Automatic grading early warning method and system for underground operation safety

Abstract

The invention discloses an automatic grading early warning method and system for underground operation safety. The method comprises the following steps that 1, physical sign parameters and underground environment parameters of a tested person are collected; step 2, establishing an early warning level of personnel signs and an early warning level of environmental parameters; step 3, performing dynamic fusion analysis on the early warning level of the personnel signs and the early warning level of the environmental parameters according to the collected parameter data to obtain a final early warning level; and 4, taking corresponding early warning issuing measures according to the final early warning grade. According to the method, the weight can be calculated according to the deviation degree of each index, each index corresponds to one weight, the accuracy is higher, interference of other grades is avoided, subjective influences are reduced, and the method is more suitable for analysis of various types of indexes.

Description

Technical Field

[0001] The present invention relates to the technical field of underground operation safety monitoring, and more specifically, to an automatic graded early warning method and system for underground operation safety. Background Art

[0002] With the continuous innovation and on-site application of coal mine technology and equipment, the mechanization, automation and intelligence level of underground coal mine production has been continuously improved, which has significantly improved the underground production conditions of coal mines, and the working environment and safety protection equipment of operators have been significantly improved. However, mining operations still face the situation of complex mining environment, objective dangers in production links, and high work intensity of personnel. Underground workers are more likely to have abnormal physiological signs, and the supervisors above the mine cannot know the physical signs of the workers in time. Once the workers have health problems, it is very easy to cause production safety accidents.

[0003] At present, the coal mine risk warning system focuses on the monitoring of non-human factors underground, such as coal mine gas and dust, but does not monitor human-related factors, and does not involve real-time monitoring of personnel's physical health and the environment in which they are located. Existing vital signs monitoring and safety warning mainly rely on the data of underground monitoring systems and fixed-point video feedback for safety warnings. This method has monitoring blind spots, and some warning behaviors require manual identification, low intelligence, and insufficient response time, which poses safety hazards. Existing risk analysis and warnings are usually separate analyses of personnel vital signs and environmental monitoring, lacking unified management of personnel vital signs and environmental monitoring related information data, and comprehensive grasp of underground conditions for accurate fusion analysis and warning.

[0004] The health monitoring of coal mine personnel involves aspects such as blood pressure, blood oxygen, body temperature, and heart rate, while environmental monitoring involves aspects such as O2, CO, CH4, and dust. A single warning value is used for each aspect, and only two states, normal and abnormal, are provided. It is impossible to determine the warning degree of personnel and environment in real time, and it is impossible to accurately and efficiently notify specific stakeholders of the corresponding abnormalities. For example, body temperature includes normal and abnormal. Abnormal body temperature includes low fever, moderate fever, and high fever. Among the three different degrees of fever, if only abnormal body temperature is used for warning, it is difficult to define the treatment measures. Treating low fever as high fever will waste rescue resources, and treating high fever as low fever will delay the treatment of personnel. Therefore, for unilateral warnings, at least three warning ranges need to be defined. Considering the mutual influence between the warning ranges of personnel health monitoring and environmental aspects, at least one warning range needs to be defined.

[0005] In addition, the existing early warning systems basically rely on the platform to issue prompts, and then the supervisors intervene. For specific early warning data, it is entirely based on the subjective judgment of the supervisors, lacking a basis for judgment, and unable to achieve standardization and intelligence. There are few similar hierarchical model frameworks in the existing mining early warning systems, and the monitoring projects do not involve the integration of multiple indicators such as people and the environment.

[0006] The existing method of fusing multiple types of indicators usually requires calculating the correlation between the indicators, such as using gray correlation analysis to calculate the correlation. The existing method has problems such as high requirements on data quality, susceptibility to subjective influence of experts, and susceptibility to interference from other classifications. When faced with multiple types of indicators, it is difficult to achieve good analysis through the existing method. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides an automatic warning method for underground personnel vital signs and environmental monitoring based on graded warning, which can solve the technical problems in the prior art of single warning value, fragmented data information, and lack of basis for warning classification.

[0008] The present invention adopts the following technical solution.

[0009] An automatic graded early warning method for underground operation safety comprises the following steps:

[0010] Step 1, collecting the physical parameters of the person being tested and the underground environment parameters;

[0011] Step 2, establishing the warning level of the physical sign parameters and the warning level of the underground environment parameters;

[0012] Step 3: dynamically integrate and analyze the warning level of personnel vital signs and the warning level of environmental parameters according to the collected parameter data to obtain the final warning level;

[0013] Step 4: Take corresponding warning issuance measures based on the final warning level.

[0014] Preferably, the physical sign parameters include at least: low blood pressure, high blood pressure, blood oxygen saturation, maximum heart rate, low body temperature and high body temperature of the person being measured;

[0015] The underground environmental parameters include: harmful gases, useful gases, and physical parameters. Harmful gases include methane, hydrogen sulfide, and carbon monoxide, and useful gases include oxygen and carbon dioxide. The physical parameters of the underground environment include temperature, humidity, noise, and vibration.

[0016] Preferably, the establishment of the warning level of personnel vital sign parameters and the warning level of underground environment parameters specifically includes:

[0017] Taking physical sign parameters and underground environmental parameters as evaluation indicators, an evaluation indicator grading table is established. Each evaluation indicator of the measured personnel is divided into n warning ranges, and each warning range is used as a warning level to obtain n warning levels. The warning levels are arranged from low to high according to the data of the warning range.

[0018] Preferably, the step 3 specifically includes:

[0019] Step 3-1, calculate the correlation between each evaluation index and each level of table, and construct a relationship matrix based on the correlation;

[0020] Step 3-2, transforming the relationship matrix to obtain a transformed relationship matrix;

[0021] Step 3-3, calculate the dimensionless quantity of each evaluation index and perform normalization processing to obtain the normalized dimensionless quantity, and obtain the weight vector based on the normalized dimensionless quantity:

[0022] Step 3-4, establish an evaluation model based on the transformed relationship matrix and weight vector, and determine the final grade range based on the maximum membership principle.

[0023] Preferably, the calculation of the correlation between each evaluation index and each level of table and the construction of a relationship matrix according to the correlation specifically includes:

[0024] Suppose the warning range corresponding to the jth warning level of the i-th evaluation index is (a ij , b ij ], the correlation between the i-th evaluation index and the j-th warning level is calculated using the following relationship: ij :

[0025] d ij =|a ij -x i |

[0026]

[0027] Where, d ij Represents the collected value x of the i-th evaluation index i To the lower boundary point a of the j-th level warning range ij The distance, b ij Indicates the upper boundary point of the j-th level warning range.

[0028] The relationship matrix constructed based on the correlation between each evaluation index and each level of table is as follows:

[0029]

[0030] Among them, t ijThe correlation degree of the jth level of the ith evaluation indicator, i∈[1,m], j∈[1,n], m is the number of evaluation indicators, and n is the number of warning levels.

[0031] Preferably, the transforming the relationship matrix to obtain the transformed relationship matrix specifically includes:

[0032] Transform Process the relationship matrix R to obtain the transformed relationship matrix as follows:

[0033]

[0034] Preferably, the steps 3-5 specifically include:

[0035] Calculate the dimensionless quantity w of each evaluation index i :

[0036]

[0037] Among them, x i is the collected value of the i-th evaluation index, is the average value of the warning classification standard of the i-th evaluation indicator;

[0038] For the dimensionless quantity w i After normalization, the normalized dimensionless quantity a is obtained. i :

[0039]

[0040] According to the normalized dimensionless quantity a of each evaluation index i Get the weight vector A:

[0041] A=(a 1 , a 2 , …a n )

[0042] Preferably, the evaluation model B is established as follows:

[0043] B=A·R′={b l1 , b l2 , ..., b ln}

[0044] In the formula, A is the weight vector, is the transformed relationship matrix, b lj is the correlation degree of each indicator at the jth warning level, and n is the number of warning levels.

[0045] The present invention also proposes an automatic graded early warning system for underground operation safety, which is used to implement the automatic graded early warning method for underground operation safety, and includes: a positioning module, a vital sign collection module, an environment collection module, a data transmission module, a dynamic analysis module, a graded early warning module and a monitoring platform;

[0046] Among them, the physical sign collection module and the environment collection module are used to collect the physical sign parameters of the measured personnel and the underground environment parameters as evaluation indicators respectively;

[0047] The data transmission module is used to send the collected evaluation index data to the dynamic analysis module;

[0048] The dynamic analysis module is used to establish the warning level of personnel vital signs and the warning level of environmental parameters;

[0049] The graded warning module dynamically integrates and analyzes the warning levels of personnel vital signs and environmental parameters based on the data collected by the acquisition module to obtain the final warning level;

[0050] The monitoring platform is used to take corresponding warning measures based on the final warning level.

[0051] The present invention also provides a terminal, comprising a processor and a storage medium;

[0052] The storage medium is used to store instructions;

[0053] The processor is used to operate according to the instructions to execute the steps of the automatic graded early warning method for underground operation safety.

[0054] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the automatic graded early warning method for underground operation safety are implemented.

[0055] The beneficial effect of the present invention is that, compared with the prior art,

[0056] 1. In order to achieve more accurate and effective early warning monitoring, the present invention sets various evaluation indicators and divides the evaluation indicators of health monitoring and environmental monitoring into multiple early warning ranges; by establishing a hierarchical early warning system, a comprehensive early warning assessment of personnel and environment is carried out, and the risk monitoring and analysis of the working environment is refined to ensure that personnel are protected from environmental risk factors and safety accidents.

[0057] 2. The present invention integrates the evaluation indicators for analysis and early warning, calculates the weight according to the degree of deviation of each indicator, and each indicator corresponds to a weight, which has higher accuracy, avoids interference from other classifications, reduces subjective influence, and is more suitable for the analysis of multiple categories of indicators. Finally, according to the maximum subordination principle, the key indicators that affect the classification in each indicator are determined, and the real-time data of personnel's physical signs and the surrounding environment are accurately monitored. For different early warning ranges, the early warning system takes different processing measures in real time to avoid subjectivity and lag caused by human misjudgment, improve disposal efficiency and accuracy, and avoid waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the automatic graded early warning method for underground operation safety in the present invention;

[0059] Figure 2 It is a structural diagram of the automatic graded early warning system for underground operation safety in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0061] like Figure 1 As shown, the present invention proposes an automatic early warning method for underground personnel vital signs and environmental monitoring based on graded early warning, which can collect and transmit the physical sign parameters and environmental parameters of underground personnel in an all-round way in real time, automatically classify according to the source and type of information, and automatically classify according to different early warning thresholds, and automatically send the early warning information according to the information content and sending range predefined in different grades. The method specifically includes the following steps:

[0062] Step 1, collecting the physical parameters of the measured personnel and the underground environment parameters as evaluation indicators;

[0063] The present invention uses at least one of a UWB positioning module, a Zigbee positioning module, an RFID positioning module, an optical fiber positioning module, an acoustic wave positioning module, and an inertial navigation positioning module to achieve real-time positioning, tracking, and management of underground personnel.

[0064] The measured physical sign parameters of the underground personnel are collected by at least one of contact, non-contact, wearable and portable collection modules. Furthermore, the physical sign parameters of the underground personnel are selected as evaluation indicators and the indicator data are collected. The physical sign parameters of the underground personnel include: basic physiological parameters, sports health parameters, medical health parameters, and environmental adaptation parameters;

[0065] Specifically, basic physiological parameters include heart rate, body temperature, and blood pressure, and the changes in physiological parameters are acquired and recorded in real time through sensors and data acquisition units;

[0066] Sports health parameters are the movement data of the human body, including the number of steps and distance traveled. The movement data is monitored and recorded in real time through sensors such as accelerometers and gyroscopes.

[0067] Medical health parameters are physiological parameters of the human body, including blood oxygen and electrocardiogram;

[0068] Environmental adaptation parameters are used to reflect the human body's adaptation to specific environmental conditions. For example, temperature sensors are used in extreme temperature environments to monitor the human body's physiological adaptability to the environment.

[0069] Further, downhole environmental parameters are selected as evaluation indicators and indicator data are collected, and the downhole environmental parameters include gas concentration and physical parameters;

[0070] The downhole environmental parameters are collected through the environmental collection module. According to the different collection parameters, the environmental collection parameter modules can be divided into the following categories:

[0071] The gas monitoring module monitors the gas concentration in the environment, including harmful gases (such as methane, hydrogen sulfide, carbon monoxide) and useful gases (such as oxygen, carbon dioxide, etc.).

[0072] The physical parameters of the underground environment, including temperature, humidity, noise, vibration, etc., are collected through the physical parameter collection module.

[0073] Step 2, establishing the warning level of personnel physical signs and the warning level of environmental parameters according to the physical sign parameters and environmental parameters;

[0074] Specifically, step 2 also includes:

[0075] Taking physical sign parameters and underground environmental parameters as evaluation indicators, an evaluation indicator grading table is established. Each evaluation indicator of the measured personnel is divided into n warning ranges, and each warning range is used as a warning level to obtain n warning levels. The warning levels are arranged from low to high according to the data of the warning range.

[0076] As shown in Table 1 below, Table 1 is an example of an evaluation index grading table established:

[0077] Table 1: Evaluation index grading table

[0078]

[0079] Step 3: Dynamically integrate and analyze the warning level of personnel vital signs and the warning level of environmental parameters to obtain the final warning level;

[0080] The dynamic analysis module determines the final levels of vital sign parameters and environmental parameters.

[0081] Establish the evaluation factor set E.

[0082] Based on the m evaluation indicators of the monitoring project (e 1 , e 2 , e 3 ,...e m ), establish the evaluation factor set E.

[0083] E={e 1 , e 2 , e 3 ,...e m}

[0084] Wherein, m is the number of evaluation index items, such as in the example of Table 1, m=10.

[0085] Establish a warning level set L.

[0086] n warning levels based on the warning range (l 1 , l 2 , l 3 ,...l n ), establish the warning level set L.

[0087] L = {l 1 , l 2 , l 3 ,...l n}

[0088] Wherein, n is the number of warning levels. For example, in the example of Table 1, the number of warning levels is 4.

[0089] Calculate the correlation between each evaluation index and each warning level, the correlation t ij represents the correlation of the jth warning level of the i-th evaluation indicator. Suppose the i-th level warning range of the i-th evaluation indicator is (a ij , b ij ], then the correlation degree t ij The following are:

[0090] d ij =|a ij -x i |

[0091]

[0092] In the formula, represents the collected value x of the i-th evaluation index i To the lower boundary point a of the j-th level warning range ij The distance, b ij Indicates the upper boundary point of the j-th level warning range.

[0093] Establish the correlation matrix T ij .

[0094]

[0095] According to the correlation matrix T ij Get the relationship matrix R.

[0096]

[0097] Take Transformation Process the relationship matrix R to obtain the transformed relationship matrix .

[0098] Calculate the dimensionless quantity w of each evaluation index i :

[0099]

[0100] Among them, x i is the measured value of the i-th evaluation index, It is the average value of the grading standard of a certain indicator.

[0101] The average value of each indicator standard is: the average value of the critical values ​​of each warning range of the indicator. For example, for the indicator low blood pressure, the critical values ​​of its four warning ranges are 60, 90, 100, and 110, then the average value of its grading standard is: (60+90+100+110) / 4=90.

[0102] For the dimensionless quantity w i After normalization, the normalized dimensionless quantity a is obtained. i :

[0103]

[0104] According to the dimensionless quantity a i Get the weight vector A:

[0105] A=(a 1 , a 2 , …a n )

[0106] Establish evaluation model B.

[0107] B=A·R′={b l1 , b l2 , ..., b ln}

[0108] b ln is the correlation degree of each indicator at level n.

[0109] According to the maximum membership principle, the final grade range is determined. The specific method is to use the maximum value b in the evaluation model lj If it belongs to level j, the final warning level is determined to be j.

[0110] b lj =max{b l1 , b l2 , b lj …, b ln}

[0111] Step 4: Take different warning measures according to the final warning level;

[0112] 1. For the first-level warning, for abnormal values, a prompt is sent to the personal terminal of the stakeholder to indicate that the specific indicator is abnormal:

[0113] Prompts abnormal physical signs, the prompt format is person + base station + distance + abnormal physical signs + abnormal parameters.

[0114] Prompts that the environment is abnormal, and the prompt format is person + base station + distance + environmental abnormality + abnormal parameters.

[0115] 2. For the second-level warning, for abnormal values, a warning is issued to the group of stakeholders, warning them that specific indicators are abnormal, and taking treatment measures.

[0116] Prompt abnormal physical signs, the prompt format is person + base station + distance + abnormal physical signs + abnormal parameters + treatment measures. The treatment measures are: please pay attention to the team members.

[0117] Prompts that the environment is abnormal. The prompt format is personnel + base station + distance + environmental abnormality + abnormal parameters + handling measures. The handling measures are: please pay attention to the team members.

[0118] 3. For the third-level warning, the monitoring platform will issue a serious warning to the base station where the stakeholders are located for abnormal values, warn them that specific indicators are abnormal, and take treatment measures.

[0119] Prompts abnormal physical signs, the prompt format is person + base station + distance + abnormal physical signs + abnormal parameters + treatment measures. The treatment measures are: ask relevant personnel to conduct investigation.

[0120] Prompts that the environment is abnormal. The prompt format is personnel + base station + distance + environmental abnormality + abnormal parameters + handling measures. The handling measures are: ask relevant personnel to evacuate.

[0121] 4. For level 4 warnings and abnormal values, the monitoring platform will send a notice to all stakeholders in the mine, reminding them that specific indicators are abnormal and taking treatment measures.

[0122] Prompts abnormal physical signs, the prompt format is personnel + base station + distance + abnormal physical signs + abnormal parameters + treatment measures. Treatment measures are: ask all personnel to provide assistance.

[0123] Prompts that the environment is abnormal. The prompt format is personnel + base station + distance + environmental abnormality + abnormal parameters + handling measures. The handling measures are: all personnel are requested to evacuate urgently.

[0124] As an optional embodiment, in step 3, the dynamic analysis module receives the monitoring value of the monitoring item in the table, and the specific level determination process is as follows:

[0125] Based on the 10 evaluation indicators (e1, e2, e3, ... e10) of the monitoring project, an evaluation factor set A is established.

[0126]

[0127] Establish warning level L.

[0128] L = {1, 2, 3, 4}

[0129] Establish the correlation function relationship T ij .

[0130]

[0131] Establish the relationship matrix R.

[0132]

[0133] Calculate the weight vector A.

[0134] A=(0.1278, 0.1045, 0.0996, 0.1097, 0.1099, 0.1011, 0.1267, 0.0643, 0.1022, 0.0542)

[0135] Calculate the evaluation model B.

[0136] B=A·R′={1.2183, 1.4263, 1.5158, 1.5012}

[0137] Based on the principle of maximum affiliation, the final level range is determined to be level three warning.

[0138] like Figure 2As shown, the present invention also proposes an automatic early warning system for underground personnel vital signs and environmental monitoring based on hierarchical early warning, the system comprising: a positioning module, a vital sign collection module, an environmental collection module, a data transmission module, a dynamic analysis module, a hierarchical early warning module and a monitoring platform;

[0139] Among them, the positioning module is at least one of a UWB positioning module, a Zigbee positioning module, an RFID positioning module, a fiber optic positioning module, an acoustic wave positioning module, and an inertial navigation positioning module, which is used to realize real-time positioning, tracking, and management of underground personnel.

[0140] The vital sign collection module is at least one of a contact type, a non-contact type, a wearable type, and a portable type.

[0141] Furthermore, the vital sign collection module is used to monitor and record human physiological parameters and transmit the collected data to the early warning platform or other equipment to achieve real-time monitoring and remote management. The vital sign collection module includes the following components:

[0142] The first module (basic physiological parameter acquisition module) collects basic physiological parameters of underground personnel, such as heart rate, body temperature, blood pressure, etc. It usually includes sensors and data acquisition units to obtain and record changes in physiological parameters in real time;

[0143] The second module (sports health monitoring module) is used to monitor the human body's sports data, such as the number of steps, distance, etc. It is usually equipped with sensors such as accelerometers and gyroscopes to monitor and record sports data in real time;

[0144] The third module (medical health monitoring module) is used to monitor the physiological parameters of the human body, such as blood oxygen monitoring module and electrocardiogram monitoring module;

[0145] The fourth module (environmental adaptation monitoring module) is used to monitor the adaptation of the human body under specific environmental conditions, such as extreme temperature environments, etc. It is usually equipped with a temperature sensor to monitor the physiological adaptability of the human body to the environment.

[0146] The environment acquisition module is at least one of a gas monitoring module and a physical parameter monitoring module.

[0147] The environment acquisition module is used to collect underground environment parameters. According to the different acquisition parameters, the environment acquisition parameter modules can be divided into the following categories:

[0148] The sixth module (gas monitoring module) is used to monitor the gas concentration in the environment, including harmful gases (such as methane, hydrogen sulfide, etc.) and useful gases (such as oxygen, carbon dioxide, etc.).

[0149] The seventh module (physical parameter acquisition module) is used to collect physical parameters of the underground environment, including temperature, humidity, noise, vibration, etc.

[0150] The data transmission module transmits the information collected by the underground personnel vital signs collection module and the environment collection module to the vital signs parameter dynamic analysis module in real time through wired, wireless and other transmission methods.

[0151] The dynamic analysis module includes a vital sign parameter dynamic analysis module and an environmental parameter dynamic analysis module.

[0152] The dynamic analysis module of vital sign parameters performs data analysis on the information transmitted by the data transmission module, and makes qualitative description and classification according to the thresholds predefined by the module.

[0153] The evaluation indicators of the physical sign collection module and the environmental collection module should be divided into at least four warning ranges, from low to high: the first warning range, the second warning range, the third warning range, and the fourth warning range. The dynamic analysis module determines the final level of the physical sign parameters and environmental parameters.

[0154] The analysis and early warning module integrates and analyzes the early warning levels of the underground personnel vital signs collection module and the environmental parameter analysis module, and gives the final decision level, which is uploaded to the monitoring platform

[0155] Analysis and early warning module fusion analysis implementation method:

[0156] The vital sign parameter dynamic analysis module transmits the warning level of the underground personnel's vital signs to the graded warning module;

[0157] The environmental parameter dynamic analysis module transmits the warning level of the underground environmental parameters to the graded warning module;

[0158] The analysis and warning module integrates and analyzes the warning levels of the physical sign parameters and environmental parameter dynamic analysis modules;

[0159] The analysis module defines the final warning level and uploads it to the monitoring platform.

[0160] Each warning level corresponds to a different warning method. The warning method for the fourth warning level is reminder, the warning method for the third warning level is warning, the warning method for the second warning level is severe warning, and the warning method for the first warning level is notice.

[0161] The communication signal is at least one of a positioning reference signal, a sounding reference signal, a signal for transmitting data information, a signal for transmitting control information, and a signal for transmitting synchronization information.

[0162] The monitoring platform automatically takes different measures for different warning levels.

[0163] The dynamic analysis module receives the monitoring values ​​of the monitoring items in the table and determines the specific levels.

[0164] The beneficial effect of the present invention is that, compared with the prior art, the mine communication positioning fusion system based on reconfigurable intelligent metasurface proposed in this paper can reduce the deployment cost of network equipment, reduce network interference and improve communication quality while realizing the underground positioning function of coal mines. The real-time collection and fusion processing of the physical parameters and environmental parameters of personnel in coal mines are realized. An automatic distribution model for early warning information is established, and different levels of distribution models and processing measures are defined to avoid manual intervention. A highly scalable multi-index fusion grading model is constructed in the dynamic analysis module, which can realize the autonomous determination and graded early warning of personnel physical parameters and environmental parameters. The early warning system is based on real-time monitoring and analysis of the underground working environment and the physiological state of workers, and according to preset safety standards and models, it warns of possible risks and performs graded processing according to the severity of the risks.

[0165] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0166] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive lists) of computer readable storage medium include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or convex structures in grooves on which instructions are stored, and any suitable combination thereof. Computer readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (e.g., light pulses by optical fiber cables), or electrical signals transmitted by wires.

[0167] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0168] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An automatic graded early warning method for underground operation safety, characterized in that: The steps include: Step 1, collecting the physical parameters of the person being tested and the underground environment parameters; Step 2, establishing the warning level of the physical sign parameters and the warning level of the underground environment parameters; Step 3: dynamically integrate and analyze the warning level of personnel vital signs and the warning level of environmental parameters according to the collected parameter data to obtain the final warning level; Step 4: Take corresponding warning issuance measures based on the final warning level.

2. The automatic graded early warning method for underground operation safety according to claim 1 is characterized in that: The physical sign parameters include at least: low blood pressure, high blood pressure, blood oxygen saturation, maximum heart rate, low body temperature and high body temperature of the person being measured; The underground environmental parameters include: harmful gases, useful gases, and physical parameters. Harmful gases include methane, hydrogen sulfide, and carbon monoxide, and useful gases include oxygen and carbon dioxide. The physical parameters of the underground environment include temperature, humidity, noise, and vibration.

3. The automatic graded early warning method for underground operation safety according to claim 1 is characterized in that: The establishment of the warning level of personnel vital sign parameters and the warning level of underground environment parameters specifically includes: Taking physical sign parameters and underground environmental parameters as evaluation indicators, an evaluation indicator grading table is established. Each evaluation indicator of the measured personnel is divided into n warning ranges, and each warning range is used as a warning level to obtain n warning levels. The warning levels are arranged from low to high according to the data of the warning range.

4. The automatic graded early warning method for underground operation safety according to claim 3 is characterized in that: The step 3 specifically includes: Step 3-1, calculate the correlation between each evaluation index and each level of table, and construct a relationship matrix based on the correlation; Step 3-2, transforming the relationship matrix to obtain a transformed relationship matrix; Step 3-3, calculate the dimensionless quantity of each evaluation index and perform normalization processing to obtain the normalized dimensionless quantity, and obtain the weight vector based on the normalized dimensionless quantity: Step 3-4, establish an evaluation model based on the transformed relationship matrix and weight vector, and determine the final grade range based on the maximum membership principle.

5. The automatic graded early warning method for underground operation safety according to claim 4 is characterized in that: The calculation of the correlation between each evaluation index and each level of table and the construction of a relationship matrix according to the correlation specifically includes: Suppose the warning range corresponding to the jth warning level of the i-th evaluation index is (a ij , b ij ], the correlation between the i-th evaluation index and the j-th warning level is calculated using the following relationship: ij : d ij =|a ij -x i | Where, d ij Represents the collected value x of the i-th evaluation index i To the lower boundary point a of the j-th level warning range ij The distance, b ij Indicates the upper boundary point of the j-th level warning range. The relationship matrix constructed based on the correlation between each evaluation index and each level of table is as follows: Among them, t ij The correlation degree of the jth level of the ith evaluation indicator, i∈[1,m], j∈[1,n], m is the number of evaluation indicators, and n is the number of warning levels.

6. The automatic graded early warning method for underground operation safety according to claim 4 is characterized in that: The transformation of the relationship matrix to obtain the transformed relationship matrix specifically includes: Transform Process the relationship matrix R to obtain the transformed relationship matrix as follows:

7. The automatic graded early warning method for underground operation safety according to claim 4 is characterized in that: The steps 3-5 specifically include: Calculate the dimensionless quantity w of each evaluation index i : Among them, x i is the collected value of the i-th evaluation index, is the average value of the warning classification standard of the i-th evaluation indicator; For the dimensionless quantity w i After normalization, the normalized dimensionless quantity a is obtained. i : According to the normalized dimensionless quantity a of each evaluation index i Get the weight vector A: <h2 style=";text-align:left;direction:ltr">A=(a1, a2,…a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> )。 8. The automatic graded early warning method for underground operation safety according to claim 4 is characterized in that: The evaluation model B established is as follows: B=A·R′={b l1 ,b l2 ,……,b ln } In the formula, A is the weight vector, is the transformed relationship matrix, b lj is the correlation degree of each indicator at the jth warning level, and n is the number of warning levels.

9. An automatic graded warning system for underground operation safety, using the automatic graded warning method for underground operation safety according to any one of claims 1 to 10, characterized in that: include: Positioning module, vital sign collection module, environment collection module, data transmission module, dynamic analysis module, graded warning module and monitoring platform; Among them, the physical sign collection module and the environment collection module are used to collect the physical sign parameters of the measured personnel and the underground environment parameters as evaluation indicators respectively; The data transmission module is used to send the collected evaluation index data to the dynamic analysis module; The dynamic analysis module is used to establish the warning level of personnel vital signs and the warning level of environmental parameters; The graded warning module dynamically integrates and analyzes the warning levels of personnel vital signs and environmental parameters based on the data collected by the acquisition module to obtain the final warning level; The monitoring platform is used to take corresponding warning measures based on the final warning level.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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