Internet of Things environment monitoring method, equipment, product and medium

Abstract

The invention discloses an Internet of Things environment monitoring method, equipment, a product and a medium, and relates to the technical field of Internet of Things. According to the method, a monitoring area is divided into an upstream reservoir area, a midstream reservoir area and a reservoir area in front of a dam based on terrain distribution data and dam position information; for an upstream reservoir area, acquiring pollutant concentration distribution data and flow rate monitoring data of multiple points, and determining a pollution source direction area based on the pollutant concentration distribution data; for the midstream reservoir area, determining a water layering stability state based on the temperature distribution data and the change trend of the dissolved oxygen content of each water layer; for the reservoir area in front of the dam, water level fluctuation data and gate opening information are obtained, water level response time and response amplitude are calculated based on the water level fluctuation data and the gate opening information, and response characteristics of the reservoir area to scheduling operation are determined based on the water level response time and the response amplitude; and determining a comprehensive environment assessment report based on the pollution source direction area, the water body layering stability state and the response characteristics. The method has the effect of improving the environmental monitoring accuracy.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, specifically to an IoT environmental monitoring method, device, product, and medium. Background Technology

[0002] In modern environmental monitoring and water resource management, reservoirs, as important water conservancy infrastructure, undertake multiple functions such as flood control, water supply, and power generation. Real-time monitoring and assessment of their water environment quality are of great significance for ensuring water resource security and ecological environment stability. With the acceleration of industrialization and the continuous improvement of urbanization, reservoirs face dual pressures from point source and non-point source pollution, and the requirements for the accuracy and timeliness of water environment monitoring technology are increasing.

[0003] Currently, in the field of reservoir environmental monitoring, a Chinese patent with publication number CN111800502A discloses a three-dimensional online environmental monitoring system and method based on LoRa. This method collects environmental information and geographical location information by setting up LoRa acquisition nodes at pre-divided grid locations in the environment to be monitored, and uses the k-nearest neighbor algorithm to identify historical environmental information with the highest similarity to the environmental information, thereby predicting environmental change early warning information and determining the movement speed and direction of pollutants exceeding the standard.

[0004] However, in practical applications, due to the characteristics of reservoir environments such as large water area, complex hydrological conditions, and uneven distribution of pollution sources, traditional grid-based monitoring methods are difficult to fully consider the functional characteristics and hydrodynamic differences of different areas of the reservoir. Therefore, relying solely on a unified grid layout and a single data analysis method for environmental monitoring can easily lead to insufficient accuracy of environmental monitoring results. Summary of the Invention

[0005] This application provides an IoT environmental monitoring method, device, product, and medium, which improves the accuracy of environmental monitoring.

[0006] The first aspect of this application provides an Internet of Things (IoT) environmental monitoring method, specifically including: Obtain topographic distribution data and dam location information of the reservoir area, and divide the monitoring area into upstream reservoir area, midstream reservoir area and dam front reservoir area based on the topographic distribution data and dam location information; For the upstream reservoir area, pollutant concentration distribution data and flow velocity monitoring data are obtained from multiple locations. Based on the pollutant concentration distribution data, the pollutant concentration gradient is calculated. Based on the pollutant concentration gradient and flow velocity monitoring data, the direction and area of ​​pollution source are determined. For the midstream reservoir area, obtain vertically stratified temperature distribution data and dissolved oxygen content of each water layer, calculate the temperature stratification coefficient based on the temperature distribution data, and determine the water stratification stability state based on the temperature stratification coefficient and the changing trend of dissolved oxygen content of each water layer. For the reservoir area in front of the dam, water level fluctuation data and gate opening information are acquired. Based on the water level fluctuation data and gate opening information, the water level response time and response amplitude are calculated. Based on the water level response time and response amplitude, the response characteristics of the reservoir area to the scheduling operation are determined. Based on the direction and region of pollution sources, the stratified stability of water bodies, and response characteristics, a comprehensive environmental assessment report is determined.

[0007] By employing the aforementioned technical solutions, topographic distribution data and dam location information of the reservoir area were acquired. Based on this data, the monitoring area was divided into upstream, midstream, and upstream reservoir areas, achieving a differentiated monitoring layout tailored to the characteristics of different reservoir areas. This avoids the blindness of traditional uniform monitoring and improves the efficiency of monitoring resource allocation. For the upstream reservoir area, multi-point pollutant concentration distribution data and flow velocity monitoring data were acquired. Based on the pollutant concentration distribution data, the pollutant concentration gradient was calculated. Then, based on the pollutant concentration gradient and flow velocity monitoring data, the direction and region of pollution sources were determined. Through correlation analysis between concentration gradient and flow velocity, the location of pollution sources was accurately pinpointed, enabling rapid location and source tracing of pollution sources. For the midstream reservoir area, vertically stratified temperature distribution data and dissolved oxygen content of each water layer were acquired. Based on the temperature distribution data, a temperature stratification coefficient was calculated. Then, based on the temperature stratification coefficient and the changing trends of dissolved oxygen content in each water layer, the stability of water stratification was determined. The temperature stratification coefficient reflects the degree of vertical mixing in the water body. Combined with dissolved oxygen changing trends, it can accurately determine whether thermal stratification exists in the water body, providing a scientific basis for predicting water quality changes and ecological risks. By acquiring water level fluctuation data and gate opening information in the reservoir area upstream of the dam, the water level response time and amplitude are calculated based on this data. Furthermore, the response time and amplitude are used to determine the reservoir area's response characteristics to scheduling operations. The water level response time and amplitude directly reflect the reservoir area's hydrodynamic response sensitivity, providing a quantitative reference for formulating precise reservoir scheduling strategies. A comprehensive environmental assessment report is generated based on the direction and region of pollution sources, the stratified stability of water bodies, and response characteristics. This report integrates monitoring information from three key dimensions: upstream pollution risk, midstream ecological status, and downstream scheduling response, forming a comprehensive and systematic reservoir environmental status evaluation system, significantly improving the accuracy of reservoir environmental monitoring.

[0008] Optionally, the real-time values ​​of pollutant concentrations at each monitoring point in the upstream reservoir area are obtained, the difference between the pollutant concentration values ​​at adjacent monitoring points is calculated, and the result of the difference calculation is divided by the distance between adjacent monitoring points to obtain the pollutant concentration gradient between adjacent monitoring points. The concentration gradients of each pollutant are spatially interpolated according to the geographical coordinates of the monitoring points to generate a pollutant concentration gradient distribution map of the upstream reservoir area. The area with the largest pollutant concentration gradient value is identified from the pollutant concentration gradient distribution map as the area with the most drastic change in pollutant concentration. The flow velocity monitoring data of each monitoring point in the area with the most drastic changes in pollutant concentration are obtained. The flow velocity direction angle and flow velocity magnitude of each monitoring point are extracted. The flow velocity direction angle is vector superimposed with the pollutant concentration gradient direction to obtain the pollutant transport direction angle of each monitoring point. The pollutant concentration gradient direction is the direction in which the pollutant concentration increases the fastest. The pollutant transport direction angle of each monitoring point is reverse-tracked and calculated. The tracking path is marked in the terrain distribution data, and the intersection area of ​​multiple reverse tracking paths is determined as the pollution source direction area.

[0009] By employing the above technical solution, real-time pollutant concentration values ​​at various monitoring points within the upstream reservoir area are obtained. The pollutant concentration values ​​at adjacent monitoring points are then subtracted, and the result is divided by the distance between adjacent monitoring points to obtain the pollutant concentration gradient between them, thus accurately quantifying the spatial rate of change in pollutant concentration. The pollutant concentration gradients are spatially interpolated according to the geographical coordinates of the monitoring points to generate a pollutant concentration gradient distribution map of the upstream reservoir area. The area with the largest pollutant concentration gradient value is identified as the area with the most drastic pollutant concentration changes. Spatial interpolation eliminates the discreteness of point monitoring, accurately locating active areas of pollutant diffusion. Flow velocity monitoring data at each monitoring point within the area of ​​most drastic pollutant concentration changes is obtained. The flow velocity direction angle and magnitude at each monitoring point are extracted. The flow velocity direction angle is vector-superimposed with the pollutant concentration gradient direction to obtain the pollutant transport direction angle at each monitoring point. Since the pollutant concentration gradient direction is the direction of the fastest increase in pollutant concentration, combining it with the flow velocity direction accurately reflects the actual transport path of pollutants in the water body. The pollutant transmission direction angle of each monitoring point is calculated in reverse, and the tracking path is marked in the terrain distribution data. The intersection area of ​​multiple reverse tracking paths is determined as the pollution source direction area. Since pollutants spread from the source to the downstream along the transmission direction, the intersection point of multiple reverse tracking transmission paths will inevitably be close to the actual pollution source location, thereby accurately locating the pollution source area and providing a precise target for pollution prevention and control.

[0010] Optionally, temperature sensor measurements at different depths in the vertical direction within the midstream reservoir area are obtained, and the measurement points are divided into three water layers: surface, middle, and bottom, according to the water depth direction. Calculate the temperature difference and depth difference between adjacent water layers, divide each temperature difference by the corresponding depth difference between water layers to obtain the temperature gradient values ​​between water layers, and sum the absolute values ​​of the temperature gradient values ​​between all water layers to obtain the temperature stratification coefficient. Obtain the dissolved oxygen content of each water layer within a preset time period, and use the difference between the maximum and minimum dissolved oxygen content within the preset time period as the range of change in dissolved oxygen content. Calculate the standard deviation of the dissolved oxygen content variation range in the surface, middle and bottom water layers, and use the standard deviation as the degree of difference in dissolved oxygen content variation range; then, weight and sum the degree of difference in dissolved oxygen content variation range with the temperature stratification coefficient to obtain the corresponding water stratification assessment value. Water stratification assessment values ​​above the preset state threshold are marked as stable stratification states, and water stratification assessment values ​​below the preset state threshold are marked as mixed states. Stable stratification states and mixed states are used as water stratification stability states.

[0011] By employing the above technical solution, temperature sensor measurements at different depths along the vertical direction within the midstream reservoir area are obtained. The measurement points are divided into three water layers—surface, middle, and bottom—according to water depth, and this vertical stratified monitoring layout covers the main thermal structural layers of the water body. The temperature and depth differences between adjacent water layers are calculated. Each temperature difference is divided by the corresponding depth difference to obtain the temperature gradient between water layers. The absolute values ​​of all temperature gradients are summed to obtain the temperature stratification coefficient, which quantitatively reflects the intensity of vertical thermal stratification in the water body. The dissolved oxygen content of each water layer is obtained within a preset time period. The difference between the maximum and minimum dissolved oxygen content within this period is taken as the variation range of dissolved oxygen content. The standard deviation of the variation ranges of dissolved oxygen content in the surface, middle, and bottom water layers is calculated as the degree of difference in dissolved oxygen content variation. The weighted sum of the degree of difference in dissolved oxygen content variation and the temperature stratification coefficient yields a water stratification assessment value. Water stratification values ​​above the preset threshold are marked as stable stratification, while those below the preset threshold are marked as mixed stratification. Since vertical water exchange is limited in stable stratification, it can affect water quality distribution and the ecological environment, while water exchange is sufficient in mixed stratification, which is conducive to water quality homogenization. Accurate judgment of the stability of water stratification provides a scientific basis for water quality management and ecological protection.

[0012] Optionally, extract the starting time and degree of change of the gate opening from the gate opening information; Identify the moment when the rate of change of water level value first exceeds the preset rate of change threshold from the water level fluctuation data, and calculate the time interval between the start time of the gate opening change and the change time as the water level response time. The change in water level value within the response time in the water level fluctuation data is taken as the response amplitude.

[0013] By employing the above technical solution, the starting time and degree of gate opening change are extracted from the gate opening information. The moment when the rate of change of water level first exceeds a preset threshold is identified from the water level fluctuation data. The time interval between the starting time and the moment of change of gate opening is calculated as the water level response time. The water level response time accurately reflects the response speed of the reservoir's hydrodynamic system to scheduling operations. The change in water level within the water level response time is used as the response amplitude. Since the response amplitude is directly related to the degree of gate opening change, the combined parameter of response amplitude and water level response time can quantitatively assess the reservoir's sensitivity to scheduling operations of different intensities, providing key hydrodynamic response parameters for formulating precise reservoir scheduling strategies and predicting scheduling effects.

[0014] Optionally, the response rate index can be obtained by calculating the ratio of the water level response time to the response amplitude; The numerical distribution of the response rate index during multiple gate operations is statistically analyzed, and the average value of the response rate index during multiple operations is calculated. Operations with response rate values ​​higher than the average are marked as high-sensitivity response characteristics, while operations with response rate values ​​not higher than the average are marked as low-sensitivity response characteristics. These high-sensitivity and low-sensitivity response characteristics are used as the response characteristics of the storage area to scheduling operations.

[0015] By employing the aforementioned technical solution, the response rate index is obtained by calculating the ratio of water level response time to response amplitude. This index comprehensively reflects the reservoir's rapid response capability to gate operation. The numerical distribution of the response rate index during multiple gate operations is statistically analyzed, and the average value of the response rate index across multiple operations is calculated. This statistical analysis of multiple operations eliminates the randomness of single operations, and the average value provides a benchmark reference for judging response sensitivity. Operations with response rate index values ​​higher than the average are marked as high-sensitivity response characteristics, while those with values ​​not higher than the average are marked as low-sensitivity response characteristics. High-sensitivity response characteristics indicate that the reservoir responds quickly and significantly to operation, requiring refined operation to avoid drastic water level fluctuations. Low-sensitivity response characteristics, on the other hand, indicate a relatively slow response to operation, allowing for larger operation amplitudes to quickly reach the target water level, thus significantly improving the accuracy of reservoir operation.

[0016] Optionally, the pollution source direction area identified in the upstream reservoir area can be overlaid with the topographic distribution data to determine the geographical coordinates and influence range boundary of the pollution source direction area. Based on the geographical coordinates and influence range boundary, the diffusion path and diffusion speed of pollutants from the pollution source direction area to the downstream can be assessed. The correlation analysis between the water stratification stability state and the temperature stratification coefficient in the middle reaches of the reservoir was conducted to identify the water exchange capacity under stable stratification and the vertical convection intensity under mixed state, and to assess the impact of different stratification states on the vertical diffusion of pollutants. By matching and analyzing the response characteristics of the reservoir area in front of the dam with the historical records of gate scheduling operations, the optimal scheduling timing corresponding to high-sensitivity response characteristics and the scheduling delay risk corresponding to low-sensitivity response characteristics are identified, and the control effect of scheduling operations on the environmental conditions of the reservoir area is evaluated. Based on the diffusion path, propagation speed, impact level, and control effectiveness, a comprehensive environmental assessment report is determined.

[0017] By employing the aforementioned technical solutions, the pollution source areas identified in the upstream reservoir area are overlaid with topographical distribution data to determine the geographical coordinates and impact boundaries of these areas. Based on these coordinates and impact boundaries, the diffusion paths and speeds of pollutants from the source areas downstream are assessed. This quantitative assessment of pollutant propagation trajectories and diffusion speeds creates conditions for timely deployment of pollution early warning and control measures. Correlation analysis between the water stratification stability state and temperature stratification coefficient in the midstream reservoir area identifies the water exchange capacity under stable stratification and the vertical convection intensity under mixed conditions. The impact of different stratification states on the vertical diffusion of pollutants is assessed. Since stable stratification restricts vertical diffusion while mixed conditions promote rapid dilution, this provides a scientific basis for selecting pollution control strategies. Matching analysis between the response characteristics of the reservoir area upstream of the dam and historical gate operation records identifies the optimal scheduling timing corresponding to high-sensitivity response characteristics and the scheduling delay risk corresponding to low-sensitivity response characteristics. Optimized scheduling timing improves the efficiency and safety of reservoir operation. Based on diffusion paths, propagation speeds, impact levels, and control effects, a comprehensive environmental assessment report is generated, forming a complete decision support system covering pollution prevention and control, ecological protection, and water resource management.

[0018] Optionally, based on the diffusion path and propagation speed, the expected arrival time and concentration decay value of pollutants at key nodes along the diffusion path are calculated to generate a spatiotemporal propagation data table of pollutants. Based on the stability state of the water stratification, when the water stratification is in a stable stratification state, the vertical retention time of pollutants is calculated using the vertical retention conversion principle, taking into account the degree of influence. When the water stratification is in a mixed state, the vertical dilution factor of pollutants is calculated using the vertical dilution conversion principle, taking into account the degree of influence. Based on the spatiotemporal propagation data of pollutants, as well as the vertical residence time or vertical dilution factor of pollutants, the final concentration and total residence time of pollutants at each location in the reservoir area are calculated, and areas where the total residence time exceeds the preset safe time threshold and the final concentration exceeds the preset safe concentration threshold are marked as high-risk areas. Based on the assessment conclusions regarding the optimal scheduling timing and scheduling delay risk in the control effect, emergency scheduling operation sequence arrangements for high-sensitivity response characteristics and preventive scheduling operation sequence arrangements for low-sensitivity response characteristics are formulated respectively. A pollution risk distribution map of the reservoir area is generated based on high-risk areas; and the pollution risk distribution map, emergency dispatch operation sequence, and preventive dispatch operation sequence together constitute a comprehensive environmental assessment report.

[0019] By employing the aforementioned technical solutions, based on the diffusion path and propagation speed, the expected arrival time and concentration decay values ​​of pollutants at key nodes along the diffusion path are calculated, generating a spatiotemporal propagation data table for pollutants, thus accurately characterizing the spatiotemporal distribution features of pollutant propagation. According to the water body stratification stability state, when the water body stratification stability state is stable, the vertical residence time of pollutants is calculated using the vertical retention conversion principle, combined with the degree of influence. When the water body stratification stability state is mixed, the vertical dilution factor of pollutants is calculated using the vertical dilution conversion principle, combined with the degree of influence. The vertical migration behavior of pollutants under different stratification states is quantitatively described. Combining the spatiotemporal propagation data table of pollutants with the vertical residence time or vertical dilution factor of pollutants, the final concentration value and total residence time of pollutants at each location within the reservoir area are calculated. Areas where the total residence time exceeds a preset safe time threshold and the final concentration value exceeds a preset safe concentration threshold are marked as high-risk areas. Since areas that simultaneously meet both concentration and time safety thresholds have significant ecological risks and health hazards, the accurate identification of high-risk areas provides a clear target for the key deployment of pollution control. Based on the assessment conclusions regarding the optimal scheduling timing and scheduling delay risks in the regulation and control effects, emergency scheduling operation sequences were formulated for high-sensitivity response characteristics and preventive scheduling operation sequences for low-sensitivity response characteristics. A pollution risk distribution map of the reservoir area was generated based on high-risk areas. The pollution risk distribution map of the reservoir area, the emergency scheduling operation sequences, and the preventive scheduling operation sequences were combined to form a comprehensive environmental assessment report, resulting in a complete environmental management plan that combines risk identification and response measures.

[0020] In a second aspect, this application provides an electronic device for IoT environmental monitoring, the electronic device comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the electronic device of the IoT environmental monitoring method to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on an electronic device for Internet of Things (IoT) environmental monitoring, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a device for monitoring an Internet of Things (IoT) environment, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the architecture of an Internet of Things (IoT) environmental monitoring system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating an IoT environmental monitoring method provided in an embodiment of this application; Figure 3 This is an exemplary hardware structure diagram of an electronic device for IoT environmental monitoring provided in an embodiment of this application. Detailed Implementation

[0024] Figure 1 An exemplary system architecture for an Internet of Things (IoT) environmental monitoring system is shown.

[0025] like Figure 1 As shown, the system architecture may include electronic device 11, network 12, and data acquisition device 13. Network 12 serves as the medium for providing a communication link between electronic device 11 and data acquisition device 13. Network 12 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0026] Users can use electronic device 11 to interact with data acquisition device 13 via network 12 to receive or send environmental monitoring data, etc. Various environmental monitoring applications can be installed on electronic device 11, such as water quality analysis applications and environmental assessment applications.

[0027] Electronic device 11 is hardware and can be various electronic devices with a display screen and data processing capabilities, including but not limited to smartphones, tablets, laptops, desktop computers, and industrial control computers.

[0028] The data acquisition device 13 can be a device that provides various environmental monitoring services, such as a sensor array for collecting and processing environmental data in a reservoir area. The data acquisition device 13 can perform preliminary processing on the collected topographic distribution data, pollutant concentration data, temperature distribution data, water level fluctuation data, etc., and can transmit the processing results (environmental monitoring data) to the electronic device 11 for further analysis and evaluation.

[0029] The following detailed explanation uses the electronic device side as an example.

[0030] This embodiment provides an IoT environmental monitoring method. Figure 2 This is a flowchart illustrating an IoT environmental monitoring method provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes steps S101 to S105: S101: Obtain topographic distribution data and dam location information of the reservoir area, and divide the monitoring area into upstream reservoir area, midstream reservoir area and dam front reservoir area based on the topographic distribution data and dam location information.

[0031] In this embodiment of the application, the terrain distribution data refers to the digital information describing the geographical features of the ground elevation, slope, water depth and other geographical features within the reservoir area, which is used to represent the shape of the bottom of the reservoir and the surrounding terrain undulations; the dam location information represents the precise location data of the main control structures of the reservoir in the geographic coordinate system, including spatial attributes such as the length, height and orientation angle of the dam.

[0032] The monitoring area refers to the spatial range of the reservoir water body that needs to be monitored, usually covering the entire water area from the reservoir inlet to the dam outlet. Among them, the upstream reservoir area refers to the water area far from the dam and mainly receiving external water input, where the water flow velocity is relatively slow and the pollutant concentration changes are more obvious; the midstream reservoir area refers to the transitional water area between the upstream reservoir area and the dam front reservoir area, where the water depth is greater and temperature stratification is easily formed; the dam front reservoir area is used to represent the water area adjacent to the dam structure, where water level changes are most sensitive to gate operation response.

[0033] Specifically, the electronic equipment acquires topographic distribution data of the reservoir area from geographic information sensors and hydrological monitoring sensors in the data acquisition device using multi-source data fusion technology. This topographic distribution data includes spatial information such as reservoir bottom contour maps, water depth measurement data, and shoreline boundary coordinates. Simultaneously, it acquires location information such as the dam's geographic coordinates, structural dimensions, and orientation angle from the positioning sensors and structural monitoring equipment in the data acquisition device. The electronic equipment establishes a reservoir spatial coordinate system based on the geographic coordinates in the dam's location information. It sorts and classifies the water depth measurement data from the topographic distribution data according to their numerical values, identifying the variation patterns of water depth values ​​from small to large and the locations of abrupt changes in water depth gradients. Furthermore, it extracts bottom slope angle information and shoreline contour shape from the topographic distribution data to calculate the total length and width of the reservoir. The electronic device divides the total length of the reservoir into three equal segments based on their distance from the dam, labeled as the long-distance segment, the medium-distance segment, and the short-distance segment. Then, it performs spatial location matching between the water areas with water depth values ​​less than a first preset depth threshold in the terrain distribution data and the long-distance segment. The successfully matched water areas are classified as the upstream reservoir area. The water areas with water depth values ​​between the first and second preset depth thresholds in the terrain distribution data are performed spatial location matching between the medium-distance segment. The successfully matched water areas are classified as the midstream reservoir area. The water areas with water depth values ​​greater than the second preset depth threshold in the terrain distribution data are performed spatial location matching between the short-distance segment. The successfully matched water areas are classified as the reservoir area in front of the dam.

[0034] S102: For the upstream reservoir area, acquire pollutant concentration distribution data and flow velocity monitoring data from multiple locations, calculate the pollutant concentration gradient based on the pollutant concentration distribution data, and determine the source area of ​​pollution based on the pollutant concentration gradient and flow velocity monitoring data.

[0035] In this embodiment, pollutant concentration distribution data refers to the pollutant content values ​​and spatial location information measured in real time at each monitoring point, including pollutant type, concentration value, and geographical coordinates; flow velocity monitoring data represents the speed and direction of water flow at each monitoring point, used to represent the water flow state and pollutant transport path; pollutant concentration gradient represents the rate of change of pollutant concentration in space; and the pollution source direction area table includes the approximate location of the pollution source and the boundary of its influence range.

[0036] Specifically, the electronic equipment acquires pollutant concentration distribution data and flow velocity monitoring data at various monitoring points via multiple pollutant concentration sensors and flow velocity sensors within the upstream reservoir area. The pollutant concentration distribution data includes the pollutant concentration value, measurement time, and geographic coordinates for each monitoring point. The flow velocity monitoring data includes the magnitude of the water flow velocity, flow direction angle, and measurement depth for each monitoring point. The electronic equipment selects spatially adjacent pairs of monitoring points, calculates the difference in pollutant concentration values ​​between each pair, divides this difference by the straight-line distance between the two monitoring points, and obtains the pollutant concentration gradient value between each pair of adjacent monitoring points. Then, all pollutant concentration gradient values ​​are spatially interpolated according to the geographic coordinates of the monitoring points to generate a pollutant concentration gradient distribution map of the upstream reservoir area. The electronic device filters out the area with the largest pollutant concentration gradient from the pollutant concentration gradient distribution map, extracts the flow direction angle information from the flow velocity monitoring data of each monitoring point in the area, performs vector synthesis operation with the direction of pollutant concentration gradient, calculates the angle of pollutant transmission direction at each monitoring point, and then performs reverse tracking calculation along the opposite direction of pollutant transmission direction, and determines the intersection area of ​​multiple reverse tracking paths as the pollution source direction area.

[0037] Based on the above embodiments, as an optional embodiment, the step of calculating the pollutant concentration gradient based on pollutant concentration distribution data and determining the pollution source direction area based on the pollutant concentration gradient and flow velocity monitoring data includes steps S201 to S204: S201: Obtain the real-time pollutant concentration values ​​of each monitoring point in the upstream reservoir area, perform a difference calculation on the pollutant concentration values ​​of adjacent monitoring points, divide the difference calculation result by the distance between adjacent monitoring points, and obtain the pollutant concentration gradient between adjacent monitoring points.

[0038] Specifically, the electronic equipment collects pollutant concentration values ​​in real time from pollutant concentration sensors at various monitoring points within the upstream reservoir area via a data acquisition device. Each sensor converts the detected pollutant concentration into a digital signal and transmits it to the data acquisition device. The data acquisition device then sends the real-time pollutant concentration values ​​from each monitoring point, along with their corresponding geographic coordinates, to the electronic equipment. Upon receiving the real-time pollutant concentration values, the electronic equipment iterates through the geographic coordinates of all monitoring points, calculates the Euclidean distance between any two monitoring points, and selects the pair of monitoring points with the smallest distance as adjacent monitoring points. It then subtracts the value of the point with the smaller pollutant concentration from the value of the point with the larger pollutant concentration in each pair of adjacent monitoring points to obtain the pollutant concentration difference calculation result for that pair. The electronic equipment divides the pollutant concentration difference calculation result for each pair of adjacent monitoring points by the corresponding distance between the adjacent monitoring points to calculate the pollutant concentration gradient between that pair of adjacent monitoring points. This calculation process is repeated until the pollutant concentration gradient between all adjacent monitoring points is obtained.

[0039] S202: Spatial interpolation of each pollutant concentration gradient is performed according to the geographical coordinates of the monitoring points to generate a pollutant concentration gradient distribution map of the upstream reservoir area. The area with the largest pollutant concentration gradient value is identified from the pollutant concentration gradient distribution map as the area with the most drastic change in pollutant concentration.

[0040] Specifically, the electronic device acquires the pollutant concentration gradient values ​​and corresponding geographic coordinates of each adjacent monitoring point. It associates each pollutant concentration gradient value with the center coordinates of the two adjacent monitoring points from which that gradient value was calculated, forming a dataset of the correspondence between pollutant concentration gradient values ​​and spatial locations. The electronic device divides the geographical area of ​​the upstream reservoir into a regular grid. Each node in the grid represents a spatial location to be interpolated. For each grid node, the electronic device calculates the distance between that node and the locations of all known pollutant concentration gradient values. It then uses an inverse distance interpolation method to calculate the interpolated pollutant concentration gradient value for that node. Known values ​​that are closer to the node contribute more to the interpolation result, while those that are farther away contribute less. After completing the interpolation calculation for all grid nodes, the electronic device grades and colors the interpolation results according to the magnitude of the pollutant concentration gradient values, generating a pollutant concentration gradient distribution map of the upstream reservoir. It then iterates through all values ​​in the pollutant concentration gradient distribution map, finds the maximum value of the pollutant concentration gradient, and marks the continuous area containing the maximum value and nearby similar values ​​as the area with the most drastic changes in pollutant concentration.

[0041] S203: Obtain flow velocity monitoring data at each monitoring point in the area with the most drastic changes in pollutant concentration, extract the flow velocity direction angle and flow velocity magnitude at each monitoring point, and perform vector superposition calculation with the pollutant concentration gradient direction to obtain the pollutant transport direction angle at each monitoring point. The pollutant concentration gradient direction is the direction in which the pollutant concentration increases the fastest.

[0042] Specifically, the electronic device acquires flow velocity sensor measurement data from all monitoring points within the area of ​​most drastic pollutant concentration changes from the data acquisition device. This data includes the water flow velocity vector information and measurement timestamp at each monitoring point. The electronic device analyzes the flow velocity sensor measurement data to extract the flow velocity direction angle and magnitude at each monitoring point. The electronic device calculates the spatial distribution characteristics of the pollutant concentration gradient within the area of ​​most drastic pollutant concentration changes. By analyzing the changes in pollutant concentration values ​​at adjacent grid points, it determines the direction of the fastest increase in pollutant concentration as the pollutant concentration gradient direction, representing it as an angle value relative to north in the geographic coordinate system. The electronic device converts the flow velocity direction angle and magnitude at each monitoring point into component forms in a Cartesian coordinate system. Similarly, it converts the pollutant concentration gradient direction and magnitude into component forms in a Cartesian coordinate system. It calculates the sum of the components of the two vectors along the X and Y axes, then converts the sum of the X-axis and Y-axis components back to polar coordinates to obtain the magnitude and direction angle of the composite vector. The direction angle of the composite vector is the pollutant transport direction angle at each monitoring point.

[0043] S204: Perform reverse tracking calculations on the pollutant transport direction angles at each monitoring point, mark the tracking paths in the terrain distribution data, and determine the intersection area of ​​multiple reverse tracking paths as the pollution source direction area.

[0044] Specifically, the electronic device acquires the pollutant transport direction angle values ​​of each monitoring point in the area with the most drastic changes in pollutant concentration. The geographic coordinates of each monitoring point are used as the starting point for reverse tracing. The pollutant transport direction angle is increased by 180 degrees and used as the reverse tracing direction angle. Path calculation is performed starting from the starting point along this reverse tracing direction angle. The electronic device sets the step size and maximum tracking distance for reverse tracing. Starting from the starting point, it calculates the geographic coordinates of the next path node along the reverse tracing direction angle according to the step size. This calculation process is repeated until the maximum tracking distance is reached or a terrain boundary is encountered. The geographic coordinates of all path nodes are connected to form a complete reverse tracing path, and the geographic coordinate information of the reverse tracing path is superimposed on the terrain distribution data for path labeling. After completing the reverse tracing path calculation for all monitoring points, the electronic device analyzes the spatial distribution relationship of each reverse tracing path, calculates the minimum distance between any two paths, and filters out the set of paths whose distance is less than a preset distance threshold. The geographic area enclosed by these path sets is determined as the intersection area of ​​the reverse tracing paths, and finally, the intersection area is marked as the pollution source direction area.

[0045] S103: For the midstream reservoir area, obtain the temperature distribution data of vertical stratification and the dissolved oxygen content of each water layer, calculate the temperature stratification coefficient based on the temperature distribution data, and determine the water stratification stability state based on the temperature stratification coefficient and the changing trend of dissolved oxygen content of each water layer.

[0046] In this embodiment, temperature distribution data refers to the water temperature values ​​measured at various water depths, including temperature values, measurement depths, and time stamps; dissolved oxygen content represents the concentration of dissolved oxygen in the water, reflecting the distribution and variation of oxygen in the water; water stratification stability represents the classification of the stability of the vertical structure of the water body, including two basic types: stable stratification and mixed state.

[0047] Specifically, the electronic equipment acquires vertically stratified temperature distribution data through temperature sensors deployed at different depths within the midstream reservoir area using a data acquisition device. These temperature sensors are deployed from the water surface to the bottom at preset depth intervals. Each temperature sensor measures the water temperature at its designated depth in real time and transmits the results to the data acquisition device. Simultaneously, dissolved oxygen sensors acquire dissolved oxygen content data for each water layer, and these sensors are paired with the temperature sensors at the same depth. The electronic equipment arranges the vertically stratified temperature distribution data in depth order, calculates the temperature difference and depth difference between adjacent water layers, divides the temperature difference by the depth difference to obtain the temperature gradient values ​​between each water layer, and sums the absolute values ​​of the temperature gradient values ​​between all water layers to obtain the temperature stratification coefficient. Simultaneously, it analyzes the changes in dissolved oxygen content in each water layer over a preset time period, identifying the patterns of increase and decrease in dissolved oxygen content as trends. The electronic device comprehensively analyzes the temperature stratification coefficient and the trend of dissolved oxygen content change in each water layer. When the temperature stratification coefficient is greater than the preset threshold and the trend of dissolved oxygen content change in each water layer is significantly different, the water stratification stability state is determined to be a stable stratification state. When the temperature stratification coefficient is less than the preset threshold and the trend of dissolved oxygen content change in each water layer is similar, the water stratification stability state is determined to be a mixed state.

[0048] Based on the above embodiments, as an optional embodiment, the step of obtaining vertically stratified temperature distribution data and dissolved oxygen content of each water layer, calculating temperature stratification coefficient based on temperature distribution data, and determining the water stratification stability state based on the temperature stratification coefficient and the changing trend of dissolved oxygen content of each water layer includes steps S301 to S305: S301: Obtain temperature sensor readings at different depths in the vertical direction within the midstream reservoir area, and divide the measurement points into three water layers: surface, middle, and bottom, according to the water depth direction.

[0049] Specifically, the electronic equipment acquires real-time measurement data from multiple temperature sensors deployed at preset depth intervals within the midstream reservoir area via a data acquisition device. The temperature sensors are deployed downwards from the water surface at equal intervals to the bottom of the reservoir. Each temperature sensor is equipped with depth identification information and geographic coordinates. The data acquisition device transmits the measurement values ​​from each temperature sensor, along with the corresponding depth location information, to the electronic equipment. After receiving the temperature sensor measurement values ​​at each depth location, the electronic equipment first determines the maximum water depth in the midstream reservoir area. Then, it divides the maximum water depth into three equal layers: all temperature sensor measurement points from the water surface to one-third of the maximum water depth are classified as the surface layer; all temperature sensor measurement points from one-third to two-thirds of the maximum water depth are classified as the middle layer; and all temperature sensor measurement points from two-thirds of the maximum water depth to the maximum water depth are classified as the bottom layer.

[0050] S302: Calculate the temperature difference and depth difference between adjacent water layers, divide each temperature difference by the corresponding depth difference between water layers to obtain the temperature gradient values ​​between water layers, and sum the absolute values ​​of the temperature gradient values ​​between all water layers to obtain the temperature stratification coefficient.

[0051] Specifically, the electronic device acquires representative temperature values ​​for each water layer in the surface, middle, and bottom temperature datasets. It calculates the average surface, middle, and bottom temperatures by averaging the measurements from all temperature sensors within each water layer. Then, it calculates the temperature difference between the surface and middle layers as the surface-to-middle layer temperature difference, and the temperature difference between the middle and bottom layers as the middle-to-bottom layer temperature difference. The electronic device also determines the center depth of each water layer, using the midpoint of the surface depth range as the surface center depth, the midpoint of the middle layer depth range as the middle layer center depth, and the midpoint of the bottom depth range as the bottom center depth. Finally, it calculates the vertical distance between the surface and middle layer center depths as the surface-to-middle layer depth difference, and the vertical distance between the middle and bottom layer center depths as the middle-to-bottom layer depth difference. The electronic device divides the temperature difference between the surface and middle layers by the depth difference between the surface and middle layers to obtain the temperature gradient between them. It also divides the temperature difference between the middle and bottom layers by the depth difference between them to obtain the temperature gradient between the middle and bottom layers. Finally, it sums these two temperature gradient values ​​to obtain the temperature stratification coefficient. The calculation logic for the temperature stratification coefficient is as follows: This coefficient reflects the degree of temperature change with depth. The system calculates this by accumulating the absolute values ​​of the rate of temperature change (i.e., the ratio of temperature difference to depth difference) between adjacent water layers in the vertical direction. Physically, this value represents the total thermal resistance of the entire water layer in the vertical direction; a higher value indicates a more pronounced thermal stratification phenomenon and a more static and stable water body; a lower value indicates a more uniform water temperature and a tendency for water mixing. The purpose of using absolute value accumulation is to quantify the total heat exchange resistance of water in the vertical direction. Regardless of whether the temperature increases or decreases with depth, as long as there is a gradient change, it represents a stratification trend. Direct accumulation may incorrectly underestimate the stratification intensity due to the cancellation of positive and negative gradients.

[0052] S303: Obtain the dissolved oxygen content of each water layer within a preset time period, and use the difference between the maximum and minimum dissolved oxygen content within the preset time period as the variation range of dissolved oxygen content.

[0053] Specifically, the electronic equipment acquires dissolved oxygen content data for each water layer within a preset time period through dissolved oxygen sensors deployed at the surface, middle, and bottom layers of the midstream reservoir area using a data acquisition device. The dissolved oxygen sensors continuously measure the dissolved oxygen concentration at each water layer according to a preset sampling frequency. The data acquisition device transmits the measurement results from each water layer's dissolved oxygen sensors, along with timestamp information, to the electronic equipment. Upon receiving the dissolved oxygen content data for each water layer within the preset time period, the electronic equipment processes the data for the surface, middle, and bottom layers respectively. From the surface dissolved oxygen content data, it selects the measurement result with the highest value as the maximum surface dissolved oxygen content and the measurement result with the lowest value as the minimum surface dissolved oxygen content. The difference between the maximum and minimum surface dissolved oxygen content is calculated to obtain the variation range of the surface dissolved oxygen content. The electronic equipment uses the same method to calculate the difference between the maximum and minimum middle layer dissolved oxygen content to obtain the variation range of the middle layer dissolved oxygen content, and calculates the difference between the maximum and minimum bottom layer dissolved oxygen content to obtain the variation range of the bottom layer dissolved oxygen content, ultimately forming the dissolved oxygen content variation range data for each water layer.

[0054] S304: Calculate the standard deviation of the dissolved oxygen content variation range in the surface, middle, and bottom water layers, and use the standard deviation as the degree of difference in dissolved oxygen content variation range. Weight the degree of difference in dissolved oxygen content variation range with the temperature stratification coefficient value to obtain the corresponding water stratification assessment value.

[0055] Specifically, the electronic device acquires data on the changes in dissolved oxygen content in the surface, middle, and bottom layers. These three data sets are compiled into a dataset, and the arithmetic mean of these three values ​​is calculated as the average level of dissolved oxygen content changes. The electronic device then calculates the deviation between each dissolved oxygen content change value and the average value, squares each deviation to obtain a squared deviation value, calculates the arithmetic mean of the three squared deviation values, and takes the square root of the arithmetic mean to obtain the standard deviation value. This standard deviation value is used to represent the degree of difference in dissolved oxygen content changes. The electronic device sets weighting coefficients for temperature stratification and dissolved oxygen content change degree. The temperature stratification coefficient is multiplied by its corresponding weighting coefficient to obtain a temperature-weighted value, and the dissolved oxygen content change degree is multiplied by its corresponding weighting coefficient to obtain a dissolved oxygen difference-weighted value. Finally, the temperature-weighted value and the dissolved oxygen difference-weighted value are added together to obtain an assessment value for the degree of water stratification. The weighting coefficients can be determined through regression analysis based on historical monitoring data or set by expert experience based on the hydrological characteristics of different seasons. In one embodiment, the weighting coefficients for both the temperature stratification coefficient and the degree of difference in dissolved oxygen content variation can be set to 0.5. S305: The water stratification assessment value that is higher than the preset state threshold is marked as a stable stratification state, and the water stratification assessment value that is not higher than the preset state threshold is marked as a mixed state. The stable stratification state and the mixed state are used as the water stratification stability state.

[0056] In this embodiment, the water stratification stability state represents the classification result of the vertical structural characteristics of the water body, including two basic types: stable stratification state and mixed state. The stable stratification state refers to a state in which the water body forms a clear stratified structure in the vertical direction with limited exchange between layers, indicating that the water body has clear stratification boundaries and relatively independent interlayer characteristics. The mixed state refers to a state in which the water body lacks a clear stratification structure in the vertical direction and the layers are fully mixed, reflecting the characteristics of active vertical convection and blurred interlayer boundaries.

[0057] Specifically, the electronic device compares the values ​​of preset state thresholds. These thresholds are determined through statistical analysis of historical water stratification data and expert experience, and are used to distinguish the critical levels of water stratification intensity. The assessed water stratification level is then compared with the preset state thresholds. When the assessed water stratification level is greater than the preset threshold, it indicates that the differences in the variation ranges of water temperature stratification coefficient and dissolved oxygen content are both at a high level, and the vertical stratification characteristics of the water body are obvious. This situation is marked as a stable stratification state. When the assessed water stratification level is less than or equal to the preset state threshold, it indicates that the differences in the variation ranges of water temperature stratification coefficient and dissolved oxygen content are relatively low, and the vertical stratification characteristics of the water body are not obvious. This situation is marked as a mixed state. Finally, the stable stratification state and the mixed state are collectively referred to as the water stratification stability state, which serves as the determination result of the water stratification characteristics in the midstream reservoir area.

[0058] S104: For the reservoir area in front of the dam, acquire water level fluctuation data and gate opening information, calculate water level response time and response amplitude based on water level fluctuation data and gate opening information, and determine the response characteristics of the reservoir area to scheduling operations based on water level response time and response amplitude.

[0059] In this embodiment of the application, water level fluctuation data refers to the measurement record information of the water level height in the reservoir area in front of the dam changing over time, including elements such as water level value, change time and fluctuation amplitude; gate opening information represents the state data of the degree of opening of the dam gate, including parameters such as opening percentage, operation time and change rate; response characteristics refer to the comprehensive characteristic description of the reservoir water level's response to the scheduling operation, including classification results such as sensitivity and response mode.

[0060] Specifically, the electronic equipment acquires water level fluctuation data through water level sensors deployed in the reservoir area in front of the dam using data acquisition devices. These sensors continuously monitor changes in water level height and record measurement timestamps. Simultaneously, it acquires gate opening information through gate monitoring equipment, which records the gate opening degree and operation time in real time. The electronic equipment analyzes the gate opening information to identify gate operation events, extracting the start time and degree of change in gate opening. Then, it analyzes the water level fluctuation data within the corresponding time period, identifying the moment when the water level begins to change significantly. It calculates the time difference between the gate operation start time and the water level change start time as the water level response time, and simultaneously calculates the change in water level from the initial value to the stable value during the response process as the response amplitude. The electronic equipment comprehensively analyzes the water level response time and amplitude. When the water level response time is short and the response amplitude is large, it indicates that the reservoir area has a high sensitivity to scheduling operations. When the water level response time is long and the response amplitude is small, it indicates that the reservoir area has a low sensitivity to scheduling operations. These high and low sensitivity characteristics are used as the reservoir area's response characteristics to scheduling operations.

[0061] Based on the above embodiments, as an optional embodiment, the step of calculating the water level response time and response amplitude based on water level fluctuation data and gate opening information includes steps S401 to S403: S401: Extract the starting time and degree of change of gate opening from the gate opening information.

[0062] Specifically, the electronic equipment acquires a continuous gate opening information data stream from the gate monitoring equipment via a data acquisition device. This data stream contains the gate opening percentage at each moment and its corresponding timestamp. The electronic equipment performs time-series analysis on this data stream. It iterates through all time points in the data stream, comparing the gate opening percentage between adjacent time points. When the difference between the gate opening percentages at adjacent time points exceeds a preset threshold, the previous time point is marked as the start time of the gate opening change, and the difference between the two time points is marked as the degree of change, thus forming a gate operation event record. The electronic equipment repeats this process until the entire gate opening information data stream is analyzed, identifying the start times of all gate opening changes and their corresponding degree of change.

[0063] S402: Identify the moment when the rate of change of water level value first exceeds the preset rate of change threshold from the water level fluctuation data, and calculate the time interval between the start time of the gate opening change and the change time as the water level response time.

[0064] Specifically, the electronic equipment acquires water level fluctuation data from the reservoir level sensors in front of the dam during the time period before and after gate operation. This data includes continuous water level measurements and corresponding timestamps. The electronic equipment then processes this data by calculating the rate of change. It iterates through the water level values ​​at adjacent time points, calculates the difference between the water level at the next time point and the previous time point, divides this difference by the time difference between the two points, and obtains the rate of change for that time period. This process is repeated to obtain the rate of change data for the entire time series. The electronic equipment then compares the calculated rate of change with a preset threshold value, identifying the point where the rate of change first exceeds the threshold as the change moment. It then calculates the time difference between the identified gate opening change start moment and the change moment, using this time difference as the water level response time.

[0065] S403: The change in water level value within the response time in the water level fluctuation data is taken as the response amplitude.

[0066] Specifically, the electronic equipment acquires the start and end time points of the water level response time. The start time point is the initial moment of the gate opening change, and the end time point is the moment when the water level change rate first exceeds a preset change rate threshold. It extracts the water level measurement values ​​corresponding to these two time points from the water level fluctuation data, reads the water level value corresponding to the start time point as the pre-response water level baseline value, and reads the water level value corresponding to the end time point as the post-response water level target value. It calculates the post-response water level target value minus the pre-response water level baseline value, and uses the absolute value of the result as the change in water level. The electronic equipment directly determines the calculated change in water level as the response amplitude, which reflects the actual regulatory effect of the water level on the gate operation within the water level response time.

[0067] Based on the above embodiments, as an optional embodiment, the step of determining the reservoir's response characteristics to scheduling operations based on water level response time and response amplitude includes steps S501 to S503: S501: The response rate index is obtained by calculating the ratio of the water level response time to the response amplitude.

[0068] Specifically, the electronic equipment uses the response amplitude value as the dividend and the water level response time value as the divisor, performing a division operation to calculate the quotient of the response amplitude value divided by the water level response time value. This quotient is determined as the response rate index. The response rate index reflects the average speed of water level change per unit time; a larger value indicates a faster and more sensitive response to gate operation, while a smaller value indicates a slower and less sensitive response. The calculation logic for the response rate index is as follows: This index reflects how quickly the reservoir water level reacts to gate operation. The system calculates the ratio of the total change in water level (water level difference) during the response process to the length of the response time. This ratio is the water level change per unit time, used to quantitatively characterize the reservoir's hydrodynamic sensitivity.

[0069] S502: Statistically analyze the numerical distribution of the response rate index during multiple gate operations, and calculate the average value of the response rate index for multiple operations.

[0070] Specifically, the electronic equipment collects records of all gate operation events occurring in the reservoir area upstream of the dam within a preset statistical time period. Each gate operation event includes corresponding water level response time, response amplitude, and response rate index values, forming a data set of response rate index values ​​from multiple gate operations. The electronic equipment performs statistical analysis on the response rate index data set, calculating the maximum, minimum, and range of response rate index values, counting the frequency of occurrence of response rate index values ​​within different value intervals, analyzing the distribution characteristics and variation patterns of response rate index values, and generating statistical results on the numerical distribution. The electronic equipment sums all values ​​in the response rate index data set, divides the sum by the total number of gate operation events, and calculates the average value of the response rate index from multiple operations. This average value serves as a benchmark reference value for measuring the response characteristics of the reservoir area.

[0071] S503: Operations with response rate index values ​​higher than the average value are marked as high-sensitivity response characteristics, and operations with response rate index values ​​not higher than the average value are marked as low-sensitivity response characteristics. High-sensitivity response characteristics and low-sensitivity response characteristics are used as the response characteristics of the storage area to scheduling operations.

[0072] Specifically, the electronic equipment acquires the response rate index values ​​for each gate operation and the calculated average response rate index value during multiple gate operations. It then compares and analyzes the response rate index values ​​for each gate operation with the average value. When the response rate index value for a gate operation is greater than the average value, it indicates that the water level response speed in that operation exceeds the overall average level, demonstrating strong sensitivity and rapid response to gate adjustments. This operation is marked as a high-sensitivity response characteristic. When the response rate index value for a gate operation is less than or equal to the average value, it indicates that the water level response speed in that operation does not exceed the overall average level, demonstrating a relatively mild response to gate adjustments. This operation is marked as a low-sensitivity response characteristic. Finally, both high-sensitivity and low-sensitivity response characteristics are collectively referred to as the reservoir's response characteristics to scheduling operations. S105: Based on the direction and region of pollution sources, the stability status of water stratification, and response characteristics, determine the comprehensive environmental assessment report.

[0073] Specifically, electronic equipment acquires pollution source direction information from upstream reservoir analysis, water stratification stability information from midstream reservoir analysis, and response characteristic information from upstream reservoir analysis, systematically integrating the monitoring and analysis results from these three reservoirs. The electronic equipment analyzes the spatial relationship between pollution source direction regions and topographic distribution data, assessing the possible paths and impact ranges of pollutant propagation from the source region to the midstream and downstream. Combined with water stratification stability analysis, it examines the vertical diffusion characteristics and residence time of pollutants in the midstream reservoir. When the water body is in a stable stratified state, vertical diffusion of pollutants is limited; when the water body is in a mixed state, vertical diffusion is sufficient. The electronic equipment, combined with the response characteristics of the upstream reservoir, evaluates the effectiveness of dispatching operations in controlling pollutant propagation. When the reservoir has high sensitivity response characteristics, dispatching operations can rapidly change water flow conditions, thus affecting pollutant distribution; when the reservoir has low sensitivity response characteristics, the control effect of dispatching operations on pollutant propagation is relatively limited. Finally, the pollution propagation path analysis, water stratification impact assessment, and dispatching control effect evaluation are integrated to form a comprehensive environmental assessment report covering pollution risk warning, environmental status determination, and dispatching recommendations.

[0074] Based on the above embodiments, as an optional embodiment, the step of determining the comprehensive environmental assessment report based on the pollution source direction region, water body stratification stability state, and response characteristics includes steps S601 to S604: S601: Overlay the pollution source direction area identified in the upstream reservoir area with the topographic distribution data to determine the geographical coordinates and influence range boundary of the pollution source direction area. Based on the geographical coordinates and influence range boundary, assess the diffusion path and propagation speed of pollutants from the pollution source direction area to the downstream.

[0075] Specifically, the electronic device spatially registers and positions the geographic boundary information of the pollution source area with the coordinate information in the topographic distribution data, identifying the specific location of the pollution source area within the topographic distribution data. From the overlay analysis results, the electronic device extracts the center point coordinates of the pollution source area as its geographic location coordinates and the set of coordinates of its outer boundary lines as the boundary of the affected area. Then, it analyzes the water flow direction and slope information in the topographic distribution data to determine the main water flow path from the pollution source area downstream. The electronic device calculates the spatial trajectory of pollutant propagation along the main water flow path, and, combined with the water depth and flow velocity information in the topographic distribution data, estimates the propagation speed of pollutants in different path segments. The spatial trajectory is determined as the diffusion path, and the velocity estimation result is determined as the propagation speed, forming a complete analysis of the propagation characteristics of pollutants from the pollution source area downstream.

[0076] S602: Correlation analysis between the water stratification stability state and temperature stratification coefficient in the midstream reservoir area is conducted to identify the water exchange capacity under stable stratification and the vertical convection intensity under mixed state, and to assess the impact of different stratification states on the vertical diffusion of pollutants.

[0077] Specifically, the electronic equipment acquires the water stratification stability classification results and temperature stratification coefficient values ​​obtained from the analysis of the midstream reservoir area. It then analyzes the correspondence between the water stratification stability and the temperature stratification coefficient. When the water stratification stability is stable, a high temperature stratification coefficient indicates a significant vertical temperature gradient. When the water stratification stability is mixed, a low temperature stratification coefficient indicates a relatively uniform vertical temperature distribution. For the stable stratification state, the electronic equipment, combined with a high temperature stratification coefficient, analyzes the formation of a clear density stratification structure in the vertical direction, indicating density barriers between different water layers that restrict material exchange, thus identifying the characteristic of limited water exchange capacity. For the mixed state, the electronic equipment, combined with a low temperature stratification coefficient, analyzes the lack of clear density stratification in the vertical direction, indicating active vertical convection, thus identifying the characteristic of sufficient vertical convection intensity. Electronic equipment assessments show that limited water exchange capacity in stable stratified states restricts the vertical diffusion rate and range of pollutants, with pollutants mainly spreading within specific water layers. Assessments also show that sufficient vertical convection intensity in mixed states promotes rapid vertical diffusion and thorough mixing of pollutants, enabling them to spread across the entire water depth. Ultimately, the results determine that different stratification states have varying degrees of impact on the vertical diffusion of pollutants.

[0078] S603: Match and analyze the response characteristics of the reservoir area in front of the dam with the historical records of gate scheduling operations to identify the optimal scheduling timing corresponding to high-sensitivity response characteristics and the scheduling delay risk corresponding to low-sensitivity response characteristics, and evaluate the control effect of scheduling operations on the environmental conditions of the reservoir area.

[0079] In this application embodiment, the optimal scheduling timing refers to the time window in which gate scheduling operations can achieve the best control effect under high sensitivity response characteristics, reflecting the optimal execution period of the scheduling operation; scheduling delay risk refers to the risk of response lag and poor effect that gate scheduling operations may produce under low sensitivity response characteristics, reflecting the uncertainty faced by the scheduling operation.

[0080] Specifically, the electronic equipment acquires the response characteristic classification results obtained from the analysis of the reservoir area in front of the dam and historical data of reservoir gate scheduling operations. The historical data includes information such as gate operation time, operation type, environmental conditions, and control results. The electronic equipment filters all operational events marked as having high sensitivity response characteristics in the historical records, extracts environmental background information for each operational event including water level status, flow conditions, and pollutant concentration levels, and analyzes the control results for each operational event, including the magnitude of water level change, response time, and degree of environmental improvement. It then calculates the proportion of operational events where the control results meet the expected targets under different environmental backgrounds, and determines the environmental background period with the highest control success rate as the optimal scheduling time. Conversely, the electronic equipment filters all operational events marked as having low sensitivity response characteristics in the historical records, extracts environmental background information and control results for each operational event, and analyzes the specific manifestations of poor control effects in each operational event, including excessively long response time, weak water level changes, and limited environmental improvement. It calculates the proportion of operational events with poor control effects under different environmental backgrounds, and determines the environmental background period with the highest control failure rate as the scheduling delay risk. Finally, it assesses that scheduling operations have a rapid and effective control effect under the optimal scheduling time, while the control effect is limited and time delay issues exist under the scheduling delay risk.

[0081] S604: Based on the diffusion path, propagation speed, degree of impact, and control effect, determine the comprehensive environmental assessment report.

[0082] Specifically, the electronic equipment acquires the diffusion path information, propagation speed data, impact assessment results, and control effect analysis results obtained from the aforementioned steps. It systematically integrates and correlates these four aspects of analysis to form a comprehensive assessment framework for the reservoir's environmental status. The electronic equipment combines diffusion path and propagation speed information to analyze the spatiotemporal propagation characteristics of pollutants in the reservoir, predicting the time points and concentration distribution of pollutants reaching various reservoir areas. It also combines the impact assessment results to analyze the vertical diffusion behavior and retention characteristics of pollutants in the midstream reservoir area, assessing the range and duration of pollutant impacts on different water depths. Finally, the electronic equipment combines the control effect analysis results to assess the feasibility and effectiveness of improving environmental conditions through gate scheduling operations, formulating scheduling strategy recommendations for different response characteristics. By integrating pollution propagation prediction, stratified impact analysis, and scheduling control recommendations, it generates a comprehensive environmental assessment report that includes environmental risk assessment, pollution control plans, and scheduling operation guidance, providing scientific basis and decision support for reservoir environmental management and emergency response.

[0083] Based on the above embodiments, as an optional embodiment, the step of determining the comprehensive environmental assessment report based on the diffusion path, propagation speed, degree of impact, and control effect includes steps S701 to S705: S701: Based on the diffusion path and propagation speed, calculate the expected arrival time and concentration decay value of pollutants at key nodes along the diffusion path, and generate a spatiotemporal propagation data table of pollutants.

[0084] In this embodiment, key nodes represent spatial locations on the diffusion path that have significant geographical or functional importance, including elements such as reservoir boundary points, water depth change points, and monitoring station locations; estimated arrival time represents the length of time required for pollutants to travel from their source location to the key node location, reflecting the temporal characteristics of pollutant propagation; concentration decay values ​​represent the degree of concentration reduction caused by dilution, degradation, and sedimentation during the propagation process, reflecting the spatial variation characteristics of pollutant concentration; and the pollutant spatiotemporal propagation data table represents information on the propagation status of pollutants at different times and spatial locations recorded in tabular form, including data elements such as time, location, and concentration.

[0085] Specifically, the electronic equipment acquires the spatial coordinates of the diffusion path and the numerical distribution of the propagation velocity. Key nodes are set at preset distance intervals along the diffusion path. These key nodes include important spatial nodes such as the boundary between the upstream and midstream reservoir areas, the boundary between the midstream reservoir and the reservoir upstream of the dam, and locations with significant water depth changes. The electronic equipment calculates the path distance of the pollutant from its source to each key node. Dividing the path distance by the propagation velocity of the corresponding path segment yields the estimated arrival time of the pollutant at each key node. Simultaneously, it considers the natural attenuation factors of the pollutant during propagation, including water dilution, biodegradation, and physical sedimentation, calculating the attenuation law of the pollutant concentration along the diffusion path to obtain the concentration attenuation value at each key node. The electronic equipment arranges and organizes the spatial coordinates, estimated arrival time, and concentration attenuation values ​​of each key node in chronological and spatial order, forming a data table containing information such as node location, arrival time, and remaining concentration, thus generating a spatiotemporal propagation data table for pollutants.

[0086] S702: Based on the stability state of water stratification, when the water stratification stability state is a stable stratification state, the vertical retention time of pollutants is calculated by applying the vertical retention conversion principle in combination with the degree of influence; when the water stratification stability state is a mixed state, the vertical dilution factor of pollutants is calculated by applying the vertical dilution conversion principle in combination with the degree of influence.

[0087] In this application embodiment, the vertical retention conversion principle refers to a calculation method for calculating the length of time pollutants remain in the vertical direction based on the characteristics of the water body's stratified structure, used to represent the time effect of the obstruction of vertical transport of pollutants under stable stratification conditions; the vertical dilution conversion principle refers to a calculation method for calculating the degree of dilution of pollutants in the vertical direction based on the mixing characteristics of the water body, used to represent the dilution effect of pollutant concentration reduction under mixed conditions; the vertical retention time of pollutants represents the length of time required for pollutants to be transported from the surface to the bottom layer in a stable stratified water body, reflecting the time delay effect of the stratification structure on the vertical diffusion of pollutants; the vertical dilution factor of pollutants represents the concentration dilution ratio of pollutants caused by vertical convection in a mixed water body, reflecting the degree of reduction of pollutant concentration due to mixing.

[0088] Specifically, the electronic equipment acquires the classification results of the water stratification stability state in the midstream reservoir area and the assessment data on the degree of impact on the vertical diffusion of pollutants, determines the specific type of water stratification stability state, and performs corresponding calculations. When the water stratification stability state is stable, the electronic equipment, combining the vertical diffusion limitation characteristics in the degree of impact, extracts the water stratification intensity value as the retardation intensity parameter, extracts the thickness values ​​of each water layer as the transmission distance parameter, and extracts the stratification density difference as the diffusion resistance parameter. Multiplying the retardation intensity parameter and the transmission distance parameter yields the basic retention factor, and multiplying the basic retention factor and the diffusion resistance parameter yields the vertical residence time of pollutants. When the water stratification stability state is mixed, the electronic equipment, combining the vertical convection promotion characteristics in the degree of impact, extracts the water mixing intensity value as the dilution intensity parameter, extracts the vertical convection velocity value as the mixing velocity parameter, and extracts the water volume value as the dilution capacity parameter. Multiplying the dilution intensity parameter and the mixing velocity parameter yields the basic dilution factor, and multiplying the basic dilution factor and the dilution capacity parameter yields the vertical dilution factor of pollutants.

[0089] The implementation of the vertical retention conversion principle is as follows: The calculation logic of this conversion principle is based on the following physical mechanism: a stable water stratification structure forms density gradients, acting as physical barriers that significantly hinder the vertical convection and diffusion of pollutants. Therefore, a calculation model based on a modified Fick diffusion law can be established, where the vertical diffusion coefficient is inversely proportional to the temperature stratification coefficient (or the Schmidt stability number derived from it). By inputting the currently calculated temperature stratification coefficient into this model, the characteristic time required for pollutants to traverse this stable water layer, i.e., the vertical retention time of the pollutants, can be calculated. The implementation of the vertical dilution conversion principle is as follows: the calculation logic of this conversion principle is based on the following physical mechanism: in a mixed state, strong vertical convection allows pollutants to be rapidly and uniformly distributed throughout the water body. Its dilution effect can be quantified as the ratio of the initial impact volume of the pollutants to the total volume of uniformly distributed pollutants after mixing. By obtaining the vertical flow velocity and combining it with the water layer thickness, the system can estimate the water exchange flux per unit time. Based on this flux, the total volume of water to which the pollutants are diluted within a specific time period can be calculated, thus obtaining the vertical dilution factor of the pollutants.

[0090] S703: Based on the spatiotemporal propagation data of pollutants, as well as the vertical residence time or vertical dilution factor of pollutants, calculate the final concentration and total residence time of pollutants at each location within the reservoir area, and mark areas where the total residence time exceeds the preset safe time threshold and the final concentration exceeds the preset safe concentration threshold as high-risk areas.

[0091] Specifically, the electronic device acquires the concentration decay values ​​and arrival time information of key nodes in the spatiotemporal propagation data table of pollutants, and simultaneously acquires the vertical residence time or vertical dilution factor data of pollutants. It then integrates the spatiotemporal propagation data with the vertical diffusion data. For reservoir locations in a stable stratified state, the electronic device uses the concentration decay values ​​in the spatiotemporal propagation data table as the baseline concentration. Combined with the vertical residence time of pollutants, it calculates that the final concentration value of pollutants at that location will remain at a high level. The arrival time in the spatiotemporal propagation data table is added to the vertical residence time to obtain the total residence time. For reservoir locations in a mixed state, the concentration decay values ​​in the spatiotemporal propagation data table are divided by the vertical dilution factor to obtain the final concentration value after vertical dilution. The arrival time is added to the vertical mixing time to obtain the total residence time. The electronic device compares the calculated total residence time for each location with a preset safe time threshold, and compares the final concentration value with a preset safe concentration threshold. Locations where both the total residence time and the final concentration value exceed the preset safe concentration threshold are selected, and the continuous area formed by connecting these locations is marked as a high-risk area.

[0092] S704: Based on the assessment conclusions regarding the optimal scheduling timing and scheduling delay risk in the control effect, formulate emergency scheduling operation sequence arrangements for high-sensitivity response characteristics and preventive scheduling operation sequence arrangements for low-sensitivity response characteristics.

[0093] Specifically, the electronic equipment acquires the assessment conclusions regarding the optimal scheduling timing and the identification results of scheduling delay risks from the control effect analysis. It then matches these assessment conclusions with the reservoir area's response characteristics, formulating scheduling operation plans adapted to different response characteristics. For highly sensitive response conditions, the electronic equipment utilizes the assessment conclusions of the optimal scheduling timing to formulate an emergency scheduling operation sequence. Periods of increased pollutant concentration and abnormal water level fluctuations are identified as emergency scheduling trigger conditions. A sequence for rapidly opening or closing gates is set, and scheduling operations are completed within a short time window before pollutants reach the reservoir area in front of the dam. This achieves the emergency control objective of pollutant dilution and transport by rapidly changing the water flow state. For low-sensitive response conditions, the electronic equipment utilizes the identification results of scheduling delay risks to formulate a preventative scheduling operation sequence. Dry seasons and periods of stable temperature stratification are identified as scheduling delay risk periods. An early warning and phased scheduling operation sequence is set, and scheduling preparations are initiated in advance before the risk period arrives. The impact of response delays is compensated by extending scheduling operation time and increasing operation frequency, achieving the preventative management objective of pollution control.

[0094] S705: Generate a pollution risk distribution map of the reservoir area based on high-risk areas. The pollution risk distribution map, the emergency dispatch operation sequence, and the preventative dispatch operation sequence will together constitute a comprehensive environmental assessment report.

[0095] Specifically, the electronic device acquires the geographic location information and boundary coordinate data of high-risk areas, overlays the spatial boundary coordinates of high-risk areas onto the reservoir topographic distribution data, and generates a visual image showing the spatial distribution of high-risk areas. The electronic device marks high-risk areas with red markers and areas within the reservoir other than high-risk areas with green markers as safe areas. Geographic coordinate grid lines and regional boundary lines are added to the image, and the specific location names and area ranges of high-risk areas are labeled, forming a reservoir pollution risk distribution map that includes the distribution of risk areas, geographic coordinate labels, and legends. The electronic device integrates the generated reservoir pollution risk distribution map with the aforementioned emergency dispatch operation sequence and preventive dispatch operation sequence. The pollution risk distribution map serves as the risk identification section, the emergency dispatch operation sequence as the emergency response section, and the preventive dispatch operation sequence as the prevention management section, forming a comprehensive environmental assessment report that includes risk assessment, emergency plans, and preventive measures. The following describes an exemplary electronic device for IoT environmental monitoring provided in the embodiments of this application. Figure 3 This is an exemplary hardware structure diagram of an electronic device for IoT environmental monitoring provided in an embodiment of this application.

[0096] In some embodiments, the electronic device for IoT environmental monitoring is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0097] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0099] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0100] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0101] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An Internet of Things (IoT) environmental monitoring method, characterized in that, The method includes: Obtain topographic distribution data and dam location information of the reservoir area, and divide the monitoring area into upstream reservoir area, midstream reservoir area and dam front reservoir area based on the topographic distribution data and dam location information; For the upstream reservoir area, pollutant concentration distribution data and flow velocity monitoring data are obtained from multiple locations. Based on the pollutant concentration distribution data, the pollutant concentration gradient is calculated. Based on the pollutant concentration gradient and flow velocity monitoring data, the direction and area of ​​pollution source are determined. For the midstream reservoir area, obtain vertically stratified temperature distribution data and dissolved oxygen content of each water layer, calculate the temperature stratification coefficient based on the temperature distribution data, and determine the water stratification stability state based on the temperature stratification coefficient and the changing trend of dissolved oxygen content of each water layer. For the reservoir area in front of the dam, water level fluctuation data and gate opening information are acquired. Based on the water level fluctuation data and gate opening information, the water level response time and response amplitude are calculated. Based on the water level response time and response amplitude, the response characteristics of the reservoir area to the scheduling operation are determined. Based on the direction and region of pollution sources, the stratified stability of water bodies, and response characteristics, a comprehensive environmental assessment report is determined.

2. The IoT environmental monitoring method according to claim 1, characterized in that, The calculation of pollutant concentration gradient based on pollutant concentration distribution data, and the determination of the pollution source direction region based on pollutant concentration gradient and flow velocity monitoring data, specifically include: The pollutant concentration values ​​at each monitoring point in the upstream reservoir area are obtained in real time. The pollutant concentration values ​​at adjacent monitoring points are calculated by difference. The result of the difference calculation is divided by the distance between adjacent monitoring points to obtain the pollutant concentration gradient between adjacent monitoring points. The concentration gradients of each pollutant are spatially interpolated according to the geographical coordinates of the monitoring points to generate a pollutant concentration gradient distribution map of the upstream reservoir area. The area with the largest pollutant concentration gradient value is identified from the pollutant concentration gradient distribution map as the area with the most drastic change in pollutant concentration. The flow velocity monitoring data of each monitoring point in the area with the most drastic changes in pollutant concentration are obtained. The flow velocity direction angle and flow velocity magnitude of each monitoring point are extracted. The flow velocity direction angle is vector superimposed with the pollutant concentration gradient direction to obtain the pollutant transport direction angle of each monitoring point. The pollutant concentration gradient direction is the direction in which the pollutant concentration increases the fastest. The pollutant transport direction angle of each monitoring point is reverse-tracked and calculated. The tracking path is marked in the terrain distribution data, and the intersection area of ​​multiple reverse tracking paths is determined as the pollution source direction area.

3. The IoT environmental monitoring method according to claim 1, characterized in that, The process of acquiring vertically stratified temperature distribution data and dissolved oxygen content of each water layer, calculating temperature stratification coefficients based on the temperature distribution data, and determining the water stratification stability state based on the changing trends of temperature stratification coefficients and dissolved oxygen content of each water layer specifically includes: Temperature sensor measurements at different depths in the vertical direction within the midstream reservoir area were obtained, and the measurement points were divided into three water layers: surface, middle, and bottom, according to the water depth direction. Calculate the temperature difference and depth difference between adjacent water layers, divide each temperature difference by the corresponding depth difference between water layers to obtain the temperature gradient values ​​between water layers, and sum the absolute values ​​of the temperature gradient values ​​between all water layers to obtain the temperature stratification coefficient. Obtain the dissolved oxygen content of each water layer within a preset time period, and use the difference between the maximum and minimum dissolved oxygen content within the preset time period as the range of change in dissolved oxygen content. Calculate the standard deviation of the dissolved oxygen content variation range in the surface, middle and bottom water layers, and use the standard deviation as the degree of difference in dissolved oxygen content variation range; then, weight and sum the degree of difference in dissolved oxygen content variation range with the temperature stratification coefficient to obtain the corresponding water stratification assessment value. Water stratification assessment values ​​above the preset state threshold are marked as stable stratification states, and water stratification assessment values ​​below the preset state threshold are marked as mixed states. Stable stratification states and mixed states are used as water stratification stability states.

4. The IoT environmental monitoring method according to claim 1, characterized in that, The calculation of water level response time and response amplitude based on water level fluctuation data and gate opening information specifically includes: Extract the starting time and degree of change of the gate opening from the gate opening information; Identify the moment when the rate of change of water level value first exceeds the preset rate of change threshold from the water level fluctuation data, and calculate the time interval between the start time of the gate opening change and the change time as the water level response time. The change in water level value within the response time in the water level fluctuation data is taken as the response amplitude.

5. The IoT environmental monitoring method according to claim 1, characterized in that, The determination of the reservoir's response characteristics to scheduling operations based on water level response time and response amplitude specifically includes: The response rate index is obtained by calculating the ratio of the water level response time to the response amplitude. The numerical distribution of the response rate index during multiple gate operations is statistically analyzed, and the average value of the response rate index during multiple operations is calculated. Operations with response rate values ​​higher than the average are marked as high-sensitivity response characteristics, while operations with response rate values ​​not higher than the average are marked as low-sensitivity response characteristics. These high-sensitivity and low-sensitivity response characteristics are used as the response characteristics of the storage area to scheduling operations.

6. The IoT environmental monitoring method according to claim 1, characterized in that, The comprehensive environmental assessment report, based on the pollution source direction region, water body stratification stability, and response characteristics, specifically includes: By overlaying the pollution source areas identified in the upstream reservoir area with topographic distribution data, the geographical coordinates and impact range boundaries of the pollution source areas are determined. Based on the geographical coordinates and impact range boundaries, the diffusion path and propagation speed of pollutants from the pollution source areas to the downstream are assessed. The correlation analysis between the water stratification stability state and the temperature stratification coefficient in the middle reaches of the reservoir was conducted to identify the water exchange capacity under stable stratification and the vertical convection intensity under mixed state, and to assess the impact of different stratification states on the vertical diffusion of pollutants. By matching and analyzing the response characteristics of the reservoir area in front of the dam with the historical records of gate scheduling operations, the optimal scheduling timing corresponding to high-sensitivity response characteristics and the scheduling delay risk corresponding to low-sensitivity response characteristics are identified, and the control effect of scheduling operations on the environmental conditions of the reservoir area is evaluated. Based on the diffusion path, propagation speed, impact level, and control effectiveness, a comprehensive environmental assessment report is determined.

7. The IoT environmental monitoring method according to claim 6, characterized in that, The comprehensive environmental assessment report, based on diffusion path, propagation speed, impact level, and control effectiveness, specifically includes: Based on the diffusion path and propagation speed, the expected arrival time and concentration decay value of pollutants at key nodes along the diffusion path are calculated, and a spatiotemporal propagation data table of pollutants is generated. Based on the stability state of the water stratification, when the water stratification is in a stable stratification state, the vertical retention time of pollutants is calculated using the vertical retention conversion principle, taking into account the degree of influence. When the water stratification is in a mixed state, the vertical dilution factor of pollutants is calculated using the vertical dilution conversion principle, taking into account the degree of influence. Based on the spatiotemporal propagation data of pollutants, as well as the vertical residence time or vertical dilution factor of pollutants, the final concentration and total residence time of pollutants at each location in the reservoir area are calculated, and areas where the total residence time exceeds the preset safe time threshold and the final concentration exceeds the preset safe concentration threshold are marked as high-risk areas. Based on the assessment conclusions regarding the optimal scheduling timing and scheduling delay risk in the control effect, emergency scheduling operation sequence arrangements for high-sensitivity response characteristics and preventive scheduling operation sequence arrangements for low-sensitivity response characteristics are formulated respectively. A pollution risk distribution map of the reservoir area is generated based on high-risk areas; and the pollution risk distribution map, emergency dispatch operation sequence, and preventive dispatch operation sequence together constitute a comprehensive environmental assessment report.

8. An electronic device for Internet of Things (IoT) environmental monitoring, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device for Internet of Things (IoT) environment monitoring, the electronic device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device for Internet of Things (IoT) environment monitoring, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

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