Environment monitoring and intelligent early warning system and device applied to intelligent environmental protection engineering

Abstract

The invention is suitable for the technical field of environmental protection, and provides an environmental monitoring and intelligent early warning system and device applied to intelligent environmental protection engineering. In the embodiment of the invention, the system collects different types of environmental parameters of a target environmental area through different types of sensors, and selects a corresponding LPWAN technology for data transmission according to key factors of each environmental parameter; the method comprises the following steps: firstly, acquiring environment parameters, then performing preprocessing, feature extraction and feature fusion on the environment parameters to obtain multivariate feature data for model analysis, and finally inputting the data into a trained environment monitoring and early warning model to determine early warning information of a target environment area. The problems that a traditional environment monitoring technology is low in data acquisition efficiency, limited in monitoring range, single in analysis method and incapable of meeting modern environment protection requirements are solved. In addition, through highly accurate parameter measurement, an efficient data transmission and processing mechanism and prediction and decision support based on artificial intelligence, perspectiveness and pertinence of environmental protection actions are ensured.

Description

Technical Field

[0001] The present application relates to the field of environmental protection technology, and in particular to an environmental monitoring and intelligent early warning system and device applied to smart environmental protection projects. Background Art

[0002] With the acceleration of industrialization and the deepening of urbanization, environmental pollution problems are becoming more and more serious. Traditional environmental monitoring technology cannot meet modern environmental protection needs due to its low data acquisition efficiency, limited monitoring range, and single analysis method. Therefore, there is an urgent need for an intelligent environmental monitoring system that can reflect environmental conditions in real time, accurately, and comprehensively, and can respond quickly based on data analysis. Summary of the invention

[0003] In view of this, the present application provides an environmental monitoring and intelligent early warning system and device applied to smart environmental protection projects to solve the problems of low data acquisition efficiency, limited monitoring range and single analysis method of traditional environmental monitoring technology.

[0004] In a first aspect, the present application provides an environmental monitoring and intelligent early warning system for smart environmental protection projects, the system comprising a multi-environmental parameter acquisition module, an Internet of Things data transmission module, a big data processing module and an intelligent early warning module; The multi-environmental parameter acquisition module includes a plurality of different types of sensors, which are used to collect different types of environmental parameters of the target environmental area through the sensors, wherein the environmental parameters include noise environmental parameters representing noise levels, air environmental parameters representing air pollution, water quality environmental parameters representing water quality, and soil environmental parameters representing soil pollution; The IoT data transmission module is used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life; The big data processing module is used to pre-process the environmental parameters, remove noise in the environmental parameters through a filtering algorithm, and process the continuous data stream in the environmental parameters through time series analysis to obtain processed environmental parameters; Extracting and fusing the processed environmental parameters to obtain characteristic data of different types of environmental parameters, and fusing the characteristic data into multivariate characteristic data for model analysis through a predetermined adaptive fusion algorithm; The intelligent early warning module is used to input the multivariate feature data into a trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

[0005] Optionally, collecting different types of environmental parameters of the target environmental area by the sensor includes: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

[0006] Optionally, the LPWAN technology includes LoRa, Sigfox, and NBIoT.

[0007] Optionally, the training process of the environmental monitoring and early warning model includes: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through a test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model.

[0008] Optionally, after determining the warning information of the target environmental area through the environmental parameters, the intelligent warning module also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

[0009] The second aspect of the present application provides an environmental monitoring and intelligent early warning device applied to a smart environmental protection project, the device comprising: A data acquisition unit, used to collect different types of environmental parameters of the target environmental area through a plurality of different types of sensors, wherein the environmental parameters include noise environmental parameters characterizing noise levels, air environmental parameters characterizing air pollution, water quality environmental parameters characterizing water quality, and soil environmental parameters characterizing soil pollution; A data transmission unit, used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life; A data processing unit, used to pre-process the environmental parameters, remove noise in the environmental parameters by a filtering algorithm, and process the continuous data stream in the environmental parameters by time series analysis to obtain processed environmental parameters; Extracting and fusing the processed environmental parameters to obtain characteristic data of different types of environmental parameters, and fusing the characteristic data into multivariate characteristic data for model analysis through a predetermined adaptive fusion algorithm; The intelligent early warning unit is used to input the multivariate feature data into a trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

[0010] Optionally, the data acquisition unit collects different types of environmental parameters of the target environmental area through the sensor, including: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

[0011] Optionally, the LPWAN technology in the data transmission unit includes LoRa, Sigfox, and NBIoT.

[0012] Optionally, the training process of the environmental monitoring early warning model in the intelligent early warning unit includes: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through a test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model.

[0013] Optionally, after determining the warning information of the target environmental area through the environmental parameters, the intelligent warning unit also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

[0014] In the embodiments provided in this application, the system collects different types of environmental parameters in the target environmental area through different types of sensors, and selects the corresponding LPWAN technology for data transmission according to the key factors of each environmental parameter; then the environmental parameters are preprocessed, feature extracted and feature fused to obtain multivariate feature data for model analysis, and finally the data is input into the trained environmental monitoring and early warning model to determine the early warning information of the target environmental area. This solves the problem that traditional environmental monitoring technology has low data acquisition efficiency, limited monitoring range, and single analysis method that cannot meet modern environmental protection needs. And this application ensures the foresight and pertinence of environmental protection actions through highly accurate parameter measurement, efficient data transmission and processing mechanism, and prediction and decision support based on artificial intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A system module diagram provided for an embodiment of the present application; Figure 2 A structural diagram of a device provided in an embodiment of the present application; Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0017] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0018] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0019] The present application provides an environmental monitoring and intelligent early warning system and device applied to smart environmental protection projects to achieve all-weather, multi-dimensional, high-precision monitoring of environmental factors such as air, water quality, soil, and noise, and can also provide early warnings and assist in decision-making based on various environmental factors.

[0020] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0021] like Figure 1 As shown, this is a module diagram of an environmental monitoring and intelligent early warning system for smart environmental protection projects provided by the present application. The system includes a multi-environmental parameter acquisition module, an Internet of Things data transmission module, a big data processing module, and an intelligent early warning module.

[0022] The functions of each module are described below: 1. Multi-environmental parameter acquisition module. This module includes several different types of sensors, which are used to collect different types of environmental parameters of the target environmental area through the sensors, wherein the environmental parameters include noise environmental parameters representing noise levels, air environmental parameters representing air pollution, water quality environmental parameters representing water quality, and soil environmental parameters representing soil pollution.

[0023] In this embodiment, the sensor may include an air pollutant sensor, a water quality monitoring sensor, a noise monitoring sensor, etc. The collection of various environmental parameters is achieved through various types of sensors.

[0024] In another embodiment, collecting different types of environmental parameters of the target environmental area by the sensor includes: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

[0025] In this embodiment, the corresponding measurement accuracy is pre-set for each environmental parameter that needs to be collected, and then the collected environmental parameters are processed according to different methods until they meet the corresponding measurement accuracy. For example, for air measurement accuracy, photoelectrocatalysis, electrochemistry and other technologies can be used to monitor air pollutants collected in the target environmental area, such as PM2.5, PM10, SO2, NOx, O3, CO, etc., so as to achieve the collected environmental parameters reaching the measurement accuracy.

[0026] For water quality measurement accuracy, sensors such as conductivity, pH, dissolved oxygen (DO), turbidity, ammonia nitrogen (NH3N), total phosphorus (TP), total nitrogen (TN) and heavy metals (such as lead, cadmium, mercury) can be integrated to ensure that the monitoring accuracy of water quality environmental parameters reaches ±3%.

[0027] As for soil measurement accuracy, spectral analysis and electrochemical sensing technology can be used to detect organic pollutants, heavy metals, etc. in the soil, so that the accuracy of the collected soil environmental parameters can reach ±4%.

[0028] For noise measurement error, the principle of sound level meter can be adopted, and the effective range can be set from 30dB to 130dB(A), with the error not exceeding ±1.5dB.

[0029] This embodiment achieves highly accurate parameter measurement by pre-setting the measurement accuracy of various environmental parameters, providing more accurate data support for subsequent intelligent early warning and decision support.

[0030] 2. IoT data transmission module. This module is used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life.

[0031] In this embodiment, the use of LPWAN (Low Power Wide Area Network) technology can ensure stable data transmission in complex environments. This technology has the characteristics of low power consumption, long-distance transmission and high-capacity connection, and is suitable for application scenarios that require long-term battery power and wide geographical distribution.

[0032] LPWAN technology includes a variety of different communication protocols, such as LoRa, SigFox, NB-IoT, etc. Each technology has its specific application scenarios and advantages.

[0033] LoRa is a LPWAN protocol based on spread spectrum technology, which provides long-range communication (usually 2-5 km in urban environments, and up to 15 km or more in suburban or open areas) and supports star network topology. The LoRa network is managed by the LoRaWAN protocol, which supports end-to-end encryption to ensure the security of data transmission. LoRa technology is suitable for applications that require long-range communication and lower data rates, such as smart cities, smart agriculture, and environmental monitoring.

[0034] SigFox is another LPWAN technology that uses the unlicensed ISM band for communication, has a very simple network architecture and low power consumption. SigFox has a lower communication rate, but its global network coverage and low-cost deployment make it an ideal choice for simple device tracking and monitoring.

[0035] NB-IoT is a LPWAN technology based on cellular networks. It uses licensed spectrum resources to provide better signal penetration and coverage. NB-IoT technology supports higher data rates and greater connection density, and is suitable for applications that require frequent communications and higher data throughput, such as smart metering and asset tracking.

[0036] The above key factors can determine the type and configuration of the selected LPWAN technology. For example, an environmental data acquisition system may need to use an NRF24L01+2.4G module or a similar wireless communication module to achieve remote data transmission, while a city-level smart water meter or gas meter may be more suitable for using NB-IoT technology for data transmission.

[0037] 3. Big data processing module. This module is used to pre-process the environmental parameters, remove the noise in the environmental parameters through filtering algorithms, and process the continuous data stream in the environmental parameters through time series analysis to obtain processed environmental parameters; The processed environmental parameters are subjected to feature extraction and fusion to obtain feature data of different types of environmental parameters, and the feature data are fused into multivariate feature data for model analysis through a predetermined adaptive fusion algorithm.

[0038] In this embodiment, preprocessing the environmental parameters includes: First, identify missing values, outliers, and duplicate values ​​from the environmental parameters, and clean them up separately. For example, for missing values, you can choose the appropriate missing value processing method based on the characteristics of the data, such as filling (filling with mean, median, mode, predicted value, etc.), deleting (if the missing ratio is too high or the missing value has no effect on the analysis), or interpolation (for time series data); for outliers, you can choose to retain, delete, or correct outliers based on the nature of the outliers and business needs; for duplicate values, you can directly perform deduplication.

[0039] For the processed environmental parameters, the characteristic data of each environmental parameter can be extracted by using methods such as PCA and ICA. Then, the characteristic data are fused into multivariate characteristic data by using adaptive fusion algorithms such as weighted fusion algorithm and Bayesian fusion method, and the multivariate characteristic data can be used for model analysis.

[0040] 4. Intelligent early warning module: This module is used to input the multivariate feature data into the trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

[0041] In this embodiment, an initial environmental monitoring and early warning model may be created first, and then the model may be optimized and trained using historical environmental parameters. The specific process is as follows: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through the test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model. The future trend of environmental parameters can be predicted by this model with an accuracy of ≥85%.

[0042] And each time the multivariate feature data is subsequently input into the trained environmental monitoring and early warning model for calculation, the model can also be iteratively optimized through the multivariate feature data, so that the environmental monitoring and early warning model can be continuously optimized and updated during use, thereby further improving the accuracy of the data.

[0043] In another embodiment, after determining the warning information of the target environmental area through the environmental parameters, the intelligent warning module also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

[0044] For example, when the monitoring value predicted by the model is about to exceed the set threshold, the corresponding warning level A is determined; or when the monitoring value exceeds the set threshold, the corresponding warning level B is determined. The system generates corresponding governance recommendation reports according to different warning levels, such as generating a mild governance plan at warning level A and an emergency governance plan at warning level B, and then pushes them to relevant departments and personnel simultaneously.

[0045] So far, completed Figure 1 Functional description of each module in .

[0046] In the embodiment of the present application, the system collects different types of environmental parameters of the target environmental area through different types of sensors, and selects the corresponding LPWAN technology for data transmission according to the key factors of each environmental parameter; then the environmental parameters are preprocessed, feature extracted and feature fused to obtain multivariate feature data for model analysis, and finally the data is input into the trained environmental monitoring and early warning model to determine the early warning information of the target environmental area. This solves the problem that traditional environmental monitoring technology has low data acquisition efficiency, limited monitoring range, and single analysis method that cannot meet modern environmental protection needs. And this application ensures the foresight and pertinence of environmental protection actions through highly accurate parameter measurement, efficient data transmission and processing mechanism, and prediction and decision support based on artificial intelligence.

[0047] The present application also provides an environmental monitoring and intelligent early warning device for use in smart environmental protection projects, such as Figure 2 As shown, the device comprises: The data acquisition unit 201 is used to collect different types of environmental parameters of the target environment area through a plurality of different types of sensors, wherein the environmental parameters include noise environmental parameters representing noise levels, air environmental parameters representing air pollution, water quality environmental parameters representing water quality, and soil environmental parameters representing soil pollution; The data transmission unit 202 is used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life; The data processing unit 203 is used to pre-process the environmental parameters, remove noise in the environmental parameters by filtering algorithms, and process the continuous data stream in the environmental parameters by time series analysis to obtain processed environmental parameters; Extracting and fusing the processed environmental parameters to obtain characteristic data of different types of environmental parameters, and fusing the characteristic data into multivariate characteristic data for model analysis through a predetermined adaptive fusion algorithm; The intelligent early warning unit 204 is used to input the multivariate feature data into a trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

[0048] In another embodiment, the data acquisition unit collects different types of environmental parameters of the target environmental area through the sensor, including: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

[0049] In another embodiment, the LPWAN technology in the data transmission unit includes LoRa, Sigfox, and NBIoT.

[0050] In another embodiment, the training process of the environmental monitoring early warning model in the intelligent early warning unit includes: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through a test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model.

[0051] In another embodiment, after determining the warning information of the target environmental area through the environmental parameters, the intelligent warning unit also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

[0052] The above embodiments of the present invention provide an environmental monitoring and intelligent early warning system applied to smart environmental protection projects, and based on the system, provide an environmental monitoring and intelligent early warning device applied to smart environmental protection projects. Through the above system and device, all-weather monitoring of environmental factors such as air, water quality, soil, and noise can be achieved.

[0053] This embodiment also discloses a computer device, such as Figure 3 As shown, the computer device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement any of the above-mentioned environmental monitoring and intelligent early warning systems applied to smart environmental protection projects.

[0054] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An environmental monitoring and intelligent early warning system applied to smart environmental protection projects, the system comprising a multi-environmental parameter acquisition module, an Internet of Things data transmission module, a big data processing module and an intelligent early warning module; The multi-environment parameter acquisition module includes a plurality of different types of sensors, which are used to acquire different types of environmental parameters of the target environmental area through the sensors, wherein: The environmental parameters include noise environmental parameters characterizing noise levels, air environmental parameters characterizing air pollution, water quality environmental parameters characterizing water quality, and soil environmental parameters characterizing soil pollution; The IoT data transmission module is used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life; The big data processing module is used to pre-process the environmental parameters, remove noise in the environmental parameters through a filtering algorithm, and process the continuous data stream in the environmental parameters through time series analysis to obtain processed environmental parameters; Extracting and fusing the processed environmental parameters to obtain characteristic data of different types of environmental parameters, and fusing the characteristic data into multivariate characteristic data for model analysis through a predetermined adaptive fusion algorithm; The intelligent early warning module is used to input the multivariate feature data into a trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

2. The system according to claim 1, characterized in that The collecting of different types of environmental parameters of the target environment area by the sensor includes: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

3. The system according to claim 1, characterized in that The LPWAN technologies include LoRa, Sigfox, and NBIoT.

4. The system according to claim 1, characterized in that The training process of the environmental monitoring and early warning model includes: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through a test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model.

5. The system according to claim 1, characterized in that After determining the warning information of the target environmental area through the environmental parameters, the intelligent warning module also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

6. An environmental monitoring and intelligent early warning device applied to a smart environmental protection project, the device comprising: A data acquisition unit, used to collect different types of environmental parameters of the target environmental area through a plurality of different types of sensors, wherein the environmental parameters include noise environmental parameters characterizing noise levels, air environmental parameters characterizing air pollution, water quality environmental parameters characterizing water quality, and soil environmental parameters characterizing soil pollution; A data transmission unit, used to determine the key factors of the environmental parameters collected by each sensor, and send the environmental parameters to the multi-environmental parameter collection module using the corresponding LPWAN technology through the key factors to ensure the stability of data transmission, wherein the key factors include transmission distance, power consumption, data rate, cost and equipment life; A data processing unit, used to pre-process the environmental parameters, remove noise in the environmental parameters by a filtering algorithm, and process the continuous data stream in the environmental parameters by time series analysis to obtain processed environmental parameters; Extracting and fusing the processed environmental parameters to obtain characteristic data of different types of environmental parameters, and fusing the characteristic data into multivariate characteristic data for model analysis through a predetermined adaptive fusion algorithm; The intelligent early warning unit is used to input the multivariate feature data into a trained environmental monitoring early warning model to determine the early warning information of the target environmental area through the environmental parameters.

7. The device according to claim 6, characterized in that The data acquisition unit collects different types of environmental parameters of the target environment area through the sensor, including: According to a preset air measurement accuracy, the air pollutants in the target environment area are measured by an air pollutant sensor to obtain the air environment parameters, wherein the air measurement accuracy is no more than 5%; According to a preset water quality measurement accuracy, the water source in the target environment area is monitored by a water quality sensor to obtain the water quality environment parameters, wherein the water quality measurement accuracy is no more than 3%; According to a preset noise measurement error, the sound in the target environment area is monitored by a noise monitoring sensor to obtain the noise environment parameter, wherein the noise measurement error is no more than 1.5 dB; According to a preset soil measurement accuracy, the soil in the target environmental area is detected by spectral analysis and electrochemical sensing technology to obtain the soil environmental parameters, wherein the soil measurement accuracy is no more than 4%.

8. The device according to claim 6, characterized in that The LPWAN technologies in the data transmission unit include LoRa, Sigfox, and NBIoT.

9. The device according to claim 6, characterized in that The training process of the environmental monitoring early warning model in the intelligent early warning unit includes: Acquire historical environmental parameters of the target environmental area, and determine historical multivariate feature data for model training according to the historical environmental parameters; Determine the training set, test set and validation set of the environmental monitoring and early warning model according to the historical multivariate feature data; The environmental monitoring and early warning model is trained using the training set data, and after each training, the performance of the environmental monitoring and early warning model is evaluated through the validation set and the parameters of the environmental monitoring and early warning model are adjusted to determine a target environmental monitoring and early warning model; The target environmental monitoring and early warning model is evaluated through a test set, and after passing the evaluation, the target environmental monitoring and early warning model is determined as a trained environmental monitoring and early warning model.

10. The device according to claim 6, characterized in that After determining the warning information of the target environmental area through the environmental parameters, the intelligent warning unit also determines the warning level according to the monitoring values ​​in the warning information, and generates a governance recommendation report according to the warning level and simultaneously pushes it to relevant departments and personnel.

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