CN113589789A - Equipment detection method and device - Google Patents
Equipment detection method and device Download PDFInfo
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- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention provides a device detection method and a device, wherein the device detection method comprises the following steps: receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by terminal monitoring target equipment; inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels. According to the equipment detection method and device provided by the invention, the image sequence data and the physical state parameters sent by the receiving terminal are input into the equipment detection model, and the equipment detection result is obtained by using the neural network model, so that the server side can timely master the working state of the production equipment, the labor cost is reduced, and the detection efficiency is improved.
Description
Technical Field
The invention relates to the technical field of industrial control, in particular to a device detection method and device.
Background
With the development of mechanization and automation technology, in the manufacturing industry, the replacement of manual work by mechanical equipment and electronic equipment has become a very common phenomenon, automatic production lines are increasingly popularized, and for various industrial equipment, the working state of the industrial equipment needs to be detected in time, so that the production progress is prevented from being influenced by faults, and dangerous accidents are avoided.
At present, the method for detecting the working state of the industrial equipment is mainly carried out in modes of manual consultation, field inspection and cache video playing, the modes are complex to operate, manpower and material resources are wasted, the condition of the equipment cannot be mastered in real time, and the detection efficiency is low.
Disclosure of Invention
The invention provides a device detection method and a device, which are used for solving the defects that the operation is complex, manpower and material resources are wasted, the condition of the device cannot be mastered in real time and the detection efficiency is low in the prior art, realizing the timely mastering of the working state of production equipment, reducing the labor cost and improving the detection efficiency.
The invention provides a device detection method, which comprises the following steps: receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by a terminal monitoring target device; inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
According to the equipment detection method provided by the invention, the equipment detection result sample data is obtained by displaying the image sequence sample data and the physical state sample parameters corresponding to the time, receiving the first input of a user and responding to the first input.
According to the equipment detection method provided by the invention, the physical state parameter is at least one of equipment real-time working current, equipment real-time working voltage, equipment real-time working power and equipment real-time working temperature.
The invention also provides a device detection method, which comprises the following steps: acquiring physical state parameters and image sequence data of target equipment; sending the physical state parameters and the image sequence data to a server side, so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
According to the device detection method provided by the invention, the sending of the physical state parameter and the image sequence data to the server side comprises the following steps: and storing the physical state parameters and the image sequence data to the server side through message middleware.
According to the device detection method provided by the invention, the acquiring of the physical state parameters and the image sequence data of the target device comprises the following steps: acquiring the physical state parameters based on a sensor, and acquiring a running image of equipment based on a camera; synthesizing the target node images into the image sequence data at a target frame rate, the target frame rate being set such that time points of the image sequence data correspond to time points of the physical state parameters.
The present invention also provides an apparatus detection device, including: the device comprises a receiving device and a processing device, wherein the receiving device is used for receiving image sequence data and physical state parameters, and the image sequence data and the physical state parameters are obtained by a terminal monitoring target device; the detection device is used for inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
The present invention also provides an apparatus detection device, including: the acquisition device is used for acquiring physical state parameters and image sequence data of the target equipment; the sending device is used for sending the physical state parameters and the image sequence data to a server side so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
The invention also provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any one of the device detection methods.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the device detection method as described in any one of the above.
According to the equipment detection method and device provided by the invention, the image sequence data and the physical state parameters sent by the receiving terminal are input into the equipment detection model, and the equipment detection result is obtained by using the neural network model, so that the server side can timely master the working state of the production equipment, the labor cost is reduced, and the detection efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a device detection method provided by the present invention;
FIG. 2 is a schematic diagram of the principle of the device detection method provided by the present invention;
FIG. 3 is a schematic structural diagram of an apparatus detecting device provided in the present invention;
FIG. 4 is a second schematic flow chart of the equipment inspection method provided in the present invention;
FIG. 5 is a second schematic structural diagram of the apparatus detecting device provided in the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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 invention.
The device detection method and apparatus of the present invention are described below with reference to fig. 1-6.
It should be noted that, when various industrial devices work, the industrial devices need to be remotely monitored and detected, and here, a terminal is arranged at the location of the industrial devices, and the terminal is used for monitoring the industrial devices and acquiring data related to the work of the industrial devices; and a server end is arranged at the remote position of the industrial equipment, the server end can be communicated with the terminal, and a user can master the working state of the industrial equipment at the server end.
As shown in fig. 1 and fig. 2, the present invention provides a device detection method, where an execution subject of the device detection method is a server side, and the device detection method includes the following steps 110 to 120.
It can be understood that the server may receive the image sequence data and the physical state parameters sent by the terminal, the number of terminals communicatively connected to the server may be multiple, each terminal corresponds to one or more production devices, and then the image sequence data and the physical state parameters received by the server may correspond to multiple production devices.
The image sequence data may be video data in which a plurality of images are arranged in time series.
The physical state parameter of the device may be a physical parameter that reflects the operation of the device, for example, the physical state parameter may be at least one of a real-time operating current of the device, a real-time operating voltage of the device, a real-time operating power of the device, and a real-time operating temperature of the device.
Of course, the physical state parameters of the device are not limited to the listed ones, and may also be other physical parameters reflecting the operation of the device.
The physical state parameters of the sensor detection equipment can be adopted, for example, the real-time working current of the equipment can be detected through a current sensor, the real-time working voltage of the equipment can be detected through a voltage sensor, the real-time working power of the equipment can be calculated on the basis of obtaining the real-time working current of the equipment and the real-time working voltage of the equipment, and the real-time working temperature of the equipment can be detected through a temperature sensor.
The image sequence data may be video data of the device captured by the camera, that is, a real-time video of the device in an operating state, or may be an image captured by the camera at a certain frequency.
The lens of the camera can be oriented to the production equipment, and the running image of the production equipment is shot according to a certain frequency, for example, the running image of the equipment can be shot once every second.
The device operation images may be synthesized into image sequence data at a certain target frame rate, for example, the device operation images may be synthesized using ffmpeg to generate image sequence data in h264 format, but the target frame rate needs to be set to ensure that the time points of the image sequence data and the time points of the physical state parameters are aligned, that is, the device operation time reflected in the image sequence data is synchronized with the device operation time reflected in the physical state parameters.
Of course, the image sequence data and the physical state parameters may not be acquired in real time, a production process key node detection algorithm may be run, an algorithm interface is called in a java jna manner, when the occurrence of a key node in the equipment is detected, the image sequence data and the corresponding physical state parameters are stored, and the key node may be a time node at which a sudden change value occurs in the physical state parameters.
Of course, the image sequence data may be marked by an image recognition algorithm, and a person or a device in the image sequence data may be marked by frame selection.
In other words, the image sequence data and the physical state parameter are arranged in time sequence, and a certain frame in the image sequence data has a corresponding physical state parameter at a time, and when the device is determined to run the image at a certain time, the physical state parameter at the time can be determined at the same time.
And 120, inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result.
The equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
It is understood that the device detection model may be a neural network model, inputs of the device detection model are image sequence data and physical state parameters, that is, two inputs, and an output of the device detection model is a device detection result.
The device detection result may indicate that a fault condition occurs in the device, for example, a current leakage phenomenon occurs in the device.
The device detection model can be trained by using the image sequence sample data, the physical state sample parameters and the corresponding device detection result sample data.
The image sequence sample data and the physical state sample parameters may be a large amount of data generated by the same equipment in the historical working process, and certainly, may also be a large amount of data generated by a plurality of equipments in the historical working process, and are saved by the server end in the historical working process, and professional personnel may analyze and mark the image sequence sample data and the physical state sample parameters, and obtain equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters.
After the training data set is obtained, the equipment detection model can be trained, and the equipment detection model with higher accuracy can be trained in the supervised learning mode.
It should be noted that the device detection model may be a convolutional neural network or a residual neural network, which is not specifically limited herein and can be selected by a person skilled in the art according to the circumstances.
Put the server end with data analysis and testing process, just can alleviate the computational stress at terminal, can rely on the advantage of server end with a plurality of terminal communication simultaneously, can acquire a large amount of training data, can improve equipment detection model's the degree of accuracy through the repeated training to equipment detection model like this.
According to the equipment detection method provided by the invention, the image sequence data and the physical state parameters sent by the receiving terminal are input into the equipment detection model, and the equipment detection result is obtained by utilizing the neural network model, so that the server side can timely master the working state of the production equipment, the labor cost is reduced, and the detection efficiency is improved.
In some embodiments, the device detection result sample data is obtained by displaying image sequence sample data and a physical state sample parameter corresponding to time, receiving a first input of a user, and responding to the first input.
It can be understood that, before the process of training the device detection model, the device detection result sample data may be obtained manually, and the specific process may be that the image sequence sample data and the physical state sample parameters are played synchronously in time and displayed to the user, and the user may analyze and mark the image sequence sample data and the physical state sample parameters on the basis of observing the image sequence sample data and the physical state sample parameters, so that a first input is given to the server, and the server can respond to the first input to obtain the device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters.
It is worth mentioning that the process of manually analyzing and marking the image sequence sample data and the physical state sample parameters is only performed before the training of the equipment detection model, and manual analysis and marking are not required in the process of applying the equipment detection model to detect the working state of the production equipment, so that the accuracy of the equipment detection model can be further improved while the working efficiency is ensured.
The device detection apparatus provided by the present invention is described below, and the device detection apparatus described below and the device detection method described above may be referred to in correspondence with each other.
As shown in fig. 3, the present invention also provides an apparatus detecting device, including: receiving means 310 and detecting means 320.
The receiving device 310 is configured to receive the image sequence data and the physical state parameter, where the image sequence data and the physical state parameter are obtained by the terminal monitoring target device;
the detection device 320 is used for inputting the image sequence data and the physical state parameters into the equipment detection model to obtain an equipment detection result;
the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
The device detection apparatus provided in the embodiment of the present application is used for executing the device detection method, and a specific implementation manner of the device detection apparatus is consistent with a method implementation manner, and the same beneficial effects can be achieved, which is not described herein again.
As shown in fig. 4, the present invention further provides an apparatus detection method, where an execution main body of the apparatus detection method is a terminal, and the terminal may be a mobile terminal, such as an electronic apparatus like a mobile phone, a tablet computer, or a notebook computer, or may also be a fixed terminal, such as a desktop computer, an industrial personal computer, or a vehicle-mounted host, and the apparatus detection method includes: as follows from step 410 to step 420.
It can be understood that the terminal can be connected with various sensors and cameras, physical state parameters of the sensor detection equipment can be adopted, for example, the real-time working current of the equipment can be detected through a current sensor, the real-time working voltage of the equipment can be detected through a voltage sensor, the real-time working power of the equipment can be calculated on the basis of obtaining the real-time working current of the equipment and the real-time working voltage of the equipment, and the real-time working temperature of the equipment can be detected through a temperature sensor.
The image sequence data may be image sequence data of the device captured by the camera, that is, a real-time video of the device in an operating state, and of course, an image of the device may also be captured by the camera at a certain frequency.
The lens of the camera can be oriented to the production equipment, and the running image of the production equipment is shot according to a certain frequency, for example, the running image of the equipment can be shot once every second.
The device operation images may be synthesized into image sequence data at a certain target frame rate, for example, the device operation images may be synthesized using ffmpeg to generate image sequence data in h264 format, but the target frame rate needs to be set to ensure that the time points of the image sequence data and the time points of the physical state parameters are aligned, that is, the device operation time reflected in the image sequence data is synchronized with the device operation time reflected in the physical state parameters.
Of course, the image sequence data and the physical state parameters may not be acquired in real time, a production process key node detection algorithm may be run, an algorithm interface is called in a java jna manner, when the occurrence of a key node in the equipment is detected, the image sequence data and the corresponding physical state parameters are stored, and the key node may be a time node at which a sudden change value occurs in the physical state parameters.
In other words, the image sequence data and the physical state parameter are arranged in time sequence, and a certain frame in the image sequence data has a corresponding physical state parameter at a time, and when the device is determined to run the image at a certain time, the physical state parameter at the time can be determined at the same time.
And step 420, sending the physical state parameters and the image sequence data to a server side, so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result.
The equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
It can be understood that the terminal can package the physical state parameters and the image sequence data and send the physical state parameters and the image sequence data to the server, and after receiving the physical state parameters and the image sequence data, the server can input the physical state parameters and the image sequence data into the device detection model and output a device detection result.
The device detection model can be a neural network model, the input of the device detection model is image sequence data and physical state parameters, namely, the device detection model has double inputs, and the output of the device detection model is a device detection result.
The device detection result may indicate that a fault condition occurs in the device, for example, a current leakage phenomenon occurs in the device.
The device detection model can be trained by using the image sequence sample data, the physical state sample parameters and the corresponding device detection result sample data.
The image sequence sample data and the physical state sample parameters may be a large amount of data generated by the same equipment in the historical working process, and certainly, may also be a large amount of data generated by a plurality of equipments in the historical working process, and are saved by the server end in the historical working process, and professional personnel may analyze and mark the image sequence sample data and the physical state sample parameters, and obtain equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters.
After the training data set is obtained, the equipment detection model can be trained, and the equipment detection model with higher accuracy can be trained in the supervised learning mode.
It should be noted that the device detection model may be a convolutional neural network or a residual neural network, which is not specifically limited herein and can be selected by a person skilled in the art according to the circumstances.
Put the server end with data analysis and testing process, just can alleviate the computational stress at terminal, can rely on the advantage of server end with a plurality of terminal communication simultaneously, can acquire a large amount of training data, can improve equipment detection model's the degree of accuracy through the repeated training to equipment detection model like this.
According to the equipment detection method provided by the invention, the image sequence data and the physical state parameters sent by the receiving terminal are input into the equipment detection model, and the equipment detection result is obtained by utilizing the neural network model, so that the server side can timely master the working state of the production equipment, the labor cost is reduced, and the detection efficiency is improved.
In some embodiments, the step 420 of sending the physical state parameter and the image sequence data to the server side includes: and storing the physical state parameters and the image sequence data to a server through message middleware.
It can be understood that the message middleware can be designed based on Kafka messages, and can adopt Kafka to gather video address data information, gather and store image sequence data to the server side, deploy nginx, add configuration items supporting image sequence data access, provide query connection of image sequence data through a gateway service, and facilitate retrieval and search of image sequence data from the server side.
The physical state parameters can be sent to the server side in a Kafka message mode and are subjected to aggregation storage.
In some embodiments, the acquiring 410 the physical state parameter of the target device and the image sequence data includes: acquiring physical state parameters based on a sensor and acquiring a running image of equipment based on a camera; and synthesizing the target node images into image sequence data according to a target frame rate, wherein the target frame rate is set to enable the time points of the image sequence data to correspond to the time points of the physical state parameters.
It can be understood that the physical state parameters of the device can be detected by the sensor, for example, the real-time working current of the device can be detected by the current sensor, the real-time working voltage of the device can be detected by the voltage sensor, the real-time working power of the device can be calculated on the basis of obtaining the real-time working current and the real-time working voltage of the device, and the real-time working temperature of the device can be detected by the temperature sensor.
The device operation images can be captured by the camera at a certain frequency, and the device operation images can be synthesized into image sequence data according to a certain target frame rate, for example, the device operation images can be synthesized by using ffmpeg to generate image sequence data in h264 format, but the target frame rate is set to ensure that the time point of the image sequence data and the time point of the physical state parameter are aligned, that is, the device operation time reflected in the image sequence data is synchronous with the device operation time reflected in the physical state parameter.
The device detection apparatus provided by the present invention is described below, and the device detection apparatus described below and the device detection method described above may be referred to in correspondence with each other.
As shown in fig. 5, the present invention also provides an apparatus detecting device, including: acquisition means 510 and transmission means 520.
An acquiring device 510, configured to acquire physical state parameters of the target apparatus and the image sequence data;
the sending device 520 is configured to send the physical state parameters and the image sequence data to the server, so that the server inputs the image sequence data and the physical state parameters into the device detection model to obtain a device detection result;
the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
The device detection apparatus provided in the embodiment of the present application is used for executing the device detection method, and a specific implementation manner of the device detection apparatus is consistent with a method implementation manner, and the same beneficial effects can be achieved, which is not described herein again.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a device detection method comprising: receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by terminal monitoring target equipment; inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
Meanwhile, the method also comprises the following steps: acquiring physical state parameters and image sequence data of target equipment; sending the physical state parameters and the image sequence data to a server side so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the device detection method provided by the above methods, the method comprising: receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by terminal monitoring target equipment; inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
Meanwhile, the method also comprises the following steps: acquiring physical state parameters and image sequence data of target equipment; sending the physical state parameters and the image sequence data to a server side so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the device detection methods provided above, the method comprising: receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by terminal monitoring target equipment; inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
Meanwhile, the method also comprises the following steps: acquiring physical state parameters and image sequence data of target equipment; sending the physical state parameters and the image sequence data to a server side so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result; the equipment detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking equipment detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A device detection method, comprising:
receiving image sequence data and physical state parameters, wherein the image sequence data and the physical state parameters are obtained by a terminal monitoring target device;
inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result;
the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
2. The device detection method according to claim 1, wherein the device detection result sample data is obtained by displaying image sequence sample data and a physical state sample parameter corresponding to time, receiving a first input of a user, and responding to the first input.
3. The device detection method of claim 1, wherein the physical state parameter is at least one of a device real-time operating current, a device real-time operating voltage, a device real-time operating power, and a device real-time operating temperature.
4. A device detection method, comprising:
acquiring physical state parameters and image sequence data of target equipment;
sending the physical state parameters and the image sequence data to a server side, so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result;
the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
5. The device detection method according to claim 4, wherein the sending the physical state parameter and the image sequence data to a server side includes:
and storing the physical state parameters and the image sequence data to the server side through message middleware.
6. The device detection method according to claim 4, wherein the acquiring of the physical state parameter of the target device and the image sequence data includes:
acquiring the physical state parameters based on a sensor, and acquiring a running image of equipment based on a camera;
synthesizing the target node images into the image sequence data at a target frame rate, the target frame rate being set such that time points of the image sequence data correspond to time points of the physical state parameters.
7. An apparatus detection device, comprising:
the device comprises a receiving device and a processing device, wherein the receiving device is used for receiving image sequence data and physical state parameters, and the image sequence data and the physical state parameters are obtained by a terminal monitoring target device;
the detection device is used for inputting the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result;
the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
8. An apparatus detection device, comprising:
the acquisition device is used for acquiring physical state parameters and image sequence data of the target equipment;
the sending device is used for sending the physical state parameters and the image sequence data to a server side so that the server side can input the image sequence data and the physical state parameters into an equipment detection model to obtain an equipment detection result;
the device detection model is obtained by training by taking image sequence sample data and physical state sample parameters as samples and taking device detection result sample data corresponding to the image sequence sample data and the physical state sample parameters as labels.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the device detection method according to any one of claims 1 to 6 are implemented when the program is executed by the processor.
10. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the device detection method according to any one of claims 1 to 6.
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Application publication date: 20211102 |