Coal mine disaster remote monitoring data analysis method, device and equipment and storage medium

Abstract

The application provides a coal mine disaster remote monitoring data analysis method, device, equipment and storage medium, wherein the method comprises the following steps: obtaining disaster prevention data and outputting corresponding standardized data; extracting entity relationship, correlation relationship and characteristic value from the standardized data and converting them into a structured data set; based on the structured data set, taking the coal mine, working face, time and disaster type dimensions as nodes and edges, generating and updating a coal mine disaster knowledge graph; based on the coal mine disaster knowledge graph and the characteristic value, performing logical comparison and rule verification to generate an analysis result and calculating a comprehensive risk assessment value to generate an early warning instruction; based on the analysis result and the early warning instruction, generating a structured monitoring report and outputting risk prompt information. Through the technical solution of the application, the monitoring and auditing efficiency and accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of coal mine safety supervision technology, and in particular to a method, device, equipment and storage medium for remote monitoring and data analysis of coal mine disasters. Background Technology

[0002] In related technologies, remote monitoring of coal mine disasters mainly relies on manual review of multi-format, heterogeneous data on disaster prevention and control, such as gas and water hazards. With the continuous increase in the total amount of data, manual verification is labor-intensive, prone to omissions and misjudgments; moreover, the data on various types of disasters is stored in a scattered manner, making it impossible to conduct cross-category and cross-time period correlation analysis, only able to verify surface information of documents, and difficult to verify the inherent correlations of the data. At the same time, regulations and standards are updated rapidly, making it difficult for manual review to keep pace with the new regulations. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Firstly, this application proposes a remote monitoring data analysis method for coal mine disasters. The method includes: acquiring disaster prevention and control data and outputting corresponding standardized data; extracting entity relationships, correlation relationships, and feature values ​​from the standardized data and converting them into a structured dataset; generating and updating a coal mine disaster knowledge graph based on the structured dataset, using coal mine, working face, time, and disaster type as nodes and edges; performing logical comparison and rule verification based on the coal mine disaster knowledge graph and the feature values ​​to generate analysis results, and calculating a comprehensive risk assessment value to generate an early warning instruction; and generating a structured monitoring report and outputting risk warning information based on the analysis results and the early warning instruction.

[0005] In one implementation, the disaster prevention data includes text files, image files, and table files. The process of acquiring the disaster prevention data and outputting corresponding standardized data includes: for the table files, calculating the invalid data missing rate; if the missing rate is less than a missing rate threshold, linear interpolation is used to complete the data using adjacent valid data; if the missing rate is greater than or equal to the missing rate threshold, null values ​​are retained and data breaks are marked, generating processed table data; for the image files, image denoising is performed using local pixel weighted average grayscale, and the image is segmented to obtain character blocks. Feature vectors are extracted and compared with a coal mine professional character feature library to calculate similarity, and the corresponding text sequence is obtained based on the similarity; for the text files, they are converted to standard character encoding to obtain text data; and the text data, the table data, and the plain text sequence are integrated into the standardized data.

[0006] In one optional implementation, the step of extracting entity relationships, association relationships, and feature values ​​from the standardized data and converting them into a structured dataset includes: inputting the plain text sequence into a fine-tuned large language model to locate entity objects; extracting predicates or prepositions as relation edges by combining attention mechanisms and dependency parsing logic to generate the association relationships; extracting two-dimensional pixel coordinates from the denoised and enhanced image data; decomposing and mapping the two-dimensional pixel coordinates into sampling time nodes and physical monitoring values; extracting the physical monitoring value of the current sampling time node and comparing it with the physical monitoring values ​​of adjacent time nodes; and determining that the physical monitoring value of the current sampling time node is greater than the physical monitoring value of the previous time node and simultaneously greater than the physical monitoring value of subsequent anomaly nodes. When measuring values, the physical monitoring values ​​at the current sampling time node are extracted as the feature values. The cell boundaries of the table data are determined using a straight line detection algorithm, and the feature values ​​located within the cell boundaries are extracted and subjected to multi-dimensional data logic verification. The multi-dimensional data logic verification includes: data range verification by comparing the feature values ​​with the design parameter safety range, time logic verification by checking the continuity of the timestamp sequence, numerical logic verification by calculating the sum of the sub-items and determining their equality, and correlation verification by checking the matching of parameters with physical coupling relationships. For feature values ​​that fail the multi-dimensional data logic verification, an abnormal data mark is added to the end of the data structure, and the feature values ​​with the added abnormal data mark are extracted as the feature values.

[0007] In one implementation, the step of generating and updating a coal mine disaster knowledge graph based on the structured dataset, using coal mine, working face, time, and disaster type as nodes and edges, includes: defining a four-dimensional ontology model in a graph database, categorized by coal mine, working face, time, and disaster type; extracting the coal mine name, working face name, and entity name from the structured dataset and concatenating them into a combined string; calculating a corresponding identifier for the combined string using a hash algorithm; creating business nodes in the four-dimensional ontology model using the identifiers; and writing the feature values ​​from the structured dataset as internal attribute data into the business nodes; extracting source node identifiers and target node identifiers from the association relationships in the structured dataset; establishing directed edges between business nodes with the source node identifiers and business nodes with the target node identifiers; and generating the coal mine disaster knowledge graph.

[0008] In one implementation, the step of generating and updating a coal mine disaster knowledge graph based on the structured dataset, using coal mine, working face, time, and disaster type as nodes and edges, further includes: when a newly received entity is received, using the identifier to locate whether there is a historical master node in the coal mine disaster knowledge graph that matches the identifier; if the query matches the historical master node, a status sub-node representing the current business review cycle is generated below the historical master node, the entity's own feature value is attached to the status sub-node, and the current status pointer of the historical master node is modified to point to the status sub-node; if the query does not match the historical master node, it is determined to be a new business object, and a brand new node is directly created below the corresponding working face and disaster type.

[0009] In one implementation, the step of performing logical comparison and rule verification based on the coal mine disaster knowledge graph and the feature values ​​to generate analysis results, and calculating a comprehensive risk assessment value to generate an early warning instruction, includes: retrieving nodes with the same name from different sources in the coal mine disaster knowledge graph; extracting the feature values ​​contained within the nodes with the same name; determining whether the units of measurement belong to the same physical quantity category; when it is determined that they belong to the same physical quantity category, converting and mapping them to the same benchmark unit and calculating the absolute value of the difference; when the absolute value of the difference is greater than a preset tolerance threshold, marking the feature values ​​as data contradiction items and storing them in an inconsistency set; initiating a global node matching query to the graph database according to the monitoring requirements in the preset safety supervision procedure list; compiling the monitoring requirements that do not match the search conditions into a missing item set; and outputting the inconsistency set and the missing item set as the analysis results.

[0010] Secondly, this application proposes a remote monitoring and data analysis system for coal mine disasters. The system includes: a multi-source data access and preprocessing module for acquiring disaster prevention and control data and outputting corresponding standardized data; a structured information intelligent extraction module for extracting entity relationships, correlation relationships, and feature values ​​from the standardized data and converting them into a structured dataset; a coal mine disaster knowledge graph construction module for generating and updating a coal mine disaster knowledge graph based on the structured dataset, with coal mine, working face, time, and disaster type as nodes and edges; an intelligent analysis and judgment engine for performing logical comparison and rule verification based on the coal mine disaster knowledge graph and the feature values ​​to generate analysis results and calculate a comprehensive risk assessment value to generate early warning instructions; and a risk warning and report generation module for generating a structured monitoring report and outputting risk warning information based on the analysis results and the early warning instructions.

[0011] In one implementation, the disaster prevention data includes text files, image files, and table files. The multi-source data access and preprocessing module can be used to: for the table files, calculate the invalid data missing rate of the table files; if the missing rate is less than the missing rate threshold, use adjacent valid data for linear interpolation to complete the data; if the missing rate is greater than or equal to the missing rate threshold, retain null values ​​and mark data faults to generate processed table data; for the image files, use local pixel weighted average grayscale to denoise the images, segment the images to obtain character blocks, extract feature vectors, and compare them with a coal mine professional character feature library to calculate similarity, and obtain the corresponding text sequence based on the similarity; for the text files, convert them to standard character encoding to obtain text data; and integrate the text data, the table data, and the plain text sequence into the standardized data.

[0012] In one optional implementation, the structured information intelligent extraction module can be used to: input the plain text sequence into a fine-tuned large language model to locate entity objects, extract predicates or prepositions as relation edges by combining attention mechanisms and dependency parsing logic, and generate the association relationship; extract two-dimensional pixel coordinates from the denoised and enhanced image data, decompose and map the two-dimensional pixel coordinates into sampling time nodes and physical monitoring values, extract the physical monitoring value of the current sampling time node and compare it with the physical monitoring values ​​of adjacent time nodes, and when it is determined that the physical monitoring value of the current sampling time node is greater than the physical monitoring value of the previous time node and is also greater than the physical monitoring value of subsequent anomaly nodes, the current sampling time node is... The physical monitoring values ​​of the nodes are used as the feature values ​​for extraction. A line detection algorithm is used to determine the cell boundaries of the table data, and feature values ​​located within the cell boundaries are extracted and subjected to multi-dimensional data logic verification. The multi-dimensional data logic verification includes: data range verification by comparing the feature values ​​with the design parameter safety range; time logic verification by checking the continuity of the timestamp sequence; numerical logic verification by calculating the sum of the sub-items and determining their equality; and correlation verification by checking the matching of parameters with physical coupling relationships. For feature values ​​that fail the multi-dimensional data logic verification, an abnormal data marker is appended to the end of the data structure, and the feature values ​​with the appended abnormal data marker are used as the extracted feature values.

[0013] In one implementation, the coal mine disaster knowledge graph construction module can be used to: define a four-dimensional ontology model in a graph database, categorized by coal mine, working face, time, and disaster type; extract the coal mine name, working face name, and entity name from the structured dataset and concatenate them into a combined string; calculate the corresponding identifier for the combined string using a hash algorithm; create business nodes in the four-dimensional ontology model using the identifier; and write the feature values ​​from the structured dataset as internal attribute data into the business nodes; extract the source node identifier and target node identifier from the association relationships in the structured dataset; establish directed edges between business nodes with the source node identifier and business nodes with the target node identifier; and generate the coal mine disaster knowledge graph.

[0014] In one implementation, the coal mine disaster knowledge graph construction module can be used to: upon receiving a newly introduced entity, use the identifier to locate whether there is a historical master node in the coal mine disaster knowledge graph that matches the identifier; if the query matches the historical master node, generate a status sub-node representing the current business review cycle under the historical master node, attach the entity's own feature value to the status sub-node, and modify the current status pointer of the historical master node to point to the status sub-node; if the query does not match the historical master node, it is determined to be a new business object, and a brand new node is directly created under the corresponding work surface and disaster type.

[0015] In one implementation, the intelligent analysis and judgment engine can be used to: retrieve nodes with the same name from different sources in the coal mine disaster knowledge graph; extract the feature values ​​contained within the nodes with the same name; determine whether the units of measurement belong to the same physical quantity category; when it is determined that they belong to the same physical quantity category, convert and map them to the same reference unit and calculate the absolute value of the difference; when the absolute value of the difference is greater than a preset tolerance threshold, mark the feature values ​​as data contradiction items and store them in an inconsistency set; initiate a global node matching query to the graph database according to the inspection requirements in the preset safety inspection procedure list, compile the inspection requirements that do not match the search conditions into a missing item set; and output the inconsistency set and the missing item set as the analysis result.

[0016] Thirdly, this application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0017] Fourthly, this application proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect.

[0018] Fifthly, this application proposes a program product comprising at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0019] The coal mine disaster remote monitoring data analysis method, device, equipment, and storage medium provided in this application standardize and preprocess heterogeneous data on various disaster prevention and control in coal mines, outputting standardized data. Then, entities, relationships, and feature values ​​are extracted from the standardized data to form a unified structured dataset. Based on this dataset, a coal mine disaster knowledge graph is built and dynamically updated, with coal mine, working face, time, and disaster type as core dimensions. Multi-dimensional logical comparisons and industry rule verifications are performed based on the knowledge graph and extracted feature values ​​to output analysis results. Simultaneously, a risk model is used to calculate a comprehensive risk value and generate tiered early warning instructions. Finally, all analysis results and early warning instructions are integrated to automatically output a monitoring report. This solution enables fully automated review of massive amounts of monitoring data, significantly improving the efficiency and accuracy of monitoring review, and can uncover hidden potential risks in the data, shifting coal mine disaster monitoring from post-event verification to pre-event risk warning.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a schematic diagram of the first embodiment provided in this application; Figure 2 This is a schematic diagram of a gas prevention and control knowledge graph provided in this application; Figure 3 This is a schematic diagram of the second embodiment provided in this application; Figure 4 This is a schematic diagram of the third embodiment provided in this application; Figure 5 This is a schematic diagram of the structure of a remote monitoring and data analysis system for coal mine disasters provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following describes a method and apparatus for remote monitoring and analyzing coal mine disaster data according to embodiments of this application, with reference to the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating a remote monitoring and data analysis method for coal mine disasters provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps: S1: Obtain disaster prevention and control data and output the corresponding standardized data.

[0026] For example, the system receives data packets pushed from the coal mine via a pre-defined application programming interface (API) and automatically parses the header information. Based on the file extension and data format characteristics contained in the header information, the system directs the raw data files to the corresponding category caches. Specifically, files with plain text extensions (such as exploration report text) are stored in the text cache, files with image extensions (such as scanned copies of ventilation system diagrams) are stored in the image cache, and files with table extensions (such as daily gas reports) are stored in the table cache. These files of different formats collectively constitute multi-source heterogeneous disaster prevention data. The disaster prevention data is then standardized to obtain standardized data.

[0027] In some embodiments, disaster prevention data includes text files, image files, and table files. Acquiring disaster prevention data and outputting corresponding standardized data includes: for table files, calculating the invalid data missing rate; if the missing rate is less than a missing rate threshold, linear interpolation is used to complete the data using adjacent valid data; if the missing rate is greater than or equal to the missing rate threshold, null values ​​are retained and data breaks are marked, generating processed table data; for image files, image denoising is performed using local pixel-weighted average grayscale, and the image is segmented to obtain character blocks. Feature vectors are extracted and compared with a coal mine professional character feature library to calculate similarity, and the corresponding text sequence is obtained based on the similarity; for text files, they are converted to standard character encoding to obtain text data; and the text data, table data, and plain text sequence are integrated into standardized data.

[0028] As an example, taking image files as an example, due to the complex working environment in coal mines or the limited resolution of scanning equipment, uploaded engineering drawings and sensor monitoring curves often contain background noise or shadows. To improve the accuracy of subsequent information extraction, the multi-source data access and preprocessing module is configured with spatial domain filtering and cleaning logic. The specific computer execution steps of this process are as follows: the system reads the original image into memory in the form of a two-dimensional pixel matrix; for each target pixel in the matrix, the system defines a local window of a set size (such as a 3×3 pixel matrix area) containing itself and its surrounding neighboring pixels; then, it reads the gray values ​​of all pixels within this window, calculates the local weighted average gray value according to the system's preset center weight logic, and uses this value to cover the original gray value of the target pixel. By traversing the entire image matrix row by row and column by column, the system effectively smooths pixel abrupt changes caused by stains or scanning artifacts, outputs denoised and enhanced image data, and performs text recognition operations on the denoised and enhanced image data to accurately convert the unstructured pixel information marked on the drawing into a machine-readable plain text sequence.

[0029] S2: Extract entity relationships, association relationships, and feature values ​​from standardized data and convert them into a structured dataset.

[0030] For example, a plain text sequence is input into a fine-tuned large language model to locate entity objects. Predicates or prepositions are extracted as relation edges using attention mechanisms and dependency parsing logic to generate associations. Two-dimensional pixel coordinates are extracted from the denoised and enhanced image data. These coordinates are decomposed and mapped to sampling time nodes and physical monitoring values. The physical monitoring value of the current sampling time node is extracted and compared with the physical monitoring values ​​of adjacent time nodes. If the physical monitoring value of the current sampling time node is determined to be greater than the physical monitoring value of the previous time node and also greater than the physical monitoring value of subsequent anomaly nodes, then the physical monitoring value of the current sampling time node is... Numerical values ​​are extracted as feature values. A line detection algorithm is used to determine the cell boundaries of the table data, and feature values ​​located within the cell boundaries are extracted and subjected to multi-dimensional data logic verification. Multi-dimensional data logic verification includes: data range verification by comparing feature values ​​with the design parameter safety range, time logic verification by checking the continuity of the timestamp sequence, numerical logic verification by calculating the sum of the sub-items and determining their equality, and correlation verification by checking the matching of parameters with physical coupling relationships. For feature values ​​that fail the multi-dimensional data logic verification, abnormal data markers are added to the end of the data structure, and feature values ​​with abnormal data markers are extracted as feature values.

[0031] S3: Based on a structured dataset, a coal mine disaster knowledge graph is generated and updated with nodes and edges representing coal mines, working faces, time, and disaster types.

[0032] For example, a four-dimensional ontology model is defined in the graph database, categorized by coal mine, working face, time, and disaster type. The coal mine name, working face name, and entity name of the entity to which the entity belongs are extracted from the structured dataset and concatenated into a combined string. The combined string is then used to generate the corresponding identifier using a hash algorithm. Business nodes are created in the four-dimensional ontology model using the identifiers, and the feature values ​​from the structured dataset are written into the business nodes as internal attribute data. The source node identifier and target node identifier contained in the association relationships in the structured dataset are extracted, and directed edges are established between business nodes with source node identifiers and business nodes with target node identifiers to generate a coal mine disaster knowledge graph.

[0033] As one example, please see Figure 2 , Figure 2 This application provides a schematic diagram of a knowledge graph for gas prevention and control, such as... Figure 2 As shown, the knowledge graph uses coal mines, working faces, time, and gas hazard types as core nodes, and various entities such as gas-related reports, monitoring sensors, ventilation equipment, borehole parameters, hidden dangers, prevention and control measures, and rectification records as branch nodes. Based on the relationship between data such as "inclusion, monitoring, existence, rectification, and supporting", the knowledge graph is built to integrate structured data extracted from texts, drawings, tables, and monitoring curves in the field of gas prevention and control with industry professional knowledge.

[0034] Please see Figure 3 , Figure 3 This is a schematic diagram of the second embodiment provided in this application. Figure 3 As shown, step S3 may include the following steps: S301, Constructing the basic data framework for a four-dimensional ontology.

[0035] For example, a four-dimensional ontology model based on coal mines, working faces, time, and disaster types is predefined in the graph database. In implementation, the system uses a combination of tree-like hierarchical and network-like methods to create classification benchmark nodes. Specifically, the system sets the coal mine as the global root node, with specific working face nodes attached to it to form a spatial distribution hierarchy. Below each working face node, disaster type benchmark nodes describing business scenarios, such as gas, water hazards, and fires, are further associated. Simultaneously, the system establishes independent timeline sequence nodes with fixed steps of year, month, and day, and establishes cross-associations with the aforementioned spatial and business nodes through time attribute edges. This four-dimensional skeleton provides fixed anchor points for the subsequent attachment and alignment of scattered entity data.

[0036] S302, Mapping and instantiation of entity nodes.

[0037] For example, when mapping and instantiating key entities extracted from text and equipment legends parsed from images, considering the phenomenon of entities with the same name existing in different working faces (e.g., methane sensor No. 1 exists in different areas), to avoid node conflicts and data overwriting errors, the system extracts the coal mine name, working face name, and entity name to which the entity belongs. The system concatenates these multiple pieces of information into a combined string, then uses a hash algorithm to calculate a globally unique identifier for this combined string, and creates a specific business node in the graph accordingly. After node creation is complete, the system reads the feature values ​​carried in the structured data (such as sensor monitoring maxima, borehole 2D coordinates) and previously added abnormal data markers, and writes this information as key-value pair data structure as internal attribute data directly into the attribute storage field of the corresponding business node.

[0038] S303, Analysis of Relationships and Construction of Multidimensional Edges.

[0039] For example, based on the establishment of entity nodes, to reflect the real business relationships between entities, the system parses the triplet association relationships in the structured dataset and the connectivity matrix of the drawings. The system extracts the source node identifier and target node identifier from the relationship results and establishes directed edges connecting the two in the graph database. To distinguish different types of business associations, the system assigns different semantic labels to the generated directed edges according to the data source. For example, the "workface-adopt-anti-collision measures" edge label extracted from the text is assigned the "technical execution logic" edge label, the "sensor-located-lane" edge label extracted from the drawing connectivity matrix is ​​assigned the "spatial containment" edge label, and the "association" edge label extracted from the table timestamp is assigned the "temporal flow" edge label. Through the construction of the above edges, isolated business nodes are connected into a mesh structure that can be traversed and queried by the computer.

[0040] S304 performs dynamic updates and version control of graph nodes.

[0041] For example, when a newly introduced feature entity is received, an index query command is triggered in the graph database. The system uses the previously generated globally unique identifier to locate whether a historical master node corresponding to the business entity already exists in the graph. If the query matches a historical master node, the system updates it using an append-only strategy. To prevent the direct writing of high-frequency detailed sensor data from creating supernodes in the graph and thus slowing down traversal performance, the system executes time-series state aggregation logic. Instead of creating an independent node for each sample, the system generates a state sub-node representing the current business review cycle (e.g., divided by day or shift) under the historical master node. The system attaches feature values ​​such as extreme values ​​or violation counts selected by the pre-processing module to this state sub-node and modifies the current state pointer of the master node to point to this latest sub-node. If the query does not match a corresponding node, the system determines it to be a newly added business object and directly creates a new node under its corresponding work surface and disaster type skeleton. This branch determination logic helps maintain the data integrity of the knowledge graph during its dynamic growth process, reducing the risk of data omissions or gaps in traceability. In addition, the system provides a manual correction interface; when the system's automatic update deviates or special business changes occur on-site, supervisors are allowed to pass overwrite commands through this interface to manually intervene and correct the node attributes or edge relationships in the graph.

[0042] S305, persistent encapsulation and status output of map data.

[0043] After the aforementioned ontology mapping, edge construction, and dynamic update operations, the system completes the assembly and status refresh of the current batch of coal mine disaster knowledge graphs. Subsequently, the system executes the graph database's transaction commit instruction, persistently writing the graph topology data in memory to the underlying storage engine. The data sharding and index creation process of the graph database's underlying storage engine can be configured by those skilled in the art based on conventional big data architectures; its underlying persistence mechanism is a well-known technology in the field and will not be elaborated upon here.

[0044] In some embodiments, based on a structured dataset, a coal mine disaster knowledge graph is generated and updated using coal mine, working face, time, and disaster type as nodes and edges. This also includes: when a newly received entity is received, using an identifier to locate whether a historical master node in the coal mine disaster knowledge graph matches the identifier; if a historical master node is matched, a status sub-node representing the current business review cycle is generated below the historical master node, the entity's own feature values ​​are attached to the status sub-node, and the current status pointer of the historical master node is modified to point to the status sub-node; if no historical master node is matched, it is determined to be a new business object, and a new node is directly created below the corresponding working face and disaster type.

[0045] S4: Based on the coal mine disaster knowledge graph and characteristic values, perform logical comparison and rule verification to generate analysis results, and calculate the comprehensive risk assessment value to generate early warning instructions.

[0046] In some embodiments, logical comparison and rule verification are performed based on a coal mine disaster knowledge graph and feature values ​​to generate analysis results, and a comprehensive risk assessment value is calculated to generate an early warning instruction. This includes: retrieving nodes with the same name from different sources in the coal mine disaster knowledge graph, extracting the feature values ​​contained within the nodes with the same name, determining whether the units of measurement belong to the same physical quantity category, converting and mapping them to the same benchmark unit when they belong to the same physical quantity category and calculating the absolute value of the difference, and marking the feature values ​​as data contradiction items and storing them in an inconsistency set when the absolute value of the difference is greater than a preset tolerance threshold; initiating a global node matching query to the graph database according to the supervision requirements in the preset safety supervision procedure list, compiling the supervision requirements that do not match the search conditions into a missing item set; and outputting the inconsistency set and the missing item set as analysis results.

[0047] As an example, please see Figure 4 , Figure 4 This is a schematic diagram of the third embodiment provided in this application, as shown below. Figure 4 As shown, step S4 may include the following steps: S401, Perform cross-modal data consistency check.

[0048] Considering that the same physical phenomenon is often recorded repeatedly in different files during actual data reporting, the system searches for nodes with similar business meanings but different data source labels in the graph to check the consistency of such data. During the verification process, the system extracts the attributes of nodes with the same name from different files (such as report text and monitoring tables). To avoid calculation anomalies caused by forced comparison of physical quantities from different sources, the system introduces a pre-judgment rule for physical dimension compatibility. The system reads the units of measurement of both entities and determines whether they belong to the same physical quantity category. If it is determined that they do not belong to the same category, resulting in the inability to perform dimension conversion, the system will suspend the current comparison operation of the related entities, generate an incomparability log, and continue polling the next graph node pair to prevent local dirty data from causing a deadlock in the global verification process; if they are determined to belong to the same physical quantity category, the system converts and maps them to the same reference dimension. After dimension unification, the system calculates the absolute value of the difference between the two values. When the absolute value of the difference is determined to be greater than the system's preset tolerance threshold (which is usually configured based on the allowable error range of the monitoring equipment, for example, set to 5% of the baseline value), the consistency verification unit marks the associated entity as a data contradiction item and stores it in the inconsistency set.

[0049] S402, Perform a content integrity scan operation based on the inventory list.

[0050] The system loads a built-in list of safety monitoring procedures into memory. This list provides a structured representation of the disaster prevention equipment or monitoring data that various types of mines must possess at specific operational stages. To avoid omissions due to differences in wording, the system calls upon a pre-built synonym mapping table for the coal mining industry. The integrity check unit uses the mandatory requirements from the procedure list as search keywords, expands the search term set using the synonym mapping table, and initiates a global node matching query to the underlying graph database. If the query result is empty, meaning there is no entity node with the corresponding name or synonym in the graph, the system determines that the disaster prevention element is missing from the uploaded data. These elements that do not match the search criteria are compiled into a missing item set by the system.

[0051] S403 involves a two-tiered technical logic deduction process that includes both superficial compliance checks and in-depth compliance assessments to identify hidden risks of formal compliance but substantive non-compliance.

[0052] As a preferred implementation, the compliance analysis unit traverses the graph to search for the starting business node (such as an abnormal data node or a specific disaster prevention project node).

[0053] First, a shallow compliance check is performed: Based on a preset industry rule base (such as coal mine safety regulations and detailed rules), the system uses rule matching to check whether the connected document nodes mention the prescribed methods, steps or parameters (for example, for water exploration operations, check whether the text nodes contain descriptions of both geophysical exploration and drilling methods).

[0054] Subsequently, based on the successful shallow rule matching, the system invokes domain expert models and machine learning algorithms to perform in-depth compliance assessment. The system utilizes a breadth-first search algorithm in the graph database, expanding outwards hierarchically along directed edges containing technical execution logic or spatial semantic labels (the maximum jump level is set to 2 to 3 levels) to obtain relevant disaster prevention technology measure nodes. For example, for nodes in geophysical anomaly areas, the system not only checks whether drilling nodes have been deployed, but also extracts the spatial coordinates, quantity, and depth attributes of the drilling nodes, inputting these into the expert model to calculate and verify whether the three-dimensional physical trajectory of the drilling logically covers the aforementioned geophysical anomaly area.

[0055] Within the specified depth, if no relevant disaster prevention technology measure nodes are found, or if the expert model determines that the coverage of disaster prevention measures or the decision-making logic fails to meet the corresponding security technology logic requirements (i.e., substantial violation), the system determines that there is a risk of failure in the spatial disaster prevention deployment or technical logic of that area. The compliance analysis unit extracts the aforementioned conflicting node relationship chains into a set of violation links, and adds the violation judgment basis and rectification direction generated by the domain expert model.

[0056] S404, issued a comprehensive risk assessment and instructions.

[0057] For example, the system summarizes the inconsistency sets, missing item sets, and violation link sets generated during the aforementioned process. To quantify the overall safety status of the mine, the risk assessment and early warning unit performs a numerical cumulative assessment based on the threat level of each anomaly set to safe production. In the specific computer execution logic, the system defines a risk accumulator variable with an initial value of zero in memory. Subsequently, the system configures differentiated basic risk scores. Since technical logic violations directly reflect substantial defects in on-site disaster prevention capabilities, their severity is usually far greater than simple missing file content or inconsistent data records. The system configures the highest basic score for violation link items, a medium basic score for missing items, and the lowest basic score for inconsistencies. After completing the parameter configuration, the system iterates through the three anomaly sets sequentially; whenever an anomaly element is read from a set, the system extracts its corresponding basic risk score and adds it to the risk accumulator. After all sets have been traversed, the final value output by the risk accumulator is the current comprehensive risk assessment value of the mine. This numerical accumulation rule ensures that the assessment results can map the true degree of danger in the mine.

[0058] S405, triggering a status response based on the calculated comprehensive risk assessment value.

[0059] For example, the acquired comprehensive risk assessment value is compared with a preset tiered early warning threshold range. This tiered early warning threshold range is pre-divided into multiple discrete numerical judgment segments, such as routine concern, general risk, significant risk, and major hidden danger. When the comprehensive risk assessment value falls into the corresponding trigger range, the system encapsulates an early warning instruction data packet containing the risk level, specific anomaly type, and spatial location identifier of the occurrence source. This data packet is pushed to the message queue of the terminal system to drive the dynamic alarm response of the front-end interface.

[0060] S5: Based on the analysis results and early warning instructions, generate a structured monitoring report and output risk warning information.

[0061] For example, data packet field integrity is verified using CRC or hash digest. If verification fails, the data packet is discarded, logged, and retransmission requested. Multiple timeouts trigger a physical alarm. Upon successful verification, core parameters are parsed and stored in memory. The system then loads the mine's geographic information model, rendering element colors and flashing states based on risk level differences to achieve spatially visualized risk alerts. Simultaneously, the backend leverages a graph database to conduct reverse tracing of the evidence chain, following the data flow to the original data access node and integrating various original data to form a complete tracing evidence set. The system then matches the corresponding report template; if no specific template exists, a fallback template is used, automatically generating standardized structured monitoring reports through data mapping. Finally, the system distributes reports according to risk level, archiving low-risk events locally and automatically pushing high-risk events to the responsible parties. An exponential backoff and retry mechanism is also included; if the push fails, manual intervention is initiated to ensure reliable alarm information transmission.

[0062] By implementing the embodiments of this application, heterogeneous data on disaster prevention and control in coal mines can be standardized and preprocessed to output standardized data. Entities, relationships, and feature values ​​are then extracted from the standardized data to form a unified structured dataset. Based on this dataset, a coal mine disaster knowledge graph is built and dynamically updated, with coal mine, working face, time, and disaster type as core dimensions. Multi-dimensional logical comparisons and industry rule verifications are performed based on the knowledge graph and extracted feature values ​​to output analysis results. Simultaneously, a comprehensive risk value is calculated using a risk model, and tiered early warning instructions are generated. Finally, all analysis results and early warning instructions are integrated to automatically output a monitoring report. This solution enables fully automated review of massive amounts of monitoring data, significantly improving the efficiency and accuracy of monitoring review, and can uncover hidden potential risks in the data, shifting coal mine disaster monitoring from post-event verification to pre-event risk warning.

[0063] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a remote monitoring and data analysis system for coal mine disasters provided in an embodiment of this application. Figure 5 As shown, the device 500 includes: a multi-source data access and preprocessing module 501, used to acquire disaster prevention and control data and output corresponding standardized data; a structured information intelligent extraction module 502, used to extract entity relationships, correlation relationships and feature values ​​from the standardized data and convert them into a structured dataset; a coal mine disaster knowledge graph construction module 503, used to generate and update a coal mine disaster knowledge graph based on the structured dataset, with coal mine, working face, time and disaster type as nodes and edges; an intelligent analysis and judgment engine 504, used to perform logical comparison and rule verification based on the coal mine disaster knowledge graph and feature values ​​to generate analysis results, and calculate a comprehensive risk assessment value to generate early warning instructions; and a risk early warning and report generation module 505, used to generate a structured monitoring report and output risk warning information based on the analysis results and early warning instructions.

[0064] In one implementation, disaster prevention data includes text files, image files, and table files. The multi-source data access and preprocessing module 501 can be used for: For table files, calculating the invalid data missing rate of the table files; if the missing rate is less than the missing rate threshold, linear interpolation is used to complete the data using adjacent valid data; if the missing rate is greater than or equal to the missing rate threshold, null values ​​are retained and data faults are marked to generate processed table data; For image files, image denoising is performed using local pixel weighted average grayscale, and the image is segmented to obtain character blocks. Feature vectors are extracted and compared with a coal mine professional character feature library to calculate similarity, and the corresponding text sequence is obtained based on the similarity; For text files, they are converted to standard character encoding to obtain text data; and the text data, table data, and plain text sequences are integrated into standardized data.

[0065] In one optional implementation, the structured information intelligent extraction module 502 can be used to: input a plain text sequence into a fine-tuned large language model to locate entity objects, extract predicates or prepositions as relation edges by combining attention mechanisms and dependency parsing logic, and generate association relationships; extract two-dimensional pixel coordinates from the denoised and enhanced image data, decompose and map the two-dimensional pixel coordinates into sampling time nodes and physical monitoring values, extract the physical monitoring value of the current sampling time node and compare it with the physical monitoring values ​​of adjacent time nodes, and when it is determined that the physical monitoring value of the current sampling time node is greater than the physical monitoring value of the previous time node, and is also greater than the physical monitoring value of subsequent anomaly nodes, then... The physical monitoring values ​​at the current sampling time point are used as feature values ​​for extraction. A line detection algorithm is used to determine the cell boundaries of the table data, and feature values ​​located within these cell boundaries are extracted and subjected to multi-dimensional data logic verification. This multi-dimensional data logic verification includes: data range verification by comparing feature values ​​with the design parameter safety range; time logic verification by checking the continuity of the timestamp sequence; numerical logic verification by calculating the sum of the sub-items and determining their equality; and correlation verification by checking the matching of parameters with physical coupling relationships. For feature values ​​that fail the multi-dimensional data logic verification, anomaly markers are appended to the end of the data structure, and the feature values ​​with the appended anomaly markers are extracted as feature values.

[0066] In one implementation, the coal mine disaster knowledge graph construction module 503 can be used to: define a four-dimensional ontology model in the graph database, categorized by coal mine, working face, time, and disaster type; extract the coal mine name, working face name, and entity name from the structured dataset and concatenate them into a combined string; calculate the corresponding identifier for the combined string using a hash algorithm; create business nodes in the four-dimensional ontology model using the identifiers; and write the feature values ​​from the structured dataset as internal attribute data into the business nodes; extract the source node identifier and target node identifier contained in the association relationships in the structured dataset; establish directed edges between business nodes with source node identifiers and business nodes with target node identifiers; and generate a coal mine disaster knowledge graph.

[0067] In one implementation, the coal mine disaster knowledge graph construction module 503 can be used to: when receiving a newly introduced entity, use the identifier to locate whether there is a historical master node in the coal mine disaster knowledge graph that matches the identifier; if a historical master node is matched, generate a status sub-node representing the current business review cycle under the historical master node, attach the entity's own feature value to the status sub-node, and modify the current status pointer of the historical master node to point to the status sub-node; if no historical master node is matched, it is determined to be a new business object, and a brand new node is created directly under the corresponding work surface and disaster type.

[0068] In one implementation, the intelligent analysis and judgment engine 504 can be used to: retrieve nodes with the same name from different sources in the coal mine disaster knowledge graph, extract the feature values ​​contained within the nodes with the same name, determine whether the units of measurement belong to the same physical quantity category, convert and map them to the same benchmark unit when they belong to the same physical quantity category and calculate the absolute value of the difference, and mark the feature values ​​as data contradiction items and store them in the inconsistency set when the absolute value of the difference is greater than the preset tolerance threshold; initiate a global node matching query to the graph database according to the inspection requirements in the preset safety supervision procedure list, compile the inspection requirements that do not match the search conditions into a missing item set; and output the inconsistency set and the missing item set as the analysis results.

[0069] It should be noted that the foregoing explanation of the embodiment of the remote monitoring data analysis method for coal mine disasters also applies to the remote monitoring data analysis system for coal mine disasters in this embodiment, and will not be repeated here.

[0070] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 6 , Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 6As shown, the electronic device 600 includes: a processor 601, and a memory 602 communicatively connected to the processor 601; the memory 602 stores computer-executable instructions; the processor 601 executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0071] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0072] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0073] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0074] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0075] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0076] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0077] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0078] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0079] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0081] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0083] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0084] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for remote monitoring and data analysis of coal mine disasters, characterized in that, include: Acquire disaster prevention and control data and output corresponding standardized data; Extract entity relationships, association relationships, and feature values ​​from the standardized data and convert them into a structured dataset; Based on the structured dataset, a coal mine disaster knowledge graph is generated and updated with coal mine, working face, time and disaster type as nodes and edges. Based on the coal mine disaster knowledge graph and the feature values, logical comparison and rule verification are performed to generate analysis results, and a comprehensive risk assessment value is calculated to generate an early warning instruction; Based on the analysis results and the early warning instructions, a structured monitoring report is generated and risk warning information is output.

2. The method as described in claim 1, characterized in that, The disaster prevention and control data includes text files, image files, and table files. The process of acquiring the disaster prevention and control data and outputting the corresponding standardized data includes: For the table file, the invalid data missing rate of the table file is calculated. If the missing rate is less than the missing rate threshold, linear interpolation is performed to complete the data using adjacent valid data. If the missing rate is greater than or equal to the missing rate threshold, null values ​​are retained and data gaps are marked to generate processed table data. For the image file, local pixel weighted average grayscale is used for image denoising, and the image is segmented to obtain character blocks. Feature vectors are extracted and compared with a coal mine professional character feature library to calculate similarity. Based on the similarity, the corresponding text sequence is obtained. The text file is converted to standard character encoding to obtain text data; The text data, the tabular data, and the plain text sequence are integrated into the standardized data.

3. The method as described in claim 2, characterized in that, The step of extracting entity relationships, association relationships, and feature values ​​from the standardized data and converting them into a structured dataset includes: The plain text sequence is input into the fine-tuned large language model to locate entity objects. Predicates or prepositions are extracted as relation edges by combining attention mechanism and dependency parsing logic to generate the association relationship. Two-dimensional pixel coordinates are extracted from the denoised and enhanced image data. The two-dimensional pixel coordinates are decomposed and mapped into sampling time nodes and physical monitoring values. The physical monitoring values ​​of the current sampling time node are extracted and compared with the physical monitoring values ​​of adjacent time nodes. When it is determined that the physical monitoring value of the current sampling time node is greater than the physical monitoring value of the previous time node and is also greater than the physical monitoring value of the subsequent anomaly node, the physical monitoring value of the current sampling time node is extracted as the feature value. The cell boundaries of the table data are determined using a line detection algorithm. Feature values ​​located within the cell boundaries are extracted and multi-dimensional data logic verification is performed. The multi-dimensional data logic verification includes: data range verification by comparing the feature values ​​with the design parameter safety range, time logic verification by checking the continuity of the timestamp sequence, numerical logic verification by calculating the sum of the sub-items and determining their equality, and correlation verification by checking the matching of parameters with physical coupling relationships. For feature values ​​that fail the multi-dimensional data logic verification, an abnormal data marker is appended to the end of the data structure, and the feature value with the appended abnormal data marker is extracted as the feature value.

4. The method as described in claim 1, characterized in that, Based on the structured dataset, a coal mine disaster knowledge graph is generated and updated, with coal mine, working face, time, and disaster type as nodes and edges, including: Define a four-dimensional ontology model in the graph database, categorized by coal mine, working face, time, and disaster type; Extract the coal mine name, working face name, and entity name of the entity in the structured dataset and concatenate them into a combined string. Calculate the corresponding identifier for the combined string using a hash algorithm. Use the identifier to create a business node in the four-dimensional ontology model and write the feature values ​​in the structured dataset as internal attribute data into the business node. Extract the source node identifier and target node identifier from the association relationships in the structured dataset, establish directed edges between business nodes with the source node identifier and business nodes with the target node identifier, and generate the coal mine disaster knowledge graph.

5. The method as described in claim 1, characterized in that, The process of generating and updating a coal mine disaster knowledge graph based on the structured dataset, using coal mine, working face, time, and disaster type as nodes and edges, also includes: Upon receiving a newly introduced entity, the identifier is used to locate whether a historical master node matching the identifier exists in the coal mine disaster knowledge graph: If the query matches the historical master node, a status sub-node representing the current business review cycle is generated below the historical master node, the characteristic value of the entity itself is attached to the status sub-node, and the current status pointer of the historical master node is modified to point to the status sub-node. If the query does not match the historical master node, it is determined to be a new business object, and a brand new node is created directly under the corresponding work surface and disaster type.

6. The method as described in claim 1, characterized in that, The process of generating analysis results by performing logical comparison and rule verification based on the coal mine disaster knowledge graph and the feature values, and calculating a comprehensive risk assessment value to generate an early warning instruction, includes: In the coal mine disaster knowledge graph, search for nodes with the same name from different sources, extract the feature values ​​contained in the nodes with the same name, determine whether the units of measurement belong to the same physical quantity category, and when it is determined that they belong to the same physical quantity category, convert and map them to the same reference unit and calculate the absolute value of the difference. When the absolute value of the difference is greater than the preset tolerance threshold, mark the feature value as a data contradiction item and store it in the inconsistency set. Based on the inspection requirements in the pre-set list of safety inspection procedures, a global node matching query is initiated to the graph database, and the inspection requirements that do not match the search conditions are compiled into a set of missing items. The inconsistency set and the missing item set are output as the analysis results.

7. A remote monitoring and data analysis system for coal mine disasters, characterized in that, include: The multi-source data access and preprocessing module is used to acquire disaster prevention and control data and output corresponding standardized data; The structured information intelligent extraction module is used to extract entity relationships, association relationships and feature values ​​from the standardized data and convert them into a structured dataset; A coal mine disaster knowledge graph construction module is used to generate and update a coal mine disaster knowledge graph based on the structured dataset, with coal mine, working face, time and disaster type as nodes and edges; The intelligent analysis and judgment engine is used to perform logical comparison and rule verification based on the coal mine disaster knowledge graph and the feature values ​​to generate analysis results, and calculate the comprehensive risk assessment value to generate early warning instructions; The risk warning and report generation module is used to generate a structured monitoring report and output risk warning information based on the analysis results and the warning instructions.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method of any one of claims 1 to 6.

10. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the program or instructions is executed by an electronic device, it implements the steps of the method according to any one of claims 1 to 6.

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