emergency disposal instruction set containing the deployment coordinates of the mobile purification equipment, the coordinates of the traffic control node and the coordinates of the pollution interception path. The application is further provided that: the generation step of the equipment maintenance instruction set is as follows: based on the device cooling alarm signal associated with the blue icon, extracting the identification information and spatial position of the monitoring equipment component; retrieving the pre-stored operation and maintenance knowledge base according to the identification information of the monitoring equipment component, matching the fault diagnosis tree and maintenance operation list of the heat dissipation equipment; combining the spatial position and the maintenance operation list to generate an equipment positioning work order, dynamically dividing the maintenance task priority; calling the pre-configured maintenance resource scheduling template according to the maintenance task priority to generate an equipment maintenance instruction set containing the replacement coordinates of the monitoring equipment component, the debugging parameters of the heat dissipation equipment and the path planning of the maintenance personnel.

[0016] The beneficial effects of the application are as follows: 1. The application captures the temperature and humidity distribution data of the instrument cabin of the monitoring substation in real time through the collection unit of the environmental perception module, simultaneously intelligently regulates and controls the multi-mode switching of the UPS power supply by the power monitoring unit, and dynamically triggers the device heat dissipation strategy of the intelligent quality control module based on the power supply state, realizes the autonomous closed-loop regulation and control of the temperature and humidity of the instrument cabin of the monitoring substation, eliminates the measurement precision decline and overheating risk of the monitoring equipment component caused by extreme temperature from the source, and guarantees the long-term stable operation of the sensor; 2. The application converts the traditional quality control process relying on manual intervention into a fully automated calibration-diagnosis-decision chain through the automatic calibration task of the intelligent quality control module and the double-signal collaborative analysis of the central decision module; at the same time, the visualization platform automatically generates the emergency disposal instruction set and the equipment maintenance instruction set according to the double-color alarm icon, realizes the cross-department collaborative scheduling of pollution event disposal and equipment fault response, and significantly improves the operation and maintenance efficiency of remote sites and the global risk control ability. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The system flowchart of the application is shown in Figure 1; Figure 2 The generation step flowchart of the calibration result in the intelligent quality control module of the application is shown in Figure 2; Figure 3The flow chart of the recording step of the heat dissipation strategy execution state of the present application; Figure 4 The flow chart of the generation of the pollution source position mark in the central decision module of the present application. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present application are described in detail below with reference to the accompanying drawings, so that the advantages and features of the present application can be more easily understood by those skilled in the art, and the protection scope of the present application can be more clearly and definitely defined.

[0019] Please refer to Figures 1-4 A real-time quality control and visualization system based on environmental monitoring data, comprising an environmental perception module, an intelligent quality control module, a central decision module and a visualization platform; The environmental perception module comprises a collection unit and a power monitoring unit deployed in a preset monitoring substation. The collection unit acquires temperature and humidity parameters of the monitoring substation in real time, and the power monitoring unit is used to regulate and control the UPS power switching state of the monitoring substation. The temperature and humidity parameters and the UPS power switching state are integrated to generate an environmental report transmitted to the intelligent quality control module. Preferably, the monitoring substation adopts an industrial-grade protection design, is configured with a multi-parameter sensor array (including temperature and humidity, PM2.5, PM10, , and other atmospheric pollutant sensors) and a dynamic environment monitoring unit (integrating water immersion, smoke sensing, access control, and video monitoring functions), and the core hardware selects an SG500 industrial gateway (supporting four independent RS485 interfaces and dual-network backup) and a KD100 micro data acquisition instrument (compatible with MQTT / Modbus protocols). Data encapsulation and encrypted transmission are realized through the HJ / T 212 standard protocol. At the same time, a UPS power multi-mode switching mechanism (city power / battery / bypass / repair bypass mode) is deployed, and the monitoring substation is arranged according to the spatial topological relationship at the boundary of the industrial park, the transportation hub, the river section, and the urban grid point, forming a globally covered perception network. The collection unit comprises a heat-sensitive sensing matrix attached to the inner wall of the instrument cabin of the monitoring substation, a humidity-sensitive sensing array suspended in the cabin space, a multi-channel signal converter electrically connected to the heat-sensitive sensing matrix and the humidity-sensitive sensing array, and a self-checking module for time domain stability checking of the converted signals of the multi-channel signal converter. The heat-sensitive sensing matrix is composed of a grid-shaped array of micro temperature probes, which can collect temperature distribution data in each area of the instrument cabin in real time. The humidity-sensitive sensing array is fixed to the geometric center point of the cabin body by a telescopic support and periodically scans the dynamic change data of the relative humidity of the air. The multi-channel signal converter converts the temperature distribution data and humidity dynamic change data into standard current signals, and the self-checking module outputs the temperature and humidity parameters after continuously detecting the fluctuation amplitude of the current signals.

[0020] The UPS power switching state of the monitoring substation in the environment perception module includes automatic switching to the battery power supply mode when the commercial power supply mode is interrupted, forced switching to the bypass power supply mode when the UPS device is overloaded or fails, and manual switching to the maintenance bypass mode under maintenance requirements; the interruption of the commercial power supply mode is triggered by the voltage fluctuation detector in real time, and the switching process is completed within 10 milliseconds through the static switch; the forced switching of the UPS device overload or failure is started after being determined by the internal diagnosis unit of the UPS, and the load is directly powered by the standby commercial power line during the switching period; the maintenance bypass mode is realized by manually operating the isolating switch, and the load is supported by the commercial power after switching while isolating the internal circuit of the UPS.

[0021] The intelligent quality control module triggers a preset automatic calibration task based on the temperature and humidity parameters in the environment report, generates a calibration result, dynamically adjusts the preset device cooling strategy in the monitoring substation according to the UPS power switching state in the environment report, records the cooling strategy execution state, and uploads the calibration result, the device cooling strategy execution state and the positioning coordinates of the monitoring substation to the central decision module after binding; The generation steps of the calibration result in the intelligent quality control module are as follows: S1, extract the temperature and humidity parameters in the environment report, compare and analyze the extracted temperature and humidity parameter values with the preset first threshold range, generate a comparison and analysis result, identify the abnormal temperature and humidity parameter values that exceed the preset first threshold range according to the comparison and analysis result, and associate the abnormal temperature and humidity parameter values to the monitoring device components in the monitoring substation; In step S1, the first threshold range specifically refers to: the temperature threshold range is 5℃~50℃, and the humidity threshold range is 10%RH~80%RH; S2, based on the identification information of the associated monitoring device components, retrieve the pre-stored calibration database to obtain the calibration program and standard parameter value of the monitoring device components, drive the preset calibration execution device to implement calibration operation on the associated monitoring device components according to the obtained calibration program and standard parameter value, and generate feedback information; The pre-stored calibration database refers to a digital storage system integrating the model, calibration procedure, standard parameter value and historical calibration record of the monitoring device components, which supports automatic retrieval and calling by associating the calibration program and standard parameters through the identification information of the monitoring device components (such as device serial number, component model); the calibration execution device refers to a physical or electromechanical device used for automatic calibration operation, and its core function is to implement standardized calibration operation on the monitoring device components according to the calibration program instructions and generate feedback data; S3, receiving feedback information, generating calibration results after analyzing and processing the original data in the feedback information, wherein the method for analyzing and processing the original data in the feedback information is: extracting the key indicators (such as gain coefficient, offset, error value) in the original data returned by the calibration execution device, comparing with the standard parameter value pre-stored in the calibration database, identifying the deviation between the actual value and the theoretical value; based on the deviation value, calculate the comprehensive error rate, and combine the temperature and humidity parameters in the environment report and the drift trend in the historical calibration record to dynamically compensate and correct the deviation; finally, according to whether the corrected error falls within the allowable tolerance range (such as ±0.5%FS), generate a "calibration qualified" "partial calibration" or "calibration failed" state label, and associate the calibration timestamp and operator electronic signature to form the calibration results.

[0022] Among them, the dynamic adjustment of the preset device cooling strategy in the intelligent quality control module in the monitoring substation is as follows: S101, identify the UPS power switching state in the environment report, when the UPS power switching state is in the mains power supply mode, enter the normal cooling strategy, set the preset cooling device reference speed and execute the continuous cooling action; when the UPS power switching state is in the battery power supply mode, enter the energy-saving cooling strategy, limit the maximum power consumption of the cooling device and execute the intermittent cooling action; when the UPS power switching state is in the bypass power supply mode, enter the compensation cooling strategy, increase the running frequency of the cooling device and execute the high-density cooling action; when the UPS power switching state is in the maintenance bypass mode, enter the manual cooling strategy, turn off the cooling device and switch to the manual start-stop mode; S102, record the power consumption parameters of the cooling device under the normal cooling strategy according to the preset period, record the speed drop data of the cooling device under the energy-saving cooling strategy according to the cooling interval, and record the frequency adjustment data of the cooling device under the compensation cooling strategy according to the action density of the cooling device; S103, based on the action execution result under the current device cooling strategy, generate a device cooling strategy execution state record associated with the identification information of the cooling device.

[0023] Among them, the cooling device refers to a set of hardware systems for regulating the temperature inside the monitoring substation, and its core function is to export the heat generated during the operation of the device by active or passive means, to ensure that the electronic components of the monitoring device components work within the safe temperature range. Active cooling devices include fan systems, compressor refrigeration devices, etc. Passive cooling devices include finned heat sinks or phase change materials.

[0024] Among them, the device cooling strategy execution state record contains the timestamp and value sequence of the power consumption parameters, speed drop data or frequency adjustment data; In Example 1, the environmental perception module of a certain industrial park monitoring substation (positioning coordinates: E118.78° N31.98°) collects real-time temperature distribution data (regional temperature difference ≤0.5°C) in the instrument cabin by attaching a thermal sensor matrix (grid-shaped micro temperature probe) to the inner wall of the cabin, while a hygroscopic sensor array (suspended in the geometric center of the cabin) periodically scans the dynamic change data of air relative humidity (sampling frequency 1 Hz). After the original data is converted into a 4-20 mA standard current signal by a multi-channel signal converter, the signal fluctuation amplitude is continuously detected (tolerance fluctuation ±0.1 mA) by a self-checking module, and the temperature and humidity parameters (temperature 28.5°C, humidity 65%RH) are output and integrated with the UPS power supply switching state (currently in battery power mode) to generate an environmental report upload. The intelligent quality control module identifies that the temperature and humidity parameters are within the threshold range (temperature 5-50°C, humidity 10%-80%RH) based on the environmental report, and when the UPS power supply is in battery power mode, triggers the energy-saving cooling strategy: limits the maximum power consumption of the cooling equipment (axial flow fan system) to the rated value of 60%, and performs intermittent cooling action (runs for 30 seconds and stops for 90 seconds). During the execution process, the system records the speed drop data (baseline speed 2500 rpm → actual speed 1500 rpm, drop 40%) according to the cooling interval, and associates the time stamp (2025-06-30 10:15:23) to generate a device cooling strategy execution state record, which contains the numerical sequence of speed drop data (10:15:23, 40, 10:17:15, 42, 10:19:08, 38) and power consumption parameters (peak power consumption 120W → average 72W), and finally binds with the positioning coordinates of the monitoring substation to upload to the central decision module.

[0025] This embodiment automatically switches to the energy-saving cooling strategy by dynamically responding to the UPS power supply state (battery power mode), significantly reducing the power consumption of the cooling equipment (40% energy saving compared to the conventional strategy), while accurately recording the time stamp sequence of the speed drop data, providing the central decision module with quantitative traceability basis for the execution state of the device cooling strategy. Combined with the real-time data of the thermal / hygroscopic sensor matrix and the stability guarantee of the self-checking module, the monitoring substation still maintains stable temperature and humidity in the instrument cabin (fluctuation ≤±0.3°C) when the power supply is abnormal, avoiding data drift caused by insufficient cooling of the sensor, improving the reliability of the monitoring data and the service life of the equipment (estimated to be extended by 25%), and optimizing the energy utilization efficiency (battery mode, extended by 3.5 hours).

[0026] The central decision module loads the calibration result, the heat dissipation strategy execution state and the positioning coordinates, generates a device cooling warning signal when the execution state of the heat dissipation strategy is abnormal, generates a pollution exceeding standard warning signal by analyzing the calibration result when the execution state of the heat dissipation strategy is normal, generates a pollution source position mark according to the positioning coordinates, associates the device cooling warning signal and the pollution exceeding standard warning signal to the pollution source position mark, and pushes to the visualization platform; The generation step of the device cooling warning signal in the central decision module is as follows: Q1, load the timestamp and numerical sequence data in the device heat dissipation strategy execution state record, and perform deviation calculation on the power consumption parameter, speed drop data or frequency adjustment data; In step Q1, the specific process of performing deviation calculation on the power consumption parameter, speed drop data or frequency adjustment data is as follows: based on the timestamp sequence in the device heat dissipation strategy execution state record, the numerical sequence of the power consumption parameter, speed drop data or frequency adjustment data is extracted in time sequence, and is compared with the reference value (such as the rated power consumption of the conventional heat dissipation strategy, the speed drop threshold of the energy saving strategy, and the frequency adjustment threshold of the compensation strategy) under the preset strategy point by point, the absolute difference value of the actual value and the reference value at each time point is calculated, and the relative deviation percentage of each data point is calculated by the formula (absolute difference value / reference value) × 100%, and finally the dynamic deviation curve is generated with the timestamp as the horizontal axis and the deviation percentage as the vertical axis, as the deviation calculation result, and as the quantitative basis for alarm judgment in step Q3; Q2, analyze the state mark of the monitoring device component and the measured value of the monitored pollutant in the calibration result, and determine the spatial position of the monitoring substation by associating the positioning coordinates of the monitoring substation; Q3, when the result of deviation calculation continuously exceeds the set deviation and the state mark of the monitoring device component in the calibration result is abnormal, generate a cooling warning signal containing the spatial position and the identification information of the monitoring device component, wherein the specific value of the set deviation is: the mild deviation threshold is ±10%, the severe deviation threshold is ±25%, and the continuous deviation time ≥5 minutes.

[0027] Among them, the generation content of the pollution source position mark in the central decision module is as follows: Q101、The central decision module loads the measured values of pollutants in the calibration results and the identification information of the monitoring device components, performs continuous frequency statistics on the measured values of pollutants exceeding the preset limit, assigns pollution marks to the measured values of pollutants exceeding the set range in the statistical results, and according to the monitoring range of the monitoring device components, the center point of the pollution mark is drawn, according to the center point of the pollution mark, the surrounding area is extended in a mesh structure, a plurality of sub-pollution marks are set at a preset monitoring distance, and the plurality of sub-pollution marks are indexed with the pollution mark, a pollution exceeding standard set is generated, and a pollution exceeding standard alarm signal is generated after the pollution exceeding standard set is processed according to the preset processing unit; Q102、According to the spatial topological relationship of the positioning coordinates of the monitoring substation, the coordinate data of all sub-pollution marks and pollution marks in the pollution exceeding standard set is extracted, a multi-level spatial grid is constructed with a preset dynamic expansion radius, and the area where the density of pollution marks or the density of sub-pollution marks in adjacent spatial grids reaches a set second threshold is automatically merged into a composite pollution block, and the identification information of all monitoring device components in the composite pollution block is bound to generate a pollution source location mark.

[0028] In the process of generating the pollution source location mark, the spatial topological relationship refers to the geographical space correlation between the positioning coordinates of the monitoring substation, including the adjacency, connectivity, directionality and spatial distribution structure of the point position (such as the relative position of the grid layout river section, traffic hub and industrial park), which is used to analyze the spatial dependence mode of the pollution diffusion path and the pollution mark; The specific value of the dynamic expansion radius is 300 meters, which is based on the density of the pollution diffusion path and the monitoring substation, and the initial radius is calculated from the center point of the pollution mark, and is gradually expanded by level (such as 1km×1km→3km×3km) to match the pollution aggregation characteristics at different scales; The specific value of the second threshold is that the density of pollution marks or sub-pollution marks in the composite pollution block is greater than or equal to 30%, that is, when the distribution density of pollution marks in adjacent spatial grid units exceeds 30% of the total area of the grid, the grid fusion logic is triggered.

[0029] Among them, after the central decision module generates the pollution source location mark according to the positioning coordinates, the latitude and longitude data of the positioning coordinates are converted into a geographical grid, the device cooling alarm signal, the pollution exceeding standard alarm signal and the pollution source location mark are filled into the geographical grid, and the codes of the geographical grid are attached respectively, a data packet is generated and pushed to the visualization platform.

[0030] The visualization platform renders a preset double-color alarm icon containing red and blue in a preset map based on the pollution source position marker, wherein the red alarm icon is associated with the pollution over-limit alarm signal and the pollution source position marker, and the blue alarm icon is associated with the equipment cooling alarm signal; an emergency disposal instruction set and an equipment maintenance instruction set are generated according to the double-color alarm icon, and are merged and issued to a preset terminal device, preferably, the terminal device specifically refers to a hardware device receiving and displaying the emergency disposal instruction set and the equipment maintenance instruction set, including a computer, a mobile device (mobile phone / tablet), a smart TV and an industrial control terminal.

[0031] In the visualization platform, the generation step of the emergency disposal instruction set includes: based on the pollution over-limit alarm signal and the pollution source position marker associated with the red icon, extracting the spatial coordinate sequence of the composite pollution block and the identification information of the monitoring equipment component; performing adjacent region clustering analysis on the composite pollution block according to the spatial topological relationship to generate a pollution diffusion path prediction model; dynamically dividing the emergency disposal priority level according to the binding relationship between the pollution diffusion path prediction model and the identification information of the monitoring equipment component; based on the priority level matching the pre-stored emergency resource scheduling scheme template, generating the emergency disposal instruction set containing the deployment coordinates of the mobile purification equipment, the coordinates of the traffic control node and the coordinates of the pollution interception path. In the visualization platform, the generation step of the emergency disposal instruction set includes: based on the pollution over-limit alarm signal and the pollution source position marker associated with the red icon, extracting the spatial coordinate sequence of the composite pollution block and the identification information of the monitoring equipment component; performing adjacent region clustering analysis on the composite pollution block according to the spatial topological relationship to generate a pollution diffusion path prediction model; dynamically dividing the emergency disposal priority level according to the binding relationship between the pollution diffusion path prediction model and the identification information of the monitoring equipment component; based on the priority level matching the pre-stored emergency resource scheduling scheme template, generating the emergency disposal instruction set containing the deployment coordinates of the mobile purification equipment, the coordinates of the traffic control node and the coordinates of the pollution interception path.

[0032] In Example 2, the central decision module loads the calibration results uploaded by a monitoring substation (positioning coordinates: E118.78° N31.98°) of an industrial park (PM2.5 sensor status marker is "partially calibrated", comprehensive error rate 0.6%>0.5% tolerance) and device cooling strategy execution state record (under energy-saving cooling strategy, speed reduction data sequence: 10:15:23, 40, 10:17:15, 42, 10:19:08, 38), executes deviation calculation: taking the speed reduction target value 40% as the basis, calculates the relative deviation percentage of the actual value at each time point (such as 10:17:15 deviation +5%), generates a dynamic deviation curve; identifies that the deviation of 3 consecutive data points is >10% (triggering moderate deviation warning) and lasts for 5 minutes, superimposes calibration state abnormalities, generates a device cooling warning signal (including spatial position and sensor ID); at the same time, analyze the PM2.5 measured value (85 μg / m³> national standard limit 35 μg / m³) in the calibration result, assign a pollution marker after counting the over-limit frequency, and construct a multi-level spatial grid (primary grid 300 meters→secondary grid 1 kilometer) with the monitoring substation as the center and a dynamic expansion radius of 300 meters, detect that the pollution marker density in the adjacent grid reaches 35% (≥ second threshold 30%), automatically fuse into a composite pollution block (bind the IDs of all monitoring device components), generate a pollution over-limit warning signal; correlate the two types of warning signals to the pollution source position marker, fill in the geographic grid code (such as GCJ-02 coordinate system grid X37Y92) to generate a data package and push it to the visualization platform; the visualization platform renders a red warning icon (associated with PM2.5 over-limit signal and composite pollution block coordinates) and a blue warning icon (associated with device cooling warning and sensor position) based on the pollution source position marker, clusters the composite pollution block according to the spatial topological relationship to generate a pollution diffusion path prediction model (dominant wind direction southeast, diffusion radius 1.2 km), dynamically divides the emergency disposal priority (industrial park boundary is the first level), matches the resource scheduling template to generate an emergency disposal instruction set (including mobile purification device deployment coordinates E118.79° N31.97°, traffic control nodes 3, interception paths 5); simultaneously search the operation and maintenance knowledge base based on the device cooling warning, match the cooling device fault diagnosis tree (axial flow fan bearing wear), generate the device maintenance instruction set (including replacement coordinates E118.78° N31.98°, debugging parameters 1500 rpm±5%, optimal path planning for maintenance personnel).

[0033] The embodiment realizes accurate correlation alarm of monitoring equipment component state anomaly and pollution exceeding standard by deviation grading judgment mechanism (mild ± 10% / severe ± 25% + for 5 minutes) of the central decision module and pollution mark space fusion logic (dynamic expansion radius 300 meters + density threshold 30%), reduces false alarm rate by 42%; the visual platform reduces pollution source positioning error to ± 50 meters (traditional method > 200 meters) based on collaborative analysis of double-color alarm icons, improves emergency disposal instruction set generation time to 90 seconds (manual decision needs 30 minutes); combined with dynamic matching of pollution diffusion path prediction model and operation and maintenance knowledge base, synchronously optimizes pollution disposal efficiency (response time of mobile purification equipment is shortened to 15 minutes) and equipment maintenance accuracy (fault positioning accuracy rate is 98%), avoids sensor data drift (PM2.5 monitoring error rate is reduced to ± 3%) caused by heat dissipation failure, supports collaborative optimization of pollution event closed-loop disposal and equipment health management, and improves global monitoring data reliability to 99.7%.

[0034] The above only describes the embodiments of the present application, and does not limit the patent range of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection range of the present application.

Claims

1. A real-time quality control and visualization system based on environmental monitoring data, characterized by: Including environmental perception module, intelligent quality control module, central decision-making module and visualization platform; An environmental perception module, comprising a collection unit and a power monitoring unit deployed in a preset monitoring substation. The collection unit acquires the temperature and humidity parameters of the monitoring substation in real time. The power monitoring unit is used to control the UPS power switching state of the monitoring substation, integrate the temperature and humidity parameters with the UPS power switching state, generate an environmental report, and transmit it to the intelligent quality control module. An intelligent quality control module triggers a preset automatic calibration task based on the temperature and humidity parameters in the environmental report, generates a calibration result, dynamically adjusts the device cooling strategy preset in the monitoring substation according to the UPS power switching status in the environmental report, records the cooling strategy execution status, and binds the calibration result and the device cooling strategy execution status with the positioning coordinates of the monitoring substation before uploading them to the central decision module; a central decision module, which loads the calibration result, the execution status of the heat dissipation strategy, and the positioning coordinates; generates a device cooling alarm signal when the execution status of the heat dissipation strategy is abnormal; and parses the calibration result to generate a pollutant exceeding standard alarm signal when the execution status of the heat dissipation strategy is normal; generates a pollution source location marker based on the positioning coordinates; associates the device cooling alarm signal and the pollutant exceeding standard alarm signal with the pollution source location marker, and pushes the result to the visualization platform; The visualization platform renders a preset two-color alarm icon containing red and blue in a preset map based on the pollution source location mark, wherein the red alarm icon is associated with the pollutant exceeding the standard alarm signal and the pollution source location mark, and the blue alarm icon is associated with the equipment cooling alarm signal. An emergency response instruction set and an equipment maintenance instruction set are generated according to the two-color alarm icon, and are merged and sent to the preset terminal device.

2. A real-time quality control and visualization system based on environmental monitoring data according to claim 1, characterized in that: The acquisition unit includes a thermal sensor matrix attached to the inner wall of the instrument cabin of the monitoring substation, a humidity sensor array suspended in the cabin space, a multi-channel signal converter electrically connected to the thermal sensor matrix and the humidity sensor array, and a self-calibration module that performs time domain stability calibration on the conversion signal of the multi-channel signal converter.

3. The real-time quality control and visualization system based on environmental monitoring data according to claim 2, characterized in that: The UPS power switching status of the monitoring substation in the environmental perception module includes automatic switching to battery power mode when the AC power supply mode is interrupted, forced switching to bypass power supply mode when the UPS equipment is overloaded or fails, and manual switching to maintenance bypass mode under maintenance requirements.

4. The real-time quality control and visualization system based on environmental monitoring data according to claim 3 is characterized by: The steps for generating the calibration results in the intelligent quality control module are as follows: S1. Extracting temperature and humidity parameters from the environmental report, comparing and analyzing the extracted temperature and humidity parameter values ​​with a preset first threshold range, generating a comparison and analysis result, identifying abnormal temperature and humidity parameter values ​​exceeding the preset first threshold range based on the comparison and analysis result, and associating the abnormal temperature and humidity parameter values ​​with monitoring equipment components in the monitoring substation; S2. Based on the identification information of the associated monitoring equipment component, retrieve the calibration procedure and standard parameter values ​​of the monitoring equipment component from a pre-stored calibration database, drive a preset calibration execution device to perform a calibration operation on the associated monitoring equipment component according to the obtained calibration procedure and standard parameter values, and generate feedback information; S3. Receive the feedback information, analyze and process the original data in the feedback information, and generate a calibration result.

5. The real-time quality control and visualization system based on environmental monitoring data according to claim 4 is characterized in that: The specific steps of dynamically adjusting the device heat dissipation strategy preset in the monitoring substation and recording the execution status of the heat dissipation strategy in the intelligent quality control module are as follows: S101. Identify the UPS power switching state in the environmental report. When the UPS power switching state is the mains power supply mode, enter the conventional cooling strategy, set a preset reference speed of the cooling device, and perform continuous cooling. When the UPS power switching state is the battery power supply mode, enter the energy-saving cooling strategy, limit the maximum power consumption of the cooling device, and perform intermittent cooling. When the UPS power switching state is the bypass power supply mode, enter the compensation cooling strategy, increase the operating frequency of the cooling device, and perform high-density cooling. When the UPS power switching state is the maintenance bypass mode, enter the manual cooling strategy, shut down the cooling device, and switch to the manual start / stop mode. S102, recording power consumption parameters of the heat dissipation device according to a preset period under a conventional heat dissipation strategy, recording speed reduction data of the heat dissipation device according to heat dissipation intervals under an energy-saving heat dissipation strategy, and recording frequency adjustment data according to the operation density of the heat dissipation device under a compensation heat dissipation strategy; S103: Based on the action execution result under the current device heat dissipation policy, associate the identification information of the heat dissipation device to generate a device heat dissipation policy execution status record.

6. The real-time quality control and visualization system based on environmental monitoring data according to claim 5, characterized in that: The device heat dissipation strategy execution status record includes a timestamp and a numerical sequence of power consumption parameters, speed reduction data or frequency adjustment data; The steps for generating the equipment temperature drop alarm signal in the central decision module are as follows: Q1. Load the timestamp and numerical sequence data in the device cooling strategy execution status record, and perform deviation calculation on the power consumption parameter, speed reduction data, or frequency adjustment data; Q2. Analyze the status marks of the monitoring equipment components and the actual measured values ​​of the pollutants monitored by the monitoring equipment components in the calibration results, and associate them with the positioning coordinates of the monitoring substation to determine the spatial position of the monitoring substation; Q3. When the result of the deviation calculation continuously exceeds the set deviation and the status of the monitoring equipment component in the calibration result is marked as abnormal, a temperature drop alarm signal containing the spatial position and identification information of the monitoring equipment component is generated.

7. The real-time quality control and visualization system based on environmental monitoring data according to claim 6, characterized in that: The generation content of the pollution source location mark in the central decision module is as follows: Q101. The central decision module loads the actual measured values ​​of pollutants and identification information of monitoring equipment components in the calibration results, performs continuous frequency statistics on the actual measured values ​​of pollutants exceeding preset limits, assigns pollution marks to the actual measured values ​​of pollutants that exceed the set range in the statistical results, and delineates the center point of the pollution mark according to the monitoring range of the monitoring equipment components. A network structure is extended to the surrounding areas based on the center point of the pollution mark, and a plurality of sub-pollution marks are set at a preset monitoring distance. The plurality of sub-pollution marks are indexed with the pollution mark to generate a pollutant exceeding standard set, and the pollutant exceeding standard set is processed by a preset processing unit to generate a pollutant exceeding standard alarm signal; Q102. According to the spatial topological relationship of the positioning coordinates of the monitoring substation, the coordinate data of all the sub-pollution marks and the pollution marks in the pollutant exceeding standard set are extracted, and a multi-level spatial grid is constructed with a preset dynamic expansion radius. The areas where the density of the pollution marks or the density of the sub-pollution marks in adjacent spatial grids reaches the set second threshold are automatically merged into a composite pollution block, and the identification information of all monitoring equipment components in the composite pollution block is bound to generate a pollution source location mark.

8. The real-time quality control and visualization system based on environmental monitoring data according to claim 7, characterized in that: After the central decision module generates the pollution source location mark based on the positioning coordinates, it converts the latitude and longitude data of the positioning coordinates into a geographic grid, fills the equipment cooling alarm signal, the pollutant exceeding the standard alarm signal and the pollution source location mark into the geographic grid, and attaches the geographic grid code to each, generates a data packet and pushes it to the visualization platform.

9. The real-time quality control and visualization system based on environmental monitoring data according to claim 8, characterized in that: The steps for generating the emergency response instruction set in the visualization platform include: extracting the spatial coordinate sequence of the complex pollution block and the identification information of the monitoring equipment components based on the pollutant exceeding the standard alarm signal and the pollution source location mark associated with the red icon; performing adjacent area clustering analysis on the complex pollution block according to the spatial topological relationship to generate a pollution diffusion path prediction model; dynamically dividing the emergency response priority levels according to the binding relationship between the pollution diffusion path prediction model and the identification information of the monitoring equipment components; and generating an emergency response instruction set containing the deployment coordinates of mobile purification equipment, traffic control node coordinates and pollutant interception path coordinates based on the priority level matching pre-stored emergency resource scheduling plan template.

10. The real-time quality control and visualization system based on environmental monitoring data according to claim 9, characterized in that: The steps for generating the equipment maintenance instruction set are as follows: based on the equipment cooling alarm signal associated with the blue icon, extract the identification information and spatial position of the monitoring equipment components; retrieve the pre-stored operation and maintenance knowledge base according to the identification information of the monitoring equipment components, and match the fault diagnosis tree and maintenance operation list of the heat dissipation equipment; generate an equipment positioning work order based on the spatial position and the maintenance operation list, and dynamically divide the maintenance task priority; call the pre-configured maintenance resource scheduling template according to the maintenance task priority, and generate an equipment maintenance instruction set including the replacement coordinates of the monitoring equipment components, the debugging parameters of the heat dissipation equipment, and the maintenance personnel path planning.

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