Implementation method and system for displaying Kanban recommendation content of science and technology management system

Abstract

The invention relates to the technical field of data recommendation, and discloses an implementation method and system for displaying billboard recommendation content of a science and technology management system, and the method comprises the steps: carrying out the data division of each billboard in the science and technology management system, and obtaining a second sub-classification corresponding to a second-level directory of each billboard; setting performance behavior information of each employee position, wherein the performance behavior information comprises work contents executed by the employees and execution time periods and execution areas corresponding to the work contents; constructing a mapping table; extracting feature vectors of the mapping table so as to obtain a feature vector set corresponding to the mapping table; inputting the feature vector set into a deep learning neural network for training to obtain a content recommendation main model; and based on an edge calculation mode, sending the content recommendation main model to each terminal. According to the invention, the working efficiency of employees is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data recommendation, and in particular to a method and system for realizing display of recommended content on a technology management system dashboard. Background Art

[0002] In order to improve the efficiency and quality of daily science and technology management, many companies have designed science and technology management systems. Science and technology management systems improve the efficiency and quality of daily science and technology management through automation and intelligent means, and realize the full process management of scientific research projects, including application, review, progress tracking, funding management, etc., thereby improving the overall level of science and technology innovation management; through standardized process management, ensure the smooth progress of science and technology innovation projects.

[0003] When collating some application materials, employees are required to log in to the company's technology management system to view the data of each dashboard. The content displayed on the dashboard homepage is the same. If the content displayed on the dashboard homepage is not the data the employee wants, the employee must search for it in the directory column of the dashboard. For new employees who have just joined the company, if the content displayed on the dashboard homepage is not what the new employee wants, they must search in the directory column, but they will not be able to find the data they want because they are not familiar with the directory column. Summary of the invention

[0004] The present invention provides a method, system and medium for implementing the display of recommended content on a technology management system dashboard. The purpose is to enable the technology management system to recommend and display on the homepage of each dashboard the data that the employee of each position needs to see during the time period when logging into the technology management system, in view of the situation where employees of each position obtain some data through the technology management system in different time periods.

[0005] The present invention is achieved through the following technical solutions: A method for implementing display of recommended content on a technology management system dashboard, comprising: Divide the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board; Setting the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory; Constructing a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, thereby forming a mapping table of the position, work content and the execution time period, execution area and second sub-classification; Extracting the feature vectors of the mapping table to obtain a feature vector set corresponding to the mapping table; The feature vector set is input into a deep learning neural network for training, thereby obtaining a content recommendation main model by displaying the recommended second sub-category path and the corresponding secondary directory name link on the homepage of each dashboard according to the login time and employee position of the technology management system; Based on the edge computing method, the content recommendation main model is sent to each terminal, and after the employee logs in to the technology management system through any of the terminals, the content recommendation main model on the login terminal is executed, thereby realizing the recommended content display on the homepage of the dashboard.

[0006] In this technical solution, since a group company is composed of multiple positions, and each position has different responsibilities and different tasks, the data to be obtained in the science and technology management system is different. Since new employees are not familiar with the science and technology management system, they often do not know where to obtain the required data on the science and technology management system dashboard. Therefore, the present invention designs a method for displaying recommended content on the dashboard. The content recommendation main model is trained by the employee's position, work content, the execution time period corresponding to the work content, the execution area, and the path of the second sub-classification number. Through the content recommendation main model, when an employee of a certain position logs in to the science and technology management system and opens a certain dashboard, the path of the second sub-classification number of the data that the employee may use and the secondary directory name corresponding to the second sub-classification are displayed in the display area of ​​the recommended content of the dashboard, and the employee can click directly. If the display area of ​​the recommended content does not display the link of the data that the employee wants, the employee can choose not to click the link in the display area of ​​the recommended content.

[0007] As an optimization, the specific process of dividing the data of each kanban board in the technology management system and obtaining the second sub-category corresponding to the secondary directory of each kanban board is as follows: Perform the first division on each kanban board in the technology management system to obtain the main classification corresponding to each kanban board type; Dividing the first-level directories contained in each of the kanban boards respectively to obtain first subcategories corresponding to each first-level directory; The second-level directories contained in each of the first-level directories are divided to obtain second subcategories corresponding to each of the second-level directories.

[0008] As an optimization, the mapping relationship is expressed as: , where Z represents the position and N represents the number corresponding to the job content. , Respectively represent the start date and end date corresponding to the work content, Indicates the path of the second sub-classification number, A indicates the main classification number corresponding to the kanban board, B indicates the first sub-classification number corresponding to the first-level directory, C indicates the second sub-classification number corresponding to the second-level directory, and D indicates the number of the execution area.

[0009] As an optimization, based on the catalog of the billboard, the first sub-category number and / or the second sub-category number are encoded in ascending order from the front to the back.

[0010] As an optimization, based on the catalog of the billboard, the first sub-category number and / or the second sub-category number are encoded in a descending order from front to back.

[0011] As an optimization, the specific process of inputting the feature vector set into the deep neural network for training is: The feature vectors of the position, execution time period and execution area are input into the deep learning neural network as input features, and the feature vector of the path of the second sub-classification number is used as an output feature to train the deep learning neural network.

[0012] As an optimization, the specific method for training the deep learning neural network is to maximize the sum of similarities between the feature vectors of the actual second sub-classification number path in the feature vector set and the feature vectors of the predicted second sub-classification number path output by the deep learning neural network as the objective function, regard one set of combined values ​​of the parameters of the deep learning neural network as the location of a black kite individual, and train the parameters of the deep learning neural network by combining the feature vector set with the improved black kite algorithm: The specific process of training the parameters of the deep learning neural network by combining the improved black kite algorithm with the feature vector set is as follows: S1, initializing algorithm parameters of the improved black-winged kite algorithm, wherein the algorithm parameters include the number of black-winged kite populations, the maximum number of iterations, and the optimization range; S2, randomly initializing the black-winged kite population; S3, respectively inputting the feature vector set into the deep learning neural network corresponding to the black-winged kite population, and calculating the fitness value generated at the location of each black-winged kite individual; wherein the fitness value is the same as the objective function; S4, reordering the black-winged kite individuals according to the fitness values; S5, performing attack behaviors and improved migration behaviors on the reordered black-winged kite individuals, thereby updating the positions of the black-winged kite individuals; S6. Determine whether the maximum number of iterations has been reached. If so, terminate the process and output the current black-winged kite population and the parameters of the deep learning neural network corresponding to the black-winged kite individual with the largest fitness value in the black-winged kite population. Otherwise, return to S3.

[0013] As an optimization, the specific formula for randomly initializing the black-winged kite population is: ; in, represents the position of the i-th black-winged kite individual, i is an integer between 1 and pop, and pop is the number of black-winged kites in the black-winged kite population. and are the lower bound and upper bound of the first dimension black-winged kite individual, i.e., the optimization range, and rand is a randomly selected value between [0,1], wherein the dimension of the black-winged kite individual is the same as the number of parameters for training the deep learning neural network; The mathematical model of the attack behavior of the black-winged kite individual is:

[0014] ; in, represents the position of the i-th black-winged kite individual in the j-th dimension and the t+1-th iteration step; represents the position of the i-th black-winged kite individual in the j-th dimension and the t-th iteration step; r is a random number between 0 and 1, p is a constant of 0.9; T is the maximum number of iterations, and t is the number of iterations completed so far; The mathematical model of the improved migration behavior of the black-winged kite individual is: ; ; in, Indicates The black-winged kite individual Peacekeeping The position in the iteration step; Indicates The black-winged kite individual Peacekeeping t The position in the iteration step; Indicates that so far The leading scorer of the dimension-th black-winged kite individual in the iteration, that is, the optimal black-winged kite individual with the largest fitness value, It means that any black-winged kite individual The first Dimension current position; Indicates In the iteration, any black-winged kite individual in The fitness value of a random position in the dimension; stands for Cauchy mutation; represents the ranking of the i-th black-winged kite individual in the j-th dimension and the t-th iteration step; The specific expression of Cauchy mutation is: ; in, x represents a random variable.

[0015] After the deep learning neural network model has determined the optimal weights and thresholds, other parameters of the deep learning neural network model are fine-tuned through training samples, such as learning rate, batch size, weight decay, learning rate decay, etc.

[0016] As an optimization, when an employee logs into the technology management system, the execution rules of the content recommendation main model include: Obtaining the employee's login account, login region, and login date, and matching the employee's position according to the login account; Matching an execution area according to the login area; Determine whether there is a matching execution time period based on the login date. If so, extract the execution time period matching the login date, the execution area matching the login area, and the feature vector of the employee's position as input features and input them into the main content recommendation model. If not, extract the next execution time period closest to the login date, and use the feature vector of the next execution time period and the employee's position as input features and input them into the main content recommendation model.

[0017] As an optimization, the implementation method also includes: Mapping and connecting each of the work contents in the execution area with the corresponding related file publishing website, and obtaining the publishing data of the related file publishing website through crawler technology, so as to update the execution time period of the work contents; The mapping table is updated according to the new execution time period, thereby updating the feature vector set, and the content recommendation main model is updated through the updated feature vector set.

[0018] The present invention also discloses a system for implementing the display of recommended content on a technology management system kanban board, which is used to execute the aforementioned method for implementing the display of recommended content on a technology management system kanban board, including: A partitioning module is used to partition the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board; A setting module, used to set the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory; A mapping table construction module, used to construct a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, so as to form a mapping table about the position, work content and the execution time period, execution area and second sub-classification; A feature extraction module, used to extract feature vectors of the mapping table to obtain a set of feature vectors corresponding to the mapping table; A training module, used for inputting the feature vector set into a deep learning neural network for training, so as to obtain a content recommendation main model by displaying the recommended second sub-category link on the homepage of each dashboard according to the login time and employee position of the technology management system; The sending module is used to send the content recommendation main model to each terminal based on edge computing, and execute the content recommendation main model on the login terminal after the employee logs in to the technology management system through any of the terminals, so as to realize the recommended content display on the homepage of the dashboard.

[0019] The present invention also discloses a storage medium storing a computer program, wherein when the computer program is executed by a processor, the method for realizing the aforementioned display of recommended content on a technology management system dashboard is implemented.

[0020] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. The present invention designs a method for implementing the display of recommended content on a bulletin board. A main content recommendation model is trained through the employee's position, work content, the execution time period corresponding to the work content, the execution area, and the path of the second sub-classification number. Through the main content recommendation model, when an employee of a certain position logs into the technology management system and opens a certain bulletin board, the path of the second sub-classification number of the data that the employee may use and the secondary directory name corresponding to the second sub-classification are displayed in the display area of ​​the recommended content of the bulletin board. If the recommended secondary directory name is what the employee wants, the employee can directly click on it. This makes it convenient for employees to directly obtain the desired data, thereby improving the efficiency of employees.

[0021] 2. The content recommendation main model is sent to each terminal through edge computing. Employees can directly recommend content through the content recommendation main model of the terminal, which reduces the calculation data of the content recommendation main model and improves the calculation efficiency of the content recommendation main model. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 It is a flow chart of a method for implementing the display of recommended content on a technology management system dashboard according to the present invention; Figure 2 This is a schematic diagram of the interface composition of the dashboard in the technology management system. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0024] This embodiment 1 provides a method for implementing the display of recommended content on a technology management system dashboard, such as Figure 1 As shown, including: S1. Divide the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board.

[0025] In the present invention, the technology management system serves as a support for the company's daily technology management work, including but not limited to the following functions, as shown in Table 1: Table 1

[0026] There are multiple dashboard module links on the homepage of the technology management system. Click one of the dashboard module links to enter the corresponding dashboard and search for data in the corresponding dashboard.

[0027] In the visual interface of a dashboard homepage, there are directories corresponding to all links of the dashboard and a display area for recommended content. The directories are displayed in a hierarchical form, such as Figure 2 The following is a schematic diagram of the high-tech enterprise module dashboard. Figure 2 In the display area, the left area is the directory display area, and the right area is the recommended content display area.

[0028] Because some employees enter the science and technology management system in order to obtain some data for filling out some project applications, and to obtain these data, the usual practice is to click on the directory bar in the left area and then enter the corresponding directory link. However, for new employees, it is not clear in which directory links of the science and technology management system the data they need to obtain is located. Therefore, it is very necessary to display the directory link of the information that the new employee wants to see in the recommended content display area.

[0029] The data to be found can usually be found in the secondary directory in the technology management system, so in the present invention, the classification of the secondary directory of each kanban board is obtained. If some kanban boards only have a primary directory, the secondary directory under the primary directory level can be set to 0, and of course, the secondary directory can also be set to empty.

[0030] In some embodiments, the specific process of dividing the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board is as follows: Perform the first division on each kanban board in the technology management system to obtain the main classification corresponding to each kanban board type; Dividing the first-level directories contained in each of the kanban boards respectively to obtain first subcategories corresponding to each first-level directory; The second-level directories contained in each of the first-level directories are divided to obtain second subcategories corresponding to each of the second-level directories.

[0031] S2. Setting the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory.

[0032] For example, in the application for high-tech enterprise projects, if the employee's position is a project application specialist, his job content is to fill out the high-tech enterprise application form for the region where the employee's company is located. Since the policy plans in each region are different, the application time varies. Therefore, the execution time period of this work content is the application time of the high-tech enterprise application policy, and the implementation area is the city where the enterprise is located.

[0033] S3. Construct a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, thereby forming a mapping table of the position, work content and the execution time period, execution area and second sub-classification.

[0034] The mapping table can be updated according to specific circumstances. The updating process is an existing process and will not be described in detail here.

[0035] In some embodiments, the mapping relationship is expressed as: , where Z represents the position and N represents the number corresponding to the job content. , Respectively represent the start date and end date corresponding to the work content, Indicates the path of the second sub-classification number, A indicates the main classification number corresponding to the kanban board, B indicates the first sub-classification number corresponding to the first-level directory, and C indicates the second sub-classification number corresponding to the second-level directory.

[0036] The positions, work contents, start and end dates, main categories, first sub-categories, and second sub-categories can be compiled according to the company's own internal rules.

[0037] In some embodiments, the first sub-classification number and / or the second sub-classification number are encoded in ascending order from front to back.

[0038] In some embodiments, based on the catalog of the kanban board, the first sub-category number and / or the second sub-category number are encoded in a descending order from front to back.

[0039] If the first classification number and the second sub-classification number are encoded in ascending order, then if the first classification number and the second sub-classification number exist, the smallest coding number is 1. When the second sub-classification does not exist, the second sub-classification number can be set to 0. Of course, it can also be set to empty.

[0040] If the first category number and the second sub-category number are coded in descending order, a maximum value can be set, which is greater than the number of any second sub-category in all the kanban boards.

[0041] S4. Extract the feature vectors of the mapping table to obtain a feature vector set corresponding to the mapping table.

[0042] In some embodiments, a position may involve multiple work contents. Therefore, there may be multiple mapping relationships for a position, that is, there may be multiple feature vectors for a position. All feature vectors corresponding to all positions are combined together to form a feature vector set.

[0043] S5. Input the feature vector set into a deep learning neural network for training, thereby obtaining a content recommendation main model by displaying the recommended second sub-classification path and the corresponding secondary directory name link on the homepage of each dashboard according to the login time of the technology management system and the employee position.

[0044] In some embodiments, the specific process of inputting the feature vector set into the deep neural network for training is: The feature vectors of the position, execution time period and execution area are input into the deep learning neural network as input features, and the feature vector of the path of the second sub-classification number is used as an output feature to train the deep learning neural network.

[0045] During specific training, the feature vectors of the position, execution time period and execution area are input into the deep learning neural network as input features. The deep learning neural network then outputs the predicted path of the second subclassification number. The predicted second subclassification number and the actual path of the second subclassification number in the mapping table are compared through a loss function to determine whether the deep learning neural network meets the requirements.

[0046] Here, the path of the second sub-category number includes the main category number corresponding to the kanban board where the second sub-category is located and the first sub-category number corresponding to the first-level directory, that is, the .

[0047] When the content recommendation main model outputs the path of the second sub-classification number, the secondary directory name corresponding to the second sub-classification can also be obtained accordingly, and finally, the path of the second sub-classification number and the secondary directory name are displayed in the display area of ​​the recommended content.

[0048] For example, in Figure 2 The recommended content display area in the , the displayed content can be: 1 / 1 / 2 RD table query.

[0049] In some embodiments, a specific method for training the deep learning neural network is to maximize the sum of similarities between the feature vectors of the actual second sub-classification number path in the feature vector set and the feature vectors of the predicted second sub-classification number path output by the deep learning neural network as the objective function, and regard a set of combined values ​​of the parameters of the deep learning neural network as the location of a black kite individual, and train the parameters of the deep learning neural network by combining the feature vector set with the improved black kite algorithm: The specific process of training the parameters of the deep learning neural network by combining the improved black kite algorithm with the feature vector set is as follows: S1, initializing algorithm parameters of the improved black-winged kite algorithm, wherein the algorithm parameters include the number of black-winged kite populations, the maximum number of iterations, and the optimization range; S2, randomly initializing the black-winged kite population; S3, respectively inputting the feature vector set into the deep learning neural network corresponding to the black-winged kite population, and calculating the fitness value generated at the location of each black-winged kite individual; wherein the fitness value is the same as the objective function; S4, reordering the black-winged kite individuals according to the fitness values; S5, performing attack behaviors and improved migration behaviors on the reordered black-winged kite individuals, thereby updating the positions of the black-winged kite individuals; S6. Determine whether the maximum number of iterations has been reached. If so, terminate the process and output the current black-winged kite population and the parameters of the deep learning neural network corresponding to the black-winged kite individual with the largest fitness value in the black-winged kite population. Otherwise, return to S3.

[0050] In some embodiments, the specific formula for randomly initializing the black-winged kite population is: ; in, represents the position of the i-th black-winged kite individual, is an integer between 1 and pop, where pop is the number of black-winged kites in the black-winged kite population. and are the lower bound and upper bound of the j-th dimension black-winged kite individual, i.e., the optimization range, and rand is a randomly selected value between [0,1], wherein the dimension of the black-winged kite individual is the same as the number of parameters for training the deep learning neural network; The mathematical model of the attack behavior of the black-winged kite individual is: ; ; in, represents the position of the i-th black-winged kite individual in the j-th dimension and the t+1-th iteration step; represents the position of the i-th black-winged kite individual in the j-th dimension and the t-th iteration step; r is a random number between 0 and 1, p is a constant of 0.9; T is the maximum number of iterations, and t is the number of iterations completed so far; The mathematical model of the improved migration behavior of the black-winged kite individual is: ; ; in, Indicates The black-winged kite individual Peacekeeping The position in the iteration step; Indicates The black-winged kite individual Peacekeeping t The position in the iteration step; Indicates that so far The iteration The leading scorer of the black-winged kite individuals, that is, the optimal black-winged kite individual with the largest fitness value, It means that any black-winged kite individual The first Dimension current position; Indicates In the iteration, any black-winged kite individual in The fitness value of a random position in the dimension; stands for Cauchy mutation; represents the ranking of the i-th black-winged kite individual in the j-th dimension and the t-th iteration step; The specific expression of Cauchy mutation is: ; in, x represents a random variable.

[0051] It should be noted that the parameters of the deep learning neural network trained by the improved Black Kite algorithm include weights and thresholds. After the deep learning neural network determines the optimal weights and thresholds, other parameters of the deep learning neural network model are fine-tuned through training samples, such as learning rate, batch size, weight decay, learning rate decay, etc.

[0052] Adding a ranking of black kite individuals to migration behavior makes full use of the current position ranking information, which is conducive to the black kite individual's position jumping out of the local optimum, allowing the black kite individual to conduct global search and optimization within a given area, thereby improving the optimization ability of the black kite algorithm.

[0053] In some embodiments, when an employee logs into the technology management system, the execution rules of the content recommendation main model include: A1. Obtain the employee's login account, login region, and login date, and match the employee's position according to the login account; A2. Determine whether there is a matching execution time period based on the login date. If so, extract the execution time period matching the login date and the feature vector of the employee's position as input features and input them into the main content recommendation model. If not, extract the next execution time period closest to the login date, and use the feature vector of the next execution time period and the employee's position as input features and input them into the main content recommendation model.

[0054] For example, when an employee logs into the technology management system, the main content recommendation model will predict the employee's login purpose based on the login account, login area and login date, that is, it will determine the latest policy requirements of the employee's company based on the login area, and check whether there is a project application form that needs to be filled out at the time of logging into the system. If so, the paths of all the second sub-classification numbers corresponding to the data that needs to fill out the project application form and the names of the secondary directories corresponding to the second sub-classifications will be presented in Figure 2In the recommended content display area, employees can directly click on the path of the second sub-classification number and the name of the corresponding second-level directory in the recommended content display area to find the relevant data for filling in the project application form.

[0055] It should be noted that the link of the path of the second sub-classification number and the name of the corresponding secondary directory is one link, that is, the main body displayed in the display area of ​​the recommended content is the path of the second sub-classification number + the name of the secondary directory.

[0056] If the time of logging into the system is not within any project application time period, it is judged that the employee has filled out the project application form in advance, and the path of the second sub-classification number corresponding to the data required for the upcoming project application policy that has been announced and is closest to the login time will be displayed in the recommended content display area.

[0057] For example, the employee logs into the system on February 1st of a certain year, but at that time, there is no project that needs to be reported. However, it has been publicly announced that the report for Project A will be submitted from February 15th to April 30th, and the report time for Project A is the project policy report time closest to the login time (February 1st). In this case, the content recommendation main model will automatically assume that the employee is filling out the report for Project A in advance. Therefore, the path of the second sub-classification number of the data required for the report for Project A will be displayed in the display area of ​​the recommended content of the dashboard related to Project A.

[0058] S6. Based on the edge computing method, the content recommendation main model is sent to each terminal, and after the employee logs in to the technology management system through any of the terminals, the content recommendation main model on the login terminal is executed, thereby realizing the recommended content display on the homepage of the dashboard.

[0059] The content recommendation main model trained in the cloud is sent to each terminal through edge computing. In this way, employees can use the content recommendation main model of the terminal to display recommended content, avoiding the situation where multiple employees use the content recommendation main model at the same time, which causes the model to calculate too much data and reduce the calculation rate.

[0060] In some embodiments, the implementation method further includes: S7, mapping and connecting each of the work contents with the corresponding related file publishing website, and obtaining the publishing data of the related file publishing website through crawler technology, so as to update the execution time period of the work contents; The mapping table is updated according to the new execution time period, thereby updating the feature vector set, and the content recommendation main model is updated through the updated feature vector set.

[0061] The content recommendation main model can be automatically updated, and the updated new content recommendation main model is then sent to each terminal, so that the content recommendation main models of each terminal are synchronized.

[0062] If the group company adds new employee positions, the main content recommendation model can also be trained in the cloud and then distributed to each terminal.

[0063] Embodiment 2 discloses a system for implementing the display of recommended content on a technology management system dashboard, which is used to execute the method for implementing the display of recommended content on a technology management system dashboard described in Embodiment 1, including: A partitioning module is used to partition the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board; A setting module, used to set the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory; A mapping table construction module, used to construct a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, so as to form a mapping table about the position, work content and the execution time period, execution area and second sub-classification; A feature extraction module, used to extract feature vectors of the mapping table to obtain a set of feature vectors corresponding to the mapping table; A training module, used for inputting the feature vector set into a deep learning neural network for training, so as to obtain a content recommendation main model by displaying the recommended second sub-category link on the homepage of each dashboard according to the login time and employee position of the technology management system; The sending module is used to send the content recommendation main model to each terminal based on edge computing, and execute the content recommendation main model on the login terminal after the employee logs in to the technology management system through any of the terminals, so as to realize the recommended content display on the homepage of the dashboard.

[0064] Embodiment 3 discloses a storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the method for displaying recommended content on a technology management system dashboard described in Embodiment 1 is implemented.

[0065] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for displaying recommended content on a technology management system dashboard, characterized in that: include: Divide the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board; Setting the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory; Constructing a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, thereby forming a mapping table of the position, work content and the execution time period, execution area and second sub-classification; Extracting the feature vectors of the mapping table to obtain a feature vector set corresponding to the mapping table; The feature vector set is input into a deep learning neural network for training, thereby obtaining a content recommendation main model by displaying the recommended second sub-category path and the corresponding secondary directory name link on the homepage of each dashboard according to the login time and employee position of the technology management system; Based on the edge computing method, the content recommendation main model is sent to each terminal, and after the employee logs in to the technology management system through any of the terminals, the content recommendation main model on the login terminal is executed, thereby realizing the recommended content display on the homepage of the dashboard.

2. According to claim 1, a method for implementing the display of recommended content on a technology management system dashboard is characterized in that: The specific process of dividing the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board is as follows: Perform the first division on each kanban board in the technology management system to obtain the main classification corresponding to each kanban board type; Dividing the first-level directories contained in each of the kanban boards respectively to obtain first subcategories corresponding to each first-level directory; The second-level directories contained in each of the first-level directories are divided to obtain second subcategories corresponding to each of the second-level directories.

3. The method for implementing the display of recommended content on a technology management system dashboard according to claim 1, characterized in that: The mapping relationship is expressed as: , where Z represents the position and N represents the number corresponding to the job content. , Respectively represent the start date and end date corresponding to the work content, Indicates the path of the second sub-classification number, A indicates the main classification number corresponding to the kanban board, B indicates the first sub-classification number corresponding to the first-level directory, C indicates the second sub-classification number corresponding to the second-level directory, and D indicates the number of the execution area.

4. The method for implementing the display of recommended content on a technology management system dashboard according to claim 3, characterized in that: Based on the catalog of the billboard, the first sub-category number and / or the second sub-category number are encoded as serial numbers from small to large from the front to the back, or based on the catalog of the billboard, the first sub-category number and / or the second sub-category number are encoded as serial numbers from large to small from the front to the back.

5. The method for implementing the display of recommended content on a technology management system dashboard according to claim 1, characterized in that: The specific process of inputting the feature vector set into the deep neural network for training is: The feature vectors of the position, execution time period and execution area are input into the deep learning neural network as input features, and the feature vector of the path of the second sub-classification number is used as an output feature to train the deep learning neural network.

6. A method for implementing the display of recommended content on a technology management system dashboard according to claim 5, characterized in that: The specific method for training the deep learning neural network is to maximize the sum of similarities between the feature vectors of the actual second sub-classification number path in the feature vector set and the feature vectors of the predicted second sub-classification number path output by the deep learning neural network as the objective function, regard one set of combined values ​​of the parameters of the deep learning neural network as the location of a black kite individual, and train the parameters of the deep learning neural network by combining the feature vector set with the improved black kite algorithm: The specific process of training the parameters of the deep learning neural network by combining the improved black kite algorithm with the feature vector set is as follows: S1, initializing algorithm parameters of the improved black-winged kite algorithm, wherein the algorithm parameters include the number of black-winged kite populations, the maximum number of iterations, and the optimization range; S2, randomly initializing the black-winged kite population; S3, respectively inputting the feature vector set into the deep learning neural network corresponding to the black-winged kite population, and calculating the fitness value generated at the location of each black-winged kite individual; wherein the fitness value is the same as the objective function; S4, reordering the black-winged kite individuals according to the fitness values; S5, performing attack behaviors and improved migration behaviors on the reordered black-winged kite individuals, thereby updating the positions of the black-winged kite individuals; S6. Determine whether the maximum number of iterations has been reached. If so, terminate the process and output the current black-winged kite population and the parameters of the deep learning neural network corresponding to the black-winged kite individual with the largest fitness value in the black-winged kite population. Otherwise, return to S3.

7. A method for implementing the display of recommended content on a technology management system dashboard according to claim 6, characterized in that: The specific formula for randomly initializing the black-winged kite population is: ； in, represents the position of the i-th black-winged kite individual, is an integer between 1 and pop, where pop is the number of black-winged kites in the black-winged kite population. and Respectively The lower and upper bounds of the black-winged kite individual, that is, the optimization range, is a value randomly selected between [0,1], wherein the dimension of the black-winged kite individual is the same as the number of parameters for training the deep learning neural network; The mathematical model of the attack behavior of the black-winged kite individual is: ； ； in, Indicates The black-winged kite individual Peacekeeping The position in the iteration step; Indicates The black-winged kite individual Peacekeeping t The position in the iteration step; r is a random number between 0 and 1, p is a constant of 0.9; T is the maximum number of iterations, and t is the number of iterations completed so far; The mathematical model of the improved migration behavior of the black-winged kite individual is: ； ； in, Indicates The black-winged kite individual Peacekeeping The position in the iteration step; Indicates The black-winged kite individual Peacekeeping t The position in the iteration step; Indicates that so far The iteration The leading scorer of the black-winged kite individuals, that is, the optimal black-winged kite individual with the largest fitness value, It means that any black-winged kite individual The first Dimension current position; Indicates In the iteration, any black-winged kite individual in The fitness value of a random position in the dimension; stands for Cauchy mutation; represents the ranking of the i-th black-winged kite individual in the j-th dimension and the t-th iteration step; The specific expression of Cauchy mutation is: ； in, x represents a random variable.

8. The method for implementing the display of recommended content on a technology management system dashboard according to claim 1, characterized in that: When an employee logs into the technology management system, the execution rules of the content recommendation main model include: Obtaining the employee's login account, login region, and login date, and matching the employee's position according to the login account; Matching an execution area according to the login area; Determine whether there is a matching execution time period based on the login date. If so, extract the execution time period matching the login date, the execution area matching the login area, and the feature vector of the employee's position as input features and input them into the main content recommendation model. If not, extract the next execution time period closest to the login date, and use the feature vector of the next execution time period and the employee's position as input features and input them into the main content recommendation model.

9. The method for implementing the display of recommended content on a technology management system dashboard according to claim 1, characterized in that: The implementation method also includes: Mapping and connecting each of the work contents in the execution area with the corresponding related file publishing website, and obtaining the publishing data of the related file publishing website through crawler technology, so as to update the execution time period of the work contents; The mapping table is updated according to the new execution time period, thereby updating the feature vector set, and the content recommendation main model is updated through the updated feature vector set.

10. A system for implementing the display of recommended content on a technology management system dashboard, used to execute the method for implementing the display of recommended content on a technology management system dashboard as claimed in any one of claims 1 to 9, characterized in that: include: A partitioning module is used to partition the data of each kanban board in the technology management system to obtain the second sub-category corresponding to the secondary directory of each kanban board; A setting module, used to set the job performance information of each employee position, wherein the job performance information includes the work content performed by the employee and the execution time period and execution area corresponding to the work content, wherein the work content includes searching for required data from the secondary directory; A mapping table construction module, used to construct a mapping relationship between the position, work content and the corresponding execution time period, execution area and second sub-classification, so as to form a mapping table about the position, work content and the execution time period, execution area and second sub-classification; A feature extraction module, used to extract feature vectors of the mapping table to obtain a set of feature vectors corresponding to the mapping table; A training module, used for inputting the feature vector set into a deep learning neural network for training, so as to obtain a content recommendation main model by displaying the recommended second sub-category link on the homepage of each dashboard according to the login time and employee position of the technology management system; The sending module is used to send the content recommendation main model to each terminal based on edge computing, and execute the content recommendation main model on the login terminal after the employee logs in to the technology management system through any of the terminals, so as to realize the recommended content display on the homepage of the dashboard.

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