Training course pushing method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a training course pushing method and device, computer equipment and a storage medium, relates to the technical field of intelligent recommendation, and is suitable for the field of financial science and technology. The method comprises the following steps: obtaining object department information, object post information and object level information, and screening selected post development route information from candidate post development route information according to the object department information, the object post information and the object level information; in response to a route selection operation of the target object on the selected post development route information, screening out target post development route information from the selected post development route information; screening out preliminary training course data from the candidate training course data according to the target post development route information, the object attribute parameters and the training mode demand information; acquiring preliminary learning progress information of the target object based on the preliminary training course data; and adjusting the preliminary training course data according to the preliminary learning progress information to obtain target training course data, and pushing the target training course data to the target object. According to the embodiment of the invention, personalized course pushing can be realized.

Description

Technical Field

[0001] The present application relates to the field of intelligent recommendation technology, applicable to the field of financial technology, and in particular to a training course push method and device, computer equipment and storage medium. Background Art

[0002] In the field of financial technology, in order to improve the professionalism of financial services, it is necessary to regularly train new and old employees so that new employees can quickly master the knowledge related to financial services and old employees can expand their knowledge of more comprehensive financial products. In related technologies, the training operation for employees is to push training courses uniformly, so that employees can automatically learn and complete relevant courses. Training courses are not pushed according to each employee's position, learning ability, etc., resulting in poor employee training results. Therefore, how to realize personalized training course push has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a training course push method and apparatus, computer equipment and storage medium, aiming to achieve personalized course push.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a training course push method, the method comprising:

[0005] Obtaining object attribute parameters and training mode requirement information of the target object; wherein the object attribute parameters include: object department information, object position information and object rank information;

[0006] Filtering selected job development route information from preset candidate job development route information according to the target department information, the target job information and the target rank information;

[0007] In response to the target object's route selection operation on the selected position development route information, filtering out target position development route information from the selected position development route information;

[0008] Screening out preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information;

[0009] Pushing the preliminary training course data to the target object, and obtaining preliminary learning progress information of the target object based on the preliminary training course data;

[0010] Adjust the preliminary training course data according to the preliminary learning progress information to obtain target training course data;

[0011] The target training course data is pushed to the target object.

[0012] In some embodiments, the selecting the selected position development route information from the preset candidate position development route information according to the target department information, the target position information and the target rank information includes:

[0013] Filtering target promotion structure information from preset candidate promotion structure information according to the target department information;

[0014] Filtering preliminary job development route information from the candidate job development route information according to the target promotion framework information;

[0015] The selected position development route information is screened out from the preliminary position development route information according to the target position information and the target rank information.

[0016] In some embodiments, the step of selecting preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information includes:

[0017] Performing vector conversion on the target position development route information, the object attribute parameters and the training mode requirement information to obtain an object vector;

[0018] Performing vector conversion on the candidate training course data to obtain a candidate course vector;

[0019] Calculate the distance between the object vector and the candidate course vector to obtain a vector distance value;

[0020] Obtain candidate course label information of each candidate training course data;

[0021] Filtering target course label information from the candidate course label information according to the vector distance value;

[0022] The preliminary training course data is screened out from the candidate training course data according to the target course label information.

[0023] In some embodiments, the vector conversion of the target position development route information, the object attribute parameters and the training mode requirement information to obtain the object vector includes:

[0024] Extracting features of the target job development route information to obtain job development route features;

[0025] Extracting features from the object attribute parameters to obtain object attribute features;

[0026] Extracting features from the training model requirement information to obtain training model requirement features;

[0027] The job development route feature, the object attribute feature and the training mode requirement feature are spliced ​​together to obtain a target feature;

[0028] The target feature is vectorized to obtain the object vector.

[0029] In some embodiments, after pushing the target training course data to the target object, the method further includes:

[0030] Acquiring the learning behavior data and target learning progress information fed back by the target object based on the target training course data;

[0031] Acquire training effect evaluation rules for the target training course data;

[0032] Performing training effect evaluation on the learning behavior data and the target learning progress information according to the training effect evaluation rule to obtain preliminary training effect evaluation data;

[0033] Sending the preliminary training effect evaluation data to a preset personnel system, and receiving evaluation feedback information fed back by the personnel system;

[0034] The preliminary training effect evaluation data is updated according to the evaluation feedback information to obtain target training effect evaluation data.

[0035] In some embodiments, the training effect evaluation rules include: evaluation demand indicator information and indicator evaluation rules;

[0036] The step of performing a training effect evaluation on the learning behavior data and the target learning progress information according to the training effect evaluation rule to obtain preliminary training effect evaluation data includes:

[0037] Extracting indicators from the learning behavior data according to the evaluation requirement indicator information to obtain learning behavior indicator parameters;

[0038] Performing indicator conversion on the target learning progress information according to the evaluation requirement indicator information to obtain a learning progress indicator parameter;

[0039] The learning behavior indicator parameter and the learning progress indicator parameter are evaluated according to the indicator evaluation rule to obtain the preliminary training effect evaluation data.

[0040] In some embodiments, after optimizing the evaluation data based on the evaluation feedback information and the preliminary training effect evaluation data to obtain the target training effect evaluation data, the method further includes:

[0041] Constructing a portrait according to the target training course data, the target training effect evaluation data, the object attribute parameters, the learning behavior data and the target learning progress information to obtain an object portrait;

[0042] Based on the object portrait, abnormality level classification is performed to obtain object abnormality level information; wherein the object abnormality level information represents the abnormality degree of the target object;

[0043] The object attribute parameters are marked according to the object abnormality level information.

[0044] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a training course push device, the device comprising:

[0045] An acquisition module is used to acquire object attribute parameters and training mode requirement information of a target object; wherein the object attribute parameters include: object department information, object position information and object rank information;

[0046] A route selection module, used to select selected position development route information from preset candidate position development route information according to the target department information, the target position information and the target rank information;

[0047] A route screening module, configured to screen target job development route information from the selected job development route information in response to the target object's route selection operation on the selected job development route information;

[0048] A course screening module, used to screen out preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information;

[0049] A learning progress acquisition module, used to push the preliminary training course data to the target object, and acquire preliminary learning progress information of the target object based on the preliminary training course data;

[0050] A course adjustment module, used to adjust the preliminary training course data according to the preliminary learning progress information to obtain target training course data;

[0051] The course push module is used to push the target training course data to the target object.

[0052] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0053] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes 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 method described in the first aspect is implemented.

[0054] The training course push method and device, computer equipment and storage medium proposed in the present application first obtain the selected job development route that meets the department, position and rank of the target object, and further determine the target job development route based on the target object's demand for job development, and then select a preliminary training course based on the target job development route, the department, position, rank and training model requirements of the target object. After the preliminary training course is pushed to the target object, the preliminary training course will be adjusted based on the target object's learning progress of the preliminary training course to obtain the target training course that meets the target object's job development route, department, position, rank, training model requirements and learning progress, thereby realizing personalized recommendation of training courses and improving the training effect of the target object. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a system architecture diagram of the training course push method provided in the embodiment of the present application;

[0056] Figure 2 is a flow chart of a training course push method provided in an embodiment of the present application;

[0057] Figure 3 yes Figure 2 Flow chart of step S202 in FIG.

[0058] Figure 4 This is a schematic diagram of selecting a job promotion map in the training course push method provided in an embodiment of the present application;

[0059] Figure 5 yes Figure 2 Flow chart of step S203 in FIG.

[0060] Figure 6 yes Figure 5 Flow chart of step S501 in FIG.

[0061] Figure 7 is a flow chart of a training course push method provided by another embodiment of the present application;

[0062] Figure 8 It is a schematic diagram of the training effect evaluation data and evaluation feedback information of the training course push method provided in the embodiment of the present application;

[0063] Fig. 9 yes Figure 7Flow chart of step S703 in FIG.

[0064] Fig.10 is a flow chart of a training course push method provided by another embodiment of the present application;

[0065] Fig.11 It is an overall flow chart of the training course push method provided in the embodiment of the present application;

[0066] Fig.12 It is a module block diagram of the training course push device provided in the embodiment of the present application;

[0067] Fig.13 It is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0071] First, some nouns involved in this application are analyzed:

[0072] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0073] Training system: A guiding document that arranges the three elements of training (lecturers, trainees, and teaching materials) in a reasonable, planned, and systematic manner to achieve certain training goals. Usually, a complete training system includes four parts: training course system, training lecturer management system, training effect evaluation, and training management system.

[0074] Learning map: refers to a series of learning activities designed with competency development path and career planning as the main axis, which is a direct reflection of employees’ learning development path within the enterprise. These learning activities may include traditional course training and other emerging learning methods, such as action learning and online learning.

[0075] Object profiling: used to understand and describe the characteristics and behavior patterns of the target object group. By collecting and analyzing multiple dimensions of object data, such as social attributes, consumption habits, preference characteristics, etc., one or more representative virtual user images are constructed.

[0076] With the development of Internet technology, employee training models and training content are becoming more and more abundant, which can improve the employee training experience. For example, in the financial field, in order to improve employees' professionalism in finance, it is necessary to push financial training courses to new employees, and push training courses on new financial products to old employees to improve employees' professionalism in finance. In related technologies, employee training courses are pushed uniformly, but in the case of high employee mobility, inconsistent employee entry time, and different acceptance abilities and positions of each employee, the uniform course push method is less flexible and cannot make personalized course push for employees, resulting in poor employee training results.

[0077] Based on this, the embodiments of the present application provide a training course push method and apparatus, computer equipment, and storage medium, which are intended to determine the job development route based on the department, position, and rank of the subject, and determine the target job development route based on the subject in the optional job development routes, and then screen the training courses according to the target job development route to screen out the training courses that meet the target job development route. At the same time, after the training course is sent to the subject, the learning progress of the subject is obtained, and the training course is adjusted according to the learning progress before being pushed. Therefore, the course push is completed based on the department, position, rank, job development route, and learning progress of different subjects, and personalized course recommendations are achieved to improve the training effect.

[0078] The training course push method and apparatus, computer equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the training course push method in the embodiments of the present application is described.

[0079] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0080] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0081] The training course push method provided in the embodiment of the present application relates to the field of intelligent recommendation technology and is applicable to the field of financial technology. The training course push method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as 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, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the training course push method, etc., but is not limited to the above forms.

[0082] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0083] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0084] Please refer to Figure 1 , Figure 1 1 is a system architecture diagram of the training course push method provided in an embodiment of the present application, which includes: at least one terminal 140, the Internet 130, a gateway 120, a server 110, etc.

[0085] The terminal 140 is a device that receives training course data and feeds back learning progress information based on the training course data, and is specifically a device controlled by the target object. It includes various forms such as desktop computers, laptops, PDAs (personal digital assistants), mobile phones, vehicle-mounted terminals, home theater terminals, and dedicated terminals. In addition, it can be a single device or a collection of multiple devices. The terminal 140 can communicate with the Internet 130 in a wired or wireless manner to exchange data.

[0086] The server 110 refers to a computer system that can provide certain services to the terminal 140, specifically, a computer system that generates personalized training course data and pushes training course data. Compared with ordinary terminals 140, the server 110 has higher requirements in terms of stability, security, performance, etc. The server 110 can be a high-performance computer in a network platform, a cluster of multiple high-performance computers, a part of a high-performance computer (such as a virtual machine), a combination of parts of multiple high-performance computers (such as virtual machines), etc. In certain application scenarios (such as receiving learning progress information, feedback information from the personnel system, etc. mentioned later), the server 110 will adjust the training course data and re-determine the training effect evaluation data of the target object.

[0087] The gateway 120 is also called an internetwork connector or a protocol converter. The gateway 120 realizes network interconnection at the transport layer and is a computer system or device that acts as a converter. The gateway 120 is a translator between two systems that use different communication protocols, data formats or languages, or even have completely different architectures. At the same time, the gateway 120 can also provide filtering and security functions. The message sent by the terminal 140 to the server 110 must be sent to the corresponding server 110 through the gateway 120. The message sent by the server 110 to the terminal 140 must also be sent to the corresponding terminal 140 through the gateway 120.

[0088] Figure 2 is an optional flowchart of the training course push method provided in the embodiment of the present application. Figure 2 The method may include but is not limited to steps S201 to S207.

[0089] Step S201, obtaining object attribute parameters and training mode requirement information of the target object; wherein the object attribute parameters include: object department information, object position information and object rank information;

[0090] Step S202, selecting selected job development route information from preset candidate job development route information according to target department information, target job information and target rank information;

[0091] Step S203, in response to the target object's route selection operation on the selected position development route information, selecting the target position development route information from the selected position development route information;

[0092] Step S204, screening out preliminary training course data from preset candidate training course data according to target position development route information, object attribute parameters and training mode requirement information;

[0093] Step S205, pushing the preliminary training course data to the target object, and obtaining the preliminary learning progress information of the target object based on the preliminary training course data;

[0094] Step S206, adjusting the preliminary training course data according to the preliminary learning progress information to obtain target training course data;

[0095] Step S207: Push the target training course data to the target object.

[0096] Steps S201 to S207 shown in the embodiment of the present application are to screen out the selected position development route information from the candidate position development route information according to the target object's object department information, object position information, and object rank information, so as to screen out the position development route that matches the target object's department, position, and rank. Then, based on the target object's selection of multiple matching selected position development route information, the target position development route information is determined to further screen out the position development route that meets the development needs of the target object. Therefore, preliminary training course data is screened out from the candidate training course data based on the target position development route information, object attribute parameters, and training mode requirement information, and the preliminary training course data is pushed to the target object to achieve personalized course push. In addition, after the course is pushed, the preliminary learning progress information of the target object based on the preliminary training course data is received, and the preliminary training course data is adjusted according to the preliminary learning progress information to obtain the target training course data, so as to obtain the training course data that meets the learning progress of the target object. It is not only suitable for the training needs of the target object's department, position, rank, training model requirements, job development requirements route and learning progress, and realizes personalized course push, but also can improve the flexibility of training course push, so as to improve the training effect of the target object.

[0097] In step S201 of some embodiments, the object department information is the department information where the target object is located, the object position information is the position information where the target object is located, the object rank information is the rank of the target object, and the training mode requirement information is the training mode required by the target object, which can be set in advance or determined according to the listening environment where the target object is located. It should be noted that different departments, positions and ranks have different matching job development route information. The training modes that the target object can choose include: on-site real-person teaching mode, real-person teaching recording mode, courseware carousel mode and live class mode, etc., and this embodiment does not make specific restrictions on the training mode.

[0098] For example, in the field of finance and insurance, professional training is required for financial service employees, and the departments of finance and insurance include: front-end department, back-end department, operation and maintenance department, customer service department, sales department, etc., and there will be multiple positions and ranks under the same department. For example, in the sales department, there are positions for accident insurance, medical insurance, and major medical insurance, etc., and the ranks are divided into manager level, supervisor level, team leader level, and employee level, etc. It can be seen that different training courses need to be pushed in different departments, positions, and ranks to adapt to the professional requirements of different financial service employees.

[0099] See also Figure 3 In some embodiments, step S202 may include but is not limited to steps S301 to S303:

[0100] Step S301, selecting target promotion structure information from preset candidate promotion structure information according to target department information;

[0101] Step S302, screening out preliminary job development route information from candidate job development route information according to the target promotion framework information;

[0102] Step S303, selecting the selected position development route information from the preliminary position development route information according to the target position information and the target rank information.

[0103] In step S301 of some embodiments, different departments have different promotion structures, so a first mapping relationship table between each reference department information and candidate promotion structure information is set in advance. Specifically, matching reference department information is found as selected department information according to the object department information, and candidate promotion structure information corresponding to the selected department information is determined as target promotion structure information according to the first mapping relationship table.

[0104] In step S302 of some embodiments, a second mapping relationship table between the reference promotion architecture information and the candidate position development route information is set in advance. When screening the preliminary position development route information, the selected promotion architecture information is first screened out from the reference promotion architecture information according to the target promotion architecture information, and the corresponding candidate position development route information is searched out in the second mapping relationship table according to the selected promotion architecture information as the preliminary position development route information. It should be noted that there are multiple preliminary position development route information corresponding to the same promotion architecture information, that is, multiple development routes under the same department are determined.

[0105] For example, if in an insurance sales department, the preliminary job development route information screened out is mainly centered around insurance business sales. Specifically, it can be the job development routes corresponding to various types of insurance positions, which are specifically divided into professional, business and expansion job development routes.

[0106] In step S303 of some embodiments, in order to further obtain a job development route that meets the job and department of the target object, job label information and rank label information are set on each preliminary job development route information. The selected job development route information can be first screened out from the preliminary job development route information based on the object job information and job label information, and then the selected job development route information can be screened out from the selected job development route information based on the object rank information and rank label information, so that the screening operation of the selected job development route information is fast and accurate. It should be noted that the candidate job development route information is divided into multiple levels, specifically according to three aspects: department, position and rank, so as to establish a multi-level candidate job development route system, so that when screening the candidate job development route, it is convenient to screen layer by layer according to the object department information, object job information and object rank information, and accurately screen out the selected job development route information that meets the department, position and rank of the target object.

[0107] Specifically, in this embodiment, the selected job development route information records the promotion requirements and welfare information of each job level in the promotion process, so a job promotion map, also called a "job map", is generated based on the selected job development route information, and nodes are used to represent promotion levels on the job promotion map, and node labels are set on the nodes to represent promotion requirements and welfare information. At the same time, the nodes corresponding to the job promotion map are marked according to the job level information of the object, and the work completion information of the target object can also be obtained, and a promotion progress bar can be set according to the work completion information and promotion requirements information, so that users can understand their current positions, future promotion routes, and the promotion progress of the next job level from the job promotion map, which is conducive to the user's career planning.

[0108] It should be noted that different positions have different promotion maps corresponding to them. For example, the promotion maps corresponding to accident insurance positions and medical insurance positions are different. This allows insurance sales personnel serving different types of insurance to determine the training courses that meet the promotion requirements from their corresponding promotion maps.

[0109] In steps S301 to S303 shown in this embodiment, by determining the matching target promotion architecture information in the candidate promotion architecture information according to the target department information, it is determined which promotion architectures the target object's department has, and then the preliminary job development route information matching the target promotion architecture information is selected, and then the selected job development route information is screened out from the preliminary job development route information based on the target job information and the target rank information. Therefore, by screening the candidate job development route information step by step according to the department, job and rank of the target object, not only the screening operation is accelerated, but also the selected job development route information can be accurately screened out.

[0110] In step S203 of some embodiments, by sending the selected job development route information to the target object, the target object can further select job development route information that meets his / her development needs from the selected job development route information. Specifically, the selected job development route information will be divided according to job development categories and presented as a job promotion map on the terminal where the target object is located. The target object can select the job promotion map of interest from the terminal to obtain the target job development route information.

[0111] For example, Figure 4 As shown, the target object is a medical insurance position, and the job grade is level 1. Three job promotion maps are displayed on the target object's terminal, which are three job promotion maps corresponding to the professional, business and development types of medical insurance positions, and the job promotion map shows the target object's current job grade and promotion route. The target object can select one of the job promotion maps and click the "Confirm" button to complete the route selection operation of the selected job development route. When the target object clicks the "Confirm" button, the training course platform on the server determines the target job development route information selected by the target object.

[0112] As disclosed above, after completing the target position development route information, it is necessary to combine the object attribute parameters and training model demand information to complete the training course push, so as to push training courses that match the target object's development route, department, position, rank and training model requirements, which is conducive to the target object to achieve rank promotion after completing the training. It should be noted that integrating the position development route into the training course recommendation can automatically push training courses that are conducive to the career development of the target object, rather than simply popularizing knowledge, which will make the target object more interested in the pushed training courses and enhance the user's experience during training.

[0113] See also Figure 5 In some embodiments, step S203 may include but is not limited to steps S501 to S506:

[0114] Step S501, performing vector conversion on target job development route information, object attribute parameters and training mode requirement information to obtain an object vector;

[0115] Step S502, performing vector conversion on the candidate training course data to obtain a candidate course vector;

[0116] Step S503, calculating the distance between the object vector and the candidate course vector to obtain a vector distance value;

[0117] Step S504, obtaining candidate course label information of each candidate training course data;

[0118] Step S505, filtering target course label information from candidate course label information according to the vector distance value;

[0119] Step S506: Filter out preliminary training course data from the candidate training course data according to the target course label information.

[0120] In steps S501 to S502 of some embodiments, during the course push process, in order to achieve efficient and accurate course push, it is necessary to convert the target job development route information, object attribute parameters and training model requirement information into object vectors, and convert the candidate training course data into candidate course vectors, so as to represent the job development, attributes and training model requirements of the target object in the form of vectors, and at the same time represent the candidate training courses in the form of vectors, so as to facilitate computer calculations and quickly and accurately screen out preliminary training course data.

[0121] In step S503 of some embodiments, the distance between the object vector and the candidate course vector is calculated, and a distance calculation function such as a cosine function, a Euclidean distance function, a Manhattan distance function, and a Chebyshev distance function can be used for calculation, and this embodiment does not impose any specific restrictions on the distance calculation function. It should be noted that this embodiment uses a pre-set course push function to calculate the course push index between the object vector and the candidate course vector, and determines the vector distance value according to the course push index. Specifically, the course push function is:

[0122]

[0123] In the formula, is the object vector, q i is the candidate course vector, P LEM (u,i) is the course push index, and F is the preset vector length, which indicates how many feature tags will affect the course push of the target object.

[0124] In steps S504 to S506 of some embodiments, the candidate course vector corresponding to the minimum vector distance value is used as the target course vector, and the candidate course label information corresponding to the target course vector is obtained as the target course label information. Finally, the candidate training course data corresponding to the target course label information is used as the preliminary training course data to achieve rapid and accurate screening of the preliminary training course data, and screen out the training course data that meets the training needs of the target object.

[0125] In steps S501 to S506 illustrated in this embodiment, the candidate training course data are screened by adopting a vector method. On the one hand, the training course data that meets the job development path, department, rank, and training demand model of the target object can be screened out, and on the other hand, the screening operation of the training course data is made simpler and more efficient.

[0126] See also Figure 6 In some embodiments, step S501 may include but is not limited to steps S601 to S605:

[0127] Step S601, extracting features of the target job development path information to obtain job development path features;

[0128] Step S602, extracting features from object attribute parameters to obtain object attribute features;

[0129] Step S603, extracting features from the training model requirement information to obtain training model requirement features;

[0130] Step S604, combining the job development route features, object attribute features and training mode requirement features to obtain target features;

[0131] Step S605, vectorize the target feature to obtain an object vector.

[0132] In step S601 of some embodiments, the job development route feature is a key feature that characterizes the target job development route information, specifically including: current job grade information, job route information, route development category, etc. This embodiment does not impose specific restrictions on the job development route feature, and mainly characterizes the content of the target job development route information. It should be noted that this embodiment sets a feature extractor to extract the job development route features in the target job development route information, and the feature extractor may include any of the following: convolutional neural network, recurrent neural network, Transformer, principal component analysis network, linear discriminant analysis network, feature pyramid, and pre-trained model.

[0133] In step S602 of some embodiments, the object attribute features are characterized as features of object attribute parameters, specifically including: object department features, object position features and object rank features, and the object attribute features are also extracted by a feature extractor, which will not be repeated here.

[0134] In step S603 of some embodiments, the training mode requirement feature is a feature of the training mode requirement information, and the training mode requirement feature is extracted from the training mode requirement information by a feature extractor.

[0135] In some embodiments, in step S604 to step S605, by splicing the job development route features, object attribute features and training mode requirement features into target features, the target features are a feature matrix formed by combining multiple features. Then, an encoder is used to encode the features in the target features one by one to obtain an object vector, and the numerical values ​​in the object vector represent the job development route features, object attribute features and training mode requirement features in the target features, so as to complete the screening of preliminary training course data based on the object vector, so as to screen out preliminary training course data that meets the job development route features, object attribute features and training mode requirement features of the target object.

[0136] In steps S601 to S605 shown in this embodiment, the job development route characteristics, object attribute characteristics and training model requirement characteristics are first extracted, and then the job development route characteristics, object attribute characteristics and training model requirement characteristics are encoded into object vectors one by one, and the job development route information, object attribute parameters and training model requirement information are represented in vector form, so that when preliminary training course data is screened based on the object vector, preliminary training course data that meets the target object's job development route, department, position, rank and training model requirements can be screened out.

[0137] In step S205 of some embodiments, as disclosed above, after the preliminary training course data is screened out, a preliminary training course link is generated based on the preliminary training course data, and the preliminary training course link is sent to the target object. The target object can obtain the preliminary training course data by clicking on the preliminary training course link, and can download the preliminary training course data on the terminal, or log in to the training course platform on the terminal to directly view the preliminary training course data for learning. While the target object is learning the preliminary training course data, the training course platform will collect the learning progress of the target object in real time to obtain preliminary learning progress information, and display the learning progress indicator bar corresponding to the preliminary learning progress information on the training course platform.

[0138] Specifically, after obtaining the preliminary learning progress information, the learning progress information of other objects under the same preliminary training course data is obtained as candidate learning progress information, and then the learning start time information of the target object is obtained, the target time range is determined according to the learning start time information and the preset time range, and the candidate learning progress information of other objects whose start learning time information is within the target time range is obtained as reference learning progress information. By comparing multiple reference learning progress information with the preliminary learning progress information to obtain learning progress comparison information, and displaying the learning progress comparison information in the form of a chart, it is convenient for training managers on the training course platform to understand the learning progress gap between the target object and other objects, which is conducive to further training optimization of the target object.

[0139] In step S206 of some embodiments, in order to adapt the target object to different training courses, the preliminary training course data is adjusted according to the preliminary learning progress information to construct target training course data that meets the learning ability of the target object. Specifically, the current target training progress information of the preliminary training course data is obtained, and the preliminary learning progress information and the target training progress information are compared to obtain progress comparison information. If the progress comparison information indicates that the preliminary learning progress information is lower than the target training progress information, the preliminary training course data will be simplified, that is, the duration, difficulty and length of the preliminary training course data will be reduced. If the progress comparison information indicates that the preliminary learning progress information is greater than the target training progress information, the preliminary training course data will be enriched, that is, the training items, content and professional knowledge in the preliminary training course data will be increased. Therefore, based on different preliminary learning progress information, different adjustment strategies will be adopted for the preliminary training course data, so that the generated target training course data is more in line with the learning ability of the target object, and personalized course push is achieved.

[0140] In step S207 of some embodiments, a target training course link is generated according to the target training course data and the target training course link is sent to the target object. The target object can directly click on the target training course link to obtain the target training course data to start training.

[0141] As disclosed above, after the optimized target training course data is pushed to the target object, it is necessary to evaluate the learning effect of the target object, and in this embodiment, the learning effect analysis combines automated analysis and manual analysis to output more accurate analysis conclusions and improve the accuracy of learning effect evaluation.

[0142] See also Figure 7 In some embodiments, after step S207, the training course push method may also include but is not limited to steps S701 to S705:

[0143] Step S701, obtaining learning behavior data and target learning progress information fed back by the target object based on the target training course data;

[0144] Step S702, obtaining training effect evaluation rules for target training course data;

[0145] Step S703, performing training effect evaluation on the learning behavior data and the target learning progress information according to the training effect evaluation rules to obtain preliminary training effect evaluation data;

[0146] Step S704, sending the preliminary training effect evaluation data to a preset personnel system, and receiving evaluation feedback information fed back by the personnel system;

[0147] Step S705: update the preliminary training effect evaluation data according to the evaluation feedback information to obtain target training effect evaluation data.

[0148] In step S701 of some embodiments, the learning behavior data characterizes the behavior of the target object in learning the target training course data, specifically including the learning time period, learning duration, learning mode, and learning repetition rate, etc. The target learning progress information is the learning progress of the target object in learning the target training course data, and the target learning progress information is updated in real time. When it is necessary to evaluate the training effect of the target object, the most recently updated target learning progress information is obtained.

[0149] In step S702 of some embodiments, the effect evaluation rules corresponding to different training course data are different, so it is necessary to obtain the effect evaluation rules corresponding to the target training course data as the training effect evaluation rules, and the training effect evaluation rules are used to evaluate the learning effect of the object receiving the target training course data.

[0150] In step S703 of some embodiments, the training effect evaluation rule is equivalent to an evaluation criterion, that is, judging whether the learning behavior data and the target learning progress meet the content of the training effect evaluation rule, and scoring the content that meets the requirements to obtain preliminary training effect evaluation data. It should be noted that the preliminary training effect evaluation data is represented by a score, and the higher the score, the better the training effect of the target object, and vice versa.

[0151] In steps S704 to S705 of some embodiments, in order to improve the accuracy of the training effect evaluation of the target object, it is necessary to send the preliminary training effect evaluation data to the personnel system to combine with the personnel system to achieve two-way evaluation. Specifically, the personnel system receives the preliminary training effect evaluation data and makes evaluation feedback information based on the preliminary training effect evaluation data. Among them, the evaluation feedback information includes: pass information and fail information, which are used to confirm whether to use the preliminary training effect evaluation data as the final target training effect evaluation data of the target object. It should be noted that if the evaluation feedback information is pass information, it indicates that the training of the target object has passed, and the preliminary training effect evaluation data is used as the target training effect evaluation data; if the evaluation feedback information is fail information, it indicates that the training of the target object has not passed, then the preliminary training effect evaluation data is set to 0, and 0 is used as the target training effect evaluation data. Therefore, the joint training course platform and the personnel system evaluate the training effect of the target object together, which can give a more accurate evaluation conclusion.

[0152] Please refer to Figure 8When the target training effect evaluation data is generated, the training course platform sends the target training effect evaluation data and the evaluation feedback information to the terminal of the target object. Then the terminal used by the target object displays the target training effect evaluation data and the evaluation feedback information, and the evaluation feedback information is a "pass" indicator mark, and the target training effect evaluation data is displayed as a score, specifically "89 points", so that the target object can understand the effect of his or her training, and it is convenient to confirm whether it is necessary to obtain the training course again, thereby improving the professionalism of the target object.

[0153] In steps S701 to S705 shown in this embodiment, after the target object learns the target training course data, the target object's learning behavior and learning progress will be regularly evaluated to evaluate the training effect of the target object, and the training effect of the target object will be evaluated together with the personnel system to obtain more accurate training effect evaluation data.

[0154] See also Fig. 9 In some embodiments, the training effect evaluation rules include: evaluation demand indicator information and indicator evaluation rules; step S703 includes but is not limited to steps S901 to S903:

[0155] Step S901, extracting indicators from learning behavior data according to evaluation demand indicator information to obtain learning behavior indicator parameters;

[0156] Step S902, converting the target learning progress information into an indicator according to the evaluation requirement indicator information to obtain a learning progress indicator parameter;

[0157] Step S903, performing indicator evaluation on the learning behavior indicator parameters and the learning progress indicator parameters according to the indicator evaluation rules to obtain preliminary training effect evaluation data.

[0158] In steps S901 to S902 of some embodiments, the evaluation demand indicator information represents the indicators required for the training effect evaluation, which may specifically be learning time, practice results, learning frequency, etc. This embodiment does not limit the specific content of the evaluation demand indicator information. According to the evaluation demand indicator information, the indicators used for the training effect evaluation are extracted from the learning behavior data as the learning behavior indicator parameters, and then according to the evaluation demand indicator information, the indicators used for the training effect evaluation are extracted from the target learning progress information as the learning progress indicator parameters, and the effect evaluation is completed by combining the learning behavior indicator parameters and the learning progress indicator parameters.

[0159] In step S903 of some embodiments, the indicator evaluation rule is specifically a scoring table, and the scoring table records the score corresponding to each evaluation requirement indicator, so the learning behavior indicator parameters and the learning progress indicator parameters are evaluated according to the indicator evaluation rule, that is, first calculate the score corresponding to each learning behavior indicator parameter in the scoring table as the behavior evaluation score, and then calculate the score corresponding to each learning progress indicator parameter in the scoring table as the progress evaluation score, and then perform weighted summation of the behavior evaluation score and the progress evaluation score to obtain preliminary training effect evaluation data.

[0160] In steps S901 to S903 shown in this embodiment, the training effect evaluation operation is made easier by extracting the learning behavior indicator parameters and learning progress indicator parameters required for the training effect evaluation, and then evaluating the learning behavior indicator parameters and learning progress indicator parameters based on the indicator evaluation rules.

[0161] See also Fig.10 In some embodiments, after step S705, the training course push method may also include but is not limited to steps S1001 to S1003:

[0162] Step S1001, constructing a portrait based on target training course data, target training effect evaluation data, object attribute parameters, learning behavior data and target learning progress information to obtain an object portrait;

[0163] Step S1002, performing abnormality level classification based on the object portrait to obtain object abnormality level information; wherein the object abnormality level information represents the abnormality degree of the target object;

[0164] Step S1003: marking the object attribute parameters according to the object abnormality level information.

[0165] In step S1001 of some embodiments, in order to facilitate training managers to view abnormal objects and improve the organization's security risk management capabilities, the target training course data, target training effect evaluation data, object attribute parameters, learning behavior data and target learning progress information are first combined to construct an object portrait, so as to display the training status and basic information of the target object in all aspects through the object portrait. Specifically, the constructed object portrait is a statistical analysis of the target object in various dimensions, such as whether the target object has completed course A, the learning time, the number of learning times, etc., so as to facilitate training managers to grasp the training status of the target object.

[0166] In step S1002 of some embodiments, the target object is classified into abnormality levels based on the object portrait to obtain object abnormality level information. Specifically, the specific content of the level classification is: first obtain the object features in the object portrait, and evaluate the object features in combination with the abnormality level evaluation rules, and the abnormality level evaluation rules record the reference features corresponding to different abnormal levels. If the object features and reference features are matched to determine the corresponding object abnormality level information. It should be noted that the object abnormality level information can be divided into no abnormality level information, low abnormality level information, medium abnormality level information and high abnormality level information, so as to determine the abnormality degree of the target object through the object abnormality level information, that is, the risk to the organization, which can specifically characterize the turnover assessment rate of the target object.

[0167] In step S1003 of some embodiments, the object attribute parameters are marked according to the object abnormality level information, that is, the risk level label is determined according to the object abnormality level information, and then the object attribute parameters of the target object are marked with the risk level label, so that when the subsequent training course is pushed, the object attribute parameters with the risk level label are obtained, so that when the training course data is pushed, it will be combined with the risk level push of the target object to achieve more accurate training course push.

[0168] In steps S1001 to S1003 shown in this embodiment, a portrait of the target object is constructed based on data, parameters and information from multiple aspects, and then the abnormality level of the target object is determined based on the object portrait. According to the abnormality level of the target object, corresponding marking operations are performed on the object attribute parameters, so that when the training management personnel push courses again, targeted courses can be pushed in combination with the abnormality level of the target object, thereby improving the personalization of course push.

[0169] Please refer to Fig.11 , Fig.11The overall process diagram of the training course push method is shown. In the embodiment of the present application, a "course learning page" and a "special topic learning page" are set on the training course platform. The "course learning page" is set with candidate training course data divided by departments, positions and departments, and the "special topic learning page" is set with special training course data. The target object can filter out multiple job promotion maps according to his / her department, position and rank in the "course learning page" and "special topic learning page", and then select the target promotion map that meets his / her job development needs from the multiple job promotion maps. The training course platform will filter out preliminary training course data from the candidate training course data based on the target promotion map, the department, position, rank and training model requirements of the target object, generate a preliminary training course link based on the preliminary training course data, and send the preliminary training course link to the target object. The target object can obtain the preliminary training course data by clicking on the preliminary training course link, and start learning based on the preliminary training course data, and the training course platform will continuously collect the learning progress of the target object based on the preliminary training course data as preliminary learning progress information. Then, the preliminary training course data is optimized according to the preliminary learning progress information, that is, the comparison result between the preliminary learning progress and the target learning progress is determined to determine the adjustment method of the preliminary training course data and obtain the target training course data that meets the target object. Then, a target training course link is generated based on the target training course data, and the target object opens the target training course link to obtain the target training course data.

[0170] After the target training course data is sent to the target object, the target object's learning behavior data and target learning progress information based on the target training course data will be collected, and the training effect of the target object will be evaluated by combining the learning behavior data and the target learning progress information to obtain a preliminary evaluation result, and the preliminary evaluation result will be sent to the personnel system, and the evaluation feedback information fed back by the personnel system will be received. Specifically, when the evaluation feedback information is passed, the training pass certificate will be sent to the target object; if the evaluation feedback information is not passed, a re-training reminder message will be sent to the target object to pass the re-training of the target object.

[0171] See also Fig.12 The embodiment of the present application also provides a training course push device, which can implement the above training course push method, and the device includes:

[0172] The acquisition module 1201 is used to acquire object attribute parameters and training mode requirement information of the target object; wherein the object attribute parameters include: object department information, object position information and object rank information;

[0173] The route selection module 1202 is used to select the selected position development route information from the preset candidate position development route information according to the target department information, target position information and target rank information;

[0174] A route screening module 1203 is used to screen target job development route information from the selected job development route information in response to the target object's route selection operation on the selected job development route information;

[0175] The course screening module 1204 is used to screen out preliminary training course data from preset candidate training course data according to target position development route information, object attribute parameters and training mode requirement information;

[0176] The learning progress acquisition module 1205 is used to push the preliminary training course data to the target object and acquire the preliminary learning progress information of the target object based on the preliminary training course data;

[0177] The course adjustment module 1206 is used to adjust the preliminary training course data according to the preliminary learning progress information to obtain the target training course data;

[0178] The course push module 1207 is used to push the target training course data to the target object.

[0179] The specific implementation of the training course pushing device is basically the same as the specific implementation of the above-mentioned training course pushing method, and will not be repeated here.

[0180] The embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above training course push method when executing the computer program. The computer device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0181] See also Fig.13 , Fig.13 The hardware structure of a computer device according to another embodiment is shown, and the computer device includes:

[0182] The processor 1301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0183] The memory 1302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1302, and the processor 1301 calls and executes the training course push method of the embodiment of this application;

[0184] Input / output interface 1303, used to implement information input and output;

[0185] The communication interface 1304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0186] A bus 1305 that transmits information between the various components of the device (e.g., the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304);

[0187] The processor 1301 , the memory 1302 , the input / output interface 1303 and the communication interface 1304 are connected to each other in communication within the device via a bus 1305 .

[0188] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned training course push method is implemented.

[0189] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0190] The training course push method and apparatus, computer equipment and storage medium provided in the embodiments of the present application first obtain a selected job development route that meets the department, position and rank of the target object, and further determine the target job development route based on the target object's demand for job development, and then select a preliminary training course based on the target job development route, the department, position, rank and training model requirements of the target object. After the preliminary training course is pushed to the target object, the preliminary training course will be adjusted based on the target object's learning progress of the preliminary training course to obtain a target training course that meets the target object's job development route, department, position, rank, allocation model requirements and learning progress, thereby realizing personalized recommendation of training courses and improving the training effect of the target object.

[0191] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0192] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0193] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, 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.

[0194] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0195] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0196] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0197] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0198] The units described above 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0201] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A training course push method, characterized in that: The method comprises: Obtaining object attribute parameters and training mode requirement information of the target object; wherein the object attribute parameters include: object department information, object position information and object rank information; Filtering selected job development route information from preset candidate job development route information according to the target department information, the target job information and the target rank information; In response to the target object's route selection operation on the selected position development route information, filtering out target position development route information from the selected position development route information; Screening out preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information; Pushing the preliminary training course data to the target object, and obtaining preliminary learning progress information of the target object based on the preliminary training course data; Adjust the preliminary training course data according to the preliminary learning progress information to obtain target training course data; The target training course data is pushed to the target object.

2. The method according to claim 1, characterized in that The selecting the selected position development route information from the preset candidate position development route information according to the target department information, the target position information and the target rank information includes: Filtering target promotion structure information from preset candidate promotion structure information according to the target department information; Filtering preliminary job development route information from the candidate job development route information according to the target promotion framework information; The selected position development route information is screened out from the preliminary position development route information according to the target position information and the target rank information.

3. The method according to claim 1, characterized in that The step of selecting preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information includes: Performing vector conversion on the target position development route information, the object attribute parameters and the training mode requirement information to obtain an object vector; Performing vector conversion on the candidate training course data to obtain a candidate course vector; Calculate the distance between the object vector and the candidate course vector to obtain a vector distance value; Obtain candidate course label information of each candidate training course data; Filtering target course label information from the candidate course label information according to the vector distance value; The preliminary training course data is screened out from the candidate training course data according to the target course label information.

4. The method according to claim 3, characterized in that The target position development route information, the object attribute parameters and the training mode requirement information are vector-converted to obtain an object vector, including: Extracting features of the target job development route information to obtain job development route features; Extracting features from the object attribute parameters to obtain object attribute features; Extracting features from the training model requirement information to obtain training model requirement features; The job development route feature, the object attribute feature and the training mode requirement feature are spliced ​​together to obtain a target feature; The target feature is vectorized to obtain the object vector.

5. The method according to any one of claims 1 to 4, characterized in that: After pushing the target training course data to the target object, the method further includes: Acquiring the learning behavior data and target learning progress information fed back by the target object based on the target training course data; Acquire training effect evaluation rules for the target training course data; Performing training effect evaluation on the learning behavior data and the target learning progress information according to the training effect evaluation rule to obtain preliminary training effect evaluation data; Sending the preliminary training effect evaluation data to a preset personnel system, and receiving evaluation feedback information fed back by the personnel system; The preliminary training effect evaluation data is updated according to the evaluation feedback information to obtain target training effect evaluation data.

6. The method according to claim 5, characterized in that The training effect evaluation rules include: evaluation demand indicator information and indicator evaluation rules; The step of performing a training effect evaluation on the learning behavior data and the target learning progress information according to the training effect evaluation rule to obtain preliminary training effect evaluation data includes: Extracting indicators from the learning behavior data according to the evaluation requirement indicator information to obtain learning behavior indicator parameters; Performing indicator conversion on the target learning progress information according to the evaluation requirement indicator information to obtain a learning progress indicator parameter; The learning behavior indicator parameter and the learning progress indicator parameter are evaluated according to the indicator evaluation rule to obtain the preliminary training effect evaluation data.

7. The method according to claim 5, characterized in that After optimizing the evaluation data based on the evaluation feedback information and the preliminary training effect evaluation data to obtain target training effect evaluation data, the method further includes: Constructing a portrait according to the target training course data, the target training effect evaluation data, the object attribute parameters, the learning behavior data and the target learning progress information to obtain an object portrait; Based on the object portrait, abnormality level classification is performed to obtain object abnormality level information; wherein the object abnormality level information represents the abnormality degree of the target object; The object attribute parameters are marked according to the object abnormality level information.

8. A training course push device, characterized in that: The device comprises: An acquisition module is used to acquire object attribute parameters and training mode requirement information of a target object; wherein the object attribute parameters include: object department information, object position information and object rank information; A route selection module, used to select selected position development route information from preset candidate position development route information according to the target department information, the target position information and the target rank information; A route screening module, configured to screen target job development route information from the selected job development route information in response to the target object's route selection operation on the selected job development route information; A course screening module, used to screen out preliminary training course data from preset candidate training course data according to the target position development route information, the object attribute parameters and the training mode requirement information; A learning progress acquisition module, used to push the preliminary training course data to the target object, and acquire preliminary learning progress information of the target object based on the preliminary training course data; A course adjustment module, used to adjust the preliminary training course data according to the preliminary learning progress information to obtain target training course data; The course push module is used to push the target training course data to the target object.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the training course pushing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the training course pushing method according to any one of claims 1 to 7 is implemented.

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