Generative intelligent control system with coal mine risk checking function

Abstract

The invention discloses a generative intelligent control system with a coal mine risk investigation function, which relates to the technical field of risk investigation and comprises a mine end, a PC end, an environment change unit, a risk constraint unit, a risk evaluation unit, a risk early warning unit and a risk investigation unit. Risk points are identified through the environment change unit, internal and external environment changes of production are obtained according to changes of the risk points, so that the production environment subsystem is established, risks possibly occurring in the coal mine production process are monitored through the risk constraint unit, and the technical management subsystem is established in combination with the risk control capability of the production environment subsystem. The overall risk state of the coal mine accident is determined through the risk evaluation unit according to the risk restriction relation between the production environment subsystem and the technical management subsystem, and the comprehensive evaluation result is reflected through the weight acquisition module and the comprehensive evaluation module, so that effective control of coal mine accident hidden danger investigation is realized, and a guarantee is provided for safe production of a coal mine.

Description

Technical Field

[0001] The present invention relates to the technical field of risk investigation, and in particular to a generative intelligent control system with a coal mine risk investigation function. Background Art

[0002] The coal mining industry is restricted and affected by the geological environment. The working environment of mines is harsh and complex. The safety management level in the coal mining industry is insufficient. The overall quality of employees varies. The coal industry has always been a high-incidence area for accidents and the safety production situation is extremely severe. In recent years, with the continuous improvement of computer information technology, the informatization construction of the coal mining industry has achieved rapid development. The safety production situation of coal mines in my country has continued to improve. However, with the increasing requirements of the coal industry for the overall level of coal mine safety, the data in some existing coal mine safety information management systems are isolated, and the coal mine safety big data has not been effectively utilized;

[0003] At present, there is a lack of sufficient understanding and attention to the investigation and control of coal mine risks. The actual application effect of the risk investigation system in many coal mines is not ideal, and it is difficult to effectively prevent and control accidents. Due to the particularity and complexity of the coal mine production environment, it poses a great threat to the safe production of coal mines. The occurrence of accident hazards is random and sudden. Accident hazard investigation provides a full-process risk management mechanism for accident prevention and control in coal mines. However, due to insufficient understanding of coal mine accident hazards, the current coal mine accident hazard investigation management system does not meet the management needs of accident hazard investigation and control, and it is difficult to play the role of accident prevention and control of coal mine accident hazard investigation. Summary of the invention

[0004] The purpose of the present invention is to provide a generative intelligent control system with a coal mine risk screening function to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: a generative intelligent control system with a coal mine risk investigation function, comprising a mine terminal and a PC terminal, wherein the mine terminal is for daily management and control of risks and hidden dangers in the coal mine, and the PC terminal is for intuitive display of risks and hidden dangers in the coal mine, and timely feedback of hidden dangers and risk control conditions found in the coal mine in the form of voice, pictures and videos;

[0006] An environmental change unit, which combines coal mine drawings with risk and hidden danger locations based on GIS to visualize risk and hidden danger information, establish a risk database, and identify risk points on the GIS map based on the risks in the risk database, and obtain changes in the internal and external production environment based on changes in risk points;

[0007] A risk constraint unit, which obtains risk constraint capabilities according to changes in the internal and external environments of the environmental change unit, and performs real-time monitoring and evaluation of environmental risks that may occur during the production process;

[0008] A risk assessment unit, which is used to determine the number of accident hidden dangers to be checked, thereby determining the risk status of the current coal mine accident, obtaining risk indicators based on the risk status, and further obtaining combination weights, thereby providing basic data for the risk warning unit;

[0009] A risk warning unit, which is used to determine the risk of potential accidents in the coal mine production system, and determine the risk assessment level and warning level of potential accidents in the coal mine production system according to the risk of potential accidents in the coal mine production system, and take corresponding investigation measures according to the corresponding warning level;

[0010] The risk investigation unit is used to extract characteristic information from the collected coal mine hidden danger data, distinguish different types of hidden dangers, and prompt the user to perform hidden danger management according to the hidden danger type.

[0011] Preferably, the step of acquiring the changes in the production environment inside and outside in the environmental change unit is:

[0012] S1. Risk identification and assessment are carried out according to risk points. During the production process, risk points are continuously generated in the risk database, and the risk database is continuously updated;

[0013] S2. Identify risk factors through production process monitoring and observe whether risk points have changed effectively. If so, continue with real-time risk assessment. If not, go to step S1;

[0014] S3. According to the result of the real-time risk assessment, determine whether the result is acceptable. If so, go to step S1. If not, perform risk control on the risk point. If the control is unsuccessful, determine that it is a dangerous state and need to issue a warning.

[0015] S4. After successful control, evaluate the control effect and observe changes in the internal and external production environment.

[0016] Preferably, when the risk constraint unit monitors environmental risks in real time, it uses sensors to monitor environmental parameters in the production process in real time, including temperature, humidity and gas concentration, and uses cameras to monitor images of the production site so as to detect abnormalities and provide feedback in a timely manner, and evaluates the possibility of environmental risks by analyzing the monitored environmental parameters and image data.

[0017] Preferably, the risk assessment unit includes a risk control module and a factor screening module. The risk control module establishes a production environment subsystem according to changes in the internal and external production environment, establishes a technical management subsystem according to the risk constraint capability, and determines the overall risk of coal mine accidents through the risk constraint relationship between the production environment subsystem and the technical management subsystem. The constraint relationship is presented by the number of potential accident hazards checked, the risk state of the production environment subsystem reflects the potential danger of the accident, and the risk constraint capability of the technical management subsystem reflects the risk control capability from the production environment subsystem.

[0018] The factor screening module determines the number of potential accident hazards to be checked through the risk function expression H=f(S,C), where H represents the number of potential accident hazards to be checked, S represents the hazard value of the production environment subsystem, and C represents the risk constraint capacity of the technical management subsystem, thereby determining the overall risk status of coal mine accidents.

[0019] Preferably, the risk assessment unit further comprises a weight acquisition module and a comprehensive evaluation module, wherein the weight acquisition module obtains risk indicators according to the risk status, determines the subjective weights of the indicators by using the hierarchical analysis method, determines the objective weights of the indicators by using the grey correlation analysis method, and allocates the subjective weights and the objective weights by using the weighted average method to obtain the combined weights;

[0020] The comprehensive evaluation module analyzes a large amount of indicator data through the cloud generator in the cloud model, and then obtains cloud maps of different indicator levels through cloud transformation. The cloud maps are intersected, reflecting the ambiguity of the level value. At the same time, the qualitative indicators are quantified. The horizontal axis represents different risk levels, and the vertical axis represents the certainty of the level. The combined weights and scores of each indicator are plotted in the shape of a cloud, which provides basic data for the risk warning unit and intuitively reflects the comprehensive evaluation results.

[0021] Preferably, the risk warning unit includes a risk reflection module and a grade classification module. The risk reflection module fills in the number of actual accident hazard inspections in the entire control system according to historical records, and uses the comparison relationship between the number of actual accident hazard inspections and the number of potential accident hazard inspections to reflect the accident hazard risk in the coal mine production system. The expression is R=H' / H, where R represents the accident hazard risk in the coal mine production system, H' represents the number of actual accident hazard inspections, and H represents the number of potential accident hazard inspections.

[0022] The grading module determines the coal mine accident hazard risk assessment grade and warning level classification according to the accident hazard risk, and divides the coal mine accident hazard risk assessment grade into four levels. When R≥1.1, there is no warning level; when 0.8≤R≤1.1, the warning level is in a low warning state; when 0.5≤R≤0.8, the warning level is in a medium warning state; when R≤0.5, the warning level is in a severe warning state. According to the corresponding warning level, corresponding investigation measures are taken.

[0023] Preferably, the risk warning unit also includes a regional inspection module, which inspects each area, analyzes the inspection information according to time, category and level dimensions, and transmits the analysis data to the PC. The risk, hidden danger distribution and inspection status can be intuitively seen through the PC. At the same time, early warnings are issued for areas with high frequency of risks and hidden dangers. The query time period can be set, and the distribution of inspection times at various locations in the mine within the time period can be queried to identify management weaknesses and strengthen supervision and inspection.

[0024] Preferably, the risk investigation unit includes a feature extraction module, a hidden danger identification module and a hidden danger association module. The feature extraction module collects a large amount of pictures, videos and text data of coal mine hidden dangers through coal mine on-site inspection and monitoring equipment, and cleans, denoises and normalizes the collected data, uses computer vision technology to extract feature information from the coal mine hidden danger data, and uses a trained convolutional neural network model to classify the coal mine hidden danger data and distinguish different types of hidden dangers;

[0025] The hidden danger identification module makes a preliminary identification and determination based on the hidden danger type, and feeds back the determination result to the PC end, including the hidden danger type and hidden danger location, prompting the user to take timely measures to deal with the hidden danger;

[0026] The hidden danger association module formulates an inspection plan, associates the monthly management risks with the hidden dangers involved in the inspection route in the plan, pushes the hidden danger risks to the PC for on-site inspection, and feeds back to the mine end after the hidden danger management is completed. When the hidden danger risks are pushed, the description of the hidden danger attributes needs to be associated with the risks in the risk database to provide data support for the subsequent hidden danger management.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention identifies risk points through an environmental change unit, obtains changes in the internal and external production environments according to changes in risk points, thereby establishing a production environment subsystem, monitors possible risks in the coal mine production process through a risk constraint unit, and establishes a technical management subsystem in combination with the risk control capability of the production environment subsystem, determines the overall risk status of coal mine accidents through a risk assessment unit according to the risk constraint relationship between the production environment subsystem and the technical management subsystem, and reflects the comprehensive assessment results through a weight acquisition module and a comprehensive evaluation module, while providing basic data for a risk warning unit, and determines the risk assessment level and warning level division of coal mine accident hazards through the risk warning unit, which helps the effective processing of the risk investigation unit, and effectively improves the quality and efficiency of coal mine accident hazard investigation and management through the risk investigation unit, realizes effective control of coal mine accident hazard investigation, and provides protection for safe production of coal mines. At the same time, the PC terminal provides tools for on-site management of coal mines to ensure the timeliness of information. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the overall system flow provided by an embodiment of the present invention;

[0030] Figure 2 A flowchart for obtaining changes in the internal and external production environment provided by an embodiment of the present invention;

[0031] Figure 3 A block diagram of the internal modules of the risk assessment unit provided by an embodiment of the present invention;

[0032] Figure 4 A block diagram of the internal modules of the risk warning unit provided by an embodiment of the present invention;

[0033] Figure 5 This is a block diagram of the internal modules of the risk investigation unit provided in an embodiment of the present invention.

[0034] In the figure: 1. Environmental change unit; 2. Risk constraint unit; 3. Risk assessment unit; 301. Risk constraint module; 302. Factor screening module; 303. Weight acquisition module; 304. Comprehensive evaluation module; 4. Risk warning unit; 401. Risk reflection module; 402. Level classification module; 403. Area inspection module; 5. Risk investigation unit; 501. Feature extraction module; 502. Hidden danger identification module; 503. Hidden danger association module. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1-5 , the present invention provides a technical solution: a generative intelligent control system with a coal mine risk investigation function, including a mine end and a PC end, the mine end is the daily management and control of risks and hidden dangers in the coal mine, and the PC end is an intuitive display of the risks and hidden dangers of the coal mine, and the hidden dangers and risk control conditions found in the coal mine are fed back in a timely manner in the form of voice, pictures and videos;

[0037] Environmental change unit 1, based on GIS, combines coal mine drawings with risk and hidden danger locations to realize the visualization of risk and hidden danger information, establish a risk database, and mark risk points on the GIS map according to the risks in the risk database, and obtain the changes in the internal and external production environment according to the changes in the risk points;

[0038] Risk constraint unit 2, which obtains risk constraint capability based on the changes in the internal and external environment of the production of environmental change unit 1, and conducts real-time monitoring and evaluation of environmental risks that may occur during the production process;

[0039] Risk assessment unit 3, which is used to determine the number of accident hidden dangers to be checked, thereby determining the risk status of the current coal mine accident, obtaining risk indicators based on the risk status, and further obtaining combination weights, providing basic data for the risk warning unit 4;

[0040] The risk warning unit 4 is used to determine the risk of potential accidents in the coal mine production system, and determine the risk assessment level and warning level of potential accidents in the coal mine production system according to the risk of potential accidents in the coal mine production system, and take corresponding investigation measures according to the corresponding warning level;

[0041] The risk investigation unit 5 is used to extract characteristic information from the collected coal mine hidden danger data, distinguish different types of hidden dangers, and prompt the user to perform hidden danger management according to the hidden danger type.

[0042] The steps for obtaining the changes in the production environment inside and outside in the environment change unit 1 are:

[0043] S1. Risk identification and assessment are carried out according to risk points. During the production process, risk points are continuously generated in the risk database, and the risk database is continuously updated;

[0044] S2. Identify risk factors through production process monitoring and observe whether risk points have changed effectively. If so, continue with real-time risk assessment. If not, go to step S1;

[0045] S3. According to the result of the real-time risk assessment, determine whether the result is acceptable. If so, go to step S1. If not, perform risk control on the risk point. If the control is unsuccessful, determine that it is a dangerous state and need to issue a warning.

[0046] S4. After successful control, evaluate the control effect and observe changes in the internal and external production environment;

[0047] When the risk constraint unit 2 monitors the environmental risk in real time, it uses sensors to monitor the environmental parameters in the production process in real time. The environmental parameters include temperature, humidity and gas concentration, and uses cameras to monitor the production site in order to detect abnormalities and provide feedback in time. The possibility of environmental risk is evaluated by analyzing the monitored environmental parameters and image data;

[0048] The risk assessment unit 3 includes a risk control module 301 and a factor screening module 302. The risk control module 301 establishes a production environment subsystem according to changes in the internal and external production environment, and establishes a technical management subsystem according to the risk constraint capability. The overall risk of coal mine accidents is determined through the risk constraint relationship between the production environment subsystem and the technical management subsystem. The constraint relationship is shown by the number of potential accident hazards checked. The risk status of the production environment subsystem reflects the potential danger of the accident. The risk constraint capability of the technical management subsystem reflects the risk control capability from the production environment subsystem.

[0049] The factor screening module 302 determines the number of potential accident hazards to be checked through the risk function expression H=f(S,C), where H represents the number of potential accident hazards to be checked, S represents the risk value of the production environment subsystem, and C represents the risk constraint capacity of the technical management subsystem, thereby determining the overall risk status of coal mine accidents;

[0050] The risk assessment unit 3 also includes a weight acquisition module 303 and a comprehensive evaluation module 304. The weight acquisition module 303 obtains risk indicators according to the risk status, uses the hierarchical analysis method to determine the subjective weight of the indicator, uses the grey correlation analysis method to determine the objective weight of the indicator, and uses the weighted average method to allocate the subjective weight and the objective weight to obtain the combined weight;

[0051] The specific analytic hierarchy process is:

[0052]

[0053] Among them, w s represents subjective weight, j, n represent the cardinality of each level, j = 1, 2, ..., n, aj Indicates indicator assignment;

[0054] Grey correlation analysis is to calculate the correlation degree of multiple factors in the system and compare the similarity of the geometric shapes of the statistical sequence curves of the system. The greater the correlation degree, the closer the geometric shapes are.

[0055] The weighted average method is as follows:

[0056] w=uw o +(1-u)w s

[0057] Among them, w o represents the objective weight, w s represents subjective weight, w represents combined weight, and u represents allocation index;

[0058] The comprehensive evaluation module 304 analyzes a large amount of indicator data through the cloud generator in the cloud model, and then obtains cloud maps of different indicator levels through cloud transformation. The cloud maps are intersected, which reflects the fuzziness of the level value. At the same time, the qualitative indicators are quantified. The horizontal axis represents different risk levels, and the vertical axis represents the degree of certainty of the level. The combined weights and scores of each indicator are drawn into the shape of a cloud, which provides basic data for the risk warning unit 4 and intuitively reflects the comprehensive evaluation results.

[0059] The core algorithm of the cloud generator is:

[0060] En i =NORM(En,He)

[0061] x i =NORM(Ex,En i )

[0062] μ i =exp(-(x i -Ex) 2 / 2(En i ) 2 )

[0063] Among them, En i represents a normal random variable, x i represents a normal random variable, μ i represents the degree of certainty, NORM represents the normal distribution function, En represents entropy, He represents hyperentropy, Ex represents the average value of the random variable, exp(·) represents the natural exponential function, and i represents the indicator sequence;

[0064] The risk warning unit 4 includes a risk reflection module 401 and a level classification module 402. The risk reflection module 401 fills in the number of actual accident hidden dangers checked in the entire control system according to the historical records, and uses the comparison relationship between the number of actual accident hidden dangers checked and the number of potential accident hidden dangers checked to reflect the accident hidden danger risk in the coal mine production system. The expression is R=H' / H, where R represents the accident hidden danger risk in the coal mine production system, H' represents the number of actual accident hidden dangers checked, and H represents the number of potential accident hidden dangers checked;

[0065] The level classification module 402 determines the risk assessment level of coal mine accident hazards and the warning level classification according to the risk of accident hazards, and divides the risk assessment level of coal mine accident hazards into four levels. When R≥1.1, there is no warning level; when 0.8≤R≤1.1, the warning level is in a low warning state; when 0.5≤R≤0.8, the warning level is in a medium warning state; when R≤0.5, the warning level is in a severe warning state. According to the corresponding warning level, corresponding investigation measures are taken;

[0066] The risk warning unit 4 also includes a regional inspection module 403, which inspects each area, analyzes the inspection information according to the time, category and level dimensions, and transmits the analysis data to the PC end, through which the risk, hidden danger distribution and inspection status can be intuitively seen, and at the same time, early warnings are issued for areas with high frequency of risk and hidden dangers. The query time period can be set, and the distribution of the number of inspections at various locations in the mine within the time period can be queried to find management weaknesses and strengthen supervision and inspection;

[0067] The risk investigation unit 5 includes a feature extraction module 501, a hidden danger identification module 502 and a hidden danger association module 503. The feature extraction module 501 collects a large amount of pictures, videos and text data of hidden dangers in coal mines through on-site inspection and monitoring equipment in coal mines, cleans, denoises and normalizes the collected data, extracts feature information from the hidden danger data in coal mines using computer vision technology, and classifies the hidden danger data in coal mines using a trained convolutional neural network model to distinguish different types of hidden dangers.

[0068] The hidden danger identification module 502 performs preliminary identification and determination according to the hidden danger type, and feeds back the determination result to the PC end, including the hidden danger type and hidden danger location, prompting the user to take timely measures to deal with the hidden danger;

[0069] The hidden danger association module 503 formulates an inspection plan, associates the monthly management risks with the hidden dangers involved in the inspection route in the plan, pushes the hidden danger risks to the PC for on-site inspection, and feeds back to the mine after the hidden danger management is completed. When the hidden danger risks are pushed, the description of the hidden danger attributes needs to be associated with the risks in the risk database to provide data support for the subsequent hidden danger management.

[0070] Working principle: When the present invention is used, the visualization of risk and hidden danger information is realized through the environmental change unit 1, a risk database is established, and risk points are marked on the GIS map according to the risks in the risk database, and the changes in the internal and external environments of the production are obtained according to the changes in the risk points. The risk constraint capability is obtained according to the changes in the internal and external environments of the production of the environmental change unit 1 through the risk constraint unit 2, and the environmental parameters in the production process are monitored in real time. The production site is monitored by the camera, and the possibility of environmental risks is evaluated by analyzing the monitored environmental parameters and image data. A production environment subsystem is established according to the changes in the internal and external environments of the production, and a technical management subsystem is established according to the risk constraint capability. The overall risk of coal mine accidents is determined through the risk constraint relationship between the production environment subsystem and the technical management subsystem, thereby determining the overall risk status of coal mine accidents, and the combined weights and score combinations of various indicators are drawn into the shape of a cloud through the comprehensive evaluation module 304, which provides basic data for the risk warning unit 4 and intuitively reflects the comprehensive evaluation results.

[0071] The number of actual accident hazard inspections is filled in the system according to historical records through the risk warning unit 4, and the comparison relationship between the number of actual accident hazard inspections and the number of potential accident hazard inspections is used to reflect the accident hazard risk in the coal mine production system. The coal mine accident hazard risk assessment level and the warning level are determined according to the accident hazard risk. The coal mine accident hazard risk assessment level is divided into four levels. According to the corresponding warning level, corresponding inspection measures are taken. Through the regional inspection module 403, early warnings are issued for areas with high frequencies of risks and hidden dangers. The system can set a query time period, and can query the distribution of the number of inspections at various locations in the mine within the time period to discover management weaknesses. The risk investigation unit 5 extracts characteristic information from the collected coal mine hidden danger data, distinguishes different types of hidden dangers, and prompts users to perform hidden danger management according to the hidden danger type.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A generative intelligent control system with coal mine risk investigation function, including a mine terminal and a PC terminal, characterized in that: The mine terminal is responsible for the daily management and control of risks and hidden dangers in the coal mine, and the PC terminal is responsible for the intuitive display of risks and hidden dangers in the coal mine, providing timely feedback on the hidden dangers and risk management and control situations found in the coal mine in the form of voice, pictures and videos; An environmental change unit (1), wherein the environmental change unit (1) combines the coal mine drawings with the risk and hidden danger locations based on GIS, realizes the visualization of risk and hidden danger information, establishes a risk database, and identifies risk points on the GIS map according to the risks in the risk database, and obtains changes in the internal and external production environment according to changes in the risk points; A risk constraint unit (2), wherein the risk constraint unit (2) obtains risk constraint capability according to changes in the internal and external environment of the production by the environmental change unit (1), and performs real-time monitoring and evaluation of environmental risks that may occur during the production process; A risk assessment unit (3), wherein the risk assessment unit (3) is used to determine the number of accident hidden dangers to be checked, thereby determining the risk status of the current coal mine accident, obtaining risk indicators based on the risk status, and further obtaining a combination weight, thereby providing basic data for the risk warning unit (4); A risk warning unit (4), the risk warning unit (4) is used to determine the risk of accident hazards in the coal mine production system, and determine the coal mine accident hazard risk assessment level and warning level classification according to the risk of accident hazards in the coal mine production system, and take corresponding investigation measures according to the corresponding warning level; The risk investigation unit (5) is used to extract characteristic information from the collected coal mine hidden danger data, distinguish different types of hidden dangers, and prompt the user to perform hidden danger management according to the hidden danger type.

2. A generative intelligent control system with coal mine risk screening function according to claim 1, characterized in that: The steps for obtaining the changes in the production environment inside and outside the production unit (1) are as follows: S1. Risk identification and assessment are carried out according to risk points. During the production process, risk points are continuously generated in the risk database, and the risk database is continuously updated; S2. Identify risk factors through production process monitoring and observe whether risk points have changed effectively. If so, continue with real-time risk assessment. If not, go to step S1; S3. According to the result of the real-time risk assessment, determine whether the result is acceptable. If so, go to step S1. If not, perform risk control on the risk point. If the control is unsuccessful, determine that it is a dangerous state and need to issue a warning. S4. After successful control, evaluate the control effect and observe changes in the internal and external production environment.

3. A generative intelligent control system with coal mine risk screening function according to claim 1, characterized in that: When the risk constraint unit (2) performs real-time monitoring of environmental risks, it uses sensors to monitor the environmental parameters in the production process in real time, the environmental parameters include temperature, humidity and gas concentration, and uses cameras to monitor the production site in an image, so as to detect abnormalities in a timely manner and provide feedback, and evaluate the possibility of environmental risks by analyzing the monitored environmental parameters and image data.

4. A generative intelligent control system with coal mine risk screening function according to claim 1, characterized in that: The risk assessment unit (3) comprises a risk control module (301) and a factor screening module (302). The risk control module (301) establishes a production environment subsystem according to changes in the internal and external production environment, and establishes a technical management subsystem according to the risk constraint capability. The overall risk of coal mine accidents is determined through the risk constraint relationship between the production environment subsystem and the technical management subsystem. The constraint relationship is shown by the number of potential accident hazards checked. The risk state of the production environment subsystem reflects the potential danger of the accident. The risk constraint capability of the technical management subsystem reflects the risk control capability from the production environment subsystem. The factor screening module (302) determines the number of potential accident hazards checked through a risk function expression H=f(S,C), wherein H represents the number of potential accident hazards checked, S represents the hazard value of the production environment subsystem, and C represents the risk constraint capability of the technical management subsystem, thereby determining the overall risk status of coal mine accidents.

5. A generative intelligent control system with coal mine risk screening function according to claim 4, characterized in that: The risk assessment unit (3) further comprises a weight acquisition module (303) and a comprehensive assessment module (304), wherein the weight acquisition module (303) obtains risk indicators according to the risk status, determines the subjective weights of the indicators by using a hierarchical analysis method, determines the objective weights of the indicators by using a grey correlation analysis method, and allocates the subjective weights and the objective weights by using a weighted average method to obtain a combined weight; The comprehensive evaluation module (304) analyzes a large amount of indicator data through the cloud generator in the cloud model, and then obtains cloud maps of different indicator levels through cloud transformation, and quantifies the qualitative indicators at the same time. The horizontal axis represents different risk levels, and the vertical axis represents the degree of certainty of the level. The combined weights and scores of each indicator are plotted in the shape of a cloud, which provides basic data for the risk warning unit (4) and intuitively reflects the comprehensive evaluation results.

6. A generative intelligent control system with coal mine risk screening function according to claim 1, characterized in that: The risk warning unit (4) comprises a risk reflection module (401) and a level classification module (402), wherein the risk reflection module (401) fills in the number of actual accident hazard inspections in the entire control system, and uses a comparative relationship between the number of actual accident hazard inspections and the number of potential accident hazard inspections to reflect the accident hazard risk in the coal mine production system, wherein the expression is R=H' / H, where R represents the accident hazard risk in the coal mine production system, H' represents the number of actual accident hazard inspections, and H represents the number of potential accident hazard inspections; The level classification module (402) determines the coal mine accident hazard risk assessment level and warning level classification according to the accident hazard risk, and divides the coal mine accident hazard risk assessment level into four levels. When R≥1.1, there is no warning level; when 0.8≤R≤1.1, the warning level is in a low warning state; when 0.5≤R≤0.8, the warning level is in a medium warning state; when R≤0.5, the warning level is in a severe warning state. According to the corresponding warning level, corresponding investigation measures are taken.

7. A generative intelligent control system with coal mine risk screening function according to claim 6, characterized in that: The risk warning unit (4) also includes a regional inspection module (403), which inspects each area, analyzes the inspection information according to the dimensions of time, category and level, and transmits the analysis data to the PC end, so that the risk, hidden danger distribution and inspection status can be intuitively viewed through the PC end, and at the same time, an early warning is issued for areas with a high frequency of risks and hidden dangers. The query time period can be set, and the distribution of the number of inspections at various locations in the mine within the time period can be queried to find management weaknesses.

8. The generative intelligent control system with coal mine risk screening function according to claim 1 is characterized by: The risk investigation unit (5) comprises a feature extraction module (501), a hidden danger identification module (502) and a hidden danger association module (503). The feature extraction module (501) collects a large amount of pictures, videos and text data of hidden dangers in coal mines through on-site inspection and monitoring equipment in coal mines, cleans, denoises and normalizes the collected data, extracts feature information from the hidden danger data in coal mines using computer vision technology, and classifies the hidden danger data in coal mines using a trained convolutional neural network model to distinguish different types of hidden dangers. The hidden danger identification module (502) performs preliminary identification and determination according to the hidden danger type, and feeds back the determination result to the PC end, including the hidden danger type and the hidden danger location, to prompt the user to take measures to deal with the hidden danger in a timely manner; The hidden danger association module (503) formulates an inspection plan, associates the monthly management risks with the hidden dangers involved in the inspection route in the plan, pushes the hidden danger risks to the PC end for on-site inspection, and feeds back to the mine end after the hidden danger management is completed. When the hidden danger risks are pushed, the description of the hidden danger attributes needs to be associated with the risks in the risk database to provide data support for the subsequent hidden danger management.

Images