Monitoring and early warning method and device for personnel aggregation risk under mine, electronic equipment and storage medium

Abstract

The invention discloses a monitoring and early warning method and device for a personnel aggregation risk under a mine, electronic equipment and a storage medium. The method comprises the following steps: uploading personnel positioning data and environment data under the mine to a message queue to form a message flow; monitoring the message queue in real time through a big data calculation engine and receiving the positioning data and the environment data in the message flow in a flow manner; analyzing the personnel positioning data and the environment data, and updating the real-time early warning grade of each region under the mine according to an analysis result; and according to the real-time early warning classification, whether aggregation risks exist in all the areas is judged. By adopting the method, the real-time performance, the throughput and the accuracy of the early warning data can be greatly improved by virtue of various advantages of a big data technology. An efficient, reliable and intelligent personnel gathering risk early warning system can be constructed, and the improvement of the safety management level and the enhancement of the emergency response capability are promoted.

Description

Technical Field

[0001] The present application relates to the field of safety technology, and in particular to a method, device, electronic equipment and storage medium for detecting and warning the risk of underground personnel gathering. Background Art

[0002] At present, the risk warning function of personnel gathering in underground coal mines mostly uses the websocket network transmission protocol to upload data, and cooperates with the OLTP (on-line transaction processing) system to analyze the data to achieve early warning of personnel gathering.

[0003] WebSocket is a protocol for full-duplex communication over a single TCP connection. WebSocket makes data exchange between clients and servers much simpler, allowing the server to actively push data to the client. In the WebSocket API, the browser and the server only need to complete a handshake to directly create a persistent connection between the two and perform two-way data transmission, that is, the traffic flow can send messages to the server and receive event-driven responses without polling the server to obtain a response. And after the connection is established, when the server and the client exchange data, the packet header used for protocol control is relatively small. Without extensions, for server-to-client content, this header size is only 2 to 10 bytes (related to the packet length); for client-to-server content, this header also needs to add an additional 4-byte mask. Because the protocol is full-duplex, the server can actively send data to the client at any time. Compared with HTTP requests, which need to wait for the client to initiate a request before the server responds, the delay is significantly less; even compared with long polling similar to Comet, it can deliver data more times in a short period of time.

[0004] Although the real-time nature of data is guaranteed using the above method, non-stream computing will cause data delays, congestion and other abnormal situations when faced with large amounts of data. Summary of the invention

[0005] The embodiments of the present application provide a monitoring and early warning method, device, electronic device and storage medium for the risk of underground personnel gathering, which are used to achieve real-time monitoring when the amount of data related to underground personnel gathering is large.

[0006] According to one aspect of the present application, a monitoring and early warning method for the risk of personnel gathering in a mine is provided, comprising:

[0007] Upload the personnel location data and environmental data in the mine to the message queue to form a message flow;

[0008] Monitor the message queue in real time through a big data computing engine and receive the positioning data and environmental data in the message stream in a streaming manner;

[0009] Analyze the personnel location data and the environmental data and update the real-time warning classification of each area in the mine according to the analysis results;

[0010] Determine whether there is a risk of aggregation in each area based on the real-time warning classification.

[0011] Furthermore, uploading the personnel location data and environmental data in the mine to the message queue to form a message flow includes:

[0012] Acquire initial positioning data of underground personnel through positioning devices worn by underground personnel;

[0013] Acquire the area where the person is currently located according to the initial positioning data;

[0014] Obtain the warning level of the previous round in the area, and generate a marker adapted to the warning level of the previous round and encapsulate it together with the initial positioning data to form personnel positioning data;

[0015] Writing the personnel positioning data and the environment data into a Kafka message stream; wherein the Kafka is provided with a personnel positioning data partition and an environment data partition, and the personnel positioning data partition has multiple partitions;

[0016] The personnel positioning data is written into the personnel positioning data partition, and the personnel positioning data is written into different personnel positioning data partitions according to the different markers; and the environment data is written into the environment data partition.

[0017] Furthermore, the real-time monitoring of the message queue by the big data computing engine and streaming receiving of the positioning data and environmental data in the message stream include:

[0018] The message stream is received in a patrol manner. In each patrol, the data in the environmental data partition is first received, and then the data in the personnel location data partition is received in a first receiving order, wherein the first receiving order is to receive partitions in order from high to low according to the warning level indicated by the marker.

[0019] Further, after first receiving the data in the environmental data partition and before receiving the data in the personnel positioning data partition according to the first receiving sequence, the method includes:

[0020] Perform real-time analysis on the environmental data obtained during the current patrol to see if there are any anomalies. If so, obtain the area where the abnormal data is located, compare the current environmental data of the area with the environmental data of the area during the previous patrol. If the environmental data deteriorates, raise the warning level of the area according to the degree of deterioration. If the environmental data is stable or optimized, keep the warning level of the area the same as the previous round.

[0021] The personnel location data partitions are reclassified according to the current highest warning level of the area where the personnel location data is stored, and the first receiving order is updated.

[0022] Furthermore, the analyzing the personnel location data and the environmental data and updating the real-time warning classification of each area in the mine according to the analysis results include:

[0023] Obtaining the ranking of each area in the first receiving order corresponding to each patrol within a preset first time, and summing the ranking of each area to obtain a comprehensive ranking index;

[0024] According to the size of the comprehensive ranking index, according to a period inversely proportional to the size of the comprehensive ranking index, combined with the analysis of the personnel positioning data and the environmental data of the area within the first time, the real-time warning grade of each area in this round is obtained.

[0025] Furthermore, the various areas are divided according to function and size, and each area has a unique identifier.

[0026] Furthermore, the first time is 1 min-5 min.

[0027] The second aspect of the present application provides a monitoring and early warning device for the risk of personnel gathering in a mine, comprising:

[0028] The data upload module uploads the personnel location data and environmental data in the mine to the message queue to form a message flow;

[0029] A data receiving module monitors the message queue in real time through a big data computing engine and receives the positioning data and environmental data in the message stream in a streaming manner;

[0030] An early warning rule updating module is used to analyze the positioning data and the environmental data and update the real-time early warning classification rules for each area in the mine according to the analysis results;

[0031] The risk judgment module judges whether there is a gathering risk in each area based on the real-time warning classification.

[0032] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method described in the first aspect of the present application.

[0033] The fourth aspect of the present application provides a computer-readable storage medium, which is used to store a computer program. When the computer program runs on a computer, the computer executes the method described in the first aspect of the present application.

[0034] In an embodiment of the present application, a monitoring and early warning method for the risk of personnel gathering in a mine is adopted, including uploading personnel positioning data and environmental data in the mine to a message queue to form a message stream; monitoring the message queue in real time through a big data computing engine and streaming the positioning data and environmental data in the message stream; analyzing the personnel positioning data and the environmental data and updating the real-time early warning classification of each area in the mine according to the analysis results; and judging whether there is a gathering risk in each area according to the real-time early warning classification.

[0035] The above method can greatly improve the real-time, throughput and accuracy of early warning data by taking advantage of the advantages of big data technology. It can build an efficient, reliable and intelligent early warning system for personnel gathering risks, which promotes the improvement of safety management level and the enhancement of emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 It is a flow chart of a method for monitoring and early warning of the risk of personnel gathering in a mine according to an embodiment of the present application.

[0038] Figure 2 It is a module diagram of a method for monitoring and early warning of the risk of gathering of people in a mine according to an embodiment of the present application.

[0039] Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] like Figure 1 The flowchart of a method for monitoring and early warning of the risk of gathering of personnel in a mine according to an embodiment of the present application is shown in the figure. As can be seen from the figure, a method for monitoring and early warning of the risk of gathering of personnel in a mine according to the present application includes the following steps:

[0043] Step S101: Upload personnel location data and environmental data in the mine to a message queue to form a message flow.

[0044] The personnel location data includes personnel ID, personnel name, personnel status, personnel mine layer, personnel x coordinate, personnel y coordinate, data time, etc. The environmental data includes tunnel width, ventilation conditions, work nature, tunnel width, ventilation conditions, work nature, oxygen concentration, temperature, humidity, etc. Different environments are suitable for different numbers of personnel. Therefore, the risk of personnel gathering needs to be considered from both environmental data and personnel data.

[0045] Step S102: Monitor the message queue in real time through the big data computing engine and stream the positioning data and environmental data in the message stream.

[0046] According to the real-time requirements, resource consumption, and complexity of data processing, choosing a suitable big data computing engine, such as Apache Flink and Apache Spark Streaming, for computing can greatly improve real-time and efficiency. It enables the system to quickly identify gatherings of people and issue warnings at the first time. This real-time performance is crucial for preventing and handling emergencies. At the same time, by using advanced algorithms and models, the computing engine can conduct in-depth analysis of massive data and accurately determine the degree and trend of gatherings of people.

[0047] In this embodiment, Apache Flink is selected. This engine has powerful state management and low-latency processing capabilities and performs well in real-time stream processing scenarios. In the big data computing engine, a corresponding stream processing job is set up, which continuously pulls data from Kafka and prepares for subsequent analysis and processing.

[0048] Step S103: Analyze the personnel location data and the environmental data and update the real-time warning classification of each area in the mine according to the analysis results. The areas are divided according to function and size, for example, the mine is divided into grids or polygonal partitions, and each area has a unique identifier.

[0049] Step S104: Determine whether there is a risk of aggregation in each area based on the real-time warning classification.

[0050] After receiving the personnel coordinate data, the big data computing engine will perform real-time analysis on the data based on the preset warning classification rules.

[0051] Warning classification rules: The rules should clearly define key parameters such as the density threshold of different work areas and the duration of gatherings. These parameters may vary depending on the nature of the work, site conditions, safety standards, etc. (Taking into account that the warning classification rules may not be fixed, they are made configurable, such as work area A, approved 10 people. Work area B, approved 5 people)

[0052] Data analysis: Use spatial analysis algorithms (such as K-means clustering, DBSCAN density clustering, etc.) to process coordinate data and identify the distribution of personnel in each work area. At the same time, combine the time dimension to analyze the dynamic changes of personnel aggregation.

[0053] Judgment logic: Based on the analysis results, determine whether each work point has reached or exceeded the preset threshold of personnel gathering. If reached, further graded assessment is performed based on the degree of gathering. Once it is determined that there is excessive gathering of personnel at a certain work point and exceeds the specified threshold, the system will immediately trigger the corresponding graded alarm mechanism.

[0054] Alarm method: Alarm methods can be diverse, including but not limited to SMS notification, email reminder, system pop-up window, voice broadcast, etc. Select the appropriate alarm method according to the degree of urgency and the characteristics of the recipient.

[0055] Alarm content: The alarm information should include key information such as the specific location of the work point, the current gathering of personnel, the gathering level, and recommended measures, so that relevant personnel can respond quickly.

[0056] Subsequent processing: After the alarm is triggered, the system should record the alarm event and generate a report for subsequent analysis as needed. At the same time, it should support linkage with other security management systems (such as emergency response systems, video surveillance systems, etc.) to achieve more comprehensive security management.

[0057] In some preferred embodiments, uploading the personnel location data and environmental data in the mine to a message queue to form a message stream includes:

[0058] Step S1011, obtaining initial positioning data of the underground personnel through a positioning device worn by the underground personnel. In this embodiment, the positioning device can be implemented by using technologies such as UWB positioning, RFID positioning, and GPS positioning.

[0059] Step S1012: Acquire the area where the person is currently located according to the initial positioning data. Specifically, based on the coordinates of the initial positioning data and the boundaries of each area, it can be determined in which area the coordinates fall.

[0060] Step S1013: Obtain the warning level of the previous round for the area, generate a marker adapted to the warning level of the previous round, and encapsulate it together with the initial positioning data to form personnel positioning data.

[0061] Step S1014: write the personnel positioning data and the environment data into a Kafka message stream; wherein the Kafka is provided with a personnel positioning data partition and an environment data partition, and the personnel positioning data partition has multiple partitions.

[0062] The personnel positioning data is written into the personnel positioning data partition, and the personnel positioning data is written into different personnel positioning data partitions according to the different markers; and the environment data is written into the environment data partition.

[0063] In the above embodiment, Kafka is used as a message middleware to achieve decoupling between the positioning subsystem and the big data computing engine. Kafka's high throughput and low latency characteristics can meet the needs of real-time data processing. Secondly, the stability and scalability of the system are also guaranteed. For example, the replication mechanism of Kafka and the fault-tolerant recovery of the big data computing engine can be used to enable the system to operate stably under high load or failure conditions. According to the data processing volume and system resource conditions, the partitions and replicas of Kafka and the resource allocation of the big data computing engine can be dynamically configured to achieve load balancing.

[0064] According to the above embodiments, the warning classification of the previous round in a certain area will affect the identifier of the location data of the current personnel in the area, so that the location data of personnel in different areas will be written into different Kafka partitions due to different identifiers, which is conducive to the optimized management of data with different risk levels. For example, different processing of data in different partitions can increase attention to possible abnormal situations and reduce attention to more routine situations.

[0065] In some preferred embodiments, the real-time monitoring of the message queue by the big data computing engine and the streaming receiving of the positioning data and the environmental data in the message stream include:

[0066] The message stream is received in a patrol manner. In each patrol, the data in the environmental data partition is first received, and then the data in the personnel location data partition is received in a first receiving order, wherein the first receiving order is to receive partitions in order from high to low according to the warning level indicated by the marker.

[0067] Since whether there is gathering or not is first related to the environmental data, when the environmental conditions are good, more people can be allowed to gather, and when the environmental conditions are poor, the gathering of people needs to be reduced. Therefore, when patrolling and receiving in this embodiment, environmental data is received first, which is conducive to judging and identifying the environment as a whole, and then targeted identification of personnel positioning data in different environments can be achieved. In particular, the personnel positioning data in areas with abnormal environmental data can be received and analyzed first to achieve the fastest response to abnormal situations.

[0068] In some preferred embodiments, after first receiving the data in the environmental data partition and before receiving the data in the personnel positioning data partition according to the first receiving sequence, the method further comprises:

[0069] Perform real-time analysis on the environmental data obtained during the current round of patrols to see if there are any anomalies. If so, obtain the area where the abnormal data is located, and compare the current environmental data of the area with the environmental data of the area during the previous patrol. If the environmental data deteriorates, increase the warning level of the area according to the degree of deterioration. If the environmental data is stable or optimized, keep the warning level of the area the same as the previous round.

[0070] The personnel location data partitions are reclassified according to the current highest warning level of the area where the personnel location data is stored, and the first receiving order is updated.

[0071] According to the above embodiments, the order of receiving personnel positioning data in each round is obtained under the influence of the warning level of the previous round and the environmental data that has changed in the current round. This method allows the system to take into account the historical regional conditions and the current temporary changes to achieve optimization of the order of receiving personnel positioning data. Take the warning level corresponding to the historical area as a relatively low level of 1 as an example, that is, it is believed that the aggregation risk of the historical areas in the partition is relatively low. If the environmental data corresponding to a certain area in the partition deteriorates in the current round, the warning level of the area is increased, for example, to level 3. Although the warning level of other areas in the partition is still level 1, the partition will be ranked at level 3 in the first receiving order.

[0072] In some optional embodiments, in the sorting of the first receiving order, if there are areas with the same highest warning level in different partitions, these partitions are sorted from most to least according to the number of areas with the highest warning level. For example, if the highest warning level of areas in the first partition and the second partition are both 3, there are 2 areas with a warning level of 3 in the first partition, and there is 1 area with a warning level of 3 in the second partition, then in the sorting of the receiving order, the first partition is before the second partition.

[0073] Since the environmental data of each round may change, this change may be just a normal fluctuation or a continuous change. Therefore, although this embodiment adjusts the order of receiving personnel positioning data according to the environmental data in each round, it is ultimately necessary to combine the environmental data and personnel positioning data to make an overall judgment on the risk of personnel gathering. At this time, it is usually necessary to collect data within a certain time span to identify the development trend and determine whether there is a real risk.

[0074] Therefore, in some preferred embodiments, the analyzing the personnel location data and the environmental data and updating the real-time warning classification of each area in the mine according to the analysis results include:

[0075] Step S1031, obtain the ranking of each area in the first receiving order corresponding to each patrol within the preset first time, and sum the ranking of each area to obtain a comprehensive ranking index. In some preferred embodiments, the stream processing job can be based on the early warning requirements of the actual coal mine, and the window is set to be based on the early warning requirements of the actual coal mine, that is, the first time, so the first time is 1min-5min, so as to aggregate and analyze the data within the specified time range.

[0076] The judgment process of the risk level of personnel gathering in each round will refer to the results of multiple patrols. The first receiving order generated by a single patrol can only reflect the risk level indicated by the environmental data of each area in a single patrol, and the comprehensive ranking index will reflect the overall risk situation of the environmental indication of each area in the first time. For example, for area A where the environmental data fluctuation deteriorates in a certain patrol and the areas corresponding to the positioning data of several personnel in the same partition as area A in this round, the corresponding comprehensive ranking index caused by this fluctuation deterioration will become higher, but if the fluctuation deterioration of the environmental data in area A is only occasional and quickly returns to normal, the first receiving order of the partition where area A is located will no longer be affected by this fluctuation in other patrols. If the fluctuation deterioration of the environmental data in area A is continuous, the first receiving order of the partition where area A is located will continue to be affected in subsequent patrols, which will cause the comprehensive ranking index of the partition where area A is located to continue to increase. The above method obtains a comprehensive ranking index from the perspective of the ranking of multiple patrols to indicate the environmental changes over a period of time. The more stable and better the environment is, the smaller the comprehensive ranking index is, and the more abnormal and continuous the environment is, the larger the comprehensive ranking index is.

[0077] In some optional embodiments, the personnel location data is written into different personnel location data partitions according to different tags, including:

[0078] The personnel positioning data is divided into partitions numbered n*m, the position in the personnel positioning data belongs to the nth partition and the marker corresponds to m warning levels; the number of the personnel positioning data partitions is N*M, wherein N is equal to the number of independent area sets, each of the independent area sets has at least one area, and the areas in the independent area sets have the same function and are interconnected; M is the number of warning levels; n belongs to N, and m belongs to M.

[0079] In this way, the entire Kafka configuration is divided into N*M partitions according to the function and location of the area. Since the areas in each independent area set have the same function and are interconnected, and the markers correspond to the warning levels of the previous round, the areas in each independent area set are prone to influence each other and obtain the same markers, so that the personnel location data belonging to the same independent area set and with the same markers will be divided into the same partition. The gathering of personnel in these areas usually affects each other. Therefore, when sorting the first receiving order according to the partition, it will affect the overall comprehensive sorting index of each personnel location data in the partition, that is, the corresponding area, so that the abnormal situation of the independent area set can be magnified, which can be easily discovered by the system.

[0080] Step S1032: According to the size of the comprehensive ranking index, in accordance with a period inversely proportional to the size of the comprehensive ranking index, combined with the analysis of the personnel positioning data and the environmental data of the area within the first time, obtain the real-time warning grade for each area in this round.

[0081] In some specific embodiments, the above analysis and warning process can be implemented through Flink / Spark. For example, use Flink's DataStream API to process real-time data streams, define a partition mapping function, calculate the partition ID based on the personnel coordinates and the ore layer code, use Flink's state (such as ValueState or ListState) to maintain the personnel count of each partition, define a risk judgment function, trigger an early warning when the count exceeds a threshold, and use Flink's Sink API to send the early warning information to the corresponding system or interface.

[0082] In the analysis process of some specific embodiments, the degree of clustering can be identified by the density clustering algorithm DBSCAN, in which the neighborhood size (ε) and the minimum number of points (MinPts) are pre-set as parameters of the clustering algorithm; for each point, check whether the number of points in its ε neighborhood is greater than or equal to MinPts, if so, mark the point as a core point; starting from any core point, find all points that are density-reachable from the point to form a cluster; for non-core points, if they are in the ε neighborhood of a core point, mark them as boundary points; points that are neither core points nor boundary points are considered noise points. Specifically, the data stream can be processed by using Flink's DataStream API and state, and the clustering logic of DBSCAN can be implemented. It can also be implemented by the DBSCAN algorithm provided by Spark MLlib, including training using the DataFrame API and MLlib's DBSCAN model, and processing real-time or batch data; the clustering effect is optimized by setting appropriate ε and MinPts parameters, and the risk of personnel gathering is judged based on the clustering results.

[0083] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides a monitoring and early warning device for the risk of personnel gathering in a mine, such as Figure 2 The figure is a schematic diagram of the structure of a monitoring and early warning device for the risk of gathering of personnel in a mine according to an embodiment of the present application. The monitoring and early warning device for the risk of gathering of personnel in a mine comprises:

[0084] The data upload module uploads the personnel location data and environmental data in the mine to the message queue to form a message flow;

[0085] A data receiving module monitors the message queue in real time through a big data computing engine and receives the positioning data and environmental data in the message stream in a streaming manner;

[0086] An early warning rule updating module is used to analyze the positioning data and the environmental data and update the real-time early warning classification rules for each area in the mine according to the analysis results;

[0087] The risk judgment module judges whether there is a gathering risk in each area based on the real-time warning classification.

[0088] In some preferred embodiments, the data uploading module is used to upload the personnel positioning data and environmental data in the mine to the message queue to form a message stream, including:

[0089] The positioning module is used to obtain the initial positioning data of the underground personnel through the positioning device worn by the underground personnel.

[0090] The area acquisition module is used to acquire the area where the person is currently located based on the initial positioning data.

[0091] The encapsulation module is used to obtain the warning level of the previous round in the area, and generate a marker adapted to the warning level of the previous round and encapsulate it together with the initial positioning data to form personnel positioning data.

[0092] A writing module is used to write the personnel positioning data and the environmental data into a Kafka message stream; wherein the Kafka is provided with a personnel positioning data partition and an environmental data partition, and the personnel positioning data partition has multiple partitions.

[0093] The personnel positioning data is written into the personnel positioning data partition, and the personnel positioning data is written into different personnel positioning data partitions according to the different markers; and the environment data is written into the environment data partition.

[0094] In some preferred embodiments, the data receiving module is used to monitor the message queue in real time through the big data computing engine and stream the positioning data and environmental data in the message stream, including:

[0095] A sequential receiving module is used to receive the message stream in a patrol manner. During each patrol, the data in the environmental data partition is first received, and then the data in the personnel location data partition is received in a first receiving order, wherein the first receiving order is to receive the partitions in order from high to low according to the warning level indicated by the marker.

[0096] In some preferred embodiments, the sequential receiving module comprises:

[0097] The environmental data anomaly query module is used to perform real-time analysis on the environmental data obtained during the current patrol to see if there are any anomalies. If so, obtain the area where the abnormal data is located, compare the current environmental data of the area with the environmental data of the previous round of the area, and if the environmental data deteriorates, increase the warning level of the area according to the degree of deterioration; if the environmental data is stable or optimized, keep the warning level of the area the same as the previous round.

[0098] The sequence updating module is used to reclassify the personnel location data partitions according to the current highest warning level of the area where the personnel location data is stored and update the first receiving order.

[0099] In some preferred embodiments, the warning rule updating module is used to analyze the positioning data and the environmental data and update the real-time warning classification rules of various areas in the mine according to the analysis results, including:

[0100] The comprehensive ranking module is used to obtain the ranking of each area in each round in the first receiving order within a preset first time, and sum the ranking of each area to obtain a comprehensive ranking index.

[0101] The analysis module is used to analyze the real-time warning classification of each area according to the size of the comprehensive ranking index, in accordance with a period inversely proportional to the size of the comprehensive ranking index, combined with the personnel positioning data and the environmental data of the area within the first time.

[0102] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0103] Based on the same inventive concept as the above-mentioned method embodiment, an electronic device is also provided in an embodiment of the present application, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the electronic device implements the control method in the above-mentioned embodiment.

[0104] In one embodiment, the electronic device may be a server. In this embodiment, the structure of the electronic device may be as follows: Figure 3 As shown, it includes a memory 2001 , a communication module 2003 and one or more processors 2002 .

[0105] The memory 2001 is used to store computer programs executed by the processor 2002. The memory 2001 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and programs required for running the instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0106] The memory 2001 may be a volatile memory, such as a random-access memory (RAM); the memory 2001 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 2001 may be any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2001 may be a combination of the above memories.

[0107] The processor 2002 may include one or more central processing units (CPU) or a digital processing unit, etc. The processor 2002 is used to implement the above-mentioned audio data processing method when calling the computer program stored in the memory 2001 .

[0108] The communication module 2003 is used to communicate with terminal devices and other servers.

[0109] The specific connection medium between the memory 2001, the communication module 2003 and the processor 2002 is not limited in the embodiment of the present application. Figure 3 In the embodiment, the memory 2001 and the processor 2002 are connected via a bus 2004. The bus 2004 is Figure 3 The arrows in the figure are used to describe the connection between other components, which is only for illustrative purposes and is not intended to be limiting. Bus 2004 can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 3 The diagram shows that only one arrow is used, but this does not mean that there is only one bus or only one type of bus.

[0110] Based on the same inventive concept as the above method embodiment, an embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium is used to store a computer program, when the computer program is run on a computer, the electronic device implements the control method in the above embodiment. The computer-readable storage medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0111] Based on the same inventive concept as the above-mentioned method embodiment, an embodiment of the present invention further provides a computer program product, the computer program product including a computer program. When the program product is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the control method according to various exemplary embodiments of the present application described above in this specification. The program product may adopt any combination of one or more readable media. These computer program commands may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the commands executed by the processor of the computer or other programmable data processing device generate commands for implementing the steps in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Claims

1. A monitoring and early warning method for the risk of personnel gathering in a mine, characterized in that: Upload the personnel location data and environmental data in the mine to the message queue to form a message flow; Monitor the message queue in real time through a big data computing engine and receive the positioning data and environmental data in the message stream in a streaming manner; Analyze the personnel location data and the environmental data and update the real-time warning classification of each area in the mine according to the analysis results; Determine whether there is a risk of aggregation in each area based on the real-time warning classification.

2. The method according to claim 1, characterized in that The method of uploading the personnel location data and environmental data in the mine to the message queue to form a message flow includes: Acquire initial positioning data of underground personnel through positioning devices worn by underground personnel; Acquire the area where the person is currently located according to the initial positioning data; Obtain the warning level of the previous round in the area, and generate a marker adapted to the warning level of the previous round and encapsulate it together with the initial positioning data to form personnel positioning data; Writing the personnel positioning data and the environment data into a Kafka message stream; wherein the Kafka is provided with a personnel positioning data partition and an environment data partition, and the personnel positioning data partition has multiple partitions; The personnel positioning data is written into the personnel positioning data partition, and the personnel positioning data is written into different personnel positioning data partitions according to the different markers; and the environment data is written into the environment data partition.

3. The method according to claim 2, characterized in that The real-time monitoring of the message queue by the big data computing engine and streaming receiving of the positioning data and the environmental data in the message stream include: The message stream is received in a patrol manner. In each patrol, the data in the environmental data partition is first received, and then the data in the personnel location data partition is received in a first receiving order, wherein the first receiving order is to receive partitions in order from high to low according to the warning level indicated by the marker.

4. The method according to claim 3, characterized in that After first receiving the data in the environment data partition and before receiving the data in the personnel positioning data partition according to the first receiving sequence, the method further comprises: Perform real-time analysis on the environmental data obtained during the current patrol to see if there are any anomalies. If so, obtain the area where the abnormal data is located, compare the current environmental data of the area with the environmental data of the area during the previous patrol. If the environmental data deteriorates, raise the warning level of the area according to the degree of deterioration. If the environmental data is stable or optimized, keep the warning level of the area the same as the previous round. The personnel location data partitions are reclassified according to the current highest warning level of the area where the personnel location data is stored, and the first receiving order is updated.

5. The method according to claim 4, characterized in that The analyzing the personnel location data and the environmental data and updating the real-time warning classification of each area in the mine according to the analysis results include: Obtaining the ranking of each area in the first receiving order corresponding to each patrol within a preset first time, and summing the ranking of each area to obtain a comprehensive ranking index; According to the size of the comprehensive ranking index, according to a period inversely proportional to the size of the comprehensive ranking index, combined with the analysis of the personnel positioning data and the environmental data of the area within the first time, the real-time warning grade of each area in this round is obtained.

6. The method according to any one of claims 1 to 5, characterized in that: The various areas are divided according to function and size, and each area has a unique identifier.

7. The method according to claim 5, characterized in that The first time is 1 min-5 min.

8. A monitoring and early warning device for the risk of personnel gathering in a mine, characterized in that: include: The data upload module uploads the personnel location data and environmental data in the mine to the message queue to form a message flow; A data receiving module monitors the message queue in real time through a big data computing engine and receives the positioning data and environmental data in the message stream in a streaming manner; An early warning rule updating module is used to analyze the positioning data and the environmental data and update the real-time early warning classification rules for each area in the mine according to the analysis results; The risk judgment module judges whether there is a gathering risk in each area based on the real-time warning classification.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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