Industrial park environment evaluation system based on cloud platform

Abstract

The invention relates to the field of environment monitoring and evaluation, in particular to an industrial park environment evaluation system based on a cloud platform. Comprising a data acquisition module, a terminal evaluation module, a cloud analysis module, an early warning module, an alarm module and a visualization module. The data acquisition module is used for acquiring environment quantitative index data and calculating environment detection parameters; the terminal evaluation module is used for evaluating environment change parameters according to the environment detection parameters and generating an alarm signal or an analysis signal through judgment; the alarm module labels and counts the environment detection parameters exceeding the standard and displays the environment detection parameters through the visualization module; the cloud analysis module is used for summarizing environment detection parameters of different positions of the park and evaluating the park environment through an artificial intelligence model to obtain an environment early warning coefficient; the early warning module generates an early warning signal and sends the early warning signal to the park management terminal; according to the invention, the environment of the industrial park can be effectively evaluated, and the intelligent requirement of the industrial park on environment monitoring evaluation is met.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring and evaluation, and in particular to an industrial park environmental evaluation system based on a cloud platform. Background Art

[0002] With the acceleration of global industrialization and urbanization, industrial parks play an important role in economic development. However, the rapid development of industrial parks has also brought about a series of problems such as environmental pollution, waste of resources and ecological damage. The monitoring, evaluation and governance of the environment in industrial parks have become the focus of attention of departments and enterprises at all levels. In recent years, with the rapid development of emerging technologies such as cloud computing, big data, and the Internet of Things, these technologies have been gradually applied to the environmental management and evaluation of industrial parks, providing new ideas for solving the problems of inefficiency, data silos and lack of real-time performance in traditional environmental assessment systems. In traditional industrial park environmental management, manual monitoring and decentralized monitoring systems are often relied on. This model has many shortcomings. The traditional environmental assessment system relies on manual data collection, has a long analysis cycle, cannot respond to environmental pollution incidents in a timely manner, and may lead to the expansion of environmental problems. In addition, the data collected is single, and environmental assessment is carried out in a simple way, which cannot be comprehensively evaluated. It lacks the analysis ability of artificial intelligence and cannot provide effective information support. Therefore, the development of an industrial park environmental assessment system based on a cloud platform is of great significance to improving the monitoring, evaluation and management of the industrial park environment. Summary of the invention

[0003] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide an industrial park environmental assessment system based on a cloud platform, which solves the problems that traditional environmental assessments require long cycles due to manual data collection and are unable to conduct timely environmental monitoring and assessment; and there are shortcomings such as single data collection, data island problems, and lack of data analysis, which lead to inaccurate analysis results, making it impossible to effectively assess the industrial park environment, and it is difficult to meet the industrial park's intelligent needs for environmental monitoring and assessment.

[0004] The purpose of the present invention can be achieved through the following technical solutions: An industrial park environmental assessment system based on a cloud platform, including: Data acquisition module, terminal evaluation module, cloud analysis module, early warning module, alarm module, visualization module; The data acquisition module collects the environmental quantitative index data in the regional location i of the industrial park and calculates the environmental detection parameters at the regional location i, including the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i ; The terminal evaluation module is used to evaluate the environmental change parameters at the area location i according to the environmental detection parameters ; If the environmental change parameters If the threshold is exceeded, the terminal evaluation module generates an alarm signal and sends the alarm signal to the alarm module; if the environmental change parameter If the threshold is not exceeded, the terminal evaluation module generates an analysis signal and sends the analysis signal to the cloud analysis module; The alarm module receives the alarm signal and obtains the environmental detection parameters at position i; the environmental detection parameters that exceed the standard are marked by calculation, and then displayed by the visualization module; the alarm module counts the number of times the environmental detection parameters exceed the standard per unit time, performs normalization processing, and sends it to the cloud analysis module for dynamic adjustment of environmental change parameters ; The cloud analysis module is used to summarize the environmental detection parameters of different locations in the park, and evaluate the park environment through artificial intelligence models based on the collected historical regional environmental data to obtain the environmental warning coefficient , if the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information and sends it to the visualization module for display; if the environmental warning coefficient If the threshold is not exceeded, the cloud analysis module sends the environmental detection parameters of different locations in the park to the visualization module; The warning module receives the warning information and identifies the location information in the warning information and the environmental detection parameters corresponding to the location; then generates a warning signal and sends the warning signal to the park management terminal; The visualization module is used to visualize the received data.

[0005] As a further solution of the present invention: the specific steps of the data acquisition module obtaining the environmental detection parameters at the regional position i are as follows: S11 Regional division: The industrial park is divided into several regional locations. Each regional location i is equipped with a sensor and each sensor has a unique device identifier, including water quality sensors, air quality sensors, and pressure sensors. The sensors in regional location i are matched with the GPS coordinates of region i; where i=1, 2, ..., n, n is a positive integer, i is the number of any regional location, and n is the total number of regional locations; S12 obtains data: obtains the water quality sampling value Pi, air quality parameter sampling value Px, and solid waste discharge Es at the regional location i; Water quality sampling values ​​Pi include pH value, COD, total nitrogen TN, and total phosphorus TP; S13 Calculate water quality parameter W i : Water quality parameter W i It is calculated by taking the weighted average of each water quality sampling value Pi. ; Among them, u represents the number of water quality sampling values ​​Pi, wi is the weight of water quality sampling value Pi, which needs to satisfy ; Si represents the standard limit of water quality, which adopts national standards or set regional standards; S14 calculates air quality parameters AQ i : Calculate the index Ix of air pollutants respectively, where Ix is equal to the ratio of the air quality reference sampling value Px to the standard limit value Sx of air pollutants; compare the index Ix of each air pollutant and take the maximum value, i.e. AQ i =max (Ix); air pollutants include PM2.5, PM10, SO2, NO2, CO, O3; AQ i =max(IPM2.5, IPM10, ISO2, INO2, ICO, IO3), take the maximum value as AQ i The value of The standard limit values ​​Sx of air pollutants adopt national standards or set regional standards; S15 Calculation of solid waste parameters SW i :Get the solid waste emission Es at the location i in the area, get the solid waste classification factor Fs according to the solid waste classification, get the solid waste recycling rate Rs of the park according to the solid waste recycling historical data, get the solid waste duration factor Ls according to the solid waste storage time, according to the formula:

[0006] Get the solid waste parameters SWi, where the treatment efficiency The value range is 0~1; z1, z2, z3, z4 are all preset proportional coefficients, and z1+z2+z3+z4=1; In order to more accurately reflect the severity of the excess, a nonlinear function is used: ; The purpose is to reduce the impact of minor excesses and highlight serious excesses; Ss represents the solid waste standard emission limit, which adopts national standards or set regional standards; Reflects whether the emission exceeds the standard. >1, indicating that the emission exceeds the standard; is the theoretical treatment efficiency of solid waste treatment in the park; The solid waste classification factor Fs reflects whether solid waste has been effectively classified and managed; classification management can significantly reduce environmental risks and improve the level of resource utilization; a scoring system is used to score: For example: Unclassified: Fs=1; Simple classification: Fs=0.5; Efficient classification Fs=0; The solid waste recycling rate Rs reflects the proportion of solid waste that is recycled. Improving the recycling rate can effectively reduce the environmental burden of the park. The actual recycling volume and total solid waste volume of multiple historical nodes are obtained, and the ratio of the actual recycling volume to the total solid waste volume at different historical nodes is calculated respectively, and then the average is taken as the current Rs value. The solid waste duration factor Ls reflects the length of time that solid waste stays in the environment. Solid waste that has not been treated for a long time may cause regional environmental pollution, such as accumulation and fermentation, groundwater pollution, etc. The solid waste detection time and solid waste emission Es are obtained. If the solid waste emission Es is greater than the preset minimum solid waste emission Emin, the accumulated solid waste storage time is recorded as the detention time Ta, and the solid waste time factor Ls is the ratio of the detention time Ta to the prescribed treatment time limit Sa; if the solid waste emission Es is less than the preset minimum solid waste emission Emin, the detention time is recalculated; the solid waste storage time is the difference between the next detection time and the previous detection time; S16 Get timestamp: Get water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i The time when the data is collected is used to obtain the timestamp of the corresponding data; S17 data storage: the data obtained from S11 to S16 are sent to the terminal evaluation module and the cloud analysis module through the network respectively for the evaluation of the industrial park environment.

[0007] As a further solution of the present invention: the terminal evaluation module evaluates the environmental change parameters at the regional position i The specific process is as follows: S21: Obtaining data: The terminal evaluation module receives the environmental detection parameters and corresponding timestamps sent by the data acquisition module; S22 calculates the cross contamination factor C: Calculate according to the formula: ; , , is the coefficient of cross contamination degree, , , Both are greater than or equal to 0 and less than or equal to 1; The cross-contamination factor C reflects the mutual influence between water, air, and solid waste; S23 calculates the change rate factor V: Calculate according to the formula: ; , , is the environmental change sensitivity coefficient, which is used to amplify the impact of rapidly changing parameters on environmental coefficients; Reflect the changing trend of environmental detection parameters and capture the potential impact of dynamic changes in environmental indicators on the environment; when a parameter changes dramatically in a short period of time, such as a sudden deterioration in air quality, the problem can be quickly reflected and an early warning can be triggered; S24 Calculate environmental change parameters :Calculated according to the formula: ; in, , , , , is the preset weight of the environmental change parameter, and ; Weight , , , , It can be flexibly adjusted according to actual needs. For example: Areas that are more sensitive to water quality can increase The area that is more concerned about air pollution will increase Value size; in , , They respectively represent the water quality sensitivity coefficient, air sensitivity coefficient, and solid waste sensitivity coefficient. When the value is greater than 1, it is more sensitive to the exceeding of the standard. When the value is less than 1, it is relatively mild to the exceeding of the standard. It will be adjusted according to the actual situation of the industrial park.

[0008] If the environmental change parameters If the threshold is exceeded, the terminal evaluation module generates an alarm signal and sends the alarm signal to the alarm module; if the environmental change parameter If the threshold is not exceeded, the terminal evaluation module generates an analysis signal and sends the analysis signal to the cloud analysis module.

[0009] As a further solution of the present invention: the alarm module receives the alarm signal and obtains the environmental detection parameters at the regional position i; the environmental detection parameters that exceed the standard are marked by calculation; if the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i If one or more parameters in the water quality parameter list are greater than the reference value 1, it is considered to be exceeded. Then it is displayed through the visualization module, with time set as the horizontal axis, and the water quality parameter W i, Air quality parameters AQ i , solid waste parameters SW i Set them as the vertical coordinates respectively, and get three broken line graphs that change with time. When the standard is exceeded, the broken line of the time period is marked with different colors; The alarm module counts the number of times the environmental detection parameters in all areas of the industrial park exceed the standard within a unit time. The unit time is set to 3 hours, and the water quality parameter W is counted separately. i , Air quality parameters AQ i , solid waste parameters SW i The number of times the standard is exceeded is recorded as NW, NAQ, and NSW respectively; Then it is normalized and sent to the cloud analysis module for dynamic adjustment of environmental change parameters. ; The normalization formula is: NWa=NW / Ntotal,NAQa=NAQ / Ntotal,NSWa=NSW / Ntotal; Among them, Ntotal = NW + NAQ + NSW; NWa, NAQa, NSWa and the annotation information of the exceeding parameters are sent to the cloud analysis module, and the environmental change parameters are adjusted by identifying the number of times the environmental detection parameters of the park exceed the standard. The preset weights of the corresponding environmental detection parameters in the calculation are sent to the terminal evaluation module, and the preset weights of the adjusted environmental detection parameters are dynamically adjusted. .

[0010] Artificial intelligence model evaluates the park environment and obtains the environmental warning coefficient The specific steps are as follows: S31 obtains detection data: obtains the environmental detection parameters at each regional position i and the corresponding timestamp; specifically, a set of environmental detection parameters recorded at different regional positions at the same timestamp; S32 obtains historical data: historical environmental data of each regional location i, including monitoring results over a certain period of time in the past; S33 obtains the data of the environmental exceeding standard event: obtains the occurrence time of the environmental exceeding standard event in history and whether it exceeds the standard; S34 data cleaning and preprocessing: fill missing values, remove outliers and standardize data obtained from S31, S32 and S33; normalize environmental detection parameters to eliminate dimensional differences; S35 obtains time series features: extracts the average value, maximum value and minimum value of environmental detection parameters within a certain time series length through the data processed by S34; for example, extracts the W iThe average, maximum and minimum values ​​of the water quality parameter W are calculated. For example, for the water quality parameter W i The time series feature set is constructed as shape = (samples, time steps, features), where samples is the number of samples, time steps is the length of the time series; features represents the number of features per unit; features contains 5 features, specifically W i , 24 hours of W i The average, maximum and minimum values ​​of W for 24 hours i The excess ratio; preferably, the 24-hour W i The rate of change of is taken as a feature; AQ i With SW i Time series feature construction and W i The same is not repeated here.

[0011] S36 model training: Using the LSTM model, the acquired time series features are used as input, and the environmental warning coefficient As training output; environmental change parameters calculated by environmental detection parameters After normalization, the training parameters are obtained, which represent the real environmental warning coefficient. ; (1) Initialize model parameters: Randomly initialize the weights and bias parameters of the LSTM layer; (2) Forward propagation: input the training data into the model and calculate the predicted value of each sample ; (3) Calculating loss: comparing predicted values and the true value , the loss function value is calculated by the mean square error (MSE); (4) Back propagation: Calculate the update direction and amplitude of the model parameters based on the gradient of the loss function; (5) Parameter update: Use the optimization algorithm Adam to update the model parameters and reduce the loss function value; (6) Iterative training: Repeat the above steps until all training data have been traversed multiple times.

[0012] S37 Environmental Warning Factor :Input the newly acquired environmental detection parameters into the model to obtain the environmental warning coefficient ; If the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information; the warning information includes the feature information contained in the features in the time series features.

[0013] After receiving the warning information, the early warning module identifies the feature information contained in the features and extracts the GPS information contained in the environmental detection parameters, which can indicate which area may have environmental problems. At this time, an early warning signal is generated and sent to the park management terminal. The park management terminal is held by managers in different areas or by dedicated personnel in the park. After receiving the early warning signal, they go to the corresponding area for environmental inspection and further judge whether environmental problems may occur manually.

[0014] The visualization module displays data in the form of graphs or charts, making it easier for park managers to monitor.

[0015] Beneficial effects of the present invention: The cloud platform-based industrial park environment assessment system of the present invention collects environmental quantitative indicators of different areas of the industrial park, obtains environmental detection parameters in the area, quickly calculates environmental change parameters through a terminal evaluation module, and issues an alarm if an abnormality exists, thereby realizing a rapid assessment of the regional environment; and an alarm module is used to annotate data, thereby facilitating an intuitive assessment of the regional environmental conditions of the industrial park; and by calculating the number of times the environmental detection parameters exceed the standard, the preset parameter coefficient is adjusted, thereby adjusting the environmental change parameters to meet the changes in the industrial park environment under different circumstances, and performing a dynamic assessment; The industrial park environment assessment system based on a cloud platform of the present invention can simulate the historical changes of the park environment through unified storage and system analysis of environmental detection parameters in various areas of the park, adopt LSTM model for training, evaluate the changes of the entire park environment, and make early warnings; in the process of data collection and analysis, it solves the shortcomings of single data collection, data island problems, lack of data analysis, etc., can more comprehensively reflect the environmental conditions of the industrial park, and provide information support for park management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 It is a structural schematic diagram of an industrial park environment assessment system based on a cloud platform in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with 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.

[0019] Embodiment 1: See also Figure 1 As shown, this embodiment is an industrial park environment assessment system based on a cloud platform, including the following modules: a data acquisition module, a terminal assessment module, a cloud analysis module, an early warning module, an alarm module, and a visualization module.

[0020] The data acquisition module collects the environmental quantitative index data in the regional location i of the industrial park and calculates the environmental detection parameters at the regional location i, including the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i .

[0021] The terminal evaluation module is used to evaluate the environmental change parameters at the area location i according to the environmental detection parameters ; If the environmental change parameters If the threshold is exceeded, the terminal evaluation module generates an alarm signal and sends the alarm signal to the alarm module; if the environmental change parameter If the threshold is not exceeded, the terminal evaluation module generates an analysis signal and sends the analysis signal to the cloud analysis module.

[0022] The alarm module receives the alarm signal and obtains the environmental detection parameters at position i; the environmental detection parameters that exceed the standard are marked by calculation, and then displayed by the visualization module; the alarm module counts the number of times the environmental detection parameters exceed the standard per unit time, performs normalization processing, and sends it to the cloud analysis module for dynamic adjustment of environmental change parameters .

[0023] The cloud analysis module is used to summarize the environmental detection parameters of different locations in the park, and evaluate the park environment through artificial intelligence models based on the collected historical regional environmental data to obtain the environmental warning coefficient , if the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information and sends it to the visualization module for display; if the environmental warning coefficient If the threshold is not exceeded, the cloud analysis module sends the environmental detection parameters of different locations in the park to the visualization module.

[0024] The early warning module receives the early warning information, and identifies the location information in the early warning information and the environmental detection parameters corresponding to the location; then generates an early warning signal, and sends the early warning signal to the park management terminal.

[0025] The visualization module is used to visualize the received data; the visualization module displays the data in the form of graphics or charts to facilitate monitoring by park managers.

[0026] Embodiment 2: Based on Example 1, this example further illustrates the specific steps of the data acquisition module obtaining the environmental detection parameters at the regional position i: S11 Regional division: The industrial park is divided into several regional locations. Each regional location i is installed with a sensor and each sensor has a unique device identifier, including water quality sensors, air quality sensors, and pressure sensors. The sensors in regional location i are matched with the GPS coordinates of region i; where i=1, 2, ..., n, n is a positive integer, i is the number of any regional location, and n is the total number of regional locations. This ensures that the data collected by the sensors in a certain area corresponds to the regional coordinates one by one, which facilitates data search and traceability.

[0027] S12 obtains data: obtains the water quality sampling value Pi, air quality parameter sampling value Px, and solid waste discharge Es at the regional location i; Water quality sampling values ​​Pi include pH value, COD, total nitrogen TN, and total phosphorus TP; In this embodiment, the sampling frequency is 30 minutes. The sampling frequency can be adjusted according to the actual situation of the park, but enough data information should be collected to avoid too little data resulting in insufficient data for training the artificial intelligence model.

[0028] S13 Calculate water quality parameter W i : Water quality parameter W i It is calculated by taking the weighted average of each water quality sampling value Pi. ; Among them, u represents the number of water quality sampling values ​​Pi, wi is the weight of water quality sampling value Pi, which needs to satisfy ; Si represents the standard limit of water quality, which adopts national standards or set regional standards.

[0029] In this embodiment, u=4, when the water quality sampling value Pi is a PH value, its weight value is 0.33; when the water quality sampling value Pi is a COD value, its weight value is 0.28; when the water quality sampling value Pi is a TN value, its weight value is 0.18; when the water quality sampling value Pi is a TP value, its weight value is 0.21; The weight of the water quality sampling value Pi is set according to the specific conditions of the park's surrounding environment. If the park is sensitive to COD values ​​or often exceeds the standard, its weight can be adjusted, thereby affecting the water quality parameter W. i The calculated value of .

[0030] S14 calculates air quality parameters AQ i : Calculate the index Ix of air pollutants respectively, where Ix is equal to the ratio of the air quality reference sampling value Px to the standard limit value Sx of air pollutants; compare the index Ix of each air pollutant and take the maximum value, i.e. AQ i =max (Ix); air pollutants include PM2.5, PM10, SO2, NO2, CO, O3; AQ i =max(IPM2.5, IPM10, ISO2, INO2, ICO, IO3), take the maximum value as AQ i The value of The standard limit values ​​Sx of air pollutants adopt national standards or set regional standards.

[0031] S15 Calculation of solid waste parameters SW i :Get the solid waste emission Es at the location i in the area, get the solid waste classification factor Fs according to the solid waste classification, get the solid waste recycling rate Rs of the park according to the solid waste recycling historical data, get the solid waste duration factor Ls according to the solid waste storage time, according to the formula: ; Get the solid waste parameters SWi, where the treatment efficiency The value range is 0~1; z1, z2, z3, z4 are all preset proportional coefficients, and z1+z2+z3+z4=1; In this embodiment, the value of z1 is 0.53, the value of z2 is 0.23, the value of z3 is 0.15, and the value of z4 is 0.09.

[0032] In order to more accurately reflect the severity of the excess, a nonlinear function is used ; The purpose is to reduce the impact of minor excesses and highlight serious excesses; Ss represents the solid waste standard emission limit, which adopts national standards or set regional standards; Reflects whether the emission exceeds the standard. >1, indicating that the emission exceeds the standard.

[0033] It is the theoretical treatment efficiency of solid waste treatment in the park.

[0034] The solid waste classification factor Fs reflects whether solid waste has been effectively classified and managed; classification management can significantly reduce environmental risks and improve the level of resource utilization; a scoring system is used to score: For example: Unclassified: Fs=1; Simple classification: Fs=0.5; Efficient classification Fs=0.

[0035] The solid waste recycling rate Rs reflects the proportion of solid waste that is recycled. Improving the recycling rate can effectively reduce the environmental burden of the park. The actual recycling volume and total solid waste volume of multiple historical nodes are obtained, and the ratio of the actual recycling volume to the total solid waste volume at different historical nodes is calculated respectively, and then the average is taken as the current Rs value.

[0036] The solid waste duration factor Ls reflects the length of time that solid waste stays in the environment. Solid waste that has not been treated for a long time may cause regional environmental pollution, such as accumulation and fermentation, groundwater pollution, etc.

[0037] The solid waste detection time and solid waste emission Es are obtained. If the solid waste emission Es is greater than the preset minimum solid waste emission Emin, the accumulated solid waste storage time is recorded as the detention time Ta, and the solid waste time factor Ls is the ratio of the detention time Ta to the prescribed treatment time limit Sa; if the solid waste emission Es is less than the preset minimum solid waste emission Emin, the detention time is recalculated; the solid waste storage time is the difference between the next detection time and the previous detection time; For example, the minimum solid waste emission Emin is set to 500 kg, the solid waste detection time is 8:30:00 on July 15, 2024, and the solid waste emission Es is 2000 kg. The solid waste detection time is obtained again at 9:30:00 on July 15, 2024, and the solid waste emission Es is 2200 kg. At this time, the solid waste storage time is 1 hour; the solid waste detection time is obtained again at 10:30:00 on July 15, 2024, and If the solid waste emission Es = 2300kg, the solid waste storage time is 1 hour. At this time, the residence time Ta is the cumulative value of the solid waste storage time, that is, the residence time Ta = 2 hours; if the solid waste detection time is 11:30:00 on July 15, 2024 and the solid waste emission Es = 300kg, the solid waste storage time will be restarted until the solid waste emission Es is greater than the preset minimum solid waste emission Emin, and the residence time Ta will be calculated.

[0038] S16 Get timestamp: Get water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i The time when the data is collected is used to obtain the timestamp of the corresponding data; For example: when collecting the water quality sampling value Pi, the pH value is collected at 8:30:15 on July 15, 2024, then the record is PH=7, 2024-07-15T08:30:15Z.

[0039] S17 data storage: the data obtained from S11 to S16 are sent to the terminal evaluation module and the cloud analysis module through the network respectively for the evaluation of the industrial park environment.

[0040] Embodiment 3: Based on any of the above embodiments, this embodiment further illustrates that the terminal evaluation module evaluates the environmental change parameters at the location i of the area The specific process is as follows: S21 Obtaining data: The terminal evaluation module receives the environmental detection parameters and corresponding timestamps sent by the data acquisition module.

[0041] S22 calculates the cross contamination factor C: Calculate according to the formula: ; , , is the coefficient of cross contamination degree, , , Both are greater than or equal to 0 and less than or equal to 1; The cross-contamination factor C reflects the mutual influence between water, air, and solid waste; For example: The accumulation of solid waste may lead to leachate contamination of water bodies.

[0042] Air pollution can cause rainwater to acidify, affecting water quality.

[0043] In this embodiment The value is 0.3. The value is 0.2. The value is 0.1.

[0044] S23 calculates the change rate factor V: Calculate according to the formula: ; , , is the environmental change sensitivity coefficient, which is used to amplify the impact of rapidly changing parameters on environmental coefficients; Reflect the changing trend of environmental detection parameters and capture the potential impact of dynamic changes in environmental indicators on the environment; when a parameter changes dramatically in a short period of time, such as a sudden deterioration in air quality, the problem can be quickly reflected and an early warning can be triggered; In this embodiment The value is 1.3. The value is 1.2. The value is 1.1.

[0045] S24 Calculate environmental change parameters :Calculated according to the formula: ; in, , , , , is the preset weight of the environmental change parameter, and ; Weight , , , , It can be flexibly adjusted according to actual needs; for example: Areas that are more sensitive to water quality can increase The area that is more concerned about air pollution will increase Value size.

[0046] in , , They represent the water quality sensitivity coefficient, air sensitivity coefficient, and solid waste sensitivity coefficient respectively. When the value is greater than 1, it is more sensitive to the exceeding of the standard. When the value is less than 1, it is relatively mild to the exceeding of the standard. It is adjusted according to the actual situation of the industrial park. In this embodiment The value is 1.2. , The value is 1.3; The value is 0.3. The value is 0.3. The value is 0.2. The value is 0.1. The value is 0.1.

[0047] If the environmental change parameters If the threshold is exceeded, the terminal evaluation module generates an alarm signal and sends the alarm signal to the alarm module; if the environmental change parameter If the threshold is not exceeded, the terminal evaluation module generates an analysis signal and sends the analysis signal to the cloud analysis module. The terminal evaluation module sends all data involved in S21 to S24 to the cloud analysis module. In this embodiment, the threshold is set to 1.46.

[0048] The alarm module receives the alarm signal and obtains the environmental detection parameters at the area position i; the environmental detection parameters that exceed the standard are marked by calculation. If the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i If one or more parameters in the water quality parameter list are greater than the reference value 1, it is considered to be exceeded. Then it is displayed through the visualization module, with time set as the horizontal axis, and the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i Set them as the vertical coordinates respectively, and get three broken line graphs that change over time. When the water quality parameter W exceeds the standard, the broken line of the time period is marked with different colors. i If the limit is exceeded between 15:00 and 16:00 on the same day, the time interval will be marked in red when the line is drawn, and the other indicators can be set to other colors; The alarm module counts the number of times the environmental detection parameters in all areas of the industrial park exceed the standard within a unit time. The unit time is set to 3 hours, and the water quality parameter W is counted separately. i , Air quality parameters AQ i , solid waste parameters SW i The number of times the standard is exceeded is recorded as NW, NAQ, and NSW respectively; Then it is normalized and sent to the cloud analysis module for dynamic adjustment of environmental change parameters. ; The normalization formula is: NWa=NW / Ntotal,NAQa=NAQ / Ntotal,NSWa=NSW / Ntotal; Among them, Ntotal = NW + NAQ + NSW; NWa, NAQa, NSWa and the annotation information of the exceeding parameters are sent to the cloud analysis module, and the environmental change parameters are adjusted by identifying the number of times the environmental detection parameters of the park exceed the standard. The preset weights of the corresponding environmental detection parameters in the calculation are sent to the terminal evaluation module, and the preset weights of the adjusted environmental detection parameters are dynamically adjusted. ; For example: after calculation, take the maximum value among NWa, NAQa, and NSWa. If NWa is the largest, it means that the main environmental problem of the current park is water quality, so it needs to be adjusted. , , The adjustment method is to add the adjustment coefficient h to the original weight value, and subtract the adjustment coefficient h from the corresponding other parameters, such as the preset parameters of NWa , after adjustment is equal to the original +h, then the default parameters of NAQa , after adjustment is equal to the original , then the preset parameters of NSWa , after adjustment is equal to the original ; The adjustment coefficient h is preset.

[0049] Embodiment 4: Based on any of the above embodiments, this embodiment further illustrates that the artificial intelligence model evaluates the park environment to obtain the environmental warning coefficient Specific steps: S31 obtains detection data: obtains the environmental detection parameters at each regional position i and the corresponding timestamp; specifically, a set of environmental detection parameters at different regional positions recorded at the same timestamp; is represented as follows: { "timestamp": "2024-07-15T10:30:00Z", "location_1": {"W i ": 1.2, "AQ i ": 1.5, "SW i ": 0.9}, "location_2": {"W i ": 0.8, "AQ i ": 1.1, "SW i ": 1.3}, ... }.

[0050] S32 obtains historical data: historical environmental data of each regional location i, including monitoring results for a certain period of time in the past; it is expressed as follows: timestamp, location, W i , AQ i , S.W. i 2024-01-01T00:00:00Z, location_1, 1.1, 1.2, 0.8 2024-01-01T01:00:00Z, location_1, 1.0, 1.3, 0.9 ....

[0051] S33 obtains the data of the environmental exceeding standard event: obtains the occurrence time of the environmental exceeding standard event in history and whether it exceeds the standard; it is expressed as follows: timestamp, location, W i _exceed, AQ i _exceed, SW i_exceed 2024-01-01T00:00:00Z, location_1, 1, 0, 0 Among them, if W i If it exceeds the standard, W i _exceed is recorded as 1, otherwise it is recorded as 0; AQ i , SW i The marking rules and W i same.

[0052] S34 data cleaning and preprocessing: fill in missing values, remove outliers and standardize data obtained from S31, S32 and S33; normalize environmental detection parameters to eliminate dimensional differences.

[0053] S35 obtains time series features: extracts the average value, maximum value and minimum value of environmental detection parameters within a certain time series length through the data processed by S34; for example, extracts the W i The average, maximum and minimum values ​​of the water quality parameter W are calculated. For example, for the water quality parameter W i The time series feature set is constructed as shape = (samples, time steps, features), where samples is the number of samples, such as 100, time steps is the length of the time series, that is, 24; features represents the number of features per unit; in this embodiment, features includes 5 features, specifically W i , 24 hours of W i The average, maximum and minimum values ​​of W for 24 hours i The excess ratio of water quality parameter W i Construct a time series feature set with shape = (100, 24, 5); preferably, you can also add 24 hours of W i The rate of change of is taken as a feature; AQ i With SW i Time series feature construction and W i The same is not repeated here.

[0054] S36 model training: Using the LSTM model, the acquired time series features are used as input, and the environmental warning coefficient As training output; environmental change parameters calculated by environmental detection parameters After normalization, the training parameters are obtained, which represent the real environmental warning coefficient. ; (1) Initialize model parameters: Randomly initialize the weights and bias parameters of the LSTM layer; (2) Forward propagation: input the training data into the model and calculate the predicted value of each sample ; (3) Calculating loss: comparing predicted values and the true value , the loss function value is calculated by the mean square error (MSE); (4) Back propagation: Calculate the update direction and amplitude of the model parameters based on the gradient of the loss function; (5) Parameter update: Use the optimization algorithm Adam to update the model parameters and reduce the loss function value; (6) Iterative training: Repeat the above steps until all training data have been traversed multiple times.

[0055] S37 Environmental Warning Factor :Input the newly acquired environmental detection parameters into the model to obtain the environmental warning coefficient ; If the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information; the warning information includes the feature information contained in the features in the time series features.

[0056] After receiving the warning information, the early warning module identifies the feature information contained in the features and extracts the GPS information contained in the environmental detection parameters, which can indicate which area may have environmental problems. At this time, an early warning signal is generated and sent to the park management terminal. The park management terminal is held by managers in different areas or by dedicated personnel in the park. After receiving the early warning signal, they go to the corresponding area for environmental inspection and further judge whether environmental problems may occur manually.

[0057] Embodiment 5: Based on any of the above embodiments, this embodiment further illustrates a data acquisition module, which includes sensors, specifically: a water quality sensor may be a COD sensor, a TP sensor, a TN sensor, a pH sensor, or a multi-parameter water quality sensor for collecting more data parameters, such as temperature, pH, ORP, conductivity, salinity, total dissolved solids, dissolved oxygen, turbidity, etc.; an air quality sensor may be a particulate matter sensor, a carbon monoxide sensor, a nitrogen oxide sensor, an ozone sensor, etc.; a pressure sensor is also included; the above sensors can be purchased directly from sensor manufacturers and will not be described in detail here.

[0058] The data acquisition module has two functions: One of its functions is to obtain data and save the collected data in a set format. The specific process is as follows: The above sensors are arranged in different industrial park areas respectively. Because each sensor has a unique device code, when the data acquisition module obtains data from a certain sensor, its device code is identified, and the database form is queried at the same time, and the device data is recorded in the corresponding data form position. The device code of the sensor in the form corresponds to the GPS coordinate data of the sensor one by one, so that the subsequent processing steps can unify the data, and when a certain data is abnormal, it can be located at a specific park location.

[0059] The second function is to calculate the environmental detection parameters at the regional location i, and calculate the water quality parameter W by obtaining the quality sampling value Pi, air quality parameter sampling value Px, and solid waste emission Es. i , Air quality parameters AQ i , solid waste parameters SW i , and complete the data recording of environmental detection parameters with the timestamp, which is used for data analysis of the terminal evaluation module and the cloud analysis module; the data acquisition module is responsible for the data collection and calculation of environmental detection parameters, reducing the occupation of cloud platform resources, with a fast response speed, and the processed data is more conducive to the later data analysis of the terminal evaluation module and the cloud platform.

[0060] Embodiment 6: Based on any of the above embodiments, this embodiment further illustrates a terminal evaluation module, which is used to store data from the data collection module offline and evaluate whether the park area environment is abnormal; The main functions of the terminal evaluation module are as follows: The first function is to receive data from the data acquisition module and store offline data; The second function is to process the received environmental detection parameters and calculate the cross contamination factor C: according to the formula: ; Obtain the cross-contamination factor C to evaluate the mutual impact between water, air and solid waste in the park; Then calculate the change rate factor V: According to the formula: ; , , is the environmental change sensitivity coefficient, which is used to amplify the impact of rapidly changing parameters on environmental coefficients; Reflect the changing trend of environmental detection parameters and capture the potential impact of dynamic changes in environmental indicators on the environment; when a parameter changes dramatically in a short period of time, such as a sudden deterioration in air quality, the problem can be quickly reflected and an early warning can be triggered; Finally, we get the environmental change parameters :Calculated according to the formula: ; against , , By setting specific values, we can adjust the sensitivity of different parks to different pollutants; The calculation can quickly assess the degree of change in the regional environment and provide a basis for data analysis in the cloud analysis module.

[0061] Embodiment 7: Based on any of the above embodiments, this embodiment further illustrates the cloud analysis module, which is used to store all data and evaluate the environmental conditions of the park based on the data storage. The environment of the park is generally in a normal state, and it is impossible to predict whether there will be problems in the future. Therefore, it is necessary to simulate and predict the probability of environmental problems through data, and obtain the environmental warning coefficient by training the artificial intelligence model. ; The main functions of the cloud analysis module are: Obtain environmental detection parameters and corresponding timestamps for all areas of the park; Train the LSTM model, use the acquired time series features as input, and use the environmental warning coefficient As training output; When the cloud analysis module performs comprehensive data analysis, the collection of environmental monitoring parameters of different regional locations is recorded at the same timestamp; the historical environmental data of each regional location i, including the monitoring results of a certain period of time in the past, constitutes a data set; the time of occurrence of environmental exceeding standard events in history and whether they exceed the standard are obtained, and if they exceed the standard, they are marked to count the number of exceeding standards; after data cleaning and preprocessing, the average value, maximum value and minimum value of environmental monitoring parameters within a certain time series length are extracted; for example, the W of 24 hours is extracted i The average, maximum and minimum values ​​of the data are obtained; the time information of the timestamp is extracted, such as year, month, day, hour, minute, etc.; then the excess ratio within a certain time series length is calculated, that is, the ratio of the number of excesses to the total number of samples within the time series length; the above information is set as a set as the time series feature; The time series feature set is of shape = (samples, time steps, features), where samples is the number of samples, time steps is the length of the time series; features indicates the number of features per unit; when performing data analysis, the cloud analysis module links and shares data with the data acquisition module, terminal evaluation module, early warning module, alarm module, and visualization module to break through data silos.

[0062] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients or thresholds in the formula are set by technical personnel in this field according to actual conditions.

[0063] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0064] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. An industrial park environment assessment system based on a cloud platform, characterized in that: include: Data acquisition module, terminal evaluation module, cloud analysis module, early warning module, alarm module, visualization module; The data acquisition module collects the environmental quantitative index data in the regional location i of the industrial park, and calculates the environmental detection parameters at the regional location i, including the water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i ; The terminal evaluation module is used to evaluate the environmental change parameters at the area position i according to the environmental detection parameters. ; If the environmental change parameters If the threshold is exceeded, the terminal evaluation module generates an alarm signal and sends the alarm signal to the alarm module; if the environmental change parameter If the threshold is not exceeded, the terminal evaluation module generates an analysis signal and sends the analysis signal to the cloud analysis module; The alarm module receives the alarm signal and obtains the environmental detection parameters at the area position i; the environmental detection parameters that exceed the standard are marked by calculation, and then displayed by the visualization module; the alarm module counts the number of times the environmental detection parameters exceed the standard per unit time, performs normalization processing, and sends it to the cloud analysis module for dynamic adjustment of environmental change parameters ; The cloud analysis module is used to summarize the environmental detection parameters at different locations in the park, and evaluate the park environment through an artificial intelligence model based on the collected historical regional environmental data to obtain the environmental warning coefficient , if the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information and sends it to the visualization module for display; if the environmental warning coefficient If the threshold is not exceeded, the cloud analysis module sends the environmental detection parameters of different locations in the park to the visualization module; The warning module receives the warning information and identifies the location information in the warning information and the environmental detection parameters corresponding to the location; Then, an early warning signal is generated and sent to the park management terminal; The visualization module is used to perform visualization processing on the received data.

2. According to the cloud platform-based industrial park environment assessment system of claim 1, it is characterized in that: The specific steps of the data acquisition module obtaining the environmental detection parameters at the regional position i are as follows: S11 Regional division: The industrial park is divided into several regional locations. Each regional location i is equipped with a sensor and each sensor has a unique device identifier, including water quality sensors, air quality sensors, and pressure sensors. The sensors in regional location i are matched with the GPS coordinates of region i. S12 obtains data: obtains the water quality sampling value Pi, air quality parameter sampling value Px, and solid waste discharge Es at the regional location i; S13 Calculate water quality parameter W i : Water quality parameter W i It is calculated by taking the weighted average of each water quality sampling value Pi. ; Among them, u represents the number of water quality sampling values ​​Pi, wi is the weight of water quality sampling value Pi, which needs to satisfy ; Si represents the standard limit of water quality; S14 calculates air quality parameters AQ i : Calculate the index Ix of air pollutants respectively, where Ix is equal to the ratio of the air quality reference sampling value Px to the standard limit value Sx of air pollutants; compare the index Ix of each air pollutant and take the maximum value, i.e. AQ i =max(ix); S15 Calculation of solid waste parameters SW i :Get the solid waste emission Es at the location i in the area, get the solid waste classification factor Fs according to the solid waste classification, and get the solid waste recycling rate Rs of the park according to the historical data of solid waste recycling, The solid waste duration factor Ls is obtained based on the solid waste storage time, according to the formula: ； Get the solid waste parameters SWi, where the treatment efficiency The value range is 0~1; z1, z2, z3, z4 are all preset proportional coefficients, and z1+z2+z3+z4=1; Ss represents the solid waste standard emission limit; S16 Get timestamp: Get water quality parameter W i , Air quality parameters AQ i , solid waste parameters SW i The time when the data is collected is used to obtain the timestamp of the corresponding data; S17 data storage: the data obtained from S11 to S16 are sent to the terminal evaluation module and the cloud analysis module through the network respectively for the evaluation of the industrial park environment.

3. According to the cloud platform-based industrial park environment assessment system of claim 2, it is characterized in that: The calculation of solid waste parameter SW i The calculation method of solid waste recycling rate Rs is as follows: obtain the actual recycling amount and total solid waste amount of multiple historical nodes, calculate the ratio of the actual recycling amount to the total solid waste amount at different historical nodes, and then take the average as the current Rs value.

4. According to claim 2, the industrial park environment assessment system based on a cloud platform is characterized in that: The calculation of solid waste parameter SW i The calculation method of the solid waste time factor Ls is as follows: obtain the solid waste detection time and solid waste emission Es. If the solid waste emission Es is greater than the preset minimum solid waste emission Emin, the accumulated solid waste storage time is recorded as the retention time Ta, and the solid waste time factor Ls is the ratio of the retention time Ta to the prescribed processing time limit Sa; if the solid waste emission Es is less than the preset minimum solid waste emission Emin, the retention time is recalculated; the solid waste storage time is the difference between the latter detection time and the previous detection time.

5. According to claim 2, the industrial park environment assessment system based on a cloud platform is characterized in that: The terminal evaluation module evaluates the environmental change parameters at the regional location i The specific process is as follows: S21: Obtaining data: The terminal evaluation module receives the environmental detection parameters and corresponding timestamps sent by the data acquisition module; S22 calculates the cross contamination factor C: Calculate according to the formula: ； , , is the coefficient of cross contamination degree, , , Both are greater than or equal to 0 and less than or equal to 1; S23 calculates the change rate factor V: Calculate according to the formula: ； , , is the environmental change sensitivity coefficient; S24 Calculate environmental change parameters : Calculated according to the formula: ； in, , , , , is the preset weight of the environmental change parameter, and ; in , , They represent water quality sensitivity coefficient, air sensitivity coefficient and solid waste sensitivity coefficient respectively.

6. The cloud platform-based industrial park environment assessment system according to claim 5 is characterized in that: The alarm module counts the number of times the environmental detection parameters exceed the standard within a unit time, and counts the water quality parameters W i , Air quality parameters AQ i , solid waste parameters SW i The number of times the standard is exceeded is recorded as NW, NAQ, and NSW respectively; Then it is normalized and sent to the cloud analysis module for dynamic adjustment of environmental change parameters. ; The normalization formula is: NWa=NW / Ntotal,NAQa=NAQ / Ntotal,NSWa=NSW / Ntotal; Among them, Ntotal=NW+NAQ+NSW.

7. The cloud platform-based industrial park environment assessment system according to claim 2 is characterized in that: The artificial intelligence model evaluates the park environment to obtain the environmental warning coefficient The specific steps are as follows: S31 obtains detection data: obtains environmental detection parameters and corresponding timestamps at each area position i; S32 obtains historical data: historical environmental data of each regional location i, including monitoring results over a certain period of time in the past; S33 obtains the data of the environmental exceeding standard event: obtains the occurrence time of the environmental exceeding standard event in history and whether it exceeds the standard; S34 data cleaning and preprocessing: fill missing values, remove outliers and standardize data obtained from S31, S32 and S33; normalize environmental detection parameters to eliminate dimensional differences; S35 obtains time series features: extracts the average value, maximum value and minimum value of the environmental detection parameters within a certain time series length through the data processed by S34; extracts time information; then calculates the excess ratio within a certain time series length, and sets the above information as a set as a time series feature; S36 model training: Using the LSTM model, the acquired time series features are used as input, and the environmental warning coefficient As training output; S37 Environmental Warning Factor :Input the newly acquired environmental detection parameters into the model to obtain the environmental warning coefficient ; If the environmental warning coefficient If the threshold is exceeded, the cloud analysis module generates warning information; the warning information includes feature information in the time series features.

8. The cloud platform-based industrial park environment assessment system according to claim 7 is characterized in that: The training steps of the LSTM model are as follows: Environmental change parameters calculated from environmental detection parameters After normalization, the training parameters are obtained, which represent the real environmental warning coefficient. ; (1) Initialize model parameters: Randomly initialize the weights and bias parameters of the LSTM layer; (2) Forward propagation: input the training data into the model and calculate the predicted value of each sample ; (3) Calculating loss: comparing predicted values and the true value , the loss function value is calculated by the mean square error MSE; (4) Back propagation: Calculate the update direction and amplitude of the model parameters based on the gradient of the loss function; (5) Parameter update: Use the optimization algorithm Adam to update the model parameters and reduce the loss function value; (6) Iterative training: Repeat the above steps until all training data have been traversed multiple times.

9. The industrial park environment assessment system based on a cloud platform according to claim 1 is characterized in that: The cloud analysis module is connected to the data acquisition module, the terminal evaluation module, the early warning module, the alarm module and the visualization module respectively through the network.

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