Coal mine monitoring system measuring point compliance analysis method and device and electronic equipment

Abstract

The invention provides a coal mine monitoring system measuring point compliance analysis method and device and electronic equipment, and the method comprises the steps: obtaining a plurality of evidence sources of measuring points in a coal mine monitoring system, and a recognition frame for the compliance analysis of the measuring points, the recognition frame comprising a plurality of candidate compliance analysis results of the measuring points; determining a basic probability distribution function of each evidence source based on the identification framework; fusing the basic probability distribution functions of the evidence sources to obtain a fused basic probability distribution function; and based on the fused basic probability distribution function, determining a target compliance analysis result of the measuring point from the identification framework. Therefore, compliance analysis can be carried out on the measuring points of the coal mine monitoring system based on the evidence theory, and multi-dimensional analysis can be accurately carried out on each measuring point. Besides, automatic measuring point compliance analysis is realized, dependence on manpower is greatly reduced, labor cost and labor intensity are remarkably reduced, and overall efficiency of coal mine safety management is improved.

Description

Technical Field

[0001] The present application relates to the field of coal mine monitoring technology, and in particular to a method, device, electronic equipment and storage medium for analyzing compliance of measurement points in a coal mine monitoring system. Background Art

[0002] At present, the coal mine monitoring system still needs to rely on manual inspections to determine whether the measuring points comply with relevant regulations. Manual judgment is inefficient and labor-intensive, and it is easy for the monitoring data to fail to meet the requirements of disaster monitoring and early warning due to untimely manual inspections, thus affecting the accuracy of disaster warnings. Summary of the Invention

[0003] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of this application is to propose a compliance analysis method for measuring points of a coal mine monitoring system.

[0005] The second purpose of this application is to propose a coal mine monitoring system measurement point compliance analysis device.

[0006] The third objective of this application is to provide an electronic device.

[0007] The fourth object of this application is to provide a computer-readable storage medium.

[0008] A fifth object of this application is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect embodiment of the present application proposes a compliance analysis method for measuring points in a coal mine monitoring system, including: obtaining multiple evidence sources for measuring points in the coal mine monitoring system, and an identification framework for compliance analysis of the measuring points, the identification framework including multiple candidate compliance analysis results of the measuring points; based on the identification framework, determining the basic probability distribution function of each evidence source; fusing the basic probability distribution functions of each evidence source to obtain a fused basic probability distribution function; based on the fused basic probability distribution function, determining the target compliance analysis result of the measuring point from the identification framework.

[0010] To achieve the above-mentioned purpose, the second aspect embodiment of the present application proposes a compliance analysis device for measuring points of a coal mine monitoring system, including: an acquisition module, used to obtain multiple evidence sources of measuring points in the coal mine monitoring system, and an identification framework for compliance analysis of the measuring points, the identification framework including multiple candidate compliance analysis results of the measuring points; a first determination module, used to determine the basic probability distribution function of each evidence source based on the identification framework; a fusion module, used to fuse the basic probability distribution functions of each evidence source to obtain a fused basic probability distribution function; a second determination module, used to determine the target compliance analysis result of the measuring point from the identification framework based on the fused basic probability distribution function.

[0011] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the coal mine monitoring system measurement point compliance analysis method as described in the first aspect embodiment above.

[0012] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by the processor, they are used to implement the coal mine monitoring system measurement point compliance analysis method as described in the first aspect embodiment above.

[0013] To achieve the above-mentioned purpose, the fifth embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the coal mine monitoring system measurement point compliance analysis method as described in the first embodiment above.

[0014] The present application provides a method, device, electronic device, and storage medium for analyzing the compliance of measurement points in a coal mine monitoring system. The method obtains multiple evidence sources for measurement points in the coal mine monitoring system, as well as an identification framework for compliance analysis of the measurement points. The identification framework includes multiple candidate compliance analysis results for the measurement points. Based on the identification framework, the basic probability distribution function of each evidence source is determined. The basic probability distribution functions of each evidence source are fused to obtain a fused basic probability distribution function. Based on the fused basic probability distribution function, the target compliance analysis result of the measurement point is determined from the identification framework. Thus, compliance analysis of the measurement points in the coal mine monitoring system can be performed based on evidence theory. Each measurement point can be accurately analyzed in multiple dimensions, and potential problems can be comprehensively and deeply investigated to ensure the reliability of the coal mine monitoring system. This provides more accurate data support for disaster warnings, effectively avoiding warning errors caused by data errors or omissions, and improving the accuracy of disaster warnings. Furthermore, automated compliance analysis of measurement points is achieved, greatly reducing reliance on manual labor, significantly reducing labor costs and labor intensity, and improving the efficiency of compliance analysis of measurement points.

[0015] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0017] Figure 1 A flow chart of a method for analyzing compliance of measurement points in a coal mine monitoring system provided in an embodiment of the present application;

[0018] Figure 2 A flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided in an embodiment of the present application;

[0019] Figure 3 A flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided in an embodiment of the present application;

[0020] Figure 4 A flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided in an embodiment of the present application;

[0021] Figure 5 This is a structural diagram of a coal mine monitoring system measurement point compliance analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0023] Coal accounts for approximately 60% of primary energy production and consumption. However, the complex nature of coal seams and the fact that most coal mines are operated underground often lead to frequent disasters such as gas, flooding, fire, roof collapse, and rock bursts, severely restricting the safe and efficient extraction of coal resources.

[0024] Disaster monitoring and early warning technology can assess the likelihood and severity of a disaster before it occurs, based on information about potential hazards and precursors. It can also issue warning signals to relevant departments or personnel, prompting them to take timely preventative measures to avoid the disaster. Currently, almost all coal mines are equipped with various types of coal mine disaster monitoring and early warning systems, including safety monitoring systems, hydrological monitoring systems, mine pressure monitoring systems, fire monitoring systems, transparent geological systems, and production and operation systems.

[0025] Accurate disaster warnings require the coordination, stable operation, and reliable data collection of all measurement points in the system. System reliability directly impacts the accuracy of disaster warnings and is crucial to their success. Accurate perception of coal mine disaster monitoring data is a prerequisite for disaster warnings. Disaster monitoring reliability determines the accuracy of early warnings. Therefore, analyzing whether a mine's current sensors meet the requirements for mine disaster monitoring is key to ensuring accurate warnings. The system should be able to assess the status of coal mine power disaster measurement points. By leveraging relevant policies, regulations, notices, and other documents, and using the acquired measurement point locations and parameter information, it can dynamically evaluate and analyze whether the mine power disaster monitoring system meets the requirements for disaster monitoring data perception, providing timely reminders to mine owners to maintain and upgrade the monitoring system.

[0026] It can be seen that the current coal mine monitoring system has the following problems:

[0027] (1) The data sources of the monitoring system all come from various types of monitoring systems that the mine has been equipped with. The monitoring data collected by the early warning system are of various types and the number of measurement points is huge.

[0028] (2) All types of early warning systems still need to rely on manual inspections to determine whether the measuring points comply with relevant regulations. Manual judgment is inefficient and labor-intensive, and it is easy for monitoring data to fail to meet the minimum requirements of disaster monitoring and early warning due to untimely manual inspections, thus affecting the accuracy of disaster warnings.

[0029] In response to the above problems, an embodiment of the present application provides a compliance analysis method for measuring points in a coal mine monitoring system, which obtains multiple evidence sources for measuring points in the coal mine monitoring system and an identification framework for compliance analysis of the measuring points. The identification framework includes multiple candidate compliance analysis results for the measuring points. Based on the identification framework, the basic probability distribution function of each evidence source is determined, and the basic probability distribution functions of each evidence source are fused to obtain a fused basic probability distribution function. Based on the fused basic probability distribution function, the target compliance analysis result of the measuring point is determined from the identification framework. Thus, compliance analysis of the measuring points in the coal mine monitoring system can be performed based on evidence theory, and each measuring point can be accurately analyzed in multiple dimensions, and potential problems can be comprehensively and deeply investigated to ensure the reliability of the coal mine monitoring system, thereby providing more accurate data support for disaster warning, effectively avoiding warning errors caused by data errors or omissions, and improving the accuracy of disaster warning. In addition, automated compliance analysis of measuring points is achieved, which greatly reduces dependence on manual labor, significantly reduces labor costs and labor intensity, and improves the efficiency of compliance analysis of measuring points.

[0030] The following describes the coal mine monitoring system measurement point compliance analysis method, device, electronic device and storage medium of the embodiments of the present application with reference to the accompanying drawings.

[0031] Figure 1 A flow chart of a method for analyzing compliance of measurement points in a coal mine monitoring system provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the method includes the following steps:

[0033] S101, obtaining multiple evidence sources of measurement points in the coal mine monitoring system and an identification framework for compliance analysis of the measurement points, the identification framework including multiple candidate compliance analysis results of the measurement points.

[0034] It should be noted that a measurement point refers to the location where monitoring information is collected, as well as the monitoring equipment used to collect this information. A coal mine monitoring system can include multiple measurement points, and there are no specific restrictions on their placement. For example, measurement points can be located in underground areas of the coal mine, such as the coal mining face, tunneling face, return air lane, and intake air lane. The monitoring information required to be collected at different measurement points may vary, and there are no specific restrictions on this information. For example, it may include gas concentration, carbon monoxide concentration, temperature, humidity, and stress. There are no specific restrictions on the monitoring equipment used, and it may include gas concentration sensors, carbon monoxide concentration sensors, temperature sensors, humidity sensors, and stress sensors.

[0035] There are not too many restrictions on the multiple evidence sources of the measuring points. For example, they may include sensor hardware evidence sources, data comparison evidence sources and spatial analysis evidence sources. The sensor hardware evidence sources may include sensor fault records, hardware self-test information, measurement range, accuracy, response time and other hardware indicators. The data comparison evidence sources may include the current monitoring information of the measuring point, the current monitoring information of other measuring points in the same area, the historical monitoring information of the measuring point, etc. The spatial analysis evidence sources may include the installation location and density of the sensor.

[0036] It is understandable that the multiple sources of evidence are different at different measurement points.

[0037] Optionally, obtaining multiple evidence sources of measurement points in the coal mine monitoring system includes at least one of the following operations:

[0038] Operation 1: Obtain the operating information, health status information, maintenance information, and actual device parameters of the target monitoring device as the first evidence source of the first measuring point. The target monitoring device is the monitoring device required to be installed at the first measuring point.

[0039] The first measuring point is any measuring point in the coal mine monitoring system. It is understandable that different measuring points may require different monitoring equipment.

[0040] The operating information of the monitoring device may include the working mode, communication information, battery power, cumulative operating time, firmware version, etc. of the monitoring device.

[0041] The health status information of the monitoring device may include fault records, self-test results, etc. of the monitoring device.

[0042] The maintenance information of the monitoring equipment may include maintenance records, calibration records, etc. of the monitoring equipment.

[0043] The actual device parameters of the monitoring device may include the measurement range, accuracy, response time, etc. of the monitoring device.

[0044] For example, measuring points 1 to 5 can be arranged on the coal mining face. Measuring points 1 to 5 are as follows:

[0045] Measuring point 1: location information 1 + gas concentration sensor 1;

[0046] Measuring point 2: location information 1 + carbon monoxide concentration sensor 1;

[0047] Measuring point 3: location information 2 + temperature sensor 1;

[0048] Measuring point 4: location information 3 + temperature sensor 2;

[0049] Measuring point 5: location information 3 + gas concentration sensor 2.

[0050] It should be noted that measuring points 1 and 2 monitor the same location, measuring points 4 and 5 monitor the same location, gas concentration sensor 1 is the monitoring equipment required to be installed at measuring point 1, carbon monoxide concentration sensor 1 is the monitoring equipment required to be installed at measuring point 2, temperature sensor 1 is the monitoring equipment required to be installed at measuring point 3, temperature sensor 2 is the monitoring equipment required to be installed at measuring point 4, and gas concentration sensor 2 is the monitoring equipment required to be installed at measuring point 5.

[0051] The operation information, health status information, maintenance information and actual equipment parameters of the gas concentration sensor 1 can be obtained as the first evidence source of the measuring point 1.

[0052] The operation information, health status information, maintenance information and actual equipment parameters of the carbon monoxide concentration sensor 1 can be obtained as the first evidence source of the measuring point 2.

[0053] The relevant contents of the first source of evidence for measurement points 3 to 5 can refer to the relevant contents of the first source of evidence for measurement points 1 and 2, and will not be repeated here.

[0054] Operation 2: Obtain the current monitoring information of the first measuring point, the current monitoring information of the second measuring point in the area to which the first measuring point belongs, and the historical monitoring information of the first measuring point as the second source of evidence for the first measuring point. The type of monitoring equipment required to be installed at the second measuring point is consistent with the type of monitoring equipment required to be installed at the first measuring point.

[0055] For example, let’s continue with measuring points 1 to 5.

[0056] The current monitoring information of measuring points 1 and 5, as well as the historical monitoring information of measuring point 1, can be obtained as the second source of evidence for measuring point 1.

[0057] The current monitoring information of measuring point 2 and the historical monitoring information of measuring point 2 can be obtained as the second evidence source of measuring point 2.

[0058] The current monitoring information of measuring points 3 and 4, as well as the historical monitoring information of measuring point 3, can be obtained as the second source of evidence for measuring point 3.

[0059] The current monitoring information of measuring points 3 and 4, as well as the historical monitoring information of measuring point 4, can be obtained as the second source of evidence for measuring point 4.

[0060] The current monitoring information of measuring points 1 and 5, as well as the historical monitoring information of measuring point 5, can be obtained as the second source of evidence for measuring point 5.

[0061] Operation 3: Obtain at least one piece of information of the actual installation location, actual monitoring area, and actual installation density of the target monitoring device in the area to which the measuring point belongs as a third source of evidence.

[0062] In this embodiment, the underground area of ​​the coal mine can be divided into multiple areas in advance. For example, the underground area of ​​the coal mine can be divided into coal mining working face, tunneling working face, return air lane, air intake lane and other areas.

[0063] The actual installation density of different monitoring devices in the area where the measuring point belongs may be different.

[0064] For example, continuing with measuring points 1 to 5, the area to which measuring points 1 to 5 belong is the coal mining face.

[0065] At least one piece of information including the actual installation position of the gas concentration sensor 1 , the actual monitoring area, and the actual installation density of the gas concentration sensor in the coal mining face can be obtained as a third evidence source of the measuring point 1 .

[0066] At least one piece of information including the actual installation position of the carbon monoxide concentration sensor 1 , the actual monitoring area, and the actual installation density of the carbon monoxide concentration sensor in the coal mining face can be obtained as a third evidence source of the measuring point 2 .

[0067] The relevant contents of the third source of evidence for measuring points 3 to 5 can refer to the relevant contents of the third source of evidence for measuring points 1 and 2, and will not be repeated here.

[0068] The identification framework is a set of multiple candidate compliance analysis results of the measurement point. There are no excessive restrictions on the identification framework. For example, the identification framework θ is as follows:

[0069] θ={Compliant recommended, Compliant not recommended, Non-compliant, Uncertain}

[0070] Among them, compliance recommendation is used to indicate that the measuring point is compliant. For example, the measuring point basically meets the specified requirements under the current monitoring conditions, and meets the disaster monitoring standards in key factors such as installation location, sensor performance and spatial layout. Data collection is reliable and no immediate adjustment or maintenance is required.

[0071] Compliance is not recommended for indicating minor non-compliance at a measuring point. For example, if some indicators at the measuring point have potential risks or do not fully meet the requirements of the rules, the mine is advised to optimize or conduct further verification based on monitoring reliability.

[0072] Non-compliance is used to indicate a serious non-compliance at a measurement point, such as a sensor hardware failure that directly leads to monitoring failure and requires immediate adjustment or maintenance.

[0073] Uncertain is used to indicate that it is currently uncertain whether the measurement point is compliant and further evidence collection is required.

[0074] S102: Based on the identification framework, determine the basic probability distribution function of each evidence source.

[0075] It should be noted that the BPA (Basic Probability Assignment) function is a function that maps all subsets in the identification framework to values ​​in the interval [0,1]. The value obtained by the BPA function is called the mass function value or mass value, which is the basic probability assignment value of the evidence source to a certain set. It represents the trust degree that the evidence source directly assigns to the set, rather than the trust degree indirectly assigned through a subset of the set.

[0076] The basic probability distribution function is formally defined as follows:

[0077] m:2 θ →[0,1]

[0078] Where m(A) is the basic probability distribution value of the evidence source to the set A. A is a subset of θ and satisfies the following conditions:

[0079]

[0080] This condition is used to indicate that the sum of the basic probability assignment values ​​for all subsets of the recognition frame is 1.

[0081] The basic probability distribution functions of different evidence sources may be different.

[0082] S103, fusing the basic probability distribution functions of the evidence sources to obtain a fused basic probability distribution function.

[0083] It should be noted that there are no restrictions on the way the basic probability distribution functions of each evidence source are integrated. For example, the Dempster combination rule can be used. The basic probability distribution functions after integration at different measurement points may be different.

[0084] For example, the basic probability distribution functions of two evidence sources are fused to obtain the following basic probability distribution function:

[0085]

[0086] in, is the conflict coefficient, which is used to measure the degree of contradiction between two sources of evidence. If K = 1, it means that the two sources of evidence are completely conflicting and cannot be integrated.

[0087] m(A) is the basic probability distribution value of the evidence source to set A after the fusion of the two evidence sources, m1(B) is the basic probability distribution value of the first evidence source to set B, and m2(C) is the basic probability distribution value of the second evidence source to set C. Both B and C are subsets of θ.

[0088] S104, based on the fused basic probability distribution function, determining the target compliance analysis result of the measurement point from the recognition framework.

[0089] In this application, based on the fused basic probability distribution function, the target compliance analysis results of the measurement points are determined from the recognition framework, including the following possible implementation methods:

[0090] Method 1: Based on the fused basic probability distribution function, the basic probability distribution value of each candidate compliance analysis result of the measuring point is determined, and the candidate compliance analysis result with the largest basic probability distribution value is used as the target compliance analysis result.

[0091] Optionally, before using the candidate compliance analysis result with the largest basic probability distribution value as the target compliance analysis result, the process further includes determining whether the largest basic probability distribution value is greater than a first set threshold value. The first set threshold value is not particularly limited and may be, for example, 0.5.

[0092] For example, the recognition framework θ is as follows:

[0093] θ={Compliant recommended, Compliant not recommended, Non-compliant, Uncertain}

[0094] The basic probability distribution function after fusion corresponding to measurement point 1 is as follows:

[0095] m({Compliant and recommended}) = 0.9, m({Compliant and not recommended}) = 0, m({Not compliant}) = 0, m({Uncertain}) = 0.1

[0096] The basic probability distribution value corresponding to "Compliance Recommendation" is the largest and is greater than the first set threshold value of 0.5. Therefore, "Compliance Recommendation" can be used as the target compliance analysis result of measurement point 1.

[0097] The basic probability distribution function after fusion corresponding to measurement point 2 is as follows:

[0098] m({Compliant and recommended}) = 0, m({Compliant and not recommended}) = 0.2, m({Not compliant}) = 0.7, m({Uncertain}) = 0.1

[0099] The basic probability distribution value corresponding to “non-compliance” is the largest and is greater than the first set threshold value of 0.5. Therefore, “non-compliance” can be used as the target compliance analysis result of measuring point 2.

[0100] The basic probability distribution function after fusion corresponding to measurement point 3 is as follows:

[0101] m({Compliant and recommended}) = 0.1, m({Compliant and not recommended}) = 0.7, m({Not compliant}) = 0.2, m({Uncertain}) = 0

[0102] The basic probability distribution value corresponding to “Compliant but Not Recommended” is the largest and is greater than the first set threshold value of 0.5. Therefore, “Compliant but Not Recommended” can be used as the target compliance analysis result of measurement point 3.

[0103] Method 2: Based on the fused basic probability distribution function, a fused trust function and / or a fused likelihood function is obtained, and based on the fused trust function and / or the fused likelihood function, the target compliance analysis result of the measurement point is determined from the identification framework.

[0104] For example, the trust function is as follows:

[0105]

[0106] Among them, Bel(A) is the lower limit of the trust of the fused evidence source for set A (also called trust), which is obtained by the sum of the basic probability distribution values ​​of the fused evidence source for set A and its subsets, and represents the sum of the trust of the fused evidence source for set A and its subsets.

[0107] For example, the likelihood function is as follows:

[0108]

[0109] Among them, Pl(A) is the upper limit of the trust of the fused evidence source to set A (also called likelihood), and the complement of set A is obtained by the fused evidence source. The basic probability distribution value of is obtained, which represents the trust degree of the fused evidence source that the set A is not false.

[0110] Optionally, based on the fused trust function and / or the fused likelihood function, the target compliance analysis result of the measuring point is determined from the identification framework, including determining the trust degree of each candidate compliance analysis result of the measuring point based on the fused trust function, and taking the candidate compliance analysis result with the highest trust degree as the target compliance analysis result.

[0111] Optionally, based on the fused trust function and / or the fused likelihood function, the target compliance analysis result of the measuring point is determined from the identification framework, including determining the likelihood of each candidate compliance analysis result of the measuring point based on the fused likelihood function, and taking the candidate compliance analysis result with the largest likelihood as the target compliance analysis result.

[0112] In summary, according to the compliance analysis method for measuring points in a coal mine monitoring system according to the embodiment of the present application, multiple evidence sources for measuring points in the coal mine monitoring system and an identification framework for compliance analysis of the measuring points are obtained. The identification framework includes multiple candidate compliance analysis results of the measuring points. Based on the identification framework, the basic probability distribution function of each evidence source is determined. The basic probability distribution function of each evidence source is fused to obtain the fused basic probability distribution function. Based on the fused basic probability distribution function, the target compliance analysis result of the measuring point is determined from the identification framework. Thus, compliance analysis of the measuring points in the coal mine monitoring system can be performed based on evidence theory. It is possible to accurately perform multi-dimensional analysis on each measuring point, comprehensively and deeply investigate potential problems, ensure the reliability of the coal mine monitoring system, and thus provide more accurate data support for disaster warning, effectively avoid warning errors caused by data errors or omissions, and improve the accuracy of disaster warning. In addition, automated compliance analysis of measuring points is achieved, which greatly reduces dependence on manual labor, significantly reduces labor costs and labor intensity, and improves the efficiency of compliance analysis of measuring points.

[0113] In the above embodiment, in step S102, the basic probability distribution function of each evidence source is determined based on the identification framework, which can be combined with Figure 2 Further understanding. Figure 2 This is a flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided by an embodiment of the present application. Figure 2 As shown, the method may include the following steps:

[0114] S201, obtaining multiple evidence sources of measurement points in the coal mine monitoring system and an identification framework for compliance analysis of the measurement points, the identification framework including multiple candidate compliance analysis results of the measurement points.

[0115] For the relevant content of step S201, please refer to the above embodiment and will not be repeated here.

[0116] S202: Identify whether a target monitoring device has a fault based on the operation information, health status information, and maintenance information.

[0117] Optionally, based on the operation information, health status information and maintenance information, identify whether the target monitoring device has a fault, including determining that the target monitoring device has a fault in response to at least one item of the operation information, health status information and maintenance information being not within a set interval, and determining that the target monitoring device has not a fault in response to the operation information, health status information and maintenance information all being within a set interval.

[0118] Optionally, at least one of the operation information, health status information, and maintenance information of the target monitoring device is used to indicate whether a fault occurs in the target monitoring device.

[0119] S203: Identify whether the actual device parameters comply with the target device parameter rules.

[0120] It should be noted that there are not too many restrictions on equipment parameter rules, for example, they may include rules such as measurement range, accuracy, response time, etc.

[0121] Taking a gas concentration sensor as an example, its measurement range should generally be 0-4% CH4, and its accuracy should be no less than ±0.1% CH4. CH4 is the chemical formula of gas (also called methane).

[0122] The mining stress sensor is required to have a self-storage function. If the data upload line is interrupted, the self-storage time is not less than 8 hours, and the maximum sampling interval is not more than 2 minutes. The stress sensor range is not less than 30MPa (megapascal), and the measurement error is not more than 0.1MPa.

[0123] S204 , determining a basic probability distribution function of the first evidence source based on the identification framework, the fault identification result of the target monitoring device, and the rule identification result of the actual device parameters.

[0124] For example, the recognition framework θ is as follows:

[0125] θ={Compliant recommended, Compliant not recommended, Non-compliant, Uncertain}

[0126] Let’s continue with measuring point 1 as an example.

[0127] If gas concentration sensor 1 is not faulty and the actual device parameters of gas concentration sensor 1 meet the target device parameter rules, the basic probability distribution function of the first evidence source corresponding to measurement point 1 is as follows:

[0128] m1({Compliance recommended})=0.9, m1({Compliance not recommended})=0, m1({Non-compliance})=0, m1({Uncertain})=0.1

[0129] If gas concentration sensor 1 fails, and / or the actual device parameters of gas concentration sensor 1 do not meet the target device parameter rules, the basic probability distribution function of the first evidence source corresponding to measurement point 1 is as follows:

[0130] m1({Compliance recommended})=0, m1({Compliance not recommended})=0.2, m1({Non-compliance})=0.7, m1({Uncertain})=0.1

[0131] S205: Fusing the basic probability distribution functions of the evidence sources to obtain a fused basic probability distribution function.

[0132] S206 , based on the fused basic probability distribution function, determining the target compliance analysis result of the measurement point from the recognition framework.

[0133] For the relevant contents of steps S205-S206, please refer to the above embodiment and will not be repeated here.

[0134] In summary, according to the coal mine monitoring system measurement point compliance analysis method of the embodiment of the present application, based on the operation information, health status information and maintenance information, it is identified whether the target monitoring equipment has a fault, and whether the actual equipment parameters comply with the target equipment parameter rules. Based on the identification framework, the fault identification results of the target monitoring equipment and the rule identification results of the actual equipment parameters, the basic probability distribution function of the first evidence source is determined. Therefore, in the case where the first evidence source includes the operation information, health status information, maintenance information and actual equipment parameters of the target monitoring equipment, the operation information, health status information and maintenance information can be taken into account to identify whether the target monitoring equipment has a fault, and to identify whether the actual equipment parameters comply with the target equipment parameter rules to determine the basic probability distribution function of the first evidence source, that is, the fault identification results of the target monitoring equipment and the rule identification results of the actual equipment parameters can be taken into account to determine the basic probability distribution of the first evidence source to the set.

[0135] In the above embodiment, in step S102, the basic probability distribution function of each evidence source is determined based on the identification framework, which can be combined with Figure 3 Further understanding. Figure 3 This is a flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided by an embodiment of the present application. Figure 3 As shown, the method may include the following steps:

[0136] S301, obtaining multiple evidence sources of measurement points in the coal mine monitoring system and an identification framework for compliance analysis of the measurement points, the identification framework including multiple candidate compliance analysis results of the measurement points.

[0137] For the relevant content of step S301, please refer to the above embodiment and will not be repeated here.

[0138] S302: Based on the second evidence source, identify whether the monitoring information of the first measuring point is abnormal.

[0139] Optionally, based on the second source of evidence, identifying whether the monitoring information of the first measuring point is abnormal includes obtaining a first difference between the current monitoring information of the first measuring point and the historical monitoring information of the first measuring point, obtaining the standard deviation and variance of the current monitoring information of the first measuring point and the historical monitoring information of the first measuring point, obtaining a second difference between the current monitoring information of the first measuring point and the average value of the current monitoring information of multiple second measuring points, and determining that the monitoring information of the first measuring point is abnormal in response to the first measuring point satisfying at least one condition of the current monitoring information of the first measuring point being not in the set interval, the first difference being greater than the second set threshold, the standard deviation being greater than the third set threshold, the variance being greater than the fourth set threshold, and the second difference being greater than the fifth set threshold.

[0140] Alternatively, in response to the first measuring point satisfying each of the conditions that the current monitoring information of the first measuring point is within the set range, the first difference is less than or equal to the second set threshold, the standard deviation is less than or equal to the third set threshold, the variance is less than or equal to the fourth set threshold, and the second difference is less than or equal to the fifth set threshold, it is determined that the monitoring information of the first measuring point is not abnormal.

[0141] S303 : Determine a basic probability distribution function of the second evidence source based on the recognition framework and the anomaly recognition result of the monitoring information of the first measurement point.

[0142] For example, the recognition framework θ is as follows:

[0143] θ={Compliant recommended, Compliant not recommended, Non-compliant, Uncertain}

[0144] Let’s continue with measuring point 1 as an example.

[0145] If the monitoring information of measurement point 1 does not show any abnormality, the basic probability distribution function of the second evidence source corresponding to measurement point 1 is as follows:

[0146] m2({Compliance recommended})=0.9, m2({Compliance not recommended})=0, m2({Non-compliance})=0, m2({Uncertain})=0.1

[0147] If the monitoring information of measurement point 1 is abnormal, the basic probability distribution function of the second evidence source corresponding to measurement point 1 is as follows:

[0148] m2({Compliance recommended})=0.1, m2({Compliance not recommended})=0.7, m2({Non-compliance})=0.2, m2({Uncertain})=0

[0149] S304: Fusing the basic probability distribution functions of the evidence sources to obtain a fused basic probability distribution function.

[0150] S305 , based on the fused basic probability distribution function, determining the target compliance analysis result of the measurement point from the recognition framework.

[0151] For the relevant contents of steps S304-S305, please refer to the above embodiment and will not be repeated here.

[0152] In summary, according to the coal mine monitoring system measurement point compliance analysis method of the embodiments of the present application, based on a second source of evidence, the presence of an anomaly in the monitoring information of the first measurement point is identified, and a basic probability distribution function for the second source of evidence is determined based on the identification framework and the anomaly identification results of the monitoring information of the first measurement point. Thus, when the second source of evidence includes at least one of the following: current monitoring information of the first measurement point, current monitoring information of a second measurement point in the area to which the first measurement point belongs, and historical monitoring information of the first measurement point, the second source of evidence can be considered to identify whether an anomaly exists in the monitoring information of the first measurement point in order to determine the basic probability distribution function of the second source of evidence. Specifically, the basic probability distribution of the second source of evidence to the set can be determined by considering the anomaly identification results of the monitoring information of the first measurement point.

[0153] In the above embodiment, in step S102, the basic probability distribution function of each evidence source is determined based on the identification framework, which can be combined with Figure 4 Further understanding. Figure 4 This is a flow chart of another method for analyzing compliance of measurement points in a coal mine monitoring system provided by an embodiment of the present application. Figure 4 As shown, the method may include the following steps:

[0154] S401, obtaining multiple evidence sources of measurement points in the coal mine monitoring system and an identification framework for compliance analysis of the measurement points, the identification framework including multiple candidate compliance analysis results of the measurement points.

[0155] For the relevant content of step S401, please refer to the above embodiment and will not be repeated here.

[0156] S402: Based on the third evidence source, identify whether the target monitoring device complies with the target installation rules.

[0157] Optionally, the target installation rule corresponds to at least one of the area to which the first measuring point belongs, the target monitoring equipment, and a coal mine operation link of the first measuring point, wherein the coal mine operation link may include blasting operation, tunneling operation, coal mining operation, etc.

[0158] Optionally, step S402 can be implemented using Web GIS (Web Geographic Information System) spatial analysis technology. Web GIS is a GIS application model based on Internet technology. It allows users to access and use geographic information data and services through a web browser, allowing users to perform operations such as geographic data analysis, map creation, and spatial data visualization without installing specialized GIS software.

[0159] In this application, based on the third evidence source, identifying whether the target monitoring device complies with the target installation rule includes at least one of the following operations:

[0160] Operation 1: Identify whether the actual installation location complies with the target installation location rule to identify whether the target monitoring device complies with the target installation location rule.

[0161] It should be noted that there are no excessive restrictions on the installation location rules. For example, at least one of the following rules may be included:

[0162] The ground sound monitoring of the mining tunnel is generally 30-200m ahead of the working face, and the excavation tunnel is generally 20-120m behind the working face;

[0163] The distance between the two sides of the drilling hole for drilling cuttings monitoring should be 10-30m, and the number of drilling holes should not be less than 3;

[0164] At the excavation working face, the number and location of gas concentration sensors and carbon monoxide concentration sensors should be reasonably arranged according to the tunnel length and excavation speed.

[0165] For example, when the target installation position rule includes that the distance between the gas concentration sensor and the coal mining face is less than the set distance, the actual distance between the target monitoring equipment and the coal mining face is obtained based on the actual installation position. In response to the actual distance being greater than the set distance, it is determined that the target monitoring equipment does not meet the target installation position rule.

[0166] Alternatively, in response to the actual distance being less than or equal to the set distance, it is determined that the target monitoring device meets the target installation location rule.

[0167] Operation 2: Identify whether the actual installation density complies with the target installation density rule of the target monitoring device in the area to which the first measuring point belongs, so as to identify whether the target monitoring device complies with the target installation density rule.

[0168] Optionally, identifying whether the actual installation density complies with the target installation density rule for the area to which the target monitoring device belongs at the first measuring point includes, in a case where the target installation density rule includes a target installation density interval for the area to which the target monitoring device belongs at the first measuring point, in response to the actual installation density not being within the target installation density interval, determining that the target monitoring device does not comply with the target installation density rule.

[0169] Alternatively, in response to the actual installation density being within the target installation density interval, it is determined that the target monitoring device complies with the target installation density rule.

[0170] Operation 3: Identify whether there are factors affecting the monitoring results of the target monitoring device in the actual monitoring area. In response to the existence of influencing factors in the actual monitoring area, determine that the target monitoring device does not comply with the target first installation rule. In response to the absence of influencing factors in the actual monitoring area, determine that the target monitoring device complies with the target first installation rule.

[0171] It should be noted that there are no excessive restrictions on influencing factors, which may include, for example, electromagnetic interference sources generated by large electromechanical equipment, high-temperature heat sources, etc. The factors affecting the monitoring results of different monitoring devices may be different.

[0172] Operation 4: Determine the target monitoring area of ​​the target monitoring device, identify whether the actual monitoring area covers the target monitoring area, and in response to the actual monitoring area not covering the target monitoring area, determine that the target monitoring device does not comply with the target second installation rule; in response to the actual monitoring area covering the target monitoring area, determine that the target monitoring device complies with the target second installation rule.

[0173] It should be noted that the target monitoring areas of different monitoring equipment may be different, and there are no excessive restrictions on the target monitoring areas. For example, they may include areas where the roof is prone to collapse, areas of stress concentration, etc.

[0174] S403: Determine a basic probability distribution function of the third evidence source based on the recognition framework and the rule recognition results of the target monitoring device.

[0175] For example, the recognition framework θ is as follows:

[0176] θ={Compliant recommended, Compliant not recommended, Non-compliant, Uncertain}

[0177] Let’s continue with measuring point 1 as an example.

[0178] If gas concentration sensor 1 meets all target installation rules, the basic probability distribution function of the third evidence source corresponding to measurement point 1 is as follows:

[0179] m3({Compliance recommended})=0.9, m3({Compliance not recommended})=0, m3({Non-compliance})=0, m3({Uncertain})=0.1

[0180] If gas concentration sensor 1 does not meet at least one target installation rule, the basic probability distribution function of the third evidence source corresponding to measurement point 1 is as follows:

[0181] m3({Compliance recommended})=0, m3({Compliance not recommended})=0.8, m3({Non-compliance})=0, m3({Uncertain})=0.2

[0182] S404: Fusing the basic probability distribution functions of the evidence sources to obtain a fused basic probability distribution function.

[0183] S405 , based on the fused basic probability distribution function, determining the target compliance analysis result of the measurement point from the recognition framework.

[0184] For the relevant contents of steps S404-S405, please refer to the above embodiment and will not be repeated here.

[0185] In summary, according to the coal mine monitoring system measurement point compliance analysis method of the embodiment of the present application, based on the third evidence source, whether the target monitoring device complies with the target installation rules is identified, and based on the identification framework and the rule identification results of the target monitoring device, the basic probability distribution function of the third evidence source is determined. Therefore, in the case where the third evidence source includes at least one piece of information among the actual installation location of the target monitoring device, the actual monitoring area, and the actual installation density in the area to which the first measuring point belongs, the third evidence source can be taken into account to identify whether the target monitoring device complies with the target installation rules to determine the basic probability distribution function of the third evidence source, that is, the rule identification results of the target monitoring device are taken into account to determine the basic probability distribution of the third evidence source to the set.

[0186] For ease of understanding, an exemplary embodiment is provided:

[0187] Step 1: Measurement point data collection and preprocessing

[0188] Multiple measurement points for gas concentration, carbon monoxide concentration, temperature, and other parameters are placed at different locations underground in the coal mine. The monitoring and early warning system collects data in real time. The collected data from the measurement points is cleaned to remove abnormal values ​​that clearly exceed the normal range.

[0189] Step 2: Establish a monitoring system measurement point information rule base

[0190] Step 2.1: Information extraction

[0191] 1. Based on relevant notices, standards, and other documents, extract measurement point installation rules for different types of sensors, such as gas sensors, carbon monoxide sensors, temperature sensors, and stress sensors. For example, ground sound monitoring in mining tunnels is generally 30-200 meters ahead of the working face, while tunneling tunnels are generally 20-120 meters behind the working face. For drill cuttings monitoring in mining tunnels, the spacing between drill holes on both sides should be 10-30 meters, and the number of drill holes should not be less than three. Measurement point installation rules refer to the requirements of regulations and other regulations for the installation of a certain number of sensors and sensor types at specific locations, including the location rules and spatial relationship rules mentioned below.

[0192] 2. Collect parameter requirements for various sensors, including measurement range, accuracy, and response time. For example, a gas sensor should typically have a measurement range of 0-4% CH₄ and an accuracy of at least ±0.1% CH₄. Mining stress sensors must have a self-storage function, with a minimum storage time of 8 hours in the event of data upload interruptions. The maximum sampling interval should be no longer than 2 minutes. The stress sensor's range should be no less than 30 MPa, and the measurement error should be no greater than 0.1 MPa.

[0193] 3. Organize monitoring plan layout requirements for different mining areas (such as coal mining faces, tunneling faces, return air lanes, and intake air lanes) and different operational links (such as blasting, tunneling, and coal mining). For example, in tunneling faces, the number and location of gas and carbon monoxide sensors should be appropriately arranged based on the length of the tunnel and the tunneling speed. Rules can be summarized for specific areas and scenarios. For example, a temporary full-range methane sensor should be added within 20 meters of the blasting site, and blasting is strictly prohibited if the methane level exceeds 1.0%.

[0194] Step 2.2: Information Structuring

[0195] Use database technology to structure the storage of the extracted information, selecting an appropriate database management system. Categorize and encode the information, classify sensor types into different categories, define detailed fields for information such as installation location and parameter range, and establish a database table containing fields such as installation location, measurement range, accuracy, and response time.

[0196] Format various rules so that they can be stored in the database in a clear and accurate manner. For example, installation location rules can be stored as coordinate ranges or relative position relationships; sensor parameter ranges can be stored as numerical intervals.

[0197] Through the above processing, a monitoring system measurement point information rule library with clear structure and complete content is established, providing an accurate basis for subsequent compliance analysis.

[0198] Step 3: Compliance analysis based on Web GIS spatial analysis technology

[0199] Step 3.1: Digitize and 3D model the tunnels, working faces, and chambers in the mine. During the modeling process, construct a 3D underground scene based on actual measurement data, including a 3D model of the tunnels, the location of the working faces, and the spatial layout of various equipment.

[0200] Step 3.2: Install location information import

[0201] The coal mine disaster monitoring system's data acquisition interface captures the actual installation location information of various sensors and systems. This information is typically expressed in coordinate format. This acquired location information is converted to a format compatible with the Web GIS system. This converted location information is then imported into the Web GIS system and overlaid with the constructed 3D geographic information model. The Web GIS interface visually displays the specific underground locations of sensors and systems, as well as their spatial relationship to the surrounding environment.

[0202] Step 3.3: GIS Compliance Analysis

[0203] Based on the location rules and spatial relationship rules in the monitoring system's measurement point information rule library, compliance analysis is performed using the spatial analysis tools of Web GIS, including distance analysis, sensor performance analysis, buffer analysis, viewshed analysis, density analysis, etc.

[0204] Distance analysis: For example, determining whether the distance between a gas sensor and the coal mining face meets regulatory requirements. Using WebGIS's distance measurement tools, the distance between the sensor and the coal mining face boundary is calculated and compared to the distance threshold specified in the rule library. If the distance exceeds the threshold, the sensor's installation location is deemed unsuitable.

[0205] Sensor performance analysis: For example, the sensors installed in the intake and return air lanes of the coal mining face in a prominent mine must be full-range or high and low concentration methane sensors.

[0206] Buffer zone analysis: Buffer zones are set for different sensor types based on their monitoring range and impact radius. A buffer zone is generated around each sensor based on its effective monitoring distance. This buffer zone is analyzed for potential interference factors, such as electromagnetic interference from large electromechanical equipment and high-temperature heat sources. The presence of such interference factors within the buffer zone can cause sensor data anomalies. Buffer zone analysis helps identify potential interference sources and ensures the reliability of monitoring data.

[0207] Field of view analysis: Determines the sensor's field of view in the underground environment—the area directly observable from the sensor's position. When monitoring hazards such as roof pressure and rock burst, the sensor's field of view is analyzed to ensure it covers key monitoring areas, such as areas prone to roof collapse and areas of stress concentration. Blind spots in the field of view can lead to the omission of important disaster precursor information. Field of view analysis allows for the optimization of sensor installation locations and monitoring angles, ensuring comprehensive and effective monitoring of underground disaster hazards.

[0208] Density Analysis: In different underground areas, such as the coal mining face, tunneling face, return air lane, and intake air lane, appropriate density reference values ​​for different sensor types are determined based on the risk of disasters and the importance of the area, combined with the monitoring system's measurement point information rule base. GIS technology is used to visualize the distribution of installed sensors and calculate density. By dividing the underground space into grid cells, the number of sensors in each cell is counted and compared to a preset reasonable density standard.

[0209] Step 4: Compliance analysis of measurement points based on evidence theory

[0210] Step 4.1: Data integration and preprocessing

[0211] Summarize the cleaned measurement point data from step 1, the rule base information established in step 2, and the GIS spatial analysis results (including distance analysis, sensor performance analysis, etc.) obtained in step 3. Associate and integrate this data by measurement point to ensure that all types of data information at each measurement point are complete and accurately matched.

[0212] For each measuring point, a data subset closely related to compliance judgment is screened out, such as the comparison data between the actual installation location data of the sensor and the installation location requirements in the rule library, the deviation data between the real-time data collected by the sensor and the specified parameter range, and the association data with surrounding environmental elements in GIS spatial analysis.

[0213] Step 4.2: Reliability Information Fusion

[0214] According to the basic principles of evidence theory, for each type of sensor, its reliability evidence body is determined in combination with the reliability data collected by it.

[0215] The identification framework θ is the set of all possible results. For the compliance analysis of coal mine measurement points, θ = {compliance recommended, compliance not recommended, non-compliance, uncertain}.

[0216] Basic probability distribution function m:2 θ →[0,1], satisfying Where m(A) represents the degree of belief in proposition A, and A is a subset of θ.

[0217] The trust function Bel(A) represents the degree of complete trust in A:

[0218]

[0219] The likelihood function Pl(A) represents the degree of belief that A is not denied (the degree of belief that proposition A is not false):

[0220]

[0221] When there are multiple pieces of evidence, Dempster's synthesis rule is used for fusion. Assume there are two basic probability distribution functions m1 and m2. The synthesized basic probability distribution function m is:

[0222]

[0223] in, is the conflict coefficient.

[0224] Step 4.3: Evidence source selection and basic probability distribution function determination

[0225] 1. Sensor hardware evidence source: Determine whether the sensor is functioning properly by monitoring hardware indicators such as sensor fault records and hardware self-test information. If a sensor issues a hardware fault alarm or detects signs of hardware aging, adjust the BPA accordingly.

[0226] If the sensor hardware is fault-free, the BPA distribution is: m1({Compliance Recommendation}) = 0.9 (the hardware is normal, strongly supporting compliance), m1({Uncertain}) = 0.1 (retaining 10% uncertainty because normal hardware does not directly equate to compliance of all indicators).

[0227] BPA adjustment during hardware failure alarm: m1({non-compliant}) = 0.7 (70% confidence level determines it is non-compliant, and the monitoring failure is directly caused by the hardware failure), m1({compliant but not recommended}) = 0.2 (20% confidence level determines it is compliant but not recommended, with a slight possibility that it can be repaired due to a temporary fault), m1({uncertain}) = 0.1 (10% uncertainty, such as a reserved attitude when the cause of the fault is unclear), m1({compliant recommended}) = 0 (the possibility of compliance recommendation is completely excluded in the event of hardware failure).

[0228] 2. Data comparison evidence source: Compare the measurement point data with data from other measurement points in the same area, historical data, and the monitoring system measurement point information rule base in step 2. If the data is stable and within the specified parameter range, this means that the data comparison is good and the measurement point is likely to be compliant.

[0229] If the real-time data is within the rule base parameter range and is stable, preset m2({Compliance Recommendation})=0.9 and m2({Uncertain})=0.1.

[0230] For example, if the real-time concentration of a gas sensor at a certain tunneling working face is 1.4% CH4 (the regulations allow 0-1.5% CH4), and the data is close to the alarm threshold for a long time, although it does not exceed the standard, it deviates from the regional average, which may indicate sensor accuracy drift or abnormalities in the monitoring area. In this case, the BPA adjustment is: m2({Compliance not recommended}) = 0.7 (abnormal data fluctuations indicate potential risks), m2({Non-compliance}) = 0.2 (if it subsequently exceeds 1.5%, it will be directly non-compliant, and some trust will be reserved in advance), and m2({Compliance recommended}) = 0.1 (currently still within the allowable range, retaining the minimum trust).

[0231] 3. Spatial analysis evidence source: Based on the spatial analysis results of step 3, if the location and performance of the sensor meet the various spatial requirements (such as distance, density, etc.) in the spatial analysis, it is considered that the measurement point is likely to be compliant. When the spatial location does not comply with the regulations, there are obvious spatial interference factors, or the performance is affected by the spatial layout, the BPA is readjusted.

[0232] The sensor installation distance is within the regulatory threshold and there are no interference sources. The preset values ​​are m3({Compliance Recommendation}) = 0.9 and m3({Uncertain}) = 0.1.

[0233] When the return air lane length of the coal mining face in high-gas and coal-gas outburst mines is greater than 1000m, a methane sensor is added in the middle of the return air lane. If the number of methane sensors installed in a return air lane meets the regulations, but their installation locations are a certain distance away from the middle of the return air lane, the BPA adjustment is: m3({Compliance not recommended}) = 0.8 (the spatial layout needs to be optimized, which affects the comprehensiveness of monitoring), m3({Uncertain}) = 0.2 (no warning abnormality has been caused yet, and uncertainty is retained).

[0234] Step 4.5: Compliance Judgment and Decision

[0235] The Dempster synthesis rule is used to fuse the basic probability distribution functions of the above evidence sources in turn. First, the conflict coefficient K is calculated, and then the fused basic probability distribution function m is calculated according to the synthesis rule.

[0236] The compliance of the measurement point is determined based on the fused basic probability distribution function. Compare m({Compliant Recommended}), m({Compliant Not Recommended}), m({Not Compliant}), and m({Uncertain}). If the maximum value among m({Compliant Recommended}) or m({Compliant Not Recommended}) is greater than the set threshold, the measurement point data is considered valid.

[0237] Therefore, the present application provides a method for analyzing compliance of measurement points in a coal mine disaster monitoring and early warning system, which can bring the following technical effects:

[0238] (1) Improve monitoring efficiency and accuracy: This invention can quickly process large amounts of complex monitoring data through automated data collection and preprocessing processes. Compared with traditional manual inspection methods, it greatly shortens data processing time and improves the timeliness and accuracy of data acquisition. After establishing a monitoring system measurement point information rule base, measurement point compliance analysis based on Web GIS spatial analysis technology and evidence theory can accurately perform multi-dimensional analysis on each measurement point, comprehensively and deeply troubleshoot potential problems, ensure the reliability of the monitoring system, and thus provide more accurate data support for disaster warnings, effectively avoiding warning errors caused by data errors or omissions.

[0239] (2) Enhanced system adaptability and stability: By systematically organizing and structurally storing the installation rules and parameter requirements for different types of sensors, as well as the monitoring plan layout requirements for different mining areas and operating links, and combining them with digital modeling and spatial analysis of the actual underground environment, the present invention enables the monitoring system to better adapt to the complex and changing underground coal mine working conditions. Whether it is adjusting the sensor position, connecting new equipment, or changing the mining area, the system can respond quickly to ensure the comprehensiveness and effectiveness of monitoring, enhance the adaptability and stability of the entire disaster monitoring and early warning system, and provide more reliable protection for coal mine safety production.

[0240] (3) Effectively reduce labor costs and labor intensity: The traditional method of relying on manual inspections to determine measurement point failures is inefficient, labor-intensive, and prone to omissions. This invention realizes automated measurement point effectiveness analysis, greatly reducing dependence on manual labor. It frees coal mine workers from tedious inspection work and allows them to focus on more valuable data analysis and decision-making, significantly reducing labor costs and labor intensity, and improving the overall efficiency of coal mine safety management.

[0241] In order to implement the above embodiment, the present application also proposes a coal mine monitoring system measurement point compliance analysis device.

[0242] Figure 5 This is a structural diagram of a coal mine monitoring system measurement point compliance analysis device provided in an embodiment of the present application.

[0243] like Figure 5 As shown, the coal mine monitoring system measurement point compliance analysis device 100 includes: an acquisition module 110 , a first determination module 120 , a fusion module 130 and a second determination module 140 .

[0244] An acquisition module 110 is configured to acquire multiple evidence sources of a measuring point in a coal mine monitoring system, and an identification framework for performing compliance analysis on the measuring point, the identification framework including multiple candidate compliance analysis results for the measuring point;

[0245] A first determination module 120 is configured to determine a basic probability distribution function for each evidence source based on the identification framework;

[0246] A fusion module 130 is used to fuse the basic probability distribution functions of each evidence source to obtain a fused basic probability distribution function;

[0247] The second determination module 140 is configured to determine the target compliance analysis result of the measurement point from the recognition framework based on the fused basic probability distribution function.

[0248] Furthermore, in a possible implementation of the embodiment of the present application, the acquisition module 110 is further configured to perform at least one of the following operations:

[0249] Obtaining operation information, health status information, maintenance information, and actual device parameters of a target monitoring device as a first source of evidence for a first measuring point, where the target monitoring device is a monitoring device required to be installed at the first measuring point;

[0250] Obtaining current monitoring information of the first measuring point, current monitoring information of a second measuring point in the area to which the first measuring point belongs, and historical monitoring information of the first measuring point as a second source of evidence for the first measuring point, wherein the type of monitoring equipment required to be installed at the second measuring point is the same as the type of monitoring equipment required to be installed at the first measuring point;

[0251] At least one piece of information of the actual installation location, the actual monitoring area, and the actual installation density of the target monitoring device in the area to which the first measuring point belongs is obtained as a third evidence source of the first measuring point.

[0252] Furthermore, in a possible implementation of an embodiment of the present application, the first determination module 120 is also used to: identify whether the target monitoring device has a fault based on the operating information, the health status information and the maintenance information; identify whether the actual device parameters comply with the target device parameter rules; and determine the basic probability distribution function of the first evidence source based on the identification framework, the fault identification results of the target monitoring device and the rule identification results of the actual device parameters.

[0253] Furthermore, in a possible implementation of an embodiment of the present application, the first determination module 120 is also used to: identify whether the monitoring information of the first measuring point is abnormal based on the second evidence source; and determine the basic probability distribution function of the second evidence source based on the identification framework and the abnormality identification result of the monitoring information of the first measuring point.

[0254] Furthermore, in a possible implementation of an embodiment of the present application, the first determination module 120 is also used to: identify whether the target monitoring device complies with the target installation rules based on the third evidence source; and determine the basic probability distribution function of the third evidence source based on the identification framework and the rule identification results of the target monitoring device.

[0255] Furthermore, in a possible implementation of the embodiment of the present application, the target installation rule has a corresponding relationship with at least one piece of information in the area to which the first measuring point belongs, the target monitoring equipment, and the coal mine operation link of the first measuring point.

[0256] Furthermore, in a possible implementation of the embodiment of the present application, the first determining module 120 is further configured to perform at least one of the following operations:

[0257] Identifying whether the actual installation location complies with a target installation location rule, so as to identify whether the target monitoring device complies with the target installation location rule;

[0258] Identify whether the actual installation density complies with a target installation density rule for the target monitoring device in the area to which the first measuring point belongs, so as to identify whether the target monitoring device complies with the target installation density rule.

[0259] Furthermore, in a possible implementation of an embodiment of the present application, the first determination module 120 is also used to: identify whether there are factors affecting the monitoring results of the target monitoring device in the actual monitoring area; in response to the presence of the influencing factors in the actual monitoring area, determine that the target monitoring device does not comply with the target first installation rule; in response to the absence of the influencing factors in the actual monitoring area, determine that the target monitoring device complies with the target first installation rule.

[0260] Furthermore, in a possible implementation of an embodiment of the present application, the first determination module 120 is also used to: determine the target monitoring area of ​​the target monitoring device; identify whether the actual monitoring area covers the target monitoring area; in response to the actual monitoring area not covering the target monitoring area, determine that the target monitoring device does not comply with the target second installation rule; in response to the actual monitoring area covering the target monitoring area, determine that the target monitoring device complies with the target second installation rule.

[0261] It should be noted that the above explanation of the embodiment of the method for analyzing compliance of measurement points in a coal mine monitoring system is also applicable to the device for analyzing compliance of measurement points in a coal mine monitoring system of this embodiment, and will not be repeated here.

[0262] In summary, the coal mine monitoring system measurement point compliance analysis device of the embodiment of the present application obtains multiple evidence sources of the measurement points in the coal mine monitoring system, and an identification framework for compliance analysis of the measurement points. The identification framework includes multiple candidate compliance analysis results of the measurement points. Based on the identification framework, the basic probability distribution function of each evidence source is determined, and the basic probability distribution function of each evidence source is fused to obtain the fused basic probability distribution function. Based on the fused basic probability distribution function, the target compliance analysis result of the measurement point is determined from the identification framework. Therefore, the compliance analysis of the measurement points of the coal mine monitoring system can be performed based on evidence theory, and each measurement point can be accurately analyzed in multiple dimensions, and potential problems can be comprehensively and deeply checked to ensure the reliability of the coal mine monitoring system, thereby providing more accurate data support for disaster warning, effectively avoiding warning errors caused by data errors or omissions, and improving the accuracy of disaster warning. In addition, automated measurement point compliance analysis is achieved, which greatly reduces dependence on manual labor, significantly reduces labor costs and labor intensity, and improves the efficiency of measurement point compliance analysis.

[0263] In order to implement the above-mentioned embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the coal mine monitoring system measurement point compliance analysis method provided in the above-mentioned embodiments.

[0264] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, which stores computer execution instructions. When the computer execution instructions are executed by a processor, they are used to implement the coal mine monitoring system measurement point compliance analysis method provided in the above embodiments.

[0265] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements the coal mine monitoring system measurement point compliance analysis method provided in the above embodiments.

[0266] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0267] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0268] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0269] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0270] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed to control or imply relative importance or implicitly indicate the number of technical features being controlled. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0271] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0272] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0273] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0274] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0275] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0276] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for analyzing compliance of measurement points in a coal mine monitoring system, characterized in that: include: Acquire multiple evidence sources for measurement points in a coal mine monitoring system, and an identification framework for compliance analysis of the measurement points, the identification framework including multiple candidate compliance analysis results for the measurement points; Determining a basic probability distribution function for each evidence source based on the identification framework; The basic probability distribution functions of each evidence source are integrated to obtain the integrated basic probability distribution function; Based on the fused basic probability distribution function, the target compliance analysis result of the measurement point is determined from the recognition framework.

2. The method according to claim 1, characterized in that The step of obtaining multiple evidence sources of measurement points in the coal mine monitoring system includes at least one of the following operations: Obtaining operation information, health status information, maintenance information, and actual device parameters of a target monitoring device as a first source of evidence for a first measuring point, where the target monitoring device is a monitoring device required to be installed at the first measuring point; Obtaining current monitoring information of the first measuring point, current monitoring information of a second measuring point in the area to which the first measuring point belongs, and historical monitoring information of the first measuring point as a second source of evidence for the first measuring point, wherein the type of monitoring equipment required to be installed at the second measuring point is the same as the type of monitoring equipment required to be installed at the first measuring point; At least one piece of information of the actual installation location, the actual monitoring area, and the actual installation density of the target monitoring device in the area to which the first measuring point belongs is obtained as a third evidence source of the first measuring point.

3. The method according to claim 2, characterized in that Determining the basic probability distribution function of each evidence source based on the identification framework includes: identifying whether the target monitoring device has a fault based on the operation information, the health status information, and the maintenance information; Identifying whether the actual device parameters comply with target device parameter rules; A basic probability distribution function of the first evidence source is determined based on the identification framework, the fault identification result of the target monitoring device, and the rule identification result of the actual device parameters.

4. The method according to claim 2, characterized in that Determining the basic probability distribution function of each evidence source based on the identification framework includes: Based on the second evidence source, identifying whether the monitoring information of the first measuring point is abnormal; A basic probability distribution function of the second evidence source is determined based on the recognition framework and an anomaly recognition result of the monitoring information of the first measurement point.

5. The method according to claim 2, characterized in that Determining the basic probability distribution function of each evidence source based on the identification framework includes: identifying, based on the third evidence source, whether the target monitoring device complies with a target installation rule; Based on the recognition framework and the rule recognition result of the target monitoring device, a basic probability distribution function of the third evidence source is determined.

6. The method according to claim 5, characterized in that The target installation rule has a corresponding relationship with at least one piece of information in the area to which the first measuring point belongs, the target monitoring equipment, and the coal mine operation link of the first measuring point.

7. The method according to claim 5, characterized in that The identifying, based on the third evidence source, whether the target monitoring device complies with the target installation rule comprises at least one of the following operations: Identifying whether the actual installation location complies with a target installation location rule, so as to identify whether the target monitoring device complies with the target installation location rule; Identify whether the actual installation density complies with a target installation density rule for the target monitoring device in the area to which the first measuring point belongs, so as to identify whether the target monitoring device complies with the target installation density rule.

8. The method according to claim 5, characterized in that The identifying, based on the third evidence source, whether the target monitoring device complies with a target installation rule includes: Identify whether there are factors affecting the monitoring results of the target monitoring device in the actual monitoring area; In response to the presence of the influencing factor in the actual monitoring area, determining that the target monitoring device does not comply with a target first installation rule; In response to the influencing factor not existing in the actual monitoring area, it is determined that the target monitoring device meets the target first installation rule.

9. The method according to claim 5, characterized in that The identifying, based on the third evidence source, whether the target monitoring device complies with a target installation rule includes: determining a target monitoring area of ​​the target monitoring device; Identifying whether the actual monitoring area covers the target monitoring area; In response to the actual monitoring area not covering the target monitoring area, determining that the target monitoring device does not comply with a target second installation rule; In response to the actual monitoring area covering the target monitoring area, it is determined that the target monitoring device complies with the target second installation rule.

10. A coal mine monitoring system measurement point compliance analysis device, characterized in that: include: an acquisition module, configured to acquire multiple evidence sources of a measuring point in a coal mine monitoring system, and an identification framework for compliance analysis of the measuring point, the identification framework including multiple candidate compliance analysis results of the measuring point; A first determination module is configured to determine a basic probability distribution function for each evidence source based on the identification framework; A fusion module is used to fuse the basic probability distribution functions of each evidence source to obtain a fused basic probability distribution function; The second determination module is configured to determine the target compliance analysis result of the measurement point from the recognition framework based on the fused basic probability distribution function.

11. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 9 when the computer program is executed by a processor.

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