Collaborative innovation system for diversified data industry cluster construction

Abstract

The invention provides a collaborative innovation system for diversified data industry cluster construction, and relates to the field of electric digital data processing, the collaborative innovation system comprises a data integration sharing module, an intelligent resource scheduling module, a collaborative innovation support module and a safety supervision management module, the data integration sharing module is used for collecting and storing data information, and the intelligent resource scheduling module is used for scheduling the data information; the intelligent resource scheduling module is used for performing management scheduling on resource information, the collaborative innovation support module is used for performing collaborative innovation analysis, and the safety supervision management module is used for performing protection processing on data safety; according to the system, multivariate data can be managed and analyzed in a centralized manner, demand data can be quickly matched according to big data analysis, and enterprises can conveniently and better carry out collaborative innovation.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing, and particularly to a collaborative innovation system for constructing a diversified data industrial cluster. Background Art

[0002] With the development of the digital economy, industrial clusters have gradually become an important model for enhancing regional competitiveness and industrial collaborative innovation capabilities. Traditional industrial clusters rely on the agglomeration effect of physical space. How to break data barriers and improve the efficiency of resource sharing and collaborative innovation has become a key challenge for industrial upgrading.

[0003] Many data collaboration systems have been developed. After a large amount of retrieval and reference, it is found that existing data collaboration systems are like the system disclosed in the publication number CN112751938B. These systems generally include a cluster job data synchronization receiving module and a cluster job data synchronization management module. The cluster job data synchronization receiving module is used to provide a data receiving end to obtain job data and perform synchronous receiving and verification of data work; the cluster job data synchronization management module is used to provide a data sending end to manage job data synchronization requests and manage data synchronization work. However, this system only synchronizes the data cluster and cannot provide further assistance for collaborative innovation. Summary of the Invention

[0004] The purpose of the present invention is to propose a collaborative innovation system for constructing a diversified data industrial cluster in view of the existing deficiencies.

[0005] The present invention adopts the following technical solutions:

[0006] A collaborative innovation system for constructing a diversified data industrial cluster includes a data integration and sharing module, an intelligent resource scheduling module, a collaborative innovation support module, and a security supervision and management module;

[0007] The data integration and sharing module is used to collect and store data information, the intelligent resource scheduling module is used to manage and schedule resource information, the collaborative innovation support module is used to conduct collaborative innovation analysis, and the security supervision and management module is used to protect data security;

[0008] The data integration and sharing module includes a data collection unit, a data storage unit, and a sharing protocol unit. The data collection unit is used to collect data information from multiple links of the industrial chain, the data storage unit is used to store and manage the collected data, and the sharing protocol unit is used to manage and operate the data sharing mechanism;

[0009] The intelligent resource scheduling module includes a resource retrieval unit, a knowledge graph construction unit, and a resource transmission unit. The resource retrieval unit is used to retrieve resource information. The knowledge graph construction unit is used to construct an industrial cluster knowledge graph. The resource transmission unit is used to transmit resource information between different nodes;

[0010] The collaborative innovation support module includes an interaction processing unit, an intelligent matching unit, and a resource trading unit. The interaction processing unit is used to provide an interaction interface for business operations. The intelligent matching unit is used to perform matching analysis on resources. The resource trading unit is used to achieve trusted trading of data resources;

[0011] The security supervision and management module includes an access control unit, a data encryption unit, and a behavior monitoring unit. The access control unit is used to control the access rights of resource data. The data encryption unit is used to encrypt the transmitted data. The behavior monitoring unit is used to supervise and analyze the behavior of users.

[0012] Further, the knowledge graph construction unit includes an entity extraction processor, a relationship reasoning processor, and a dynamic relationship register. The entity extraction processor is used to extract key entity information from resource data. The relationship reasoning processor is used to mine the association relationships between entity information. The dynamic relationship register is used to dynamically store the association data of entities;

[0013] The relationship reasoning processor calculates the association degree Pa between two entity information according to the following formula:

[0014]

[0015] Where n1 is the occurrence times of the first entity, n2 is the occurrence times of the second entity, and n3 is the co-occurrence times of these two entities.

[0016] Further, the interaction processing unit includes a requirement editing processor, a pattern recognition processor, and a task output processor. The requirement editing processor is used to edit collaborative information. The pattern matching processor is used to perform recognition analysis on the collaboration pattern. The task output processor outputs a matching task based on the recognition result;

[0017] The pattern recognition processor generates fixed entities, requirement items, and requirement entities based on the edited content. Each requirement item corresponds to multiple requirement entities. The pattern recognition processor selects a corresponding requirement entity for each requirement item and forms a target pattern with the fixed entity, and calculates the collaboration degree Pc of the target pattern according to the following formula:

[0018]

[0019] Where n strThe number of entity combinations containing strong associations in the target pattern, n wek The number of entity combinations containing weak associations in the target pattern, Pa i，j Represents the association degree between the i-th entity and the j-th entity in the target pattern, α is the strong coefficient, and β is the weak coefficient;

[0020] When the cooperation degree exceeds the set threshold, the target pattern is recognized as a cooperation pattern, and the corresponding demand entity is sent to the task output processor.

[0021] Furthermore, the intelligent matching unit includes a task management processor, a graph matching processor, and a data feedback processor. The task management processor is used to manage the matching tasks. The graph matching processor processes the matching tasks based on the knowledge graph. The data feedback processor is used to feedback the matching results and obtain the corresponding data;

[0022] The graph matching processor finds the enterprises containing the demand entity in the knowledge graph based on the demand entity in the matching task, and at the same time obtains the actual cases containing the demand entity in the enterprise, and calculates the matching value VM of the demand entity of the enterprise according to the following formula:

[0023]

[0024] Where n is the number of actual cases obtained, m is the total number of cases of the enterprise, k i Is the weight value of the demand entity in the i-th actual case, and B(i) is the benefit value of the i-th actual case;

[0025] The graph matching processor selects the enterprise with the largest matching value as the matching result.

[0026] Furthermore, the resource trading unit includes a trading application processor, a trading signature processor, and a resource execution processor. The trading application processor is used to submit the user's trading application. The trading signature processor is used for both trading parties to review the trading content and conduct signature confirmation. The resource execution processor performs resource delivery based on the valid transaction.

[0027] The beneficial effects achieved by the present invention are:

[0028] This system has efficient data integration and sharing, breaks data islands. On this basis, it can perform precise matching through big data analysis and user demand analysis, reduce invalid matching, improve the cooperation efficiency of enterprises, scientific research institutions, and investors, and promote the transformation of technological achievements.

[0029] To enable a further understanding of the features and technical content of the present invention, please refer to the following detailed description of the present invention and the attached drawings. However, the attached drawings are only provided for reference and illustration, and are not used to limit the present invention. Brief Description of the Drawings

[0030] Figure 1 This is a schematic diagram of the overall structural framework of the present invention;

[0031] Figure 2 This is a schematic diagram of the composition of the data integration and sharing module of the present invention;

[0032] Figure 3 This is a schematic diagram of the composition of the intelligent resource scheduling of the present invention;

[0033] Figure 4 This is a schematic diagram of the composition of the collaborative innovation support module of the present invention;

[0034] Figure 5 This is a schematic diagram of the composition of the safety supervision and management module of the present invention;

[0035] Figure 6 This is a comparison table of the data effects of the present invention's system with different systems. Detailed Description of the Preferred Embodiments

[0036] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only simple schematic illustrations and are not drawn according to actual sizes, which is hereby stated in advance. The following embodiments will further detail the related technical content of the present invention, but the disclosed content is not intended to limit the protection scope of the present invention.

[0037] Embodiment 1

[0038] This embodiment provides a collaborative innovation system for the construction of a diversified data industrial cluster, combined with Figure 1 , including a data integration and sharing module, an intelligent resource scheduling module, a collaborative innovation support module, and a safety supervision and management module;

[0039] The data integration and sharing module is used to collect and store data information, the intelligent resource scheduling module is used to manage and schedule resource information, the collaborative innovation support module is used to conduct collaborative innovation analysis, and the safety supervision and management module is used to protect data security;

[0040] The data integration and sharing module includes a data collection unit, a data storage unit, and a sharing protocol unit. The data collection unit is used to collect data information from multiple links of the industrial chain, the data storage unit is used to store and manage the collected data, and the sharing protocol unit is used to manage the data sharing mechanism;

[0041] The intelligent resource scheduling module includes a resource retrieval unit, a knowledge graph construction unit, and a resource transmission unit. The resource retrieval unit is used to retrieve resource information. The knowledge graph construction unit is used to construct an industrial cluster knowledge graph. The resource transmission unit is used to transmit resource information between different nodes;

[0042] The collaborative innovation support module includes an interaction processing unit, an intelligent matching unit, and a resource trading unit. The interaction processing unit is used to provide an interaction interface for business operations. The intelligent matching unit is used to perform matching analysis on resources. The resource trading unit is used to realize the trusted trading of data resources;

[0043] The security supervision and management module includes an access control unit, a data encryption unit, and a behavior monitoring unit. The access control unit is used to control the access rights of resource data. The data encryption unit is used to encrypt the transmitted data. The behavior monitoring unit is used to supervise and analyze the behavior of users.

[0044] The knowledge graph construction unit includes an entity extraction processor, a relationship reasoning processor, and a dynamic relationship register. The entity extraction processor is used to extract key entity information from resource data. The relationship reasoning processor is used to mine the association relationships between entity information. The dynamic relationship register is used to dynamically store the association data of entities;

[0045] The relationship reasoning processor calculates the association degree Pa between two entity information according to the following formula:

[0046]

[0047] where n1 is the number of occurrences of the first entity, n2 is the number of occurrences of the second entity, and n3 is the number of times these two entities co-occur.

[0048] The interaction processing unit includes a requirement editing processor, a pattern recognition processor, and a task output processor. The requirement editing processor is used to edit collaborative information. The pattern matching processor is used to perform recognition analysis on the collaborative pattern. The task output processor outputs a matching task based on the recognition result;

[0049] The pattern recognition processor generates a fixed entity, requirement items, and requirement entities based on the edited content. Each requirement item corresponds to multiple requirement entities. The pattern recognition processor selects a corresponding requirement entity for each requirement item and forms a target pattern with the fixed entity, and calculates the collaboration degree Pc of the target pattern according to the following formula:

[0050]

[0051] where nstr is the number of entity combinations with strong associations in the target pattern, n wek is the number of entity combinations with weak associations in the target pattern, Pa i，j represents the association degree between the i-th entity and the j-th entity in the target pattern, α is the strong coefficient, and β is the weak coefficient;

[0052] When the cooperation degree exceeds the set threshold, the target pattern is recognized as a cooperation pattern, and the corresponding demand entity is sent to the task output processor.

[0053] The intelligent matching unit includes a task management processor, a graph matching processor, and a data feedback processor. The task management processor is used to manage the matching tasks. The graph matching processor processes the matching tasks based on the knowledge graph. The data feedback processor is used to feedback the matching results and obtain the corresponding data;

[0054] Based on the demand entity in the matching task, the graph matching processor finds the enterprise containing the demand entity in the knowledge graph, and at the same time obtains the actual cases containing the demand entity in the enterprise, and calculates the matching value VM of the enterprise demand entity according to the following formula:

[0055]

[0056] where n is the number of actual cases obtained, m is the total number of cases of the enterprise, k i is the weight value of the demand entity in the i-th actual case, and B(i) is the benefit value of the i-th actual case;

[0057] The graph matching processor selects the enterprise with the largest matching value as the matching result.

[0058] The resource trading unit includes a trading application processor, a trading signature processor, and a resource execution processor. The trading application processor is used to submit the user's trading application. The trading signature processor is used for the trading parties to review the trading content and conduct signature confirmation. The resource execution processor performs resource delivery based on the valid transaction.

[0059] Embodiment II.

[0060] This embodiment includes all the content of Embodiment I, and provides a collaborative innovation system for the construction of a diversified data industry cluster, including a data integration and sharing module, an intelligent resource scheduling module, a collaborative innovation support module, and a security supervision and management module;

[0061] The data integration and sharing module is used to collect and store data information. The intelligent resource scheduling module is used to manage and schedule resource information. The collaborative innovation support module is used to conduct collaborative innovation analysis. The security supervision and management module is used to protect data security;

[0062] Combined with Figure 2 , the data integration and sharing module includes a data acquisition unit, a data storage unit, and a sharing protocol unit. The data acquisition unit is used to collect data information from multiple links of the industrial chain. The data storage unit is used to store and manage the collected data. The sharing protocol unit is used to manage and operate the data sharing mechanism;

[0063] Combined with Figure 3 , the intelligent resource scheduling module includes a resource retrieval unit, a knowledge graph construction unit, and a resource transmission unit. The resource retrieval unit is used to retrieve resource information. The knowledge graph construction unit is used to construct an industrial cluster knowledge graph. The resource transmission unit is used to transmit resource information between different nodes;

[0064] Combined with Figure 4 , the collaborative innovation support module includes an interaction processing unit, an intelligent matching unit, and a resource trading unit. The interaction processing unit is used to provide an interaction interface for business operations. The intelligent matching unit is used to perform matching analysis on resources. The resource trading unit is used to realize the trusted trading of data resources;

[0065] Combined with Figure 5 , the safety supervision and management module includes an access control unit, a data encryption unit, and a behavior monitoring unit. The access control unit is used to control the access rights of resource data. The data encryption unit is used to encrypt the transmitted data. The behavior monitoring unit is used to supervise and analyze the behavior of users;

[0066] The data acquisition unit includes a multi-source adapter, a data cleaning processor, and a metadata annotator. The multi-source adapter is used to interface with data sources in different industries. The data cleaning processor is used to eliminate invalid data and name the data with standardized fields. The metadata annotator is used to automatically generate data tags;

[0067] The data storage unit includes a distributed storage cluster, a cold and hot grading controller, and a data version manager. The distributed storage cluster is used to store multi-modal data. The cold and hot grading controller dynamically migrates data according to the access frequency. The data version manager is used to record the data change history and support version rollback and traceability;

[0068] The sharing protocol unit includes an intelligent contract processor, a privacy gateway processor, and a compliance check processor. The intelligent contract processor is used to define data sharing rules and automatically execute contract terms based on the rules. The privacy gateway processor is used to process privacy data to realize applications in an encrypted state. The compliance check processor is used to check whether the retrieved data complies with the sharing rules;

[0069] The resource retrieval unit includes a semantic parsing processor, a distributed indexing processor, and a dynamic feedback processor. The semantic parsing processor is used to convert the user's natural language query into a graph query statement. The distributed indexing processor is used to accelerate the retrieval of massive resources through fuzzy matching and approximate neighbor search. The dynamic feedback processor optimizes the retrieval sorting weight according to the user's behavior;

[0070] The graph construction unit includes an entity extraction processor, a relationship reasoning processor, and a dynamic relationship register. The entity extraction processor is used to extract key entity information from resource data. The relationship reasoning processor is used to mine the association relationships between entity information. The dynamic relationship register is used to dynamically store the association data of entities;

[0071] The relationship reasoning processor calculates the association degree Pa between two entity information according to the following formula:

[0072]

[0073] where n1 is the number of occurrences of the first entity, n2 is the number of occurrences of the second entity, and n3 is the number of times these two entities co-occur;

[0074] The relationship reasoning processor divides the association degree into three levels: strong association, weak association, and no association, retains the strong association and weak association information and records it in the dynamic relationship register;

[0075] The resource transmission unit includes a bandwidth optimization processor, a resume interrupted transfer processor, and a transmission audit processor. The bandwidth optimization processor is used to dynamically allocate the transmission path. The resume interrupted transfer processor is used to resume data transmission after the transmission is interrupted. The transmission audit processor is used to record the resource flow and generate an audit report;

[0076] The interaction processing unit includes a requirement editing processor, a pattern recognition processor, and a task output processor. The requirement editing processor is used to edit collaboration information. The pattern matching processor is used to identify and analyze the collaboration pattern. The task output processor outputs a matching task based on the recognition result;

[0077] The pattern recognition processor generates fixed entities, requirement items, and requirement entities based on the edited content. Each requirement item corresponds to multiple requirement entities. The pattern recognition processor selects a corresponding requirement entity for each requirement item and forms a target pattern with the fixed entity, and calculates the collaboration degree Pc of the target pattern according to the following formula:

[0078]

[0079] where n strThe number of entity combinations containing strong associations in the target pattern, n wek The number of entity combinations containing weak associations in the target pattern, Pa i，j Indicates the association degree between the i-th entity and the j-th entity in the target pattern. α is the strong coefficient and β is the weak coefficient;

[0080] When the cooperation degree exceeds the set threshold, the target pattern is recognized as a cooperation pattern, and the corresponding demand entity is sent to the task output processor;

[0081] The intelligent matching unit includes a task management processor, a graph matching processor, and a data feedback processor. The task management processor is used to manage the matching tasks. The graph matching processor processes the matching tasks based on the knowledge graph. The data feedback processor is used to feedback the matching results and obtain the corresponding data;

[0082] Based on the demand entity in the matching task, the graph matching processor finds the enterprises containing the demand entity in the knowledge graph, and at the same time obtains the actual cases containing the demand entity in the enterprise, and calculates the matching value VM of the demand entity of the enterprise according to the following formula:

[0083]

[0084] Where n is the number of actual cases obtained, m is the total number of cases of the enterprise, k i Is the weight value of the demand entity in the i-th actual case, and B(i) is the benefit value of the i-th actual case;

[0085] The graph matching processor selects the enterprise with the largest matching value as the matching result;

[0086] The resource trading unit includes a trading application processor, a trading signature processor, and a resource execution processor. The trading application processor is used to submit the user's trading application. The trading signature processor is used for the trading parties to review the trading content and perform signature confirmation. The resource execution processor performs resource delivery based on the valid transaction;

[0087] The access control unit includes a permission record processor, a dynamic token generator, and an audit log tracker. The permission record processor is used to record user permissions and perform permission verification. The dynamic token generator is used to generate short-term valid access tokens. The audit log tracker is used to record the user's access operations;

[0088] The data encryption unit includes an encryption calculation processor, a decryption calculation processor, and a key cycle manager. The encryption calculation processor is used to encrypt the data content. The decryption calculation processor is used to decrypt the data content. The key cycle manager is used to automatically rotate the key;

[0089] The behavior monitoring unit includes an anomaly detection processor, a traceability and forensics processor, and an adaptive response processor. The anomaly detection processor is used to detect attack behaviors, the traceability and forensics processor is used to trace the data leakage path, and the adaptive response processor is used to execute corresponding response measures;

[0090] Both i and j appearing in the above text are ordinal numbers used to represent serial numbers and have no actual meaning.

[0091] Some code information of this system is described as follows:

[0092] class DataIntegrationSharingModule:

[0093] def __init__(self):

[0094] self.data_storage = {}

[0095] def collect_data(self, source, data):

[0096] self.data_storage[source] = data

[0097] def get_data(self, source):

[0098] return self.data_storage.get(source, None)

[0099] def manage_sharing_protocol(self, protocol):

[0100] print(f"Applying sharing protocol: {protocol}")

[0101] class IntelligentResourceSchedulingModule:

[0102] def __init__(self):

[0103] self.resources = {}

[0104] def add_resource(self, name, info):

[0105] self.resources[name] = info

[0106] def retrieve_resource(self, name):

[0107] return self.resources.get(name, None)

[0108] def construct_knowledge_graph(self):

[0109] return {key: val for key, val in self.resources.items()}

[0110] def transfer_resource(self, source, destination):

[0111] print(f"Transferring resource from {source} to {destination}")

[0112]

[0113] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated.

Claims

1. A collaborative innovation system for the construction of a diversified data industry cluster, characterized in that, It includes a data integration and sharing module, an intelligent resource scheduling module, a collaborative innovation support module, and a security supervision and management module; The data integration and sharing module is used to collect and store data information, the intelligent resource scheduling module is used to manage and schedule resource information, the collaborative innovation support module is used to conduct collaborative innovation analysis, and the security supervision and management module is used to protect data security; The data integration and sharing module includes a data collection unit, a data storage unit, and a sharing protocol unit. The data collection unit is used to collect data information from multiple links of the industrial chain, the data storage unit is used to store and manage the collected data, and the sharing protocol unit is used to manage the data sharing mechanism; The intelligent resource scheduling module includes a resource retrieval unit, a graph construction unit, and a resource transmission unit. The resource retrieval unit is used to retrieve resource information, the graph construction unit is used to construct an industrial cluster knowledge graph, and the resource transmission unit is used to transmit resource information between different nodes; The collaborative innovation support module includes an interaction processing unit, an intelligent matching unit, and a resource trading unit. The interaction processing unit is used to provide an interaction interface for business operations, the intelligent matching unit is used to perform matching analysis on resources, and the resource trading unit is used to achieve trusted trading of data resources; The security supervision and management module includes an access control unit, a data encryption unit, and a behavior monitoring unit. The access control unit is used to control the access rights of resource data, the data encryption unit is used to encrypt the transmitted data, and the behavior monitoring unit is used to supervise and analyze the behavior of users; 2. The collaborative innovation system for constructing a diversified data industrial cluster according to claim 1, wherein The graph construction unit includes an entity extraction processor, a relationship reasoning processor, and a dynamic relationship register. The entity extraction processor is used to extract key entity information from resource data, the relationship reasoning processor is used to mine the association relationships between entity information, and the dynamic relationship register is used to dynamically store the association data of entities; The relationship reasoning processor calculates the association degree Pa between two entity information according to the following formula: Where, n1 is the occurrence times of the first entity, n2 is the occurrence times of the second entity, and n3 is the times of co-occurrence of these two entities.

3. The collaborative innovation system for constructing a diversified data industrial cluster according to claim 2, characterized in that, The interaction processing unit includes a requirement editing processor, a pattern recognition processor, and a task output processor. The requirement editing processor is used to edit collaborative information, the pattern matching processor is used to perform recognition analysis on the collaborative pattern, and the task output processor outputs a matching task based on the recognition result; The pattern recognition processor generates a fixed entity, a requirement item, and a requirement entity based on the edited content. Each requirement item corresponds to multiple requirement entities. The pattern recognition processor selects a corresponding requirement entity for each requirement item and forms a target pattern with the fixed entity, and calculates the collaboration degree Pc of the target pattern according to the following formula: Among them, n str is the number of entity combinations with strong associations in the target pattern, n wek is the number of entity combinations with weak associations in the target pattern, Pa i，j represents the association degree between the i-th entity and the j-th entity in the target pattern, α is the strong coefficient, and β is the weak coefficient; When the collaboration degree exceeds the set threshold, the target pattern is recognized as a collaborative pattern, and the corresponding requirement entity is sent to the task output processor.

4. A collaborative innovation system for the construction of a diversified data industry cluster as described in claim 3, characterized in that, The intelligent matching unit includes a task management processor, a graph matching processor, and a data feedback processor. The task management processor is used to manage matching tasks. The graph matching processor processes matching tasks based on a knowledge graph. The data feedback processor is used to feedback matching results and obtain corresponding data; Based on the demand entity in the matching task, the graph matching processor finds the enterprise containing the demand entity in the knowledge graph, and simultaneously obtains the actual cases containing the demand entity in the enterprise, and calculates the matching value VM of the demand entity of the enterprise according to the following formula: where n is the number of actual cases obtained, m is the number of all cases of the enterprise, and k i is the weight value of the requirement entity in the i-th actual case, and B(i) is the benefit value of the i-th actual case; The graph matching processor selects the enterprise with the largest matching value as the matching result.

5. The collaborative innovation system for constructing a diversified data industrial cluster according to claim 4, wherein The resource trading unit includes a transaction application processor, a transaction signature processor, and a resource execution processor. The transaction application processor is used to submit the user's transaction application. The transaction signature processor is used for both trading parties to review the transaction content and conduct signature confirmation. The resource execution processor performs resource delivery based on a valid transaction.

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