Water environment monitoring system and method based on big data analysis

Abstract

The invention discloses a water environment monitoring system and method based on big data analysis, relates to the technical field of environment monitoring, and solves the technical problems that only monitoring of water quality parameters is concerned, the influence of biological factors, human activity factors and the like on the water environment is ignored, and the overall monitoring and evaluation accuracy of the water environment is reduced. Through the water environment data acquisition module, various water environment related data including water quality, hydrology, organisms, crowds, pollution sources and other information can be widely collected, comprehensive monitoring of the water environment is realized, and the influence of biological factors and human activity factors on the water environment is comprehensively considered. The calculation of the biological diversity index and the biological pollution index reflects the structure and health condition of the biocenosis, the water quality monitoring and early warning module can perform real-time monitoring and evaluation according to the water environment value, and through comparison with the threshold value and matching with the water quality grade interval, the abnormal condition of the water quality is found in time, and early warning information or an evaluation report is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a water environment monitoring system and method based on big data analysis. Background Art

[0002] In recent years, the rapid development of information technology has greatly improved the ability to acquire, store and process data. Big data analysis technology has been widely used in various fields, and water environment monitoring is no exception.

[0003] The patent with publication number CN118671294A discloses a water environment monitoring system and method based on big data analysis. The system includes a theoretical water quality analysis module, a water quality anomaly detection module, a control measure matching module and an implementation effect monitoring module.

[0004] The above patent uses historical water quality data and weather influences to evaluate theoretical water quality in an unpolluted environment and timely adjust anomaly detection thresholds. It can detect potential pollution incidents in a timely manner, prevent missed reports and false alarms, and reduce environmental and public health risks. It matches the type of water quality anomaly with the management strategies of different water bodies, improves the pertinence and efficiency of management measures, ensures the optimal allocation and use of resources through cost evaluation, improves the economy of water environment management, and monitors the changes in water quality before and after the implementation of management measures to evaluate the management effect in real time, adjust the management strategy in a timely manner, and ensure continuous improvement and maintenance of water quality.

[0005] However, some existing water environment monitoring systems only focus on monitoring water quality parameters when in use, while ignoring the impact of biological factors, human activities and other factors on the water environment, reducing the accuracy of overall monitoring and assessment of the water environment. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a water environment monitoring system and method based on big data analysis, which solves the problem of only focusing on the monitoring of water quality parameters while ignoring the impact of biological factors, human activity factors, etc. on the water environment, thereby reducing the accuracy of overall monitoring and evaluation of the water environment.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A water environment monitoring system based on big data analysis, comprising:

[0008] A water environment data collection module, used for transmitting the collected water environment data to the distribution model building module;

[0009] A distribution model building module is used to build a corresponding water environment distribution model according to the acquired water environment data, and transmit the built water environment distribution model to the adaptive analysis module;

[0010] The adaptive analysis module is used to obtain the regional parameters of the analysis area according to the water environment distribution model, calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and the pollution source distribution parameters in the regional parameters, and sum the biological impact index and the pollution source index to obtain the water environment value, and transmit the water environment value to the water quality monitoring and early warning module;

[0011] The water quality monitoring and early warning module is used to monitor and analyze the water quality of the analysis area according to the acquired water environment value, evaluate the water quality of the analysis area to generate evaluation information, and transmit the evaluation information to the monitoring information output unit;

[0012] The monitoring information output module is used to display the acquired evaluation information and warning information to the corresponding operators.

[0013] As a further solution of the present invention: the specific method of establishing the water environment distribution model by the distribution model establishment module is:

[0014] Clean the collected water environment data, remove outliers and erroneous data, and standardize the data to obtain preprocessed data;

[0015] A standard model is selected based on the acquired preprocessed data, and the demand parameters are obtained based on the standard model. The acquired demand parameters are substituted into the standard model to optimize the model. Then historical data is obtained to calibrate the standard model, and the simulation results of the model are compared with the actual data. The parameters of the standard model are adjusted through the least squares method to obtain the water environment distribution model.

[0016] As a further solution of the present invention: the specific method in which the adaptive analysis module calculates the biological impact index according to the biological parameters in the regional parameters is:

[0017] Based on the water environment model, regional parameters are obtained, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters, and biological impact factors and pollution source factors are calculated respectively;

[0018] The calculation method of the biological impact index is as follows: obtain the biological species in the analysis area and record them as i, where i=1, 2, ..., j, where j represents the biological species, and obtain the number of different biological species i and record them as Li, and calculate the total number of biological species in the analysis area and record them as Lz, then substitute the obtained parameters into the formula The biodiversity index is calculated, where pi represents the ratio of the number of the i-th biological species to the total number. The specific calculation method is: , j is the species of organism;

[0019] Take any biological species i as the analysis object, assign a value to the pollution tolerance of the analysis object, and so on, assign values ​​to all biological species i, and then calculate the biological pollution index of the biological species based on the obtained biological species assignment. The specific calculation formula is , the biological pollution index B is calculated according to the formula, where Fi represents the value corresponding to the i-th biological species, and Lz is the total number of organisms;

[0020] The calculated biodiversity index H is summed with the biological pollution index B to obtain the biological impact index H+B.

[0021] As a further solution of the present invention: the specific method in which the adaptive analysis module calculates the pollution source index according to the population distribution parameters and the pollution source distribution parameters in the regional parameters is:

[0022] The calculation method of the pollution source index is as follows: obtain the total population in the analysis area, record it as R, and obtain the total domestic sewage discharge per unit time, record it as K, then obtain the per capita water consumption, record it as S1, and obtain the per capita discharge, record it as S2, and substitute the obtained parameters into the formula The sewage discharge coefficient S is calculated, and the unit sewage discharge coefficient is calculated here. Substitute the obtained parameters into the formula The domestic sewage discharge intensity per unit time W1 is calculated;

[0023] Similarly, the above analysis process calculates the industrial wastewater discharge intensity per unit time W2.

[0024] As a further solution of the present invention: the specific method of the adaptive analysis module to sum the biological impact index and the pollution source index to obtain the water environment value is:

[0025] Substitute the calculated biological impact index H+B and pollution source index W1+W2 into the formula The water environment value PC is calculated, where and They represent the weight coefficients of the biological impact index and the pollution coefficient index respectively, and Fa is the coefficient of the air pollutant converted into COD after deposition, where Fa represents the deposition flux of the ath air pollutant;

[0026] At the same time, the acquired water environment value PC is transmitted to the water quality monitoring and early warning module.

[0027] As a further solution of the present invention: the specific manner in which the water quality monitoring and early warning module monitors and analyzes the water quality of the analysis area according to the acquired water environment value is:

[0028] The water environment value PC of the analysis area is obtained, and the water environment value PC is compared with the threshold value PY. If the water environment value PC is greater than the threshold value PY, it means that the water quality in the analysis area is abnormal, and an early warning message is generated and transmitted to the monitoring information output module. On the contrary, if the water environment value PC is less than the threshold value PY, it means that there is no abnormality in the water quality of the analysis area, and a water quality assessment signal is generated, and the water quality assessment signal is further analyzed.

[0029] As a further solution of the present invention: the specific method in which the water quality monitoring and early warning module analyzes the water quality assessment signal is:

[0030] With time t as a cycle, the water environment value PC is monitored and analyzed, and the water environment value PC is matched with the water quality grade interval. At the same time, the corresponding evaluation information is generated based on the matching result, and the evaluation information is transmitted to the monitoring information output module.

[0031] A water environment monitoring method based on big data analysis, the method specifically comprises the following steps:

[0032] Step 1: Obtain water environment data of the analysis area, and preprocess the water environment data to obtain preprocessed data;

[0033] Step 2: Establish a corresponding water environment distribution model based on the obtained preprocessed data, and obtain regional parameters of the analysis area based on the water environment distribution model, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters;

[0034] Step 3: Calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and the pollution source distribution parameters, and sum the biological impact index and the pollution source index to obtain the water environment value;

[0035] Step 4: Monitor and analyze the water quality of the analysis area based on the obtained water environment values, and evaluate the water quality of the analysis area to generate evaluation information.

[0036] Beneficial Effects

[0037] The present invention provides a water environment monitoring system and method based on big data analysis. Compared with the prior art, it has the following beneficial effects:

[0038] The present invention can collect a wide range of water environment-related data, including water quality, hydrology, biology, population and pollution sources, etc., through the water environment data acquisition module, to achieve comprehensive monitoring of the water environment, and comprehensively consider the impact of biological factors and human activity factors on the water environment. The calculation of the biodiversity index and the biological pollution index reflects the structure and health of the biological community. The water quality monitoring and early warning module can monitor and evaluate in real time according to the water environment value, and timely detect water quality abnormalities and generate early warning information or evaluation reports by comparing with the threshold and matching with the water quality grade interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a block diagram of the system principle of the present invention;

[0040] Figure 2 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] For example, see Figure 1 The present application provides a water environment monitoring system based on big data analysis, including: a water environment data acquisition module, a distribution model building module, an adaptive analysis module, a water quality monitoring and early warning module and a monitoring information output module. Figure 1 It can be known that the functional modules mentioned above are connected by unidirectional wires.

[0043] Water environment data acquisition module: This module is used to collect water environment data in the analysis area and transmit the collected water environment data to the distribution model establishment module. The specific water environment data collected includes data on various water quality parameters such as dissolved oxygen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen, pH value, heavy metal content, etc., and hydrological information such as water level, flow, and flow rate is obtained.

[0044] The distribution model establishment module is used to establish a corresponding water environment distribution model according to the acquired water environment data, and transmit the established water environment distribution model to the adaptive analysis module. The specific method of establishing the water environment distribution model is:

[0045] Clean the collected water environment data to remove abnormal values ​​and erroneous data. For example, if the dissolved oxygen data of a water quality monitoring point suddenly shows a value far beyond the normal range, it may be caused by sensor failure and needs to be corrected or eliminated. At the same time, standardize the data to obtain preprocessed data;

[0046] A standard model is selected based on the obtained pre-processed data, and demand parameters are obtained based on the standard model, and the demand parameters here are expressed as parameters required by the standard model. For example, for a hydrodynamic model, it is necessary to determine parameters such as river channel roughness and hydraulic slope; for a water quality model, it is necessary to determine parameters such as the degradation rate and diffusion coefficient of pollutants; for an ecological model, it is necessary to determine parameters such as the growth rate, mortality rate, and reproduction rate of organisms. At the same time, the obtained demand parameters are substituted into the standard model to optimize the model, and then historical data is obtained to calibrate the standard model, and the simulation results of the model are compared with the actual data. The parameters of the standard model are adjusted by the least squares method to obtain a water environment distribution model. For example, in the calibration of the water quality model, the simulated dissolved oxygen concentration and the actually monitored dissolved oxygen concentration are compared, and the difference between the two is minimized by adjusting parameters such as the degradation rate of pollutants. The establishment of the water environment distribution model here is an existing technology, which specifically includes the following steps:

[0047] The first step is problem identification and goal setting;

[0048] Step 2: Model selection -> model principle;

[0049] The third step is to obtain basic information;

[0050] The fourth step is model mesh division -> model generalization;

[0051] Step 5: Setting the initial conditions of boundary conditions;

[0052] Step 6: Sensitivity analysis and parameter calibration;

[0053] Step 7: Model application and deployment;

[0054] At the same time, the obtained water environment distribution model is transmitted to the adaptive analysis module.

[0055] The adaptive analysis module is used to obtain the regional parameters of the analysis area according to the transmitted water environment distribution model, and calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and pollution source distribution parameters in the regional parameters, and sum the biological impact index and the pollution source index to obtain the water environment value, and transmit the water environment value to the water quality monitoring and early warning module. The specific calculation method of the water environment value is as follows:

[0056] Based on the water environment model, regional parameters are obtained, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters, and biological impact factors and pollution source factors are calculated respectively;

[0057] The calculation method of the biological impact index is as follows: obtain the biological species in the analysis area and record them as i, where i=1, 2, ..., j, where j represents the biological species, and obtain the number of different biological species i and record them as Li, and calculate the total number of biological species in the analysis area and record them as Lz, then substitute the obtained parameters into the formula The biodiversity index is calculated, where pi represents the ratio of the number of the i-th biological species to the total number. The specific calculation method is: , j is the biological species. For example, in a river ecosystem, there may be many types of organisms, such as phytoplankton, zooplankton, fish, benthic organisms, etc.;

[0058] In a specific example, suppose that in an ecological survey of the analysis area, after detailed classification and statistics, the following species and numbers of organisms are found, as shown in Table 1: Table 1 Ecological survey table of the analyzed area

[0059]

[0060] First, calculate the total number of organisms LZ = 500 + 300 + 200 + 150 + 80 + 50 + 120 + 80 = 1580, then calculate the proportion of each species, and finally calculate P1 = 0.0316, P2 = 0.1899, P3 = 0.1266, P4 = 0.0949, P5 = 0.0506, P6 = 0.0316, P7 = 0.0759, P8 = 0.0506, and then calculate the biodiversity index according to the above formula to be 1.82.

[0061] Take any biological species i as the analysis object, assign a value to the pollution tolerance of the analysis object, and the pollution tolerance can be classified into very sensitive, moderately sensitive, pollution-tolerant and highly pollution-tolerant, and assign values ​​of -2, -1, 1 and 2 respectively. Specifically, the organisms that are very sensitive to pollution are assigned a value of -2, the organisms that are moderately sensitive are assigned a value of -1, the pollution-tolerant organisms are assigned a value of 1, the highly pollution-tolerant organisms are assigned a value of 2, and so on. All biological species i are assigned values, and then the biological pollution index of the biological species is calculated according to the obtained biological species assignments. The specific calculation formula is: , the biological pollution index B is calculated according to the formula, where Fi represents the value corresponding to the i-th biological species, and Lz is the total number of organisms;

[0062] In a specific example, suppose that during the biological contamination assessment of the ecosystem in the analysis area, the following biological species and their numbers are found, as shown in Table 2 below: Table 2 Analysis area pollution tolerance assessment table

[0063]

[0064] First, calculate the total biomass Lz=110, and then calculate the biocontamination index according to the formula After calculation, the biological pollution index B is 0. Generally speaking, the closer the biological pollution index is to positive values, the more pollution-tolerant or highly pollution-tolerant organisms there are in the ecosystem, which may suggest that the water body is polluted to a certain extent; and the closer the index is to negative values, the larger the proportion of very sensitive and moderately sensitive organisms, and the relatively cleaner the water body.

[0065] The calculated biodiversity index H is summed with the biological pollution index B to obtain the biological impact index H+B.

[0066] The pollution source index is calculated as follows: the total population in the analysis area is recorded as R, and the total domestic sewage discharge per unit time is recorded as K. The total domestic sewage discharge can be obtained through sewage discharge monitoring equipment (such as installing a flow meter at the total outlet of the sewage network). The regional population can be obtained from the census data or the local government's demographic department. Then, the per capita water consumption is recorded as S1, and the per capita discharge is recorded as S2. Substitute the obtained parameters into the formula The sewage discharge coefficient S is calculated, and the unit sewage discharge coefficient is calculated here. Substitute the obtained parameters into the formula The domestic sewage discharge intensity per unit time W1 is calculated, and the unit time here is expressed as one day;

[0067] Similarly, the above analysis process calculates the industrial wastewater discharge intensity W2 per unit time, and what is calculated here is the total industrial wastewater discharge intensity W2 of multiple products. For example, a chemical company produces two products A and B. The production water consumption of product A is 500 cubic meters / day, and the wastewater discharge is 400 cubic meters / day. Its wastewater discharge coefficient is 400 / 500=0.8; the production water consumption of product B is 300 cubic meters / day, and the wastewater discharge is 240 cubic meters / day. Its wastewater discharge coefficient is 240 / 300=0.8. Then the calculated wastewater discharge coefficients are summed and the mean is calculated.

[0068] The calculated domestic sewage discharge intensity per unit time W1 and industrial sewage discharge intensity per unit time W2 are summed to obtain the pollution source index W1+W2.

[0069] Next, substitute the calculated biological impact index H+B and pollution source index W1+W2 into the formula to calculate the water environment value PC, where and respectively represent the weight coefficients of the biological impact index and the pollution coefficient index. These coefficients can be determined through experimental data, model calibration, or expert experience. Fa is the coefficient for converting the air pollutant into COD after deposition, where Fa represents the deposition flux of the a-th air pollutant (such as the deposition fluxes of sulfur dioxide and nitrogen oxides). At the same time, transmit the obtained water environment value PC to the water quality monitoring and early warning module.

[0070] Water quality monitoring and early warning module. This module is used to monitor and analyze the water quality of the analysis area based on the obtained water environment value, evaluate the water quality of the analysis area to generate evaluation information, and transmit the evaluation information to the monitoring information output unit. The specific method for generating the evaluation information is as follows:

[0071] Obtain the water environment value PC of the analysis area, and at the same time compare the water environment value PC with the threshold PY. The specific value of the threshold is set by the operator. If the water environment value PC is greater than the threshold PY, it means that the water quality of the analysis area is abnormal, and at the same time generate a warning message and transmit it to the monitoring information output module. On the contrary, if the water environment value PC is less than the threshold PY, it means that the water quality of the analysis area is not abnormal, and the non-abnormality here means that there is no abnormality at the current time, and generate a water quality evaluation signal, and further analyze the water quality evaluation signal;

[0072] Monitor and analyze the water environment value PC with time t as the cycle, and match the water environment value PC with the water quality grade interval. The specific value of the water quality grade interval here is calculated comprehensively based on physical indicators, chemical indicators, and biological indicators, and the specific value is set by the operator. At the same time, generate corresponding evaluation information based on the matching result and transmit the evaluation information to the monitoring information output module.

[0073] In a specific example, the water quality is divided into Class I, Class II, Class III, Class IV, and Class V. Among them, Class I water quality: PC≤0.2, Class II water quality: 0.2<PC≤0.4, Class III water quality: 0.4<PC≤0.6, Class IV water quality: 0.6<PC≤0.8, Class V water quality: 0.8<PC. Suppose when monitoring the water environment value PC of the analysis area, the obtained value is PC = 0.45. According to the set threshold range, 0.4<PC≤0.6, so the water quality grade of the analysis area is Class III. Further, it is known that after appropriate treatment, it can be used for aquaculture and swimming and other activities, but it cannot be directly used as a drinking water source because Class III water needs to be deeply treated to be used for domestic drinking, thus generating evaluation information.

[0074] Monitoring information output module, which is used to display the acquired evaluation information and warning information to the corresponding operators.

[0075] For example 2, please refer to Figure 2 The present application provides a water environment monitoring method based on big data analysis, which specifically includes the following steps:

[0076] Step 1: Acquire water environment data of the analysis area, and preprocess the water environment data to obtain preprocessed data;

[0077] Step 2: Establish a corresponding water environment distribution model based on the obtained preprocessed data, and obtain regional parameters of the analysis area based on the water environment distribution model, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters;

[0078] Step 3: Calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and the pollution source distribution parameters, and sum the biological impact index and the pollution source index to obtain the water environment value, and the processing method here is the same as the analysis process of the adaptive analysis module in Example 1;

[0079] Step 4: Monitor and analyze the water quality of the analysis area according to the obtained water environment value, and evaluate the water quality of the analysis area to generate evaluation information. The specific method of generating the evaluation information here is the same as the processing process of the water quality monitoring and early warning module in Example 1.

[0080] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0081] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A water environment monitoring system based on big data analysis, characterized in that: include: A water environment data collection module, used for transmitting the collected water environment data to the distribution model building module; A distribution model building module is used to build a corresponding water environment distribution model according to the acquired water environment data, and transmit the built water environment distribution model to the adaptive analysis module; The adaptive analysis module is used to obtain the regional parameters of the analysis area according to the water environment distribution model, calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and the pollution source distribution parameters in the regional parameters, and sum the biological impact index and the pollution source index to obtain the water environment value, and transmit the water environment value to the water quality monitoring and early warning module; The water quality monitoring and early warning module is used to monitor and analyze the water quality of the analysis area according to the acquired water environment value, evaluate the water quality of the analysis area to generate evaluation information, and transmit the evaluation information to the monitoring information output unit; The monitoring information output module is used to display the acquired evaluation information and warning information to the corresponding operators.

2. A water environment monitoring system based on big data analysis according to claim 1, characterized in that: The specific method of establishing the water environment distribution model by the distribution model establishment module is: Clean the collected water environment data, remove outliers and erroneous data, and standardize the data to obtain preprocessed data; A standard model is selected based on the acquired preprocessed data, and the demand parameters are obtained based on the standard model. The acquired demand parameters are substituted into the standard model to optimize the model. Then historical data is obtained to calibrate the standard model, and the simulation results of the model are compared with the actual data. The parameters of the standard model are adjusted through the least squares method to obtain the water environment distribution model.

3. A water environment monitoring system based on big data analysis according to claim 1, characterized in that: The specific method in which the adaptive analysis module calculates the biological impact index according to the biological parameters in the regional parameters is: Based on the water environment model, regional parameters are obtained, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters, and biological impact factors and pollution source factors are calculated respectively; The calculation method of the biological impact index is as follows: obtain the biological species in the analysis area and record them as i, where i=1, 2, ..., j, where j represents the biological species, and obtain the number of different biological species i and record them as Li, and calculate the total number of biological species in the analysis area and record them as Lz, then substitute the obtained parameters into the formula The biodiversity index is calculated, where pi represents the ratio of the number of the i-th biological species to the total number. The specific calculation method is: , j is the species of organism; Take any biological species i as the analysis object, assign a value to the pollution tolerance of the analysis object, and so on, assign values ​​to all biological species i, and then calculate the biological pollution index of the biological species based on the obtained biological species assignment. The specific calculation formula is , the biological pollution index B is calculated according to the formula, where Fi represents the value corresponding to the i-th biological species, and Lz is the total number of organisms; The calculated biodiversity index H is summed with the biological pollution index B to obtain the biological impact index H+B.

4. The water environment monitoring system based on big data analysis according to claim 1 is characterized in that: The specific method in which the adaptive analysis module calculates the pollution source index according to the population distribution parameters and pollution source distribution parameters in the regional parameters is: The calculation method of the pollution source index is as follows: obtain the total population in the analysis area, record it as R, and obtain the total domestic sewage discharge per unit time, record it as K, then obtain the per capita water consumption, record it as S1, and obtain the per capita discharge, record it as S2, and substitute the obtained parameters into the formula The sewage discharge coefficient S is calculated, and the unit sewage discharge coefficient is calculated here. Substitute the obtained parameters into the formula The domestic sewage discharge intensity per unit time W1 is calculated; Similarly, the above analysis process calculates the industrial wastewater discharge intensity per unit time W2.

5. The water environment monitoring system based on big data analysis according to claim 1 is characterized in that: The specific method of the adaptive analysis module to sum the biological impact index and the pollution source index to obtain the water environment value is: Substitute the calculated biological impact index H+B and pollution source index W1+W2 into the formula The water environment value PC is calculated, where and They represent the weight coefficients of the biological impact index and the pollution coefficient index, respectively, and Fa is the coefficient of the air pollutant converted into COD after deposition in the analysis area, where Fa represents the deposition flux of the ath air pollutant; At the same time, the acquired water environment value PC is transmitted to the water quality monitoring and early warning module.

6. The water environment monitoring system based on big data analysis according to claim 1 is characterized in that: The specific method in which the water quality monitoring and early warning module monitors and analyzes the water quality of the analysis area according to the acquired water environment value is: The water environment value PC of the analysis area is obtained, and the water environment value PC is compared with the threshold value PY. If the water environment value PC is greater than the threshold value PY, it means that the water quality in the analysis area is abnormal, and an early warning message is generated and transmitted to the monitoring information output module. On the contrary, if the water environment value PC is less than the threshold value PY, it means that there is no abnormality in the water quality of the analysis area, and a water quality assessment signal is generated, and the water quality assessment signal is further analyzed.

7. A water environment monitoring system based on big data analysis according to claim 6, characterized in that: The specific method in which the water quality monitoring and early warning module analyzes the water quality assessment signal is as follows: With time t as a cycle, the water environment value PC is monitored and analyzed, and the water environment value PC is matched with the water quality grade interval. At the same time, the corresponding evaluation information is generated based on the matching result, and the evaluation information is transmitted to the monitoring information output module.

8. A water environment monitoring method based on big data analysis, used to implement a water environment monitoring system based on big data analysis as claimed in any one of claims 1 to 7, characterized in that: The method specifically comprises the following steps: Step 1: Obtain water environment data of the analysis area, and preprocess the water environment data to obtain preprocessed data; Step 2: Establish a corresponding water environment distribution model based on the obtained preprocessed data, and obtain regional parameters of the analysis area based on the water environment distribution model, and the regional parameters include biological parameters, population distribution parameters and pollution source distribution parameters; Step 3: Calculate the biological impact index according to the biological parameters in the regional parameters, calculate the pollution source index according to the population distribution parameters and the pollution source distribution parameters, and sum the biological impact index and the pollution source index to obtain the water environment value; Step 4: Monitor and analyze the water quality of the analysis area based on the obtained water environment values, and evaluate the water quality of the analysis area to generate evaluation information.

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