Environmental data anomaly monitoring system and method based on information collection
By using a dynamic monitoring network and source tracing analysis, combined with automatic air quality monitoring stations and vehicle-mounted sensors, the problem of monitoring blind spots in densely populated urban pollution source areas has been solved. This has enabled real-time tracking of pollutant diffusion trajectories and accurate identification of high emission states, reducing the false alarm rate and pinpointing the emission sources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing environmental data monitoring systems are sparsely deployed in areas with dense mobile pollution sources, such as major urban roads and transportation hubs, resulting in many monitoring blind spots and a lack of ability to track the diffusion trajectory and dynamic changes of pollutants. Furthermore, traditional monitoring relies on a single concentration threshold for judgment, leading to a high rate of misjudgment of high emission states.
By utilizing existing automatic air quality monitoring stations and vehicle-mounted micro sensors, a dynamic monitoring network is constructed through a dynamic monitoring network construction unit. This network combines dynamic curve analysis to identify high-trend trajectories and uses a source tracing analysis unit to pinpoint emission sources, replacing single concentration thresholds with quantitative indicators.
It achieves high-density coverage of major urban roads, accurately identifies high-emission states, reduces the false alarm rate, ensures the accuracy and real-time nature of source tracing analysis, and pinpoints the specific location of pollutant diffusion paths.
Smart Images
Figure CN121745478A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of environmental data anomaly monitoring, in particular to an environmental data anomaly monitoring system and method based on information collection. BACKGROUND
[0002] In the current field of environmental data anomaly monitoring, urban air quality monitoring mainly relies on fixedly deployed air quality automatic monitoring stations. For example, a patent with application number 2024102855760 discloses an environmental monitoring abnormal data identification method. The method collects relevant values in the monitoring area through a data collection module and sends the relevant values to a data processing module. By processing and analyzing real-time data, it can determine whether the environment is polluted and the growth rate of relevant factors in a timely manner, so that monitoring personnel can timely discover environmental abnormal values and make response strategies.
[0003] That is, although the current monitoring stations can provide standard pollutant concentration data, the monitoring station layout is sparse and fixed, and it is difficult to cover the main roads of the city, transportation hubs and other mobile pollution source intensive areas, resulting in many monitoring blind spots and the inability to fully capture the spatial distribution differences of pollutants in the area. In addition, traditional monitoring mainly relies on static data collection and can only obtain pollutant concentration values at specific points. It lacks the ability to track the diffusion trajectory and dynamic change trend of pollutants, making it difficult to reflect the dynamic characteristics of the whole process from emission to diffusion of pollutants. Moreover, traditional high emission state identification mostly relies on single concentration threshold judgment, lacks quantitative analysis of dynamic indicators such as pollutant concentration change rate and fluctuation law, resulting in high misjudgment and omission rate of high emission state.
[0004] Therefore, a solution is proposed. SUMMARY
[0005] The present application is to solve the above-mentioned problems and proposes an environmental data anomaly monitoring system and method based on information collection.
[0006] The purpose of the present application can be achieved by the following technical solution: an environmental data anomaly monitoring system based on information collection, comprising an anomaly monitoring platform, wherein the anomaly monitoring platform is communicatively connected to: a dynamic monitoring network building unit for building a dynamic monitoring network for the environmental monitoring area; a dynamic monitoring unit for performing regional dynamic monitoring based on dynamic monitoring network data analysis after building the dynamic monitoring network; a trace analysis unit for setting a trace analysis trajectory based on the dynamic monitoring result and analyzing all trace analysis trajectories in the environmental monitoring area.
[0007] Further, the process of the dynamic monitoring network building unit is as follows: Using the existing air quality automatic monitoring station, continuously obtain the minute or hour level concentration data of its standard pollutants, and mark it as environmental change data; select taxis and bus teams covering the main roads of the city, and install customized vehicle-mounted micro environmental monitoring sensor modules on the roof of the vehicle; Divide the environmental monitoring area into regular grids, each grid point containing a time-varying data sequence; record the total value of pollutants in the environmental monitoring area according to the air quality automatic monitoring station, and according to the data collection of the vehicle-mounted micro environmental monitoring sensor module, the rising trend of pollutants at each position in the internal grid of the monitoring area is obtained, and a dynamic monitoring network is constructed.
[0008] Further, the process of the dynamic monitoring unit is as follows: According to the rising trend of pollutants at each position in the internal grid of the dynamic monitoring network, the positions are connected, and the pollutants at each position are recorded to form the environmental change data curve at a single time, and according to the continuous monitoring of the environmental area, the environmental change data curve corresponding to each adjacent time is collected and a dynamic curve graph is constructed; According to the distribution of the rising trend of pollutants at each position, high-trend trajectory identification is performed, that is, the positions with high trend are connected to construct a high-trend trajectory, the slope value corresponding to the environmental change data curve of the high-trend trajectory is obtained, and if the slope value corresponding to the environmental change data curve of the high-trend trajectory exceeds the set slope threshold, the current trajectory order of the high-trend trajectory is marked as an increasing order; if the slope value corresponding to the environmental change data curve of the high-trend trajectory does not exceed the set slope threshold, the current trajectory order of the high-trend trajectory is marked as a decreasing order.
[0009] Further, according to the increasing order and decreasing order of the high-trend trajectory, the dynamic curve graph is analyzed: When the high-trend trajectory is in the increasing order, the rising span of the minimum value of the environmental change data of the grid position in the slope rising stage in the dynamic curve graph is obtained; when the high-trend trajectory is in the decreasing order, the peak value increase frequency of the floating span of the environmental change data of the grid position in the slope falling stage in the dynamic curve graph is obtained; If the rising span of the minimum value of the environmental change data of the grid position in the slope rising stage in the dynamic curve graph exceeds the rising span threshold, or the peak value increase frequency of the floating span of the environmental change data of the grid position in the slope falling stage in the dynamic curve graph exceeds the increase frequency threshold, it is concluded that the current high-trend trajectory is in a high-emission state, and is marked as an environmental parameter intensification trajectory; If the rising span of the minimum value of the grid position environment change data in the rising stage of the slope in the dynamic curve diagram does not exceed the rising span threshold, and the increasing frequency of the peak value of the grid position environment change data floating span in the falling stage of the slope in the dynamic curve diagram does not exceed the increasing frequency threshold, it is inferred that the current high trend trajectory is in a low emission state, and is marked as an environmental parameter stable trajectory.
[0010] Further, the process of the trace analysis unit is as follows: The grid position corresponding to the environmental parameter intensification trajectory is analyzed to obtain the environmental change data floating trend of the corresponding grid position at a single time, and the grid positions with the same trend are screened; if the grid positions with the same trend are adjacent and the interval time length of the environmental change data floating time is lower than the set interval time length threshold, the grid positions with the same trend are connected and marked as a trace analysis trajectory; otherwise, if there are grid positions with different trends between the grid positions with the same trend, or the interval time length of the environmental change data floating time is higher than the set interval time length threshold, the corresponding grid positions with the same trend are not merged into the same trace analysis trajectory.
[0011] Further, after setting the trace analysis trajectories in the environmental monitoring area, all the trace analysis trajectories are analyzed: The time when the environmental change data of the grid position in the trace analysis trajectory appears floating is obtained, and the earliest time is marked as the starting floating time point; if the order of the floating grid positions corresponding to the adjacent floating times after the starting floating time point is consistent with the trace analysis trajectory, the grid position corresponding to the starting floating time point is marked as the source position; otherwise, the grid position of the starting floating time point and the grid position corresponding to the adjacent floating time are obtained, and are marked in the trace analysis trajectory, and the grid positions between the grid position of the starting floating time point and the grid position corresponding to the adjacent floating time are analyzed synchronously.
[0012] Further, the grid position of the starting floating time point and the adjacent floating time are marked as the grid starting point and the grid ending point respectively, and the grid positions between them are marked as the turning points; according to the environmental change data of the grid starting point and the grid ending point, the current environmental change data trend and the corresponding numerical range are obtained; if the environmental change data of the turning point is in the numerical range, and the corresponding numerical values of the environmental change data of the grid starting point and the turning point, and the turning point and the grid ending point are in the same trend, the current grid starting point is marked as the trace point; If the environmental change data of the turning point is not in the numerical range, or the corresponding numerical values of the environmental change data of the grid starting point and the turning point, and the turning point and the grid ending point are not in the same trend, the turning points are screened, and the turning point closest to the grid ending point and the environmental change data floating trend of the turning point and the grid ending point are consistent with the floating trend corresponding to the current trace analysis trajectory, then the corresponding turning point is marked as the trace point; The trace point is sent to the abnormality monitoring platform, and the abnormality monitoring platform performs environmental parameter monitoring control on the trace point after receiving the trace point.
[0013] In the application, an environmental data abnormality monitoring method based on information collection is also provided, and the specific steps are as follows: Step 1: environmental change data collection, and dynamic monitoring network construction on the environmental monitoring area during the collection process; Step 2: grid division of the dynamic monitoring network, and environmental change parameter collection and analysis on each grid position; Step 3: high trend trajectory identification and construction of a high trend trajectory, and sorting of the trend order of the grid positions in the current trajectory according to the real-time trajectory; Step 4: dynamic curve analysis according to the increasing order and decreasing order of the high trend trajectory, to obtain an environmental parameter aggravation trajectory; Step 5: after obtaining the environmental parameter aggravation trajectory, performing emission source tracing on the environmental parameter aggravation trajectory.
[0014] Compared with the prior art, the application has the following beneficial effects: 1. The dynamic monitoring network construction unit utilizes the existing air quality automatic monitoring station data and combines the design of the vehicle-mounted miniature environmental monitoring sensor module, on the one hand, to realize the reuse of existing monitoring resources and reduce the system deployment cost, and on the other hand, to install sensors on the selected taxis and buses on the main roads of the city to accurately compensate for the monitoring blind area of the fixed monitoring station in the mobile pollution source dense area, and to realize the all-around and high-density coverage of the environmental monitoring area. The step of dividing the environmental monitoring area into regular grids and constructing the dynamic monitoring network binds the dispersed multi-source data with the specific spatial positions, forms the grid data sequence changing with time, and lays a foundation for subsequent tracking of the spatial diffusion trajectory of the pollutants, to solve the problem that the traditional monitoring data is not associated with the spatial positions.
[0015] 2. The step of constructing the dynamic curve in the dynamic monitoring unit connects the pollutant data of each grid position and concatenates the curves at adjacent time points, to intuitively present the spatio-temporal dynamic change rule of the pollutant concentration, break through the limitation that the traditional static data cannot reflect the change trend, and through the design of the high trend trajectory identification and the increasing / decreasing order marking, the data analysis deviation caused by the fixed collection order is abandoned, the order characteristics of the change of the pollutant concentration are identified in real time, and the real-time and accuracy of the trajectory analysis are ensured. The step of distinguishing the high emission state from the low emission state by judging the relationship between the rising span and the peak increase frequency and the corresponding threshold value adopts the quantitative index to replace the traditional single concentration threshold value judgment, avoids the interference of subjective factors, and significantly improves the accuracy of the high emission state identification, so as to lock the core target for subsequent tracing analysis and reduce the invalid tracing work.
[0016] 3. The step of screening adjacent grids with the same trend in the traceability analysis unit and constructing a traceability analysis track. By setting a time interval threshold and a trend consistency condition, the interference of irrelevant grid data is effectively excluded, ensuring the consistency of the traceability track with the pollutant diffusion path, and avoiding the problem of track confusion in traditional traceability. The design of determining the starting floating time point and locking the traceability point through the multi-node analysis of the starting point, the ending point and the turning point fully considers the track changes in the pollutant diffusion process, accurately locates the emission source by verifying the numerical range and trend of environmental change data, and solves the pain point that traditional traceability methods are difficult to lock the specific location of the moving pollution source, providing accurate location basis for subsequent targeted control. BRIEF DESCRIPTION OF DRAWINGS
[0017] In order to facilitate those skilled in the art to understand, the present application will be further described below in conjunction with the drawings.
[0018] Figure 1 The system principle block diagram of the present application; Figure 2 The method flow chart of the present application. DETAILED DESCRIPTION
[0019] In order to make the person in this technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor belong to the scope of protection of the present application.
[0020] In this paper, "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily refer to the same embodiment, nor is it independent or alternative to other embodiments. The person skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.
[0021] Please refer to Figure 1 As shown in the figure, the environmental data anomaly monitoring system based on information collection includes an anomaly monitoring platform, wherein the anomaly monitoring platform is communicatively connected with a dynamic monitoring network building unit, a dynamic monitoring unit and a traceability analysis unit. The anomaly monitoring platform generates a dynamic monitoring network building signal and sends it to the dynamic monitoring network building unit. After receiving the dynamic monitoring network building signal, the dynamic monitoring network building unit builds a dynamic monitoring network for the environmental monitoring area. Using the existing air quality automatic monitoring station, continuously obtain the minute or hour level concentration data of its standard pollutants (such as PM2.5, PM10, NO2, SO2); and mark as environmental change data; Select the taxi and bus fleet covering the main roads of the city, install customized vehicle-mounted micro environmental monitoring sensor modules on the roof of the vehicle; Specifically, gas sensors: low-cost, low-power micro sensors for monitoring PM2.5, NOx, etc.; Positioning module: high-precision GPS module, real-time recording of vehicle latitude, longitude, speed, direction; Vehicle diagnostic system interface: optional access, obtain engine operating condition (such as idle, acceleration) data, assist in judging the source of emissions; Divide the environmental monitoring area into regular grids (such as 100m*100m), each grid point contains a time-varying data sequence; According to the air quality automatic monitoring station, record the total value of pollutants in the environmental monitoring area, and according to the data collection of the vehicle-mounted micro environmental monitoring sensor module, monitor the rising trend of pollutants at each position in the internal grid of the monitoring area, and build a dynamic monitoring network; After completing the dynamic monitoring network, generate a dynamic monitoring signal and send it to the dynamic monitoring unit; After receiving the dynamic monitoring signal, the dynamic monitoring unit analyzes the data of the dynamic monitoring network to monitor the area dynamically; According to the rising trend of pollutants at each position in the internal grid of the dynamic monitoring network, connect each position and record the pollutants to form an environmental change data curve at a single time, and according to the continuous monitoring of the environmental area, collect the environmental change data curves corresponding to each adjacent time and build a dynamic curve graph; According to the distribution of the rising trend of pollutants at each position, identify the high trend trajectory, that is, connect the positions with high trend to build a high trend trajectory, obtain the slope value corresponding to the environmental change data curve corresponding to the high trend trajectory, and if the slope value corresponding to the environmental change data curve corresponding to the high trend trajectory exceeds the set slope threshold, mark the current trajectory order of the high trend trajectory as an increasing order; If the slope value corresponding to the environmental change data curve corresponding to the high trend trajectory does not exceed the set slope threshold, mark the current trajectory order of the high trend trajectory as a decreasing order; This order is the current trajectory data obtained according to the sensor data statistics order at the same time, which avoids the need for real-time identification when collecting at multiple positions according to a fixed collection order, that is, through order division, the pollutants can be effectively monitored; According to the increasing order and decreasing order of the high trend trajectory, analyze the dynamic curve graph: When the high trend trajectory is in the increasing order, obtain the rising span of the minimum value of the environmental change data of the grid position in the slope rising stage in the dynamic curve graph; If the high trend trajectory is in a decreasing order, the peak value increase frequency of the floating span of the grid position environmental change data in the slope decreasing stage of the dynamic curve is obtained; If the rising span of the minimum value of the grid position environmental change data in the slope increasing stage of the dynamic curve exceeds the rising span threshold, or the peak value increase frequency of the floating span of the grid position environmental change data in the slope decreasing stage of the dynamic curve exceeds the increase frequency threshold, it is inferred that the current high trend trajectory is in a high emission state, and is marked as an environmental parameter intensification trajectory; If the rising span of the minimum value of the grid position environmental change data in the slope increasing stage of the dynamic curve does not exceed the rising span threshold, and the peak value increase frequency of the floating span of the grid position environmental change data in the slope decreasing stage of the dynamic curve does not exceed the increase frequency threshold, it is inferred that the current high trend trajectory is in a low emission state, and is marked as an environmental parameter stable trajectory; After obtaining the environmental parameter intensification trajectory, a traceability analysis signal is generated and sent to a traceability analysis unit; After receiving the traceability analysis signal, the traceability analysis unit performs emission source traceability on the environmental parameter intensification trajectory; The grid positions corresponding to the environmental parameter intensification trajectory are analyzed, the environmental change data floating trend of the corresponding grid positions at a single time is obtained, and the grid positions with the same trend are screened; if the grid positions with the same trend are adjacent and the interval time length between the floating times of the environmental change data is lower than a set interval time length threshold, the grid positions with the same trend are connected and marked as a traceability analysis trajectory; otherwise, if there are grid positions with different trends between the grid positions with the same trend, or the interval time length between the floating times of the environmental change data is higher than the set interval time length threshold, the corresponding grid positions with the same trend are not merged into the same traceability analysis trajectory; After completing the setting of the traceability analysis trajectories in the environmental monitoring area, all the traceability analysis trajectories are analyzed: The times at which the environmental change data of the grid positions corresponding to the traceability analysis trajectories appear to float are obtained, and the earliest time is marked as a starting floating time point; if the order of the floating grid positions corresponding to the adjacent floating times after the starting floating time point is consistent with the traceability analysis trajectory, the grid position corresponding to the starting floating time point is marked as a source position; otherwise, the grid position of the starting floating time point and the grid positions corresponding to the adjacent floating times are obtained, and are marked in the traceability analysis trajectory, and the grid positions between the grid position of the starting floating time point and the grid positions corresponding to the adjacent floating times are analyzed synchronously; Mark the grid position of the starting floating time point and the adjacent floating time points as the grid starting point and the grid ending point respectively, and mark the grid positions between them as turning points; according to the environmental change data of the grid starting point and the grid ending point, and the current environmental change data trend and the corresponding numerical range are obtained, if the turning point corresponding environmental change data is in the numerical range, and the grid starting point and the turning point, the turning point and the grid ending point, the corresponding environmental change data corresponding to the numerical value is in the same trend, then the current grid starting point is marked as a trace point; If the turning point corresponding environmental change data is not in the numerical range, or the grid starting point and the turning point, the turning point and the grid ending point, the corresponding environmental change data corresponding to the numerical value is not in the same trend, then the turning point is screened, and the turning point closest to the grid ending point, and the environmental change data floating trend of the turning point and the grid ending point is consistent with the current trace analysis trajectory corresponding floating trend, then the corresponding turning point is marked as a trace point; The trace point is sent to the abnormal monitoring platform, and the abnormal monitoring platform receives the trace point and performs environmental parameter monitoring control.
[0022] Please refer to Figure 2 The present application also proposes an environmental data abnormality monitoring method based on information collection, and the specific steps are as follows: Step one, environmental change data collection, and dynamic monitoring network construction is carried out on the environmental monitoring area during the collection process; Step two, grid division is carried out on the dynamic monitoring network, and environmental change parameter collection and analysis are carried out on each grid position; Step three, high trend trajectory identification and construction, according to real-time trajectory, the trend order of the grid position in the current trajectory is screened; Step four, dynamic curve analysis according to the increasing order and decreasing order of the high trend trajectory, and the environmental parameter aggravation trajectory is obtained; Step five, after obtaining the environmental parameter aggravation trajectory, the environmental parameter aggravation trajectory is traced to the source of emission.
[0023] The threshold or the preset value, the preset range and the like are set for result comparison and analysis, so as to determine whether it is good or bad, and the size of the threshold is determined according to the large model analysis of sample data and the combination of artificial experience, and the input storage is set, and the threshold can also be adjusted appropriately according to the seasonal or rational influence condition; The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to provide the best illustration of the application and its practical application to those skilled in the art and to enable those skilled in the art to best utilize the application. The application is limited only by the claims and their full scope and equivalents.
Claims
1. An environmental data anomaly monitoring system based on information acquisition, characterized in that, This includes an anomaly monitoring platform, whose communication connections include: The dynamic monitoring network construction unit is responsible for constructing a dynamic monitoring network for the environmental monitoring area. The dynamic monitoring unit performs regional dynamic monitoring based on the data analysis of the dynamic monitoring network after the dynamic monitoring network is completed. The source tracing analysis unit sets the source tracing analysis trajectory based on the dynamic monitoring results and analyzes all source tracing analysis trajectories within the environmental monitoring area.
2. The environmental data anomaly monitoring system based on information acquisition according to claim 1, characterized in that, The process of building a dynamic monitoring network unit is as follows: Utilize existing automatic air quality monitoring stations to continuously acquire minute-level or hourly concentration data of standard pollutants and label them as environmental change data; select taxi and bus fleets covering major urban roads and install customized vehicle-mounted miniature environmental monitoring sensor modules on their roofs; The environmental monitoring area is divided into regular grids, with each grid point containing a data sequence that changes over time. Based on the total pollutant value recorded by the automatic air quality monitoring station and the data collected by the vehicle-mounted micro environmental monitoring sensor module, the pollutant rise trend at each location within the grid of the monitoring area is analyzed, and a dynamic monitoring network is constructed.
3. The environmental data anomaly monitoring system based on information acquisition according to claim 2, characterized in that, The process of the dynamic monitoring unit is as follows: Based on the pollutant rise trend at each location within the internal grid of the dynamic monitoring network, the locations are connected and the pollutants at each location are recorded to form an environmental change data curve at a single moment. Based on continuous monitoring of the environmental area, the environmental change data curves corresponding to each adjacent moment are collected and a dynamic curve graph is constructed. Based on the distribution of pollutant upward trends at various locations, high-trend trajectories are identified. This involves connecting the locations of high-trend points to construct high-trend trajectories and obtaining the slope values of the corresponding environmental change data curves. If the slope value of the corresponding environmental change data curve for a high-trend trajectory exceeds a set slope threshold, the current trajectory order of the high-trend trajectory is marked as increasing; if the slope value of the corresponding environmental change data curve for a high-trend trajectory does not exceed the set slope threshold, the current trajectory order of the high-trend trajectory is marked as decreasing.
4. The environmental data anomaly monitoring system based on information acquisition according to claim 3, characterized in that, Dynamic curve analysis is performed based on the increasing and decreasing order of the high trend trajectory: When the upward trend trajectory is in ascending order, obtain the range of the lowest value of the data with changes in grid position environment during the upward slope phase in the dynamic curve; when the upward trend trajectory is in descending order, obtain the frequency of the increase in the peak value of the data with changes in grid position environment during the downward slope phase in the dynamic curve. If the increase span of the lowest value of the grid position environmental change data in the rising phase of the slope in the dynamic curve exceeds the increase span threshold, or if the increase frequency of the peak value of the grid position environmental change data in the falling phase of the slope in the dynamic curve exceeds the increase frequency threshold, then it is inferred that the current high trend trajectory is in a high emission state and is marked as an environmental parameter aggravation trajectory. If the increase span of the lowest value of the grid location environmental change data during the rising phase of the slope in the dynamic curve does not exceed the increase span threshold, and the increase frequency of the peak value of the grid location environmental change data during the falling phase of the slope in the dynamic curve does not exceed the increase frequency threshold, then it is inferred that the current high trend trajectory is in a low emission state and is marked as a stable environmental parameter trajectory.
5. The environmental data anomaly monitoring system based on information acquisition according to claim 4, characterized in that, The process of the source tracing analysis unit is as follows: The grid positions corresponding to the trajectory of environmental parameter aggravation are analyzed to obtain the fluctuation trend of environmental change data at the grid position at a single moment, and the grid positions with the same trend are filtered; if the grid positions with the same trend are adjacent and the time interval of the fluctuation of environmental change data is less than the set time interval threshold, the grid positions with the same trend are connected and marked as the source analysis trajectory. Conversely, if there are non-trend grid positions among the grid positions with the same trend, or if the time interval between data fluctuations due to environmental changes exceeds the set time interval threshold, then the corresponding grid positions with the same trend will not be merged into the same source analysis trajectory.
6. The environmental data anomaly monitoring system based on information acquisition according to claim 5, characterized in that, After setting the source tracing analysis trajectories within the environmental monitoring area, all source trajectories are analyzed: Obtain the time when the environmental change data fluctuates corresponding to the grid position within the source tracing analysis trajectory, and mark the earliest time as the starting floating time point. If the order of the floating grid positions corresponding to adjacent floating times after the starting floating time point is consistent with the source tracing analysis trajectory, then mark the grid position corresponding to the starting floating time point as the source position; otherwise, obtain the grid position of the starting floating time point and the grid positions corresponding to adjacent floating times, and mark them within the source tracing analysis trajectory. Perform synchronous analysis on the grid positions between the grid position of the starting floating time point and the grid positions corresponding to adjacent floating times.
7. The environmental data anomaly monitoring system based on information acquisition according to claim 6, characterized in that, Mark the grid position at the starting floating moment and the adjacent floating moment as the grid start point and grid end point, respectively, and mark the grid positions in between as inflection points; based on the environmental change data at the grid start point and grid end point, obtain the current environmental change data trend and corresponding numerical range. If the environmental change data corresponding to the inflection point is within the numerical range, and the corresponding values of the environmental change data at the grid start point and the inflection point, and the inflection point and the grid end point are in the same trend, then mark the current grid start point as the source point. If the environmental change data corresponding to the inflection point is not within the numerical range, or if the corresponding values of the environmental change data between the grid start point and the inflection point, or between the inflection point and the grid end point, are not in the same trend, then the inflection points are filtered out. The inflection point closest to the grid end point, and whose fluctuation trend of environmental change data between the inflection point and the grid end point is consistent with the fluctuation trend of the current source analysis trajectory, is then marked as the source point. The source tracing point is sent to the anomaly monitoring platform, which then monitors and controls the environmental parameters of the source tracing point.
8. A method for monitoring environmental data anomalies based on information collection, characterized in that, The specific steps for applying the environmental data anomaly monitoring system based on information acquisition as described in any one of claims 1-7 are as follows: Step 1: Collect environmental change data and establish a dynamic monitoring network for the environmental monitoring area during the data collection process; Step 2: Divide the dynamic monitoring network into grids and collect and analyze environmental change parameters at each grid location; Step 3: Identify and construct high trend trajectories, and filter the trend order of grid positions within the current trajectory based on the real-time trajectory; Step 4: Perform dynamic curve analysis based on the increasing and decreasing order of the high trend trajectory to obtain the trajectory of environmental parameter aggravation; Step 5: After obtaining the trajectory of environmental parameter aggravation, trace the emission sources of the trajectory of environmental parameter aggravation.