Inspection work order generation method and device based on inspection video, equipment and medium

Abstract

The invention relates to an inspection work order generation method and device based on an inspection video, equipment and a storage medium, and the method comprises the steps: obtaining the inspection video of a to-be-inspected area, analyzing and recognizing the inspection video, judging whether there is an abnormal image frame in the inspection video, and if yes, generating an inspection work order; and if yes, determining an abnormal inspection area corresponding to the abnormal image frame and an abnormal object in the abnormal inspection area, searching a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object, generating a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object, and sending the target work order to the target user. When an abnormal object is found in the inspection process, a corresponding work order is generated and automatically assigned to a related responsibility unit for processing, an area with a problem is accurately positioned, the problems that no process record exists in traditional inspection and the problem is not real are solved, and the inspection efficiency is improved. And the inspection personnel can check the authenticity of rectification through the inspection video and the work order after rectification in a related responsible unit, so that the inspection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of automatic inspection technology, and in particular to a method, device, equipment and storage medium for generating an inspection work order based on an inspection video. Background Art

[0002] At present, during the inspection process of project park inspections, fixed hidden danger inspections, fine decoration indoor acceptance inspections and other projects under construction and delivery, the inspection personnel mainly manually record the problems that need to be rectified and take photos during the inspection process. In this way, there is no recorded data of the entire inspection process, and when there are many problems that need to be rectified, it is difficult for the rectification personnel to accurately locate the location of the problem. It also takes a lot of time for the inspection personnel to verify the authenticity of the rectification, resulting in low inspection efficiency.

[0003] Therefore, how to improve inspection efficiency has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] In view of the above, the present application provides a method, device, equipment and storage medium for generating inspection work orders based on inspection videos, the purpose of which is to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for generating an inspection work order based on an inspection video, the method comprising:

[0006] Obtain inspection video of the area to be inspected;

[0007] Parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video;

[0008] If so, determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area;

[0009] According to the abnormal inspection area and the abnormal object, searching for a target user associated with the abnormal object;

[0010] A target work order is generated based on the abnormal image frame, the abnormal inspection area and the abnormal object, and the target work order is sent to the target user.

[0011] In a second aspect, the present application provides a device for generating an inspection work order based on an inspection video, the device comprising:

[0012] Acquisition module: used to obtain the inspection video of the area to be inspected;

[0013] Judgment module: used to parse and identify the inspection video, and determine whether there are abnormal image frames in the inspection video;

[0014] Determination module: used for determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the time domain of the abnormal inspection area if there is an abnormal image frame in the inspection video;

[0015] Search module: used to search for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object;

[0016] A generation module is used to generate a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object, and send the target work order to the target user.

[0017] In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0018] Memory, used to store computer programs;

[0019] The processor is used to implement the inspection work order generation method based on the inspection video as described in any embodiment of the first aspect when executing the program stored in the memory.

[0020] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for generating an inspection work order based on the inspection video as described in any embodiment of the first aspect is implemented.

[0021] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0022] This application obtains the inspection video of the area to be inspected, parses and identifies the inspection video, determines whether there is an abnormal image frame in the inspection video, and if so, determines the abnormal inspection area and abnormal object in the abnormal inspection area corresponding to the abnormal image frame, finds the target user associated with the abnormal object according to the abnormal inspection area and abnormal object, generates a target work order based on the abnormal image frame, abnormal inspection area and abnormal object, and sends the target work order to the target user. When an abnormal object is found during the inspection process, a corresponding work order can be generated and automatically assigned to the relevant responsible unit for processing, accurately locating the area where the problem occurs, solving the problem of no process record and unreal problem in traditional inspections. After the relevant responsible unit has rectified the problem, the inspection personnel can verify the authenticity of the rectification through the inspection video and work order, thereby improving the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 This is a flow chart of an embodiment of a method for generating an inspection work order based on inspection videos of the present application;

[0026] Figure 2 This is a module diagram of an embodiment of a device for generating inspection work orders based on inspection videos of the present application;

[0027] Figure 3 A schematic diagram of an electronic device embodiment of the present application;

[0028] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0030] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0031] This application provides a method for generating an inspection work order based on inspection videos. Figure 1 , which is a schematic diagram of a method flow of an embodiment of a method for generating an inspection work order based on inspection videos of the present application. The method can be executed by an electronic device, which can be implemented by software and / or hardware. The method for generating an inspection work order based on inspection videos includes:

[0032] Step S10: Obtaining the inspection video of the area to be inspected;

[0033] Step S20: parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video;

[0034] Step S30: If yes, determine the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area;

[0035] Step S40: searching for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object;

[0036] Step S50: generating a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object, and sending the target work order to the target user.

[0037] In this embodiment, the inspection video can be an inspection video shot by the inspector holding a 360 panoramic camera or wearing a wearable device such as a helmet equipped with a 360 camera, while walking on the project site or conducting a special inspection activity. The inspection scene of this embodiment can be a project park inspection, a demonstration area inspection, a fixed hidden danger inspection, a sample acceptance, a rest point acceptance, a fine decoration indoor acceptance, a post-landscape evaluation and other inspection scenes. It can be understood that in actual applications, it can also be other inspection scenes for inspection and quality management of projects under construction and delivery, which are not specifically limited here.

[0038] Taking the inspection scene of fine decoration indoor acceptance as an example, the area to be inspected is a three-bedroom and two-living room unit in a certain building. After obtaining the inspection video of the area to be inspected, the inspection video can be parsed to determine the image frame set of each sub-area in the inspection video, such as the image frame set of the living room, dining room, master bedroom, balcony and other sub-areas. Since each sub-area has key inspection items in actual inspections, the corresponding anomaly recognition model is pre-trained for the abnormal type of each key inspection item, that is, the anomaly recognition model has a mapping relationship with the anomaly type, for example, a model for identifying whether the wall has been bumped.

[0039] By inputting the image frames into the abnormal recognition model, it is possible to identify whether there are abnormal image frames in the inspection video. Abnormal image frames refer to image frames with abnormal types, for example, image frames identifying that the wall has been bumped, image frames identifying that the gaps between floor tiles are uneven, etc. Specifically, parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video include:

[0040] Parsing the inspection video to obtain an image frame containing an area identification code;

[0041] Based on the image frames containing the area identification code, the inspection video is divided into an image frame set corresponding to at least one sub-area, wherein the area to be inspected includes the multiple sub-areas;

[0042] Using multiple pre-trained anomaly recognition models to identify the image frame set corresponding to each sub-region to obtain a recognition result;

[0043] It is determined whether there are abnormal image frames in the inspection video according to the recognition result.

[0044] By parsing the inspection video, image frames containing area identification codes can be obtained. The area identification code refers to an identification used to distinguish sub-areas in the inspection area, for example, a QR code to distinguish sub-areas such as the living room, dining room, and master bedroom. When inspecting a sub-area, the user can first take a picture or scan the area identification code of the sub-area. For example, before inspecting the master bedroom, first take a picture or scan the area identification code of the master bedroom. The area identification code can be one or more combinations of information codes, numbers, letters, and character strings. The image frame of the area identification code can be a QR code image frame, a barcode image frame, etc.

[0045] The inspection video is pre-labeled according to the chronological order of the inspection, so after obtaining the image frame containing the area identification code, the inspection video can be divided into an image frame set corresponding to at least one sub-area. For example, the order of inspecting the areas is the living room, dining room, and master bedroom. Combined with the area identification codes of the living room, dining room, and master bedroom, the image frame sets corresponding to the sub-areas of the living room, dining room, and master bedroom can be determined respectively.

[0046] For the image frame set corresponding to each sub-area, multiple pre-trained abnormality recognition models are used to identify the image frame set corresponding to each sub-area to obtain the corresponding recognition result of each sub-area. The recognition result mark of the image frame represents whether the image frame is an abnormal image frame. Therefore, it can be determined whether there are abnormal image frames in the inspection video.

[0047] Furthermore, the above step of “dividing the inspection video into at least one set of image frames corresponding to a sub-region based on the image frames containing the region identification code” specifically includes:

[0048] Parsing the region identification code, and determining the sub-region corresponding to each image frame according to the parsing result;

[0049] According to the sub-region corresponding to each image frame, the image frames in the inspection video are divided according to the sub-region to obtain an image frame set corresponding to at least one sub-region.

[0050] By parsing the area identification code, the sub-area represented by the area identification code can be obtained. For example, by parsing the area identification code of the living room, it can be determined that the area identification code represents the area of ​​the living room. By parsing the area identification code of the dining room, it can be determined that the area identification code represents the area of ​​the living room. Since each frame of the inspection video is numbered in chronological order, if the inspection order is the living room, dining room, and master bedroom, it can be determined that after parsing the area identification code of the living room, the image frames before parsing the area identification code of the dining room belong to the image frames of the living room, that is, these image frames correspond to this sub-area of ​​the living room. After obtaining the sub-area corresponding to each image frame, the image frames in the inspection video can be divided according to the sub-area to obtain an image frame set corresponding to at least one sub-area.

[0051] Furthermore, the above step of “determining whether there are abnormal image frames in the inspection video” further includes:

[0052] If there is no abnormal image frame in the inspection video, the information that the inspection area has passed the inspection is submitted, and the inspection video is saved to a preset storage path.

[0053] If there are no abnormal image frames in the inspection video, it means that the inspection area has passed the acceptance. At this time, the information that the inspection area has passed the inspection is submitted, and the inspection video is saved to the preset storage path so that the inspection video of the inspection area can be viewed at any time later.

[0054] If there are abnormal image frames in the inspection video, it is necessary to determine the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area. The abnormal inspection area refers to the sub-area to which the abnormal image frame belongs. For example, if the abnormal image frame indicates that the wall of the master bedroom is bumped, the abnormal inspection area is the sub-area of ​​the master bedroom, and the abnormal object is the bumped wall.

[0055] After determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area, find the target user (i.e., responsible person or responsible unit) associated with the abnormal object according to the abnormal inspection area and the abnormal object. For example, if the abnormal inspection area is the sub-area of ​​the master bedroom and the abnormal object is a bumped wall, find the person or responsible unit associated with the construction of the master bedroom. Specifically, find the target user associated with the abnormal object according to the abnormal inspection area and the abnormal object, including:

[0056] Determining candidate users associated with the abnormal inspection area;

[0057] According to the identification information of the abnormal object, a target user associated with the abnormal object is searched from the candidate users.

[0058] For example, if the abnormal inspection area is the master bedroom sub-area, and the abnormal object is the east wall of the master bedroom, the responsible person or responsible unit associated with the construction of the main bedroom (recorded as candidate users) is first determined, and based on the identification information of the east wall of the master bedroom, the responsible person or responsible unit associated with the east wall construction is searched from the candidate users (recorded as target users).

[0059] Filling the abnormal image frame, the abnormal inspection area and the abnormal object into the preset work order template can generate a target work order, and send the target work order to the target user for the target user to make corrections. Specifically, generating a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object includes:

[0060] Receiving an abnormal description text input by an inspection user based on the abnormal inspection area and the abnormal object;

[0061] Determine the location information of the abnormal inspection area;

[0062] The abnormal image frame, the abnormal description text and the location information are filled into a preset work order template to generate a target work order.

[0063] Inspection users can also manually input abnormal description text for abnormal inspection areas and abnormal objects. The abnormal description text is used to characterize the problems or phenomena existing in the abnormal inspection areas and abnormal objects, and can also be the rectification direction of abnormal objects, etc. The location information indicated by the abnormal inspection area refers to the sub-area information. By filling the abnormal image frame, abnormal description text and location information into the preset work order template, a complete target work order can be generated and sent to the relevant responsible person or responsible unit for rectification.

[0064] In one embodiment, after sending the target work order to the target user, the method further includes:

[0065] Receive the image information and text information fed back by the target user based on the target work order, so that the inspection user can submit the inspection information of the area to be inspected after confirming that the image information and the text information meet the requirements, and save the inspection video to a preset storage path.

[0066] After the relevant responsible person or responsible unit has rectified the abnormal object, they can upload image information and text information. The image information can be the image of the abnormal object after rectification, and the text information can be the description of the abnormal object after rectification. The inspection user can view the image information and text information uploaded by the target user. After the inspection user determines that the image information and text information meet the requirements, he can submit the information that the inspection area has passed the inspection, that is, the information that the inspection area has completed rectification or acceptance, and save the inspection video to the preset storage path. So that the inspection video of the inspection area can be viewed at any time later.

[0067] Reference Figure 2 As shown, it is a functional module diagram of the inspection work order generation device 100 based on the inspection video of the present application.

[0068] The inspection work order generation device 100 based on inspection video described in the present application can be installed in an electronic device. According to the functions implemented, the inspection work order generation device 100 based on inspection video can include an acquisition module 110, a judgment module 120, a determination module 130, a search module 140 and a generation module 150. The module described in the present application can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0069] In this embodiment, the functions of each module / unit are as follows:

[0070] Acquisition module 110: used to acquire inspection video of the area to be inspected;

[0071] The judgment module 120 is used to analyze and identify the inspection video, and judge whether there is an abnormal image frame in the inspection video;

[0072] Determination module 130: for determining an abnormal inspection area corresponding to the abnormal image frame and an abnormal object in the time domain of the abnormal inspection area if there is an abnormal image frame in the inspection video;

[0073] Search module 140: used to search for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object;

[0074] The generating module 150 is used to generate a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object, and send the target work order to the target user.

[0075] In one embodiment, parsing and identifying the inspection video and determining whether there are abnormal image frames in the inspection video include:

[0076] Parsing the inspection video to obtain an image frame containing an area identification code;

[0077] Based on the image frames containing the area identification code, the inspection video is divided into an image frame set corresponding to at least one sub-area, wherein the area to be inspected includes the multiple sub-areas;

[0078] Using multiple pre-trained anomaly recognition models to identify the image frame set corresponding to each sub-region to obtain a recognition result;

[0079] It is determined whether there are abnormal image frames in the inspection video according to the recognition result.

[0080] In one embodiment, the step of dividing the inspection video into at least one set of image frames corresponding to a sub-region based on the image frame containing the region identification code includes:

[0081] Parsing the region identification code, and determining the sub-region corresponding to each image frame according to the parsing result;

[0082] According to the sub-region corresponding to each image frame, the image frames in the inspection video are divided according to the sub-region to obtain an image frame set corresponding to at least one sub-region.

[0083] In one embodiment, searching for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object includes:

[0084] Determining candidate users associated with the abnormal inspection area;

[0085] According to the identification information of the abnormal object, a target user associated with the abnormal object is searched from the candidate users.

[0086] In one embodiment, generating a target work order based on the abnormal image frame, the abnormal inspection area, and the abnormal object includes:

[0087] Receiving an abnormal description text input by an inspection user based on the abnormal inspection area and the abnormal object;

[0088] Determine the location information of the abnormal inspection area;

[0089] The abnormal image frame, the abnormal description text and the location information are filled into a preset work order template to generate a target work order.

[0090] In one embodiment, the inspection work order generation device 100 based on the inspection video further includes a feedback module, which is used to:

[0091] Receive the image information and text information fed back by the target user based on the target work order, so that the inspection user can submit the inspection information of the area to be inspected after confirming that the image information and the text information meet the requirements, and save the inspection video to a preset storage path.

[0092] In one embodiment, the determining whether there is an abnormal image frame in the inspection video further includes:

[0093] If there is no abnormal image frame in the inspection video, the information that the inspection area has passed the inspection is submitted, and the inspection video is saved to a preset storage path.

[0094] Reference Figure 3, which is a schematic diagram of a preferred embodiment of the electronic device of the present application.

[0095] The electronic device includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other via the communication bus 114;

[0096] The memory 113 is used to store computer programs, for example, a program for generating inspection work orders based on inspection videos;

[0097] In some embodiments, the processor 111 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 111 is generally used to control the overall operation of the electronic device, such as performing control and processing related to data interaction or communication. In this embodiment, the processor 111 is used to run the program code stored in the memory 113 or process data, such as running the program code of the inspection work order generation program based on the inspection video.

[0098] The communication interface 112 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and the communication interface 112 may also be used to establish a communication connection between the electronic device and other electronic devices.

[0099] The memory 113 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 113 can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory 113 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device. Of course, the memory 113 can also include both the internal storage unit of the electronic device and its external storage device. In this embodiment, the memory 11 is generally used to store the operating system and various computer programs installed in the electronic device, such as the program code of the inspection work order generation program based on the inspection video, etc. In addition, the memory 113 can also be used to temporarily store various types of data that have been output or are to be output.

[0100] Figure 3 Only an electronic device having components 111 - 114 is shown, but it is understood that implementing all of the components shown is not a requirement, and more or fewer components may alternatively be implemented.

[0101] In one embodiment of the present application, the processor 111 is used to execute the program stored in the memory 113 to implement the inspection work order generation method based on the inspection video provided by any of the above method embodiments, including:

[0102] Obtain inspection video of the area to be inspected;

[0103] Parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video;

[0104] If so, determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area;

[0105] According to the abnormal inspection area and the abnormal object, searching for a target user associated with the abnormal object;

[0106] A target work order is generated based on the abnormal image frame, the abnormal inspection area and the abnormal object, and the target work order is sent to the target user.

[0107] For a detailed description of the above steps, please refer to the above Figure 1 Description of a flowchart of an embodiment of a method for generating an inspection work order based on inspection videos.

[0108] In addition, the embodiment of the present application also proposes a computer-readable storage medium, which may be non-volatile or volatile. The computer-readable storage medium includes a data storage area and a program storage area, and the program storage area stores a patrol work order generation program based on patrol inspection videos. When the patrol work order generation program based on patrol inspection videos is executed by a processor, the following operations are implemented:

[0109] Obtain inspection video of the area to be inspected;

[0110] Parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video;

[0111] If so, determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area;

[0112] According to the abnormal inspection area and the abnormal object, searching for a target user associated with the abnormal object;

[0113] A target work order is generated based on the abnormal image frame, the abnormal inspection area and the abnormal object, and the target work order is sent to the target user.

[0114] The specific implementation of the computer-readable storage medium of the present application is roughly the same as the specific implementation of the above-mentioned inspection work order generation method based on inspection video, and will not be repeated here.

[0115] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0117] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0118] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0119] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for generating an inspection work order based on inspection videos, characterized in that: The method comprises: Obtain inspection video of the area to be inspected; Parsing and identifying the inspection video, and determining whether there are abnormal image frames in the inspection video; If so, determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the abnormal inspection area; According to the abnormal inspection area and the abnormal object, searching for a target user associated with the abnormal object; A target work order is generated based on the abnormal image frame, the abnormal inspection area and the abnormal object, and the target work order is sent to the target user.

2. The method for generating an inspection work order based on inspection video according to claim 1, characterized in that: The parsing and identifying the inspection video, and determining whether there is an abnormal image frame in the inspection video, includes: Parsing the inspection video to obtain an image frame containing an area identification code; Based on the image frames containing the area identification code, the inspection video is divided into an image frame set corresponding to at least one sub-area, wherein the area to be inspected includes the multiple sub-areas; Using multiple pre-trained anomaly recognition models to identify the image frame set corresponding to each sub-region to obtain a recognition result; It is determined whether there are abnormal image frames in the inspection video according to the recognition result.

3. The method for generating an inspection work order based on inspection video according to claim 2, characterized in that: The step of dividing the inspection video into at least one set of image frames corresponding to a sub-region based on the image frame containing the region identification code comprises: Parsing the region identification code, and determining the sub-region corresponding to each image frame according to the parsing result; According to the sub-region corresponding to each image frame, the image frames in the inspection video are divided according to the sub-region to obtain an image frame set corresponding to at least one sub-region.

4. The method for generating an inspection work order based on inspection video according to claim 1, characterized in that: The step of searching for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object includes: Determining candidate users associated with the abnormal inspection area; According to the identification information of the abnormal object, a target user associated with the abnormal object is searched from the candidate users.

5. The method for generating an inspection work order based on inspection video according to claim 1, characterized in that: The generating a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object includes: Receiving an abnormal description text input by an inspection user based on the abnormal inspection area and the abnormal object; Determine the location information of the abnormal inspection area; The abnormal image frame, the abnormal description text and the location information are filled into a preset work order template to generate a target work order.

6. The method for generating an inspection work order based on inspection video according to claim 1, characterized in that: After sending the target work order to the target user, the method further includes: Receive the image information and text information fed back by the target user based on the target work order, so that the inspection user can submit the inspection information of the area to be inspected after confirming that the image information and the text information meet the requirements, and save the inspection video to a preset storage path.

7. The method for generating an inspection work order based on inspection video according to claim 1, characterized in that: The determining whether there is an abnormal image frame in the inspection video also includes: If there is no abnormal image frame in the inspection video, the information that the inspection area has passed the inspection is submitted, and the inspection video is saved to a preset storage path.

8. A device for generating inspection work orders based on inspection videos, characterized in that: The device comprises: Acquisition module: used to obtain the inspection video of the area to be inspected; Judgment module: used to parse and identify the inspection video, and determine whether there are abnormal image frames in the inspection video; Determination module: used for determining the abnormal inspection area corresponding to the abnormal image frame and the abnormal object in the time domain of the abnormal inspection area if there is an abnormal image frame in the inspection video; Search module: used to search for a target user associated with the abnormal object according to the abnormal inspection area and the abnormal object; A generation module is used to generate a target work order based on the abnormal image frame, the abnormal inspection area and the abnormal object, and send the target work order to the target user.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the inspection work order generation method based on inspection video as described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating an inspection work order based on an inspection video according to any one of claims 1 to 7 is implemented.

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