Distributed industrial park atmospheric pollution total emission calculation method and system

Abstract

The invention provides a method and system for calculating the total atmospheric pollution emission amount of an industrial park based on distribution, and solves the problems that an existing total amount simulation technology can only carry out accounting for a single target, the accounting efficiency is low, and large-scale dynamic accounting cannot be carried out. Configuring to form a standard configuration file in a JSON format and outputting the standard configuration file; s2, deploying simulation inversion software to carry out park pollution emission intensity total amount accounting according to the standardized configuration file; s3, constructing a distributed computing cluster of a master-slave structure; for each slave node, N pieces of independent simulation inversion software and at least one piece of monitoring software are deployed, and completion of tasks with higher priorities is accelerated through an intelligent scheduling algorithm; for the master node, information reported by the slave node at an interval t moment needs to be received, and the state of the slave node is maintained; and S4, deploying an operation and maintenance module to perform analysis and early warning on the running state of the distributed computing cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring and protection, and in particular to a method and system for calculating the total amount of atmospheric pollution emissions from industrial parks based on distributed management. Background Art

[0002] The total amount of air pollution emissions is an important basis for atmospheric environmental control. Previously, the calculation of total emissions was mainly divided into three methods: the actual measurement method, which calculates the total amount of emissions through online monitoring instruments or manual sampling monitoring. This method can only calculate pollutants emitted in an organized manner through the outlet; the coefficient method, which calculates enterprise emissions through production capacity conversion or production and emission coefficients. This type of method requires professionals to verify the data, and the calculation cycle is usually in years; the model method, which inverts the total amount of emissions through environmental monitoring data. Currently, dynamic calculation of the total amount of organized and unorganized emissions can be carried out, but due to differences in model selection and parameter settings, it is difficult to carry out large-scale calculations, and the calculation results of different parks are difficult to compare horizontally.

[0003] The above method still has the following limitations: 1. The existing total volume simulation technology can only calculate a single target and cannot perform large-scale dynamic calculations; 2. Existing technologies use different configuration schemes for different parks, lack standards, and the results are difficult to compare horizontally; 3. The existing target accounting process has low efficiency in resource allocation and poor synchronization in task mobilization, making it difficult to achieve efficient accounting of multiple pollution sources in large-scale industrial parks.

[0004] To this end, we propose a distributed industrial park atmospheric pollution emission calculation method and system to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art. The present invention proposes a distributed method and system for calculating the total amount of atmospheric pollution emissions from industrial parks, which can realize large-scale synchronous dynamic accounting of the total amount of atmospheric pollution emissions from industrial parks.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for calculating the total amount of atmospheric pollution emissions from industrial parks based on distributed distribution, comprising the following steps: S1. Based on the basic information and data of the industrial park, a standard configuration file in JSON format is configured and output; S2. Deploy simulation inversion software to calculate the total amount of pollution emission intensity in the park based on standardized configuration files; S3, build a distributed computing cluster with a master-slave structure; For each slave node, deploy N independent simulation inversion software and at least one monitoring software, where the monitoring software regularly sends the computing resource status of the slave node to the master node. When computing resources are limited, the intelligent scheduling algorithm is used to speed up the completion of high-priority tasks. The specific steps are as follows: for the calculation of a pollution factor in a certain park, the priority is to achieve synchronous real-time calculation, assisted by correction factors, and synchronously optimized through evolutionary algorithms to select the individual with the maximum fitness as the current task allocation plan; For the master node, it needs to receive information reported by the slave node at intervals of t and maintain the status of the slave node. If no information is received for 2t consecutive times, it is considered that the slave node is offline; S4. Deploy the operation and maintenance module to analyze and warn the operating status of the distributed computing cluster.

[0007] Furthermore, the standard configuration file in step S1 includes park simulation area information, monitoring point information and pollution point information.

[0008] Furthermore, the specific steps of configuring the standard configuration file are as follows: Collect basic information and data of the industrial park, including regional scope, regional pollution source information, ground monitoring point information, and other pollution source point information around the park; According to the collected information, calculate the simulated longitude and latitude range of the park, where the minimum longitude of the simulation range is the minimum longitude of all points minus the offset, the maximum longitude is the maximum longitude of all points plus the offset, the minimum latitude is the minimum latitude of all points minus the offset, and the maximum latitude is the maximum latitude of all points plus the offset. The offset is set to 0.005; Divide the simulation grid according to the grid resolution or the number of grids; Mark the interference points outside the park, set the pollution source points outside the park, and mark whether each point belongs to the inside of the park. In the simulation and inversion of pollution diffusion, consider the pollution sources inside and outside the park. When calculating the total emissions, only the pollution sources inside the park are calculated; Outputs a standard configuration file in JSON format.

[0009] Furthermore, the park simulation area information includes the longitude and latitude of the southwest corner and the northeast corner of the simulation grid, the XY grid resolution, and the number of XY direction grids, wherein the X direction is the direction of longitude change and the Y direction is the direction of latitude change; the monitoring point information includes the point name, point code, point longitude and latitude, and point altitude; the pollution source point information includes the pollution source point name, point longitude and latitude, emission altitude, temperature, flow rate, outlet diameter, and whether it belongs to the interior of the park.

[0010] Furthermore, the specific steps of the total amount accounting in step S2 are to obtain real-time data corresponding to each monitoring point from the database, synchronously calculate the contribution of the diffusion process from the pollution point to the monitoring point, reversely infer the emission intensity of the pollution source based on the real-time detection concentration, add up the emission intensities of all points marked as park points, and obtain the overall emission intensity of the park.

[0011] Furthermore, in the distributed computing cluster of step S3, an independent coded SID and M park accounting tasks are assigned to each simulation inversion software, and an independent coded PID is assigned to each park accounting task. The master node synchronously maintains a correspondence table between the park accounting task PID and the simulation inversion software instance SID.

[0012] Furthermore, the specific calculation method of the intelligent scheduling algorithm in step S3 is: in is the correction factor of task i; The hour difference between the current time and the time the park has completed accounting; Define the computing power requirements of a single park and a single factor as constants, then the total computing power of computing server j can be set to , indicating that it can be performed simultaneously Calculation of an instance.

[0013] Furthermore, when M parks and factors are calculated by N node servers, the specific calculation steps of the evolutionary algorithm are as follows: Initialize DNA to ,in represents the first base of the k-th individual in generation 0, Indicates that computing task 1 is numbered Node server computing; Calculate the fitness of individual k in generation 0, According to the genetic algorithm, the 10% individuals with the highest fitness in the 0th generation are selected to generate offspring through base interaction and mutation; Repeat the above steps until the maximum fitness of the group does not increase for three consecutive rounds; The individual DNA with the largest fitness is selected as the current task allocation scheme.

[0014] Furthermore, in step S4, the specific deployment steps of the operation and maintenance module are: Park accounting warning: analyze the delay of park accounting time according to the calculation result database, and generate accounting warning if the delay exceeds T hours; Calculate the instance warning, analyze the instance SID corresponding to the accounting warning park PID, calculate the number of warning PIDs corresponding to all SIDs and divide it by M as the warning rate. When the warning rate of the same SID exceeds the threshold, issue a warning for the instance; Early warning for computing nodes, early warning for offline slave nodes that are under maintenance of the master node, and early warning for slave node disk usage that exceeds the threshold.

[0015] According to the above-mentioned distributed industrial park air pollution emission total amount calculation method, a system is constructed, including: Configuration module, which is used to configure and output standard configuration files in JSON format based on the basic information and data of the industrial park; The total volume accounting module is equipped with simulation inversion software to perform total volume accounting of the park based on the standardized configuration files output by the configuration module; The distributed computing module is built based on the master-slave node structure, and intelligently schedules high-priority tasks to speed up completion based on the computing resources of the slave nodes. The master node is responsible for maintaining the status of the slave nodes. The operation and maintenance module is used to analyze and warn the operating status of the distributed computing cluster.

[0016] Compared with the prior art, the beneficial effects of the present invention include: 1. The present invention provides a large-scale synchronous dynamic accounting technology for the total amount of atmospheric pollution emissions in industrial parks, which solves the problems of computing power allocation and efficient operation and maintenance. It has been verified that the system can support the synchronous accounting of hundreds of provincial industrial parks; 2. The present invention standardizes the model operation parameter settings by standardizing the park configuration files and model configuration files, so that the accounting results of different parks are horizontally comparable, providing support for management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosure of the present invention is described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. In the accompanying drawings, the same reference numerals are used to refer to the same components. Among them: Figure 1 A schematic diagram of a calculation method flow chart according to an embodiment of the present invention is shown; Figure 2 Schematically shows a park system configuration effect diagram proposed according to an embodiment of the present invention; Figure 3 A table diagram of simulation inversion software configuration items proposed according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0018] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0019] According to one embodiment of the present invention, Figure 1-Figure 3 Shown.

[0020] like Figure 1 As shown, in this embodiment, for the distributed industrial park atmospheric pollution emission total amount calculation method process, the specific steps can be summarized as follows: S1. Based on the basic information and data of the industrial park, a standard configuration file in JSON format is configured and output; S2, deploy simulation inversion software to perform total volume accounting based on standardized configuration files; S3, build a distributed computing cluster with a master-slave structure; For each slave node, deploy N independent simulation inversion software and at least one monitoring software, where the monitoring software regularly sends the computing resource status of the slave node to the master node. When computing resources are limited, the intelligent scheduling algorithm is used to speed up the completion of high-priority tasks. The specific steps are as follows: for the calculation of a pollution factor in a certain park, the priority is to achieve synchronous real-time calculation, assisted by correction factors, and synchronously optimized through evolutionary algorithms to select the individual with the maximum fitness as the current task allocation plan; For the master node, it needs to receive information reported by the slave node at intervals of t and maintain the status of the slave node. If no information is received for 2t consecutive times, it is considered that the slave node is offline; S4. Deploy the operation and maintenance module to analyze and warn the operating status of the distributed computing cluster.

[0021] Similarly, the system constructed according to the above calculation method is also within the protection scope of the present invention. The following is a further description of the above method and the actual system module composition: 1. Configure the module 1.1 Collection of basic information of the park Collect basic information and data of the application area, including regional scope, regional pollution source information (including the latitude and longitude of the pollution source point, pollutant emission intensity, outlet height, outlet temperature, outlet flow rate, outlet diameter, etc.), ground monitoring point information, including the point latitude and longitude, height and ground meteorological monitoring data (air pressure, temperature, humidity, wind speed, wind direction), and other pollution source point information around the park.

[0022] 1.2 Build standardized configuration Based on the collected information, the simulated longitude and latitude range of the park is calculated, where the minimum longitude of the simulation range is the minimum longitude of all points (including pollution sources and monitoring sites) minus offset, and the offset is set to 0.005; the maximum longitude is the maximum longitude of all points (including pollution sources and monitoring sites) plus offset; the minimum latitude is the minimum latitude of all points (including pollution sources and monitoring sites) minus offset, and the offset is set to 0.005; the maximum latitude is the maximum latitude of all points (including pollution sources and monitoring sites) plus offset.

[0023] The simulation grid can be divided according to the grid resolution or the number of grids. The grid resolution is generally set to 100 meters, and the number of grids is divided evenly according to the number of grids entered by the user in the longitude and latitude directions.

[0024] Mark the interference points outside the park, set the pollution source points including those outside the park, and mark whether each point belongs to the inside of the park. In the pollution diffusion simulation and inversion, consider the pollution sources inside and outside the park. When calculating the total emissions, only the pollution sources within the park are calculated.

[0025] Output standard configuration file, the configuration file is in the format of: 1) Park simulation area information: the latitude and longitude of the southwest and northeast corners of the simulation grid, the XY grid resolution, and the number of grids in the XY direction (the X direction is the direction of longitude change, and the Y direction is the direction of latitude change); 2) Monitoring point information: including point name, point code (corresponding to the database), point longitude and latitude, and point height H; 3) Pollution source location information: including the name of the pollution source, the longitude and latitude of the location, the discharge height H, temperature, flow rate, outlet diameter, and whether it is inside the park.

[0026] 2. Total volume accounting module 2.1 Simulation inversion software The simulation inversion software performs total volume accounting based on the standardized configuration file. The specific steps include: 1) Read the park configuration JSON file information, standardize the location information, and convert the longitude and latitude in the JSON file into X and Y coordinates in local coordinates in meters, with the southwest corner of the simulation grid as the rectangular coordinate origin; 2) According to the simulation range information, read the satellite elevation DEM data, calculate the ground height h of each monitoring point and pollution source location, and the final height h0=h+H of the monitoring and pollution source point input model; 3) Obtain real-time data from the database by numbering the monitoring point, including point pollution concentration data, temperature, air pressure, humidity, wind speed, and wind direction; 4) Calculate the diffusion process from the pollution source to the monitoring site and obtain the contribution c, in units of mg·S / Kg·m3, which means that when the emission intensity is 1kg / s, the pollutant concentration at the monitoring point is c mg / m3; 5) Reversely estimate the emission intensity of pollution sources based on the actual monitored concentration; 6) Add up the emission intensities of all points marked within the park to obtain the overall emission intensity of the park.

[0027] The simulation inversion software can be uniformly parameterized to ensure the comparability of calculation results in different parks. The configuration items can be referred to Figure 3 The table shows the details and will not be elaborated here.

[0028] As for the specific calculation steps of the above-mentioned contribution degree c, point emission intensity and overall emission intensity of the park, please refer to the Chinese invention patent with announcement number CN118643299B. The patent name is a method and system for real-time tracing and visualization analysis of air pollution. The corresponding formula and calculation process have been disclosed in detail in the specification [0054-0068], which will not be repeated here.

[0029] 3. Distributed computing module The computing cluster adopts a master-slave structure, which is divided into master nodes and slave nodes.

[0030] For each slave node, deploy N independent simulation inversion software and 1 monitoring software. Among them, the monitoring software regularly sends operation status information to the master node. The operation status information includes: the computing resource status of the slave node, including CPU occupancy, RAM occupancy, and disk occupancy. Each simulation inversion software is assigned an independent coded SID and M park accounting tasks. Each park accounting task is assigned an independent coded PID. Generally, N takes a value of 10 and M takes a value of 8.

[0031] For the master node, it needs to receive information reported by the slave node at intervals of t and maintain the status of the slave node. If no information is received for 2t consecutive times, the slave node is considered offline, and t is taken as 10 minutes. The master node also maintains the corresponding relationship table between the park accounting task PID and the simulation inversion software instance SID.

[0032] When the aforementioned cluster computing resources are limited, that is, when there are many computing tasks, it is necessary to use an intelligent scheduling algorithm to speed up the completion of tasks with higher priority.

[0033] For the calculation of a pollution factor in a certain park, the priority is to achieve synchronous real-time calculation, assisted by correction factors. The calculation method is: in is the correction factor for task i, the default value is 1.0, and it can be adjusted according to business needs; It is the difference in hours between the current time and the time the park has completed the accounting.

[0034] The computing power requirement for a single park and a single factor can be considered as a constant. For computing server j, its total computing power can be set to , indicating that it can be performed simultaneously Calculation of an instance.

[0035] Intelligent optimization is performed using evolutionary algorithms. When M parks and factors are calculated by N node servers, optimization is performed according to the following method.

[0036] 1) Initialize DNA to ,in represents the first base of the k-th individual in generation 0, Indicates that computing task 1 is numbered Node server computing.

[0037] 2) Calculate the fitness of individual k in generation 0: 3) Select the 10% individuals with the highest fitness in the 0th generation according to the genetic algorithm, and generate offspring through base interaction and mutation; 4) Repeat steps 1-3 until the maximum fitness of the group does not increase for three consecutive rounds; 5) Select the individual DNA with the maximum fitness as the current task allocation plan.

[0038] 4. Operation and maintenance module The operation and maintenance module mainly realizes the analysis of the running status of the distributed high-performance cluster to achieve efficient operation and maintenance of large-scale calculations. The operation and maintenance module mainly links the cluster master node and the calculation result database, including: Park accounting warning: according to the calculation result database, the park accounting time delay is analyzed. If the delay exceeds T hours, a calculation warning is generated, and T is set to 24 hours. Compute instance warning, analyze the instance SID corresponding to the accounting warning park PID, calculate the number of warning PIDs corresponding to all SIDs and divide it by M as the warning rate. When the warning rate of the same SID exceeds the threshold, issue a warning for the instance. The threshold is 60%; Computing node warning: Warning for offline slave nodes due to master node maintenance, and warning for slave node disk usage exceeding the threshold, with the threshold value being 80%.

[0039] In addition, the above-mentioned method steps and system can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0040] The technical scope of the present invention is not limited to the contents in the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. A distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks, characterized in that: The steps include: S1. Based on the basic information and data of the industrial park, a standard configuration file in JSON format is configured and output; S2. Deploy simulation inversion software to calculate the total amount of pollution emission intensity in the park based on standardized configuration files; S3, build a distributed computing cluster with a master-slave structure; For each slave node, deploy N independent simulation inversion software and at least one monitoring software, where the monitoring software regularly sends the computing resource status of the slave node to the master node. When computing resources are limited, the intelligent scheduling algorithm is used to speed up the completion of high-priority tasks. The specific steps are as follows: for the calculation of a pollution factor in a certain park, the priority is to achieve synchronous real-time calculation, assisted by correction factors, and synchronously optimized through evolutionary algorithms to select the individual with the maximum fitness as the current task allocation plan; For the master node, it needs to receive information reported by the slave node at intervals of t and maintain the status of the slave node. If no information is received for 2t consecutive times, it is considered that the slave node is offline; S4. Deploy the operation and maintenance module to analyze and warn the operating status of the distributed computing cluster.

2. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 1 is characterized in that: The standard configuration file in step S1 includes park simulation area information, monitoring point information and pollution point information.

3. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 2 is characterized in that: The specific steps for configuring the standard configuration file are as follows: Collect basic information and data of the industrial park, including regional scope, regional pollution source information, ground monitoring point information, and other pollution source point information around the park; According to the collected information, calculate the simulated longitude and latitude range of the park, where the minimum longitude of the simulation range is the minimum longitude of all points minus the offset, the maximum longitude is the maximum longitude of all points plus the offset, the minimum latitude is the minimum latitude of all points minus the offset, and the maximum latitude is the maximum latitude of all points plus the offset. The offset is set to 0.005; Divide the simulation grid according to the grid resolution or the number of grids; Mark the interference points outside the park, set the pollution source points outside the park, and mark whether each point belongs to the inside of the park. In the simulation and inversion of pollution diffusion, consider the pollution sources inside and outside the park. When calculating the total emissions, only the pollution sources inside the park are calculated; Outputs a standard configuration file in JSON format.

4. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 2 is characterized in that: The park simulation area information includes the longitude and latitude of the southwest corner and northeast corner of the simulation grid, the XY grid resolution, and the number of grids in the XY direction, where the X direction is the direction of longitude change and the Y direction is the direction of latitude change; the monitoring point information includes the point name, point code, point longitude and latitude, and point height; the pollution source point information includes the pollution source point name, point longitude and latitude, emission height, temperature, flow rate, outlet diameter, and whether it belongs to the interior of the park.

5. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 3 is characterized in that: The specific steps of the total amount accounting in step S2 are to obtain the real-time data of each monitoring point from the standard configuration file, synchronously calculate the contribution of the diffusion process from the pollution point to the monitoring point, reversely infer the emission intensity of the pollution source based on the real-time detection concentration, add up the emission intensities of all marked points in the park, and obtain the overall pollution emission intensity of the park.

6. The distributed method for calculating the total amount of air pollution emissions from industrial parks according to claim 1 is characterized in that: In the distributed computing cluster of step S3, an independent coded SID and M park accounting tasks are allocated to each simulation inversion software, and an independent coded PID is allocated to each park accounting task. The master node synchronously maintains a correspondence table between the park accounting task PID and the simulation inversion software instance SID.

7. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 1 is characterized in that: The specific calculation method of the intelligent scheduling algorithm in step S3 is: in is the correction factor of task i; The hour difference between the current time and the time the park has completed accounting; Define the computing power requirements of a single park and a single factor as constants, then the total computing power of computing server j can be set to , indicating that it can be performed simultaneously Calculation of an instance.

8. The distributed method for calculating the total amount of air pollution emissions from industrial parks according to claim 7 is characterized in that: When M parks and pollution factors are calculated by N node servers, the specific calculation steps of the evolutionary algorithm are as follows: Initialize DNA to ,in represents the first base of the k-th individual in generation 0, Indicates that computing task 1 is numbered Node server computing; Calculate the fitness of individual k in generation 0, According to the genetic algorithm, the 10% individuals with the highest fitness in the 0th generation are selected to generate offspring through base interaction and mutation; Repeat the above steps until the maximum fitness of the group does not increase for three consecutive rounds; The individual DNA with the largest fitness is selected as the current task allocation scheme.

9. The distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to claim 6 is characterized by: In step S4, the specific deployment steps of the operation and maintenance module are: Park accounting warning: analyze the delay of park accounting time according to the calculation result database, and generate accounting warning if the delay exceeds T hours; Calculate the instance warning, analyze the instance SID corresponding to the accounting warning park PID, calculate the number of warning PIDs corresponding to all SIDs and divide it by M as the warning rate. When the warning rate of the same SID exceeds the threshold, issue a warning for the instance; Early warning for computing nodes, early warning for offline slave nodes that are under maintenance of the master node, and early warning for slave node disk usage that exceeds the threshold.

10. A system constructed based on a distributed method for calculating the total amount of atmospheric pollution emissions from industrial parks according to any one of claims 1 to 9, characterized in that: include: Configuration module, which is used to configure and output standard configuration files in JSON format based on the basic information and data of the industrial park; The total volume accounting module is equipped with simulation inversion software to perform total volume accounting of the park based on the standardized configuration files output by the configuration module; The distributed computing module is built based on the master-slave node structure, and intelligently schedules high-priority tasks to speed up completion based on the computing resources of the slave nodes. The master node is responsible for maintaining the status of the slave nodes. The operation and maintenance module is used to analyze and warn the operating status of the distributed computing cluster.

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