Coal-rock identification method, device and storage medium

Abstract

The present disclosure proposes a coal-rock identification method, device and storage medium. The method includes: acquiring an image to be identified of a coal mining face, and determining the target working condition of the coal mining face based on the image to be identified, and using a target recognition algorithm corresponding to the target working condition to identify the image to be identified, and judging whether there is a rock area on the coal mining face. The target algorithm can be adaptively selected according to the actual working condition of the coal mining face to perform coal-rock identification, thereby improving the robustness and accuracy of coal-rock identification.

Description

Technical Field

[0001] The present disclosure relates to the field of image detection technology, and in particular to a coal-rock identification method, device and storage medium. Background Art

[0002] In the process of coal mining, it is necessary to identify the coal seam and the rock layer. In the actual production process, the operator generally relies on visual inspection and hearing to identify the coal-rock area. With the development of technology, it is also proposed to solve the problem of coal-rock boundary identification through image processing. However, due to the complex underground environment, high concentration of dust and water mist, low light source illumination and uneven lighting, the use of general image recognition algorithms will affect the accuracy of coal-rock identification. Summary of the invention

[0003] The present application proposes a coal-rock identification method, device and storage medium, aiming to solve one of the technical problems in the related art at least to a certain extent.

[0004] The first aspect of the present application proposes a coal-rock identification method, including: obtaining an image to be identified of a coal mining face; determining a target working condition to which the coal mining face belongs based on the image to be identified; and using a target recognition algorithm corresponding to the target working condition to identify the image to be identified to determine whether there is a rock area on the coal mining face.

[0005] The second aspect of the present application proposes a coal-rock identification device, including: a first acquisition module, used to acquire an image to be identified of a coal mining face; a first determination module, used to determine the target working condition to which the coal mining face belongs based on the image to be identified; and an identification module, used to use a target recognition algorithm corresponding to the target working condition to identify the image to be identified, and determine whether there is a rock area on the coal mining face.

[0006] The third aspect embodiment of the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the coal rock identification method of the embodiment of the present application.

[0007] The fourth aspect embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the coal rock identification method disclosed in the embodiment of the present application.

[0008] In this embodiment, by acquiring the image to be identified of the coal mining face, determining the target working condition of the coal mining face based on the image to be identified, and using the target recognition algorithm corresponding to the target working condition to identify the image to be identified, it is determined whether there is a rock area on the coal mining face. The target algorithm can be adaptively selected according to the actual working condition of the coal mining face to perform coal and rock identification, thereby improving the robustness and accuracy of coal and rock identification.

[0009] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0011] Figure 1 is a flow chart of a coal-rock identification method provided according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic diagram of the structure of a coal mining machine provided according to an embodiment of the present disclosure;

[0013] Figure 3 is a schematic diagram of the structure of a coal-rock identification device provided according to an embodiment of the present disclosure;

[0014] Figure 4 is a flow chart of a coal-rock identification method provided according to another embodiment of the present disclosure;

[0015] Figure 5 is a flow chart of classification model training and application provided according to an embodiment of the present disclosure;

[0016] Figure 6 It is a schematic diagram of the overall process of coal-rock identification provided according to an embodiment of the present disclosure;

[0017] Figure 7 is a schematic diagram of a coal-rock identification device provided according to another embodiment of the present disclosure;

[0018] Figure 8 A block diagram of an exemplary computer device suitable for implementing embodiments of the present application is shown. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0020] In response to the technical problem mentioned in the background technology that the underground environment of coal mines is complex, the dust and water mist concentrations are high, the light source illumination is low and the lighting is uneven, and the use of general image recognition algorithms will affect the accuracy of coal and rock recognition, the technical solution of this embodiment provides a coal and rock recognition method, which is explained below in conjunction with a specific embodiment.

[0021] It should be noted that the executor of the coal rock identification method of this embodiment can be a coal rock identification device, which can be implemented by software and / or hardware, and can be configured in an electronic device, which can include but is not limited to a terminal, a server, etc.

[0022] Figure 1 is a flow chart of a coal-rock identification method provided according to an embodiment of the present disclosure, such as Figure 1 As shown, the method includes:

[0023] S101: Acquire the image to be identified of the coal mining face.

[0024] In the disclosed embodiment, the image to be identified of the coal mining face is firstly acquired.

[0025] Among them, the coal mining face image that needs to be identified can be called an image to be identified, and the image to be identified can be an image collected in real time, and the image to be identified can be one frame of image or multiple frames of image, which is not limited.

[0026] In some embodiments, the image to be identified may be collected during the coal mining process of a coal mining machine, wherein the coal mining machine may be equipped with one or more coal-rock identification devices for collecting the image to be identified.

[0027] For example, Figure 2 is a schematic diagram of the structure of a coal mining machine provided according to an embodiment of the present disclosure, such as Figure 2 As shown, coal-rock identification devices can be respectively arranged on both sides of the coal mining machine to ensure that when the coal mining machine works in both directions, coal-rock images of the area to be cut can be collected in advance, that is, images to be identified of the coal mining face can be obtained.

[0028] also, Figure 3 is a schematic diagram of the structure of a coal-rock identification device provided according to an embodiment of the present disclosure, such as Figure 3As shown, the coal and rock identification device may include an explosion-proof box, an annular light source, a visible light camera and an embedded industrial computer, wherein the annular light source is used to provide fill light when the illumination of the imaging environment is too low, the visible light camera is used to collect the image to be identified and transmit it to the embedded industrial computer, and the embedded industrial computer is used to identify the image to be identified. That is to say, the coal and rock identification method provided in this embodiment can be executed by an embedded industrial computer.

[0029] S102: Determine the target working condition of the coal mining face according to the image to be identified.

[0030] After the image to be identified is acquired as described above, the embodiment of the present disclosure can further determine the target working condition of the coal mining face according to the image to be identified.

[0031] Among them, the embodiments of the present disclosure can establish a variety of typical working conditions based on the common environment underground in a coal mine (ambient light illumination, dust concentration, water mist concentration, etc.), and the working condition to which the coal mining face in the image to be identified belongs can be called the target working condition, that is, different images to be identified can correspond to different target working conditions.

[0032] In some embodiments, the target operating condition may be determined by manual selection, or may be determined by an algorithm, or may be determined by any other possible method, without limitation.

[0033] S103: Using a target recognition algorithm corresponding to the target working condition to recognize the image to be recognized, and determining whether there is a rock area on the coal mining face.

[0034] Among them, the embodiments of the present disclosure can set multiple image recognition algorithms (or models) for multiple typical working conditions, for example: setting a corresponding image recognition algorithm for each typical working condition, or setting one image recognition algorithm for multiple working conditions, without limitation.

[0035] The image recognition algorithm corresponding to the target working condition can be called a target recognition algorithm. In some embodiments, the image recognition algorithm can be pre-configured in the embedded industrial computer of the coal-rock recognition device. The embedded industrial computer can determine the corresponding target recognition algorithm according to the target working condition, and further use the target recognition algorithm to identify the image to be identified, and determine whether there is a rock area on the coal mining face, for example: identify whether there is a coal-rock boundary line in the image to be identified, so as to determine whether there is a rock area. Therefore, the coal mining machine can be guided to adaptively adjust the cutting height and traction speed, etc., to avoid the coal mining machine cutting the rock.

[0036] In some embodiments, the above-mentioned image recognition algorithm can be based on artificial intelligence models such as cluster analysis, support vector machine, neural network, deep learning, etc., or can be any other possible algorithm model, without limitation.

[0037] In this embodiment, by acquiring the image to be identified of the coal mining face, determining the target working condition of the coal mining face based on the image to be identified, and using the target recognition algorithm corresponding to the target working condition to identify the image to be identified, it is determined whether there is a rock area on the coal mining face. The target algorithm can be adaptively selected according to the actual working condition of the coal mining face to perform coal and rock identification, thereby improving the robustness and accuracy of coal and rock identification.

[0038] Figure 4 is a flow chart of a coal-rock identification method provided according to an embodiment of the present disclosure, such as Figure 4 As shown, the method includes:

[0039] S401: Acquire the image to be identified of the coal mining face.

[0040] The specific description of S401 can be found in the above embodiment and will not be repeated here.

[0041] S402: Determine image features of the image to be recognized.

[0042] In the operation of determining the target working condition of the coal mining face according to the image to be identified, the disclosed embodiment can first determine the image features of the image to be identified, that is, perform feature extraction on the image to be identified, wherein the image features are related to the environment underground in the coal mine.

[0043] In some embodiments, the image features include, for example, texture features, color features, contour features, and any other possible image features for evaluating the working environment, without limitation.

[0044] S403: Calculate image features using a pre-trained classification model to determine target working conditions.

[0045] After the image features of the image to be identified are determined, a pre-trained classification model is further used to calculate the image features to determine the target working condition. The classification model may be, for example, a support vector machine classifier, or may be a classification model of any other structure, without limitation. Thus, using a classification model to determine the target working condition can improve the calculation speed and the accuracy of classification.

[0046] Some embodiments, Figure 5 is a flow chart of classification model training and application provided according to an embodiment of the present disclosure, such as Figure 5 As shown, during the classification model training process, multiple training images are first obtained.

[0047] Among them, an image data set can be established according to typical working conditions in a coal mine, and the image data set includes multiple training images.

[0048] Furthermore, training image features contained in the plurality of training images are determined respectively, wherein the features extracted from the training images can be referred to as training image features, that is, training image features are extracted respectively for the images of each typical working condition.

[0049] In some embodiments, according to the special environment of coal mines, the training image features extracted in this embodiment may include at least one of the following: texture features, color features, contour features, and more specifically, may also include color histogram distribution, HOG features, Haar features, gray-level co-occurrence matrix features, and LBP features, etc., without limitation.

[0050] Furthermore, the disclosed embodiment uses ambient illumination and visibility as classification criteria for working conditions according to the actual environment underground in a coal mine, wherein ambient illumination is used to describe the ambient brightness underground, and visibility is used to describe the dust and water mist conditions underground. In other words, multiple working condition categories (i.e., multiple typical working conditions) are determined based on ambient illumination and visibility.

[0051] For example, in the embodiment of the present disclosure, the ambient illumination can be divided into three types of illumination: low illumination, medium illumination and high illumination, and the visibility can be divided into three types of visibility: low visibility, medium visibility and high visibility. According to different combinations of ambient illumination and visibility, 9 typical working conditions can be determined.

[0052] It is understandable that the above examples only use ambient illumination and visibility as classification criteria for working conditions. In actual applications, other factors can also be used as classification criteria to obtain a variety of working condition categories, and there is no limitation to this.

[0053] Furthermore, based on the training image features and multiple working condition categories, a classification model is trained, for example, a support vector machine classifier is trained, or other classification models are trained, without limitation. Thus, this embodiment can extract features for the actual environment of a coal mine and determine the working condition category, so that the trained classification model can meet the application requirements in the mine.

[0054] S404: Determine a target enhancement method corresponding to the target operating condition.

[0055] In addition, the embodiments of the present disclosure may also configure corresponding image enhancement methods (or algorithms) for images of different working conditions, and the enhancement method corresponding to the target working condition may be referred to as a target enhancement method.

[0056] In some embodiments, the image enhancement algorithms under different working conditions include, for example, histogram equalization, homomorphic filtering, Retinex algorithm and improved or combined algorithms thereof, which are not limited thereto.

[0057] For example, the target working condition is high visibility and low illumination, the overall environment of the coal mining face is dark, and strong light halos appear locally. In this case, the histogram equalization method can be used to process the image to be identified, suppress the strong light and increase the image brightness in the low-brightness area. That is to say, when the target working condition is high visibility and low illumination, the determined target enhancement method is the histogram equalization method.

[0058] For example, under working conditions with high dust and water mist concentration, low visibility (i.e., low visibility) and good illumination (high illumination), the medium in the atmosphere will bring serious noise to the image. A non-local mean denoising algorithm (i.e., target enhancement method) can be used to suppress noise such as dust in downhole images.

[0059] S405: Processing the image to be recognized using a target enhancement method.

[0060] That is to say, the target enhancement method is used to perform image enhancement processing on the image to be identified. Therefore, the enhanced image is clearer, which is conducive to the subsequent identification of the image to be identified and improves the recognition accuracy.

[0061] S406: Use a target recognition algorithm corresponding to the target working condition to recognize the image to be recognized, and determine whether there is a rock area on the coal mining face.

[0062] The specific description of S406 can be found in the above embodiment and will not be repeated here.

[0063] In this embodiment, by obtaining the image to be identified of the coal mining face, and determining the target working condition of the coal mining face according to the image to be identified, and using the target recognition algorithm corresponding to the target working condition to identify the image to be identified, it is determined whether there is a rock area on the coal mining face, and the target algorithm can be adaptively selected according to the actual working condition of the coal mining face for coal and rock identification, thereby improving the robustness and accuracy of coal and rock identification. In addition, using a classification model to determine the target working condition can increase the calculation speed and also improve the accuracy of classification. Moreover, the target enhancement method is used to enhance the image to be identified, and the enhanced image is clearer, which is conducive to the subsequent identification of the image to be identified and improves the recognition accuracy.

[0064] Figure 6 is a schematic diagram of the overall process of coal rock identification provided according to an embodiment of the present disclosure, such as Figure 6 As shown, first, the image features of the coal-rock image (image to be identified) are extracted from the perspectives of ambient illumination and visibility, and then the support vector machine model is used to determine the target working condition. Furthermore, the coal-rock image is enhanced by a target working condition-specific image enhancement method, and finally a general or special coal-rock recognition model is selected for coal-rock recognition.

[0065] Figure 7FIG. 1 is a schematic diagram of a coal rock identification device provided according to another embodiment of the present disclosure. Figure 7 As shown, the coal-rock identification device 70 includes:

[0066] The first acquisition module 701 is used to acquire the image to be identified of the coal mining face;

[0067] A first determination module 702 is used to determine the target working condition of the coal mining face according to the image to be identified; and

[0068] The recognition module 703 is used to recognize the image to be recognized by using a target recognition algorithm corresponding to the target working condition to determine whether there is a rock area on the coal mining face.

[0069] In some embodiments, the first determination module 702 includes: a determination submodule for determining image features of an image to be identified; and a calculation submodule for calculating the image features using a pre-trained classification model to determine a target operating condition.

[0070] In some embodiments, the device also includes: a second acquisition module for acquiring multiple training images; a second determination module for respectively determining the training image features contained in the multiple training images; a classification module for determining multiple working condition categories based on ambient illumination and visibility; and a training module for training a classification model based on the training image features and multiple working condition categories.

[0071] In some embodiments, the device 70 further includes: a third determination module, configured to determine a target enhancement method corresponding to the target working condition; and an image enhancement module, configured to process the image to be identified using the target enhancement method.

[0072] In some embodiments, the training image features include at least one of the following: texture features, color features, contour features; ambient illumination is divided into low illumination, medium illumination, and high illumination; visibility is divided into low visibility, medium visibility, and high visibility.

[0073] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0074] In order to implement the above embodiments, the present application also proposes a computer program product. When the instruction processor in the computer program product executes, the coal rock identification method proposed in the above embodiments of the present application is executed.

[0075] Figure 8 A block diagram of an exemplary computer device suitable for implementing embodiments of the present application is shown. Figure 8 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0076] like Figure 8 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0077] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0078] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0079] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 Not shown, often called a "hard drive").

[0080] although Figure 8Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0081] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0082] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0083] The processing unit 16 executes various functional applications by running the programs stored in the system memory 28, such as implementing the coal rock identification method mentioned in the above embodiment.

[0084] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0085] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

[0086] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0087] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0088] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0089] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0091] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0093] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A coal-rock identification method, characterized in that: include: Acquire the image to be identified of the coal mining face; Determine the target working condition of the coal mining face according to the image to be identified, wherein the target working condition is to establish a plurality of typical working conditions according to the ambient light illumination, dust concentration, and water mist concentration in the coal mine, and the working condition of the coal mining face in the image to be identified is called the target working condition; and Using a target recognition algorithm corresponding to the target working condition to recognize the image to be recognized, and judging whether there is a rock area on the coal mining face, wherein the target recognition algorithm is an image recognition algorithm corresponding to the target working condition; Before using the target recognition algorithm corresponding to the target working condition to recognize the image to be recognized, the method further includes: determining a target enhancement method corresponding to the target operating condition; and The target enhancement method is used to process the image to be identified.

2. The method according to claim 1, characterized in that Determining the target working condition of the coal mining face according to the image to be identified includes: Determining image features of the image to be recognized; and The image features are calculated using a pre-trained classification model to determine the target working condition.

3. The method according to claim 2, characterized in that Before calculating the image features using the pre-trained classification model, the method further includes: Obtain multiple training images; respectively determining training image features contained in the plurality of training images; Determine multiple operating conditions based on ambient illumination and visibility; and The classification model is trained based on the training image features and the multiple operating condition categories.

4. The method according to claim 3, characterized in that in, The training image features include at least one of the following: texture features, color features, and contour features; The ambient illumination is divided into low illumination, medium illumination and high illumination; The visibility is divided into low visibility, medium visibility and high visibility.

5. A coal-rock identification device, characterized in that: include: The first acquisition module is used to acquire the image to be identified of the coal mining face; A first determination module is used to determine the target working condition to which the coal mining face belongs according to the image to be identified, wherein the target working condition is a plurality of typical working conditions established according to the ambient light illumination, dust concentration, and water mist concentration in the coal mine, and the working condition to which the coal mining face belongs in the image to be identified is called the target working condition; and an identification module, used to identify the image to be identified by using a target identification algorithm corresponding to the target working condition, and determine whether there is a rock area on the coal mining face, wherein the target identification algorithm is an image identification algorithm corresponding to the target working condition; A third determination module is used to determine a target enhancement method corresponding to the target operating condition; and An image enhancement module is used to process the image to be identified using the target enhancement method.

6. The device according to claim 5, characterized in that The first determining module includes: A determination submodule, used to determine the image features of the image to be identified; and The calculation submodule is used to calculate the image features using a pre-trained classification model to determine the target working condition.

7. The device according to claim 6, characterized in that The device also includes: A second acquisition module is used to acquire multiple training images; A second determination module, used to respectively determine the training image features contained in the plurality of training images; A classification module for determining multiple operating condition categories based on ambient illumination and visibility; and A training module is used to train the classification model based on the training image features and the multiple working condition categories.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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