CN111578446A - Coal mine ventilation equipment detection method, equipment and medium - Google Patents
Coal mine ventilation equipment detection method, equipment and medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F7/00—Ventilation
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Abstract
The application discloses a method, equipment and a medium for detecting coal mine ventilation equipment, which comprise the following steps: acquiring running state data inside the coal mine ventilation equipment through detection equipment; converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model; and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model. The embodiment of the application acquires the internal running state data of the coal mine ventilation equipment through the detection equipment, and finally determines the state information of the current coal mine ventilation system through the pre-trained coal mine ventilation equipment detection model, so that the coal mine ventilation equipment can be maintained as early as possible when the coal mine ventilation equipment is abnormal, and the occurrence of greater loss is avoided.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method, equipment and a medium for detecting coal mine ventilation equipment.
Background
The normal operation of the coal mine ventilation equipment is related to the mine production operation, and if the coal mine ventilation equipment fails, the underground air can not be normally ventilated, so that serious production safety accidents can be caused, the explosion danger can occur, and the lives of workers can be threatened. When the coal mine ventilation equipment has high failure risk, the coal mine ventilation equipment needs to be maintained regularly. However, the conventional maintenance cannot avoid accidents, and if the coal mine ventilation equipment fails and is not discovered, great loss may be brought.
Disclosure of Invention
In view of this, the embodiment of the application provides a method, a device and a medium for detecting a coal mine ventilation device, which are used for solving the problem that the detection method in the prior art cannot well find the fault of the coal mine ventilation device, and may bring great loss.
The embodiment of the application adopts the following technical scheme:
the embodiment of the application provides a method for detecting coal mine ventilation equipment, which comprises the following steps:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
Further, before the detection device acquires the operation state data inside the coal mine ventilation device, the method further comprises the following steps:
constructing an initial coal mine ventilation equipment detection model;
constructing a data set, wherein the data set comprises image data of a plurality of coal mine ventilation devices which are normal and image data of a plurality of coal mine ventilation devices which are abnormal;
and obtaining a coal mine ventilation equipment detection model meeting the requirements according to the data set and the initial coal mine ventilation equipment detection model.
Further, the obtaining of a coal mine ventilation equipment detection model meeting the requirements according to the data set and the initial coal mine ventilation equipment detection model specifically includes:
dividing the data set into a training set and a verification set according to a preset proportion;
leading the training set into a deep learning model, and obtaining a coal mine ventilation equipment detection model to be adjusted through a plurality of training periods;
evaluating the coal mine ventilation equipment detection model to be adjusted according to the verification set to obtain an evaluation result;
and adjusting the hyper-parameters according to the evaluation result, and determining a coal mine ventilation equipment detection model meeting the conditions.
Further, the deep learning model is a residual error neural network model.
Further, after the data set is constructed, the method further comprises:
and preprocessing the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set according to a preset mode so as to eliminate the noise in the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set.
Further, the preset mode comprises an opening operation, a closing operation and a binarization operation.
Further, the detection device is a radio frequency signal capture device arranged outside the coal mine ventilation device.
Furthermore, the radio frequency signal capture device acquires the running state data inside the coal mine ventilation equipment through a WIFI signal.
The embodiment of the application still provides a colliery ventilation equipment check out test set, equipment includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
The embodiment of the application also provides a detection medium for the coal mine ventilation equipment, which stores computer executable instructions, wherein the computer executable instructions are set as follows:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the embodiment of the application acquires the internal running state data of the coal mine ventilation equipment through the detection equipment, and finally determines the state information of the current coal mine ventilation system through the pre-trained coal mine ventilation equipment detection model, so that the coal mine ventilation equipment can be maintained as early as possible when the coal mine ventilation equipment is abnormal, and the occurrence of greater loss is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a detection method of a coal mine ventilation device according to one embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a detection method for a coal mine ventilation device provided in the second embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a detection system for a coal mine ventilation device provided in the second embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for detecting a coal mine ventilation device according to an embodiment of the present disclosure, where the following steps may be performed by a detection system for a coal mine ventilation device according to the embodiment of the present disclosure, and specifically include:
and S101, the coal mine ventilation equipment detection system acquires the running state data inside the coal mine ventilation equipment through the detection equipment.
Step S102, the coal mine ventilation equipment detection system converts the running state data inside the coal mine ventilation equipment into image data, and inputs the image data into a pre-trained coal mine ventilation equipment detection model.
And S103, determining the state information of the coal mine ventilation equipment by the coal mine ventilation equipment detection system according to the coal mine ventilation equipment detection model.
The embodiment of the application acquires the internal running state data of the coal mine ventilation equipment through the detection equipment, and finally determines the state information of the current coal mine ventilation system through the pre-trained coal mine ventilation equipment detection model, so that the coal mine ventilation equipment can be maintained as early as possible when the coal mine ventilation equipment is abnormal, and the occurrence of greater loss is avoided.
Correspondingly to the embodiment of the present specification, fig. 2 is a schematic flow chart of a method for detecting a coal mine ventilation device provided in the second embodiment of the present specification, where the following steps may be executed by a detection system for a coal mine ventilation device in the embodiment of the present specification, and specifically include:
step S201, the coal mine ventilation equipment detection system constructs an initial coal mine ventilation equipment detection model.
Step S202, the coal mine ventilation equipment detection system constructs a data set, wherein the data set comprises a plurality of normal image data of coal mine ventilation equipment and a plurality of abnormal image data of the coal mine ventilation equipment.
In step S202 in the embodiment of this specification, the detection system of the coal mine ventilation equipment obtains operation state data inside the coal mine ventilation equipment through the detection equipment, where the operation state data includes state data of normal operation and state data of abnormal operation of the coal mine ventilation equipment. The coal mine ventilation equipment detection system converts all the internal operation state data of the coal mine ventilation equipment into image data. The image data can be a waveform diagram, namely, the operation state data inside the coal mine ventilation equipment is represented in the form of the waveform diagram.
The data in the data set may be for a coal mine ventilation unit of 20 seconds in length and at a sampling frequency of 500 HZ.
After this step, the embodiments of this specification may further perform the following steps:
and preprocessing the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set according to a preset mode so as to eliminate the noise in the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set.
The preset mode may include an open operation, a close operation, and a binarization operation.
And S203, the coal mine ventilation equipment detection system obtains a coal mine ventilation equipment detection model meeting the requirements according to the data set and the initial coal mine ventilation equipment detection model.
In step S203 in the embodiment of this specification, this step specifically includes:
dividing the data set into a training set and a verification set according to a preset proportion, wherein the proportion of the training set to the verification set can be 7: 3;
leading the training set into a deep learning model, and obtaining a coal mine ventilation equipment detection model to be adjusted through a plurality of training periods;
evaluating the coal mine ventilation equipment detection model to be adjusted according to the verification set to obtain an evaluation result;
and adjusting the hyper-parameters according to the evaluation result, and determining a coal mine ventilation equipment detection model meeting the conditions.
It should be noted that, because the operating parameters of the coal mine ventilation equipment are different between the normal state and the fault state, the radio frequency signals of the radio frequency signal capturing equipment outside the coal mine ventilation equipment are different, and the displayed images are different. The characteristics of the images are analyzed by machine learning, a model is generated by training a deep neural network, the model is deployed on a server in a production environment, and radio frequency signal capturing equipment is connected with the server to perform real-time detection.
It should be noted that the deep learning model may be a residual neural network model.
And the trained detection model of the coal mine ventilation equipment can be deployed on a server. The coal mine ventilation equipment detection model can judge whether the current coal mine ventilation equipment has faults or not, the date of the faults and the specific fault position, and real-time monitoring can be achieved on a display screen of a server.
And S204, the coal mine ventilation equipment detection system acquires the running state data inside the coal mine ventilation equipment through the detection equipment.
Step S205, the coal mine ventilation equipment detection system converts the operation state data in the coal mine ventilation equipment into image data, and inputs the image data into a pre-trained coal mine ventilation equipment detection model.
And S206, the coal mine ventilation equipment detection system determines the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
It should be noted that the normal operation of the coal mine ventilation equipment is related to the mine production operation, and if the coal mine ventilation equipment fails, the underground air cannot be normally ventilated, which may cause serious production safety accidents, may cause explosion risks and endanger the lives of workers. When the coal mine ventilation equipment has high failure risk, the coal mine ventilation equipment needs to be maintained regularly. However, the conventional maintenance cannot avoid accidents, and if the coal mine ventilation equipment fails and is not discovered, great loss may be brought. In order to solve the above problems, in the embodiments of the present description, the radio frequency signal capture device may capture the operating state data inside the coal mine ventilation equipment, convert the operating state data inside the coal mine ventilation equipment into image data, and determine the state information of the coal mine ventilation equipment through a coal mine ventilation equipment detection model, so as to achieve an effect of monitoring the operating state of the coal mine ventilation equipment in real time. Once the coal mine ventilation equipment is found to be out of order, a notice can be sent out in time, and maintenance personnel can maintain the coal mine ventilation equipment, so that the occurrence of heavy loss is avoided.
In the radio frequency signal proposed in the embodiments of the present invention, when an alternating current flows through a conductor, an alternating magnetic field is generated around the conductor, and the alternating magnetic field induces an alternating electric field in the vicinity of the conductor, and the electromagnetic wave is formed and radiated outward by repeating the cycle. Generally, the higher the frequency, the more pronounced this phenomenon, i.e., the higher the transmission efficiency.
The detection device is a radio frequency signal capture device arranged outside the coal mine ventilation device. The radio frequency signals can be WIFI signals, and the detection equipment can acquire the running state data in the coal mine ventilation equipment through the WIFI signals.
The WIFI signal is a wireless network signal, almost all smart phones, tablet computers and notebook computers support WIFI internet surfing, and the WIFI signal is a wireless network transmission technology which is used most widely at present. WIFI signals are typically information carriers between a transmitter and a receiver. One study by the massachusetts institute of technology shows that WIFI also expands the human senses, enabling viewing of objects that pass through the wall. Embodiments of the present description may utilize this technique to capture radio frequency signals generated by internal structures across the enclosure of a coal mine ventilation unit and convert the signals into an image as input data to a detection model of the coal mine ventilation unit.
It should be noted that the detection device in the embodiment of the present description acquires the operation state data inside the coal mine ventilation device, and may send the data to the detection model of the coal mine ventilation device through the antenna unit. The detection device may be a radio frequency signal capture device located externally to the coal mine ventilation device. The radio frequency signal capturing device can acquire the running state data in the coal mine ventilation device through WIFI signals.
The detection device can be an MIMO system, the MIMO system can greatly improve the channel capacity, a plurality of antennas are used at a sending end and a receiving end, and a plurality of channels are formed between the sending end and the receiving end. An obvious characteristic of the MIMO system is that the MIMO system has a very high spectrum utilization efficiency, and obtains gains in both reliability and effectiveness by using space resources on the basis of fully utilizing existing spectrum resources, at the cost of increasing the processing complexity of a transmitting end and a receiving end.
It should be noted that, in the embodiments of the present disclosure, radio frequency signals may be used to sense the interior of the coal mine ventilation equipment, and the MIMO system may use a linearly polarized broadband antenna array to transmit, receive and record signals to reconstruct an environment image, so as to obtain dynamic information of the interior of the coal mine ventilation equipment. This manner of obtaining internal information through coal mine ventilation equipment is similar to radar and sonar imaging.
Further, in the embodiments of the present disclosure, it is proposed that the data in the coal mine ventilation equipment is acquired by the rf signal capturing device, that is, a part of the rf signal passes through the outer shell of the coal mine ventilation equipment, and may be reflected back to the internal structural information, and a signature with an imprint is left in the internal information. The next time the radio frequency signal is transmitted, the structures inside the housing can be imaged by simply recapturing these reflections. The working principle of the radio frequency signal capturing device is as follows: reflections outside the device housing are separated from reflections inside the housing according to the arrival time of the signal and a sub-nanosecond delay is determined to filter the sparkling effect.
It should be noted that multiple antennas in a MIMO system may encode their transmissions such that the signal is zero at the predetermined receiving antenna. MIMO systems use this function (nulling) to eliminate interference to unwanted receivers and also to eliminate reflections from static objects (including equipment housings, static objects within housings).
Further, the rf signal capturing device in the embodiments of the present disclosure may have two transmitting antennas and one receiving antenna. The radio frequency signal acquisition device can operate in two phases:
in a first phase, measuring channels from each of two transmit antennas connected to its receive antenna;
in the second phase, the two transmit antennas use the channel measurements of the first phase to null the signals at the receive antennas.
It is noted that since the wireless signals (including reflections) are linearly combined on the medium, only reflections of moving objects between the two phases are captured in the second phase. The reflection for static objects, including walls, will be zero.
It should be noted that the radio frequency signal capturing device may use a frequency band of WIFI OFDM ISM2.4 GHz and a signal in general WIFI hardware. The radio frequency signal acquisition device may be a three antenna MIMO device: where two antennas are used for transmission and one antenna is used for reception. It comprises two main components: 1. the first component eliminates the flash reflected from the housing by performing a MIMO nulling scoring approach; 2. the second component tracks moving objects by treating the object itself as an antenna array using inverse SAR (synthetic aperture radar) techniques.
It should be noted that the embodiments of the present specification may use USRP (universal software radio peripheral) N210 software radio and SBX to construct a radio frequency capture device daughter board. MIMO systems may use LP0965 directional antennas, which may provide 6dBi gain. The device consists of three USRPs connected to an external clock, which can act as a MIMO system. Two USRPs are used for transmission and one for reception. MIMO zeroing is directly implemented in an UHD (Ultra High Definition) driver so that an effect of real-time execution can be achieved.
Implementing standard WIFIOFDM (orthogonal frequency division multiplexing) modulation codes in the UHD code; each OFDM symbol consists of 64 subcarriers, which include direct current. The zeroing process is performed on a subcarrier basis. Channel measurements across different subcarriers are combined to improve the signal-to-noise ratio. Since USRP cannot process signals at 20MHz in real time, the bandwidth of the transmitted signal is reduced to 5MHz, so that the return-to-zero operation can still run in real time.
In the prior art, in order to maintain coal mine ventilation equipment, a sensor needs to be added into the coal mine ventilation equipment so as to monitor and collect data. However, for the coal mine ventilation equipment, the difficulty of adding a sensor inside the coal mine ventilation equipment is high, and the accuracy of collected data is not high, so that the prediction result is not accurate enough. In the above-mentioned scheme that this specification embodiment provided, can collect data in the outside of colliery ventilation equipment, need not add extra sensor, all can adopt same data acquisition mode to the equipment of different models, it is convenient to deploy, and the data collected is more accurate reliable, and is better to colliery ventilation equipment's maintenance effect.
Referring to fig. 3, a schematic structural diagram of a detection system of a coal mine ventilation device is shown, the detection device is deployed outside the to-be-detected ventilation device and used for acquiring operation data inside the coal mine ventilation device, converting the operation data into image data, then sending the image data to a coal mine ventilation device detection model through a link channel (WIFI), and finally determining operation information of the coal mine ventilation device through the coal mine ventilation device detection model.
The embodiment of the application acquires the internal running state data of the coal mine ventilation equipment through the detection equipment, and finally determines the state information of the current coal mine ventilation system through the pre-trained coal mine ventilation equipment detection model, so that the coal mine ventilation equipment can be maintained as early as possible when the coal mine ventilation equipment is abnormal, and the occurrence of greater loss is avoided.
The embodiment of the application still provides a colliery ventilation equipment check out test set, equipment includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
The embodiment of the application also provides a detection medium for the coal mine ventilation equipment, which stores computer executable instructions, wherein the computer executable instructions are set as follows:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for detecting coal mine ventilation equipment is characterized by comprising the following steps:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
2. The method for detecting coal mine ventilation equipment as claimed in claim 1, wherein before the obtaining of the operational status data inside the coal mine ventilation equipment by the detection equipment, the method further comprises:
constructing an initial coal mine ventilation equipment detection model;
constructing a data set, wherein the data set comprises image data of a plurality of coal mine ventilation devices which are normal and image data of a plurality of coal mine ventilation devices which are abnormal;
and obtaining a coal mine ventilation equipment detection model meeting the requirements according to the data set and the initial coal mine ventilation equipment detection model.
3. The method for detecting coal mine ventilation equipment according to claim 2, wherein the step of obtaining a coal mine ventilation equipment detection model meeting requirements according to the data set and the initial coal mine ventilation equipment detection model specifically comprises the following steps:
dividing the data set into a training set and a verification set according to a preset proportion;
leading the training set into a deep learning model, and obtaining a coal mine ventilation equipment detection model to be adjusted through a plurality of training periods;
evaluating the coal mine ventilation equipment detection model to be adjusted according to the verification set to obtain an evaluation result;
and adjusting the hyper-parameters according to the evaluation result, and determining a coal mine ventilation equipment detection model meeting the conditions.
4. The coal mine ventilation equipment detection method of claim 3, wherein the deep learning model is a residual neural network model.
5. The coal mine ventilation equipment detection method of claim 2, wherein after constructing the data set, the method further comprises:
and preprocessing the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set according to a preset mode so as to eliminate the noise in the normal image data of the plurality of coal mine ventilation devices and the abnormal images of the plurality of coal mine ventilation devices in the data set.
6. The coal mine ventilation equipment detection method as claimed in claim 5, wherein the preset mode comprises an opening operation, a closing operation and a binarization operation.
7. The method of detecting coal mine ventilation equipment as claimed in claim 1, wherein the detection equipment is radio frequency signal capture equipment disposed outside the coal mine ventilation equipment.
8. The coal mine ventilation equipment detection method according to claim 7, wherein the radio frequency signal capture device obtains the operation state data inside the coal mine ventilation equipment through WIFI signals.
9. A coal mine ventilation equipment detection device, the device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
10. A coal mine ventilation equipment detection medium storing computer executable instructions, the computer executable instructions configured to:
acquiring running state data inside the coal mine ventilation equipment through detection equipment;
converting the running state data inside the coal mine ventilation equipment into image data, and inputting the image data into a pre-trained coal mine ventilation equipment detection model;
and determining the state information of the coal mine ventilation equipment according to the coal mine ventilation equipment detection model.
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