CN-121144723-B - GIS dynamic environment monitoring and early warning system based on multisource sensing network

CN121144723BCN 121144723 BCN121144723 BCN 121144723BCN-121144723-B

Abstract

The invention discloses a GIS dynamic environment monitoring and early warning system based on a multi-source sensing network, which belongs to the technical field of satellite navigation positioning and comprises a data acquisition module, a model generation module, an evolution analysis module, an early warning module and a closed loop feedback module, wherein the data acquisition module is connected with a preset satellite positioning signal and a sensor environment data stream generated by the multi-source sensing network in real time, the model generation module is used for generating an environment event trigger mark according to abnormal parameters in the sensor environment data stream, the evolution analysis module is used for dividing a triggered environment event into the following four stages based on positioning point coordinates and associated abnormal parameters in the environment event trigger mark, and the early warning module is used for generating a first early warning result and a second early warning result if the triggered environment event is in the stage, and the closed loop feedback module is used for dynamically adjusting the early warning module according to the first early warning result and the second early warning result. The early warning of the pollutant migration track is realized by generating the predicted path coordinate set, and the hysteresis of the passive marking point position of the traditional system in the peak stage is effectively overcome.

Inventors

KE JIANGLIN
Zhou Zuoyang
ZOU LIAN
ZHU WEIWEI

Assignees

江西益欣环境科技有限公司

Dates

Publication Date: 20260512
Application Date: 20250821

Claims (6)

1. The GIS dynamic environment monitoring and early warning system based on the multi-source sensing network is characterized by comprising a data acquisition module, a model generation module, an evolution analysis module, an early warning module and a closed loop feedback module; the data acquisition module is used for accessing a preset satellite positioning signal and a sensor environment data stream generated by a multi-source sensing network in real time and synchronously recording the positioning point coordinates of the satellite positioning signal; The model generation module screens and associates positioning point coordinates in the satellite positioning signals according to abnormal parameters in the sensor environment data stream, inputs the abnormal parameters and the positioning point coordinates into a preset space-time association model, and generates an environment event trigger mark; The evolution analysis module divides the triggered environmental event into four phases, namely a starting phase, a development phase, a peak phase and a fading phase, based on the locating point coordinates and the associated abnormal parameters in the environmental event trigger mark; The early warning module generates a predicted path coordinate set according to the locating point coordinate and the abnormal parameter and takes the predicted path coordinate set as a first early warning result if the triggered environmental event is in a starting stage or a development stage, continuously executes the first early warning result if the triggered environmental event is in a peak stage, and combines the descending rate of the abnormal parameter and the density reduction trend of the locating point coordinate of the satellite positioning signal to generate a degradation mark and takes the degradation mark as a second early warning result if the triggered environmental event is in a fading stage; the generating step of the first early warning result in the early warning module is as follows: Q1, extracting a data subset of which the locating point coordinates and associated abnormal parameters exceed the maximum deviation amount of the abnormal parameters in a starting stage or a development stage, and constructing a diffusion initial point set; Q2, calculating a cluster center migration vector of the positioning point coordinates in the diffusion initial point set, generating a spatial diffusion weight by correlating the number rising rate of the abnormal parameters of the cluster center migration vector, sequencing the positioning point coordinates based on the spatial diffusion weight, and constructing a spatial diffusion model; Q3, simulating a diffusion track of the positioning point coordinates in the diffusion initial point set according to the spatial diffusion model, and predicting a positioning point coordinate set in a future period to form a predicted path coordinate set; Q4, matching the predicted path coordinate set with geographic information in a preset GIS platform, and generating a visualized early warning map as a first early warning result; The generation step of the second early warning result in the early warning module comprises the following steps: q101, screening abnormal parameters which continuously descend in the environmental event at the fading stage and accord with the historical abnormal fluctuation rule and associated locating point coordinates thereof, and constructing a fading analysis point set; Q102, calculating the space density change slope of the coordinates of the locating points in the regression analysis point set, and generating a comprehensive regression factor by correlating the descending rate of the corresponding abnormal parameters; Q103, dynamically grading the regression analysis point set according to the comprehensive regression factor, namely generating an acceleration degradation mark according to the space density change slope of the locating point coordinate when the comprehensive regression factor exceeds a set range, generating a gradual degradation mark according to the descending rate of the abnormal parameter when the comprehensive regression factor is within the set range, generating a stable degradation mark based on the density reduction trend of the locating point coordinate when the comprehensive regression factor is smaller than the set range, and carrying out degradation processing on the first early warning result according to the acceleration degradation mark, the gradual degradation mark and the stable degradation mark to generate a second early warning result; and the closed-loop feedback module is used for dynamically adjusting the early-warning module according to the first early-warning result, the second early-warning result and the phase information of the current environmental event determined by the evolution analysis module.
2. The GIS dynamic environment monitoring and early warning system based on the multi-source sensor network of claim 1, wherein satellite positioning signals in the data acquisition module are derived from Beidou/GPS/Galileo satellite navigation system signals, and the satellite positioning signals comprise positioning point coordinates and a first timestamp; the sensor environment data stream is generated by a multi-source sensing network composed of a deployed air quality monitoring station, a water quality floating sensor and a temperature and humidity remote sensing terminal, a data packet is generated after compression and encryption and is transmitted back, a unique identifier of acquisition equipment in the multi-source sensing network and a second time stamp are embedded in the data packet, and the second time stamp is aligned with a first time stamp in a satellite positioning signal.
3. The GIS dynamic environment monitoring and early warning system based on the multi-source sensor network according to claim 2, wherein the method for generating the space-time correlation model in the model generating module is as follows: s1, selecting historical first environmental data collected by an air quality monitoring station in the sensor environmental data stream, historical second environmental data collected by a water quality floating sensor and historical third environmental data collected by a temperature and humidity remote sensing terminal as primary training samples, inputting the primary training samples into a preset training model, and extracting training parameters in the training model to generate a historical abnormal fluctuation rule; S2, selecting real-time first environmental data acquired by an air quality monitoring station in the sensor environmental data stream, real-time second environmental data acquired by a water quality floating sensor and real-time third environmental data acquired by a temperature and humidity remote sensing terminal as optimized training samples, and inputting the optimized training samples into a training model trained in the step S1 to calculate real-time dynamic deviation; and S3, screening locating point coordinates and abnormal parameters corresponding to the real-time dynamic deviation with the matching degree higher than a preset threshold according to the matching degree of the real-time dynamic deviation and the historical abnormal fluctuation rule, and dynamically adjusting the association weight of the preliminary training sample and the optimized training sample to the training model by combining the training parameters in the historical abnormal fluctuation rule to generate a space-time association model.
4. The GIS dynamic environment monitoring and early warning system based on the multi-source sensor network according to claim 3, wherein the step of generating the historical abnormal fluctuation rule in the step S1 is as follows: s11, respectively calculating the mean value and the maximum deviation of the fluctuation amplitude in a preset history period based on the first environmental data, the second environmental data and the third environmental data in the preliminary training sample, and inputting the mean value and the maximum deviation into the training model to establish an initial fluctuation baseline; And S12, dynamically adjusting the weight of the average value and the maximum deviation of the fluctuation amplitude in the history period by combining the initial fluctuation baseline according to the fluctuation frequency of the history first environmental data, the history second environmental data and the history third environmental data in the history period in the preliminary training sample, and generating a history abnormal fluctuation rule.
5. The GIS dynamic environment monitoring and early warning system based on the multi-source sensor network according to claim 4 is characterized in that the specific content of the evolution analysis module for classifying triggered environment events is that if the initial abnormal parameters associated with the positioning point coordinates in the environment event trigger marks exceed the maximum deviation amount of the abnormal parameters predicted by the space-time association model according to the abnormal parameters for the first time, the starting stage is judged, if the number of the abnormal parameters in the environment event trigger marks continuously rises and the number of the positioning point coordinates increases, the development stage is judged, if the fluctuation range of the abnormal parameters predicted by the space-time association model according to the abnormal parameters in the environment event trigger marks is narrowed and the variation amount of the predicted abnormal parameter dynamic deviation approaches a preset tolerance, the peak stage is judged, and if the number of the abnormal parameters in the environment event trigger marks continuously falls and the density of the positioning point coordinates is reduced, the development stage is judged.
6. The GIS dynamic environment monitoring and early warning system based on the multi-source sensing network is characterized in that the closed loop feedback module receives the first early warning result, the second early warning result and the phase information of the current environment event determined by the evolution analysis module, analyzes that a predicted path coordinate set in the first early warning result in a preset first period is fused with an abnormal point diffusion track in the satellite positioning signal associated with the actual abnormal parameter to generate a path offset rate, and compares the actual abnormal parameter change slope of the sensor environment data stream with the associated positioning point coordinate distribution density of the satellite positioning signal to generate a predicted deviation based on a degradation mark in the second early warning result in a preset second period, and dynamically adjusts the first early warning result and the second early warning result according to the path offset rate or the predicted deviation.

Description

GIS dynamic environment monitoring and early warning system based on multisource sensing network Technical Field The invention relates to the technical field of satellite navigation positioning, in particular to a GIS dynamic environment monitoring and early warning system based on a multi-source sensing network. Background GIS (geographic information system) is used as a comprehensive technical platform integrating space positioning, environment sensing and data analysis, acquires geographic coordinates in real time through satellite navigation positioning technology (such as Beidou/GPS), and fuses a multi-source sensor network to monitor environmental parameter changes, thereby providing dynamic decision support for scenes such as disaster early warning, pollution tracing and the like. The working principle of the existing GIS dynamic environment monitoring and early warning system is that geographic coordinates of a target area are acquired in real time through satellite navigation positioning technology (such as Beidou/GPS), environment parameter data streams uploaded by a multi-source sensor network (such as temperature and humidity, water quality and air quality monitoring equipment) are synchronously received, sensor data and positioning coordinates are subjected to static association and then are superimposed on a GIS platform, early warning marks are triggered when certain point environment parameters exceed a preset fixed threshold value, whether manual intervention analysis parameter mutation is caused by a real environment event or equipment failure or not is finally output on the GIS map. The prior art has the following defects that firstly, the response is lagged, the diffusion path (such as pollutant migration track) of an environmental event cannot be predicted, the point position can be passively marked only in the peak stage of the environmental event, the intervention time in the development stage is lost, and secondly, the degradation time of the environmental event is judged by relying on manual experience in the resolution stage of the environmental event, so that early warning is easily released prematurely or the invalid response period is prolonged. Therefore, it is needed to provide a GIS dynamic environment monitoring and early warning system based on a multi-source sensor network to solve the above-mentioned problems. Disclosure of Invention The invention aims to overcome the defects that in the prior art, the response is lagged, the diffusion path (such as pollutant migration track) of an environmental event cannot be predicted, the point position can be passively marked only in the peak stage of the environmental event, the intervention time in the development stage is lost, the degradation time is judged by relying on manual experience in the fading stage of the environmental event, early warning is easy to be released prematurely or the invalid response period is prolonged, and a GIS dynamic environment monitoring and early warning system based on a multi-source sensing network is provided. In order to solve the technical problems, the invention adopts a technical scheme that a GIS dynamic environment monitoring and early warning system based on a multi-source sensing network is provided, and comprises a data acquisition module, a model generation module, an evolution analysis module, an early warning module and a closed loop feedback module; the data acquisition module is used for accessing a preset satellite positioning signal and a sensor environment data stream generated by a multi-source sensing network in real time and synchronously recording the positioning point coordinates of the satellite positioning signal; The model generation module screens and associates positioning point coordinates in the satellite positioning signals according to abnormal parameters in the sensor environment data stream, inputs the abnormal parameters and the positioning point coordinates into a preset space-time association model, and generates an environment event trigger mark; The evolution analysis module divides the triggered environmental event into four phases, namely a starting phase, a development phase, a peak phase and a fading phase, based on the locating point coordinates and the associated abnormal parameters in the environmental event trigger mark; The early warning module generates a predicted path coordinate set according to the locating point coordinate and the abnormal parameter and takes the predicted path coordinate set as a first early warning result if the triggered environmental event is in a starting stage or a development stage, continuously executes the first early warning result if the triggered environmental event is in a peak stage, and combines the descending rate of the abnormal parameter and the density reduction trend of the locating point coordinate of the satellite positioning signal to generate a degradation mark and takes the degradation mark as a second