Mine safety detection method, device, equipment and medium

Abstract

The invention relates to a mine safety detection method, device, equipment and medium, and is applied to the technical field of mine safety, the method comprises the following steps: obtaining historical data and monitoring data, the historical data comprising historical monitoring data and historical evaluation data; analyzing the historical data to obtain standard data; determining abnormal information based on the standard data and the monitoring data; and performing early warning based on the abnormal information. The method has the effect of improving the efficiency and accuracy of mine safety detection.

Description

Technical Field

[0001] The present application relates to the technical field of mine safety, and in particular to a mine safety detection method, device, equipment and medium. Background Art

[0002] As an important resource mining site, mine safety has always attracted much attention. However, due to the complex and changeable environment of mines, various safety hazards occur from time to time, posing a serious threat to the life and property safety of mine workers and the environment. Therefore, it is of great significance to strengthen mine safety detection and early warning work.

[0003] At present, mine safety inspections are completed through manual inspections. However, manual inspections not only consume a lot of manpower and material resources, but are also prone to errors and omissions due to human sensory, judgment, and energy limitations, resulting in low efficiency and accuracy in safety inspections. Summary of the invention

[0004] In order to improve the efficiency and accuracy of mine safety detection, the present application provides a mine safety detection method, device, equipment and medium.

[0005] In the first aspect, the present application provides a mine safety detection method, which adopts the following technical solution: A mine safety detection method, comprising: Acquiring historical data and monitoring data, wherein the historical data includes historical monitoring data and historical evaluation data; Analyzing the historical data to obtain standard data; Determine abnormal information based on the standard data and the monitoring data; An early warning is issued based on the abnormal information.

[0006] By adopting the above technical solution, standard data is obtained by analyzing historical data instead of using fixed standard data, thereby improving the reliability of standard data. Safety inspections are carried out based on the standard data, thereby improving the accuracy of safety inspections. Through real-time data collection and automated data analysis, the efficiency of mine safety inspections is improved.

[0007] Optionally, the determining the abnormal information based on the standard data and the monitoring data includes: Get weather information; Determine a safe distance and a safe speed based on the weather information and the standard data; Determine the monitoring data of vehicle type as vehicle operation data, wherein the vehicle operation data includes vehicle position and driving speed; calculating a vehicle distance based on the vehicle position; Abnormal information is determined based on the vehicle distance, the safety distance, the travel speed, and the safety speed.

[0008] By adopting the above technical solution, weather conditions are taken into account when determining the safe distance and safe speed, that is, the impact of the external environment on vehicle driving is taken into account, so that the determined safe distance and safe speed are more reliable. Safety inspections are carried out based on the safe distance and safe speed, which improves the reliability of mine safety inspections and can promptly detect dangerous behaviors during vehicle driving, thereby reminding the driver and reducing the occurrence of traffic accidents.

[0009] Optionally, the safety distance includes an opposite safety distance, the safety speed includes an opposite safety speed, the vehicle operation data includes a driving direction, and determining the abnormal information based on the vehicle distance, the safety distance, the driving speed, and the safety speed includes: If the vehicle distance is less than the opposite safety distance, the vehicles corresponding to the vehicle distance are determined to be the first vehicle and the second vehicle; Determine a direction association between the first vehicle and the second vehicle based on the driving direction, wherein the direction association includes driving in opposite directions and driving in the same direction; If the direction is associated with traveling in opposite directions, the driving speed corresponding to the first vehicle is determined as a first driving speed; and the driving speed corresponding to the second vehicle is determined as a second driving speed; If the first driving speed and / or the second driving speed is greater than the oncoming safety speed, abnormal information is determined based on the direction association, the oncoming safety speed, the first driving speed, and the second driving speed.

[0010] By adopting the above technical solution, the actual situation of narrow roads in mines is fully taken into consideration, and the concepts of opposite safety distance and opposite safety speed are introduced, the relative position and speed relationship between vehicles can be considered more comprehensively, thereby improving the reliability of abnormal information, that is, improving the accuracy of mine safety detection.

[0011] Optionally, the determining the abnormal information based on the standard data and the monitoring data includes: Acquire vehicle history data and driver driving information, wherein the driver driving information includes driving experience; Determining the number of vehicle anomalies based on the vehicle historical data; determining a vehicle grade based on the number of vehicle anomalies and the driving age; determining a standard load based on the vehicle grade and the standard data; determining the vehicle load based on the monitoring data; If the vehicle load is greater than the standard load, abnormal information is determined based on the vehicle load and the standard load.

[0012] By adopting the above technical scheme, when determining the vehicle grade, the number of vehicle abnormalities and the driving age are taken into account, that is, the vehicle conditions and the driver's conditions are fully considered, so that the determined vehicle grade is more accurate and reliable, and different standard loads are set for vehicles of different grades, which improves the safety of the vehicle transportation process. Safety inspections are carried out according to the standard loads, which improves the accuracy of safety inspections.

[0013] Optionally, the determining the abnormal information based on the standard data and the monitoring data includes: Get worker scheduling information; Determine the monitoring data whose data type is a video type as video data; Performing image recognition on the video data to obtain work information, wherein the work information includes worker information and operation information; Determine personnel abnormality based on the worker scheduling information and the worker information; determining an abnormal operation condition based on the operation information; Abnormal information is determined based on the personnel abnormal situation and the operation abnormal situation.

[0014] By adopting the above technical solution, when conducting safety inspections, video data is identified through image recognition technology, and abnormal conditions of workers can be obtained in a timely manner through worker information, worker scheduling information and operation information, without the need for manual investigation, thereby improving the efficiency of safety inspections.

[0015] Optionally, the determining the abnormal information based on the standard data and the monitoring data includes: Determine the monitoring data whose data type is an environmental type as environmental data; Dividing the environmental data into a plurality of environmental data combinations based on positions corresponding to the environmental data; Calculate the average value of the environmental data in each of the environmental data combinations; Calculate the environmental data interval based on the average value and the preset difference value; Abnormal information is determined based on the environmental data interval and the environmental data.

[0016] By adopting the above technical solution, environmental data with close distances are divided into an environmental data combination, the average value of all environmental data in each environmental data combination is calculated, and abnormal environmental data is determined based on the average value. When conducting mine safety inspections, the environmental characteristics of the region are fully taken into consideration, thereby improving the accuracy of the inspection.

[0017] Optionally, the determining the abnormal information based on the standard data and the monitoring data includes: Obtain mining information and production information; Modeling the mine based on the digital twin technology and the mine information to obtain a three-dimensional model of the mine; Synchronizing the monitoring data to the three-dimensional model of the mine; Simulating the production process based on the three-dimensional mine model and the production information to obtain a simulation result; Abnormal information is determined based on the simulation result.

[0018] By adopting the above technical solution and building a three-dimensional mine model through digital twin technology, the production process can be simulated through the three-dimensional mine model before production. Dangerous situations in the production process can be predicted in advance, so that the production plan can be adjusted in time, thereby improving the safety of mine production.

[0019] In a second aspect, the present application provides a mine safety detection device, which adopts the following technical solution: A mine safety detection device, comprising: A data acquisition module, used to acquire historical data and monitoring data, wherein the historical data includes historical monitoring data and historical evaluation data; A data analysis module, used for analyzing the historical data to obtain standard data; An abnormality determination module, used to determine abnormal information based on the standard data and the monitoring data; The abnormality warning module is used to issue an early warning based on the abnormal information.

[0020] By adopting the above technical solution, standard data is obtained by analyzing historical data instead of using fixed standard data, thereby improving the reliability of standard data. Safety inspections are carried out based on the standard data, thereby improving the accuracy of safety inspections. Through real-time data collection and automated data analysis, the efficiency of mine safety inspections is improved.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a processor, wherein the processor is coupled to a memory; The memory stores a computer program that can be loaded by a processor and executes the mine safety detection method described in any one of the first aspects.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the mine safety detection method described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a mine safety detection method provided in an embodiment of the present application.

[0024] Figure 2 It is a structural block diagram of a mine safety detection device provided in an embodiment of the present application.

[0025] Figure 3 It is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application is further described in detail below in conjunction with the accompanying drawings.

[0027] The embodiment of the present application provides a mine safety detection method, which can be executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0030] like Figure 1 As shown, a mine safety detection method, the main process of the method is described as follows (steps S101 to S104): Step S101: Acquire historical data and monitoring data.

[0031] The historical data includes historical monitoring data and historical evaluation data. The historical evaluation data is abnormal evaluation data corresponding to the historical monitoring data, and has a one-to-one correspondence with the historical monitoring data. The historical data is obtained from the database.

[0032] Sensors are installed at multiple locations in the mine, such as temperature sensors, humidity sensors, and smoke concentration sensors, etc., which are used to monitor various environmental data in the mine. At the same time, multiple cameras are installed in the mine to shoot the mine conditions. There are various equipment in the mine, such as excavation equipment and transportation vehicles, etc. The monitoring data includes data types, and the data types include environment types, vehicle types, and video types, etc. The monitoring data is obtained from sensors, cameras, and various equipment in real time.

[0033] Step S102: Analyze historical data to obtain standard data.

[0034] Among them, the standard data is the reference data for determining whether there are any abnormalities in the monitoring data.

[0035] Use data analysis tools to analyze historical data to obtain standard data, and store the standard data in a standard database. The standard database can be updated in real time or at preset intervals. The data analysis tool can be EXCEL, Python, or MATLAB. The preset period can be half a month, one month, or three months, and there is no specific limitation here.

[0036] Step S103: determine abnormal information based on standard data and monitoring data.

[0037] The monitoring data is compared with the standard data to determine whether there is any abnormality in the mine and obtain the corresponding abnormal information, which includes the abnormal type and the corresponding abnormal data.

[0038] Specifically, abnormal information is determined based on standard data and monitoring data, including: obtaining weather information; determining a safe distance and a safe speed based on weather information and standard data; determining monitoring data with a vehicle type as vehicle operation data, and the vehicle operation data includes a vehicle position and a driving speed; calculating a vehicle distance based on the vehicle position; and determining abnormal information based on the vehicle distance, safe distance, driving speed, and safe speed.

[0039] In this embodiment, weather information is obtained from the meteorological department, and standard data includes the safe distance and safe speed for vehicle driving in various weather conditions. The standard database is queried based on the weather information to obtain the safe distance and safe speed for vehicle driving under the weather information. The vehicle operation data of all vehicles is found from the monitoring data, and the distance between every two vehicles, i.e., the vehicle distance, is calculated based on the vehicle positions. If the vehicle distance is less than the safe distance and / or the driving speed is greater than the safe speed, there is an abnormality with the vehicle, and the abnormality type is abnormal vehicle driving. The abnormal information also includes the vehicle distance and / or driving speed and the corresponding abnormal vehicle.

[0040] Specifically, abnormal information is determined based on vehicle distance, safety distance, driving speed and safety speed, including: if the vehicle distance is less than the opposite safety distance, the vehicles corresponding to the vehicle distance are determined as the first vehicle and the second vehicle; the direction association between the first vehicle and the second vehicle is determined based on the driving direction, and the direction association includes opposite driving and same direction driving; if the direction association is opposite driving, the driving speed corresponding to the first vehicle is determined as the first driving speed; the driving speed corresponding to the second vehicle is determined as the second driving speed; if the first driving speed and / or the second driving speed is greater than the opposite safety speed, abnormal information is determined based on the direction association, the opposite safety speed, the first driving speed and the second driving speed.

[0041] Among them, the safety distance includes the safety distance towards each other and the safety distance in the same direction. The safety distance towards each other is the safety distance when two vehicles are driving towards each other, and the safety distance towards each other is greater than the safety distance in the same direction. The safety speed includes the safety speed towards each other and the safety speed in the same direction, and the safety speed towards each other is less than the safety speed in the same direction. The vehicle operation data includes the driving direction, and the driving direction is described in the form of the starting point and the end point of the road section. For example, if the starting point of the road section where the vehicle is located is A and the end point is B, the driving direction can be from A to B or from B to A.

[0042] In mines, many sections of roads are narrow in width, or even have only one lane. If two vehicles are traveling towards each other on this section of road, safety accidents are likely to occur, so the distance between vehicles and the speed of travel need to be limited.

[0043] When the abnormality type is vehicle driving abnormality, the abnormality information further includes an abnormality level, and the abnormality level includes a first abnormality level and a second abnormality level, and the first abnormality level is more serious than the second abnormality level.

[0044] In this embodiment, when the vehicle distance between two vehicles is less than the safety distance towards each other, the two vehicles corresponding to the vehicle distance are respectively determined as the first vehicle and the second vehicle, and the direction association between the first vehicle and the second vehicle is determined according to the driving directions of the two vehicles. If the driving directions of the two vehicles are the same, the direction association is driving in the same direction; if the driving directions of the vehicles are inconsistent, the direction association is driving in opposite directions.

[0045] When the vehicle distance between two vehicles is less than the opposite safety distance and the direction association is opposite driving, there is a vehicle driving abnormality, the driving speed corresponding to the first vehicle is determined as the first driving speed, and the driving speed corresponding to the second vehicle is determined as the second driving speed. If the first driving speed and the second driving speed are both less than or equal to the opposite safety speed, the abnormality level is the second abnormality level; if the first driving speed and / or the second driving speed is greater than the opposite safety speed, the abnormality level is the first abnormality level.

[0046] When the direction association is same-direction driving, the relationship between the vehicle distance and the same-direction safety distance is determined. If the vehicle distance between the two vehicles is greater than or equal to the same-direction safety distance, there is no vehicle driving abnormality; if the vehicle distance between the two vehicles is less than the same-direction safety distance, there is a vehicle driving abnormality. If the first driving speed and the second driving speed are both less than or equal to the same-direction safety speed, the abnormality level is the second abnormality level; if the first driving speed and / or the second driving speed is greater than the same-direction safety speed, the abnormality level is the first abnormality level.

[0047] When there is a vehicle driving abnormality, the abnormality level, the direction association of the abnormality, the abnormal speed, the abnormal vehicle, and the safe speed at this time are determined as abnormality information.

[0048] Specifically, abnormal information is determined based on standard data and monitoring data, including: obtaining vehicle historical data and driver driving information, the driver driving information including driving experience; determining the number of vehicle abnormalities based on vehicle historical data; determining the vehicle grade based on the number of vehicle abnormalities and driving experience; determining the standard load based on the vehicle grade and standard data; determining the vehicle load based on monitoring data; if the vehicle load is greater than the standard load, determining abnormal information based on the vehicle load and the standard load.

[0049] In this embodiment, vehicle information, vehicle historical data and driver driving information are obtained from a database, the vehicle historical data includes historical vehicle operation data and historical vehicle abnormal data, the driver driving information includes driver personal information and driving experience, the driver personal information may be name, ID number, etc., the number of historical vehicle abnormal data corresponding to each vehicle, that is, the number of vehicle abnormalities, the database stores the correspondence between the number of vehicle abnormalities, driving experience and vehicle grade, the corresponding vehicle grade is searched from the database according to the vehicle information, the number of vehicle abnormalities and driving experience, the standard load corresponding to the vehicle grade is searched from the standard database according to the vehicle grade, the vehicle load is searched from the monitoring data, if the vehicle load is greater than the standard load, there is an abnormality, the abnormality type is vehicle load abnormality, the vehicle information, vehicle load and standard load are determined as abnormal information.

[0050] Specifically, abnormal information is determined based on standard data and monitoring data, including: obtaining worker scheduling information; determining monitoring data of video type as video data; performing image recognition on video data to obtain work information, which includes worker information and operation information; determining personnel abnormalities based on worker scheduling information and worker information; determining operation abnormalities based on operation information; and determining abnormal information based on personnel abnormalities and operation abnormalities.

[0051] In this embodiment, worker scheduling information is obtained from a database, monitoring data of a video type is searched from the monitoring data, and the monitoring data is determined to be video data. The video data is identified by image recognition technology to obtain worker information in the video. The video data is deeply identified by a machine learning model to obtain worker operation information, which includes safety equipment wearing information. The machine learning model is a deep learning model that can identify the safety equipment wearing status of workers, and is a model obtained by training with a large amount of sample data.

[0052] The identified worker information is compared with the worker scheduling information to determine whether the worker is working according to the worker scheduling information. If a worker does not work according to the worker scheduling information, there is an exception, and the exception type is personnel exception. The worker information corresponding to the worker and the exception time are determined as personnel exceptions. Otherwise, there is no exception.

[0053] The situation of workers wearing safety equipment is obtained based on the safety equipment wearing information. If a worker does not wear safety equipment as required, there is an exception, and the exception type is equipment wearing exception. The worker information corresponding to the worker and the abnormal time are determined as abnormal operation. Otherwise, there is no exception.

[0054] Abnormal personnel situations and abnormal operation situations are jointly identified as abnormal information.

[0055] Specifically, abnormal information is determined based on standard data and monitoring data, including: determining monitoring data with an environmental type as environmental data; dividing the environmental data into multiple environmental data combinations based on the positions corresponding to the environmental data; calculating the average value of the environmental data in each environmental data combination; calculating the environmental data interval based on the average value and a preset difference; and determining abnormal information based on the environmental data interval and the environmental data.

[0056] In this embodiment, monitoring data whose data type is an environmental type is searched from the monitoring data, and the monitoring data is determined to be environmental data, the environmental data is data collected by the sensor, the position corresponding to the environmental data is the position of the sensor that collects the environmental data, the distance between the position corresponding to each environmental data and the position corresponding to other environmental data is calculated, all environmental data are traversed, and when an environmental data is traversed, multiple distances corresponding to the environmental data are compared with a preset distance, and environmental data corresponding to distances less than the preset distance are divided into an environmental data combination with the environmental data, until all environmental data are traversed, each environmental data can exist in multiple environmental data combinations, if there is a completely consistent environmental data combination, only one is retained, wherein the preset distance is pre-set by the staff according to the mine situation, and is not specifically limited here.

[0057] Calculate the average value of all environmental data in each environmental data combination, add the preset difference to the average value to obtain the upper limit of the environmental interval, and subtract the preset difference from the average value to obtain the lower limit of the environmental interval, so as to obtain the environmental data interval corresponding to each environmental data combination. Since there are environmental data corresponding to multiple environmental data intervals, if the environmental data is within all corresponding environmental data intervals, then there is no abnormality in the environmental data; otherwise, there is an abnormality in the environmental data, and the abnormal type is environmental abnormality. The abnormal environmental data and the corresponding environmental data interval are determined as abnormal information.

[0058] Specifically, abnormal information is determined based on standard data and monitoring data, including: obtaining mine information and production information; modeling the mine based on digital twin technology and mine information to obtain a three-dimensional mine model; synchronizing monitoring data to the three-dimensional mine model; simulating the production process based on the three-dimensional mine model and production information to obtain simulation results; and determining abnormal information based on the simulation results.

[0059] In this embodiment, detailed physical data of the mine, i.e., mine information, is obtained from a database or management personnel, and the mine information is three-dimensionally modeled through digital twin technology to obtain a three-dimensional model of the mine. The monitoring data collected in real time is synchronized to the three-dimensional model of the mine in real time, and production information is obtained from the database. When there is new production information, the production information is input into the three-dimensional model of the mine to simulate the production process to obtain simulation results. The simulation results include the presence and absence of abnormalities. If there is an abnormality, the abnormal situation in the simulation result and the production information are jointly determined as abnormal information.

[0060] Step S104: Issue an early warning based on the abnormal information.

[0061] The abnormal information is sent to the corresponding abnormal personnel, so as to warn the abnormal personnel so that they can adjust the abnormality in time, and store the abnormal information in the database for subsequent viewing.

[0062] Figure 2 A structural block diagram of a mine safety detection device 200 provided in an embodiment of the present application.

[0063] like Figure 2 As shown, the mine safety detection device 200 mainly includes: The data acquisition module 201 is used to acquire historical data and monitoring data, where the historical data includes historical monitoring data and historical evaluation data; The data analysis module 202 is used to analyze the historical data to obtain standard data; An abnormality determination module 203, used to determine abnormal information based on standard data and monitoring data; The abnormal warning module 204 is used to issue an early warning based on abnormal information.

[0064] As an optional implementation of this embodiment, the abnormality determination module 203 is also specifically used to determine abnormal information based on standard data and monitoring data, including: obtaining weather information; determining a safe distance and a safe speed based on weather information and standard data; determining monitoring data with a vehicle type as a vehicle operation data, and the vehicle operation data includes a vehicle position and a driving speed; calculating a vehicle distance based on a vehicle position; and determining abnormal information based on a vehicle distance, a safe distance, a driving speed, and a safe speed.

[0065] As an optional implementation manner of this embodiment, the safety distance includes an opposite safety distance, the safety speed includes an opposite safety speed, the vehicle operation data includes a driving direction, and the abnormality determination module 203 is also specifically used to determine abnormal information based on the vehicle distance, the safety distance, the driving speed and the safety speed, including: if the vehicle distance is less than the opposite safety distance, the vehicles corresponding to the vehicle distance are determined as the first vehicle and the second vehicle; the direction association between the first vehicle and the second vehicle is determined based on the driving direction, and the direction association includes opposite driving and same direction driving; if the direction association is opposite driving, the driving speed corresponding to the first vehicle is determined as the first driving speed; the driving speed corresponding to the second vehicle is determined as the second driving speed; if the first driving speed and / or the second driving speed is greater than the opposite safety speed, the abnormal information is determined based on the direction association, the opposite safety speed, the first driving speed and the second driving speed.

[0066] As an optional implementation of this embodiment, the abnormality determination module 203 is also specifically used to determine abnormal information based on standard data and monitoring data, including: obtaining vehicle historical data and driver driving information, the driver driving information including driving experience; determining the number of vehicle abnormalities based on vehicle historical data; determining the vehicle grade based on the number of vehicle abnormalities and driving experience; determining the standard load based on the vehicle grade and standard data; determining the vehicle load based on monitoring data; if the vehicle load is greater than the standard load, determining the abnormal information based on the vehicle load and the standard load.

[0067] As an optional implementation of this embodiment, the abnormality determination module 203 is also specifically used to determine abnormal information based on standard data and monitoring data, including: obtaining worker scheduling information; determining monitoring data with a video data type as video data; performing image recognition on the video data to obtain work information, the work information includes worker information and operation information; determining personnel abnormalities based on worker scheduling information and worker information; determining operation abnormalities based on operation information; and determining abnormal information based on personnel abnormalities and operation abnormalities.

[0068] As an optional implementation of this embodiment, the abnormality determination module 203 is also specifically used to determine abnormal information based on standard data and monitoring data, including: determining monitoring data with an environmental data type as environmental data; dividing the environmental data into multiple environmental data combinations based on the positions corresponding to the environmental data; calculating the average value of the environmental data in each environmental data combination; calculating the environmental data interval based on the average value and a preset difference; and determining abnormal information based on the environmental data interval and the environmental data.

[0069] As an optional implementation of this embodiment, the abnormality determination module 203 is also specifically used to determine abnormal information based on standard data and monitoring data, including: obtaining mine information and production information; modeling the mine based on digital twin technology and mine information to obtain a three-dimensional mine model; synchronizing the monitoring data to the three-dimensional mine model; simulating the production process based on the three-dimensional mine model and production information to obtain simulation results; and determining abnormal information based on the simulation results.

[0070] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0071] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] Figure 3 A structural block diagram of an electronic device 300 provided in an embodiment of the present application.

[0074] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0075] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned mine safety detection method; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and data related to the application. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0076] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0077] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the mine safety detection method given in the above embodiment.

[0078] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0079] The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0080] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned mine safety detection method are implemented.

[0081] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0082] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0083] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A mine safety detection method, characterized in that: include: Acquiring historical data and monitoring data, wherein the historical data includes historical monitoring data and historical evaluation data; Analyzing the historical data to obtain standard data; Determine abnormal information based on the standard data and the monitoring data; An early warning is issued based on the abnormal information.

2. The method according to claim 1, characterized in that The determining of abnormal information based on the standard data and the monitoring data includes: Get weather information; Determine a safe distance and a safe speed based on the weather information and the standard data; Determine the monitoring data of vehicle type as vehicle operation data, wherein the vehicle operation data includes vehicle position and driving speed; calculating a vehicle distance based on the vehicle position; Abnormal information is determined based on the vehicle distance, the safety distance, the travel speed, and the safety speed.

3. The method according to claim 2, characterized in that The safety distance includes an opposite safety distance, the safety speed includes an opposite safety speed, the vehicle operation data includes a driving direction, and the abnormal information is determined based on the vehicle distance, the safety distance, the driving speed, and the safety speed, including: If the vehicle distance is less than the opposite safety distance, the vehicles corresponding to the vehicle distance are determined to be the first vehicle and the second vehicle; Determine a direction association between the first vehicle and the second vehicle based on the driving direction, wherein the direction association includes driving in opposite directions and driving in the same direction; If the direction is associated with traveling in opposite directions, the driving speed corresponding to the first vehicle is determined as a first driving speed; and the driving speed corresponding to the second vehicle is determined as a second driving speed; If the first driving speed and / or the second driving speed is greater than the oncoming safety speed, abnormal information is determined based on the direction association, the oncoming safety speed, the first driving speed, and the second driving speed.

4. The method according to claim 1, characterized in that: The determining of abnormal information based on the standard data and the monitoring data includes: Acquire vehicle history data and driver driving information, wherein the driver driving information includes driving experience; Determining the number of vehicle anomalies based on the vehicle historical data; determining a vehicle grade based on the number of vehicle anomalies and the driving age; determining a standard load based on the vehicle grade and the standard data; determining the vehicle load based on the monitoring data; If the vehicle load is greater than the standard load, abnormal information is determined based on the vehicle load and the standard load.

5. The method according to claim 1, characterized in that The determining of abnormal information based on the standard data and the monitoring data includes: Get worker scheduling information; Determine the monitoring data whose data type is a video type as video data; Performing image recognition on the video data to obtain work information, wherein the work information includes worker information and operation information; Determine personnel abnormality based on the worker scheduling information and the worker information; determining an abnormal operation condition based on the operation information; Abnormal information is determined based on the personnel abnormal situation and the operation abnormal situation.

6. The method according to claim 1, characterized in that The determining of abnormal information based on the standard data and the monitoring data includes: Determine the monitoring data whose data type is an environmental type as environmental data; Dividing the environmental data into a plurality of environmental data combinations based on positions corresponding to the environmental data; Calculate the average value of the environmental data in each of the environmental data combinations; Calculate the environmental data interval based on the average value and the preset difference value; Abnormal information is determined based on the environmental data interval and the environmental data.

7. The method according to claim 1, characterized in that The determining of abnormal information based on the standard data and the monitoring data includes: Obtain mining information and production information; Modeling the mine based on the digital twin technology and the mine information to obtain a three-dimensional model of the mine; Synchronizing the monitoring data to the three-dimensional model of the mine; Simulating the production process based on the three-dimensional mine model and the production information to obtain a simulation result; Abnormal information is determined based on the simulation result.

8. A mine safety detection device, characterized in that: include: A data acquisition module, used to acquire historical data and monitoring data, wherein the historical data includes historical monitoring data and historical evaluation data; A data analysis module, used for analyzing the historical data to obtain standard data; An abnormality determination module, used to determine abnormal information based on the standard data and the monitoring data; The abnormal warning module is used to issue an early warning based on the abnormal information.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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