Job training method, device, equipment and storage medium based on visual self-inspection

Abstract

The present application discloses a method and device, equipment, and medium for job training based on visual self-inspection, the method comprising: obtaining enterprise information, the enterprise information comprising production and operation site information and job information, the job information being used to indicate the work content and job responsibilities of the job positions set by the enterprise; determining a self-inspection area according to the production and operation site information, the self-inspection area being a specific work area that the enterprise needs to inspect in order to ensure compliance with production and operation specifications; obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the job responsibilities, the self-inspection standard data being self-inspection data that complies with production and operation specifications; and conducting job training for the personnel to be trained according to the self-inspection standard data. The use of the embodiments of the present application is conducive to simplifying the redundant data of the current job training system in the catering service industry, unifying relevant specifications, and improving its operating efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of visual job training, and in particular to a job training method and device, equipment, and storage medium based on visual self-inspection. Background Art

[0002] Job training in the catering service industry is of great significance for improving service quality, ensuring customer satisfaction and promoting the sustainable development of enterprises. For example, through systematic training, employees can master professional service skills and knowledge; for another example, regular food safety training can help employees understand the latest food safety regulations and operating specifications, and reduce the occurrence of food safety accidents. However, at present, the training of relevant practitioners is mainly carried out by each enterprise itself, and the relevant specifications are not unified. The training data are all-encompassing, resulting in a huge and bloated training system and low operating efficiency. Therefore, how to simplify the redundant data of the current job training system in the catering service industry, unify the relevant specifications, and improve its operating efficiency is a technical problem that needs to be solved. Summary of the invention

[0003] The present application provides a job training method and device, equipment, and storage medium based on visual self-inspection, which is conducive to simplifying the redundant data of the current job training system in the catering service industry, unifying relevant specifications, and improving its operating efficiency.

[0004] In a first aspect, the present application provides a job training method based on visual self-examination, the method comprising:

[0005] Obtaining enterprise information, which refers to data and information in the process of enterprise operation and management, including production and operation site information and job information, which is used to indicate the work content and job responsibilities of the job positions set up by the enterprise;

[0006] Determine the self-inspection area based on the production and operation site information, where the self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications;

[0007] Obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that complies with production and operation specifications;

[0008] The personnel to be trained are provided with job training according to the self-inspection standard data.

[0009] The above-mentioned job training method based on visual self-inspection obtains enterprise information, which refers to data and information in the process of enterprise operation and management. Enterprise information includes production and operation site information and job information. Job information is used to indicate the work content and job responsibilities of the jobs set by the enterprise; the self-inspection area is determined according to the production and operation site information. The self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications; self-inspection standard data of the self-inspection area is obtained, and the personnel to be trained in the self-inspection area are determined according to the work content and job responsibilities. The self-inspection standard data refers to the self-inspection data that meets the production and operation specifications; and job training is performed on the personnel to be trained according to the self-inspection standard data.

[0010] Optionally, the performing job training on the personnel to be trained according to the self-examination standard data includes:

[0011] Generate theoretical knowledge training data and practical animation training data according to the self-examination standard data;

[0012] Conducting job theory training for the personnel to be trained according to the theoretical knowledge training data;

[0013] When it is determined that the theoretical training results of the trainee are qualified, the trainee is provided with practical training according to the practical animation training data;

[0014] When it is determined that the trainee has passed the job practical training, it is determined that the trainee has passed the job training and obtained the job qualification.

[0015] Optionally, before determining that the post theory training of the trainee is qualified, the method further includes:

[0016] Generate a theoretical knowledge training question bank according to the theoretical knowledge training data;

[0017] Extracting a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface;

[0018] Receiving, in the answer input area of ​​the job theory training interface, answer data of the user for the theoretical knowledge training test questions displayed in the test question display area;

[0019] Whether the theoretical training results of the position of the trainee are qualified is determined according to the answer data.

[0020] Optionally, before determining that the trainee's post practical training performance is qualified, the method further includes:

[0021] Acquire the photographed actual operation image of the person to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data;

[0022] When the actual operation image of the current self-examination content matches the theoretical operation image, it is determined that the post practical training result of the person to be trained is qualified.

[0023] Optionally, before the actual operation image of the current self-check content matches the theoretical operation image, the method further includes:

[0024] Using an image recognition algorithm to identify a target area corresponding to the current self-examination content;

[0025] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content, and providing a first confidence level for whether the current self-inspection content meets the licensing requirements;

[0026] When the first confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0027] Optionally, the method further includes:

[0028] When the first confidence level is lower than a preset threshold, obtaining actual operation images of other self-check contents in the check item corresponding to the current self-check content;

[0029] Identify the target area corresponding to the current self-inspection content based on the actual operation images of other self-inspection contents;

[0030] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content identified in the actual operation image of other self-inspection contents, and giving a second confidence level;

[0031] When the second confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0032] Optionally, before determining that the trainee's post practical training performance is qualified, the method further includes:

[0033] Generate a three-dimensional virtual operation scene of the self-inspection area by virtual reality technology and determine a theoretical operation process according to the practical animation training data;

[0034] Detecting the actual operation process of the person to be trained on the virtual device in the three-dimensional virtual operation scene;

[0035] When the matching degree between the actual operation process and the theoretical operation process is greater than or equal to a first preset matching degree, it is determined that the result of the job practical training of the person to be trained is qualified.

[0036] Since the self-inspection area is determined based on the production and operation site information in the enterprise information, different enterprises have different production and operation site information, so it is highly targeted and can reduce the self-inspection of areas that are not in their production and operation site information, thereby reducing the content of self-inspection; secondly, self-inspection standard data refers to self-inspection data that complies with production and operation specifications. The production and operation specifications followed by various regions are the same, rather than simply adopting the experience summarized by the enterprise itself, so that unified standards can be achieved; finally, according to the self-inspection standard data, the training personnel are trained in their respective positions, rather than training all specifications to the same position, thereby reducing the training content of the same position, generating targeted training processes, and thus improving its operating efficiency.

[0037] In a second aspect, the present application further provides a job training device based on visual self-inspection, the device comprising:

[0038] An information acquisition unit, used to acquire enterprise information, wherein the enterprise information refers to data and materials in the process of enterprise operation and management, and the enterprise information includes production and operation site information and job information, and the job information is used to indicate the work content and job responsibilities of the job positions set by the enterprise;

[0039] A self-inspection area determination unit, configured to determine a self-inspection area according to the production and operation site information, wherein the self-inspection area refers to a specific work area that the enterprise needs to inspect in order to ensure compliance with production and operation specifications;

[0040] a data determination unit, used to obtain self-inspection standard data of the self-inspection area and determine the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that meets the production and operation specifications;

[0041] The job training unit is used to carry out job training for the personnel to be trained according to the self-examination standard data.

[0042] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor;

[0043] The memory is used to store computer programs;

[0044] The processor is used to execute the computer program and implement the above-mentioned job training method based on visual self-inspection when executing the computer program.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the job training method based on visual self-inspection as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For catering practitioners, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 is a structural schematic diagram of a computer device provided in an embodiment of the present application;

[0048] Figure 2 It is a schematic flow chart of a job training method based on visual self-examination provided in an embodiment of the present application;

[0049] Figure 3 It is a structural schematic diagram of a job training device based on visual self-inspection provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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 creative work are within the scope of protection of this application.

[0051] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0052] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0053] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] See also Figure 1 , Figure 1 1 is a schematic diagram of a computer device 100 provided in an embodiment of the present application. The computer device 100 may include a processor 1001 and a memory 1002, wherein the processor 1001 and the memory 1002 may be connected via a bus, and the bus may be any applicable bus such as an Inter-integrated Circuit (I2C) bus.

[0055] Exemplarily, the computer device 100 may be a server or a terminal. The server may be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal may be an electronic device such as a smartphone, a tablet computer, a laptop computer, and a desktop computer.

[0056] The memory 1002 may include a storage medium and an internal memory. The storage medium may store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor 1001 may execute the job training method based on visual self-examination described in any embodiment.

[0057] The processor 1001 is used to provide computing and control capabilities and support the operation of the entire computer device 100 .

[0058] The processor 1001 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other types of processors such as programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. A general-purpose processor may be a microprocessor, or a general-purpose processor may be any conventional processor.

[0059] In one embodiment, the processor 1001 is used to run a computer program stored in the memory 1002 to implement the following steps:

[0060] Obtaining enterprise information, which refers to data and information in the process of enterprise operation and management, including production and operation site information and job information, which is used to indicate the work content and job responsibilities of the job positions set up by the enterprise;

[0061] Determine the self-inspection area based on the production and operation site information, where the self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications;

[0062] Obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that complies with production and operation specifications;

[0063] The personnel to be trained are provided with job training according to the self-inspection standard data.

[0064] In one embodiment, when implementing the job training for the personnel to be trained according to the self-check standard data, the processor 1001 is used to implement:

[0065] Generate theoretical knowledge training data and practical animation training data according to the self-examination standard data;

[0066] Conducting job theory training for the personnel to be trained according to the theoretical knowledge training data;

[0067] When it is determined that the theoretical training results of the trainee are qualified, the trainee is provided with practical training according to the practical animation training data;

[0068] When it is determined that the trainee has passed the job practical training, it is determined that the trainee has passed the job training and obtained the job qualification.

[0069] In one embodiment, before implementing the determination that the trainee's post theory training score is qualified, the processor 1001 is further configured to implement:

[0070] Generate a theoretical knowledge training question bank according to the theoretical knowledge training data;

[0071] Extracting a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface;

[0072] Receiving, in the answer input area of ​​the job theory training interface, answer data of the user for the theoretical knowledge training test questions displayed in the test question display area;

[0073] In one embodiment, the processor 1001 is further configured to implement, before implementing the step of determining whether the trainee's post practical training score is qualified, the following steps are implemented:

[0074] Acquire the photographed actual operation image of the person to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data;

[0075] When the actual operation image of the current self-examination content matches the theoretical operation image, it is determined that the post practical training result of the person to be trained is qualified.

[0076] In one embodiment, before matching the actual operation image of the current self-check content with the theoretical operation image, the processor 1001 is further configured to:

[0077] Using an image recognition algorithm to identify a target area corresponding to the current self-examination content;

[0078] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content, and providing a first confidence level for whether the current self-inspection content meets the licensing requirements;

[0079] When the first confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0080] In one embodiment, the processor 1001 is further configured to implement:

[0081] When the first confidence level is lower than a preset threshold, obtaining actual operation images of other self-check contents in the check item corresponding to the current self-check content;

[0082] Identify the target area corresponding to the current self-inspection content based on the actual operation images of other self-inspection contents;

[0083] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content identified in the actual operation image of other self-inspection contents, and giving a second confidence level;

[0084] When the first confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0085] In one embodiment, before implementing the step of determining whether the trainee's post practical training is qualified, the processor 1001 is further configured to implement:

[0086] Generate a three-dimensional virtual operation scene of the self-inspection area by virtual reality technology and determine a theoretical operation process according to the practical animation training data;

[0087] Detecting the actual operation process of the person to be trained on the virtual device in the three-dimensional virtual operation scene;

[0088] When the matching degree between the actual operation process and the theoretical operation process is greater than or equal to a first preset matching degree, it is determined that the result of the job practical training of the person to be trained is qualified.

[0089] Some embodiments of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Figure 2 , Figure 2 is a schematic flow chart of a job training method based on visual self-examination provided in an embodiment of the present application. Figure 2 As shown, the job training method based on visual self-examination may include steps S101 to S104.

[0090] Step S101, obtaining enterprise information, wherein the enterprise information refers to data and information in the process of enterprise operation and management, and the enterprise information includes production and operation site information and position information, and the position information is used to indicate the work content and job responsibilities of the job positions set by the enterprise.

[0091] It should be noted that the way to obtain enterprise information can be direct or indirect. Direct acquisition is based on the unique identification information provided by the enterprise, which can be directly input by the user or identified by uploaded documents. Indirect acquisition is determined through intelligent question and answer with the user. When directly acquiring, it can be further verified through the Internet to further determine the correctness of the information. Indirect acquisition can facilitate users of unregistered enterprises to obtain and authenticate enterprise information, reducing food safety risks caused by unregistered or unlicensed enterprises in society.

[0092] In some embodiments, the user may be asked about the production and operation site information through intelligent dialogue, and the production and operation site information includes generating an example diagram of the business site layout and equipment layout. The method may include:

[0093] In the question area of ​​the main business option details display interface, ask the user whether there is a production and operation site layout diagram;

[0094] If it exists, receiving the layout diagram of the production and operation site uploaded by the user;

[0095] If it does not exist, fill in the blanks for the area of ​​production and operation sites in the question area of ​​the main business option details display interface;

[0096] The specific category of the operator's main business format is determined based on the area filled in by the user for the production and operation site area, and an example diagram of the business site layout and equipment arrangement is displayed and generated based on the specific category of the main business format.

[0097] Among them, the example diagram of the production and operation site layout and equipment layout can change dynamically with the area input by the operator. If the area is within the area range of micro-catering, the example diagram of the production and operation site layout and equipment layout is the example diagram of the production and operation site layout and equipment layout of micro-catering; if the area is within the area range of small-sized catering, the example diagram of the production and operation site layout and equipment layout is the example diagram of the production and operation site layout and equipment layout of small-sized catering; if the area is within the area range of medium-sized catering, the example diagram of the production and operation site layout and equipment layout is the example diagram of the production and operation site layout and equipment layout of medium-sized catering. The example diagram of the production and operation site layout and equipment layout of the corresponding surface is displayed in real time through dynamic changes, which is convenient for operators to compare, determine and adjust, and find a place that suits their actual operation, so that the information of the production and operation site is more accurate.

[0098] It can be seen that in the embodiment of the present application, even if the user does not have a production and operation site layout map, an approximate production and operation site layout map can be generated through intelligent dialogue, which is conducive to the subsequent self-inspection area division, rather than conducting a general self-inspection of all areas, regardless of whether the area exists in the production and operation site.

[0099] For example, the job positions in a catering service unit include store manager, chef, assistant chef, waiter, receptionist, cleaner, etc. The job content and job responsibilities of the store manager are to be responsible for the operation and management of the entire restaurant, including the formulation of business plans, cost control, employee management, customer service, etc. The job content and job responsibilities of the chef are to be responsible for the overall operation of the kitchen, including menu design, food procurement, food quality control, and management and training of the kitchen team. The job content and job responsibilities of the assistant chef are to assist the chef in preparing ingredients and cooking dishes. The job content and job responsibilities of the waiter are to receive customers, introduce the menu, take orders, serve dishes, clean the table, etc. It is also necessary to pay attention to observe customer needs and respond in a timely manner to provide quality service. The job content and job responsibilities of the receptionist are to greet guests at the door, guide them to sit down, and sometimes they are also responsible for answering phone reservations. The job content and job responsibilities of the cleaner are to be responsible for the cleaning and disinfection of tableware, kitchen hygiene cleaning, etc., to ensure compliance with food safety standards.

[0100] Step S102: determine a self-inspection area based on the production and operation site information, where the self-inspection area refers to a specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications.

[0101] For example, the production and operation site can be divided into two major areas: dining area and food processing area. The food processing area can be further divided into receiving area, warehouse area, primary processing area, tableware washing area / room, tableware cleaning area / room, hot food processing area, cold food room, plant-based cold food production area, raw food room, homemade beverage area, pastry processing area, cold processed pastry room, meal preparation area / room, auxiliary area, simple production and sales area, etc. The dining area can be divided into the front door welcoming area, front desk management area, customer dining area, etc. At least one of the above areas can be determined based on the production and operation site information of the enterprise. According to the production and operation specifications, the self-inspection area can then be determined from the above at least one area.

[0102] Step S103, obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the job responsibilities, wherein the self-inspection standard data refers to self-inspection data that complies with production and operation specifications.

[0103] Among them, the self-inspection standard data can be determined based on the rules and regulations published on relevant websites or the business norms recommended by the industry. The personnel to be trained in the self-inspection area are determined based on the work content and job responsibilities. For example, the store manager, waiter, and greeter belong to the dining area, and the head chef, assistant chef, and cleaner belong to the food processing area; further, the head chef can belong to the hot food processing area, cold food room, plant-based cold food production area, raw food room, homemade beverage area, pastry processing area, cold processed pastry room, assistant chef can belong to the primary processing area, auxiliary area, simple production and sales area, etc., cleaners can belong to the tableware washing area / room, tableware cleaning area / room, store manager can belong to the front desk management area, waiter can belong to the customer dining area, and greeter can belong to the front door welcoming area.

[0104] Step S104: conducting job training for the personnel to be trained according to the self-examination standard data.

[0105] In some embodiments, the step of providing job training to the personnel to be trained according to the self-examination standard data includes:

[0106] Generate theoretical knowledge training data and practical animation training data according to the self-examination standard data;

[0107] Conducting job theory training for the personnel to be trained according to the theoretical knowledge training data;

[0108] When it is determined that the theoretical training results of the trainee are qualified, the trainee is provided with practical training according to the practical animation training data;

[0109] When it is determined that the trainee has passed the job practical training, it is determined that the trainee has passed the job training and obtained the job qualification.

[0110] Exemplarily, the theoretical knowledge training data of a store manager may include, for example, an overview of the catering industry, restaurant operation and management processes, cost control methods, customer service skills, employee management and motivation, legal and regulatory requirements, etc. The theoretical knowledge training data of a head chef may include, for example, the basics of food safety, menu design principles, use and maintenance of kitchen equipment, food procurement and storage, dish innovation methods, team collaboration and management, etc. The theoretical knowledge training data of an assistant chef may include, for example, basic cooking skills, commonly used seasonings and their combinations, knife training, hygiene operation specifications, food identification and processing, etc. The theoretical knowledge training data of a waiter may include, for example, etiquette and communication skills, menu interpretation and recommendation, ordering system operation, wine and beverage knowledge, emergency handling procedures, etc. The theoretical knowledge training data of a greeter may include, for example, reception etiquette, reservation management, customer information recording, seating arrangement principles, customer complaint handling skills, etc. The theoretical knowledge training data of a cleaner may include, for example, instructions for use of detergents, disinfection procedures, kitchen hygiene standards, garbage classification and processing, equipment maintenance common sense, etc.

[0111] Exemplarily, the practical animation training data can be an animation produced based on the above-mentioned theoretical knowledge training data. Only when the theoretical training results of the position are qualified, the trainees will be given practical training based on the practical animation training data. Different trainings can be entered by clicking on different modules, such as the "theoretical knowledge training" module and "practical animation training" module set on the main interface. When the theoretical knowledge training is not completed or passed, the "practical animation training" background is not loaded, the module cannot be clicked, and is displayed in a dark color different from the "theoretical knowledge training", thereby saving the memory occupied by the operation of the self-inspection training system and speeding up the operation of the self-inspection training system.

[0112] The above-mentioned job training method based on visual self-inspection obtains enterprise information, which refers to data and information in the process of enterprise operation and management. Enterprise information includes production and operation site information and job information. Job information is used to indicate the work content and job responsibilities of the jobs set by the enterprise; the self-inspection area is determined according to the production and operation site information. The self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications; self-inspection standard data of the self-inspection area is obtained, and the personnel to be trained in the self-inspection area are determined according to the work content and job responsibilities. The self-inspection standard data refers to the self-inspection data that meets the production and operation specifications; and job training is performed on the personnel to be trained according to the self-inspection standard data.

[0113] Since the self-inspection area is determined based on the production and operation site information in the enterprise information, different enterprises have different production and operation site information, so it is highly targeted and can reduce the self-inspection of areas that are not in their production and operation site information, thereby reducing the content of self-inspection; secondly, self-inspection standard data refers to self-inspection data that complies with production and operation specifications. The production and operation specifications followed by various regions are the same, rather than simply adopting the experience summarized by the enterprise itself, so that unified standards can be achieved; finally, according to the self-inspection standard data, the training personnel are trained in their respective positions, rather than training all specifications to the same position, thereby reducing the training content of the same position, generating targeted training processes, and thus improving its operating efficiency.

[0114] In some embodiments, before determining that the trainee's theoretical training result is qualified, the method further includes:

[0115] Generate a theoretical knowledge training question bank according to the theoretical knowledge training data;

[0116] Extracting a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface;

[0117] Receiving, in the answer input area of ​​the job theory training interface, answer data of the user for the theoretical knowledge training test questions displayed in the test question display area;

[0118] Whether the theoretical training results of the position of the trainee are qualified is determined according to the answer data.

[0119] Specifically, generating a theoretical knowledge training question bank according to the theoretical knowledge training data includes:

[0120] Acquire theoretical knowledge training data; simplify the theoretical knowledge training data based on part-of-speech coding to obtain simplified theoretical knowledge training data and corresponding part-of-speech coding files, wherein the simplified theoretical knowledge training data retains key information of the theoretical knowledge training data, and reduces the theoretical knowledge training data

[0121] complexity; generate a theoretical knowledge training question bank based on the simplified theoretical knowledge training data and the corresponding part-of-speech coding files.

[0122] More specifically, the theoretical knowledge training data is simplified based on the part-of-speech coding to obtain simplified theoretical knowledge training data and corresponding part-of-speech coding files, including:

[0123] Determine keywords and non-keywords in the theoretical knowledge training data based on the self-examination content;

[0124] Deleting non-keywords in the theoretical knowledge training data, and processing the non-keywords in the theoretical knowledge training data with keywords retained, to obtain simplified theoretical knowledge training data;

[0125] Generate the part-of-speech coding file corresponding to the simplified theoretical knowledge training data based on the part-of-speech coding.

[0126] More specifically, generating a theoretical knowledge training question bank based on simplified theoretical knowledge training data and corresponding part-of-speech coding files includes:

[0127] According to the corresponding part-of-speech coding file, answers are extracted and questions are generated according to the semantics of each word in each sentence in the simplified theoretical knowledge training data; a theoretical knowledge training question bank is constructed based on the extracted answers and generated questions.

[0128] Among them, according to the part of speech classification and coding, each type of keyword is coded by one letter, and the keywords can be divided into: self-inspection content, self-inspection object, self-inspection location, attribute, existence and indicator word. The above six types of keywords are respectively coded by d, o, l, s, y and the indicator word itself.

[0129] The present invention can greatly simplify the complex theoretical knowledge training question bank, retaining the key information of the theoretical knowledge training question bank and improving the structurality of the theoretical knowledge training question bank. In addition, the method based on part-of-speech encoding has universal applicability. As long as the corresponding words in the theoretical knowledge training question bank are collected, it can be applied to the generation of various self-examination content questions.

[0130] Specifically, the extracting of a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface includes:

[0131] Based on the entropy reduction principle, a preset number of theoretical knowledge training test questions are extracted from the theoretical knowledge training question bank and displayed one by one in the test question display area of ​​the job theory training interface.

[0132] Furthermore, based on the entropy reduction principle, a preset number of theoretical knowledge training test questions are extracted from the theoretical knowledge training test bank and displayed one by one in the test question display area of ​​the job theory training interface, including:

[0133] Extract keywords from the job description and job responsibilities;

[0134] Calculating the degree of correlation between each theoretical knowledge training question in the theoretical knowledge training question bank and the keywords of the job content and job responsibilities, wherein the degree of correlation is represented by an entropy value;

[0135] Select a preset number of theoretical knowledge training test questions from large to small entropy values ​​and display them one by one in the test question display area of ​​the job theory training interface.

[0136] The entropy reduction principle is: the larger the entropy value, the higher the degree of association, and the smaller the entropy value, the lower the degree of association. Based on the order from large to small, a preset number of theoretical knowledge training test questions are screened out from the theoretical knowledge training question bank for display, so as to reduce the theoretical knowledge training test questions with conditional entropy, and prevent the displayed theoretical knowledge training test questions from having a low degree of association with the positions of the trainees, thereby greatly weakening the purpose of training. For example, it can prevent the theoretical knowledge training test questions for training chefs from being assigned to waiters.

[0137] In an example of the present application, in the job theory training interface, the user can directly click on the picture corresponding to the option to generate answer data in the answer input area, or enter the answer data in the answer input area and click the picture corresponding to the option to complete the answer. The operation space is larger, which is more conducive to the user to quickly complete the answer and prevent wrong selection; entering the answer data in the answer input area also provides an input option for devices that cannot be touched, which can be adapted to different devices, and the selected answer options are directly displayed and not easy to be misselected; and the method of clicking on the picture corresponding to the option and generating answer data in the answer input area allows users who choose to click the picture corresponding to the option to complete the answer to further check whether they have made a wrong selection. Finally, the results of the job theory training of the trainee to be trained are judged based on the answer data, that is, the score is calculated based on the questions he answered correctly, and then see whether the score exceeds the set passing score.

[0138] It can be seen that in the embodiment of the present application, after completing the theoretical knowledge training, a theoretical knowledge training question bank is generated through the theoretical knowledge training data, and questions are extracted for examination, which can further consolidate the theoretical knowledge training data.

[0139] Practical animation training is different from theoretical knowledge training. Answering test questions cannot effectively test the learning outcomes. Therefore, this application provides two visualization methods to test the results of practical animation training.

[0140] In a specific embodiment of the present application, the result of the practical animation training is checked by an image recognition method, and the method specifically includes:

[0141] Acquire the photographed actual operation image of the person to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data;

[0142] When the actual operation image of the current self-examination content matches the theoretical operation image, it is determined that the post practical training result of the person to be trained is qualified.

[0143] The self-inspection content is used to instruct the operator to regularly conduct self-inspection and assessment of whether the management and operations in the self-inspection area comply with the requirements of food safety laws and regulations and internal management systems. Actual operation images refer to images used to demonstrate and explain an actual operation, and theoretical operation images refer to images used to demonstrate and explain a theoretical operation.

[0144] Exemplarily, the store manager needs to be responsible for the entire production and operation site. For example, the production and operation site needs to be equipped with safety and fire-fighting facilities. Then the self-inspection area can be the food preparation area, and the current self-inspection content can be whether the safety and fire-fighting facilities are set up in the food preparation area, and whether the safety and fire-fighting facilities are in the standard position. Using the embodiment of the present application, the store manager can set up the safety and fire-fighting facilities in the food preparation area, and then take a picture of the actual operation image, and match the actual operation image with the theoretical operation image. Specifically, for example, if the safety and fire-fighting facilities are fire extinguishers, when matching, first identify whether the number meets the requirement of at least one fire extinguisher per 50 square meters, and then match the position of the fire extinguisher in the actual operation image with the position of the fire extinguisher in the theoretical operation image to see if it is within the preset area.

[0145] Specifically, when the actual operation image of the current self-check content matches the theoretical operation image, the method further includes:

[0146] Using an image recognition algorithm to identify a target area corresponding to the current self-examination content;

[0147] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content, and providing a first confidence level for whether the current self-inspection content meets the licensing requirements;

[0148] When the first confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0149] Exemplarily, the image recognition algorithm may be, for example, a region-based convolutional neural network (R-CNN) algorithm, a Fast R-CNN algorithm, a Faster R-CNN algorithm, a Mask R-CNN algorithm, a You Only Look Once (YOLO) algorithm, etc.

[0150] For example, the internal management system can be the management system within the industry, or the management system within the association, etc.

[0151] Furthermore, the method further comprises:

[0152] Determine a plurality of input texts according to the texts of a plurality of food safety regulations and standard documents and a plurality of internal management system standard documents, each input text includes a plurality of words, and each word has a corresponding word vector;

[0153] Preprocessing the input text to obtain multiple keywords, wherein the preprocessing includes but is not limited to word segmentation, removal of stop words or punctuation marks, part-of-speech restoration, and word frequency statistics;

[0154] determining at least one keyword from the plurality of keywords;

[0155] Determine a plurality of related sentences according to the at least one keyword, each of the related sentences including some of the at least one keyword;

[0156] Calculate the correlation feature vectors between multiple related sentences;

[0157] Inferring the association relationship between multiple files based on the association feature vector;

[0158] Determine a target standard file from the plurality of food safety regulations and standard files according to the association relationship;

[0159] Establish licensing requirements based on the stated objective standards document.

[0160] The embodiment of the present application can filter files with low relevance or files with high duplication according to the association relationship between multiple food safety regulations and standard files and multiple food safety regulations and standard files, and obtain strongly relevant standard files for formulating licensing requirements.

[0161] Exemplarily, the confidence can be determined by the number of target points that need to be matched in the current area, and the ratio of the number of matched target points to the number of target points that need to be matched is the confidence; the confidence can be determined by the image quality and the number of matched target points, and the ratio of the number of matched target points to the number of target points that need to be matched and the product of the quality of the actual operation image. For example, if the number of target points that need to be matched is 100, the number of matched target points is 90, and the quality of the actual operation image is 90%, then according to the first determination method mentioned above, the confidence can be determined to be 90%, and according to the first determination method mentioned above, the confidence can be determined to be 90%×90%=81%. The second confidence determination method not only considers the number of matching target points, but also the quality of the actual operation image. The actual operation image is manually shot and uploaded, and there may be poor quality that causes the confidence calculated by the image recognition algorithm to be inaccurate, so the second confidence determination method is more accurate.

[0162] Based on the second confidence determination method, in some embodiments, the method further includes:

[0163] When the first confidence level is lower than a preset threshold, obtaining actual operation images of other self-check contents in the check item corresponding to the current self-check content;

[0164] Identify the target area corresponding to the current self-inspection content based on the actual operation images of other self-inspection contents;

[0165] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content identified in the actual operation image of other self-inspection contents, and giving a second confidence level;

[0166] When the second confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0167] When the embodiment of the present application cannot determine whether the current self-inspection content complies with the licensing provisions based on the actual operation image corresponding to the current self-inspection content, the actual operation image uploaded by other self-inspection contents is introduced to determine whether the current self-inspection content complies with the licensing provisions. The user does not need to upload the actual operation diagram corresponding to the current self-inspection content again, thereby facilitating the operator to improve the self-inspection efficiency. It should be noted that the method of the embodiment of the present application cannot be used as a reference through other secondary recognitions, because the current recognition is mostly for the recognition of a single target, and there are no related photos before and after it. The method of the present application is derived from the limitations of the special scenarios of the embodiment of the present application, thereby reducing the re-upload of pictures. Other recognitions can only upload multiple photos for the same recognition target, while the present application can achieve multiple recognitions by uploading a photo once for the same recognition target.

[0168] In order to better illustrate the effect of the embodiment of the present application, the current self-inspection content is a wall skirt as an example. When the current self-inspection content is whether the wall skirt is laid to the top, when the user's self-inspection content is whether the entrance is equipped with a wash basin and whether it is equipped with a tool cleaning pool, the actual operation images uploaded can at least partially reflect whether the wall skirt is laid to the top. Therefore, when the actual operation images uploaded by the user for the self-inspection content of whether the wall skirt is laid to the top cannot determine whether the self-inspection content is in compliance, further judgment is made through the actual operation images uploaded by the user for the self-inspection content of whether the entrance is equipped with a wash basin and whether it is equipped with a tool cleaning pool, thereby achieving the purpose of diagnosis.

[0169] Furthermore, the method further comprises:

[0170] When the second confidence level is lower than a preset threshold, a target rectification strategy is generated based on a preset strategy generation model and the actual operation image, so that the user can rectify errors in the actual operation image based on the target rectification strategy.

[0171] Furthermore, before generating the target rectification strategy based on the preset strategy generation model and the actual operation image, the method further includes:

[0172] Initialize the model parameters of each network layer in the initial random deep network model, and generate the survival probability parameters of each network layer based on preset rules;

[0173] Based on the survival probability parameters of each network layer, a target network layer and an optimized network layer are determined in each network layer, wherein the target network layer is a network layer to be trained, and the optimized network layer is a network layer for which training needs to be skipped;

[0174] Based on the preset training sample set and the initial model parameters, the optimization network layer is skipped, and the target network layer is trained until the preset convergence condition is reached to obtain the strategy generation model.

[0175] Specifically, the determining of the target network layer and the optimizing network layer in each network layer based on the survival probability parameters of each network layer includes:

[0176] Obtain any value within a preset value range as a random parameter of a network layer;

[0177] Compare the survival probability parameter of each network layer with its corresponding random parameter;

[0178] Based on the comparison results of each network layer, the network layer whose survival probability parameter is not less than the random parameter is determined as the target network layer, and the network layer whose survival probability parameter is less than the random parameter is determined as the optimized network layer.

[0179] Among them, the survival probability can be set as:

[0180]

[0181] in, is the current network layer number, is the total number of network layers, It is the survival probability of the output layer in the previous round. The initial value can be taken in the first round. The value is generally low, such as less than 50%.

[0182] Through the above method, the present application provides a random depth mechanism based on the survival probability corresponding to each network layer. Based on the random depth mechanism, some network layers can be randomly skipped during the training process, reducing the amount of forward and backward propagation calculations during model training, thereby reducing the computational complexity during model training; and only part of the network layers are used in each training, so that the training time is significantly shortened while ensuring the convergence of the model, preventing the model from overfitting, and enhancing the robustness of the model to abnormal data and noise data.

[0183] In another specific embodiment of the present application, the results of the practical animation training are verified by virtual reality technology. Similarly, when the trainees are given job practical training based on the practical animation training data, the practical animation can also be presented by virtual reality technology, so that users can experience the practical process in an immersive way. The method specifically includes:

[0184] Generate a three-dimensional virtual operation scene of the self-inspection area by virtual reality technology and determine a theoretical operation process according to the practical animation training data;

[0185] Detecting the actual operation process of the person to be trained on the virtual device in the three-dimensional virtual operation scene;

[0186] When the matching degree between the actual operation process and the theoretical operation process is greater than or equal to a first preset matching degree, it is determined that the result of the job practical training of the person to be trained is qualified.

[0187] Among them, virtual reality (VR) is a computer technology that allows users to immerse themselves in a highly realistic virtual world by simulating environments, scenes or physical existence. The matching degree can be determined based on the number of matched nodes in the operation process and the number of nodes in the total process. The first preset matching degree can be, for example, 99%, 95%, 90%, etc., which is not limited here.

[0188] In the embodiments of the present application, firstly, VR technology can better imitate the practical animation training scene, so that users can be immersed in the scene and better experience the actual operation. On the other hand, it does not need to arrange the actual operation scene on the spot, which can also save costs; secondly, unlike the above embodiments, the theoretical operation process in this application is obtained based on the practical animation training data, and the data of the comparison group is directly obtained through the practical animation training data, rather than indirectly through the theoretical operation data, and the comparison result is more accurate; in addition, the actual operation process and the theoretical operation process are compared in the embodiments of the present application, which is a comparison of the process, while the actual operation image and the theoretical operation image used in the previous embodiment are compared, which is a comparison of the results, which can effectively verify the standardization of the operation process and reduce the misjudgment of the match caused by accurate results and inaccurate processes. The above-mentioned first embodiment is applicable to the result inspection of static self-examination content, and this embodiment is applicable to the result inspection of dynamic self-examination content.

[0189] See also Figure 3 , Figure 3 2 is a schematic diagram of a job training device 200 based on visual self-checking provided in an embodiment of the present application. The device comprises:

[0190] An information acquisition unit 210 is used to acquire enterprise information, which refers to data and information in the process of enterprise operation and management. The enterprise information includes production and operation site information and job information. The job information is used to indicate the work content and job responsibilities of the job positions set by the enterprise;

[0191] A self-inspection area determination unit 220, configured to determine a self-inspection area according to the production and operation site information, wherein the self-inspection area refers to a specific work area that the enterprise needs to inspect in order to ensure compliance with production and operation specifications;

[0192] A data determination unit 230 is used to obtain self-inspection standard data of the self-inspection area and determine the personnel to be trained in the self-inspection area according to the work content and the job responsibilities, wherein the self-inspection standard data refers to self-inspection data that meets the production and operation specifications;

[0193] The job training unit 240 is used to perform job training on the personnel to be trained according to the self-examination standard data.

[0194] In one embodiment, when the job training is performed on the personnel to be trained according to the self-check standard data, the job training unit 240 is specifically used to:

[0195] Generate theoretical knowledge training data and practical animation training data according to the self-examination standard data;

[0196] Conducting job theory training for the personnel to be trained according to the theoretical knowledge training data;

[0197] When it is determined that the theoretical training results of the trainee are qualified, the trainee is provided with practical training according to the practical animation training data;

[0198] When it is determined that the trainee has passed the job practical training, it is determined that the trainee has passed the job training and obtained the job qualification.

[0199] In one embodiment, when the job theory training is performed on the trainee according to the theoretical knowledge training data, the job training unit 240 is specifically used to:

[0200] Generate a theoretical knowledge training question bank according to the theoretical knowledge training data;

[0201] Extracting a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface;

[0202] Receiving, in the answer input area of ​​the job theory training interface, answer data of the user for the theoretical knowledge training test questions displayed in the test question display area;

[0203] Whether the theoretical training results of the position of the trainee are qualified is determined according to the answer data.

[0204] In one embodiment, the job training unit 240 is further specifically used for:

[0205] Acquire the photographed actual operation image of the person to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data;

[0206] When the actual operation image of the current self-examination content matches the theoretical operation image, it is determined that the post practical training result of the person to be trained is qualified.

[0207] In one embodiment, the job training unit 240 is further specifically used for:

[0208] Using an image recognition algorithm to identify a target area corresponding to the current self-examination content;

[0209] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content, and providing a first confidence level for whether the current self-inspection content meets the licensing requirements;

[0210] When the first confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0211] In one embodiment, the job training unit 240 is further specifically used for:

[0212] When the first confidence level is lower than a preset threshold, obtaining actual operation images of other self-check contents in the check item corresponding to the current self-check content;

[0213] Identify the target area corresponding to the current self-inspection content based on the actual operation images of other self-inspection contents;

[0214] Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content identified in the actual operation image of other self-inspection contents, and giving a second confidence level;

[0215] When the second confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content matches the theoretical operation image.

[0216] In one embodiment, the job training unit 240 is further specifically used for:

[0217] Generate a three-dimensional virtual operation scene of the self-inspection area by virtual reality technology and determine a theoretical operation process according to the practical animation training data;

[0218] Detecting the actual operation process of the person to be trained on the virtual device in the three-dimensional virtual operation scene;

[0219] When the matching degree between the actual operation process and the theoretical operation process is greater than or equal to a first preset matching degree, it is determined that the result of the job practical training of the person to be trained is qualified.

[0220] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0221] The embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, which includes program instructions, and the processor executes the program instructions to implement any of the job training methods based on visual self-checking provided in the embodiments of the present application. For example, the computer program is loaded by the processor and can execute the following steps:

[0222] Obtaining enterprise information, which refers to data and information in the process of enterprise operation and management, including production and operation site information and job information, which is used to indicate the work content and job responsibilities of the job positions set by the enterprise;

[0223] Determine the self-inspection area based on the production and operation site information, where the self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications;

[0224] Obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that complies with production and operation specifications;

[0225] The personnel to be trained are provided with job training according to the self-inspection standard data.

[0226] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0227] The computer-readable storage medium may be an internal storage unit of the computer device of the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital card (Secure Digital, SD), a flash card (Flash Card), etc. equipped on the computer device.

[0228] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A job training method based on visual self-inspection, characterized in that: The method comprises: Obtaining enterprise information, which refers to data and information in the process of enterprise operation and management, including production and operation site information and job information, which is used to indicate the work content and job responsibilities of the job positions set up by the enterprise; Determine the self-inspection area based on the production and operation site information, where the self-inspection area refers to the specific work area that the enterprise needs to inspect to ensure compliance with production and operation specifications; Obtaining self-inspection standard data of the self-inspection area and determining the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that complies with production and operation specifications; Conducting job training for the personnel to be trained based on the self-inspection standard data, including: Generate theoretical knowledge training data and practical animation training data according to the self-examination standard data; Conducting job theory training for the personnel to be trained according to the theoretical knowledge training data; When it is determined that the theoretical training results of the trainee are qualified, the trainee is provided with practical training according to the practical animation training data; Acquire the photographed actual operation image of the person to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data; Using an image recognition algorithm to identify a target area corresponding to the current self-examination content; Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content, and providing a first confidence level for whether the current self-inspection content meets the licensing requirements; When the first confidence level is higher than or equal to a preset threshold, determining that the actual operation image of the current self-check content matches the theoretical operation image; When the first confidence level is lower than a preset threshold, obtaining actual operation images of other self-check contents in the check item corresponding to the current self-check content; Identify the target area corresponding to the current self-inspection content based on the actual operation images of other self-inspection contents; Determining whether the current self-inspection content meets the licensing requirements according to the target area corresponding to the current self-inspection content identified in the actual operation image of other self-inspection contents, and giving a second confidence level; When the second confidence level is higher than or equal to a preset threshold, determining that the actual operation image of the current self-check content matches the theoretical operation image; When the actual operation image of the current self-examination content matches the theoretical operation image, it is determined that the post practical training of the trainee is qualified; When it is determined that the trainee has passed the job practical training, it is determined that the trainee has passed the job training and obtained the job qualification.

2. The job training method based on visual self-examination according to claim 1 is characterized in that: Before determining that the training personnel have passed the post theory training, the method further includes: Generate a theoretical knowledge training question bank according to the theoretical knowledge training data; Extracting a preset number of theoretical knowledge training test questions from the theoretical knowledge training question bank and displaying them one by one in the test question display area of ​​the job theory training interface; Receiving, in the answer input area of ​​the job theory training interface, answer data of the user for the theoretical knowledge training test questions displayed in the test question display area; Whether the theoretical training results of the position of the trainee are qualified is determined according to the answer data.

3. The job training method based on visual self-inspection according to claim 2 is characterized in that: Before determining that the trainee's post practical training result is qualified, the method further includes: Generate a three-dimensional virtual operation scene of the self-inspection area by virtual reality technology and determine a theoretical operation process according to the practical animation training data; Detecting the actual operation process of the person to be trained on the virtual device in the three-dimensional virtual operation scene; When the matching degree between the actual operation process and the theoretical operation process is greater than or equal to a first preset matching degree, it is determined that the result of the job practical training of the person to be trained is qualified.

4. A job training device based on visual self-inspection, characterized in that: The device comprises: An information acquisition unit, used to acquire enterprise information, wherein the enterprise information refers to data and materials in the process of enterprise operation and management, and the enterprise information includes production and operation site information and job information, and the job information is used to indicate the work content and job responsibilities of the job positions set by the enterprise; A self-inspection area determination unit, configured to determine a self-inspection area according to the production and operation site information, wherein the self-inspection area refers to a specific work area that the enterprise needs to inspect in order to ensure compliance with production and operation specifications; a data determination unit, used to obtain self-inspection standard data of the self-inspection area and determine the personnel to be trained in the self-inspection area according to the work content and the work responsibilities, wherein the self-inspection standard data refers to self-inspection data that meets the production and operation specifications; The job training unit is used to generate theoretical knowledge training data and practical operation animation training data according to the self-examination standard data; conduct job theory training for the personnel to be trained according to the theoretical knowledge training data; when it is determined that the job theory training of the personnel to be trained is qualified, conduct job practical operation training for the personnel to be trained according to the practical operation animation training data; obtain the actual operation image of the personnel to be trained in the self-examination area for the current self-examination content, and determine the theoretical operation image of the current self-examination content according to the theoretical knowledge training data; use an image recognition algorithm to identify the target area corresponding to the current self-examination content; judge whether the current self-examination content meets the licensing requirements according to the target area corresponding to the current self-examination content, and give a first confidence level whether the current self-examination content meets the licensing requirements; when the first confidence level is higher than or equal to a preset threshold, determine whether the current self-examination content meets the licensing requirements. The actual operation image of the self-check content is matched with the theoretical operation image; when the first confidence level is lower than a preset threshold, the actual operation images of other self-check contents in the verification item corresponding to the current self-check content are obtained; the target area corresponding to the current self-check content is identified according to the actual operation images of other self-check contents; whether the current self-check content meets the licensing requirements is judged according to the target area corresponding to the current self-check content identified in the actual operation images of other self-check contents, and a second confidence level is given; when the second confidence level is higher than or equal to a preset threshold, it is determined that the actual operation image of the current self-check content is matched with the theoretical operation image; when the actual operation image of the current self-check content is matched with the theoretical operation image, it is determined that the job practical training of the person to be trained is qualified; when it is determined that the job practical training of the person to be trained is qualified, it is determined that the person to be trained has passed the job training and obtained the qualification to take up the job.

5. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the job training method based on visual self-inspection as described in any one of claims 1 to 3 when executing the computer program.

6. 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 a processor, the job training method based on visual self-inspection as described in any one of claims 1 to 3 is implemented.

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