Intelligent mining lamp lighting inspection method, device, equipment and storage medium

Abstract

The present application provides a method, device, equipment and storage medium for intelligent mining lamp lighting inspection, the above method includes: applied to a lighting control system, including: obtaining lighting area data and equipment data, wherein the equipment data includes the lighting unit data and the sensor unit data; transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface; retrieving the inspection configuration data in a preset configuration database to generate an inspection task; based on the inspection timing of the inspection task, the inspection interface is intercepted to obtain an inspection diagram; the inspection diagram is input into a pre-trained detection model for abnormality detection to obtain a detection result item; when the detection result item is characterized as an abnormal item, an alarm message is generated. The present application embodiment provides a solution for timely detection of mining lamp failures.

Description

Technical Field

[0001] The present application relates to the field of intelligent lighting technology, and in particular to an intelligent mining lamp lighting inspection method, device, equipment and storage medium. Background Art

[0002] With the development of science and technology, the intelligence level of mining lamps is getting higher and higher, but the scenes in mines are complex and changeable. When mining lamps fail, safety situations are prone to occur. Traditional methods generally rely on manual inspections, which are often not discovered until a failure occurs. In some areas that are not often visited, it is especially difficult to detect whether a lamp failure occurs.

[0003] Therefore, it is necessary to provide a method for timely discovering the failure of mining lamps. Summary of the invention

[0004] The main purpose of this application is to provide an intelligent mining lamp lighting inspection method, device, computer equipment and storage medium, aiming to solve the technical problem that the existing lighting solutions fail to detect faulty mining lamps in a timely manner.

[0005] To achieve the above objectives, the present application provides an intelligent mining lamp lighting inspection method, which is applied to a lighting control system. The method comprises:

[0006] Acquire lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data;

[0007] Transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface;

[0008] Retrieve the inspection configuration data from the preset configuration database and generate inspection tasks;

[0009] Based on the inspection time sequence of the inspection task, the inspection interface is intercepted to obtain an inspection diagram;

[0010] Inputting the inspection graph into a pre-trained detection model to perform anomaly detection to obtain a detection result item;

[0011] When the detection result item is characterized as an abnormal item, an alarm message is generated.

[0012] Optionally, the device data further includes inspection robot data, and when the detection result item is characterized as an abnormal item, generating alarm information includes:

[0013] When the detection result item is characterized as an abnormal item, extracting the lighting unit data and the sensor data corresponding to the abnormal item to calculate the position, and obtaining abnormal position information;

[0014] Performing path synthesis based on the lighting area data and the abnormal location information to generate an inspection path;

[0015] The inspection path is transmitted to the inspection robot to guide the inspection robot to navigate to the abnormal position,

[0016] When the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and alarm information to be verified is generated and transmitted back to the lighting control system.

[0017] Optionally, when the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and after generating and transmitting the to-be-verified alarm information back to the lighting control system, the method includes:

[0018] Extracting the first environment brightness and the first device data included in the alarm information to be verified;

[0019] Comparing the first environment brightness and the first device data using the standard environment brightness and the standard device data in the configuration database to obtain comparison difference data;

[0020] In the case where there is a difference in the comparison difference data, extracting the inspection diagram when the detection result item is characterized as an abnormal item for double verification to generate an abnormal inspection result;

[0021] When the inspection result is characterized as abnormal confirmation, an abnormal rectification report is generated using the abnormal position, the first ambient brightness, the first device data, and the inspection map.

[0022] Optionally, the retrieving inspection configuration data in a preset configuration database to generate an inspection task includes:

[0023] The inspection item set is obtained, and the inspection task is generated using the abnormal item category, abnormal indicator item, inspection frequency and inspection sequence contained in the inspection configuration data.

[0024] Optionally, after generating the inspection task, the method further includes:

[0025] Calculating the execution time of the inspection task according to the inspection frequency and the inspection sequence;

[0026] Determining a data maintenance period of the inspection task based on the execution time;

[0027] When the inspection task is in the data maintenance period, update data is imported to perform maintenance and update on the lighting control system.

[0028] Optionally, the inspection map is input into a pre-trained detection model for abnormality detection to obtain a detection result item, specifically including:

[0029] For each inspection map, based on the identification image corresponding to each target inspection identification in the inspection map, the corresponding inspection image features are extracted through an image feature extraction network;

[0030] Identify the inspection mark corresponding to each inspection image feature and classify it as the mark signal feature information of the inspection image;

[0031] The inspection map is subjected to abnormality detection using the identification feature information to obtain a detection result.

[0032] Optionally, before inputting the inspection graph into a pre-trained detection model for abnormality detection, the method further includes:

[0033] Obtain abnormal inspection atlas;

[0034] Annotate each abnormal inspection graph in the abnormal inspection graph set to obtain a training set including abnormal type and abnormal coordinates;

[0035] The training set is used to train the detection model to be trained to obtain the trained detection model.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides an intelligent mining lamp lighting inspection device, which is characterized in that it is applied to a lighting control system and includes:

[0037] An acquisition module, used for acquiring lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data;

[0038] An interactive module, used to transmit the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface;

[0039] The first generation module is used to retrieve the inspection configuration data in the preset configuration database and generate the inspection task;

[0040] An interception module, used to intercept the inspection interface based on the inspection sequence of the inspection task to obtain an inspection diagram;

[0041] A detection module, used for inputting the inspection map into a pre-trained detection model to perform anomaly detection and obtain a detection result item;

[0042] The second generating module is used to generate warning information when the detection result item is characterized as an abnormal item.

[0043] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0044] The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the intelligent mining lamp lighting inspection methods proposed in the embodiments of the present application are implemented.

[0045] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0046] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of any one of the intelligent mining lamp lighting inspection methods proposed in the embodiments of the present application are implemented.

[0047] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0048] The present application provides a method, device, equipment and storage medium for intelligent mining lamp lighting inspection, the above method includes: applied to a lighting control system, including: obtaining lighting area data and equipment data, wherein the equipment data includes the lighting unit data and the sensor unit data; transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface; retrieving the inspection configuration data in a preset configuration database to generate an inspection task; based on the inspection timing of the inspection task, the inspection interface is intercepted to obtain an inspection diagram; the inspection diagram is input into a pre-trained detection model for abnormality detection to obtain a detection result item; when the detection result item is characterized as an abnormal item, an alarm message is generated. The present application embodiment provides a solution for timely detection of mining lamp failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0051] Figure 2 It is a flow chart of the intelligent mining lamp lighting inspection method provided by the embodiment of the present application;

[0052] Figure 3It is a structural schematic diagram of an embodiment of an intelligent mining lamp lighting inspection device provided in an embodiment of the present application;

[0053] Figure 4 It is a basic structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The intelligent mining lamp lighting inspection method provided in the embodiment of the present application is applied to the intelligent mining lamp lighting inspection device. Unless otherwise defined, all technical and scientific terms used in this article have the same meaning as those generally understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0057] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0058] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social online platform software, etc.

[0059] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0060] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0061] It should be noted that the intelligent mining lamp lighting inspection method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the intelligent mining lamp lighting inspection device is generally arranged in the server / terminal device.

[0062] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0063] Please refer to Figure 2 , shows a flow chart of an embodiment of the intelligent mining lamp lighting inspection method proposed in this application. The embodiment of this application can acquire and process relevant data based on artificial intelligence technology.

[0064] The intelligent mining lamp lighting inspection method provided in the embodiment of the present application comprises the following steps:

[0065] S210: acquiring lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data;

[0066] In this embodiment, the lighting area data refers to the area data where lighting equipment is arranged, which may be data of mines, mountain roads, roads, factories and office areas where lighting equipment is arranged. The lighting area data may be GIS data or GPS data, which is not limited here.

[0067] Acquire lighting area data and device data. In the present invention, the device data arranged in the lighting area data refers to data transmitted by the lighting unit, and the sensor transmission refers to data of the lighting system.

[0068] S220: transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface;

[0069] The present invention is applied to a lighting control system, which receives the returned lighting area data, lighting unit data and the number of sensor units, accesses the initial interactive interface, generates an interactive display interface, and renders the interface using a rendering component to obtain an inspection interface, wherein the inspection interface displays the distribution of lighting areas and lighting units, and displays the parameters of each sensor unit in real time.

[0070] S230 retrieves the inspection configuration data in the preset configuration database and generates an inspection task;

[0071] In this embodiment, the configuration database is used to store all configuration data of the lighting control system, and the inspection configuration data contained in the configuration database is retrieved to generate the inspection task.

[0072] Optionally, the retrieving inspection configuration data in a preset configuration database to generate an inspection task includes:

[0073] Obtain the inspection item set, and generate the inspection task by using the abnormal item category, abnormal indicator item, inspection frequency and inspection sequence contained in the inspection configuration data.

[0074] Optionally, after generating the inspection task, the method further includes:

[0075] Calculate the execution time of the inspection task based on the inspection frequency and inspection sequence; determine the data maintenance period of the inspection task based on the execution time; when the inspection task is in the data maintenance period, import the updated data and perform maintenance and update on the lighting control system

[0076] S240: intercepting the inspection interface based on the inspection sequence of the inspection task to obtain an inspection map.

[0077] In this embodiment, the inspection task also generates an inspection map by taking a screenshot of the inspection sequence execution interface. It should be noted that while generating the inspection map, the real-time parameters of the current inspection item are also retrieved and compared with the inspection map. Figure 1 One to one correspondence.

[0078] S250: input the inspection map into a pre-trained detection model to perform abnormality detection to obtain a detection result item;

[0079] In an embodiment, for each inspection map, based on the identification image corresponding to each target inspection identification in the inspection map, the corresponding inspection image feature is extracted through an image feature extraction network;

[0080] Identify the inspection mark corresponding to each inspection image feature and classify it as the mark signal feature information of the inspection image;

[0081] The inspection map is subjected to abnormality detection using the identification feature information to obtain a detection result.

[0082] S260: When the detection result item is characterized as an abnormal item, an alarm message is generated.

[0083] Optionally, the device data further includes inspection robot data, and when the detection result item is characterized as an abnormal item, generating alarm information includes:

[0084] When the detection result item is characterized as an abnormal item, extracting the lighting unit data and the sensor data corresponding to the abnormal item to calculate the position, and obtaining abnormal position information;

[0085] Performing path synthesis based on the lighting area data and the abnormal location information to generate an inspection path;

[0086] The inspection path is transmitted to the inspection robot to guide the inspection robot to navigate to the abnormal position,

[0087] When the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and alarm information to be verified is generated and transmitted back to the lighting control system.

[0088] In another preferred embodiment, when the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and after generating a warning information to be verified and transmitting it back to the lighting control system, the method includes:

[0089] Extracting the first environment brightness and the first device data included in the alarm information to be verified;

[0090] Comparing the first environment brightness and the first device data using the standard environment brightness and the standard device data in the configuration database to obtain comparison difference data;

[0091] In the case where there is a difference in the comparison difference data, extracting the inspection diagram when the detection result item is characterized as an abnormal item for double verification to generate an abnormal inspection result;

[0092] When the inspection result is characterized as abnormal confirmation, an abnormal rectification report is generated using the abnormal position, the first ambient brightness, the first device data, and the inspection map.

[0093] In another preferred embodiment, before inputting the inspection graph into a pre-trained detection model for abnormality detection, the method further includes:

[0094] Obtain abnormal inspection atlas;

[0095] Annotate each abnormal inspection graph in the abnormal inspection graph set to obtain a training set including abnormal type and abnormal coordinates;

[0096] The training set is used to train the detection model to be trained to obtain the trained detection model

[0097] A method, device, equipment and storage medium for intelligent mining lamp lighting inspection, the method includes: being applied to a lighting control system, including: obtaining lighting area data and equipment data, wherein the equipment data includes the lighting unit data and the sensor unit data; transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface; retrieving the inspection configuration data in a preset configuration database to generate an inspection task; intercepting the inspection interface based on the inspection timing of the inspection task to obtain an inspection diagram; inputting the inspection diagram into a pre-trained detection model for abnormality detection to obtain a detection result item; when the detection result item is characterized as an abnormal item, generating an alarm message. A solution for timely detecting faults of mining lamps is provided in an embodiment of the present application.

[0098] See also Figure 3 The embodiment of the present application provides an intelligent mining lamp lighting inspection device 300, which is applied to a lighting control system. The intelligent mining lamp lighting inspection device 300 includes:

[0099] An acquisition module 310, configured to acquire lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data;

[0100] An interactive module 320 is used to transmit the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface;

[0101] The first generating module 330 is used to retrieve the inspection configuration data in the preset configuration database and generate the inspection task;

[0102] The interception module 340 is used to intercept the inspection interface based on the inspection sequence of the inspection task to obtain an inspection map;

[0103] A detection module 350 is used to input the inspection map into a pre-trained detection model to perform anomaly detection and obtain a detection result item;

[0104] The second generating module 360 ​​is used to generate warning information when the detection result item is characterized as an abnormal item.

[0105] Optionally, the interaction module 320 is specifically configured to:

[0106] The inspection item set is obtained, and the inspection task is generated using the abnormal item category, abnormal indicator item, inspection frequency and inspection sequence contained in the inspection configuration data.

[0107] Optionally, the intelligent mining lamp lighting inspection device 300 further includes a training subunit, which is specifically used for:

[0108] Get abnormal inspection atlas;

[0109] Annotate each abnormal inspection graph in the abnormal inspection graph set to obtain a training set including abnormal type and abnormal coordinates;

[0110] The training set is used to train the detection model to be trained to obtain the trained detection model.

[0111] Optionally, the detection module 350 is specifically used to:

[0112] For each inspection map, based on the identification image corresponding to each target inspection identification in the inspection map, the corresponding inspection image features are extracted through an image feature extraction network;

[0113] Identify the inspection mark corresponding to each inspection image feature and classify it as the mark signal feature information of the inspection image;

[0114] The inspection map is subjected to abnormality detection using the identification feature information to obtain a detection result.

[0115] Optionally, the second generating module 360 ​​is specifically configured to:

[0116] When the detection result item is characterized as an abnormal item, extracting the lighting unit data and the sensor data corresponding to the abnormal item to calculate the position, and obtaining abnormal position information;

[0117] Performing path synthesis based on the lighting area data and the abnormal location information to generate an inspection path;

[0118] The inspection path is transmitted to the inspection robot to guide the inspection robot to navigate to the abnormal position,

[0119] When the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and alarm information to be verified is generated and transmitted back to the lighting control system.

[0120] To solve the above technical problems, the present application also provides a computer device. Figure 4, Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0121] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0122] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0123] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as the program code of the intelligent mining lamp lighting inspection method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0124] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the program code stored in the memory 41 or process data, such as running the program code of the intelligent mining lamp lighting inspection method.

[0125] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0126] The present application also provides another implementation mode, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores the intelligent mining lamp lighting inspection program, and the intelligent mining lamp lighting inspection program can be executed by at least one processor so that the at least one processor executes the steps of the intelligent mining lamp lighting inspection method as described above.

[0127] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware online platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0128] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0129] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An intelligent mining lamp lighting inspection method, characterized in that: Applied to lighting control systems, including: Acquire lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data; Transmitting the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface; Retrieve the inspection configuration data from the preset configuration database and generate inspection tasks; Based on the inspection time sequence of the inspection task, the inspection interface is intercepted to obtain an inspection diagram; Inputting the inspection graph into a pre-trained detection model to perform anomaly detection to obtain a detection result item; When the detection result item is characterized as an abnormal item, an alarm message is generated.

2. The method according to claim 1, characterized in that The equipment data also includes inspection robot data, and when the detection result item is characterized as an abnormal item, an alarm information is generated, including: When the detection result item is characterized as an abnormal item, extracting the lighting unit data and the sensor data corresponding to the abnormal item to calculate the position, and obtaining abnormal position information; Performing path synthesis based on the lighting area data and the abnormal location information to generate an inspection path; The inspection path is transmitted to the inspection robot to guide the inspection robot to navigate to the abnormal position, When the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and alarm information to be verified is generated and transmitted back to the lighting control system.

3. The method according to claim 2, characterized in that When the inspection robot navigates to an abnormal position, the mounted camera device is used to obtain image information of the abnormal position, and after generating and transmitting the to-be-verified alarm information back to the lighting control system, the method includes: Extracting the first environment brightness and the first device data included in the alarm information to be verified; Comparing the first environment brightness and the first device data using the standard environment brightness and the standard device data in the configuration database to obtain comparison difference data; In the case where there is a difference in the comparison difference data, extracting the inspection diagram when the detection result item is characterized as an abnormal item for double verification to generate an abnormal inspection result; When the inspection result is characterized as abnormal confirmation, an abnormal rectification report is generated using the abnormal position, the first ambient brightness, the first device data, and the inspection map.

4. The method according to claim 1, characterized in that The step of retrieving the inspection configuration data in the preset configuration database and generating the inspection task includes: The inspection item set is obtained, and the inspection task is generated using the abnormal item category, abnormal indicator item, inspection frequency and inspection sequence included in the inspection configuration data.

5. The method according to claim 4, characterized in that After the inspection task is generated, the method further includes: Calculating the execution time of the inspection task according to the inspection frequency and the inspection sequence; Determining a data maintenance period of the inspection task based on the execution time; When the inspection task is in the data maintenance period, update data is imported to perform maintenance and update on the lighting control system.

6. The method according to claim 1, characterized in that The inspection map is input into a pre-trained detection model for abnormality detection to obtain a detection result item, specifically including: For each inspection map, based on the identification image corresponding to each target inspection identification in the inspection map, the corresponding inspection image features are extracted through an image feature extraction network; Identify the inspection mark corresponding to each inspection image feature and classify it as the mark signal feature information of the inspection image; The inspection map is subjected to abnormality detection using the identification feature information to obtain a detection result.

7. The method according to claim 6, characterized in that Before inputting the inspection graph into a pre-trained detection model for abnormality detection, the method further includes: Get abnormal inspection atlas; Annotate each abnormal inspection graph in the abnormal inspection graph set to obtain a training set including abnormal type and abnormal coordinates; The training set is used to train the detection model to be trained to obtain the trained detection model.

8. An intelligent mining lamp lighting inspection device, characterized in that: Applied to lighting control systems, including: An acquisition module, used for acquiring lighting area data and device data, wherein the device data includes the lighting unit data and the sensor unit data; An interactive module, used to transmit the lighting area data, the lighting unit data and the sensor unit data to the lighting control system for interactive display to obtain an inspection interface; The first generation module is used to retrieve the inspection configuration data in the preset configuration database and generate the inspection task; An interception module, used to intercept the inspection interface based on the inspection sequence of the inspection task to obtain an inspection diagram; A detection module, used for inputting the inspection map into a pre-trained detection model to perform anomaly detection and obtain a detection result item; The second generating module is used to generate warning information when the detection result item is characterized as an abnormal item.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the intelligent mining lamp lighting inspection method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the intelligent mining lamp lighting inspection method according to any one of claims 1 to 7 are implemented.

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