Self-rescuer intelligent device management system

Abstract

The invention relates to the technical field of intelligent device management, and particularly discloses a self-rescuer intelligent device management system which comprises a mine virtual scene building module, a self-rescuer state monitoring module, a mine environment data monitoring module, a self-rescuer wearing behavior monitoring module, a self-rescuer management abnormity analysis module and a self-rescuer management abnormity early warning module. The mine three-dimensional virtual scene is constructed, real-time monitoring and early warning of the self-rescuer state, the environment data and the personnel wearing behavior are achieved, the first early warning coefficient, the second early warning coefficient and the third early warning coefficient are obtained through analysis, and then the management abnormal index is obtained through analysis; the management abnormity early warning module analyzes the management abnormity state of the self-rescuer and carries out abnormity early warning, reliable guarantee is provided for life safety of mine personnel, multi-source data are analyzed through an intelligent algorithm, an equipment-environment-behavior coupling risk model is established, and the real-time performance and the intelligent level of mine safety management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent device management, and in particular to an intelligent device management system for a self-rescuer. Background Art

[0002] The mining of resources like coal and metals creates complex and dangerous mine environments, with accidents like gas explosions, water seepage, and fires a common occurrence. Self-rescuers, crucial equipment for protecting the lives of underground workers, are crucial for ensuring timely and effective protection in the event of an accident. However, with the advancement of digital and intelligent technologies, traditional self-rescuer management models are no longer able to meet the demand for real-time, precise, and comprehensive management of these devices.

[0003] Traditional self-rescuer intelligent device management systems are mainly based on a single sensor and a simple data processing platform. As for the status of the self-rescuer, only some basic parameters can be monitored through limited built-in sensors, and data is collected and recorded manually on a regular basis. The management of personnel wearing behavior relies on irregular on-site inspections by underground management personnel, and lacks a real-time early warning mechanism. The handling of abnormal situations is mostly based on simple judgment methods. Once the data exceeds the preset range, a general early warning signal is issued, lacking comprehensive analysis and precise positioning of complex situations. In addition, the traditional system lacks a collaborative linkage mechanism and cannot form an effective data closed loop. It is difficult to conduct a comprehensive and in-depth analysis of the abnormal status of self-rescuer management, which reduces the accuracy and timeliness of the early warning. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a self-rescuer intelligent device management system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a self-rescuer intelligent device management system, comprising a mine virtual scene construction module, a self-rescuer status monitoring module, a mine environment data monitoring module, a self-rescuer wearing behavior monitoring module, a self-rescuer management abnormality analysis module, and a self-rescuer management abnormality early warning module; Constructing a mine virtual scene module: Using structured light scanning and lidar to collect 3D point cloud data of the target mine, constructing a mine virtual scene, and mapping the self-rescuer status data and dynamic environment data into the mine virtual scene; Self-rescuer status monitoring module: Based on the mine virtual scene, the module collects the oxygen concentration, battery power, and leakage rate of each target self-rescuer, analyzes them to obtain the first warning coefficient, and makes a judgment on it. If the status is normal, the mine environment data monitoring module is executed; otherwise, the first warning information is generated; Mine environment data monitoring module: Based on the mine virtual scene, it obtains mine environment data, analyzes it to obtain the second warning coefficient, and judges it. If it is judged that the environmental state is abnormal, the self-rescuer wearing behavior monitoring module is executed and the second warning information is generated; Self-rescuer wearing behavior monitoring module: The module uses underground cameras to obtain images of each mine worker wearing a self-rescuer, analyzes them to obtain a third warning coefficient, and evaluates them. If the behavior is assessed as normal, the self-rescuer management abnormality analysis module is executed; otherwise, a third warning information is generated; Self-rescuer management abnormality analysis module: used to analyze the management abnormality status of each target self-rescuer and obtain the management abnormality index of each target self-rescuer; Self-rescuer management abnormality warning module: Based on the management abnormality index of each target self-rescuer, it conducts abnormality evaluation on the management status of each target self-rescuer, outputs the management abnormality evaluation result, and issues early warning information for abnormal data.

[0006] Preferably, the execution method of constructing the mine virtual scene module is as follows: Structured light scanning and lidar are used to collect three-dimensional point cloud data of the target mine, and a virtual mine scene is constructed. The self-rescuer information is registered in the virtual mine scene through software, and the identity information of each mine personnel is bound to the corresponding self-rescuer. Each target self-rescuer and each mine personnel are numbered with the same serial number, specifically 1, 2, 3, ...j..., m, where j represents the number of each target self-rescuer and also the number of each mine personnel, and m represents the total number of target self-rescuers.

[0007] Preferably, the execution mode of the self-rescuer status monitoring module is as follows: Obtain the oxygen concentration of each target self-rescuer and extract the maximum oxygen concentration allowed by the self-rescuer from the management database and the minimum allowable oxygen concentration , respectively substitute them into the formula , get the oxygen concentration deviation of the jth target self-rescuer, where represents the oxygen concentration of the jth self-rescuer; According to the status monitoring data of each target self-rescuer, the status warning coefficient of each target self-rescuer is analyzed. The calculation formula is as follows: ,in, FWf j represents the state warning coefficient of the j-th target self-rescuer, which is marked as the first warning coefficient of the j-th self-rescuer. Indicates the preset standard oxygen concentration deviation, represents the battery capacity of the jth target self-rescuer, Indicates the preset standard battery level. represents the gas leakage rate of the jth target self-rescuer, Indicates the preset standard gas leakage rate, Represent the weight coefficients of oxygen concentration deviation, battery power, and gas leakage rate respectively, and ; Based on the first warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the first warning coefficient of each target self-rescuer and compare it with the preset first warning coefficient threshold. If the first warning coefficient of a target self-rescuer is less than the preset first warning coefficient threshold, it is judged that the status of the target self-rescuer is normal and no abnormal alarm is required. The mine environment data monitoring module is further executed. Otherwise, it is judged that the status of the target self-rescuer is abnormal, and the first warning information is generated, and the number corresponding to the self-rescuer with abnormal status is sent to the mobile terminal.

[0008] Preferably, the gas leakage rate of the j-th target self-rescuer is obtained as follows: Use the pressure sensor to collect the internal gas pressure value of each target self-rescuer at the preset time interval within the preset period , and record the collection time node t i , i represents the number of each time node, i=1, 2, 3, ..., n, n represents the total time nodes collected, j represents the number of each target self-rescuer, j=1, 2, 3, ..., m, m represents the total number of target self-rescuers; By formula , calculate the gas leakage rate of the jth target self-rescuer Lr j , V j represents the volume of the jth target self-rescuer, R represents the universal gas constant, Tk represents the thermodynamic temperature, Indicates that at the i+1th time node The internal gas pressure value corresponding to the j-th target self-rescuer, Indicates that at the i-th time node The internal gas pressure value corresponding to the j-th target self-rescuer.

[0009] Preferably, the execution mode of the mine environment data monitoring module is as follows: Based on the mine virtual scene, the mine environment data is obtained. The mine environment data includes gas concentration, ambient temperature and dust concentration. Based on the mine environment data, the environmental abnormality coefficient of each target self-rescuer is analyzed. The specific calculation formula is as follows: ,in, Swc j represents the environmental abnormality coefficient of the j-th target self-rescuer, which is marked as the second warning coefficient representing the j-th target self-rescuer. represents the environment of the jth target self-rescuer p Gas concentration, Indicates the p The safety threshold of gas concentration, represents the ambient temperature of the jth target self-rescuer, Indicates the preset ambient temperature safety threshold. represents the dust concentration in the environment of the jth target self-rescuer, Indicates the safety threshold of dust concentration, p Indicates the number of each gas type, p =1, 2, 3, ..., k, where k represents the total number of gas types; Based on the second warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the second warning coefficient of each target self-rescuer and compare it with the preset second warning coefficient threshold. If the second warning coefficient of a target self-rescuer is less than the preset second warning coefficient threshold, it is judged that the environmental state of the target self-rescuer is normal and there is no need to issue an alarm message for wearing a self-rescuer to escape. If the second warning coefficient of a target self-rescuer is greater than or equal to the preset second warning coefficient threshold, it is judged that the environmental state of the target self-rescuer is abnormal and it is necessary to wear a self-rescuer to escape. The self-rescuer wearing behavior monitoring module is further executed and a second warning message is generated.

[0010] Preferably, the execution mode of the self-rescuer wearing behavior monitoring module is as follows: The images of the mine personnel wearing self-rescuers are obtained through underground cameras. The wearing angles of each mine personnel are extracted using image analysis technology, and the standard range of the target self-rescuer wearing angles is extracted from the management database. , analyze the abnormal coefficient of wearing behavior of each mine personnel, and the calculation formula is as follows: ,in, Twc j represents the abnormal coefficient of wearing behavior of the j-th target self-rescuer, which is marked as the third warning coefficient representing the j-th target self-rescuer. represents the actual wearing angle of the j-th mine worker, Indicates the preset standard wearing angle. represents the wearing time of the j-th mine worker, wt Indicates the preset standard wearing time. Represent the weight coefficients of wearing angle and wearing time respectively, and ; Based on the third warning coefficient of each target self-rescuer, select the corresponding management method to manage each target self-rescuer; Read the third warning coefficient of each target self-rescuer and compare it with the preset third warning coefficient threshold. If the third warning coefficient of a target self-rescuer is less than the preset third warning coefficient threshold, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is normal, and no abnormal alarm is required. Further execute the self-rescuer management abnormality analysis module. If the third warning coefficient of a target self-rescuer is greater than or equal to the preset third warning coefficient threshold, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is abnormal, generate the third warning information, and send the corresponding number of the self-rescuer to the mobile terminal.

[0011] Preferably, the execution mode of the self-rescuer management abnormality analysis module is as follows: Read the first warning coefficient of each target self-rescuer FWf j , the second warning coefficient Swc j , the third warning coefficient Twc j , analyze the abnormal management status of each target self-rescuer and obtain the abnormal management index of each target self-rescuer. The calculation formula is as follows: ,in, represents the management abnormality index of the j-th target self-rescuer, They respectively represent the preset first warning coefficient threshold, the preset second warning coefficient threshold, and the preset third warning coefficient threshold.

[0012] Preferably, the execution mode of the self-rescuer management abnormality warning module is as follows: Read the management anomaly index of each target self-rescuer, perform an anomaly assessment on the management status of each target self-rescuer, compare the management anomaly index of a target self-rescuer with the preset management anomaly index threshold, if the management anomaly index of a target self-rescuer is less than the preset management anomaly index threshold, then the management status of the target self-rescuer is judged to be normal, otherwise, the management status of the target self-rescuer is judged to be abnormal, mark the corresponding self-rescuer's management status abnormal data as a management anomaly assessment result, output the management anomaly assessment result, and issue a warning message for the abnormal data.

[0013] As described above, the self-rescuer intelligent device management system provided by the present invention has at least the following beneficial effects: The present invention provides a self-rescuer intelligent device management system, which constructs a three-dimensional virtual scene of the mine through structured light scanning and laser radar, integrates a self-rescuer status monitoring module, a mine environmental data monitoring module and a self-rescuer wearing behavior monitoring, and realizes real-time monitoring and early warning of the self-rescuer status, environmental data and personnel wearing behavior. The first early warning coefficient, the second early warning coefficient and the third early warning coefficient are obtained by analysis, and the management abnormality index is calculated by fusing the three early warning coefficients to realize all-round intelligent management and control of underground safety; the management abnormality early warning module analyzes the management abnormality status of each target self-rescuer and issues an abnormality early warning. When facing complex underground situations, managers can make reasonable and effective decisions based on scientific analysis results, optimize resource allocation, minimize accident risks, and ensure the life safety of underground workers. Intelligent algorithms are introduced to analyze multi-source data, and an equipment-environment-behavior coupling risk model is established, which improves the real-time and intelligent level of mine safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without inventive effort.

[0015] Figure 1 This is a structural diagram of a self-rescuer intelligent device management system of the present invention.

[0016] Figure 2 This is a schematic diagram of the electronic device structure of a self-rescuer intelligent device management system of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1 As shown, the present invention provides a self-rescuer intelligent device management system, including a mine virtual scene construction module, a self-rescuer status monitoring module, a mine environment data monitoring module, a self-rescuer wearing behavior monitoring module, a self-rescuer management abnormality analysis module, and a self-rescuer management abnormality early warning module; Constructing a mine virtual scene module: Using structured light scanning and lidar to collect 3D point cloud data of the target mine, constructing a mine virtual scene, and mapping the self-rescuer status data and dynamic environment data into the mine virtual scene; In this embodiment, it should be specifically explained that the execution method of constructing the mine virtual scene module is as follows: Structured light scanning and lidar are used to collect three-dimensional point cloud data of the target mine, and a virtual mine scene is constructed. The self-rescuer information is registered in the virtual mine scene through software, and the identity information of each mine personnel is bound to the corresponding self-rescuer. Each target self-rescuer and each mine personnel are numbered with the same serial number, specifically 1, 2, 3, ...j..., m, where j represents the number of each target self-rescuer and also the number of each mine personnel, and m represents the total number of target self-rescuers. The self-rescuer information is registered through software to achieve unique binding between miners and self-rescuers, ensuring that responsibilities are assigned to individuals.

[0019] Self-rescuer status monitoring module: Based on the mine virtual scene, the module collects the oxygen concentration, battery power, and leakage rate of each target self-rescuer, analyzes them to obtain the first warning coefficient, and makes a judgment on it. If the status is normal, the mine environment data monitoring module is executed; otherwise, the first warning information is generated; In this embodiment, it should be specifically explained that the execution mode of the self-rescuer status monitoring module is as follows: Obtain the oxygen concentration of each target self-rescuer and extract the maximum oxygen concentration allowed by the self-rescuer from the management database and the minimum allowable oxygen concentration , respectively substitute them into the formula , get the oxygen concentration deviation of the jth target self-rescuer, where represents the oxygen concentration of the jth self-rescuer; It should be noted that, in a specific embodiment, the maximum oxygen concentration allowed by the self-rescuer is Set to 19.5%, the minimum allowed oxygen concentration Set to 23.5%, when the oxygen concentration of the self-rescuer is lower than 19.5%, it will cause hypoxia, causing dizziness, fatigue and even suffocation; when the oxygen concentration of the self-rescuer is higher than 23.5%, it will increase the risk of oxygen poisoning and fire hazards.

[0020] According to the status monitoring data of each target self-rescuer, the status warning coefficient of each target self-rescuer is analyzed. The calculation formula is as follows: ,in, FWf j represents the state warning coefficient of the j-th target self-rescuer, which is marked as the first warning coefficient of the j-th self-rescuer. Indicates the preset standard oxygen concentration deviation, represents the battery capacity of the jth target self-rescuer, Indicates the preset standard battery level. represents the gas leakage rate of the jth target self-rescuer, Indicates the preset standard gas leakage rate, Represent the weight coefficients of oxygen concentration deviation, battery power, and gas leakage rate respectively, and ; Based on the first warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the first warning coefficient of each target self-rescuer and compare it with the preset first warning coefficient threshold. If the first warning coefficient of a target self-rescuer is less than the preset first warning coefficient threshold, it is judged that the status of the target self-rescuer is normal and no abnormal alarm is required. The mine environment data monitoring module is further executed. Otherwise, it is judged that the status of the target self-rescuer is abnormal, and the first warning information is generated, and the number corresponding to the self-rescuer with abnormal status is sent to the mobile terminal.

[0021] The first warning information refers to the abnormal status of the target self-rescuer. The number corresponding to the target self-rescuer with abnormal status is sent to the mobile terminal, and a yellow warning message is issued to remind maintenance personnel to maintain the designated target self-rescuer. The abnormal status of the self-rescuer specifically includes abnormal oxygen concentration, abnormal battery power, and abnormal gas leakage rate.

[0022] In this embodiment, it should be specifically explained that the method for obtaining the gas leakage rate of the j-th target self-rescuer is as follows: Use the pressure sensor to collect the internal gas pressure value of each target self-rescuer at the preset time interval within the preset period , and record the collection time node t i , i represents the number of each time node, i=1, 2, 3, ..., n, n represents the total time nodes collected, j represents the number of each target self-rescuer, j=1, 2, 3, ..., m, m represents the total number of target self-rescuers; By formula , calculate the gas leakage rate of the jth target self-rescuer Lr j , V j represents the volume of the jth target self-rescuer, R represents the universal gas constant, Tk represents the thermodynamic temperature, Indicates that at the i+1th time node The internal gas pressure value corresponding to the j-th target self-rescuer, Indicates that at the i-th time node The internal gas pressure value corresponding to the j-th target self-rescuer.

[0023] It should be noted that, in a specific embodiment, the preset period can be set to one day, and the preset time interval can be set to three seconds. R It represents the universal gas constant, and its specific value is: R=8.314J / (mol·K).

[0024] Mine environment data monitoring module: Based on the mine virtual scene, it obtains mine environment data, analyzes it to obtain the second warning coefficient, and judges it. If it is judged that the environmental state is abnormal, the self-rescuer wearing behavior monitoring module is executed and the second warning information is generated; In this embodiment, it should be specifically explained that the execution method of the mine environment data monitoring module is as follows: Based on the mine virtual scene, obtain mine environmental data, the mine environmental data including gas concentration, ambient temperature and dust concentration; the gas concentration includes but is not limited to methane concentration, oxygen concentration, carbon monoxide concentration and hydrogen sulfide concentration; Based on the mine environment data, the environmental anomaly coefficient of each target self-rescuer is analyzed. The specific calculation formula is as follows: ,in, Swc j represents the environmental abnormality coefficient of the j-th target self-rescuer, which is marked as the second warning coefficient representing the j-th target self-rescuer. represents the environment of the jth target self-rescuer p Gas concentration, Indicates the p The safety threshold of gas concentration, represents the ambient temperature of the jth target self-rescuer, Indicates the preset ambient temperature safety threshold. represents the dust concentration in the environment of the jth target self-rescuer, Indicates the safety threshold of dust concentration, p Indicates the number of each gas type, p =1, 2, 3, ..., k, where k represents the total number of gas types; Based on the second warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the second warning coefficient of each target self-rescuer, and compare it with the preset second warning coefficient threshold value. If the second warning coefficient of a target self-rescuer is less than the preset second warning coefficient threshold value, it is judged that the environmental state of the target self-rescuer is normal, and there is no need to issue an alarm message for wearing a self-rescuer to escape. If the second warning coefficient of a target self-rescuer is greater than or equal to the preset second warning coefficient threshold value, it is judged that the environmental state of the target self-rescuer is abnormal, and it is necessary to wear a self-rescuer to escape, further execute the self-rescuer wearing behavior monitoring module, and generate a second warning message; The second warning information refers to the abnormality of the environmental state of the target self-rescuer. The number corresponding to the target self-rescuer with the abnormal environmental state is sent to the mobile terminal, and a red warning message is issued to remind miners to wear self-rescuers to escape. The environmental abnormality data is marked in the virtual scene of the mine, and the abnormal area is located through the built-in GPS of the self-rescuer and nearby personnel are notified.

[0025] Self-rescuer wearing behavior monitoring module: The module uses underground cameras to obtain images of each mine worker wearing a self-rescuer, analyzes them to obtain a third warning coefficient, and evaluates them. If the behavior is assessed as normal, the self-rescuer management abnormality analysis module is executed; otherwise, a third warning information is generated; In this embodiment, it should be specifically explained that the execution mode of the self-rescuer wearing behavior monitoring module is as follows: The images of the mine personnel wearing self-rescuers are obtained through underground cameras. The wearing angles of each mine personnel are extracted using image analysis technology, and the standard range of the target self-rescuer wearing angles is extracted from the management database. , analyze the abnormal coefficient of wearing behavior of each mine personnel, and the calculation formula is as follows: ,in, Twc j represents the abnormal coefficient of wearing behavior of the j-th target self-rescuer, which is marked as the third warning coefficient representing the j-th target self-rescuer. represents the actual wearing angle of the j-th mine worker, Indicates the preset standard wearing angle. represents the wearing time of the jth mine worker, wt Indicates the preset standard wearing time. Represent the weight coefficients of wearing angle and wearing time respectively, and ; It should be noted that the number of the mine personnel is the same as the number of the target self-rescuer.

[0026] Based on the third warning coefficient of each target self-rescuer, select the corresponding management method to manage each target self-rescuer; Read the third warning coefficient of each target self-rescuer, and compare it with the preset third warning coefficient threshold value. If the third warning coefficient of a target self-rescuer is less than the preset third warning coefficient threshold value, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is normal, and no abnormal alarm is required. Further execute the self-rescuer management abnormality analysis module. If the third warning coefficient of a target self-rescuer is greater than or equal to the preset third warning coefficient threshold value, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is abnormal, generate a third warning information, and send the corresponding number of the self-rescuer to the mobile terminal; The third warning information refers to the abnormal behavior of the mine personnel wearing the self-rescuer corresponding to the target self-rescuer. A training needs report is generated for the data on the abnormal behavior of the mine personnel wearing the self-rescuer and uploaded to the management center, which will review and arrange the corresponding training methods and time.

[0027] It should be specifically noted that, in a specific embodiment, It can be set to 0.6, It can be set to 0.4. The wearing angle of the self-rescuer directly affects the sealing and oxygen supply functions of the self-rescuer. Angle deviation will cause toxic gas intrusion or oxygen supply failure, which has an immediate fatal risk and requires special attention. Insufficient wearing time is a cumulative risk, which is not instantly fatal and is easier to monitor and remind, and the risk urgency is relatively low.

[0028] Self-rescuer management abnormality analysis module: used to analyze the management abnormality status of each target self-rescuer and obtain the management abnormality index of each target self-rescuer; In this embodiment, it should be specifically explained that the execution mode of the self-rescuer management abnormality analysis module is as follows: Read the first warning coefficient of each target self-rescuer FWf j , the second warning coefficient Swc j , the third warning coefficient Twc j , analyze the abnormal management status of each target self-rescuer and obtain the abnormal management index of each target self-rescuer. The calculation formula is as follows: ,in, represents the management abnormality index of the j-th target self-rescuer, They respectively represent the preset first warning coefficient threshold, the preset second warning coefficient threshold, and the preset third warning coefficient threshold.

[0029] Self-rescuer management abnormality warning module: Based on the management abnormality index of each target self-rescuer, it conducts abnormality evaluation on the management status of each target self-rescuer, outputs the management abnormality evaluation result, and issues early warning information for abnormal data.

[0030] In this embodiment, it should be specifically explained that the execution method of the self-rescuer management abnormality warning module is as follows: Read the management anomaly index of each target self-rescuer, perform an anomaly assessment on the management status of each target self-rescuer, compare the management anomaly index of a target self-rescuer with the preset management anomaly index threshold, if the management anomaly index of a target self-rescuer is less than the preset management anomaly index threshold, then the management status of the target self-rescuer is judged to be normal, otherwise, the management status of the target self-rescuer is judged to be abnormal, mark the corresponding self-rescuer's management status abnormal data as a management anomaly assessment result, output the management anomaly assessment result, and issue a warning message for the abnormal data.

[0031] Example 2 According to an exemplary embodiment, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned self-rescuer intelligent device management system by calling the computer program stored in the memory.

[0032] Figure 2 It is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement a self-rescuer intelligent device management system provided in the above-mentioned various method embodiments.

[0033] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for input and output.

[0034] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the self-rescuer intelligent device management system.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0037] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0038] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A self-rescuer intelligent device management system, characterized in that: include: Constructing a mine virtual scene module: Using structured light scanning and lidar to collect 3D point cloud data of the target mine, constructing a mine virtual scene, and mapping the self-rescuer status data and dynamic environment data into the mine virtual scene; Self-rescuer status monitoring module: Based on the mine virtual scene, the module collects the oxygen concentration, battery power, and leakage rate of each target self-rescuer, analyzes them to obtain the first warning coefficient, and makes a judgment on it. If the status is normal, the mine environment data monitoring module is executed; otherwise, the first warning information is generated; Mine environment data monitoring module: Based on the mine virtual scene, it obtains mine environment data, analyzes it to obtain the second warning coefficient, and judges it. If it is judged that the environmental state is abnormal, the self-rescuer wearing behavior monitoring module is executed and the second warning information is generated; Self-rescuer wearing behavior monitoring module: The module uses underground cameras to obtain images of each mine worker wearing a self-rescuer, analyzes them to obtain a third warning coefficient, and evaluates them. If the behavior is assessed as normal, the self-rescuer management abnormality analysis module is executed; otherwise, a third warning information is generated; Self-rescuer management abnormality analysis module: used to analyze the management abnormality status of each target self-rescuer and obtain the management abnormality index of each target self-rescuer; Self-rescuer management abnormality warning module: Based on the management abnormality index of each target self-rescuer, it conducts abnormality evaluation on the management status of each target self-rescuer, outputs the management abnormality evaluation result, and issues early warning information for abnormal data.

2. A self-rescuer intelligent device management system according to claim 1, characterized in that: The execution method of constructing the mine virtual scene module is as follows: Structured light scanning and lidar are used to collect three-dimensional point cloud data of the target mine, and a virtual mine scene is constructed. The self-rescuer information is registered in the virtual mine scene through software, and the identity information of each mine personnel is bound to the corresponding self-rescuer. Each target self-rescuer and each mine personnel are numbered with the same serial number, specifically 1, 2, 3, ...j..., m, where j represents the number of each target self-rescuer and also the number of each mine personnel, and m represents the total number of target self-rescuers.

3. A self-rescuer intelligent device management system according to claim 1, characterized in that: The execution mode of the self-rescuer status monitoring module is as follows: Obtain the oxygen concentration of each target self-rescuer and extract the maximum oxygen concentration allowed by the self-rescuer from the management database and the minimum allowable oxygen concentration , respectively substitute them into the formula , get the oxygen concentration deviation of the jth target self-rescuer, where represents the oxygen concentration of the jth self-rescuer; According to the status monitoring data of each target self-rescuer, the status warning coefficient of each target self-rescuer is analyzed. The calculation formula is as follows: ,in, FWf j represents the state warning coefficient of the j-th target self-rescuer, which is marked as the first warning coefficient of the j-th self-rescuer. Indicates the preset standard oxygen concentration deviation, represents the battery capacity of the jth target self-rescuer, Indicates the preset standard battery level. represents the gas leakage rate of the jth target self-rescuer, Indicates the preset standard gas leakage rate, Represent the weight coefficients of oxygen concentration deviation, battery power, and gas leakage rate respectively, and ; Based on the first warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the first warning coefficient of each target self-rescuer and compare it with the preset first warning coefficient threshold. If the first warning coefficient of a target self-rescuer is less than the preset first warning coefficient threshold, it is judged that the status of the target self-rescuer is normal and no abnormal alarm is required. The mine environment data monitoring module is further executed. Otherwise, it is judged that the status of the target self-rescuer is abnormal, and the first warning information is generated, and the number corresponding to the self-rescuer with abnormal status is sent to the mobile terminal.

4. A self-rescuer intelligent device management system according to claim 3, characterized in that: The method for obtaining the gas leakage rate of the j-th target self-rescuer is as follows: Use the pressure sensor to collect the internal gas pressure value of each target self-rescuer at the preset time interval within the preset period , and record the collection time node t i , i represents the number of each time node, i=1, 2, 3, ..., n, n represents the total time nodes collected, j represents the number of each target self-rescuer, j=1, 2, 3, ..., m, m represents the total number of target self-rescuers; By formula , calculate the gas leakage rate of the jth target self-rescuer Lr j , V j represents the volume of the jth target self-rescuer, R represents the universal gas constant, Tk represents the thermodynamic temperature, Indicates that at the i+1th time node The internal gas pressure value corresponding to the j-th target self-rescuer, Indicates that at the i-th time node The internal gas pressure value corresponding to the j-th target self-rescuer.

5. The self-rescuer intelligent device management system according to claim 1, characterized in that: The execution mode of the mine environment data monitoring module is as follows: Based on the mine virtual scene, the mine environment data is obtained. The mine environment data includes gas concentration, ambient temperature and dust concentration. Based on the mine environment data, the environmental abnormality coefficient of each target self-rescuer is analyzed. The specific calculation formula is as follows: ,in, Swc j represents the environmental abnormality coefficient of the j-th target self-rescuer, which is marked as the second warning coefficient representing the j-th target self-rescuer. represents the environment of the jth target self-rescuer p Gas concentration, Indicates the p The safety threshold of gas concentration, represents the ambient temperature of the jth target self-rescuer, Indicates the preset ambient temperature safety threshold. represents the dust concentration in the environment of the jth target self-rescuer, Indicates the safety threshold of dust concentration, p Indicates the number of each gas type, p =1, 2, 3, ..., k, where k represents the total number of gas types; Based on the second warning coefficient of each target self-rescuer, a corresponding management method is selected to manage each target self-rescuer; Read the second warning coefficient of each target self-rescuer and compare it with the preset second warning coefficient threshold. If the second warning coefficient of a target self-rescuer is less than the preset second warning coefficient threshold, it is judged that the environmental state of the target self-rescuer is normal and there is no need to issue an alarm message for wearing a self-rescuer to escape. If the second warning coefficient of a target self-rescuer is greater than or equal to the preset second warning coefficient threshold, it is judged that the environmental state of the target self-rescuer is abnormal and it is necessary to wear a self-rescuer to escape. The self-rescuer wearing behavior monitoring module is further executed and a second warning message is generated.

6. A self-rescuer intelligent device management system according to claim 1, characterized in that: The execution mode of the self-rescuer wearing behavior monitoring module is as follows: The images of the mine personnel wearing self-rescuers are obtained through underground cameras. The wearing angles of each mine personnel are extracted using image analysis technology, and the standard range of the target self-rescuer wearing angles is extracted from the management database. , analyze the abnormal coefficient of wearing behavior of each mine personnel, and the calculation formula is as follows: ,in, Twc j represents the abnormal coefficient of wearing behavior of the j-th target self-rescuer, which is marked as the third warning coefficient representing the j-th target self-rescuer. represents the actual wearing angle of the j-th mine worker, Indicates the preset standard wearing angle. represents the wearing time of the j-th mine worker, wt Indicates the preset standard wearing time. Represent the weight coefficients of wearing angle and wearing time respectively, and ; Based on the third warning coefficient of each target self-rescuer, select the corresponding management method to manage each target self-rescuer; Read the third warning coefficient of each target self-rescuer and compare it with the preset third warning coefficient threshold. If the third warning coefficient of a target self-rescuer is less than the preset third warning coefficient threshold, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is normal, and no abnormal alarm is required. Further execute the self-rescuer management abnormality analysis module. If the third warning coefficient of a target self-rescuer is greater than or equal to the preset third warning coefficient threshold, it is judged that the wearing behavior of the mine personnel of the target self-rescuer is abnormal, generate the third warning information, and send the corresponding number of the self-rescuer to the mobile terminal.

7. The self-rescuer intelligent device management system according to claim 1, characterized in that: The execution mode of the self-rescuer management abnormality analysis module is as follows: Read the first warning coefficient of each target self-rescuer FWf j , the second warning coefficient Swc j , the third warning coefficient Twc j , analyze the abnormal management status of each target self-rescuer and obtain the abnormal management index of each target self-rescuer. The calculation formula is as follows: ,in, represents the management abnormality index of the j-th target self-rescuer, They respectively represent the preset first warning coefficient threshold, the preset second warning coefficient threshold, and the preset third warning coefficient threshold.

8. The self-rescuer intelligent device management system according to claim 1, characterized in that: The execution mode of the self-rescuer management abnormal warning module is as follows: Read the management anomaly index of each target self-rescuer, perform an anomaly assessment on the management status of each target self-rescuer, compare the management anomaly index of a target self-rescuer with the preset management anomaly index threshold, if the management anomaly index of a target self-rescuer is less than the preset management anomaly index threshold, then the management status of the target self-rescuer is judged to be normal, otherwise, the management status of the target self-rescuer is judged to be abnormal, mark the corresponding self-rescuer's management status abnormal data as a management anomaly assessment result, output the management anomaly assessment result, and issue a warning message for the abnormal data.

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