Data management and control method and system based on identification analysis middleware oriented to mine safety production

Abstract

The invention provides a data management and control method and system based on mine safety production-oriented identification analysis middleware, and relates to the technical field of data management and control. Mine safety production identification analysis middleware is constructed, and underground multi-type terminal original identification data is received to obtain a to-be-processed packet; then coding and error correction are carried out on the to-be-processed packet to obtain a tundish; analyzing the tundish through a lightweight deep learning model, packaging the tundish into a standardized data object, and checking security information by using a national cryptographic SM4 algorithm in combination with an encrypted dynamic mapping library, and binding to obtain an enhanced object; the method comprises the following steps: acquiring mine identification data, performing electronic fence processing to obtain an early warning result, performing verification and display at a cloud to obtain a work order, and performing dynamic authentication to strengthen management and control, thereby realizing whole process processing from acquisition, analysis and early warning of the mine identification data to cloud management and control, and further ensuring safe and efficient management and control of the data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management and control, in particular to a data management and control method and system based on an identification analysis middleware for mine safety production. BACKGROUND

[0002] The underground mine environment is complex and has high safety risks, and it is necessary to real-time manage and control key information such as personnel position, equipment state, and gas concentration. At present, multiple types of collection terminals have been deployed, but the data sources are scattered, the formats are different, and the transmission is easily disturbed. At the same time, it is necessary to realize reliable data processing, safe transmission, and "collection, early warning, and disposal" whole-process rapid response, and an integrated data management and control method is urgently needed.

[0003] At present, the existing data management and control method for mine safety production transmits the original data collected by the underground terminal to the ground system after simple processing, then gives a unique identification to the equipment and personnel through the industrial internet identification, the associated information is stored in a centralized database, and the information is retrieved by using conventional language when queried; the edge end determines the abnormality according to the fixed threshold value to trigger the local alarm, while the cloud end summarizes and displays the received information, and the management personnel manually analyzes and assigns the operation, and records the operation process to the local log, and the permission is distributed according to the pre-set role.

[0004] However, the most important disadvantage of the prior art is the lack of integrated management and control scheme, specifically: there is no unified processing carrier for data, and the format difference causes poor coherence in subsequent processing; the efficiency and safety of safety information query are insufficient; the edge early warning and the cloud end run independently; the log and permission management cannot meet the needs of operation traceability and flexible authentication, and thus the whole-process management and control cannot be realized. SUMMARY

[0005] The purpose of the present application is to provide a data management and control method and system based on an identification analysis middleware for mine safety production, to solve the problem that the prior art cannot realize whole-process management and control.

[0006] To solve the above technical problems, in a first aspect, the present application provides a data management and control method based on an identification analysis middleware for mine safety production, comprising:

[0007] An identification analysis middleware for mine safety production is constructed, the original identification data of multiple types of terminals in the mine is received through the identification analysis middleware to obtain an original data packet, and a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model;

[0008] Based on the standardized data object and a pre-set encrypted dynamic mapping library, a national encryption SM4 algorithm is used for priority query to obtain safety information, the standardized data object is bound with the safety information to obtain a safety data object;

[0009] The safety data object is processed in real time by using an electronic fence dynamic matching algorithm to obtain a warning result.

[0010] The warning result is verified in the cloud through a two-way authentication mode of a digital certificate to obtain a verification result, and the warning result is displayed and analyzed through a digital twin model based on the verification result to obtain disposal work order data. Meanwhile, all operation data is recorded to generate a tamper-proof safety audit log, and dynamic authentication is performed by using an attribute-based access control module to strengthen cloud management and control.

[0011] Optionally, the safety data object is processed in real time by using an electronic fence dynamic matching algorithm to obtain a warning result, including:

[0012] The safety data object is received by an edge processing module of the identification analysis middleware.

[0013] Based on the personnel three-dimensional coordinates in the safety data object and preset electronic fence boundary information, an electronic fence dynamic matching algorithm is used to perform real-time matching processing of the coordinates and the boundary to obtain a matching relationship between the personnel three-dimensional coordinates and the dangerous area. When it is detected based on the matching relationship that the personnel three-dimensional coordinates fall into the dangerous area, a warning instruction is generated.

[0014] Based on the warning instruction, a control signal is sent to a local audible and visual alarm corresponding to the dangerous area to obtain a warning result.

[0015] Optionally, based on the personnel three-dimensional coordinates in the safety data object and preset electronic fence boundary information, an electronic fence dynamic matching algorithm is used to perform real-time matching processing of the coordinates and the boundary to obtain a matching relationship between the personnel three-dimensional coordinates and the dangerous area. When it is detected based on the matching relationship that the personnel three-dimensional coordinates fall into the dangerous area, a warning instruction is generated, including:

[0016] Based on the personnel three-dimensional coordinates and preset electronic fence boundary information, an electronic fence dynamic matching algorithm is used to calculate the positional relationship between the coordinate point corresponding to the personnel three-dimensional coordinates and the polygon boundary corresponding to each dangerous area to obtain the spatial positional relationship between each personnel three-dimensional coordinate and each dangerous area.

[0017] Based on the spatial positional relationship, it is judged whether the personnel is in a dangerous area. When it is identified based on the judgment result that the personnel three-dimensional coordinates fall into the dangerous area, a warning instruction with corresponding warning level and position information is generated based on the dangerous level of the dangerous area and the personnel three-dimensional coordinates.

[0018] Optionally, based on the check result, the early warning result is displayed and analyzed through a digital twin model to obtain treatment work order data, and meanwhile, all operation data are recorded to generate a non-tamperable security audit log, including:

[0019] Based on the check result, an encrypted transmission channel is established.

[0020] Based on the encrypted transmission channel, the early warning result is transmitted to a cloud center database in an asynchronous manner, the early warning result is mapped into a digital twin model through the cloud center database, and a visual panoramic view is obtained.

[0021] The panoramic view is analyzed to obtain treatment work order data, and based on the treatment work order data and a preset treatment rule library, task assignment processing is performed to obtain a task processing result.

[0022] Based on the task processing result, the early warning result, the treatment work order data, and operation data in the task processing process are recorded into a distributed storage system to generate a security audit log.

[0023] Optionally, the panoramic view is analyzed to obtain treatment work order data, and based on the treatment work order data and a preset treatment rule library, task assignment processing is performed to obtain a task processing result, including:

[0024] A spatial analysis method is used to analyze and process the panoramic view to obtain a dangerous area that needs to be treated in priority.

[0025] Based on the spatial distribution characteristics and the danger level parameters of the dangerous area, treatment work order data containing a treatment position, treatment content, and treatment priority are generated.

[0026] Based on the treatment work order data and a preset treatment rule library, a task assignment strategy and an execution time limit requirement are determined.

[0027] According to the task assignment strategy and the execution time limit requirement, the treatment work order data is assigned to a corresponding responsible personnel terminal device, and the responsible personnel terminal device processes based on the treatment work order data to obtain a task processing result.

[0028] Optionally, based on the standardized data object and a preset encrypted dynamic mapping library, a national cryptographic SM4 algorithm is used for priority query to obtain security information, the standardized data object is bound with the security information to obtain a security data object, including:

[0029] An analytic engine is adopted to receive the standardized data object, and based on the standardized data object, a dynamic cache encrypted by a national secret SM4 algorithm is used for a priority query, when the safety information is queried, a first query result containing the safety information is obtained;

[0030] Alternatively, when the safety information is not queried, based on the standardized data object, a preset encrypted dynamic mapping library is queried by using a block chain smart contract to obtain a second query result containing the safety information, and at the same time, a cache synchronization mechanism is used to update the safety information in the second query result to the dynamic cache encrypted by the national secret SM4 algorithm;

[0031] Based on the first query result or the second query result, the standardized data object and the corresponding safety information are associated and bound to generate a safety data object.

[0032] Optionally, the standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, comprising:

[0033] The original data packet is encoded and error-corrected to obtain an intermediate data packet;

[0034] A signal quality evaluation module of a lightweight deep learning model is used to perform time-frequency feature analysis on the intermediate data packet to obtain a time-frequency feature analysis result, and when the intermediate data packet is identified as a data packet with transmission distortion risk based on the time-frequency feature analysis result, a retransmission mechanism is triggered to obtain a reliable data packet;

[0035] The reliable data packet is subjected to data structure analysis to obtain a device identification sequence and corresponding monitoring data readings;

[0036] The device identification sequence is subjected to format unification processing to obtain a standard format sequence, and the monitoring data readings are subjected to unit unification conversion according to corresponding physical quantity types to obtain standard data values;

[0037] The standard format sequence and the standard data values are combined and packaged according to a preset data structure to obtain a standardized data object.

[0038] In a second aspect, the present application provides a data management and control system based on an identification analysis middleware for mine safety production, comprising:

[0039] A construction module is used to construct an identification analysis middleware for mine safety production, and the original identification data of a mine underground multi-type terminal is received by the identification analysis middleware to obtain an original data packet, and a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model;

[0040] The query module is used to perform a priority query based on the standardized data object and the preset encrypted dynamic mapping library, using the national cryptographic SM4 algorithm to obtain security information, and bind the standardized data object with the security information to obtain a secure data object;

[0041] The processing module is used to process the security data object in real time using an electronic fence dynamic matching algorithm to obtain early warning results;

[0042] The analysis module is used to verify the warning results in the cloud through two-way digital certificate authentication, obtain the verification results, display and analyze the warning results based on the verification results, obtain the handling work order data through a digital twin model, record all operation data, generate an immutable security audit log, and use an attribute-based access control module to perform dynamic authentication to strengthen cloud management.

[0043] Thirdly, this application provides an electronic device, comprising:

[0044] Memory, used to store computer programs;

[0045] A processor, used to implement the steps of the data management method based on identifier resolution middleware for mine safety production as described in the first aspect above when executing the computer program.

[0046] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the data management method based on an identifier resolution middleware for mine safety production as described in the first aspect above.

[0047] The data management method based on identifier resolution middleware for mine safety production provided in this application has the following beneficial effects:

[0048] This application first addresses the issue of fragmented data sources by using an identifier resolution middleware to uniformly receive data from various types of underground terminals. Next, based on the original data packets, a lightweight deep learning model is used to generate standardized data objects, providing a unified data carrier for subsequent security information queries. Then, the national cryptographic algorithm SM4 is used to prioritize queries of the encrypted dynamic mapping library, improving efficiency while ensuring data query security. Finally, security information is bound to the standardized data objects to enrich the data dimensions, thus giving the data both identifier and security control attributes, thereby providing complete data support for real-time early warning.

[0049] Then, the electronic fence dynamic matching algorithm is used to process safety data objects in real time, and can quickly identify dangerous conditions of personnel or equipment without relying on the cloud. This can meet the real-time safety early warning needs of underground mines and further reduce the probability of delayed risk response. Subsequently, the security of the early warning results transmitted to the cloud is ensured through two-way digital certificate authentication.

[0050] Furthermore, by using digital twin models for visualization and analysis to improve the efficiency of security situation awareness, it can help generate handling work orders quickly. Finally, by using tamper-proof security audit logs to ensure operation traceability, and by using attribute-based dynamic authentication to prevent unauthorized access to sensitive data, the security and standardization of cloud management are comprehensively strengthened.

[0051] Furthermore, this application clearly defines the middleware edge processing module as the execution subject, thereby ensuring the localization and efficiency of data processing; by relying on the three-dimensional coordinates of personnel to carry out electronic fence matching, the accuracy of dangerous area identification can be improved, and control signals can be sent directly to the local audible and visual alarms, further shortening the early warning response link, avoiding intermediate delays, and thus more efficiently ensuring that underground personnel stay away from dangerous areas, thereby strengthening the timeliness and pertinence of safety early warning. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a data management method based on an identifier resolution middleware for mine safety production, provided in an embodiment of this application;

[0054] Figure 2 This is a schematic diagram illustrating a specific implementation of a data management method based on an identifier resolution middleware for mine safety production, provided in an embodiment of this application.

[0055] Figure 3 A schematic diagram of the structure of a data management and control system based on an identifier resolution middleware for mine safety production, provided in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0057] In the data management and control methods for mine safety production, the existing methods have the following obvious shortcomings: underground data sources are scattered, formats are messy, and there is a lack of a unified processing carrier; safety information queries cannot balance efficiency and safety performance; there is a gap between edge early warning and cloud-based processing, and operation traceability is poor and permissions are rigid, which cannot form a complete control loop and restricts the accuracy of management.

[0058] To address this, this application proposes a data management method based on an identifier resolution middleware for mine safety production. The core idea of ​​this method is as follows: First, a dedicated identifier resolution middleware is used to uniformly receive underground data, which is then processed into standardized objects, solving the data integration problem. Next, an encryption library and the national cryptographic standard SM4 algorithm are combined to enable priority querying of safety information, ensuring both efficiency and security. Then, an electronic fence algorithm is used to achieve real-time local early warning, followed by cloud-based verification. Visualization and analysis using a digital twin model improve the efficiency of safety situation awareness. Finally, tamper-proof security audit logs ensure operational traceability, and attribute-based dynamic authentication prevents unauthorized access to sensitive data, thereby comprehensively strengthening the security and standardization of cloud-based management. This solution realizes a complete process design from data collection to cloud-based management, effectively compensating for the shortcomings of existing technologies in integration, security, and real-time performance, and providing more reliable technical support for mine safety production.

[0059] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] The core of this application is to provide a data management method based on an identifier resolution middleware for mine safety production. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0061] S101. Construct an identifier resolution middleware for mine safety production. Receive raw identifier data from various types of terminals in the mine through the identifier resolution middleware, obtain raw data packets, and generate standardized data objects based on the raw data packets and a lightweight deep learning model.

[0062] Among them, the identifier resolution middleware refers to a dedicated data processing carrier designed for mine safety production, which can connect to various types of underground terminals, process identifier data, and connect to subsequent safety queries and control.

[0063] Raw identification data refers to the initial data collected by various types of underground terminals, containing equipment identification information and monitoring data; raw data packets refer to the data set formed after the middleware receives the raw identification data.

[0064] The lightweight deep learning model refers to a miniaturized AI model adapted to edge computing scenarios in mines, which includes a signal quality assessment module for analyzing data signal characteristics and parsing data. It should be noted that the specific structure of the model is not specifically limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0065] In step 101, an identifier resolution middleware for mine safety production is constructed. This middleware can connect to various types of terminals in the mine, including Ultra Wide Band (UWB) positioning base stations with conformal antenna design, positioning tags worn by personnel, and other equipment sensors. The middleware then collects raw identifier data containing equipment identity information and monitoring data through these terminals and integrates them to form raw data packets.

[0066] In one specific implementation, step S101 involves generating standardized data objects based on the original data packet and a lightweight deep learning model, including:

[0067] Step 1011: Encode and correct the original data packet to obtain an intermediate data packet.

[0068] For example, an enhanced mining communication protocol based on RS-FEC encoding is used to encode and correct the original data packets. Specifically, redundant check codes are first added to the original data packets, and then the errors caused by strong electromagnetic interference and high dust in the underground environment are detected through the redundant check codes. Subsequently, the detected errors are repaired using RS-FEC encoding logic, resulting in intermediate data packets with improved data integrity.

[0069] Furthermore, this step in the embodiments of this application can enable the intermediate data packets to meet the input conditions of a lightweight deep learning model.

[0070] Step 1012: The signal quality assessment module using a lightweight deep learning model performs time-frequency feature analysis on the intermediate data packets to obtain time-frequency feature analysis results. When a data packet with transmission distortion risk is identified based on the time-frequency feature analysis results, a retransmission mechanism is triggered to obtain a reliable data packet.

[0071] In step 1021, a lightweight deep learning model is started. First, the signal quality assessment module of the model performs time-frequency feature analysis on the intermediate data packets to obtain the time-frequency feature analysis results. Then, based on the results, it is determined whether there is a risk of transmission distortion in the intermediate data packets. If there is a risk of transmission distortion, a retransmission mechanism is triggered, and the corresponding terminal is requested to retransmit the data to obtain a reliable data packet with the required data accuracy.

[0072] It should be noted that the specific structural design of the signal quality assessment module is not limited in the embodiments of this application, and can be set accordingly according to the actual situation.

[0073] Step 1013: Perform data structure parsing on the reliable data packet to obtain the device identification sequence and the corresponding monitoring data reading.

[0074] Among them, the equipment identification sequence refers to the ordered set of identifiers that represent the unique identity of downhole equipment after parsing; the monitoring data readings refer to the specific values ​​of equipment operating parameters, environmental parameters, etc., collected by the terminal after parsing.

[0075] In step 1013, information is extracted according to the correspondence between terminal type and data category to obtain the device identification sequence representing the unique identity of the downhole equipment and the monitoring data readings collected by the corresponding terminal.

[0076] Step 1014: Perform format unification processing on the device identification sequence to obtain a standard format sequence, and perform unit unification conversion on the monitoring data readings according to the corresponding physical quantity type to obtain standard data values.

[0077] In step 1014, the device identifiers with different initial formats are adjusted to a preset unified format. At the same time, the monitoring data readings are converted to units according to their corresponding physical quantity types, and the monitoring data with non-standard units are converted to preset standard units, so as to obtain a standard format sequence with consistent format and standard data values ​​with unified units.

[0078] Step 1015: Combine and encapsulate the standard format sequence and the standard data value according to a preset data structure to obtain a standardized data object.

[0079] This application achieves unified reception of data from multiple types of terminals in underground mines by constructing a dedicated identifier resolution middleware, which can solve the problem of scattered data sources from different terminals and difficulty in centralized processing; subsequently, the enhanced mining communication protocol based on RS-FEC encoding can effectively repair data transmission errors caused by strong electromagnetic and high dust environments underground, thereby improving data integrity.

[0080] Then, through a signal quality assessment and retransmission mechanism using a lightweight deep learning model, potentially distorted data was further filtered and repaired, ensuring data reliability. Furthermore, by standardizing the format of device identifiers and the unit of monitoring data, it is possible to avoid connection barriers in subsequent security queries and early warning processing due to differences in data formats.

[0081] S102. Based on the standardized data object and the preset encrypted dynamic mapping library, the national cryptographic SM4 algorithm is used for priority query to obtain security information. The standardized data object is then bound to the security information to obtain a secure data object.

[0082] The preset encrypted dynamic mapping library refers to a two-layer storage structure that stores the association between mining equipment identification and safety information. One layer is a dynamic cache encrypted using the national cryptographic SM4 algorithm, and the other layer is a storage unit based on blockchain technology. The whole structure is used to provide safety information query services.

[0083] The SM4 algorithm, a national standard cryptographic algorithm, is a block cipher used to encrypt data in dynamic caches, ensuring the security of data storage and retrieval. For a description of the algorithm, please refer to relevant technical documents, which will not be elaborated here.

[0084] In one specific implementation, step S102 includes:

[0085] Step 1021: Using a parsing engine, receive the standardized data object. Based on the standardized data object, perform priority querying using a dynamic cache encrypted with the national cryptographic SM4 algorithm. When security information is found, obtain the first query result containing the security information.

[0086] Among them, the parsing engine refers to the processing module used to receive standardized data objects and execute security information query logic. It integrates non-line-of-sight path recognition and suppression algorithms, which can optimize the query accuracy of location-related data.

[0087] Dynamic caching refers to the storage area in the memory of the identifier resolution middleware within the encrypted dynamic mapping library. It is encrypted using the national cryptographic algorithm SM4 and is used to prioritize the storage of frequently queried security information, thereby improving query efficiency.

[0088] In step 1021, a parsing engine integrating non-line-of-sight path recognition and suppression algorithms receives the standardized data object generated in step S101. The parsing engine first performs non-line-of-sight path recognition and suppression algorithms on the positioning data in the standardized data object, and eliminates the influence of non-line-of-sight interference on the positioning data to optimize data accuracy.

[0089] Based on the optimized standardized data object, and using the device identifier as the retrieval basis, a priority query request is initiated to the dynamic cache encrypted with the national cryptographic SM4 algorithm. After the dynamic cache receives the request, it decrypts the data in the retrieval area using the national cryptographic SM4 algorithm. If security information matching the device identifier is found, a first query result containing that security information is generated.

[0090] Step 1022, or, if no security information is found, based on the standardized data object, a blockchain smart contract is used to query a preset encrypted dynamic mapping library to obtain a second query result containing security information. At the same time, a cache synchronization mechanism is used to update the security information in the second query result to the dynamic cache encrypted by the national cryptographic SM4 algorithm.

[0091] Among them, blockchain smart contracts refer to pre-set programs deployed in blockchain storage units, which are used to execute the logic of querying the association between identifiers and security information, and to ensure that the query results are tamper-proof.

[0092] The cache synchronization mechanism refers to the mechanism of encrypting the security information queried from the blockchain smart contract and updating it to the dynamic cache, which is used to supplement the dynamic cache data and improve the efficiency of subsequent queries.

[0093] In step 1022, if the parsing engine fails to retrieve matching security information after querying the dynamic cache, it sends a smart contract call request to the blockchain storage unit of the encrypted dynamic mapping library based on the optimized standardized data object to trigger the blockchain smart contract to execute the preset association query logic. The smart contract retrieves the matching security information from the blockchain storage unit with the device identifier as the input parameter and generates a second query result containing the security information.

[0094] Simultaneously, a cache synchronization mechanism is activated. The parsing engine first encrypts the security information in the second query result using the national standard SM4 algorithm, then updates the encrypted security information to the dynamic cache. If the dynamic cache capacity has reached a preset limit, the LRU replacement algorithm is used to filter out the least recently used data in the dynamic cache and replace it, ensuring that the new security information is successfully stored in the dynamic cache. A description of the LRU replacement algorithm can be found in relevant technical documents and will not be elaborated here.

[0095] Step 1023: Based on the first query result or the second query result, associate and bind the standardized data object with the corresponding security information to generate a security data object.

[0096] In step 1023, the parsing engine extracts security information from the query result if it is the first query result and also extracts security information from the second query result. The extracted security information is then associated and bound with the corresponding standardized data object according to the correspondence of "identifier, monitoring data, and security control information". Finally, a security data object containing basic data and security control information is generated to provide data support for subsequent real-time early warning steps.

[0097] This application effectively optimizes the accuracy of location-type data in standardized data objects through the non-line-of-sight path recognition and suppression algorithm integrated into the parsing engine, which can provide a more reliable retrieval basis for security information query; and adopts a dynamic cache priority query mechanism encrypted with the national cryptographic SM4 algorithm, which not only ensures the security of the data query process, but also greatly improves the query efficiency of high-frequency security information.

[0098] Subsequently, the immutability of security information was ensured through the query method of blockchain smart contracts, thereby improving the credibility of query results. Finally, the combination of cache synchronization mechanism and LRU replacement algorithm not only realized the real-time replenishment of dynamic cache data, but also efficiently managed cache space, thereby avoiding cache overflow problems.

[0099] S103. Using an electronic fence dynamic matching algorithm, the security data object is processed in real time to obtain an early warning result.

[0100] In one specific implementation, such as Figure 2 As shown, step S103 includes:

[0101] Step 1031: Receive the security data object through the edge processing module of the identifier resolution middleware.

[0102] Among them, the edge processing module of the identifier resolution middleware refers to the processing unit deployed at the edge of the mine underground. It can receive and process security data objects nearby to achieve low-latency data processing. This embodiment does not specifically limit the specific structure of this module, and can be set accordingly according to the actual situation.

[0103] For example, in a mining operation area, the edge processing module of the identifier resolution middleware receives the generated TAG008 safety data object, which contains the three-dimensional coordinates of the personnel (25, 22, 18), the preset electronic fence boundary coordinate range is (15, 12, 13) to (35, 32, 23), and the hazard level is level two.

[0104] Step 1032: Based on the three-dimensional coordinates of the personnel in the safety data object and the preset electronic fence boundary information, the electronic fence dynamic matching algorithm is used to perform real-time matching processing of coordinates and boundaries to obtain the matching relationship between the three-dimensional coordinates of the personnel and the dangerous area. When the three-dimensional coordinates of the personnel are detected to fall into the dangerous area based on the matching relationship, an early warning command is generated.

[0105] Among them, the three-dimensional coordinates of personnel refer to the real-time location data of personnel recorded in the safety data object, including X-axis, Y-axis and Z-axis coordinate values, which are used to locate the spatial location of personnel; the preset electronic fence boundary information refers to the predefined spatial range data of the mine's dangerous area, represented by polygon boundary coordinates, and is associated with the danger level of the dangerous area, which is used to define the safe and dangerous areas.

[0106] The electronic fence dynamic matching algorithm is an algorithm used to calculate the relative position of a person's three-dimensional coordinates to the boundary of the electronic fence, which can determine in real time whether a person has entered a dangerous area; the warning command is a command generated when a person enters a dangerous area, which includes the warning level, the person's location information and the dangerous area information, and is used to trigger an alarm action.

[0107] Step 1032 may specifically include the following steps:

[0108] Step a1: Based on the three-dimensional coordinates of the personnel and the preset electronic fence boundary information, the electronic fence dynamic matching algorithm is used to calculate the positional relationship between the coordinate points corresponding to the three-dimensional coordinates of the personnel and the polygonal boundaries corresponding to each dangerous area, so as to obtain the spatial positional relationship between the three-dimensional coordinates of each personnel and each dangerous area.

[0109] For example, the edge processing module can initiate a dynamic matching algorithm for electronic fences and can use the ray-mapping method to determine whether a person's coordinates are within a danger zone. The ray-mapping formula can be: Where P represents the spatial relationship between the personnel's coordinates and the boundary of the danger zone polygon, and N represents the number of intersections between the ray drawn from the personnel's two-dimensional coordinates along the positive X-axis and the polygon boundary;

[0110] Subsequently, starting from the two-dimensional coordinates (25, 22) of TAG008, a ray is drawn along the positive X-axis. The two-dimensional boundary vertices of the danger zone are (15, 12), (35, 12), (35, 32), and (15, 32) respectively. The ray only intersects the boundary edge (35, 12)-(35, 32) at the point (35, 22). Therefore, we get N=1. Since N is an odd number, we determine the spatial position relationship P as "inside" according to the formula.

[0111] Step a2: Based on the spatial relationship, determine whether the person is in the danger zone. When the judgment result indicates that the person's three-dimensional coordinates fall into the danger zone, generate an early warning instruction with corresponding warning level and location information based on the danger level of the danger zone and the person's three-dimensional coordinates.

[0112] For example, if the Z-axis coordinate of a person's three-dimensional coordinates is 18 meters and falls within the range of the preset electronic fence boundary coordinates of 13 to 23 meters, then the person is ultimately determined to be in a dangerous area. Subsequently, based on the danger level of level two, the edge processing module generates an early warning instruction, which includes the warning level of level two, the warning time of 15:10 on [Date], the person's identifier TAG008, and the location coordinates (25, 22, 18).

[0113] Step 1033: Based on the warning instruction, send a control signal to the local audible and visual alarm corresponding to the dangerous area to obtain the warning result.

[0114] Among them, the local audible and visual alarm refers to the alarm equipment installed near various dangerous areas in the mine, which can receive control signals and issue alarm prompts through sound and light; the early warning result refers to the alarm result formed after the local audible and visual alarm responds to the control signal, indicating that the early warning action has been completed.

[0115] For example, based on the warning command, the edge processing module sends a control signal to the local audible and visual alarm near the dangerous area in the mining operation area. The alarm is preset with a buzzer sound intensity of 78 decibels and an LED flashing frequency of 3 times / second. The parameter setting is based on the minimum effective warning requirements of underground alarm devices in the mining safety standard. After receiving the signal, the alarm immediately starts the alarm according to the preset parameters, forming a warning result.

[0116] Meanwhile, the edge processing module temporarily stores the warning event and TAG008's trajectory data for nearly 8 minutes in local persistent storage. The trajectory data includes 16 consecutive sets of coordinates (23, 20, 18), (23.5, 20.5, 18), ..., (25, 22, 18). The time interval is calculated as (8 × 60) seconds ÷ 16 sets = 30 seconds, that is, a set of coordinates is recorded every 30 seconds. Then, when the network bandwidth utilization drops to 35% at 15:13, which is lower than the preset asynchronous synchronization trigger threshold of 45%, the edge processing module will batch synchronize the warning event and trajectory data to the mine cloud center database through the asynchronous transmission protocol.

[0117] This application uses the edge processing module of the identifier resolution middleware to process safety data objects locally, which can avoid the delay of data transmission to the cloud for processing. Combined with the electronic fence dynamic matching algorithm, it can determine in real time whether personnel have entered dangerous areas, which greatly improves the timeliness of early warning and thus effectively protects the safety of underground personnel.

[0118] S104. The warning result is verified in the cloud through two-way digital certificate authentication to obtain the verification result. Based on the verification result, the warning result is displayed and analyzed through a digital twin model to obtain the handling work order data. At the same time, all operation data is recorded to generate an unalterable security audit log. Dynamic authentication is performed using an attribute-based access control module to strengthen cloud management.

[0119] Among them, the two-way digital certificate authentication method refers to a security mechanism for mutual identity verification between the cloud and the edge. Both parties need to present a valid digital certificate. If the verification is successful, the identity is confirmed to be legitimate, which is used to ensure the security of the transmission and verification of early warning results.

[0120] The verification result refers to the result obtained by the cloud after verifying the legality of the source of the warning result and the integrity of the data through two-way authentication of digital certificates. It is divided into two categories: qualified and unqualified.

[0121] An attribute-based access control module is a module that dynamically determines whether to allow access to data or perform operations based on user attributes.

[0122] In one specific implementation, step S104 includes:

[0123] Step 1041: Based on the verification result, establish an encrypted transmission channel.

[0124] In step 1041, based on the qualified verification result, the cloud and the edge establish an encrypted transmission channel using an encryption protocol. This channel can provide real-time encryption protection for the data transmitted subsequently to prevent the data from being stolen or tampered with during transmission. If the verification result is unqualified, the certificate problem must be investigated and two-way authentication must be completed again. The encrypted transmission channel is established only after a qualified verification result is obtained.

[0125] For example, the cloud first receives the TAG008 warning result generated in step S103. Then, the cloud initiates two-way digital certificate authentication, which involves the cloud sending a verification request to the edge of the work area, and the edge returning a digital certificate containing the unique identifier of the edge node and a validity period until December 202X. The cloud queries the signature information of the certificate through the certificate authority to confirm that the signature is valid and has not expired. At the same time, the edge sends a verification request to the cloud, and the cloud returns a digital certificate containing the mine headquarters identifier and a validity period until June 202Y, thus obtaining a successful verification result from the edge.

[0126] Subsequently, based on the successful verification result, both parties exchanged a list of supported cipher suites using the TLS 1.3 protocol, and ultimately selected the "TLS_AES_256_GCM_SHA384" cipher suite. Then, the cloud sent a random number to the edge, and the edge returned the random number along with its own certificate. After verifying the certificate, the cloud generated a pre-master key, encrypted it with the edge's public key, and sent it to the edge. Next, the edge decrypted the pre-master key using its own private key to obtain the pre-master key. Both parties then generated a session key using a pseudo-random function based on the pre-master key and the previously exchanged random number. Finally, the cloud and the edge sent each other a Finished message and encrypted it with the session key to confirm that the channel encryption was effective, thus completing the establishment of the encrypted transmission channel.

[0127] Step 1042: Based on the encrypted transmission channel, the early warning result is transmitted to the cloud central database in an asynchronous manner. Through the cloud central database, the early warning result is mapped to the digital twin model to obtain a visualized panoramic view.

[0128] Among them, asynchronous mode refers to a non-real-time data transmission method that does not block other cloud services and can transmit data in batches when the network is idle to avoid data congestion; digital twin model refers to a virtual model that replicates the physical scene of a mine and can map the location and safety status of real equipment and personnel.

[0129] It should be noted that the embodiments of this application do not specifically limit the specific type of asynchronous mode or the specific structure of the digital twin model.

[0130] In step 1042, the early warning results are transmitted to the cloud central database in an asynchronous manner based on the encrypted transmission channel. Subsequently, the cloud central database receives the data and converts the data format to adapt it to the data format requirements of the digital twin model and the Geographic Information System (GIS) map. Then, the converted early warning results are mapped onto the digital twin model or GIS map, and the early warning location is marked in the virtual scene and the early warning level is distinguished by different colors to form a panoramic view that can intuitively present the safety status of the entire mine.

[0131] For example, the early warning results are transmitted asynchronously to the cloud central database, which has a storage capacity of 100TB. After receiving the data, it is converted into JSON format to adapt to the requirements of the digital twin model and GIS map. Subsequently, after the conversion is completed, the cloud marks the location information (25, 22, 18) in the early warning results as red solid dots and annotates the early warning content. This is synchronously mapped to the digital twin model and GIS map of the mining operation area to form a panoramic view, where the red solid dots represent the level 2 early warning.

[0132] Step 1043: Analyze the panoramic view to obtain the work order data. Based on the work order data and the preset work rule library, perform task assignment processing to obtain the task processing result.

[0133] The preset handling rule library refers to a database that stores the mine's preset early warning handling logic and task assignment standards, including the handling process and time limit corresponding to different hazard levels. In this embodiment, the content and number of each rule in the rule library are not specifically limited.

[0134] Step 1043 may specifically include the following steps:

[0135] Step b1: Use spatial analysis methods to analyze and process the panoramic view to obtain the dangerous areas that need to be prioritized for handling.

[0136] In step b1, spatial analysis methods are used to analyze and process the warning areas in the panoramic view, and the spatial distance between each dangerous area is calculated. Then, dangerous areas that are spatially adjacent and have similar danger levels are grouped together to obtain the dangerous areas that need to be dealt with first.

[0137] For example, when using spatial analysis methods to analyze panoramic views, the Euclidean distance formula is called. Calculate the distance between the danger zone corresponding to TAG008 and the adjacent warning zone, where d represents the spatial distance between the centers of the two zones. , , This represents the center coordinates of the danger zone corresponding to TAG008 (25, 22, 18). , , The coordinates of the center of the adjacent warning area are (27, 24, 17); then, the coordinate values ​​are substituted into the formula to calculate... Meters; then the preset rule is that dangerous areas with a spatial distance of ≤5 meters are grouped into the same cluster, so these two areas are grouped into one dangerous area.

[0138] Step b2: Based on the spatial distribution characteristics and hazard level parameters of the hazardous area, generate disposal work order data that includes disposal location, disposal content, and disposal priority.

[0139] For example, the formula for calculating the coordinates of the cluster center is: ,in, , , Representing the coordinates of the cluster center, substituting the coordinates into the value... ;

[0140] Then, based on the spatial distribution characteristics of the cluster, namely: located in the middle of the No. 2 mining face, near the main roadway, with 3 workers active in the vicinity, the evacuation passage is about 8 meters away from the cluster center and the hazard level parameter is level two, corresponding to "high risk of personnel exposure, requiring rapid intervention but not life-threatening at present", the disposal location was determined to be: the coordinates of the cluster center (26, 23, 17.5), and the additional label "middle of the No. 2 mining face, near the evacuation passage" was added to clarify the specific location;

[0141] Secondly, based on the pre-set handling content template for the Level 2 hazard level, "Level 2 warnings for personnel should include 'personnel within the evacuation area and on-site risk detection'", and then supplemented with details based on the cluster characteristics, it is "evacuate all personnel in the cluster to the safe zone of the evacuation route, and use a gas detector to detect the gas concentration in and around the cluster within 5 meters". Finally, the priority is determined according to the hazard level and the scope of impact, resulting in "high priority" corresponding to the Level 2 hazard level. Since there are 3 people in the cluster, the rule of "if the number of people involved is ≥2, the priority is increased by 1 level" is met, and the final handling priority is determined to be "high priority" to form complete handling work order data.

[0142] Step b3: Based on the work order data and the preset work rule library, determine the task assignment strategy and execution time limit requirements.

[0143] For example, by calling the preset handling rule library to determine the task assignment strategy and execution time limit requirements, the first step is to extract the key information of "high priority, level 2 warning, and No. 2 sampling face" from the handling work order data and match it with the rule in the rule library that "high priority work orders are assigned to those with corresponding regional operation qualifications and safety management experience ≥ 5 years".

[0144] The second step is to query the cloud-based personnel management database to filter out the responsible personnel who "hold a No. 2 mining face operation permit, have 5.5 years of safety management experience, and are currently available", and determine the task assignment strategy as "directly assigning the work order data to the responsible personnel's industrial tablet terminal".

[0145] The third step is to calculate the execution time limit. In the rule base, "Execution Time Limit = Hazard Level Coefficient × Basic Time Limit + Personnel Evacuation Coefficient × Distance Coefficient". The hazard level coefficient corresponds to a level 2 warning and is set to 1.0. The basic time limit is set to 1 hour. The personnel evacuation coefficient is set to 0.1 because it involves 3 people. The distance coefficient is set to 0.1 because the evacuation route is 8 meters away. Substituting these values, the execution time limit is calculated to be 1.0 × 1 + 0.1 × 0.1 × 1 = 1.01 hours. Following the rule of "rounding up to the nearest 15 minutes", the final execution time limit is determined to be 1 hour and 15 minutes.

[0146] Step b4: According to the task assignment strategy and the execution time limit requirements, the work order data is assigned to the corresponding responsible personnel's terminal devices. The responsible personnel's terminal devices process the work order data to obtain the task processing results.

[0147] Among them, the terminal equipment of the responsible personnel refers to the terminal carried or used by the responsible personnel that can receive and process work order data.

[0148] Step 1044: Based on the task processing results, record the warning results, the handling work order data, and the operation data during the task processing into the distributed storage system to generate a security audit log.

[0149] Among them, the distributed storage system refers to a system that adopts a multi-node storage architecture to store early warning results, work order data, and operation data, and has the characteristics of data immutability and high reliability; the security audit log refers to a log file that records all operation data and is stored in the distributed storage system for subsequent security traceability.

[0150] In step 1044, based on the task processing results, the warning results, handling work order data and all operation data in the task processing process are uploaded to the distributed storage system. The distributed storage system stores these data through a multi-node storage architecture to ensure that the data is tamper-proof.

[0151] Meanwhile, the stored data is organized according to time sequence and operation type to generate security audit logs. Throughout the cloud processing flow, an attribute-based access control module is used for dynamic authentication: when a user requests access to data or performs an operation, the module verifies the user's attribute information in real time to determine whether the user's attributes meet the preset access or operation permission requirements. If they do, the corresponding permissions are granted; otherwise, access or operation is denied, thereby strengthening the security of cloud data management and operation.

[0152] example As Based on the task processing results, the cloud uploads the warning results, work order data and operation data, including the warning verification time of 15:05, work order assignment time of 15:16, task feedback time of 15:46, and operator / administrator data, to the distributed storage system. This system contains 3 storage nodes and is deployed on cloud servers A, B and C respectively. After the data is stored synchronously on multiple nodes, it is organized into a security audit log in the format of "time-operation-data content".

[0153] Then, when an administrator requests to view the security audit log, the attribute-based access control module verifies that their attribute information indicates they are a security management position with a permission level of 3. According to the preset rules, level 3 and above permissions allow viewing security audit logs, so the module dynamically grants the administrator access permission. When an intern requests to view the log, the module verifies that their attribute information indicates they are an intern with a permission level of 1, which does not meet the viewing permission requirements, so the access request is denied.

[0154] This application ensures the security of the transmission and verification of early warning results through two-way digital certificate authentication, thereby avoiding the risks of identity forgery and data tampering; subsequently, by transmitting data through an encrypted transmission channel and asynchronous method, it ensures data security without affecting the operation of other cloud services;

[0155] The early warning results are then mapped onto a digital twin model or GIS map to form a panoramic view, which can intuitively display the safety situation of the entire mine and thus improve the efficiency of safety situation awareness. Then, spatial analysis methods are used to identify dangerous areas and generate disposal work orders. Combined with a preset rule base, tasks are assigned to ensure that the handling of early warning events is orderly and efficient, and to avoid chaotic handling.

[0156] Subsequently, during the processing, data is stored in a distributed storage system and an immutable security audit log is generated, enabling traceability of operations and facilitating subsequent compliance checks and problem tracing. Finally, the attribute-based access control module provides dynamic authentication, which can accurately control data access permissions based on the user's real-time attributes, thereby preventing unauthorized access to sensitive data.

[0157] Figure 3 This application provides a schematic diagram of the structure of a data management system based on an identifier resolution middleware for mine safety production, as shown in the embodiment of the present application. Figure 3 The system may include:

[0158] Module 31 is used to build an identifier resolution middleware for mine safety production. The middleware receives raw identifier data from various types of terminals in the mine, obtains raw data packets, and generates standardized data objects based on the raw data packets and a lightweight deep learning model.

[0159] The query module 32 is used to perform a priority query based on the standardized data object and the preset encrypted dynamic mapping library, using the national cryptographic SM4 algorithm to obtain security information, and bind the standardized data object with the security information to obtain a secure data object.

[0160] The processing module 33 is used to process the security data object in real time using an electronic fence dynamic matching algorithm to obtain an early warning result.

[0161] The analysis module 34 is used to verify the warning result in the cloud through two-way authentication of digital certificates, obtain the verification result, display and analyze the warning result based on the verification result through a digital twin model, obtain the handling work order data, record all operation data, generate an immutable security audit log, and use an attribute-based access control module to perform dynamic authentication to strengthen cloud management.

[0162] The data management system based on the identifier resolution middleware for mine safety production in this application embodiment is used to implement the aforementioned data management method based on the identifier resolution middleware for mine safety production. Therefore, the specific implementation of the data management system based on the identifier resolution middleware for mine safety production can be found in the embodiment section of the data management method based on the identifier resolution middleware for mine safety production above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0163] like Figure 4 As shown, this application also provides an electronic device, including: a memory 41 for storing a computer program; and a processor 42 for executing the computer program to implement the steps of the data management method based on the identifier resolution middleware for mine safety production described above.

[0164] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described data management methods based on identifier resolution middleware for mine safety production.

[0165] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0166] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the data management method based on an identifier resolution middleware for mine safety production.

[0167] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0168] The above provides a detailed description of a data management method and system based on an identifier resolution middleware for mine safety production, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A data management and control method based on mine safety production-oriented identification analysis middleware, characterized in that, The application relates to a mine safety production-oriented identification analysis middleware, and the original identification data of a plurality of types of terminals underground is received through the identification analysis middleware to obtain an original data packet, a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, a security information is obtained by adopting a national encryption SM4 algorithm for a prior query based on the standardized data object and a preset encryption dynamic mapping library, the standardized data object is bound with the security information to obtain a security data object, the security data object is processed in real time by adopting an electronic fence dynamic matching algorithm to obtain an early warning result, the early warning result is verified in the cloud through a digital certificate bidirectional authentication mode to obtain a verification result, the early warning result is displayed and analyzed through a digital twin model based on the verification result to obtain disposal work order data, meanwhile, all operation data is recorded to generate an unalterable safety audit log, and dynamic authentication is performed by using an attribute-based access control module to strengthen cloud management and control. The application relates to a mine safety production-oriented identification analysis middleware, and the original identification data of a plurality of types of terminals underground is received through the identification analysis middleware to obtain an original data packet, a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, a security information is obtained by adopting a national encryption SM4 algorithm for a prior query based on the standardized data object and a preset encryption dynamic mapping library, the standardized data object is bound with the security information to obtain a security data object, the security data object is processed in real time by adopting an electronic fence dynamic matching algorithm to obtain an early warning result, the early warning result is verified in the cloud through a digital certificate bidirectional authentication mode to obtain a verification result, the early warning result is displayed and analyzed through a digital twin model based on the verification result to obtain disposal work order data, meanwhile, all operation data is recorded to generate an unalterable safety audit log, and dynamic authentication is performed by using an attribute-based access control module to strengthen cloud management and control. The application relates to a mine safety production-oriented identification analysis middleware, and the original identification data of a plurality of types of terminals underground is received through the identification analysis middleware to obtain an original data packet, a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, a security information is obtained by adopting a national encryption SM4 algorithm for a prior query based on the standardized data object and a preset encryption dynamic mapping library, the standardized data object is bound with the security information to obtain a security data object, the security data object is processed in real time by adopting an electronic fence dynamic matching algorithm to obtain an early warning result, the early warning result is verified in the cloud through a digital certificate bidirectional authentication mode to obtain a verification result, the early warning result is displayed and analyzed through a digital twin model based on the verification result to obtain disposal work order data, meanwhile, all operation data is recorded to generate an unalterable safety audit log, and dynamic authentication is performed by using an attribute-based access control module to strengthen cloud management and control. The application relates to a mine safety production-oriented identification analysis middleware, and the original identification data of a plurality of types of terminals underground is received through the identification analysis middleware to obtain an original data packet, a standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, a security information is obtained by adopting a national encryption SM4 algorithm for a prior query based on the standardized data object and a preset encryption dynamic mapping library, the standardized data object is bound with the security information to obtain a security data object, the security data object is processed in real time by adopting an electronic fence dynamic matching algorithm to obtain an early warning result, the early warning result is verified in the cloud through a digital certificate bidirectional authentication mode to obtain a verification result, the early warning result is displayed and analyzed through a digital twin model based on the verification result to obtain disposal work order data, meanwhile, all operation data is recorded to generate an unalterable safety audit log, and dynamic authentication is performed by using an attribute-based access control module to strengthen cloud management and control. ​ 2. The method of claim 1, wherein, ​ ​ ​ ​ 3. The method of claim 2, wherein, ​ ​ ​ 4. The method of claim 1, wherein, ​ ​ Based on the encrypted transmission channel, the early warning result is transmitted to a cloud center database in an asynchronous manner, the early warning result is mapped into a digital twin model through the cloud center database, and a visual panoramic view is obtained; The panoramic view is analyzed to obtain treatment work order data, and based on the treatment work order data and a preset treatment rule library, task assignment processing is performed to obtain a task processing result; Based on the task processing result, the early warning result, the treatment work order data, and operation data in the task processing process are recorded in a distributed storage system to generate a security audit log.

5. The method of claim 4, wherein, The analysis of the panoramic view to obtain treatment work order data, and based on the treatment work order data and a preset treatment rule library, task assignment processing is performed to obtain a task processing result, includes: A spatial analysis method is used to analyze and process the panoramic view to obtain a dangerous area that needs to be treated in priority; Based on the spatial distribution characteristics and danger level parameters of the dangerous area, treatment work order data containing treatment location, treatment content and treatment priority are generated; Based on the treatment work order data and a preset treatment rule library, a task assignment strategy and an execution time limit requirement are determined; According to the task assignment strategy and the execution time limit requirement, the treatment work order data is assigned to the corresponding responsible personnel terminal device, and the task processing result is obtained by processing the treatment work order data based on the responsible personnel terminal device.

6. The method of claim 1, wherein, The standardized data object and the preset encrypted dynamic mapping library are used to perform priority query using the SM4 algorithm to obtain security information, and the standardized data object and the security information are bound to obtain a security data object, including: An analysis engine is used to receive the standardized data object, and based on the standardized data object, the dynamic cache encrypted by the SM4 algorithm is used to perform priority query, and when the security information is queried, a first query result containing the security information is obtained; Or, when the security information is not queried, based on the standardized data object, the encrypted dynamic mapping library is queried by using a blockchain smart contract, a second query result containing the security information is obtained, and at the same time, the security information in the second query result is updated to the dynamic cache encrypted by the SM4 algorithm by using a cache synchronization mechanism; Based on the first query result or the second query result, the standardized data object and the corresponding security information are associated and bound to generate a security data object.

7. The method of claim 1, wherein, The standardized data object is generated based on the original data packet in combination with a lightweight deep learning model, including: The original data packet is encoded and error-corrected to obtain an intermediate data packet; A signal quality evaluation module of a lightweight deep learning model is used to analyze the time-frequency characteristics of the intermediate data packet to obtain a time-frequency characteristic analysis result, and when the intermediate data packet is identified as a data packet with transmission distortion risk based on the time-frequency characteristic analysis result, a retransmission mechanism is triggered to obtain a reliable data packet; The reliable data packet is data-structured and parsed to obtain a device identification sequence and corresponding monitoring data readings; The device identification sequence is uniformly processed in format to obtain a standard format sequence, and the monitoring data readings are uniformly converted in unit according to corresponding physical quantity types to obtain standard data values; The standard format sequence and the standard data values are combined and packaged according to a preset data structure to obtain a standardized data object.

8. A data management and control system based on a mine production safety-oriented identification analysis middleware, characterized in that, Comprise: The construction module is used for constructing an identification analysis middleware for mine safety production, receiving original identification data of multiple types of terminals in a mine through the identification analysis middleware to obtain an original data packet, and generating a standardized data object based on the original data packet and in combination with a lightweight deep learning model; The query module is used for performing prior query by using a national encryption SM4 algorithm based on the standardized data object and a preset encrypted dynamic mapping library to obtain safety information, binding the standardized data object with the safety information to obtain a safety data object; The processing module is used for performing real-time processing on the safety data object by using an electronic fence dynamic matching algorithm to obtain an early warning result; The analysis module is used for checking the early warning result in the cloud through a digital certificate bidirectional authentication mode to obtain a checking result, displaying and analyzing the early warning result through a digital twin model based on the checking result to obtain disposal work order data, recording all operation data to generate a tamper-proof safety audit log, and performing dynamic authentication by using an attribute-based access control module to strengthen cloud management and control.

9. An electronic device, comprising: Comprise: The memory is used for storing a computer program; The processor is used for executing the computer program to realize the steps of the data management and control method based on the identification analysis middleware for mine safety production according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the data management and control method based on the identification analysis middleware for mine safety production according to any one of claims 1 to 7.

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