A method, system and medium for managing scientific and technological innovation project data

Abstract

The invention discloses a method, system and medium for managing scientific and technological innovation project data; it relates to the technical field of data management; it obtains the task template of a target kanban and the content data of remaining kanbans, and parses the target task from the task template of the target kanban; based on the fuzzy association calculation of the target task and the task template of the remaining kanban, the associated task template of each target task is screened; the task data of each target task is generated according to the associated task template; the task data of all target tasks are integrated to generate the content data of the target kanban; this scheme improves the existing technology of scientific and technological management projects in terms of methods, integrates multiple kanbans to manage scientific and technological innovation project data, and realizes the association relationship between each kanban through fuzzy association calculation and standard association calculation methods, finds the associated task template based on the results of fuzzy association calculation and standard association calculation, realizes the automatic filling of different task templates under different kanbans, and greatly reduces the manual workload.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a method, system and medium for managing data of scientific and technological innovation projects. Background Art

[0002] In order to improve the efficiency and quality of scientific research management, promote the transformation of scientific research results, and support the career development of scientific researchers, so as to enhance the company's competitiveness in the field of scientific research, many companies have designed scientific research results management systems. Scientific research results management systems improve the efficiency and quality of scientific research work through automation and intelligent means, and can achieve full-process management of scientific research projects, including application, review, progress tracking, and funding management, thereby improving the overall level of scientific research management; through standardized process management, ensure the smooth progress of scientific research projects. From project application, project establishment, execution to conclusion, each step has clear processes and standards, which reduces the arbitrariness of management and improves the standardization of project management. It provides companies with a platform for the evaluation and transformation of scientific and technological achievements, helps scientific researchers evaluate and commercialize scientific and technological achievements, and improves the transformation effect and economic value of scientific research results.

[0003] The current scientific research results management system is relatively specialized, and is mainly responsible for the overall management and tracking of the project itself. At the application level of the project output results, it still requires manpower to organize and summarize them. For group companies with a wide range of business operations, not only is data management complicated, but the labor cost is also higher. Summary of the invention

[0004] The technical problem to be solved by the present invention is that the current scientific research achievement management system is highly specific, and mainly coordinates the management and tracking of the project itself. At the application level of the project output results, it still requires manpower to organize and summarize; for group companies with a wide range of business operations, not only is data management complicated, but the labor cost is also greater; the purpose of the present invention is to provide a scientific and technological innovation project data management method, system and medium, to improve the existing technology management project technology, integrate multiple dashboards to manage scientific and technological innovation project data, and realize the association relationship between each dashboard through fuzzy association calculation and standard association calculation methods, find the associated task template based on the results of fuzzy association calculation and standard association calculation, and realize the automatic filling of different task templates under different dashboards, thereby greatly reducing the manual workload.

[0005] The present invention is achieved through the following technical solutions:

[0006] This solution provides a method for managing scientific and technological innovation project data, including:

[0007] Obtaining the task template of the target board and the content data of the remaining boards, and parsing the target task from the task template of the target board; the content data includes a plurality of task templates, each of which is composed of a plurality of tasks and task data corresponding to each task;

[0008] Perform fuzzy correlation calculation based on the target task and the task templates of the remaining Kanban boards to filter out the associated task templates of each target task;

[0009] Generate task data for each target task according to the associated task template: parse the associated task from the associated task template; perform standard association calculation on the target task and the associated task, filter out the target associated task of each target task, extract the task data of the target associated task, and generate the task data of the target task according to the task data of the target associated task;

[0010] The task data of all target tasks are integrated to generate the content data of the target dashboard.

[0011] Working principle of this scheme: the current scientific research achievement management system is highly specific, mainly coordinating and tracking the project itself, and at the application level of the project output results, it still needs to consume manpower to organize and summarize; for group companies with a wide range of business, not only is data management complex, but the manpower cost is also greater; this scheme sets up multiple dashboards for simultaneous management while managing the data of scientific and technological innovation projects; there are many overlapping contents between the task templates of different dashboards, such as the additional deduction dashboard requires the cost data in the scientific and technological innovation project dashboard, and the high-tech enterprise management dashboard requires the intellectual property status data in the intellectual property dashboard; if the task templates under the dashboards at each application level are manually statistically reported, the workload is cumbersome and large; this scheme provides a scientific and technological innovation project data management method, improves the existing technology management project technology, integrates multiple dashboards for scientific and technological innovation project data management, and realizes the correlation between each dashboard through fuzzy correlation calculation and standard correlation calculation methods, finds the related task templates based on the results of fuzzy correlation calculation and standard correlation calculation, and realizes the automatic filling of different task templates under different dashboards, greatly reducing the manual workload.

[0012] A further optimization scheme is to perform fuzzy association calculation based on the target task and the task templates of the remaining kanban boards to screen out the associated task templates of each target task, including the following method:

[0013] Construct a keyword set for each target task;

[0014] Based on the keyword set, fuzzy association calculation is performed on the task templates of all remaining kanban boards to obtain a fuzzy association value P;

[0015] A fuzzy correlation threshold Pe is preset, and if the fuzzy correlation value of the task template of the current remaining kanban is greater than Pe, the task template of the current remaining kanban is determined to be the associated task template of the current target task.

[0016] A further optimization scheme is that the fuzzy correlation calculation is performed on the task templates of all remaining kanban boards based on the keyword set to obtain a fuzzy correlation value P; including the method:

[0017] Get the keyword set of the target task and set the weight of each keyword;

[0018] Perform a keyword search on all remaining boards and task templates of remaining boards:

[0019] Get the remaining board l The name and introduction text constitute the main database set {L0}, and the total number of words in the main database set is N; get the remaining kanban l The names and introduction texts of the R task templates constitute the slave database set {L1}, ..., {LR}; the total number of words in each slave database set is {M1}, ..., {MR}; the total number of words in the slave database is M = M1+...+MR;

[0020] Search for keyword i in the main database set {L0} and the slave database set {L1}, ..., {LR} to obtain: the number of hits of keyword i in the main database set {L0} is L0 i ; The number of hits of keyword i in the database set {L1}, ..., {LR} is L1 respectively i , …, LR i ;

[0021] The fuzzy correlation values ​​of the task templates of each remaining kanban board are calculated based on the search results.

[0022] A further optimization scheme is that the fuzzy correlation value of the task template of each remaining kanban is calculated according to the search results, including the method:

[0023] Remaining Kanban l The fuzzy association value P of the task template r about the keyword i lri for:

[0024] ;

[0025] a=(L1 i +…+LR i ) / R;

[0026] b=L0 i ;

[0027] in, represents the weight of keyword i; n represents the total number of keywords; ln represents the logarithmic function; R represents the total number of keywords; a represents the first hit factor; b represents the second hit factor.

[0028] Remaining Kanban l The sum of the fuzzy association values ​​of all keywords in the task template r is used as the remaining kanban l The fuzzy association value of the task template r.

[0029] A further optimization scheme is that the task data of each target task is generated according to the associated task template, including the method:

[0030] Parse the associated tasks from the associated task template and construct the associated task feature quantity;

[0031] The semantic value is set based on the associated task feature quantity, and the Fourier expansion coefficient vector of each semantic value is calculated;

[0032] A data similarity threshold is set to filter out the semantic values ​​of the Fourier expansion coefficient vector that meet the data similarity threshold, so as to filter out the associated tasks corresponding to the Fourier expansion coefficient vector as the target associated tasks.

[0033] A further optimization scheme is that the method for constructing the associated task feature quantity includes:

[0034] Get the name and introduction text of each related task, and construct the data text of each related task;

[0035] Based on the data text, construct the feature quantity of each related task according to the formula f=(β*R) / h;

[0036] Among them, β represents the total number of hits of searching for all keywords in the data text of the associated task; h represents the total number of characters in the data text; and R represents the total number of keywords.

[0037] A further optimization scheme is to set the semantic value based on the associated task feature quantity and calculate the Fourier expansion coefficient vector of each semantic value; including the method:

[0038] Take e as the discrete numerical sequence and u as the repeated data approximate value sequence for Fourier transformation to determine the semantic value y of the associated task feature j j :

[0039] γ = (f + ue) / R + I';

[0040] ;

[0041] Among them, I' represents the semantic numerical adjacent relationship representation; γ represents the conversion parameter; v represents the Lagrange multiplier.

[0042] A further optimization scheme is that the task data of the target task is generated according to the task data of the target-associated task, including the method:

[0043] Extracting task data of target-related tasks;

[0044] The task data of the target-associated task is deduplicated and reorganized to generate the task data of the current task.

[0045] This solution also provides a scientific and technological innovation project data management system, which is used to implement the above-mentioned scientific and technological innovation project data management method, and the system includes:

[0046] The acquisition module is used to obtain the task template of the target board and the content data of the remaining boards, and parse the target task from the task template of the target board; the content data includes multiple task templates, each task template consists of multiple tasks and task data corresponding to each task;

[0047] The first calculation module is used to perform fuzzy association calculation based on the target task and the task templates of the remaining kanban board, and screen out the associated task templates of each target task;

[0048] The second calculation module is used to generate task data of each target task according to the associated task template: parse the associated task from the associated task template; perform standard association calculation on the target task and the associated task, screen out the target associated task of each target task, extract the task data of the target associated task, and generate the task data of the target task according to the task data of the target associated task;

[0049] The generation module is used to integrate the task data of all target tasks to generate the content data of the target dashboard.

[0050] The present solution also provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement a method for managing scientific and technological innovation project data as described above.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] The present invention provides a method, system and medium for managing scientific and technological innovation project data. The method improves on the existing technology for managing scientific and technological projects, integrates multiple dashboards to manage scientific and technological innovation project data, and realizes the association relationship between various dashboards through fuzzy association calculation and standard association calculation methods. On the basis of the results of fuzzy association calculation and standard association calculation, the associated task templates are found, and the automatic filling of different task templates under different dashboards is realized, which greatly reduces the manual workload.

[0053] The present invention provides a method, system and medium for managing scientific and technological innovation project data; fuzzy correlation calculation or standard correlation calculation is performed at the level of dashboard and task template, and at the level of task template and task in order to reduce the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0055] Figure 1 This is a flowchart of the data management method for scientific and technological innovation projects;

[0056] Figure 2 A flowchart of a method for screening task templates associated with a target task;

[0057] Figure 3 This is a schematic diagram of the structure of the scientific and technological innovation project data management system;

[0058] Figure 4 This is a schematic diagram of the interface of the scientific and technological innovation project data management system. DETAILED DESCRIPTION

[0059] 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.

[0060] The current scientific research results management system is highly specialized, mainly managing and tracking the project itself, and at the application level of the project output results, it also requires manpower to organize and summarize; for group companies with a wide range of business, not only is data management complicated, but the manpower cost is also higher. In view of this, this solution provides the following embodiments to solve the above technical problems.

[0061] Embodiment 1: This embodiment provides a method for managing scientific and technological innovation project data, such as Figure 1 As shown, including:

[0062] Step 1: Obtain the task template of the target board and the content data of the remaining boards, and parse the target task from the task template of the target board; the content data includes multiple task templates, each task template consists of multiple tasks and task data corresponding to each task;

[0063] This solution sets up multiple dashboards for simultaneous management while managing the data of scientific and technological innovation projects; with scientific and technological innovation projects as the basic dashboard, other dashboards mainly include dashboards designed according to the application of scientific and technological innovation project results, such as project results transformation dashboard, scientific and technological achievements dashboard, high-tech enterprise management dashboard, intellectual property dashboard, journal association dashboard, additional deduction dashboard, etc., and each dashboard stores relevant data according to the task template; each dashboard has a corresponding task template, such as the high-tech enterprise management dashboard has task templates such as high-tech enterprise certification form filling, high-tech enterprise certification policy, equipment management, scientific research project management, and high-tech enterprise certification cost inquiry set around the high-tech enterprise certification process; the task template contains one or more tasks, and the task content needs to be filled in the corresponding task;

[0064] There are many overlapping contents between the task templates of different dashboards. For example, the additional deduction dashboard requires the expense data in the science and technology innovation project dashboard, and the high-tech enterprise management dashboard requires the intellectual property status data in the intellectual property dashboard. If the task templates under the dashboards at each application level are manually statistically reported, the workload is cumbersome and large. This solution provides a science and technology innovation project data management method, which improves the existing science and technology management project technology, integrates multiple dashboards to manage science and technology innovation project data, and realizes the correlation between each dashboard through fuzzy correlation calculation and standard correlation calculation methods. On the basis of the fuzzy correlation calculation and standard correlation calculation results, the related task templates are found, and the automatic filling of different task templates under different dashboards is realized, which greatly reduces the manual workload.

[0065] Step 2: Perform fuzzy correlation calculation based on the target task and the task templates of the remaining kanbans to select the associated task templates of each target task; the associated task templates described in this solution mainly refer to the task templates in other kanbans that are associated with the task content of the target task; Figure 2 As shown, step 2 specifically includes the following sub-steps:

[0066] S21, constructing a keyword set for each target task; a classification algorithm such as a support vector machine and a random forest may be used to determine the keyword set for the target task, and determine the weight of each keyword in the keyword set;

[0067] S22, based on the keyword set, fuzzy correlation calculation is performed on the task templates of all remaining kanban boards to obtain a fuzzy correlation value P; this step specifically includes the following method:

[0068] S221, obtaining a keyword set of the target task and setting a weight for each keyword;

[0069] S222, perform keyword search on all remaining kanbans and task templates of the remaining kanbans:

[0070] S223, get the remaining kanban l The name and introduction text constitute the main database set {L0}, and the total number of words in the main database set is N; get the remaining kanban l The names and introduction texts of the R task templates constitute the slave database set {L1}, ..., {LR}; the total number of words in each slave database set is {M1}, ..., {MR}; the total number of words in the slave database is M = M1+ ...+MR;

[0071] S224, search for keyword i in the main database set {L0} and the slave database sets {L1}, ..., {LR} to obtain: the number of hits of keyword i in the main database set {L0} L0 i ; The number of hits of keyword i in the database set {L1}, ..., {LR} is L1 i , …, LR i ;

[0072] S225, calculating the fuzzy correlation value of the task template of each remaining kanban according to the search results; this step specifically includes the following method:

[0073] Remaining Kanban l The fuzzy association value P of the task template r about the keyword i lri for:

[0074] ;

[0075] a=(L1 i +…+LR i ) / R;

[0076] b=L0 i ;

[0077] in, represents the weight of keyword i; n represents the total number of keywords; ln represents the logarithmic function; R represents the total number of keywords;

[0078] Remaining Kanban l The sum of the fuzzy association values ​​of all keywords in the task template r is used as the remaining kanban l The fuzzy association value of the task template r.

[0079] The prerequisite for fuzzy association calculation and standard association calculation is to have accurate task templates, names and introductions of tasks and dashboards. Generally, the names and introductions are established when the data management system is created, so they can be used directly here.

[0080] S23, presetting a fuzzy correlation threshold Pe, if the fuzzy correlation value of the task template of the current remaining kanban is greater than Pe, determining that the task template of the current remaining kanban is the associated task template of the current target task.

[0081] Step 3, generating task data of each target task according to the associated task template: parsing the associated task from the associated task template; performing standard association calculation on the target task and the associated task, screening out the target associated task of each target task, extracting the task data of the target associated task, and generating the task data of the target task according to the task data of the target associated task;

[0082] This step specifically includes the following sub-steps:

[0083] S31, parsing the associated tasks from the associated task template and constructing the associated task feature quantity; this step specifically includes:

[0084] S311, obtaining the name and introduction text of each related task, and constructing the data text of each related task;

[0085] S312, constructing feature quantities of each associated task based on the data text according to the formula f=(β*R) / h;

[0086] Among them, β represents the total number of hits of searching for all keywords in the data text of the associated task; h represents the total number of characters in the data text; and R represents the total number of keywords.

[0087] S32, setting semantic values ​​based on the associated task feature quantity, and calculating the Fourier expansion coefficient vector of each semantic value;

[0088] Specifically, e is used as a discrete numerical sequence and u is used as a repeated data approximate value sequence for Fourier transformation to determine the semantic value y of the associated task feature j. j :

[0089] ;

[0090] γ = (f + ue) / R + I';

[0091] Among them, I' represents the semantic numerical adjacent relationship representation; γ represents the conversion parameter; v represents the Lagrange multiplier.

[0092] S33, setting a data similarity threshold, screening out the semantic values ​​of the Fourier expansion coefficient vector that meet the data similarity threshold, and screening out the associated tasks corresponding to the Fourier expansion coefficient vector as the target associated tasks; this step specifically includes the following steps:

[0093] S331, calculate the character association relationship of each Fourier expansion coefficient vector: G=L j-z Ey j / y z ; L j-z represents the relative semantic distance of the zth character in the associated task feature j; y zIt represents the semantic value of the zth character after the sliding window and smoothing operation are set; E represents the sliding window size; G represents the character association value of the Fourier expansion coefficient vector;

[0094] S332, calculate the similarity judgment value X=(p+x) / Ge1 of each Fourier expansion coefficient vector; when the similarity judgment value X exceeds the threshold Xe, retain the current Fourier expansion coefficient vector; p represents the semantic numerical distribution probability of different data; x is the Fourier expansion coefficient; e1 represents a discrete sequence of one period.

[0095] Step 4: Generate the content data of the target dashboard by integrating the task data of all target tasks. This step specifically includes the following methods:

[0096] S41, extracting task data of target-related tasks;

[0097] S42, deduplicate and reorganize the task data of the target-related tasks to generate task data of the current task.

[0098] Some task content data contain more content, and it is not realistic to perform fuzzy association calculation or standard association calculation based on task content data. Therefore, the fuzzy association calculation or standard association calculation of this scheme is performed at the level of the dashboard and task template, and at the level of the task template and task, respectively, to reduce the amount of calculation.

[0099] Embodiment 2: This embodiment provides a scientific and technological innovation project data management system, which is used to implement a scientific and technological innovation project data management method described in Embodiment 1, such as Figure 3 As shown, the system comprises:

[0100] The acquisition module is used to obtain the task template of the target board and the content data of the remaining boards, and parse the target task from the task template of the target board; the content data includes multiple task templates, each task template consists of multiple tasks and task data corresponding to each task;

[0101] The first calculation module is used to perform fuzzy association calculation based on the target task and the task templates of the remaining kanban board, and screen out the associated task templates of each target task;

[0102] The second calculation module is used to generate task data of each target task according to the associated task template: parse the associated task from the associated task template; perform standard association calculation on the target task and the associated task, screen out the target associated task of each target task, extract the task data of the target associated task, and generate the task data of the target task according to the task data of the target associated task;

[0103] The generation module is used to integrate the task data of all target tasks to generate the content data of the target dashboard.

[0104] like Figure 4 As shown, the journal association dashboard of this solution includes manuscript submission task template, journal registration task template, manuscript management task template, journal management task template, etc. The manuscript submission task template includes tasks such as manuscript name, project source, manuscript author, and delivery target. The task content corresponding to each task in the manuscript submission task template, journal registration task template, manuscript management task template, and journal management task template is filled in according to the method of this solution.

[0105] Embodiment 3: This embodiment provides a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement a method for managing scientific and technological innovation project data as described in Embodiment 1. Specifically, the following steps are performed:

[0106] Step 1: Obtain the task template of the target board and the content data of the remaining boards, and parse the target task from the task template of the target board; the content data includes multiple task templates, each task template consists of multiple tasks and task data corresponding to each task;

[0107] Step 2: Perform fuzzy correlation calculation based on the target task and the task templates of the remaining kanban boards to filter out the associated task templates of each target task;

[0108] Step 3, generating task data of each target task according to the associated task template: parsing the associated task from the associated task template; performing standard association calculation on the target task and the associated task, screening out the target associated task of each target task, extracting the task data of the target associated task, and generating the task data of the target task according to the task data of the target associated task;

[0109] Step 4: Generate content data of the target dashboard by integrating the task data of all target tasks.

[0110] 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 managing scientific and technological innovation project data, characterized in that: include: Obtaining the task template of the target board and the content data of the remaining boards, and parsing the target task from the task template of the target board; the content data includes a plurality of task templates, each of which is composed of a plurality of tasks and task data corresponding to each task; Perform fuzzy correlation calculation based on the target task and the task templates of the remaining Kanban boards to filter out the associated task templates of each target task; Included methods: Construct a keyword set for each target task; Based on the keyword set, fuzzy association calculation is performed on the task templates of all remaining kanban boards to obtain a fuzzy association value P; Included methods: Get the keyword set of the target task and set the weight of each keyword; Perform a keyword search on all remaining boards and task templates of remaining boards: Get remaining boards l The name and introduction text constitute the main database set {L0}, and the total number of words in the main database set is N; get the remaining kanban l The names and introduction texts of the R task templates constitute the slave database set {L1}, ..., {LR}; the total number of words in each slave database set is {M1}, ..., {MR}; the total number of words in the slave database is M=M1+...+MR; searching for keyword i in the main database set {L0} and the slave database set {L1}, ..., {LR} obtains: the number of hits of keyword i in the main database set {L0} is L0 i ; The number of hits of keyword i in the database set {L1}, ..., {LR} is L1 i , …, LR i ; Calculate the fuzzy correlation value of the task template of each remaining kanban board according to the search results; A fuzzy correlation threshold Pe is preset. If the fuzzy correlation value of the task template of the current remaining kanban is greater than Pe, the task template of the current remaining kanban is determined to be the associated task template of the current target task. Generate task data for each target task according to the associated task template: parse the associated tasks from the associated task template; Perform standard correlation calculation on the target task and the associated tasks, screen out the target associated tasks of each target task, extract the task data of the target associated tasks, and generate the task data of the target task according to the task data of the target associated tasks; The task data of all target tasks are integrated to generate the content data of the target dashboard.

2. A method for managing scientific and technological innovation project data according to claim 1, characterized in that: The method of calculating the fuzzy correlation value of the task template of each remaining kanban board according to the search results includes: Remaining Kanban l The fuzzy association value P of the task template r about the keyword i lri for: ； a=( L1 i +…+LR i ) / R; b=L0 i ; in, represents the weight of keyword i; n represents the total number of keywords; ln represents the logarithmic function; R represents the total number of keywords; a represents the first hit factor; b represents the second hit factor; Remaining Kanban l The sum of the fuzzy association values ​​of all keywords in the task template r is used as the remaining kanban l The fuzzy association value of the task template r.

3. A method for managing scientific and technological innovation project data according to claim 2, characterized in that: The method of generating task data for each target task according to the associated task template includes: Parse the associated tasks from the associated task template and construct the associated task feature quantity; The semantic value is set based on the associated task feature quantity, and the Fourier expansion coefficient vector of each semantic value is calculated; A data similarity threshold is set to filter out the semantic values ​​of the Fourier expansion coefficient vector that meet the data similarity threshold, so as to filter out the associated tasks corresponding to the Fourier expansion coefficient vector as the target associated tasks.

4. A method for managing scientific and technological innovation project data according to claim 3, characterized in that: The method for constructing the associated task feature quantity includes: Get the name and introduction text of each related task, and construct the data text of each related task; Based on the data text, construct the feature quantity of each related task according to the formula f=(β*R) / h; Among them, β represents the total number of hits of searching for all keywords in the data text of the associated task; h represents the total number of characters in the data text; and R represents the total number of keywords.

5. A method for managing scientific and technological innovation project data according to claim 4, characterized in that: The method of setting semantic values ​​based on the associated task feature quantity and calculating the Fourier expansion coefficient vector of each semantic value includes: Take e as the discrete numerical sequence and u as the repeated data approximate value sequence for Fourier transformation to determine the semantic value y of the associated task feature j j : γ = (f + ue) / R + I'; ； Among them, I' represents the semantic numerical adjacent relationship representation; γ represents the conversion parameter; v represents the Lagrange multiplier.

6. A method for managing scientific and technological innovation project data according to claim 1, characterized in that: The method of generating the task data of the target task according to the task data of the target-associated task includes: Extract task data of the target-associated task; and generate task data of the current task after de-duplication and reorganization of the task data of the target-associated task.

7. A scientific and technological innovation project data management system, characterized in that: A method for managing scientific and technological innovation project data according to any one of claims 1 to 6, the system comprising: The acquisition module is used to obtain the task template of the target board and the content data of the remaining boards, and parse the target task from the task template of the target board; the content data includes multiple task templates, each task template consists of multiple tasks and task data corresponding to each task; The first calculation module is used to perform fuzzy association calculation based on the target task and the task templates of the remaining kanban boards, and screen out the associated task templates of each target task; The second calculation module is used to generate task data of each target task according to the associated task template: parse the associated task from the associated task template; perform standard association calculation on the target task and the associated task, screen out the target associated task of each target task, extract the task data of the target associated task, and generate the task data of the target task according to the task data of the target associated task; The generation module is used to integrate the task data of all target tasks to generate the content data of the target dashboard.

8. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement a method for managing scientific and technological innovation project data as described in any one of claims 1 to 6.

Images