CN118261738A

CN118261738A - Coal mine safety compliance analysis and research method and device based on large model

Info

Publication number: CN118261738A
Application number: CN202410531873.9A
Authority: CN
Inventors: 刘林; 龚浩杰; 王玺荣; 请求不公布姓名
Original assignee: Tehuakemai Xi'an Information Technology Co ltd
Current assignee: Tehuakemai Xi'an Information Technology Co ltd
Filing date: 2024-04-29
Publication date: 2024-06-28

Abstract

The application provides a coal mine safety compliance analysis and judgment method and device based on a large model, a computer readable medium and electronic equipment. The coal mine safety compliance analysis and judgment method based on the large model comprises the following steps: collecting an instance data set and rule data in the coal mine production process; determining a data structure corresponding to the compliance information based on the coal mine production scene; extracting sample characteristics related to coal mine production safety compliance from an example data set and the rule data; generating a model structure and parameters based on the sample characteristics and the data structure, and constructing a safety compliance large model; and obtaining the information to be detected, generating a corresponding judgment result in the safety compliance large model, and giving out a standard rule corresponding to the judgment result. The scheme not only can improve the detection efficiency of the coal mine production compliance and the comprehensiveness of the compliance detection, reduce the labor cost, but also can realize the real-time monitoring and dynamic management of the coal mine production safety.

Description

Coal mine safety compliance analysis and research method and device based on large model

Technical Field

The application relates to the technical field of computers, in particular to a coal mine safety compliance analysis and judgment method and device based on a large model, a computer readable medium and electronic equipment.

Background

The working environment of the coal mine is complex and changeable, and a plurality of potential safety risks exist in the management process. By strictly adhering to safety regulations and standards, enterprises can ensure that miners are fully protected in the process of operation management, and the probability of accidents is reduced, so that the life safety of the miners is ensured. In addition, the coal mine production safety compliance is also of great significance in maintaining social harmony and stability.

The following problems mainly exist in the traditional coal mine production safety compliance management: the traditional coal mine production safety compliance detection relies on manual inspection and regular safety inspection, and the mode is low in efficiency and easy to miss some fine potential safety hazards; in addition, because the coal mine production environment is complex, the safety hidden trouble is various, the manual detection method is difficult to cover the whole surface, and key safety hidden trouble is easy to miss; traditional coal mine production safety compliance detection also has the problem of complicated and non-uniform standard. The detection standards of different areas and different enterprises may be different, so that potential safety hazards are ignored or misjudged in the detection process, and potential hazards are brought to the safety production of the coal mine. In conclusion, the problem of low safety compliance efficiency and low precision in coal mine production exists.

Disclosure of Invention

The embodiment of the application provides a coal mine safety compliance analysis and judgment method and device based on a large model, a computer readable medium and electronic equipment, and further solves the problem that coal mine production safety compliance efficiency and accuracy are low to at least a certain extent.

Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.

According to one aspect of the application, a coal mine safety compliance analysis and judgment method based on a large model is provided, which comprises the following steps: collecting an instance data set and rule data in the coal mine production process; determining a data structure corresponding to the pre-generated compliance information based on a coal mine production scene; extracting sample features related to coal mine production safety compliance from the example data set and the rule data; generating a model structure and parameters based on the sample characteristics and the data structure, and constructing a safety compliance large model; and obtaining information to be detected, generating a corresponding judgment result in the safety compliance large model, and giving out a standard rule corresponding to the judgment result.

In the present application, based on the foregoing scheme, after collecting the instance data set and the rule data in the coal mine production process, the method further includes: preprocessing the instance data set and the rule data to obtain preprocessed data; the pretreatment includes cleaning, integration and standardization.

In the present application, based on the foregoing, the extracting the sample feature related to coal mine production safety compliance from the example data set and the rule data includes: determining a rule matrix based on the rule data; determining a backbone parameter of the rule data based on the eigenvalue and eigenvector of the rule matrix; and projecting the preprocessed data onto the trunk parameters to generate sample features related to coal mine production safety compliance.

In the present application, based on the foregoing scheme, the preprocessing the instance data set and the rule data includes: deleting duplicate data records in the instance dataset and rule data; filling missing values in the example data set and the rule data according to the distribution characteristics of the data, or deleting records containing the missing values; outliers in the instance dataset and rule data are identified and processed.

In the present application, based on the foregoing solution, the generating a model structure and parameters based on the sample features and the data structure, and constructing a safety compliance large model includes: carrying out normalized encoding on the sample characteristics based on the data structure to generate standard characteristics; generating model structures and parameters based on the model information; and training the model structure and parameters based on the standard characteristics to generate a safety compliance large model.

Based on the foregoing scheme, the method for obtaining the information to be detected, generating a corresponding determination result in the safety compliance large model, and giving out standard regulations corresponding to the determination result, includes: acquiring information to be detected input by a user; loading the weight, structure and parameters of the constructed safety compliance large model; and inputting the information to be detected into the safety compliance large model for processing, and outputting a corresponding judging result and related standard regulations based on the data structure.

In the present application, based on the foregoing scheme, the obtaining the information to be detected, generating a corresponding determination result in the large safety compliance model, and giving a standard rule corresponding to the determination result, further includes: based on the type of the standard regulations, acquiring a report template corresponding to the standard regulations; and generating a safety compliance report by combining the report template based on the information to be detected, the judging result and the standard regulations.

According to one aspect of the application, a coal mine safety compliance analysis and judgment device based on a large model is provided, comprising:

the acquisition unit is used for collecting an example data set and rule data in the coal mine production process;

The structure unit is used for determining a data structure corresponding to the pre-generated compliance information based on the coal mine production scene;

an extraction unit for extracting sample features related to coal mine production safety compliance from the instance data set and the rule data;

the model unit is used for generating a model structure and parameters based on the sample characteristics and the data structure, and constructing a safety compliance large model;

And the judging unit is used for acquiring the information to be detected, generating a corresponding judging result in the safety compliance large model, and giving out a standard rule corresponding to the judging result.

According to one aspect of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a large model based coal mine safety compliance analysis and research method as described in the above embodiments.

According to an aspect of the present application, there is provided an electronic apparatus including: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the coal mine safety compliance analysis and research method based on the large model.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the coal mine safety compliance analysis and research method based on the large model provided in the various alternative implementations.

In the technical scheme of the application, an example data set and rule data in the coal mine production process are collected; determining a data structure corresponding to the pre-generated compliance information based on a coal mine production scene; extracting sample features related to coal mine production safety compliance from the example data set and the rule data; generating a model structure and parameters based on the sample characteristics and the data structure, and constructing a safety compliance large model; and obtaining information to be detected, generating a corresponding judgment result in the safety compliance large model, and giving out a standard rule corresponding to the judgment result. According to the technical scheme, a large amount of data in the coal mine production process is subjected to deep analysis and processing by using a large model, and potential safety hazards are automatically identified through model learning and training. The method not only can improve the detection efficiency of the coal mine production compliance and the comprehensiveness of the compliance detection and reduce the labor cost, but also can realize the real-time monitoring and dynamic management of the coal mine production safety.

The coal mine production safety compliance detection method based on the large model can be combined with specific coal mine production environments and safety standards, customized development is suitable for safety compliance detection models of different coal mines, and the coal mine production safety compliance standards are unified and standardized, so that potential safety hazards can be identified and early-warned more accurately, and the safety and stability of coal mine production are improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 schematically illustrates a flow chart of a coal mine safety compliance analysis and research method based on a large model in one embodiment of the application.

FIG. 2 schematically illustrates a flow chart of generating sample features in one embodiment of the application.

Fig. 3 schematically illustrates a schematic diagram of a coal mine safety compliance analysis and research device based on a large model in an embodiment of the present application.

Fig. 4 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

The implementation details of the technical scheme of the application are explained in detail as follows:

FIG. 1 shows a flow chart of a large model based coal mine safety compliance analysis and decision methodology in accordance with one embodiment of the present application. Referring to fig. 1, the method for analyzing and judging coal mine safety compliance based on the large model at least comprises steps S110 to S150, and is described in detail as follows:

in step S110, example data sets and rule data in the coal mine production process are collected.

First, an example data set during coal mine production is collected, including equipment data and environmental data generated during production, such as production operation data, equipment operational status data, environmental monitoring data, and the like. Such data may come from a number of sources, such as sensors, monitoring equipment, production management systems, etc.

Specifically, the example data set in this embodiment includes example data in each case. For example, the sand well is used for mining the coal seam which is easy to self-ignite, and the special design for preventing and extinguishing fire adopts comprehensive fire prevention measures which mainly adopt yellow mud grouting and are assisted by spraying inhibitor. Inspection finds that: the grouting pump of the mine ground yellow mud grouting station is not connected with a grouting pipeline, the lower yellow mud grouting pipeline is about 500 meters away from the 30105 fully-mechanized mining face, the 30105 fully-mechanized mining face is not provided with a stopping agent spraying device and a beam tube monitoring system, and the 30105 fully-mechanized mining face returns about 17.5 meters from 4 months to 3 days to 20 days.

Specifically, the rule data in this embodiment is used to represent a compliance standard used when the data is subjected to compliance, and at least includes a production rule and an evaluation rule. For example, the fourth clause of the second hundred sixty of the coal mine safety regulations, the twelfth item (one) of the coal mine major production safety accident potential judgment standard, and the like.

In one embodiment of the application, after collecting the instance data set and the rule data in the coal mine production process, the method further comprises:

preprocessing the instance data set and the rule data to obtain preprocessed data; the pretreatment includes cleaning, integration and standardization.

Specifically, the data cleaning mainly removes noise and abnormal values in the data, so that the data is more accurate and reliable. The method comprises the following specific steps:

Deleting duplicate data records in the dataset to avoid redundant and misleading analysis;

filling up missing values in the data according to the distribution characteristics of the data, or deleting records containing the missing values;

outliers in the data, i.e., those data points that deviate significantly from the normal range, are identified and processed. The processing method includes deleting outliers or replacing them with appropriate values.

Further, in order to ensure a certain reliability during the training process in this embodiment, during the process of identifying and processing the abnormal values in the instance data set and the rule data, the method specifically includes:

Detecting each data type in a data set formed by the instance data set and the rule data, and acquiring a standard range corresponding to each data type;

Acquiring an actual numerical value corresponding to each data type in a data set;

based on the standard range [ min, max ] and the actual value val_act, calculating a data anomaly parameter par_ abn as follows:

wherein α represents an abnormality factor determined from the history data, min, and max represent a minimum value and a maximum value in the standard range, respectively. When the calculated abnormal data parameters are greater than or equal to the set threshold, abnormal data are judged, and the data are deleted, so that the comprehensiveness of the data in the training process is ensured, and the reliability of the training process is enhanced.

Data integration during preprocessing is mainly to combine multiple data sources into one data source to increase the data volume and provide more comprehensive information. The method comprises the following specific steps:

Selecting and extracting a specific subset of data from the original data source;

Transmitting the extracted data subset to a target location, such as a data center or an analysis platform;

and carrying out association processing on the cleaned data according to the new data organization logic, and enhancing the internal connection of the data.

The data integration is performed by the above process in a centralized manner, in which a plurality of data sources are combined into one data source, so as to increase the data volume and provide more comprehensive information.

In step S120, a data structure corresponding to the pre-generated compliance information is determined based on the coal mine production scenario.

In one embodiment of the application, based on the coal mine production scene, the corresponding data structure is designed in advance according to the type of the combed compliance information. The system can comprise a database table structure, data fields, data types and the like, and can also comprise information such as related data display sequences, information arrangement modes and the like.

Factors such as data integrity, consistency, queriability, and scalability are considered in designing a data structure. At the same time, the storage and access efficiency of data is considered, and the data exchange and sharing requirements with other systems are considered.

In step S130, sample features related to coal mine production safety compliance are extracted from the example data set and the rule data.

In one embodiment of the application, the characteristics related to the coal mine production safety compliance are extracted from the preprocessed data, and the sample characteristics related to the compliance are extracted from the example data set and the rule data by simplifying huge data volume, so that accurate and reliable compliance judgment standards are formed conveniently.

Optionally, the features include: equipment failure rate, environmental pollutant concentration, employee operation standardization level, etc. By selecting representative features, the accuracy and efficiency of subsequent model training can be improved.

As shown in fig. 2, in one embodiment of the application, extracting sample features related to coal mine production safety compliance from the example data set and the rule data includes:

s210, determining a rule matrix based on the rule data;

s220, determining backbone parameters of the rule data based on the eigenvalues and eigenvectors of the rule matrix;

and S230, projecting the preprocessed data onto the trunk parameters to generate sample features related to coal mine production safety compliance.

Specifically, in this embodiment, the covariance matrix corresponding to the rule data is generated and calculated based on the rule data as the rule matrix. And carrying out eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors. Wherein the eigenvalues represent the variance in the rule data and the eigenvectors represent the principal directions in the rule data.

Then, based on the eigenvalue gamma _i and the eigenvector v _i of the rule matrix, the backbone parameter par_rea of the rule data is determined as follows:

Wherein i represents the identification corresponding to the characteristic value and the characteristic vector, and k represents the number of the characteristic values. The method is convenient for extracting sample characteristics related to coal mine production safety compliance from the pretreatment data by quantifying compliance standards in the rule data through the process.

And then, in the preprocessing data, projecting the preprocessing data onto the trunk parameters to generate sample characteristics related to coal mine production safety compliance. The dimension reduction of the data is realized, key information in the original data is reserved in the dimension reduced data, and the dimension of the data is reduced, so that the subsequent analysis and processing are facilitated. The extracted sample characteristics related to the coal mine production safety compliance are applied to actual safety management and analysis. These features can be used to assess the safety of a coal mine, identify potential safety risks, and guide the formulation and implementation of safety measures.

In step S140, a model structure and parameters are generated based on the sample features and the data structure, and a safety compliance large model is constructed.

In this embodiment, the processed data set is divided into a training set, a validation set, and a test set. The training set is used for learning the model, the verification set is used for adjusting the super parameters of the model, and the test set is used for evaluating the performance of the model.

In one embodiment of the application, generating model structures and parameters based on the sample features and the data structure, constructing a safety compliance large model includes:

Carrying out normalized encoding on the sample characteristics based on the data structure to generate standard characteristics;

Generating model structures and parameters based on the model information;

And training the model structure and parameters based on the standard characteristics to generate a safety compliance large model.

In one embodiment of the application, the appropriate model structure is selected based on the complexity of the problem and the nature of the data. For safety compliance problems, classification, regression or clustering tasks may be involved, so that corresponding model structures, such as neural networks, decision trees, random forests, etc., may be selected. The model is trained using a training set. Parameters of the model are continuously and iteratively updated through an optimization algorithm (such as gradient descent, adam and the like) so as to minimize a loss function, improve the performance of the model on a training set and finally generate a safety compliance large model.

In step S150, information to be detected is obtained, a corresponding determination result is generated in the safety compliance large model, and standard regulations corresponding to the determination result are given.

The constructed safety compliance large model is deployed into an actual application scene and is used for predicting, analyzing and monitoring the coal mine production safety compliance. And continuously monitoring and updating the model according to the actual application requirements so as to adapt to the continuously-changing safe compliance environment.

In one embodiment of the present application, obtaining information to be detected, generating a corresponding determination result in the safety compliance large model, and giving out standard regulations corresponding to the determination result, including:

Acquiring information to be detected input by a user;

Loading the weight, structure and parameters of the constructed safety compliance large model;

And inputting the information to be detected into the safety compliance large model for processing, and outputting a corresponding judging result and related standard regulations based on the data structure.

The method comprises the steps of obtaining information to be detected input by a user, loading weights, structures and parameters of a built safety compliance large model, inputting the information to be detected into the safety compliance large model for processing, and outputting corresponding judging results and relevant standard regulations based on the data structure. And determining corresponding standard regulations in the safety compliance large model according to the information to be detected, and sending out early warning signals in time to inform related personnel to process when the model finds potential safety hazards or illegal behaviors. Meanwhile, the production process can be adjusted and optimized according to the early warning result, and the safety risk is reduced.

In one embodiment of the present application, the method further includes, after obtaining information to be detected, generating a corresponding determination result in the safety compliance large model, and providing a standard rule corresponding to the determination result:

based on the type of the standard regulations, acquiring a report template corresponding to the standard regulations;

and generating a safety compliance report by combining the report template based on the information to be detected, the judging result and the standard regulations.

Specifically, specific contents are filled on the basis of the report template. First, the introduction section of the report is written, and the background and purpose of the report are briefly described. In the text part, the information to be detected is detailed, including the basic conditions of time, place, object and the like of detection. And then, showing the judging result, wherein the judging result comprises the judgment of whether each detection item is in compliance or not, and the basis and the process of the judgment. Meanwhile, the judgment result is explained and explained by referring to the related standard regulations. If a problem or a condition not meeting the standard is found in the detection process, the nature, severity and possible influence of the problem should be described in detail and corresponding improvement measures or suggestions should be made.

The following describes an embodiment of the device of the present application, which can be used to execute the coal mine safety compliance analysis and determination method based on the large model in the above embodiment of the present application. It will be appreciated that the apparatus may be a computer program (including program code) running in a computer device, for example the apparatus being an application software; the device can be used for executing corresponding steps in the method provided by the embodiment of the application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for analyzing and judging coal mine safety compliance based on the large model.

FIG. 3 shows a block diagram of a large model-based coal mine safety compliance analysis and decision making apparatus in accordance with one embodiment of the present application.

Referring to fig. 3, a coal mine safety compliance analysis and judgment device based on a large model according to an embodiment of the present application includes:

an acquisition unit 310 for collecting an instance data set and rule data in a coal mine production process;

A structural unit 320, configured to determine a data structure corresponding to the pre-generated compliance information based on the coal mine production scenario;

An extraction unit 330 for extracting sample features related to coal mine production safety compliance from the example data set and the rule data;

A model unit 340, configured to generate a model structure and parameters based on the sample features and the data structure, and construct a safety compliance large model;

And the judging unit 350 is configured to obtain information to be detected, generate a corresponding judging result in the safety compliance large model, and provide a standard rule corresponding to the judging result.

It should be noted that, the computer system 400 of the electronic device shown in the drawings is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

Among them, the computer system 400 includes a central processing unit (Central Processing Unit, CPU) 401 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage section 408 into a random access Memory (Random Access Memory, RAM) 403. In the RAM 403, various programs and data required for the system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An Input/Output (I/O) interface 405 is also connected to bus 404.

The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. When executed by a Central Processing Unit (CPU) 401, performs the various functions defined in the system of the present application.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.

As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. The coal mine safety compliance analysis and judgment method based on the large model is characterized by comprising the following steps of:

Collecting an instance data set and rule data in the coal mine production process, wherein the instance data set at least comprises equipment data and environment data generated in the production process, and the rule data at least comprises a production rule and an evaluation rule;

determining a data structure corresponding to the pre-generated compliance information based on a coal mine production scene;

Extracting sample features related to coal mine production safety compliance from the example data set and the rule data;

Generating a model structure and parameters based on the sample characteristics and the data structure, and constructing a safety compliance large model;

And obtaining information to be detected, generating a corresponding judgment result in the safety compliance large model, and giving out a standard rule corresponding to the judgment result.

2. The method of claim 1, further comprising, after collecting the instance data set and the rule data in the coal mine production process:

Preprocessing the instance data set and the rule data to obtain preprocessed data; wherein the pretreatment comprises cleaning, integration and standardization.

3. The method of claim 2, wherein extracting sample features related to coal mine production safety compliance from the example dataset and the rule data comprises:

determining a rule matrix based on the rule data;

Determining a backbone parameter of the rule data based on the eigenvalue and eigenvector of the rule matrix;

and projecting the preprocessed data onto the trunk parameters to generate sample features related to coal mine production safety compliance.

4. The method of claim 2, wherein preprocessing the instance dataset and rule data comprises:

Deleting duplicate data records in the instance dataset and rule data;

Filling missing values in the example data set and the rule data according to the distribution characteristics of the data, or deleting records containing the missing values;

outliers in the instance dataset and rule data are identified and processed.

5. The method of claim 1, wherein generating model structures and parameters based on the sample features and the data structure, constructing a safety compliance large model comprises:

Generating model structures and parameters based on the model information;

6. The method of claim 1, wherein obtaining information to be detected, generating a corresponding determination result in the safety compliance large model, and giving out standard regulations corresponding to the determination result, comprises:

Acquiring information to be detected input by a user;

7. The method according to claim 1, wherein after obtaining information to be detected, generating a corresponding determination result in the safety compliance large model, and giving a standard rule corresponding to the determination result, further comprising:

8. Colliery safety compliance analysis and research judgement device based on big model, its characterized in that includes:

9. A computer readable medium having stored thereon a computer program which when executed by a processor implements the large model based coal mine safety compliance analysis and research method of any one of claims 1 to 7.

10. An electronic device, comprising:

One or more processors;

A storage means for storing one or more programs that when executed by the one or more processors cause the one or more processors to implement the large model-based coal mine safety compliance analysis and research method of any one of claims 1 to 7.