Accident emergency response and environmental impact evaluation method based on environmental protection housekeeper

Abstract

The invention relates to the field of environmental protection prediction, and provides an accident emergency response and environmental influence evaluation method based on an environmental protection housekeeper, and the method comprises the steps: obtaining the historical data of industrial equipment based on artificial intelligence, and building a pollutant discharge prediction model and an emergency scheme prediction model; establishing a database based on the pollutant emission prediction model and the emergency plan prediction model, wherein the database comprises industrial equipment information and a time continuous model group corresponding to industrial equipment; real-time environment information of the industrial equipment is monitored in real time based on the Internet of Things, the real-time environment information comprises real-time environment influence factor parameters and real-time pollutant emission, the industrial equipment is monitored in real time based on the real-time environment information and a database, real-time comparison nodes are obtained, and a corresponding prediction emergency scheme is implemented. When the system is used, scientific evaluation and quick response of the industrial environment can be realized, and powerful support is provided for environmental influence evaluation and accident emergency response service of environmental protection households.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection prediction, and more specifically, to an accident emergency response and environmental impact assessment method based on an environmental steward. Background Art

[0002] In order to cope with complex environmental issues and challenges, enterprises need professional environmental stewards to provide a full range of environmental services. At present, environmental stewards in chemical industrial parks generally use manual prediction methods in accident emergency response and environmental impact assessment. However, manual prediction methods often have limitations in thinking. It is difficult to predict environmental impact factors with very small probabilities. When a small probability accident occurs, manual emergency response will lead to low efficiency of accident emergency response, resulting in a "lock the fold after the horse has bolted" effect, and failing to prepare for a rainy day. It poses a great threat to the safety of personnel in the industrial park and even the earth's environment. Summary of the invention

[0003] The purpose of the present invention is to provide an accident emergency response and environmental impact assessment method based on environmental steward to solve the shortcomings of the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: an accident emergency response and environmental impact assessment method based on environmental steward, comprising the following steps: Based on artificial intelligence, historical data of industrial equipment is obtained to establish pollutant emission prediction models and emergency plan prediction models; Establishing a database based on the pollutant emission prediction model and the emergency plan prediction model, wherein the database includes industrial equipment information and a time-continuous model group of corresponding industrial equipment; Based on the Internet of Things, real-time environmental information of industrial equipment is monitored in real time. The real-time environmental information includes real-time environmental impact factor parameters and real-time pollutant emissions. Based on the real-time environmental information and the database, real-time monitoring of industrial equipment is performed to obtain real-time comparison nodes and implement corresponding prediction and emergency plans.

[0005] In a preferred embodiment, the step of acquiring historical data of industrial equipment based on artificial intelligence to establish a pollutant emission prediction model and an emergency plan prediction model includes: Obtaining historical operation data of industrial equipment, wherein the historical operation data includes historical industrial equipment numbers, corresponding historical industrial equipment parameters, corresponding historical environmental impact factors, and corresponding historical pollutant emissions, and inputting the historical operation data as parameters into a deep learning model for training to obtain a pollutant emission prediction model; The historical plan data of the industrial equipment is obtained, wherein the historical plan data includes historical emergency plans, corresponding historical pollutant numbers and corresponding historical pollutant emissions, and the historical operation data is input as a parameter into the deep learning model for training to obtain an emergency plan prediction model.

[0006] In a preferred embodiment, the step of establishing a database based on the pollutant emission prediction model and the emergency plan prediction model includes: Based on the pollutant emission prediction model, the simulated industrial equipment information range is obtained, and the simulated industrial equipment information range includes the industrial equipment number and the simulated industrial equipment parameter range, wherein the simulated industrial equipment parameter range includes the simulated process parameter range and the simulated raw material parameter range, the simulated process parameter range includes the simulated processing speed range and the simulated transmission speed range, and the simulated raw material parameter range includes the simulated raw material name range, the simulated processing raw material amount range, the simulated raw material concentration range, the simulated pollutant number range, the simulated pollutant name range and the simulated pollutant control equipment efficiency parameter range; Based on the simulated industrial equipment information range, pollutant emission prediction model and emergency plan prediction model, a time-continuous model group of industrial equipment is established, and the time-continuous model group is bound to the corresponding industrial equipment number and stored in the database.

[0007] In a preferred embodiment, the step of establishing a time-continuous model group of industrial equipment based on the simulated industrial equipment information range, the pollutant emission prediction model and the emergency plan prediction model includes: Acquire all situations within the simulated industrial equipment information range as a simulated industrial equipment parameter group, acquire all situations of simulated environmental impact factor parameters within a preset range as a simulated environmental impact factor parameter group, wherein the simulated environmental impact factor parameters include temperature parameters, humidity parameters, and air pressure parameters, and pair and combine the elements in the simulated industrial equipment parameter group with the elements in the simulated environmental impact factor group one by one to obtain an initial condition group; Based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model, a linear model group with the same initial condition is obtained, and multiple linear model groups with the same initial condition are repeatedly obtained until the initial conditions of the initial condition group are all in the selected state to obtain multiple linear model groups with different initial conditions as the time continuous model group.

[0008] In a preferred embodiment, the step of obtaining a linear model group with the same initial conditions based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model comprises: Based on the initial condition group, randomly select an initial condition of an unselected state as the target condition and mark it as the starting point of the monitoring time, set the state of the target condition corresponding to the initial condition to the selected state, and input the target condition and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information, wherein the prediction information includes a predicted pollutant number and a corresponding predicted pollutant emission amount; If the predicted pollutant emissions are within the environmental evaluation threshold range, a simulated environmental impact factor in the simulated environmental impact factor group is randomly selected as a simulation condition, and the corresponding moment on the simulation condition mark is used as a monitoring time node according to the preset monitoring time period. The simulation condition and the preset monitoring time period are input as parameters into the pollutant emission prediction model to obtain prediction information, until the predicted pollutant emissions exceed the environmental evaluation threshold range to obtain multiple monitoring time nodes, the prediction information is bound to the corresponding monitoring time nodes, the last monitoring time node is marked as an abnormal time node, and the monitoring time node before the abnormal time node is marked as a prevention time node. Based on the emergency plan prediction model, the predicted emergency plan for the prevention time node and the abnormal time node is obtained, the corresponding predicted emergency plan is bound to the corresponding monitoring time node, and multiple monitoring time nodes are combined in time order to obtain a linear model. This step is repeated within the expected number of times to obtain multiple linear models with the same initial conditions as a linear model group with the same initial conditions.

[0009] In a preferred embodiment, the step of inputting the target initial conditions and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information includes: Based on the pollutant emission prediction model, the predicted pollutant names generated by industrial equipment are obtained according to the target initial conditions, the predicted pollutant numbers are determined according to the predicted pollutant names, the benchmark standards and prediction standards of the simulated environmental impact factors are obtained, and the environmental impact ratio is obtained according to the environmental impact ratio calculation formula, where the environmental impact ratio calculation formula is: ,in, is the environmental impact ratio, To simulate the benchmark standard of environmental impact factors, To simulate the prediction criteria of environmental impact factors, To simulate environmental impact factors, is the number of simulated environmental impact factors; The predicted pollutant emissions are calculated based on the environmental impact ratio according to the predicted pollutant emissions formula, where the predicted pollutant emissions formula is: ,in, To predict pollutant emissions, As the basic emission, is the process parameter, is the raw material parameter, is the environmental impact ratio, is the pollutant control equipment efficiency parameter, It is the preset monitoring time period; The predicted pollutant emissions are bound to the predicted pollutant numbers to obtain the predicted information.

[0010] In a preferred embodiment, the step of monitoring industrial equipment in real time based on real-time environmental information and database to obtain real-time comparison nodes and implement corresponding prediction emergency plans includes: Using sensors of industrial equipment to obtain initial temperature parameters, initial humidity parameters and initial air pressure parameters of industrial equipment in real time as initial environmental influencing factor parameters, and determine real-time industrial equipment parameters; The real-time industrial equipment parameters and the initial environmental impact factor parameters are used as initial comparison conditions, and a linear model group with the same initial conditions is obtained in the database based on the initial comparison conditions; Based on the linear model group with the same initial conditions, real-time monitoring of industrial equipment is carried out to obtain the corresponding prediction emergency plan and implement it.

[0011] In a preferred embodiment, the step of performing real-time monitoring on industrial equipment based on the linear model group with the same initial condition to obtain the corresponding prediction emergency plan and implement it includes: Based on the linear model group with the same initial conditions, a real-time environmental impact factor parameter and real-time pollutant emission are obtained every preset monitoring time period as a real-time comparison node; Based on the real-time comparison node and the database, the corresponding linear model is obtained. Based on the linear model, it is determined whether the monitoring time node corresponding to the real-time comparison node is a prevention time node or an abnormal time node. If so, the corresponding prediction emergency plan is obtained and implemented.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention can realize full-process monitoring and prediction of industrial equipment by simulating the operating environment of industrial equipment to establish a time-continuous model group, which has a good auxiliary effect on environmental impact assessment. It can accurately predict whether environmental impact factors will have an impact on the environment when artificial prediction is impossible, and can accurately give corresponding emergency plans, which can improve the efficiency of accident emergency response, realize scientific assessment and rapid response of industrial environment, and provide strong support for environmental stewards' environmental impact assessment and accident emergency response services. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0016] Example 1, please refer to Figure 1 As shown, the accident emergency response and environmental impact assessment method based on the environmental steward described in this embodiment includes the following steps: S1. Based on artificial intelligence, historical data of industrial equipment is obtained to establish a pollutant emission prediction model and an emergency plan prediction model; S2. Establishing a database based on the pollutant emission prediction model and the emergency plan prediction model, wherein the database includes industrial equipment information and a time-continuous model group of corresponding industrial equipment; S3. Real-time monitoring of the real-time environmental information of industrial equipment based on the Internet of Things, the real-time environmental information includes real-time environmental impact factor parameters and real-time pollutant emissions, real-time monitoring of industrial equipment based on real-time environmental information and database to obtain real-time comparison nodes and implement corresponding prediction emergency plans; As described in the above steps S1-S3, in order to cope with complex environmental problems and challenges, enterprises need professional environmental stewards to provide comprehensive environmental services. At present, environmental stewards in chemical industrial parks generally use manual prediction methods for accident emergency response and environmental impact assessment, and manual prediction methods often have limitations in thinking. For environmental impact factors with very small probabilities, it is difficult to predict them manually. When a small probability accident occurs, manual emergency response to the accident will lead to low efficiency of the accident emergency response, resulting in the effect of mending the fold after the sheep have been lost, and failing to achieve the role of preparing for a rainy day. It poses a great threat to the safety of personnel in the industrial park and even the earth's environment. The present application simulates the operating environment of industrial equipment to establish a time-continuous model group to achieve full-process monitoring and prediction of industrial equipment, which has a good auxiliary effect on environmental impact assessment. In the event of manual prediction, it can accurately predict whether the environmental impact factor will have an impact on the environment, and can accurately give the corresponding emergency plan, which can improve the efficiency of accident emergency response, realize scientific assessment and rapid response of the industrial environment, and provide strong support for the environmental impact assessment and accident emergency response services of environmental stewards; In one embodiment, the step S1 of acquiring historical data of industrial equipment based on artificial intelligence to establish a pollutant emission prediction model and an emergency plan prediction model includes: S11, obtaining historical operation data of industrial equipment, wherein the historical operation data includes historical industrial equipment numbers, corresponding historical industrial equipment parameters, corresponding historical environmental impact factors, and corresponding historical pollutant emissions, and inputting the historical operation data as parameters into a deep learning model for training to obtain a pollutant emission prediction model; S12, obtaining historical plan data of industrial equipment, wherein the historical plan data includes historical emergency plans, corresponding historical pollutant numbers and corresponding historical pollutant emissions, and inputting the historical operation data as parameters into a deep learning model for training to obtain an emergency plan prediction model; As described in the above steps S11-S12, firstly, the historical industrial equipment number, the corresponding historical industrial equipment parameters, the corresponding historical environmental impact factors and the corresponding historical pollutant emissions of the industrial equipment are collected as historical operation data. The historical industrial equipment number here is used to uniquely identify a piece of equipment. The historical industrial equipment parameters here refer to the historical process parameters and the historical raw material parameters. The historical process parameters here refer to the processing speed and transmission speed when the industrial equipment is running. The historical raw material parameters here refer to the historical raw material name, the historical processing raw material quantity, the historical processing raw material concentration, the historical pollutant number, the historical pollutant name and the historical pollutant control equipment efficiency parameter. The historical environmental impact factor here refers to the historical temperature, historical humidity and historical air pressure. Then, the historical operation data is input as a parameter into the deep learning model for training to obtain a pollutant emission prediction model. Then, the historical emergency plan, the corresponding historical pollutant number and the corresponding historical pollutant emission are collected as historical plan data. The historical plan data here refers to the solution data manually adopted when the historical event occurs. Then, the historical operation data is input as a parameter into the deep learning model for training to obtain an emergency plan prediction model, which is used to analyze the historical data to obtain the prediction model, which has a good auxiliary effect on the environmental impact assessment. In one embodiment, the step S2 of establishing a database based on the pollutant emission prediction model and the emergency plan prediction model includes: S21. Based on the pollutant emission prediction model, the simulated industrial equipment information range is obtained, wherein the simulated industrial equipment information range includes the industrial equipment number and the simulated industrial equipment parameter range, wherein the simulated industrial equipment parameter range includes the simulated process parameter range and the simulated raw material parameter range, wherein the simulated process parameter range includes the simulated processing speed range and the simulated transmission speed range, and the simulated raw material parameter range includes the simulated raw material name range, the simulated processing raw material amount range, the simulated raw material concentration range, the simulated pollutant number range, the simulated pollutant name range and the simulated pollutant control equipment efficiency parameter range; S22, establishing a time-continuous model group of industrial equipment based on the simulated industrial equipment information range, the pollutant emission prediction model and the emergency plan prediction model, and binding the time-continuous model group with the corresponding industrial equipment number and storing it in a database; As described in the above steps S21-S22, firstly, according to the historical operation data of the pollutant emission prediction model, the industrial equipment number and the simulated industrial equipment parameter range are collected as the simulated industrial equipment information range, where the industrial equipment number is used to uniquely identify an industrial equipment, and the simulated industrial equipment parameter range includes a simulated process parameter range and a simulated raw material parameter range, wherein the simulated process parameter range includes a simulated processing speed range and a simulated transmission speed range, and the simulated raw material parameter range includes a simulated raw material name range, a simulated processing raw material amount range, a simulated raw material concentration range, a simulated pollutant number range, a simulated pollutant name range and a simulated pollutant control equipment efficiency parameter range, which are used to determine the value range of various parameters in the industrial equipment parameters. The scope plays an auxiliary role in the subsequent establishment of the time-continuous model group. Then, the time-continuous model group of industrial equipment is established according to the simulated industrial equipment information range, the pollutant emission prediction model and the emergency plan prediction model. The time-continuous model group here is composed of multiple linear model groups with different initial conditions, and the linear model groups with different initial conditions are composed of multiple linear model groups with the same initial conditions. The linear model group with the same initial conditions is composed of multiple linear models with the same initial conditions. It is used to predict various abnormal events caused by industrial equipment under different environmental conditions and give corresponding emergency plans. The time-continuous model group is bound to the corresponding industrial equipment number and stored in the database, which can provide strong support for the environmental impact assessment and accident emergency response services of environmental stewards. In one embodiment, the step S22 of establishing a time-continuous model group of industrial equipment based on the simulated industrial equipment information range, the pollutant emission prediction model and the emergency plan prediction model includes: S221, obtaining all situations within the range of simulated industrial equipment information as a simulated industrial equipment parameter group, obtaining all situations of simulated environmental impact factor parameters within a preset range as a simulated environmental impact factor parameter group, wherein the simulated environmental impact factor parameters include temperature parameters, humidity parameters, and air pressure parameters, and pairing and combining the elements in the simulated industrial equipment parameter group with the elements in the simulated environmental impact factor group one by one to obtain an initial condition group; S222, based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model, a linear model group with the same initial condition is obtained, and multiple linear model groups with the same initial condition are repeatedly obtained until the initial conditions of the initial condition group are all in the selected state to obtain multiple linear model groups with different initial conditions as the time continuous model group; As described in the above steps S221-S222, firstly, all situations of the simulated industrial equipment parameters are collected through the simulated industrial equipment information range and combined as a simulated industrial equipment parameter group. For example, a chemical equipment number in the simulated industrial equipment information range is 0001, the simulated processing speed range of this equipment is 20L / h-100L / h, the simulated transmission speed range is 0.8L / s-1.5L / s, the raw material name range of this equipment is sodium hydroxide, hydrochloric acid, sulfuric acid, carbon dioxide, etc., and the raw material quantity range is 10kg of sodium hydroxide, 10kg of hydrochloric acid, Sulfuric acid 10kg, etc., the raw material concentration range is 90% sodium hydroxide, 90% hydrochloric acid, 60% sulfuric acid, 100% carbon dioxide, etc., the simulated pollutant name range is benzene, toluene, xylene, sulfur dioxide, etc., the raw material name range here is the data stored in the database in advance manually, the pollutant name range here is the data stored in the database in advance corresponding to the raw material name range, the corresponding simulated pollutant number range is W001, W0002, W003, W0004, etc., the simulated pollutant control equipment efficiency parameter range is 20%-30%, the model All parameters within the simulated industrial equipment parameter range are arranged and combined to obtain all situations as the industrial equipment parameter group, and all situations of the simulated environmental impact factor parameters within the preset range are collected as the simulated environmental impact factor parameter group, wherein the simulated environmental impact factor parameters include temperature parameters, humidity parameters and air pressure parameters, and then the elements in the simulated industrial equipment parameter group are paired and combined with the elements in the simulated environmental impact factor group one by one to obtain the initial condition group, which is used to cover all possibilities of the operating environment of the industrial equipment, and can better utilize the prediction model to make the subsequent time-continuous model group comprehensive and adaptable. Finally, based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model, a linear model group with the same initial condition is obtained, wherein the linear model group with the same initial condition is composed of multiple linear models with the same initial conditions, and multiple linear model groups with the same initial condition are repeatedly obtained until the initial conditions of the initial condition group are all in the selected state to obtain multiple linear model groups with different initial conditions as the time-continuous model group, which is used to simulate the operating environment of the industrial equipment to establish a time-continuous model group to realize the full-process monitoring and prediction of the industrial equipment, and has a good auxiliary effect on the environmental impact assessment; In one embodiment, the step S222 of obtaining a linear model group with the same initial conditions based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model includes: S2221. Based on the initial condition group, randomly select an initial condition that is not in the selected state as the target condition and mark it as the starting point of the monitoring time, set the state of the target condition corresponding to the initial condition to the selected state, and input the target condition and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information, wherein the prediction information includes the predicted pollutant number and the corresponding predicted pollutant emission amount; S2222. If the predicted pollutant emission is within the environmental evaluation threshold, a simulated environmental impact factor in the simulated environmental impact factor group is randomly selected as a simulation condition, and the corresponding moment on the simulation condition mark is used as a monitoring time node according to the preset monitoring time period. The simulation condition and the preset monitoring time period are input as parameters into the pollutant emission prediction model to obtain prediction information, until the predicted pollutant emission exceeds the environmental evaluation threshold to obtain multiple monitoring time nodes, the prediction information is bound to the corresponding monitoring time nodes, the last monitoring time node is marked as an abnormal time node, and the monitoring time node before the abnormal time node is marked as a prevention time node. Based on the emergency plan prediction model, a predicted emergency plan for the prevention time node and the abnormal time node is obtained, and the corresponding predicted emergency plan is bound to the corresponding monitoring time node. Multiple monitoring time nodes are combined in time order to obtain a linear model, and this step is repeated within the expected number of times to obtain multiple linear models with the same initial conditions as a linear model group with the same initial conditions; In one embodiment, the step S2221 of inputting the target initial conditions and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information includes: S22211. Based on the pollutant emission prediction model, obtain the predicted pollutant names generated by industrial equipment according to the target initial conditions, determine the predicted pollutant numbers according to the predicted pollutant names, obtain the benchmark standards and prediction standards of the simulated environmental impact factors, and obtain the environmental impact ratio according to the environmental impact ratio calculation formula, where the environmental impact ratio calculation formula is: ,in, is the environmental impact ratio, To simulate the benchmark standard of environmental impact factors, To simulate the prediction criteria of environmental impact factors, To simulate environmental impact factors, is the number of simulated environmental impact factors; S22212. Calculate the predicted pollutant emissions based on the environmental impact ratio according to the predicted pollutant emissions formula, where the predicted pollutant emissions formula is: ,in, To predict pollutant emissions, As the basic emission, is the process parameter, is the raw material parameter, is the environmental impact ratio, is the pollutant control equipment efficiency parameter, It is the preset monitoring time period; S22213. Binding the predicted pollutant emission amount and the predicted pollutant number to obtain prediction information; As described in the above steps S2221-S2222, first, randomly select an initial condition in the initial condition group that is not in the selected state as the target condition and mark it as the starting point of the monitoring time, set the state of the target condition corresponding to the initial condition to the selected state, and then input the target condition and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information, wherein the prediction information includes the predicted pollutant number and the corresponding predicted pollutant emission, which is used to determine whether the predicted pollutant emission in the current time period is within the environmental evaluation threshold range. The environmental evaluation threshold range here refers to the range in which the predicted pollutant emission will not have an impact on the environment. If the predicted pollutant emission is within the environmental evaluation threshold range, then the simulated environmental impact is randomly selected. A simulated environmental impact factor in the factor group is used as a simulation condition, and then the simulation condition and the preset monitoring time period are input as parameters into the pollutant emission prediction model to obtain prediction information. The algorithm for obtaining the prediction information here is to obtain the predicted pollutant name generated by the industrial equipment according to the target initial conditions based on the pollutant emission prediction model, determine the predicted pollutant number according to the predicted pollutant name, and obtain the benchmark standard and prediction standard of the simulated environmental impact factor. The benchmark standard here refers to the ideal environmental impact factor parameter that can stably control the pollutant emission. The prediction standard here refers to the predicted environmental impact factor parameter, and then the environmental impact ratio is obtained according to the environmental impact ratio calculation formula, where the environmental impact ratio calculation formula is: ,in, is the environmental impact ratio, To simulate the benchmark standard of environmental impact factors, To simulate the prediction criteria of environmental impact factors, To simulate environmental impact factors, To simulate the number of environmental impact factors, for example, the baseline standard of environmental impact factors is 25℃, 20%RH, 101.325kPa, and the current prediction standard is 28℃, 25%RH, 105.325kPa, then the environmental impact ratio is , based on the environmental impact ratio, the predicted pollutant emissions are calculated according to the predicted pollutant emissions formula, where the predicted pollutant emissions formula is: ,in, To predict pollutant emissions, As the basic emission, is the process parameter, is the raw material parameter, is the environmental impact ratio, is the pollutant control equipment efficiency parameter, The basic emission here refers to the remaining pollutant emission in the previous time period, and then the predicted pollutant emission is calculated. For example, the remaining pollutant emission in the previous time period is 20kg of sulfur dioxide and 10kg of hydrogen sulfide. The industrial parameters are processing speed 20kg / h, transmission speed 2.88kg / h, raw material parameters are oil, 2t, 20%, natural gas, 1t, 10%, and the pollutant control equipment efficiency parameter is 20%. The preset monitoring time period is 1h, then the predicted pollutant emission is , the predicted sulfur dioxide emissions are 23.52kg, , the predicted hydrogen sulfide emission is 15.86kg, the predicted pollutant emission is bound to the predicted pollutant number to obtain the prediction information, until the predicted pollutant emission exceeds the environmental assessment threshold range to obtain multiple monitoring time nodes, the last monitoring time node is marked as an abnormal time node, the monitoring time node before the abnormal time node is marked as a prevention time node, based on the emergency plan prediction model to obtain the predicted emergency plan of the prevention time node and the abnormal time node, the corresponding predicted emergency plan is bound to the corresponding monitoring time node, and the multiple monitoring time nodes are combined according to the time sequence to obtain a linear model, and this step is repeated within the expected number of times to obtain multiple linear models with the same initial conditions as the linear model group with the same initial conditions, which can accurately predict whether the environmental impact factor will have an impact on the environment when manual prediction is not possible, and can accurately give the corresponding emergency plan; In one embodiment, the step S3 of real-time monitoring of industrial equipment based on real-time environmental information and database to obtain real-time comparison nodes and implement corresponding prediction emergency plans includes: S31, using sensors of industrial equipment to obtain in real time initial temperature parameters, initial humidity parameters, and initial air pressure parameters of the industrial equipment as initial environmental impact factor parameters, and determining real-time industrial equipment parameters; S32, using the real-time industrial equipment parameters and the initial environmental impact factor parameters as initial comparison conditions, and obtaining a linear model group with the same initial conditions in a database based on the initial comparison conditions; S33, based on the linear model group with the same initial conditions, real-time monitoring of industrial equipment is performed to obtain corresponding prediction emergency plans and implement them; In one embodiment, the step S33 of performing real-time monitoring on industrial equipment based on the linear model group with the same initial condition to obtain the corresponding prediction emergency plan and implement it includes: S331, based on the linear model group with the same initial conditions, obtaining a real-time environmental impact factor parameter and a real-time pollutant emission as a real-time comparison node every preset monitoring time period; S332, based on the real-time comparison node and the database, a corresponding linear model is obtained, and based on the linear model, whether the monitoring time node corresponding to the real-time comparison node is a prevention time node or an abnormal time node is determined, and if so, a corresponding prediction emergency plan is obtained and implemented; As described in the above steps S31-33, first, the initial temperature parameters, initial humidity parameters and initial air pressure parameters of the industrial equipment are obtained in real time using the sensors of the industrial equipment as the initial environmental impact factor parameters, and the real-time industrial equipment parameters are determined. The real-time industrial equipment parameters and the initial environmental impact factor parameters are used as initial comparison conditions. Based on the initial comparison conditions, a linear model group with the same initial conditions is obtained in the database. Based on the linear model group with the same initial conditions, a real-time environmental impact factor parameter and a real-time pollutant emission are obtained every preset monitoring time period as a real-time comparison node. Based on the real-time comparison node and the database, a corresponding linear model is obtained. If the monitoring time node corresponding to the real-time comparison node is a prevention time node or an abnormal time node, a corresponding linear model is obtained on the corresponding linear model. The corresponding prediction emergency plan is implemented. If the real-time comparison node cannot find the corresponding linear model in the database, multiple real-time comparison nodes are combined into a real-time learning model in chronological order, and all nodes in the real-time learning model are marked as learning nodes. The learning model is input into the pollutant emission prediction model and the emergency plan prediction model to establish a corresponding linear model, and a linear model within the preset range of each parameter in several learning nodes is established. Finally, the established linear model is stored in the database to obtain the actual operating environment of industrial equipment and find the corresponding prediction model in the database. It can improve the efficiency of accident emergency response, realize scientific evaluation and rapid response of industrial environment, and provide strong support for environmental protection stewards' environmental impact assessment and accident emergency response services.

[0017] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An accident emergency response and environmental impact assessment method based on environmental steward, characterized in that: The following steps are involved: Based on artificial intelligence, historical data of industrial equipment is obtained to establish pollutant emission prediction models and emergency plan prediction models; Establishing a database based on the pollutant emission prediction model and the emergency plan prediction model, wherein the database includes industrial equipment information and a time-continuous model group of corresponding industrial equipment; Based on the Internet of Things, real-time environmental information of industrial equipment is monitored in real time. The real-time environmental information includes real-time environmental impact factor parameters and real-time pollutant emissions. Based on the real-time environmental information and the database, real-time monitoring of industrial equipment is performed to obtain real-time comparison nodes and implement corresponding prediction and emergency plans.

2. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 1, characterized in that: The steps of acquiring historical data of industrial equipment based on artificial intelligence to establish a pollutant emission prediction model and an emergency plan prediction model include: Obtaining historical operation data of industrial equipment, wherein the historical operation data includes historical industrial equipment numbers, corresponding historical industrial equipment parameters, corresponding historical environmental impact factors, and corresponding historical pollutant emissions, and inputting the historical operation data as parameters into a deep learning model for training to obtain a pollutant emission prediction model; The historical plan data of the industrial equipment is obtained, wherein the historical plan data includes historical emergency plans, corresponding historical pollutant numbers and corresponding historical pollutant emissions, and the historical operation data is input as a parameter into the deep learning model for training to obtain an emergency plan prediction model.

3. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 1, characterized in that: The step of establishing a database based on the pollutant emission prediction model and the emergency plan prediction model includes: Based on the pollutant emission prediction model, the simulated industrial equipment information range is obtained, and the simulated industrial equipment information range includes the industrial equipment number and the simulated industrial equipment parameter range, wherein the simulated industrial equipment parameter range includes the simulated process parameter range and the simulated raw material parameter range, the simulated process parameter range includes the simulated processing speed range and the simulated transmission speed range, and the simulated raw material parameter range includes the simulated raw material name range, the simulated processing raw material amount range, the simulated raw material concentration range, the simulated pollutant number range, the simulated pollutant name range and the simulated pollutant control equipment efficiency parameter range; Based on the simulated industrial equipment information range, pollutant emission prediction model and emergency plan prediction model, a time-continuous model group of industrial equipment is established, and the time-continuous model group is bound to the corresponding industrial equipment number and stored in the database.

4. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 3 is characterized in that: The step of establishing a time-continuous model group of industrial equipment based on the simulated industrial equipment information range, the pollutant emission prediction model and the emergency plan prediction model includes: Acquire all situations within the simulated industrial equipment information range as a simulated industrial equipment parameter group, acquire all situations of simulated environmental impact factor parameters within a preset range as a simulated environmental impact factor parameter group, wherein the simulated environmental impact factor parameters include temperature parameters, humidity parameters, and air pressure parameters, and pair and combine the elements in the simulated industrial equipment parameter group with the elements in the simulated environmental impact factor group one by one to obtain an initial condition group; Based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model, a linear model group with the same initial condition is obtained, and multiple linear model groups with the same initial condition are repeatedly obtained until the initial conditions of the initial condition group are all in the selected state to obtain multiple linear model groups with different initial conditions as the time continuous model group.

5. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 4 is characterized in that: The step of obtaining a linear model group with the same initial conditions based on the initial condition group, the emergency plan prediction model and the pollutant emission prediction model comprises: Based on the initial condition group, randomly select an initial condition of an unselected state as the target condition and mark it as the starting point of the monitoring time, set the state of the target condition corresponding to the initial condition to the selected state, and input the target condition and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information, wherein the prediction information includes a predicted pollutant number and a corresponding predicted pollutant emission amount; If the predicted pollutant emissions are within the environmental evaluation threshold range, a simulated environmental impact factor in the simulated environmental impact factor group is randomly selected as a simulation condition, and the corresponding moment on the simulation condition mark is used as a monitoring time node according to the preset monitoring time period. The simulation condition and the preset monitoring time period are input as parameters into the pollutant emission prediction model to obtain prediction information, until the predicted pollutant emissions exceed the environmental evaluation threshold range to obtain multiple monitoring time nodes, the prediction information is bound to the corresponding monitoring time nodes, the last monitoring time node is marked as an abnormal time node, and the monitoring time node before the abnormal time node is marked as a prevention time node. Based on the emergency plan prediction model, the predicted emergency plan for the prevention time node and the abnormal time node is obtained, the corresponding predicted emergency plan is bound to the corresponding monitoring time node, and multiple monitoring time nodes are combined in time order to obtain a linear model. This step is repeated within the expected number of times to obtain multiple linear models with the same initial conditions as a linear model group with the same initial conditions.

6. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 5, characterized in that: The step of inputting the target initial conditions and the preset monitoring time period as parameters into the pollutant emission prediction model to obtain prediction information includes: Based on the pollutant emission prediction model, the predicted pollutant names generated by industrial equipment are obtained according to the target initial conditions, the predicted pollutant numbers are determined according to the predicted pollutant names, the benchmark standards and prediction standards of the simulated environmental impact factors are obtained, and the environmental impact ratio is obtained according to the environmental impact ratio calculation formula, where the environmental impact ratio calculation formula is: ,in, is the environmental impact ratio, To simulate the benchmark standard of environmental impact factors, To simulate the prediction criteria of environmental impact factors, To simulate environmental impact factors, is the number of simulated environmental impact factors; The predicted pollutant emissions are calculated based on the environmental impact ratio according to the predicted pollutant emissions formula, where the predicted pollutant emissions formula is: ,in, To predict pollutant emissions, As the basic emission, is the process parameter, is the raw material parameter, is the environmental impact ratio, is the pollutant control equipment efficiency parameter, It is the preset monitoring time period; The predicted pollutant emissions are bound to the predicted pollutant numbers to obtain the predicted information.

7. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 1 is characterized in that: The steps of monitoring industrial equipment in real time based on real-time environmental information and database to obtain real-time comparison nodes and implement corresponding prediction emergency plans include: Using sensors of industrial equipment to obtain initial temperature parameters, initial humidity parameters and initial air pressure parameters of industrial equipment in real time as initial environmental influencing factor parameters, and determine real-time industrial equipment parameters; The real-time industrial equipment parameters and the initial environmental impact factor parameters are used as initial comparison conditions, and a linear model group with the same initial conditions is obtained in the database based on the initial comparison conditions; Based on the linear model group with the same initial conditions, real-time monitoring of industrial equipment is carried out to obtain the corresponding prediction emergency plan and implement it.

8. The accident emergency response and environmental impact assessment method based on environmental steward as claimed in claim 7, characterized in that: The steps of performing real-time monitoring on industrial equipment based on the linear model group with the same initial conditions to obtain corresponding prediction emergency plans and implement them include: Based on the linear model group with the same initial conditions, a real-time environmental impact factor parameter and real-time pollutant emission are obtained every preset monitoring time period as a real-time comparison node; Based on the real-time comparison node and the database, the corresponding linear model is obtained. Based on the linear model, it is determined whether the monitoring time node corresponding to the real-time comparison node is a prevention time node or an abnormal time node. If so, the corresponding prediction emergency plan is obtained and implemented.

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