Project full-cycle data management method and system for school-enterprise collaborative research and development

Abstract

This invention relates to the field of project lifecycle data management technology, specifically to a project lifecycle data management method and system for collaborative research and development between universities and enterprises. The system includes a phase migration instruction generation module, which continuously monitors whether preset phase migration trigger conditions are met during project progress and generates corresponding phase migration instructions when these conditions are met. This invention constructs a three-dimensional data partitioning model that orthogonally combines organizational domains, lifecycle phase domains, and data type domains, and defines dynamic mapping rules that automatically update data entity coordinates according to phase migration trigger conditions, achieving a smooth transition of data ownership as the project progresses. Furthermore, the introduction of a synchronization window mechanism provides a controllable bidirectional access buffer period during data migration, ensuring business continuity before and after migration, and automatically converging permissions and generating audit logs when the window expires.

Description

Technical Field

[0001] This invention relates to the field of project lifecycle data management technology, specifically to a project lifecycle data management method and system for collaborative research and development between universities and enterprises. Background Technology

[0002] Industry-university collaborative research and development (ICR) refers to an innovative model in which universities and enterprises collaborate across organizations based on shared research and development goals, from project definition and technological breakthroughs to deliverables. Unlike research and development activities within a single organization, the data generated during industry-university collaborative research and development includes both procedural data such as experimental records and code iteration logs, and deliverable data such as delivery documents and intellectual property application documents. Furthermore, the ownership of data access changes dynamically as the project progresses. During the project initiation phase, the enterprise primarily defines the requirements; during the research and development execution phase, both parties need to share core design materials; and during the acceptance and delivery phase, the intellectual property rights and data carriers of the final deliverables need to be transferred to the enterprise.

[0003] Currently, existing collaborative R&D platforms on the market generally employ static role-based access control lists or organizational domain divisions for data isolation, failing to support a smooth transition of data ownership as the project lifecycle progresses. When a project transitions from the R&D execution phase to the acceptance phase, managers must manually modify permissions in batches or manually migrate data storage locations to change ownership, a cumbersome process prone to security oversights. Furthermore, current methods for assessing the contributions of project participants often rely on simple summaries of code commits or task completion counts, lacking comprehensive quantitative analysis based on factors such as cross-organizational interactions and time investment. In addition, a large amount of intermediate data generated during the project (such as iterative versions of design schemes, experimental failure records, and review comments) lacks value differentiation. Without dynamic storage and backup resource management based on value valuation, storage costs increase, and high-value data is not effectively protected. Therefore, there is an urgent need for a project lifecycle data management method and system that adapts to the characteristics of cross-organizational collaboration and supports dynamic data ownership management. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for full-cycle data management of projects in university-enterprise collaborative research and development, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for full-cycle data management of projects in university-enterprise collaborative research and development, the method comprising: S1. Construct a three-dimensional data partitioning model orthogonally defined by organizational dimension coordinate axes, life cycle dimension coordinate axes, and data type dimension coordinate axes, which is used to define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects; each data partition in the three-dimensional data partitioning model corresponds to a unique identifier composed of a triplet of coordinates; S2. Obtain the original project records generated or updated in real time during the project execution process from both university clients and enterprise clients; construct standard data entities corresponding to the original project records; and determine the initial organization dimension coordinates, initial life cycle dimension coordinates, and initial data type dimension coordinates corresponding to each standard data entity based on the source information, project stage information at the time of collection, and data content characteristics of the obtained standard data entities. Then, allocate each standard data entity to the corresponding data partition in the three-dimensional data partitioning model for storage and establish an association table between standard data entities and storage partitions. S3. During the project's progress, continuously monitor whether the preset stage migration trigger conditions are met; and when the preset stage migration trigger conditions are met, generate a corresponding stage migration instruction, which includes a list of identifiers of the standard data entities to be migrated and the target lifecycle dimension coordinate values. S4. Execute the obtained stage migration instructions, and for each standard data entity to be migrated in the identifier list in the obtained stage migration instructions, update the lifecycle dimension coordinate value in the triplet coordinates of the current standard data entity to be migrated to the corresponding target lifecycle dimension coordinate value in the three-dimensional data partition model, so as to change the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition; and add a corresponding record of the data partition before migration and the data partition after migration for the corresponding standard data entity to be migrated in the obtained association table. S5. During the time from the start to the completion of the dynamic migration operation of the data partition, through a preset synchronization window period, both university clients and enterprise clients are allowed to perform read operations on the standard data entities to be migrated with the highest permissions granted in the current data partition and the target data partition. When the synchronization window expires, remove the access permissions of the university client and the enterprise client to the standard data entity to be migrated located in the current data partition, while retain the access permissions to access the corresponding data entity through the target data partition; and generate an audit log record containing the data entity identifier, the coordinates of the triple before migration, the coordinates of the triple after migration, the migration trigger timestamp, and the start and end timestamps of the synchronization window.

[0006] This invention constructs a three-dimensional data partitioning model that orthogonally combines organizational domains, lifecycle stage domains, and data type domains, and defines dynamic mapping rules that automatically update data entity coordinates as the migration stage triggers conditions. This achieves a smooth transition of data ownership as the project progresses. At key nodes such as acceptance and delivery, manual batch modification of permissions is unnecessary, reducing the risk of permission mismatch or data residue caused by human operation. Furthermore, the introduction of a synchronization window mechanism provides a controllable bidirectional access buffer period during the data migration process, ensuring business continuity before and after migration, and ensuring the traceability of migration operations through automatic permission convergence and audit log generation when the window expires.

[0007] Furthermore, the set of coordinate values ​​for the organizational dimension coordinate axis in S1 includes the university domain, the enterprise domain, and the common domain; The set of coordinate values ​​for the lifecycle dimension coordinate axis includes the project initiation stage, R&D execution stage, acceptance and delivery stage, and post-project stage; The set of coordinate values ​​for the data type dimension coordinate axis includes process data, result data, and management data.

[0008] Furthermore, in the process of constructing the standard data entity corresponding to the original project record in S2, the types of the standard data entity include project entity, task entity, personnel entity, document entity, code entity, experimental data entity, review entity, and delivery entity; Each type of standard data entity contains the following basic attribute fields: globally unique identifier, data generation timestamp, triplet coordinate value, version number label, and extended attribute field group; The extended attribute field group includes a university-side extended field group and an enterprise-side extended field group. The university-side extended field group is used to store custom attributes that are only visible to university clients; the enterprise-side extended field group is used to store custom attributes that are only visible to enterprise clients.

[0009] Furthermore, the stage migration triggering condition in S3 is a confirmation signal that the project moves from the current life cycle stage to the next life cycle stage. The confirmation signal includes: a stage deliverable submission event in which both the university client and the enterprise client have completed digital signature confirmation, a preset milestone node expiration event, or a stage review conclusion of passing.

[0010] Furthermore, in the process of changing the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition in S4, the dynamic migration of the data partition is performed based on a preset dynamic mapping rule table. The dynamic mapping rule table includes the organizational dimension coordinate value change rules corresponding to different types of standard data entities during the conversion at different lifecycle stages.

[0011] The organizational dimension coordinate value change rules included in the preset dynamic mapping rule table in this invention are as follows: When a project moves from the initiation stage to the R&D execution stage, update the organizational dimension coordinate values ​​of all standard data entities whose data types are related to output data from the enterprise domain to the common domain. When a project moves from the R&D execution phase to the acceptance and delivery phase, the organizational dimension coordinate values ​​of all standard data entities whose data types are related to outcome data are updated from the common domain to the enterprise domain.

[0012] Furthermore, the method also includes: S6. When executing data operation requests issued by university clients or enterprise clients, generate operation behavior logs based on each data operation behavior; calculate the individual contribution value of each project participant based on the operation behavior logs corresponding to each project participant, the type of corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The formulas used to calculate the individual contribution value of each project participant are as follows: ; in, This represents the individual contribution value of the i-th project participant; This represents the number of operation behavior log entries for the i-th project participant performing the k-th preset operation behavior; n represents the number of preset operation behavior types. This represents the preset operation type weight value corresponding to the preset k-th type of operation behavior; This represents the role weight factor corresponding to the preset role assumed by the i-th project participant in the project; The quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time is represented by the quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time. This represents the quotient of the number of operation behavior log entries performed by the i-th project participant across the university and enterprise domains, divided by the total number of operation behavior log entries performed by the i-th project participant. When calculating the individual contribution value among project participants, this invention, based on the full collection of operation behavior logs, utilizes a multi-dimensional weighted summation algorithm that includes operation type weight, role weight, participation time data, and operation behavior log entries for cross-university and enterprise domain interactions to calculate the individual contribution value of each participant. The statistical analysis of this contribution value provides an objective and auditable reference for both universities and enterprises in matters such as authorship of results and profit distribution.

[0013] In this invention, each operation behavior log contains at least the following log fields: a globally unique personnel identifier of the operation initiator, a timestamp of the operation behavior reception, a globally unique identifier of the operated data entity, an operation behavior type identifier, and operation content summary information; the operation behavior corresponding to the operation behavior type identifier includes at least: document creation, document content editing, source code submission, defect report creation, defect report status update, review comments submission, test case execution passed, test case execution failed, and data annotation completed.

[0014] Furthermore, S5 also includes: According to a preset scoring cycle or when a data value assessment request is received, the value score of the data entity stored in each data partition is calculated; and the value score calculation result of the data entity is compared with the preset first scoring threshold and second scoring threshold respectively, and the corresponding storage management strategy is executed according to the comparison result, wherein the first scoring threshold is greater than the second scoring threshold; If the value score of the obtained data entity is greater than or equal to the preset first score threshold, then the first-level storage strategy is executed on the corresponding data entity, retaining all the content and all historical versions of the corresponding data entity, and performing multi-copy off-site backup. If the calculated value score of the obtained data entity is greater than or equal to the second scoring threshold and the calculated value score of the obtained data entity is less than the preset first scoring threshold, then it is determined that the second-level storage strategy will be implemented for the corresponding data entity, the content of the corresponding data entity will be compressed and stored, only the most recent N historical versions will be retained, and periodic archiving will be performed; where N is a preset constant. If the value score of the obtained data entity is less than the preset second score threshold, then the third-level storage strategy is executed on the corresponding data entity. The basic attribute field values ​​of the corresponding data entity are extracted and retained, and the specific content and historical versions of the corresponding data entity are deleted to free up storage space.

[0015] Furthermore, the calculation formulas involved in obtaining the value score of the data entity are as follows: ; in, This represents the value score calculation result for data entity b; The quality compliance score of data entity b is represented by the following. The value is equal to the quotient of the difference between the actual value of the preset quality indicator corresponding to the life cycle stage of data entity b at the time of its creation and the target threshold, divided by the target threshold. This represents the preset stage weight value corresponding to the lifecycle stage that data entity b is in at the time of its creation; This represents the time decay factor that binds the time difference between the data generation timestamp and the current time of data entity b in the data preset form; This represents the additional score for the associated output value of data entity b. When data entity b is marked as associated with one or more pre-registered output records in the constructed standard data entity and storage partition association table, it is determined that... If it equals the preset positive value; otherwise, it is judged. It equals 0.

[0016] A project lifecycle data management system for university-industry collaborative research and development, including: The 3D partitioning model construction module is used to construct a 3D data partitioning model and define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects. The relationship building module is used to obtain the original project records generated or updated in real time during project execution; construct the standard data entities corresponding to the original project records; determine the initial organization dimension coordinate values, initial life cycle dimension coordinate values ​​and initial data type dimension coordinate values ​​corresponding to each standard data entity; and allocate each standard data entity to the corresponding data partition in the three-dimensional data partition model for storage, and establish a relationship table between standard data entities and storage partitions. The phase transition instruction generation module is used to continuously monitor whether the preset phase transition trigger conditions are met during the project progress; and generate the corresponding phase transition instructions when the preset phase transition trigger conditions are met. The phase migration execution management module is used to execute the obtained phase migration instructions. For each standard data entity to be migrated in the identifier list of the obtained phase migration instructions, it changes the storage location of the standard data entity to be migrated from the current data partition to the target data partition in the three-dimensional data partitioning model; and adds a corresponding record of the data partition before and after migration for the corresponding standard data entity in the obtained association table. The synchronization window audit management module is used to manage the access permissions of university clients and enterprise clients in the current data partition and the target data partition during the time from the start to the completion of the dynamic migration operation of the data partition and when the synchronization window expires. It generates corresponding audit log records. The Operation Behavior and Contribution Value Management module is used to generate operation behavior logs based on each data operation request issued by university clients or enterprise clients; and to calculate the individual contribution value of each project participant based on the operation behavior logs corresponding to each project participant, the type of corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The data value assessment and hierarchical storage module is used to calculate the value of data entities stored in each data partition according to a preset scoring period or when a data value assessment request is received; and compare the calculated value of the data entities with the preset first scoring threshold and second scoring threshold respectively, and execute the corresponding storage management strategy based on the comparison result.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) This invention constructs a three-dimensional data partitioning model that orthogonally combines organizational domain, lifecycle stage domain and data type domain, and defines a dynamic mapping rule that automatically updates the coordinates of data entities as the stage migration triggers the conditions, thereby achieving a smooth transition of data ownership as the project progresses; at key nodes such as acceptance and delivery, there is no need to manually modify permissions in batches, reducing the risk of permission mismatch or data residue caused by human operation; and the introduction of the synchronous window period mechanism provides a controllable bidirectional access buffer period during the data migration process, which not only ensures the business continuity before and after the migration, but also ensures the traceability of the migration operation through automatic permission convergence and audit log generation when the window period expires. (2) When calculating the individual contribution value of project participants, this invention uses a multi-dimensional weighted summation algorithm that includes operation type weight, role weight, participation time data, and operation behavior logs of cross-university and enterprise domain interaction operations to calculate the individual contribution value of each participant. The statistics of this contribution value provide an objective and auditable reference for both universities and enterprises in the process of attribution of results and distribution of benefits. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of the project lifecycle data management system for collaborative research and development between universities and enterprises, as described in this invention. Figure 2 This is a flowchart illustrating the full-cycle data management method for collaborative research and development between universities and enterprises, as described in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This embodiment provides a full-lifecycle data management system for university-enterprise collaborative research and development, including: The 3D partitioning model construction module is used to construct a 3D data partitioning model and define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects. The relationship building module is used to obtain the original project records generated or updated in real time during project execution; construct the standard data entities corresponding to the original project records; determine the initial organization dimension coordinate values, initial life cycle dimension coordinate values ​​and initial data type dimension coordinate values ​​corresponding to each standard data entity; and allocate each standard data entity to the corresponding data partition in the three-dimensional data partition model for storage, and establish a relationship table between standard data entities and storage partitions. The phase transition instruction generation module is used to continuously monitor whether the preset phase transition trigger conditions are met during the project progress; and generate the corresponding phase transition instructions when the preset phase transition trigger conditions are met. The phase migration execution management module is used to execute the obtained phase migration instructions. For each standard data entity to be migrated in the identifier list of the obtained phase migration instructions, it changes the storage location of the standard data entity to be migrated from the current data partition to the target data partition in the three-dimensional data partitioning model; and adds a corresponding record of the data partition before and after migration for the corresponding standard data entity in the obtained association table. The synchronization window audit management module is used to manage the access permissions of university clients and enterprise clients in the current data partition and the target data partition during the time from the start to the completion of the dynamic migration operation of the data partition and when the synchronization window expires. It generates corresponding audit log records. The Operation Behavior and Contribution Value Management module is used to generate operation behavior logs based on each data operation request issued by university clients or enterprise clients; and to calculate the individual contribution value of each project participant based on the operation behavior logs corresponding to each project participant, the type of corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The data value assessment and hierarchical storage module is used to calculate the value of data entities stored in each data partition according to a preset scoring period or when a data value assessment request is received; and compare the calculated value of the data entities with the preset first scoring threshold and second scoring threshold respectively, and execute the corresponding storage management strategy based on the comparison result.

[0021] This embodiment also provides a method for full-cycle data management of projects for university-enterprise collaborative research and development, such as... Figure 2 As shown, the method includes: S1. Construct a three-dimensional data partitioning model orthogonally defined by organizational dimension coordinate axes, life cycle dimension coordinate axes, and data type dimension coordinate axes, which is used to define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects; each data partition in the three-dimensional data partitioning model corresponds to a unique identifier composed of a triplet of coordinates; The set of coordinate values ​​for the organizational dimension coordinate axis in S1 includes the university domain, the enterprise domain, and the common domain; The set of coordinate values ​​for the lifecycle dimension coordinate axis includes the project initiation stage, R&D execution stage, acceptance and delivery stage, and post-project stage; The set of coordinate values ​​for the data type dimension coordinate axis includes process data, result data, and management data.

[0022] S2. Obtain the original project records generated or updated in real time during the project execution process from both university clients and enterprise clients; construct standard data entities corresponding to the original project records; and determine the initial organization dimension coordinates, initial life cycle dimension coordinates, and initial data type dimension coordinates corresponding to each standard data entity based on the source information, project stage information at the time of collection, and data content characteristics of the obtained standard data entities. Then, allocate each standard data entity to the corresponding data partition in the three-dimensional data partitioning model for storage and establish an association table between standard data entities and storage partitions. In the process of constructing the standard data entity corresponding to the original project record in S2, the types of the standard data entity include project entity, task entity, personnel entity, document entity, code entity, experimental data entity, review entity, and delivery entity; Each type of standard data entity contains the following basic attribute fields: globally unique identifier, data generation timestamp, triplet coordinate value, version number label, and extended attribute field group; The extended attribute field group includes a university-side extended field group and an enterprise-side extended field group. The university-side extended field group is used to store custom attributes that are only visible to university clients; the enterprise-side extended field group is used to store custom attributes that are only visible to enterprise clients.

[0023] S3. During the project's progress, continuously monitor whether the preset stage migration trigger conditions are met; and when the preset stage migration trigger conditions are met, generate a corresponding stage migration instruction, which includes a list of identifiers of the standard data entities to be migrated and the target lifecycle dimension coordinate values. The stage migration triggering condition in S3 is a confirmation signal that the project moves from the current life cycle stage to the next life cycle stage. The confirmation signal includes: a stage deliverable submission event in which both the university client and the enterprise client have completed digital signature confirmation, a preset milestone node expiration event, or a stage review conclusion of passing.

[0024] S4. Execute the obtained stage migration instructions, and for each standard data entity to be migrated in the identifier list in the obtained stage migration instructions, update the lifecycle dimension coordinate value in the triplet coordinates of the current standard data entity to be migrated to the corresponding target lifecycle dimension coordinate value in the three-dimensional data partition model, so as to change the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition; and add a corresponding record of the data partition before migration and the data partition after migration for the corresponding standard data entity to be migrated in the obtained association table. In step S4, during the process of changing the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition, the dynamic migration of the data partition is performed based on a preset dynamic mapping rule table. The dynamic mapping rule table includes the organizational dimension coordinate value change rules corresponding to different types of standard data entities during the conversion at different lifecycle stages.

[0025] Specifically, the preset dynamic mapping rule table includes the following rules for changing organizational dimension coordinate values: When a project moves from the initiation stage to the R&D execution stage, update the organizational dimension coordinate values ​​of all standard data entities whose data types are related to output data from the enterprise domain to the common domain. When a project moves from the R&D execution phase to the acceptance and delivery phase, the organizational dimension coordinate values ​​of all standard data entities whose data types are related to outcome data are updated from the common domain to the enterprise domain.

[0026] S5. During the time from the start to the completion of the dynamic migration operation of the data partition, through a preset synchronization window period, both university clients and enterprise clients are allowed to perform read operations on the standard data entities to be migrated with the highest permissions granted in the current data partition and the target data partition. When the synchronization window expires, remove the access permissions of the university client and the enterprise client to the standard data entity to be migrated located in the current data partition, while retain the access permissions to access the corresponding data entity through the target data partition; and generate an audit log record containing the data entity identifier, the coordinates of the triple before migration, the coordinates of the triple after migration, the migration trigger timestamp, and the start and end timestamps of the synchronization window.

[0027] The S5 also includes: According to a preset scoring period or upon receiving a data value assessment request, the value scores of data entities stored in each data partition are calculated. The calculation formulas involved in obtaining the value score calculation results of data entities in this embodiment are as follows: ; in, This represents the value score calculation result for data entity b; The quality compliance score of data entity b is represented by the following. The value is equal to the quotient of the difference between the actual value of the preset quality indicator corresponding to the life cycle stage of data entity b at the time of its creation and the target threshold, divided by the target threshold. This represents the preset stage weight value corresponding to the lifecycle stage that data entity b is in at the time of its creation; This represents the time decay factor that binds the time difference between the data generation timestamp and the current time of data entity b in the data preset form; This represents the additional score for the associated output value of data entity b. When data entity b is marked as associated with one or more pre-registered output records in the constructed standard data entity and storage partition association table, it is determined that... If it equals the preset positive value; otherwise, it is judged. It equals 0.

[0028] The value score of the obtained data entity is compared with the preset first score threshold and second score threshold respectively, and the corresponding storage management strategy is executed according to the comparison result. The first score threshold is greater than the second score threshold. If the value score of the obtained data entity is greater than or equal to the preset first score threshold, then the first-level storage strategy is executed on the corresponding data entity, retaining all the content and all historical versions of the corresponding data entity, and performing multi-copy off-site backup. If the calculated value score of the obtained data entity is greater than or equal to the second scoring threshold and the calculated value score of the obtained data entity is less than the preset first scoring threshold, then it is determined that the second-level storage strategy will be implemented for the corresponding data entity, the content of the corresponding data entity will be compressed and stored, only the most recent N historical versions will be retained, and periodic archiving will be performed; where N is a preset constant. If the value score of the obtained data entity is less than the preset second score threshold, then the third-level storage strategy is executed on the corresponding data entity. The basic attribute field values ​​of the corresponding data entity are extracted and retained, and the specific content and historical versions of the corresponding data entity are deleted to free up storage space.

[0029] The method further includes: S6. When executing data operation requests issued by university clients or enterprise clients, an operation behavior log is generated based on each data operation behavior. In this embodiment, each operation behavior log contains at least the following log fields: a globally unique identifier of the operation initiator, a timestamp of the operation behavior reception, a globally unique identifier of the operated data entity, an operation behavior type identifier, and operation content summary information. The operation behavior corresponding to the operation behavior type identifier includes at least: document creation, document content editing, source code submission, defect report creation, defect report status update, review comments submission, test case execution passing, test case execution failing, and data annotation completion. The individual contribution value of each project participant is calculated based on the operation behavior logs corresponding to each project participant, the type of the corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The formulas used to calculate the individual contribution value of each project participant are as follows: ; in, This represents the individual contribution value of the i-th project participant; This represents the number of operation behavior log entries for the i-th project participant performing the k-th preset operation behavior; n represents the number of preset operation behavior types. This represents the preset operation type weight value corresponding to the preset k-th type of operation behavior; This represents the role weight factor corresponding to the preset role assumed by the i-th project participant in the project; The quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time is represented by the quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time. This represents the quotient of the number of operation behavior log entries performed by the i-th project participant in cross-university domain and enterprise domain interaction operations, divided by the total number of operation behavior log entries performed by the i-th project participant.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A project whole-cycle data management method for school-enterprise collaborative research and development, characterized in that, The method includes: S1. Construct a three-dimensional data partitioning model orthogonally defined by organizational dimension coordinate axes, life cycle dimension coordinate axes, and data type dimension coordinate axes, which is used to define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects; each data partition in the three-dimensional data partitioning model corresponds to a unique identifier composed of a triplet of coordinates; S2. Obtain the original project records generated or updated in real time during the project execution process from both university clients and enterprise clients; construct standard data entities corresponding to the original project records; and determine the initial organization dimension coordinates, initial life cycle dimension coordinates, and initial data type dimension coordinates corresponding to each standard data entity based on the source information, project stage information at the time of collection, and data content characteristics of the obtained standard data entities. Then, allocate each standard data entity to the corresponding data partition in the three-dimensional data partitioning model for storage and establish an association table between standard data entities and storage partitions. S3. During the project's progress, continuously monitor whether the preset stage migration trigger conditions are met; and when the preset stage migration trigger conditions are met, generate a corresponding stage migration instruction, which includes a list of identifiers of the standard data entities to be migrated and the target lifecycle dimension coordinate values. S4. Execute the obtained stage migration instructions, and for each standard data entity to be migrated in the identifier list in the obtained stage migration instructions, update the lifecycle dimension coordinate value in the triplet coordinates of the current standard data entity to be migrated to the corresponding target lifecycle dimension coordinate value in the three-dimensional data partition model, so as to change the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition; and add a corresponding record of the data partition before migration and the data partition after migration for the corresponding standard data entity to be migrated in the obtained association table. S5. During the time from the start to the completion of the dynamic migration operation of the data partition, through a preset synchronization window period, both university clients and enterprise clients are allowed to perform read operations on the standard data entities to be migrated with the highest permissions granted in the current data partition and the target data partition. When the synchronization window expires, remove the access permissions of the university client and the enterprise client to the standard data entity to be migrated located in the current data partition, while retain the access permissions to access the corresponding data entity through the target data partition; and generate an audit log record containing the data entity identifier, the coordinates of the triple before migration, the coordinates of the triple after migration, the migration trigger timestamp, and the start and end timestamps of the synchronization window.

2. The project full-cycle data management method for school-enterprise collaborative research and development according to claim 1, characterized in that, The set of coordinate values ​​for the organizational dimension coordinate axis in S1 includes the university domain, the enterprise domain, and the common domain; The set of coordinate values ​​for the lifecycle dimension coordinate axis includes the project initiation stage, R&D execution stage, acceptance and delivery stage, and post-project stage; The set of coordinate values ​​for the data type dimension coordinate axis includes process data, result data, and management data.

3. The project full-cycle data management method for school-enterprise collaborative research and development according to claim 1, characterized in that, In the process of constructing the standard data entity corresponding to the original project record in S2, the types of the standard data entity include project entity, task entity, personnel entity, document entity, code entity, experimental data entity, review entity, and delivery entity; Each type of standard data entity contains the following basic attribute fields: globally unique identifier, data generation timestamp, triplet coordinate value, version number label, and extended attribute field group; The extended attribute field group includes a university-side extended field group and an enterprise-side extended field group. The university-side extended field group is used to store custom attributes that are only visible to university clients; the enterprise-side extended field group is used to store custom attributes that are only visible to enterprise clients.

4. The project full-cycle data management method for school-enterprise collaborative research and development according to claim 1, characterized in that, The stage migration triggering condition in S3 is a confirmation signal that the project moves from the current life cycle stage to the next life cycle stage. The confirmation signal includes: a stage deliverable submission event in which both the university client and the enterprise client have completed digital signature confirmation, a preset milestone node expiration event, or a stage review conclusion of passing.

5. The project full-cycle data management method for school-enterprise collaborative research and development according to claim 3, characterized in that, In step S4, during the process of changing the storage ownership of the standard data entity to be migrated from the current data partition to the target data partition, the dynamic migration of the data partition is performed based on a preset dynamic mapping rule table. The dynamic mapping rule table includes the organizational dimension coordinate value change rules corresponding to different types of standard data entities during the conversion at different lifecycle stages.

6. The project full-cycle data management method for school-enterprise collaborative research and development according to claim 1, characterized in that, The method further includes: S6. When executing data operation requests issued by university clients or enterprise clients, generate operation behavior logs based on each data operation behavior; calculate the individual contribution value of each project participant based on the operation behavior logs corresponding to each project participant, the type of corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The formulas used to calculate the individual contribution value of each project participant are as follows: ； in, This represents the individual contribution value of the i-th project participant; This represents the number of operation behavior log entries for the i-th project participant performing the k-th preset operation behavior; n represents the number of preset operation behavior types. This represents the preset operation type weight value corresponding to the preset k-th type of operation behavior; This represents the role weight factor corresponding to the preset role assumed by the i-th project participant in the project; The quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time is represented by the quotient of the duration between the first and last effective operation times of the i-th project participant and the total duration of the project from its initiation to the statistical time. This represents the quotient of the number of operation behavior log entries performed by the i-th project participant in cross-university domain and enterprise domain interaction operations, divided by the total number of operation behavior log entries performed by the i-th project participant.

7. The method for full-cycle data management of projects for university-enterprise collaborative R&D as described in claim 1, characterized in that, The S5 also includes: According to a preset scoring cycle or when a data value assessment request is received, the value score of the data entity stored in each data partition is calculated; and the value score calculation result of the data entity is compared with the preset first scoring threshold and second scoring threshold respectively, and the corresponding storage management strategy is executed according to the comparison result, wherein the first scoring threshold is greater than the second scoring threshold; If the value score of the obtained data entity is greater than or equal to the preset first score threshold, then the first-level storage strategy is executed on the corresponding data entity, retaining all the content and all historical versions of the corresponding data entity, and performing multi-copy off-site backup. If the calculated value score of the obtained data entity is greater than or equal to the second scoring threshold and the calculated value score of the obtained data entity is less than the preset first scoring threshold, then it is determined that the second-level storage strategy will be implemented for the corresponding data entity, the content of the corresponding data entity will be compressed and stored, only the most recent N historical versions will be retained, and periodic archiving will be performed; where N is a preset constant. If the value score of the obtained data entity is less than the preset second score threshold, then the third-level storage strategy is executed on the corresponding data entity. The basic attribute field values ​​of the corresponding data entity are extracted and retained, and the specific content and historical versions of the corresponding data entity are deleted to free up storage space.

8. The method for full-cycle data management of projects for university-enterprise collaborative R&D as described in claim 1, characterized in that, The calculation formulas involved in obtaining the value score of a data entity are as follows: ； in, This represents the value score calculation result for data entity b; This represents the quality compliance score of data entity b. The value is equal to the quotient of the difference between the actual value of the preset quality indicator corresponding to the life cycle stage of data entity b at the time of its creation and the target threshold, divided by the target threshold. This represents the preset stage weight value corresponding to the lifecycle stage that data entity b is in at the time of its creation; This represents the time decay factor that binds the time difference between the data generation timestamp and the current time of data entity b in the data preset form; This represents the additional score for the associated output value of data entity b. When data entity b is marked as associated with one or more pre-registered output records in the constructed standard data entity and storage partition association table, it is determined that... If it equals the preset positive value; otherwise, it is judged. It equals 0.

9. A project lifecycle data management system that applies the project lifecycle data management method for university-enterprise collaborative R&D as described in any one of claims 1 to 8, characterized in that, include: The 3D partitioning model construction module is used to construct a 3D data partitioning model and define the storage location and access boundaries of data entities generated in university-enterprise collaborative R&D projects. The relationship building module is used to obtain the original project records generated or updated in real time during project execution; construct the standard data entities corresponding to the original project records; determine the initial organization dimension coordinate values, initial life cycle dimension coordinate values ​​and initial data type dimension coordinate values ​​corresponding to each standard data entity; and allocate each standard data entity to the corresponding data partition in the three-dimensional data partition model for storage, and establish a relationship table between standard data entities and storage partitions. The phase migration instruction generation module is used to continuously monitor whether the preset phase migration trigger conditions are met during the project progress. And when the preset stage transition trigger conditions are met, the corresponding stage transition instruction is generated; The phase migration execution management module is used to execute the obtained phase migration instructions. For each standard data entity to be migrated in the identifier list of the obtained phase migration instructions, it changes the storage location of the standard data entity to be migrated from the current data partition to the target data partition in the three-dimensional data partitioning model; and adds a corresponding record of the data partition before and after migration for the corresponding standard data entity in the obtained association table. The Synchronization Window Audit Management Module is used to manage the access permissions of university clients and enterprise clients in the current data partition and the target data partition during the time from the start to the completion of the dynamic migration operation of the data partition and when the synchronization window expires, through a preset synchronization window period. And generate corresponding audit log records; The Operation Behavior and Contribution Value Management module is used to generate operation behavior logs based on each data operation request issued by university clients or enterprise clients; and to calculate the individual contribution value of each project participant based on the operation behavior logs corresponding to each project participant, the type of corresponding operation behavior in each operation behavior log, and the preset contribution value rules. The data value assessment and hierarchical storage module is used to calculate the value of data entities stored in each data partition according to a preset scoring period or when a data value assessment request is received; and compare the calculated value of the data entities with the preset first scoring threshold and second scoring threshold respectively, and execute the corresponding storage management strategy based on the comparison result.

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