A management system for improving data transmission quality

Abstract

The present invention discloses a management system for improving the quality of data transmission, which belongs to the technical field of data management, and specifically comprises: a database comprises storage partitions of several levels, each storage partition of each level stores technical data of a corresponding level; enterprise parameters of data application enterprises are collected; the technical value of the application enterprises is calculated according to the enterprise parameters, and a technical rating of the application enterprises is generated, the application enterprises that meet the preset ratings are screened out, and marked as preferred enterprises, and the reference value of the preferred enterprises to the enterprise is calculated according to the public materials of the preferred enterprises, and the shared value of the preferred enterprises is jointly measured according to the reference value and the technical rating, and sorted; the technical data of the enterprise is encrypted by an encryption algorithm, and the encrypted technical data is compressed, and the compressed technical data is transmitted in sequence according to the sorting of the preferred enterprises; the present invention improves the utilization rate of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a management system for improving data transmission quality. Background Art

[0002] Data sharing refers to the exchange and sharing of data and data products between parameter systems at different levels and in different enterprises. Data resources are shared with others in order to achieve more reasonable resource allocation. It is an important means to improve the utilization rate of data resources and avoid repeated waste in data collection, storage and management. For R&D companies, since the data in the R&D field are numerous and complex, technical exchanges among multiple companies are undoubtedly an effective means to improve the technical level. Therefore, data sharing is particularly important for high-tech enterprises.

[0003] However, data sharing does not mean unlimited sharing. Sharing data with applicant companies at will will only cause data redundancy and waste. Unlimited data sharing will also make it impossible to screen effective key data from the outside world. A large amount of data irrelevant to the company will affect the extraction of important data. Therefore, the actual purpose of data sharing should be to improve the utilization rate of data transmission, avoid waste of resources caused by repeated data transmission, form data complementarity between related companies, screen technology in a targeted manner, and enhance enterprise development, rather than simply data interoperability. Summary of the invention

[0004] The purpose of the present invention is to provide a management system for improving the quality of data transmission and solve the following technical problems:

[0005] Improve the utilization rate of data transmission and avoid waste of resources caused by repeated data transmission.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A management system for improving data transmission quality, comprising:

[0008] A database for storing the technical data of the enterprise, wherein the database includes storage partitions of several levels, and each storage partition of a level stores technical data of a corresponding level;

[0009] Data collection module, used to collect enterprise parameters of data application enterprises;

[0010] A data processing module is used to calculate the technical value of the applicant enterprise according to the enterprise parameters, generate the technical rating of the applicant enterprise, screen out the applicant enterprises that meet the preset rating, mark them as preferred enterprises, calculate the reference value of the preferred enterprises for the enterprise according to the public materials of the preferred enterprises, measure the shared value of the preferred enterprises according to the reference value and the technical rating, and sort the preferred enterprises from large to small according to the shared value;

[0011] A data transmission module is used to encrypt the technical data of the enterprise through an encryption algorithm, compress the encrypted technical data, and transmit the compressed technical data in sequence according to the ranking of the preferred enterprises;

[0012] The enterprise parameters collected by the data collection module include patent indicators, project indicators and talent indicators. The patent indicators include patent type, patent quantity and patent ranking; the project indicators include project type, project quantity and project level; the talent indicators include the number of talent titles, title level and the number of technical contracts.

[0013] As a further solution of the present invention: the process of the data processing module calculating the technical value of the applicant enterprise is:

[0014] The various enterprise parameters of the applicant enterprises are normalized, and the entropy values ​​of the normalized enterprise parameters are calculated respectively by the entropy weight method. The weights of the enterprise parameters are allocated according to the entropy values. The technical value of the applicant enterprises is calculated based on the weights of the enterprise parameters and the actual data. The technical rating of the applicant enterprises is divided based on the technical value.

[0015] As a further solution of the present invention: the process of selecting the preferred enterprises by the data processing module is as follows:

[0016] The applicant enterprises whose technical ratings obtained by the entropy weight method are greater than the preset ratings are determined as pending preferred enterprises; the pending preferred enterprises are subject to secondary verification, the weights of various indicators of the pending preferred enterprises are recalculated based on the hierarchical analysis method, and the secondary ratings of the pending preferred enterprises are generated, the results of the secondary ratings are used as the final technical ratings of the pending preferred enterprises, and the pending preferred enterprises whose secondary ratings are greater than the preset ratings are marked as preferred enterprises.

[0017] As a further solution of the present invention: the process of the data processing module calculating the shared value of the applicant enterprise is:

[0018] The public materials are public patent documents. The patent types and quantities of the selected enterprises are obtained respectively. The number of patents of each selected enterprise in the same patent field as the enterprise's existing patents is counted according to the patent classification number and marked as pending patents;

[0019] Collect the names and abstracts of the company's existing patents, generate a set of patent descriptions for the company, calculate the frequency of each word in the set of patent descriptions for the company, and mark the top m words with the highest frequency as the company's patent dictionary, where m is a preset value;

[0020] Similarly, according to the name and abstract of each preferred enterprise's pending patent, a preferred enterprise patent description set is generated, and the preferred enterprise patent description set is segmented and split into several words. The words are converted into vectors and then input into a text classification model. The text classification model includes a BERT layer, a BiLSTM layer, and a CRF layer. The context feature data of any word vector is deeply learned, and the probability of each word vector corresponding to the entity is output to generate an entity sequence. The entity sequence is input into the CRF layer for sorting to obtain the entity sequence that optimizes the objective function, and the common words in the entity sequence are filtered; and the optimized entity sequence is screened based on the patent dictionary of this enterprise to screen out target entities that are not related to the patent dictionary of this enterprise, and the proportion of target entities of each pending patent to all entities is calculated. The pending patents with a proportion greater than 80% are marked as target patents, and the number of target patents of each preferred enterprise is counted, which is the reference value. The product of the number of target patents and the technical rating is marked as shared value, and the preferred enterprises are sorted from large to small according to the shared value.

[0021] As a further solution of the present invention: the technical rating includes n levels, and the storage partitions of the database are also divided into n levels. The storage partitions of each level store corresponding technical data, and only the technical data of the corresponding level is transmitted to the applicant enterprise of the corresponding technical rating.

[0022] Beneficial effects of the present invention:

[0023] The present invention conducts a comprehensive evaluation of the applicant enterprises according to the enterprises' own parameters, not only obtaining the technical value of the applicant enterprises, but also obtaining the degree of similarity and reference value between the applicant enterprises and the enterprise, so as to select enterprises with qualified technical levels and capable of forming technical complementarity with the enterprise, so as to facilitate similar enterprises to quickly extract information beneficial to their own development, avoid the waste of resources and efficiency caused by repeated information collection, and realize common development among enterprises; and by conducting multiple accurate differentiated technical ratings on the applicant enterprises, it avoids the misjudgment of high-quality enterprises, realizes priority technology sharing with high-tech enterprises, and further improves the efficiency of information sharing among enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 As shown, the present invention is a management system for improving data transmission quality, comprising:

[0028] A database for storing the technical data of the enterprise, wherein the database includes storage partitions of several levels, and each storage partition of a level stores technical data of a corresponding level;

[0029] Data collection module, used to collect enterprise parameters of data application enterprises;

[0030] A data processing module is used to calculate the technical value of the applicant enterprise according to the enterprise parameters, generate the technical rating of the applicant enterprise, screen out the applicant enterprises that meet the preset rating, mark them as preferred enterprises, calculate the reference value of the preferred enterprises for the enterprise according to the public materials of the preferred enterprises, measure the shared value of the preferred enterprises according to the reference value and the technical rating, and sort the preferred enterprises from large to small according to the shared value;

[0031] The data transmission module is used to encrypt the technical data of the enterprise through an encryption algorithm, compress the encrypted technical data, and transmit the compressed technical data in sequence according to the ranking of the preferred enterprises.

[0032] In another preferred embodiment of the present invention, the enterprise parameters collected by the data collection module include patent indicators, project indicators and talent indicators. The patent indicators include patent type, number of patents and patent ranking; the project indicators include project type, number of projects and project level; the talent indicators include the number of talent titles, title level and the number of technical contracts.

[0033] Furthermore, the weights also include: paper weight (divided into core and non-core journals, expert and institution rankings), patent weight (divided into patent type, patent awards, applicant, inventor, patent holder rankings), award weight (divided into national, provincial and ministerial levels, municipal and departmental levels, expert and institution rankings), platform weight (divided into national and provincial levels), standard weight (divided into national standards, industry standards, local standards, standard awards), project weight (divided into major, key, general, national and provincial levels), talent title weight (divided into national and provincial levels, academician, Yangtze River, ten thousand people, thousand people and other levels).

[0034] In another preferred embodiment of the present invention, the process of the data processing module calculating the technical value of the applicant enterprise is:

[0035] The various enterprise parameters of the applicant enterprises are normalized, and the entropy values ​​of the normalized enterprise parameters are calculated respectively by the entropy weight method. The weights of the enterprise parameters are allocated according to the entropy values. The technical value of the applicant enterprises is calculated based on the weights of the enterprise parameters and the actual data. The technical rating of the applicant enterprises is divided based on the technical value.

[0036] According to the basic principles of information theory, information is a measure of the order of a system; and entropy is a measure of the disorder of a system. The absolute values ​​of the two are equal but the signs are opposite. According to the characteristics of entropy, the randomness and disorder of an event can be judged by calculating the entropy value. The degree of discreteness of an indicator can also be judged by the entropy value. The greater the discreteness of the indicator, the greater the influence (weight) of the indicator on the comprehensive evaluation. For example, if the sample data have the same value under a certain indicator, the influence of the indicator on the overall evaluation is 0, and the weight is 0. The entropy weight method is an objective weighting method. Because it only depends on the discreteness of the data itself, the entropy weight method is used to determine the weight of the evaluation parameters of the enterprise to ensure the objectivity and fairness of the results.

[0037] In another preferred embodiment of the present invention, the process of the data processing module screening the preferred enterprises is:

[0038] The applicant enterprises whose technical ratings obtained by the entropy weight method are greater than the preset ratings are determined as pending preferred enterprises; the pending preferred enterprises are subject to secondary verification, the weights of various indicators of the pending preferred enterprises are recalculated based on the hierarchical analysis method, and the secondary ratings of the pending preferred enterprises are generated, the results of the secondary ratings are used as the final technical ratings of the pending preferred enterprises, and the pending preferred enterprises whose secondary ratings are greater than the preset ratings are marked as preferred enterprises.

[0039] The analytic hierarchy process breaks down complex problems into component elements, and divides them into levels such as goals, criteria, and plans according to the dominant relationship between each element, forming a hierarchical structure. It relies on a certain mathematical model to quantitatively process and express the results of qualitative analysis. Since key enterprises have higher information sharing authority, they are subject to secondary verification to further obtain accurate ratings of applicant enterprises and prevent misjudgment of key enterprises, which would affect information sharing efficiency.

[0040] In another preferred embodiment of the present invention, the process of the data processing module calculating the shared value of the applicant enterprise is:

[0041] The public materials are public patent documents. The patent types and quantities of the selected enterprises are obtained respectively. The number of patents of each selected enterprise in the same patent field as the enterprise's existing patents is counted according to the patent classification number and marked as pending patents;

[0042] Collect the names and abstracts of the company's existing patents, generate a set of patent descriptions for the company, calculate the frequency of each word in the set of patent descriptions for the company, and mark the top m words with the highest frequency as the company's patent dictionary, where m is a preset value;

[0043] Similarly, according to the name and abstract of each preferred enterprise's pending patent, a preferred enterprise patent description set is generated, and the preferred enterprise patent description set is segmented and split into several words. The words are converted into vectors and then input into a text classification model. The text classification model includes a BERT layer, a BiLSTM layer, and a CRF layer. The context feature data of any word vector is deeply learned, and the probability of each word vector corresponding to the entity is output to generate an entity sequence. The entity sequence is input into the CRF layer for sorting to obtain the entity sequence that optimizes the objective function, and the common words in the entity sequence are filtered; and the optimized entity sequence is screened based on the patent dictionary of this enterprise to screen out target entities that are not related to the patent dictionary of this enterprise, and the proportion of target entities of each pending patent to all entities is calculated. The pending patents with a proportion greater than 80% are marked as target patents, and the number of target patents of each preferred enterprise is counted, which is the reference value. The product of the number of target patents and the technical rating is marked as shared value, and the preferred enterprises are sorted from large to small according to the shared value.

[0044] In another preferred embodiment of the present invention, the technical rating includes n levels, and the storage partitions of the database are also divided into n levels. The storage partitions of each level store corresponding technical data, and only the technical data of the corresponding level is transmitted to the applicant enterprise of the corresponding technical rating.

[0045] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A management system for improving data transmission quality, characterized in that: include: A database for storing the technical data of the enterprise, wherein the database includes storage partitions of several levels, and each storage partition of a level stores technical data of a corresponding level; Data collection module, used to collect enterprise parameters of data application enterprises; A data processing module is used to calculate the technical value of the applicant enterprise according to the enterprise parameters, generate the technical rating of the applicant enterprise, screen out the applicant enterprises that meet the preset rating, mark them as preferred enterprises, calculate the reference value of the preferred enterprises for the enterprise according to the public materials of the preferred enterprises, measure the shared value of the preferred enterprises according to the reference value and the technical rating, and sort the preferred enterprises from large to small according to the shared value; A data transmission module is used to encrypt the technical data of the enterprise through an encryption algorithm, compress the encrypted technical data, and transmit the compressed technical data in sequence according to the ranking of the preferred enterprises; The enterprise parameters collected by the data collection module include patent indicators, project indicators and talent indicators. The patent indicators include patent type, patent quantity and patent ranking. The project indicators include project type, project quantity and project level. The talent indicators include the number of talent titles, title level and number of technical contracts. The process of calculating the shared value of the applicant enterprise by the data processing module is as follows: The public materials are public patent documents. The patent types and quantities of the selected enterprises are obtained respectively. The number of patents of each selected enterprise in the same patent field as the enterprise's existing patents is counted according to the patent classification number and marked as pending patents; Collect the names and abstracts of the company's existing patents, generate a set of patent descriptions for the company, calculate the frequency of each word in the set of patent descriptions for the company, and mark the top m words with the highest frequency as the company's patent dictionary, where m is a preset value; Similarly, according to the name and abstract of each preferred enterprise's pending patent, a preferred enterprise patent description set is generated, and the preferred enterprise patent description set is segmented and split into several words. The words are converted into vectors and then input into a text classification model. The text classification model includes a BERT layer, a BiLSTM layer, and a CRF layer. The context feature data of any word vector is deeply learned, and the probability of each word vector corresponding to the entity is output to generate an entity sequence. The entity sequence is input into the CRF layer for sorting to obtain the entity sequence that optimizes the objective function, and the common words in the entity sequence are filtered; and the optimized entity sequence is screened based on the patent dictionary of this enterprise to screen out target entities that are not related to the patent dictionary of this enterprise, and the proportion of target entities of each pending patent to all entities is calculated. The pending patents with a proportion greater than 80% are marked as target patents, and the number of target patents of each preferred enterprise is counted, which is the reference value. The product of the number of target patents and the technical rating is marked as shared value, and the preferred enterprises are sorted from large to small according to the shared value.

2. A management system for improving data transmission quality according to claim 1, characterized in that: The process of calculating the technical value of the applicant enterprise by the data processing module is as follows: The various enterprise parameters of the applicant enterprises are normalized, and the entropy values ​​of the normalized enterprise parameters are calculated respectively by the entropy weight method. The weights of the enterprise parameters are allocated according to the entropy values. The technical value of the applicant enterprises is calculated based on the weights of the enterprise parameters and the actual data. The technical rating of the applicant enterprises is divided based on the technical value.

3. A management system for improving data transmission quality according to claim 2, characterized in that: The process of selecting the preferred enterprises by the data processing module is as follows: The applicant enterprises whose technical ratings obtained by the entropy weight method are greater than the preset ratings are determined as pending preferred enterprises; the pending preferred enterprises are subject to secondary verification, the weights of various indicators of the pending preferred enterprises are recalculated based on the hierarchical analysis method, and the secondary ratings of the pending preferred enterprises are generated, the results of the secondary ratings are used as the final technical ratings of the pending preferred enterprises, and the pending preferred enterprises whose secondary ratings are greater than the preset ratings are marked as preferred enterprises.

4. A management system for improving data transmission quality according to claim 3, characterized in that: The technical rating includes n levels, and the storage partitions of the database are also divided into n levels. The storage partitions of each level store corresponding technical data, and only the technical data of the corresponding level is transmitted to the applicant enterprise of the corresponding technical rating.

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