CN117689218B

CN117689218B - Intelligent management and control system suitable for industrial enterprise production site environment risk

Info

Publication number: CN117689218B
Application number: CN202410157095.1A
Authority: CN
Inventors: 张智良; 李晶; 林莎
Original assignee: Chengdu Gongxi Technology Co ltd
Current assignee: Chengdu Gongxi Technology Co ltd
Priority date: 2024-02-04
Filing date: 2024-02-04
Publication date: 2024-04-12
Anticipated expiration: 2044-02-04
Also published as: CN117689218A

Abstract

The invention provides an intelligent management and control system suitable for environmental risks in industrial enterprise production sites, which relates to the field of data processing and comprises the following components: the environment monitoring module comprises a plurality of types of environment data used for collecting production sites; the environment monitoring module is also used for establishing a plurality of monitoring point association maps; the edge calculation module is used for determining the environmental risk information of the production field area; the pipe network monitoring module is used for determining the damage risk and the mixed drainage risk of the pipe network; the treatment monitoring module is used for determining parallel risks of production and treatment; the hazardous waste monitoring module is used for determining the mixing risk and the hazardous waste leakage risk of a hazardous waste placement area of the production site; the operation monitoring module is used for acquiring operation image information of the production site and determining operation risk of the production site; the risk management and control module is used for managing and controlling the production field environment and has the advantages of improving the accuracy and comprehensiveness of monitoring the production field environment of industrial enterprises and the quality of intelligent management and control of the production field environment risk.

Description

Intelligent management and control system suitable for industrial enterprise production site environment risk

Technical Field

The invention relates to the field of data processing, in particular to an intelligent management and control system suitable for environmental risks in industrial enterprises production sites.

Background

Along with the development of industrial economy, china enters a new development stage, the development foundation is firmer, the development conditions are deeply changed, the further development faces new opportunities and challenges, the main responsibility of enterprises is increasingly clear, the safety control gateway is moved forward instead of being processed afterwards for safety production requirements, the risk control and the risk early warning prediction are targeted, the safety supervision work is actually performed, the safety risk early warning of industrial production is effectively improved by adding the Internet of things monitoring technology, and the capability of restraining the occurrence of safety accidents is also improved.

However, the existing industrial production safety risk early warning methods still have some problems: in the prior art, the early warning signal is generally sent only when the safety risk of industrial production is monitored, and related technicians can be timely notified to carry out safety inspection before, but the safety problem of industrial production is sometimes serious, especially for certain dangerous chemical industrial production processes, the reasons for risk are more than one, the technicians need to check the reasons for risk one by one to find and solve the safety problem, the prior art cannot help the personnel to save the time for searching the reasons for risk, and the ineffective inspection time is increased for the checking process of the reasons for not causing the production safety risk, so that more serious consequences can occur, even the whole industrial production process is influenced, and great cost loss is caused.

Therefore, it is necessary to provide an intelligent management and control system suitable for industrial enterprises to increase the accuracy and comprehensiveness of monitoring the industrial enterprises' production site environment and the quality of intelligent management and control of the production site environment risk.

Disclosure of Invention

The invention provides an intelligent management and control system suitable for industrial enterprise production site environment risk, which comprises the following components: the environment monitoring module comprises a plurality of environment monitoring units, wherein each target monitoring point is provided with one environment monitoring unit, and the environment monitoring units are used for collecting various environmental data of a production field; the environment monitoring module is also used for establishing a plurality of monitoring point association maps, wherein one monitoring point association map is used for representing the association relation of a plurality of target monitoring points on one type of environment data; the edge computing module comprises a plurality of edge computing units, wherein one edge computing unit performs data interaction with at least one environment monitoring unit, and the edge computing unit is used for determining environmental risk information of a production field area based on the plurality of monitoring point association maps and acquired environment data of a plurality of types of production fields acquired by the at least one environment monitoring unit; the pipe network monitoring module is used for acquiring flow rate information and real-time weather information of the sewage pipe network of the production site and determining pipe network damage risks and mixed discharge risks based on the flow rate information and the real-time weather information of the sewage pipe network of the production site; the control monitoring module is used for acquiring the operation information of the production equipment and the operation information of the control equipment on the production site, and determining the parallel risk of production and control based on the operation information of the production equipment and the operation information of the control equipment on the production site; the hazardous waste monitoring module is used for acquiring image information of the hazardous waste placement area of the production site and determining the mixing risk and the hazardous waste leakage risk of the hazardous waste placement area of the production site; the operation monitoring module is used for acquiring operation image information of the production site and determining operation risks of the production site; the risk management and control module is used for managing and controlling the production site environment based on the risk of damage of the pipe network, the mixed discharge risk, the parallel risk of production and treatment, the mixed discharge risk, the risk of dangerous waste leakage, the operation risk and the production site area environment risk information uploaded by the plurality of edge computing units.

Further, the system also comprises a monitoring point determining module, which is used for acquiring the image information of the production area of the production site and determining a plurality of target monitoring points based on the image information of the production area of the production site; the monitoring point determining module determines a plurality of target monitoring points through the following processes, including: determining position information of a plurality of devices included in the production site and association relations among the plurality of devices based on image information of a production area of the production site; acquiring type information and parameter information of each device included in the production field; generating layout information of the production site based on the position information of a plurality of devices included in the production site, the association relation among the plurality of devices, the type information and the parameter information of each device; and determining a plurality of target monitoring points based on the layout information of the production site.

Further, the monitoring point determining module determines a plurality of target monitoring points based on the layout information of the production field, including: acquiring layout information of a plurality of sample production sites; for any one of the sample production sites, calculating the layout similarity between the layout information of the production site and the layout information of the sample production site; judging whether at least one target production site exists in the plurality of sample production sites or not based on the layout similarity between the layout information of the production sites and the layout information of each sample production site; when judging that at least one target production field exists, determining a plurality of target monitoring points based on monitoring point distribution information corresponding to each target production field; and when judging that at least one target production site does not exist, determining a plurality of target monitoring points based on the position information, the type information and the parameter information of each device included in the production site.

Further, the environment monitoring module establishes a plurality of monitoring point association maps, including: acquiring a plurality of types of acquired environmental data of the plurality of environmental monitoring units in a plurality of simulation environments; based on a plurality of types of collected environmental data of the plurality of environmental monitoring units in a plurality of simulation environments, determining correlation coefficients of any two environmental monitoring units in each type of environmental data; and establishing a plurality of monitoring point association maps based on the correlation coefficient of any two environmental monitoring units in each type of environmental data.

Further, the edge computing module is further configured to establish a data interaction relationship between the plurality of environmental monitoring units and the plurality of edge computing units based on the plurality of monitoring point association maps, where the plurality of environmental monitoring units and the plurality of edge computing units interact data based on the data interaction relationship, and specifically includes: calculating the association degree of the monitoring units of any two environment monitoring units based on the association maps of the plurality of monitoring points; based on the association degree of any two environment monitoring units, clustering the plurality of environment monitoring units to determine a plurality of monitoring unit clustering clusters; for each monitoring unit cluster, determining the data processing requirement corresponding to the monitoring unit cluster based on the data processing requirement corresponding to the environment monitoring unit included in the monitoring unit cluster; when the data processing requirement corresponding to the monitoring unit cluster is smaller than or equal to a preset data processing requirement threshold, taking the monitoring unit cluster as a target monitoring unit cluster; when the data processing requirement corresponding to the monitoring unit cluster is greater than a preset data processing requirement threshold, performing secondary clustering on a plurality of environmental monitoring units included in the monitoring unit cluster based on the data processing requirement corresponding to each environmental monitoring unit included in the monitoring unit cluster and the monitoring unit association degree of any two environmental monitoring units, and determining a plurality of monitoring unit cluster groups included in the monitoring unit cluster and the data processing requirement corresponding to each monitoring unit cluster group; and establishing a data interaction relationship between the plurality of environment monitoring units and the plurality of edge computing units based on the data processing requirements corresponding to each target monitoring unit cluster and the data processing requirements corresponding to each monitoring unit cluster group.

Further, the environment monitoring unit comprises a temperature monitoring component, a humidity monitoring component, a smoke monitoring component, a dust monitoring component, a sound monitoring component, a carbon dioxide monitoring component and a carbon monoxide monitoring component.

Further, the edge calculating unit determines environmental risk information of a production field region based on the plurality of monitoring point association maps and the acquired environmental data of a plurality of types of production fields acquired by the at least one environmental monitoring unit, including: for each target monitoring unit cluster, determining a first environment data matrix corresponding to the environment data of the plurality of types corresponding to the target monitoring unit cluster based on the environment data of the plurality of types of production sites collected by each environment monitoring unit included in the target monitoring unit cluster, and determining environment risk information of a production site area corresponding to the target monitoring unit cluster based on the first environment data matrix; for each monitoring unit clustering group, determining a second environment data matrix corresponding to the plurality of types of environment data corresponding to the monitoring unit clustering group based on the plurality of types of environment data of the production site collected by each environment monitoring unit included in the monitoring unit clustering group, and determining environment risk information of the production site area corresponding to the monitoring unit clustering group based on the second environment data matrix.

Further, the edge calculating unit determines a first environmental data matrix corresponding to the plurality of types of environmental data corresponding to the target monitoring unit cluster based on the plurality of types of environmental data of the production site collected by each environmental monitoring unit included in the target monitoring unit cluster, and the first environmental data matrix comprises: generating a first initial environment data matrix corresponding to each type of environment data based on a plurality of types of environment data of a production site collected by each environment monitoring unit included in the target monitoring unit cluster, wherein a row vector of the first initial environment data matrix is one type of environment data collected by one environment monitoring unit included in the target monitoring unit cluster at a plurality of data collection time points; and carrying out data denoising and correction on the first initial environmental data matrix corresponding to each type of environmental data, and determining the first environmental data matrix corresponding to the plurality of types of environmental data corresponding to the target monitoring unit cluster.

Further, the edge computing unit performs data denoising and correction on the first initial environmental data matrix corresponding to each type of environmental data, and determines a first environmental data matrix corresponding to a plurality of types of environmental data corresponding to the target monitoring unit cluster, including: denoising and correcting the first initial environment data matrix corresponding to the temperature type to generate a first environment data matrix corresponding to the temperature type; denoising and correcting the first initial environment data matrix corresponding to the humidity type to generate a first environment data matrix corresponding to the humidity type; denoising the first initial environmental data matrix corresponding to the smoke type to generate a denoised first initial environmental data matrix corresponding to the smoke type; for each smoke row vector of a first initial environment data matrix corresponding to the denoised smoke type, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the smoke row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, and carrying out first correction on the smoke row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the smoke row vector to generate a first initial environment data matrix corresponding to the smoke type after the first correction; denoising the first initial environmental data matrix corresponding to the dust type to generate a denoised first initial environmental data matrix corresponding to the dust type; for each dust row vector of a first initial environment data matrix corresponding to the denoised dust type, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the dust row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, and carrying out first correction on the dust row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the dust row vector to generate a first initial environment data matrix corresponding to the dust type after the first correction; for each environmental monitoring unit included in the target monitoring unit cluster, determining a first corrected smoke line vector corresponding to the environmental monitoring unit based on a first initial environmental data matrix corresponding to the first corrected smoke type, generating a smoke concentration change curve, determining a first corrected dust line vector corresponding to the environmental monitoring unit based on the first initial environmental data matrix corresponding to the first corrected dust type, generating a dust concentration change curve, performing empirical mode decomposition on the smoke concentration change curve, generating at least one smoke content modal component and a smoke residual, performing empirical mode decomposition on the dust concentration change curve, generating at least one dust content modal component and a dust residual, performing a second correction on the first corrected dust line vector corresponding to the environmental monitoring unit and the first corrected smoke line vector based on the at least one smoke content modal component and the smoke residual and the at least one dust content modal component and the dust residual, and generating a second corrected dust vector corresponding to the environmental monitoring unit and a second corrected dust vector; generating a first environment data matrix corresponding to the smoke type and a first environment data matrix corresponding to the dust type based on the second corrected dust row vector and the second corrected smoke row vector corresponding to each environment monitoring unit included in the target monitoring unit cluster.

Further, the risk management and control module manages and controls the production site environment based on the risk of pipe network damage, the risk of mixed discharge, the risk of parallel production and treatment, the risk of mixed discharge, the risk of hazardous waste leakage, the operation risk and the production site regional environment risk information uploaded by the plurality of edge computing units, and comprises: determining a risk type of the production site based on the production site area environment risk information uploaded by the plurality of edge computing units; determining a target management and control operation based on the risk type of the production site; and controlling the production field environment based on the target control operation.

Compared with the prior art, the intelligent management and control system for environmental risks in industrial enterprise production sites has the following beneficial effects:

1. the method comprises the steps of acquiring image information of a production area of a production field, pertinently setting a plurality of target monitoring points, reducing subsequent invalid data acquisition, guaranteeing comprehensiveness of production field environment monitoring, realizing distributed processing of various types of environment data of the production field, acquired by a plurality of environment monitoring units, through a plurality of edge computing units, improving instantaneity of intelligent management and control of the production field environment risk, monitoring damage and mixed discharge of a sewage pipe network of the production field, monitoring synchronous operation of treatment equipment and production equipment, monitoring mixed discharge and dangerous waste leakage of a dangerous waste placement area, monitoring operation of the production field, and managing and controlling the production field environment based on the pipe network damage risk, mixed discharge risk, production and treatment parallel risk, mixed discharge risk, dangerous waste leakage risk, operation risk and production field area environment risk information uploaded by a plurality of edge computing units, and improving accuracy and comprehensiveness of industrial enterprise production field environment monitoring and intelligent management and control quality of the production field environment risk.

2. The method has the advantages that the related information of a plurality of sample production sites is collected in advance, the reference data is provided for determining a plurality of target monitoring points of the subsequent production sites, the plurality of target monitoring points of the production sites can be accurately and rapidly determined, the acquisition of subsequent invalid data is reduced, and meanwhile the comprehensiveness of the environmental monitoring of the production sites is ensured.

3. By arranging a plurality of edge computing units, the distributed processing of the environmental data of the production site collected by the plurality of environmental monitoring units can be realized, compared with centralized processing of transmission data to a center, the real-time performance is higher, on the basis, the monitoring unit association degree of any two environmental monitoring units is determined by establishing a plurality of monitoring point association maps, the plurality of environmental monitoring units are clustered at least once, the grouping of the plurality of environmental monitoring units is realized, the subsequent edge computing units can determine the environmental risk information of the production site region based on the environmental data of the production site collected by the target monitoring unit cluster or the plurality of environmental monitoring units collected by the monitoring unit cluster group, and compared with the determination of the environmental risk information of the production site region based on the environmental data of the production site collected by the single monitoring unit cluster group, the accuracy is higher.

4. The temperature and the humidity can influence the detection of the smoke concentration and the dust concentration, so that the smoke line vector is required to be corrected for the first time based on an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the smoke line vector, the dust line vector is required to be corrected for the first time based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the dust line vector, and the smoke and the dust can mutually influence in the detection process of the smoke concentration and the dust concentration, so that the dust line vector and the first environmental data matrix corresponding to the dust type after the first correction corresponding to the environmental monitoring unit are required to be corrected for the second time based on at least one smoke content modal component and a smoke residual and at least one dust content modal component and a dust residual, and the first environmental data matrix corresponding to the dust type after the first correction corresponding to the smoke type is generated, and the accuracy of the environmental risk information of a production field area which is determined later is improved.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic block diagram of an intelligent environmental risk management and control system suitable for use in an industrial enterprise production site, according to some embodiments of the present disclosure;

FIG. 2 is a schematic flow chart of determining a plurality of target monitoring points, according to some embodiments of the present disclosure;

FIG. 3 is a flow diagram illustrating a process for establishing a data interaction relationship between a plurality of environmental monitoring units and a plurality of edge computing units, according to some embodiments of the present disclosure;

fig. 4 is a schematic flow chart of data denoising and correction for a first initial environmental data matrix corresponding to each type of environmental data according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

Fig. 1 is a schematic diagram of a module suitable for an intelligent management and control system for environmental risk in a production site of an industrial enterprise according to some embodiments of the present disclosure, as shown in fig. 1, a system suitable for intelligent management and control for environmental risk in a production site of an industrial enterprise may include a monitoring point determining module, an environmental monitoring module, an edge computing module, and a risk management and control module. The respective modules are described in detail in order below.

The monitoring point determination module may be configured to obtain image information of a production area of a production site and determine a plurality of target monitoring points based on the image information of the production area of the production site.

The target monitoring point may be a location in the production environment where environmental data acquisition is required.

FIG. 2 is a schematic flow diagram of determining a plurality of target monitoring points, as shown in FIG. 2, according to some embodiments of the present disclosure, in some embodiments, the monitoring point determination module determines the plurality of target monitoring points based on image information of a production area of a production site, including:

determining position information of a plurality of devices included in a production site and association relations among the plurality of devices based on image information of a production area of the production site;

acquiring type information and parameter information of each device included in a production field, wherein the type of the device can be determined based on the function of the device;

Generating layout information of the production site based on the position information of the plurality of devices, the association relation among the plurality of devices, the type information and the parameter information of each device, wherein the position information of the plurality of devices is included in the production site;

a plurality of target monitoring points are determined based on layout information of the production site.

In particular, production sites of different industries may include different types of equipment. For example, a production site in the food industry may include cleaning equipment, sterilization equipment, water treatment equipment, sorting equipment, quick freezing equipment, drying equipment, filling equipment, extraction equipment, heat exchange equipment, transport equipment, and the like. As another example, a production site in the chemical industry may include a reactor, a separator, a dryer, a distillation column, a stirrer, and the like.

The parameter information that can be different for different devices can be different, for example, the parameter information for cleaning devices at the production site of the food industry can include product capacity, cleaning mode, disinfection mode, etc. As another example, parameter information for a separator at a production site in the chemical industry may include size, microwave power, overall machine power, etc.

The association relationship among the plurality of devices can represent the matching relationship among any two devices in the process of producing the product.

As shown in fig. 2, in some embodiments, the monitoring point determination module determines a plurality of target monitoring points based on layout information of a production site, including:

obtaining layout information of a plurality of sample production sites, wherein the sample production sites can be production sites which belong to the same industry as the current production sites and are determined by target monitoring points;

for any sample production site, calculating the layout similarity between the layout information of the production site and the layout information of the sample production site;

judging whether at least one target production site exists in the plurality of sample production sites or not based on the layout similarity between the layout information of the production sites and the layout information of each sample production site;

when judging that at least one target production site exists, determining at least one target monitoring point based on monitoring point distribution information corresponding to each target production site;

and when judging that at least one target production site does not exist, determining a plurality of target monitoring points based on the position information, the type information and the parameter information of each device included in the production site.

Specifically, for any one sample production site, the layout similarity between the layout information of the production site and the layout information of the sample production site can be calculated by the similarity determination model based on the layout information of the production site and the layout information of the sample production site. The similarity determination model may be a machine learning model such as an artificial neural network (Artificial Neural Network, ANN) model, a recurrent neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, or a bi-directional recurrent neural network (BRNN) model.

The target production site may be a sample production site having a layout similarity to layout information of the current production site greater than a preset layout similarity threshold.

The monitoring point distribution information corresponding to the target production site may include location information of a plurality of target monitoring points corresponding to the target production site. When judging that one target production site exists, the position information of a plurality of target monitoring points of the current production site can be determined based on the position information of a plurality of target monitoring points corresponding to the target production site. When judging two or more than two target production sites, the plurality of target monitoring points corresponding to each target production site can be fused to generate the position information of the plurality of target monitoring points of the current production site. For example, the target production field 1 corresponds to the target monitoring point 1, the target monitoring point 2 and the target monitoring point 3, the target production field 2 corresponds to the target monitoring point 4, the target monitoring point 5 and the target monitoring point 6, wherein the target monitoring point 1 and the target monitoring point 4 are both positioned on the equipment A, and after the target production field 1 and the target production field 2 correspond to the target monitoring points are fused, the plurality of target monitoring points of the current production field are generated and comprise the target monitoring point 1, the target monitoring point 2, the target monitoring point 3, the target monitoring point 5 and the target monitoring point 6.

When judging that at least one target production site does not exist, determining the influence condition of each device on the production environment based on the type information and the parameter information of each device included in the production site, further determining the influence on the production environment, and determining a plurality of target monitoring points based on the position information of the target devices.

It can be understood that the method can accurately and rapidly determine the plurality of target monitoring points of the production field by collecting the related information of the plurality of sample production fields in advance and providing reference data for the determination of the plurality of target monitoring points of the subsequent production field, so that the comprehensiveness of the environmental monitoring of the production field is ensured while the acquisition of the subsequent invalid data is reduced.

The environmental monitoring module may include a plurality of environmental monitoring units.

Each target monitoring point is provided with an environment monitoring unit, and the environment monitoring units are used for collecting various environmental data of the production site.

In some embodiments, the environmental monitoring unit includes a temperature monitoring component, a humidity monitoring component, a smoke monitoring component, a dust monitoring component, a sound monitoring component, a carbon dioxide monitoring component, and a carbon monoxide monitoring component.

It is to be appreciated that the environmental monitoring unit may also include components for acquiring other types of environmental data. For example, the environmental monitoring unit may also include toxic gas monitoring components at the production site of the chemical industry. The type of components comprised by the environmental monitoring unit may vary depending on the actual situation at the production site.

The environment monitoring module is also used for establishing a plurality of monitoring point association maps.

One monitoring point association graph is used for representing the association relation of a plurality of target monitoring points on one type of environmental data.

In some embodiments, the environment monitoring module establishes a plurality of monitoring point association maps, including:

acquiring a plurality of types of acquired environmental data of a plurality of environmental monitoring units in a plurality of simulation environments;

based on a plurality of types of collected environmental data of a plurality of environmental monitoring units in a plurality of simulation environments, determining a correlation coefficient of any two environmental monitoring units in each type of environmental data;

and based on the correlation coefficient of any two environmental monitoring units in each type of environmental data, establishing a plurality of monitoring point correlation maps.

The simulation environment can simulate the environment change condition of each position of the current production environment in the production process of the product. For any two environmental monitoring units, the similarity of each type of environmental data of the two environmental monitoring units in each simulation environment can be determined based on the collected multiple types of environmental data of the two environmental monitoring units in each simulation environment, so that the correlation coefficient of the two environmental monitoring units in each type of environmental data is determined.

For example, taking environmental data of a temperature type as an example, the environmental monitoring module may determine the correlation coefficient of any two environmental monitoring units at the environmental data of the temperature type based on the following formula:

wherein,for the correlation coefficient of the ith environmental monitoring unit and the jth environmental monitoring unit in the environmental data of the temperature type, < ->For preset parameters, < >>In the g-th simulation environment, the similarity of the environmental data of the ith environmental monitoring unit and the jth environmental monitoring unit in the temperature type is +.>In the g-th simulation environment, the fluctuation parameter of the temperature data acquired by the i-th environment monitoring unit in the temperature type environment data is +.>In the g-th simulation environment, the fluctuation parameter of the temperature data acquired by the j-th environment monitoring unit in the temperature type environment data is +.>The ith environmental monitoring unit in the g-th simulated environment +.>Temperature data collected at each time point, +.>For the total number of time points in the simulation environment of the g-th species, +.>The jth environmental monitoring unit is the jth environmental monitoring unit in the g simulation environment>Temperature data collected at each time point.

The manner of determining the correlation coefficient of any two environmental monitoring units in other types of environmental data is similar to that of determining the correlation coefficient of any two environmental monitoring units in temperature types of environmental data, and is not repeated here.

When the correlation coefficient of any two environmental monitoring units in certain type of environmental data is larger than a preset correlation coefficient threshold, the two environmental monitoring units have a correlation relationship, on a monitoring point correlation map corresponding to the type of environmental data, two nodes representing the two environmental monitoring units can be connected through edges, the length of the edges can represent the correlation coefficient of the two environmental monitoring units in the type of environmental data, and the larger the correlation coefficient is, the shorter the edges are.

The edge computing module may include a plurality of edge computing units, wherein one edge computing unit is in data interaction with at least one environmental monitoring unit. The edge computing unit and the environment monitoring unit can conduct data interaction through a wireless network.

The edge computing module can establish a data interaction relationship between the plurality of environment monitoring units and the plurality of edge computing units based on the plurality of monitoring point association patterns, and the plurality of environment monitoring units and the plurality of edge computing units interact data based on the data interaction relationship.

FIG. 3 is a schematic flow chart of establishing a data interaction relationship between a plurality of environmental monitoring units and a plurality of edge computing units according to some embodiments of the present disclosure, as shown in FIG. 3, in some embodiments, the edge computing module establishes a data interaction relationship between a plurality of environmental monitoring units and a plurality of edge computing units based on a plurality of monitoring point association maps, including:

Calculating the association degree of the monitoring units of any two environment monitoring units based on the association maps of the plurality of monitoring points;

based on the association degree of the monitoring units of any two environment monitoring units, clustering the plurality of environment monitoring units to determine a plurality of monitoring unit clustering clusters;

for each monitoring unit cluster, determining the data processing requirement corresponding to the monitoring unit cluster based on the data processing requirement corresponding to the environment monitoring unit included in the monitoring unit cluster;

when the data processing requirement corresponding to the monitoring unit cluster is smaller than or equal to a preset data processing requirement threshold, taking the monitoring unit cluster as a target monitoring unit cluster;

when the data processing requirement corresponding to the monitoring unit cluster is larger than a preset data processing requirement threshold, performing secondary clustering on a plurality of environmental monitoring units included in the monitoring unit cluster based on the data processing requirement corresponding to each environmental monitoring unit included in the monitoring unit cluster and the monitoring unit association degree of any two environmental monitoring units, and determining a plurality of monitoring unit cluster groups included in the monitoring unit cluster and the data processing requirement corresponding to each monitoring unit cluster group;

Based on the data processing requirements corresponding to each target monitoring unit cluster and the data processing requirements corresponding to each monitoring unit cluster group, a data interaction relationship between a plurality of environment monitoring units and a plurality of edge computing units is established.

Specifically, for any two environmental monitoring units, based on a plurality of monitoring point association maps, the correlation coefficient of the two environmental monitoring units in each type of environmental data can be determined, the correlation coefficients of the two environmental monitoring units in each type of environmental data are weighted and summed, and the monitoring unit association degree of the two environmental monitoring units is obtained through calculation.

The plurality of environmental monitoring units can be clustered according to a k-means clustering algorithm (k-means clustering algorithm) based on the monitoring unit association degree of any two environmental monitoring units, a plurality of monitoring unit clustering clusters are determined, and when the monitoring unit association degree between a certain environmental monitoring unit and an environmental monitoring unit corresponding to a certain first clustering center is greater than a first preset monitoring unit association degree threshold value, the environmental monitoring units can be clustered to the monitoring unit clustering cluster where the first clustering center is located.

The method comprises the steps that a plurality of environment monitoring units included in a monitoring unit cluster can be subjected to secondary clustering at least once according to a k-means clustering algorithm (k-means clustering algorithm) based on the monitoring unit association degree and constraint conditions of any two environment monitoring units included in the monitoring unit cluster, a plurality of monitoring unit cluster groups included in the monitoring unit cluster are determined, when the monitoring unit association degree between a certain environment monitoring unit and the environment monitoring unit corresponding to a certain second clustering center is larger than a second preset monitoring unit association degree threshold, the environment monitoring unit can be clustered to the monitoring unit cluster where the second clustering center is located until the data processing requirement corresponding to each monitoring unit cluster group obtained after the secondary clustering is smaller than a preset data processing requirement threshold, and the second preset monitoring unit association degree threshold can be adjusted based on the monitoring unit association degree of any two environment monitoring units included in the monitoring unit cluster.

When the data processing requirement corresponding to the monitoring unit cluster is greater than a preset data processing requirement threshold, the data processing requirement corresponding to the monitoring unit cluster is greater than the maximum calculation power load threshold of a single edge calculation unit, so that a plurality of environment monitoring units included in the monitoring unit cluster are required to be subjected to secondary clustering, and the monitoring unit cluster is divided into a plurality of monitoring unit cluster groups.

It can be understood that by arranging a plurality of edge computing units, distributed processing of various types of environmental data of a production field acquired by the plurality of environmental monitoring units can be realized, compared with centralized processing of the transmission data to a center, the real-time performance is higher, on the basis, by establishing a plurality of monitoring point association maps, the monitoring unit association degree of any two environmental monitoring units is determined, the plurality of environmental monitoring units are clustered at least once, grouping of the plurality of environmental monitoring units is realized, so that the subsequent edge computing units can determine the environmental risk information of the production field based on various types of environmental data of the production field acquired by a target monitoring unit cluster or a plurality of environmental monitoring units included in a monitoring unit cluster group, and compared with determining the environmental risk information of the production field based on various types of environmental data of the production field acquired by a single monitoring unit cluster group, the accuracy is higher.

The edge computing unit can be used for determining environmental risk information of a production field area based on the plurality of monitoring point association maps and the acquired plurality of types of environmental data of the production field acquired by the at least one environmental monitoring unit.

In some embodiments, the edge computing unit determines production site area environmental risk information based on the plurality of monitoring point association maps and the acquired plurality of types of environmental data of the production site acquired by the at least one environmental monitoring unit, including:

for each target monitoring unit cluster, determining a first environment data matrix corresponding to the plurality of types of environment data corresponding to the target monitoring unit cluster based on the plurality of types of environment data of the production site collected by each environment monitoring unit included in the target monitoring unit cluster, and determining environment risk information of a production site area corresponding to the target monitoring unit cluster based on the first environment data matrix;

for each monitoring unit cluster group, determining a second environment data matrix corresponding to the plurality of types of environment data corresponding to the monitoring unit cluster group based on the plurality of types of environment data of the production site collected by each environment monitoring unit included in the monitoring unit cluster group, and determining environment risk information of the production site area corresponding to the monitoring unit cluster group based on the second environment data matrix.

Specifically, for each cluster of target monitoring units, a corresponding first risk determination model may be established and trained, through which the first risk determination model is passed. And determining the environmental risk information of the production field area corresponding to the target monitoring unit cluster based on a first environmental data matrix corresponding to the plurality of types of environmental data corresponding to the target monitoring unit cluster through a first risk determination model. For each cluster of monitoring units a corresponding second risk determination model may be established and trained, by means of which. And determining the environmental risk information of the production field area corresponding to the monitoring unit cluster group based on a second environmental data matrix corresponding to the plurality of types of environmental data corresponding to the monitoring unit cluster group through a second risk determination model. The first risk determination model and the second risk determination model may be machine learning models such as an artificial neural network (Artificial Neural Network, ANN) model, a recurrent neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, a bi-directional recurrent neural network (BRNN) model, and the like. The production site area environmental risk information may include information of a location in the area where there is a risk, a risk type of the location (e.g., temperature abnormality, humidity abnormality, sound abnormality, etc.), and the like.

In some embodiments, the edge computing unit determines, based on the plurality of types of environmental data of the production site collected by each environmental monitoring unit included in the target monitoring unit cluster, a first environmental data matrix corresponding to the plurality of types of environmental data corresponding to the target monitoring unit cluster, including:

generating a first initial environment data matrix corresponding to each type of environment data based on a plurality of types of environment data of a production site collected by each environment monitoring unit included in the target monitoring unit cluster, wherein a row vector of the first initial environment data matrix is one type of environment data collected by one environment monitoring unit included in the target monitoring unit cluster at a plurality of data collection time points;

and carrying out data denoising and correction on a first initial environmental data matrix corresponding to each type of environmental data, and determining a first environmental data matrix corresponding to a plurality of types of environmental data corresponding to the target monitoring unit cluster.

Fig. 4 is a schematic flow chart of data denoising and correction for a first initial environmental data matrix corresponding to each type of environmental data according to some embodiments of the present disclosure, as shown in fig. 4, in some embodiments, an edge computing unit performs data denoising and correction for a first initial environmental data matrix corresponding to each type of environmental data, and determines a first environmental data matrix corresponding to a plurality of types of environmental data corresponding to a cluster of target monitoring units, including:

Denoising and correcting a first initial environmental data matrix corresponding to a temperature type to generate a first environmental data matrix corresponding to the temperature type, specifically, for each row vector of the first environmental data matrix corresponding to the temperature type, determining a temperature difference value between temperature data collected by one environmental monitoring unit at each data collection time point and temperature data collected by an adjacent time point, determining a noise point according to the difference value, for example, taking temperature data collected by data collection time points, the temperature difference value of which is greater than a preset temperature difference value, of which is collected by a plurality of adjacent time points before and after the noise point is removed, determining a plurality of temperature-related environmental monitoring units with relevance relation to the environmental monitoring unit in the temperature type based on a monitoring point relevance map corresponding to the temperature type, and performing data complementation and data complementation on the row vector of the denoised first environmental data matrix corresponding to the environmental monitoring unit and the denoised first environmental data matrix corresponding to the environmental monitoring unit based on a plurality of temperature-related environmental monitoring units with relevance relation to the temperature type through a temperature data correction model, namely performing data complementation and complete data complementation on the row vector of the denoised first environmental data matrix corresponding to the environmental monitoring unit;

Denoising and correcting the first initial environment data matrix corresponding to the humidity type to generate a first environment data matrix corresponding to the humidity type, wherein the mode of generating the first environment data matrix corresponding to the humidity type is similar to that of generating the first environment data matrix corresponding to the temperature type, and the description is omitted herein;

denoising the first initial environmental data matrix corresponding to the smoke type to generate a denoised first initial environmental data matrix corresponding to the smoke type, wherein the denoising mode of the first initial environmental data matrix corresponding to the smoke type is similar to that of the first environmental data matrix corresponding to the temperature type, and is not repeated here;

for each smoke row vector of a first initial environment data matrix corresponding to the smoke type after denoising, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the smoke row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, carrying out first correction on the smoke row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the smoke row vector, generating a first initial environment data matrix corresponding to the smoke type after first correction, specifically, for each smoke row vector, generating an auxiliary correction temperature change curve based on the temperature row vector corresponding to the smoke row vector in the first environment data matrix corresponding to the temperature type by an environment monitoring unit corresponding to the smoke row vector, generating an auxiliary correction humidity change curve based on the temperature row vector corresponding to the first environment data matrix corresponding to the humidity type by the environment monitoring unit corresponding to the smoke row vector, carrying out first correction on the smoke row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the smoke row vector by the smoke data correction model, and carrying out first correction on the smoke row vector after first correction, namely generating smoke initial environment data corresponding to the smoke row vector after first initial correction matrix;

Denoising the first initial environmental data matrix corresponding to the dust type to generate a denoised first initial environmental data matrix corresponding to the dust type, wherein the denoising mode of the first initial environmental data matrix corresponding to the dust type is similar to that of the first environmental data matrix corresponding to the temperature type, and is not repeated here;

for each dust row vector of the first initial environment data matrix corresponding to the denoised dust type, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the dust row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, and carrying out first correction on the dust row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the dust row vector, wherein the mode of carrying out first correction on the dust row vector is similar to the mode of carrying out first correction on the smoke row vector, and is not repeated here;

for each environmental monitoring unit included in the target monitoring unit cluster, determining a first corrected smoke line vector corresponding to the environmental monitoring unit based on a first initial environmental data matrix corresponding to the first corrected smoke type, generating a smoke concentration change curve, determining a first corrected dust line vector corresponding to the environmental monitoring unit based on the first initial environmental data matrix corresponding to the first corrected dust type, generating a dust concentration change curve, performing empirical mode decomposition on the smoke concentration change curve, generating at least one smoke content modal component and smoke residual, performing empirical mode decomposition on the dust concentration change curve, generating at least one dust content modal component and smoke residual, performing a second correction on the first corrected dust line vector corresponding to the environmental monitoring unit and the first corrected smoke line vector based on the at least one smoke content modal component and smoke residual and the at least one dust content modal component and the dust residual, and specifically performing a second correction on the second corrected dust line vector corresponding to the environmental monitoring unit and the second corrected dust line vector corresponding to the environmental monitoring unit by combining the at least one smoke content modal component and the first corrected dust line vector and the second corrected smoke residual;

Generating a first environment data matrix corresponding to the smoke type and a first environment data matrix corresponding to the dust type based on the second corrected dust row vector and the second corrected smoke row vector corresponding to each environment monitoring unit included in the target monitoring unit cluster.

The temperature data correction model, the smoke data correction model and the joint correction model may be machine learning models such as an artificial neural network (Artificial Neural Network, ANN) model, a cyclic neural network (Recurrent Neural Networks, RNN) model, a Long Short-Term Memory (LSTM) model, a bidirectional cyclic neural network (BRNN) model, and the like.

It can be understood that the temperature and the humidity will affect the detection of the smoke concentration and the dust concentration, so that the smoke line vector needs to be corrected for the first time based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the smoke line vector, the dust line vector needs to be corrected for the first time based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the dust line vector, and the smoke and the dust will affect each other in the detection process of the smoke concentration and the dust concentration, so that the dust line vector and the smoke line vector after the first correction corresponding to the environment monitoring unit need to be corrected for the second time based on at least one smoke content modal component and smoke residual and at least one dust content modal component and dust residual, so as to generate a first environment data matrix corresponding to the smoke type and a first environment data matrix corresponding to the dust type, thereby improving the accuracy of the environment risk of the production field area which is determined later.

The manner of generating the second environmental data matrix is similar to that of generating the first environmental data matrix, and will not be described again here.

The pipe network monitoring module can be used for acquiring flow rate information and real-time weather information of the sewage pipe network on the production site, and determining the damage risk and the mixed drainage risk of the pipe network based on the flow rate information and the real-time weather information of the sewage pipe network on the production site. For example, the obtained real-time weather information indicates that the current weather is sunny, but according to the flow rate information of the sewage pipe network, if the sewage pipe for draining rainwater is monitored, the sewage pipe for draining rainwater is possibly damaged, on the basis of the flow rate information of the sewage pipe network, whether the sewage pipe for draining production sewage is draining sewage can be monitored, if so, according to the flow rate information of the sewage pipe network on the production site, the sewage draining time of the sewage pipe for draining production sewage and the sewage draining time of the sewage pipe for draining rainwater are determined, and whether mixed draining occurs between the sewage pipe for draining rainwater and the sewage pipe for draining production sewage is judged.

The control monitoring module can be used for acquiring the operation information of the production equipment and the operation information of the control equipment on the production site, and determining the parallel risk of production and control based on the operation information of the production equipment and the operation information of the control equipment on the production site.

Specifically, according to the operation information of the production equipment and the operation information of the treatment equipment on the production site, whether the treatment equipment is always in an operation state or not is determined in the operation process of the production equipment on the production site.

The dangerous waste monitoring module can be used for acquiring image information of a dangerous waste placement area of a production site and determining mixing risk and dangerous waste leakage risk of the dangerous waste placement area of the production site.

Specifically, the dangerous waste monitoring module can identify labels on the strong acid storage tank and the strong base storage tank placed in the dangerous waste placement area according to the image information of the dangerous waste placement area of the production site, and judge whether the situation of mixed placement of strong acid and strong base exists in the same dangerous waste placement area. The dangerous waste monitoring module can identify whether liquid exists in the diversion trench of the dangerous waste placement area according to the image information of the dangerous waste placement area of the production site, and judge whether dangerous waste leakage occurs.

The operation monitoring module can be used for acquiring operation image information of the production site and determining operation risks of the production site.

Specifically, the operation monitoring module can identify the action of the production equipment on the production site according to the operation image information of the production equipment on the production site, and judge whether the production equipment on the production site has an irregular action. And the actions of operators on the production site can be identified according to the operation image information of the production equipment on the production site, and whether the operators on the production site generate the non-compliance actions can be judged.

The risk management and control module can be used for managing and controlling the production site environment based on the risk of damage of the pipe network, the risk of mixed discharge, the parallel risk of production and treatment, the risk of mixed discharge, the risk of hazardous waste leakage, the operation risk and the production site area environment risk information uploaded by the plurality of edge computing units.

For example, when judging that the sewage pipeline is damaged, mixed-discharged, the treatment equipment does not run along with the production equipment, whether the mixed-discharged condition of strong acid and strong alkali exists in the same dangerous waste placement area, dangerous waste leakage, the production equipment on the production site generates an irregular action and an operator on the production site generates an irregular action, corresponding prompt information is generated, and broadcasting is performed on the production site and/or the prompt information is sent to a mobile terminal (such as a mobile phone) of an administrator on the production site.

In some embodiments, the risk management module manages a production site environment based on production site area environment risk information uploaded by the plurality of edge computing units, including:

determining the risk type of a production site based on the environmental risk information of the production site area uploaded by the plurality of edge computing units;

determining target management and control operation based on the risk type of the production field;

And controlling the production field environment based on the target control operation.

Specifically, the control operation may include cooling, drying, humidifying, stopping production, fire fighting, and the like. Each management and control operation can correspond to a risk type and management and control equipment, and after the target management and control operation is determined based on the risk type of the production field, the corresponding management and control equipment can be controlled to execute the target management and control operation to manage and control the production field environment.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. Intelligent management and control system suitable for industrial enterprise production scene environmental risk, characterized by comprising:

the environment monitoring module comprises a plurality of environment monitoring units, wherein each target monitoring point is provided with one environment monitoring unit, and the environment monitoring units are used for collecting various environmental data of a production field;

The environment monitoring module is also used for establishing a plurality of monitoring point association maps, wherein one monitoring point association map is used for representing the association relation of a plurality of target monitoring points on one type of environment data;

the edge computing module comprises a plurality of edge computing units, wherein one edge computing unit performs data interaction with at least one environment monitoring unit, and the edge computing unit is used for determining environmental risk information of a production field area based on the plurality of monitoring point association maps and acquired environment data of a plurality of types of production fields acquired by the at least one environment monitoring unit;

the pipe network monitoring module is used for acquiring flow rate information and real-time weather information of the sewage pipe network of the production site and determining pipe network damage risks and mixed discharge risks based on the flow rate information and the real-time weather information of the sewage pipe network of the production site;

the control monitoring module is used for acquiring the operation information of the production equipment and the operation information of the control equipment on the production site, determining the parallel risk of production and control based on the operation information of the production equipment and the operation information of the control equipment on the production site, and particularly determining whether the control equipment is always in an operation state or not in the operation process of the production equipment on the production site according to the operation information of the production equipment and the operation information of the control equipment on the production site;

The hazardous waste monitoring module is used for acquiring image information of the hazardous waste placement area of the production site and determining the mixing risk and the hazardous waste leakage risk of the hazardous waste placement area of the production site;

the operation monitoring module is used for acquiring operation image information of the production site and determining operation risks of the production site;

the risk management and control module is used for managing and controlling the production site environment based on the risk of damage of the pipe network, the mixed discharge risk, the parallel risk of production and treatment, the mixed discharge risk, the risk of dangerous waste leakage, the operation risk and the production site area environment risk information uploaded by the plurality of edge computing units.

2. The intelligent management and control system for environmental risks of a production site of an industrial enterprise according to claim 1, further comprising a monitoring point determining module, configured to obtain image information of a production area of the production site, and determine a plurality of target monitoring points based on the image information of the production area of the production site;

the monitoring point determining module determines a plurality of target monitoring points through the following procedures:

acquiring image information of a production area of the production site;

determining position information of a plurality of devices included in the production site and association relations among the plurality of devices based on image information of a production area of the production site;

Acquiring type information and parameter information of each device included in the production field;

generating layout information of the production site based on the position information of a plurality of devices included in the production site, the association relation among the plurality of devices, the type information and the parameter information of each device;

and determining a plurality of target monitoring points based on the layout information of the production site.

3. The intelligent environmental risk management and control system for a production site of an industrial enterprise according to claim 2, wherein the monitoring point determining module determines a plurality of target monitoring points based on layout information of the production site, including:

acquiring layout information of a plurality of sample production sites;

for any one of the sample production sites, calculating the layout similarity between the layout information of the production site and the layout information of the sample production site;

judging whether at least one target production site exists in the plurality of sample production sites or not based on the layout information of the production sites and the layout similarity between the layout information of each sample production site, wherein the target production site is a sample production site with the layout similarity of the layout information of the current production site being greater than a preset layout similarity threshold;

When judging that at least one target production field exists, determining a plurality of target monitoring points based on monitoring point distribution information corresponding to each target production field;

4. An intelligent environmental risk management and control system for industrial enterprises according to any one of claims 1-3, wherein the environmental monitoring module establishes a plurality of monitoring point association maps, comprising:

acquiring a plurality of types of acquired environmental data of the plurality of environmental monitoring units in a plurality of simulation environments;

based on a plurality of types of collected environmental data of the plurality of environmental monitoring units in a plurality of simulation environments, determining correlation coefficients of any two environmental monitoring units in each type of environmental data;

and establishing a plurality of monitoring point association maps based on the correlation coefficient of any two environmental monitoring units in each type of environmental data.

5. The intelligent management and control system for environmental risk in a production field of an industrial enterprise according to claim 4, wherein the edge computing module is further configured to establish a data interaction relationship between the plurality of environmental monitoring units and the plurality of edge computing units based on the plurality of monitoring point association maps, and the data interaction relationship between the plurality of environmental monitoring units and the plurality of edge computing units is based on the data interaction relationship, and specifically includes:

based on the association degree of any two environment monitoring units, clustering the plurality of environment monitoring units to determine a plurality of monitoring unit clustering clusters;

when the data processing requirement corresponding to the monitoring unit cluster is greater than a preset data processing requirement threshold, performing secondary clustering on a plurality of environmental monitoring units included in the monitoring unit cluster based on the data processing requirement corresponding to each environmental monitoring unit included in the monitoring unit cluster and the monitoring unit association degree of any two environmental monitoring units, and determining a plurality of monitoring unit cluster groups included in the monitoring unit cluster and the data processing requirement corresponding to each monitoring unit cluster group;

And establishing a data interaction relationship between the plurality of environment monitoring units and the plurality of edge computing units based on the data processing requirements corresponding to each target monitoring unit cluster and the data processing requirements corresponding to each monitoring unit cluster group.

6. The intelligent environmental risk management and control system for industrial enterprises according to claim 5, wherein the environmental monitoring unit comprises a temperature monitoring component, a humidity monitoring component, a smoke monitoring component, a dust monitoring component, a sound monitoring component, a carbon dioxide monitoring component and a carbon monoxide monitoring component.

7. The intelligent management and control system for environmental risk of industrial enterprise production site of claim 6, wherein the edge computing unit determines environmental risk information of the production site area based on the plurality of monitoring point association maps and the acquired environmental data of the production site collected by the at least one environmental monitoring unit, and comprises:

for each target monitoring unit cluster, determining a first environment data matrix corresponding to the plurality of types of environment data corresponding to the target monitoring unit cluster based on the plurality of types of environment data of the production site collected by each environment monitoring unit included in the target monitoring unit cluster, and determining environment risk information of a production site area corresponding to the target monitoring unit cluster based on the first environment data matrix, wherein each type of environment data corresponds to one first environment data matrix;

For each monitoring unit clustering group, determining a second environment data matrix corresponding to the plurality of types of environment data corresponding to the monitoring unit clustering group based on the plurality of types of environment data of the production site collected by each environment monitoring unit included in the monitoring unit clustering group, and determining environment risk information of the production site area corresponding to the monitoring unit clustering group based on the second environment data matrix, wherein each type of environment data corresponds to one second environment data matrix.

8. The intelligent environmental risk management and control system for an industrial enterprise production site according to claim 7, wherein the edge computing unit determines a first environmental data matrix corresponding to a plurality of types of environmental data corresponding to the target monitoring unit cluster based on a plurality of types of environmental data of a production site collected by each environmental monitoring unit included in the target monitoring unit cluster, and the first environmental data matrix comprises:

And carrying out data denoising and correction on the first initial environmental data matrix corresponding to each type of environmental data, and determining the first environmental data matrix corresponding to the plurality of types of environmental data corresponding to the target monitoring unit cluster.

9. The intelligent environmental risk management and control system for industrial enterprises according to claim 8, wherein the edge computing unit performs data denoising and correction on the first initial environmental data matrix corresponding to each type of environmental data, determines a first environmental data matrix corresponding to a plurality of types of environmental data corresponding to the target monitoring unit cluster, and comprises:

denoising and correcting the first initial environment data matrix corresponding to the temperature type to generate a first environment data matrix corresponding to the temperature type;

denoising and correcting the first initial environment data matrix corresponding to the humidity type to generate a first environment data matrix corresponding to the humidity type;

denoising the first initial environmental data matrix corresponding to the smoke type to generate a denoised first initial environmental data matrix corresponding to the smoke type;

for each smoke row vector of a first initial environment data matrix corresponding to the denoised smoke type, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the smoke row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, and carrying out first correction on the smoke row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the smoke row vector to generate a first initial environment data matrix corresponding to the smoke type after the first correction;

Denoising the first initial environmental data matrix corresponding to the dust type to generate a denoised first initial environmental data matrix corresponding to the dust type;

for each dust row vector of a first initial environment data matrix corresponding to the denoised dust type, generating an auxiliary correction temperature change curve and an auxiliary correction humidity change curve corresponding to the dust row vector based on the first environment data matrix corresponding to the temperature type and the first environment data matrix corresponding to the humidity type, and carrying out first correction on the dust row vector based on the auxiliary correction temperature change curve and the auxiliary correction humidity change curve corresponding to the dust row vector to generate a first initial environment data matrix corresponding to the dust type after the first correction;

for each environmental monitoring unit included in the target monitoring unit cluster, determining a first corrected smoke line vector corresponding to the environmental monitoring unit based on a first initial environmental data matrix corresponding to the first corrected smoke type, generating a smoke concentration change curve, determining a first corrected dust line vector corresponding to the environmental monitoring unit based on the first initial environmental data matrix corresponding to the first corrected dust type, generating a dust concentration change curve, performing empirical mode decomposition on the smoke concentration change curve, generating at least one smoke content modal component and a smoke residual, performing empirical mode decomposition on the dust concentration change curve, generating at least one dust content modal component and a dust residual, performing a second correction on the first corrected dust line vector corresponding to the environmental monitoring unit and the first corrected smoke line vector based on the at least one smoke content modal component and the smoke residual and the at least one dust content modal component and the dust residual, and generating a second corrected dust vector corresponding to the environmental monitoring unit and a second corrected dust vector;

Generating a first environment data matrix corresponding to the dust type based on the second corrected dust row vector corresponding to each environment monitoring unit included in the target monitoring unit cluster, and generating a first environment data matrix corresponding to the smoke type based on the second corrected smoke row vector corresponding to each environment monitoring unit included in the target monitoring unit cluster.

10. The intelligent management and control system for environmental risks in a production site of an industrial enterprise according to any one of claims 1 to 3, wherein the risk management and control module manages the environmental of the production site based on risk of pipe network breakage, risk of mixed discharge, parallel risk of production and management, risk of mixed discharge, risk of hazardous waste leakage, risk of operation, and environmental risk information of the production site uploaded by the plurality of edge computing units, and includes:

determining a risk type of the production site based on the production site area environment risk information uploaded by the plurality of edge computing units;

determining a target management and control operation based on the risk type of the production site;