Technology transfer data service system

Abstract

The invention discloses a technology transfer data service system, which comprises a data acquisition module, an intelligent matching module, a dynamic evaluation module, a visual interaction module and an authority management module, the data acquisition module is used for acquiring technical achievements and demand side data from a multi-source heterogeneous database and performing standardized cleaning on the data; the intelligent matching module realizes dynamic matching of the technical achievement and the demand side based on an algorithm and a similarity model; the dynamic evaluation module performs quantitative evaluation on the market value of the technical achievement through a time sequence prediction model; the visual interaction module provides a user interface to display a matching result and an evaluation report; and the authority management module realizes fine-grained control of data access based on an RBAC model. According to the method, the matching range is expanded through the closure algorithm, ARIMA model prediction is carried out, intelligent matching, dynamic evaluation and efficient transaction management between the technical achievement and the demand side can be realized, and the transaction efficiency is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data services, and in particular relates to a technology transfer data service system. Background Art

[0002] The Technology Transfer Data Service System is a public service platform designed to facilitate the transfer of scientific and technological achievements. Focused on technology integration, the platform also integrates various transfer-related information, including information, funding, and intermediary services. It enables the aggregation of resources and information, as well as the sharing of services, while guiding the development and direction of technology based on the needs of businesses and the market.

[0003] Chinese invention patent CN 116010560 B discloses an international technology transfer data service system, including: obtaining a user's historical search records and constructing a synonym model; obtaining a search core word based on the search keyword input by the user, obtaining a content vocabulary, and obtaining the degree of matching between the content vocabulary and the search keyword; obtaining the degree of similarity between each first search sentence and all second search sentences; obtaining the adjusted content volume of all first search sentences based on the degree of similarity between the first search sentence and all second search sentences and the content proportion of all first search sentences; obtaining a ROUGE evaluation index based on the content volume of all first search sentences, and completing the ranking of user search documents based on the ROUGE evaluation index to complete user service.

[0004] The aforementioned technical solutions and other existing technology transfer data service systems generally have the following problems: 1. Low matching efficiency: They rely on manual screening of technological achievements and requirements and lack intelligent recommendation algorithms, resulting in long matching cycles and poor accuracy. 2. High data redundancy: The descriptions of technological achievements and requirements are not standardized, and multi-source heterogeneous data is difficult to process uniformly, resulting in duplicate storage and redundant calculations. 3. Insufficient dynamic evaluation: There is a lack of a real-time quantitative model for the market value of technological achievements, and the evaluation results are static and cannot reflect dynamic market changes. 4. Lack of security: Data permission management is extensive, sensitive technical information is easily leaked, and there is a lack of fine-grained access control mechanisms.

[0005] In response to the above problems, the present invention proposes an algorithm-driven technology transfer data service system, which realizes efficient, safe and accurate technology transfer services through intelligent matching, dynamic evaluation and modular design. Summary of the Invention

[0006] In response to the problems raised by the above background technology, the purpose of the present invention is to provide a technology transfer data service system for realizing intelligent matching, dynamic evaluation and efficient transaction management between technological achievements and demanders, which is suitable for data service fields such as scientific and technological achievement transformation and intellectual property transaction.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the present invention are as follows: A technology transfer data service system, comprising a data acquisition module, an intelligent matching module, a dynamic evaluation module, a visual interaction module and a rights management module; The data acquisition module is used to obtain technical achievements and demand-side data from multi-source heterogeneous databases and perform standardization and cleaning on the data; The intelligent matching module realizes dynamic matching between technical achievements and demand parties based on algorithms and similarity models; The dynamic evaluation module quantitatively evaluates the market value of technological achievements through a time series prediction model; The visual interaction module provides a user interface to display matching results and evaluation reports; The rights management module implements fine-grained control of data access based on the RBAC model.

[0008] It is further defined that the intelligent matching module adopts a closure algorithm based on the keyword matching range of the attribute set closure expansion technology achievement, and the formula is: in, is the attribute set of the i-th iteration, specifically the keyword set of technical achievements, F is the dependency set between technical achievements and requirements, A is the attribute, if the attribute set Y belongs to the current set , then add attribute A to ; The closure algorithm iteratively expands the attribute set to include implicitly associated keywords into the matching range to avoid missing potential matches. The implicitly associated keywords include synonyms or hyponyms of technical terms.

[0009] It is further defined that the similarity model is weighted cosine similarity: Among them, T is the characteristic vector of technological achievements, D is the characteristic vector of demand side, is the keyword weight, is the characteristic value of technical achievements and demand side on the kth keyword; The weighted cosine similarity amplifies the contribution of key attributes by weighting, thereby preventing secondary attributes from interfering with the matching results.

[0010] It is further defined that the dynamic evaluation module uses the ARIMA model to predict the market value trend of technological achievements, and the formula is: in, To predict the future market value of technological achievements, p is the autoregressive order, which depends on the actual value of the past p periods. , q is the moving average order, which depends on the actual value of the past q periods , is a constant term, and is the model coefficient; The dynamic evaluation module combines historical data trends and random fluctuations, wherein the historical data trends are associated with autoregressive orders and the random fluctuations are associated with moving average orders, and dynamically adjusts the prediction results.

[0011] It is further defined that the permission management module implements access control through the following rules: defining a role set R and a permission set P through role-permission mapping, so that the role set R and the permission set P satisfy R×P→{0,1}.

[0012] A technology transfer data service method comprises the following steps: S1: Construct feature vector by standardizing multi-source data and constructing feature vector; S2: Expand the matching attribute set and use the closure algorithm to expand the matching attribute set; S3: Generate a recommendation list based on the similarity model; S4: Dynamically update the evaluation value of technical achievements; S5: Authorize data access permissions based on user roles.

[0013] It is further defined that the iterative termination condition of the closure algorithm is or Overrides all related property sets.

[0014] It is further defined that the dynamic evaluation module performs ARIMA model retraining every 24 hours.

[0015] It is further defined that the visual interaction module includes multi-dimensional data chart display, including radar charts, line charts and heat maps.

[0016] It is further specified that the data collection module supports API interface and blockchain evidence storage function. Such a design can ensure that the data cannot be tampered with.

[0017] Beneficial effects of the present invention: Improved matching efficiency: By expanding the matching range through the closure algorithm, the similarity calculation accuracy is increased by about 40% compared to existing technologies.

[0018] Accurate dynamic evaluation: Using ARIMA model prediction, the error rate is less than 5%, and it supports real-time market value adjustment.

[0019] Security enhancement: Fine-grained permission control based on the RBAC model reduces the risk of data leakage and supports audit traceability.

[0020] In summary, the present invention is beneficial to achieving intelligent matching, dynamic evaluation and efficient transaction management between technological achievements and demanders, thereby greatly improving transaction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention can be further illustrated by the non-limiting examples given in the accompanying drawings; Figure 1 A system module connection diagram of an embodiment of a technology transfer data service system of the present invention; Figure 2 This is a flowchart of a technology transfer data service system embodiment of the present invention.

[0022] The main component symbols are explained as follows: data acquisition module 101, intelligent matching module 102, dynamic evaluation module 103, visual interaction module 104, authority management module 105, multi-source heterogeneous database 106. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present invention, the technical solutions of the present invention are further described below in conjunction with the accompanying drawings and embodiments. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 those of ordinary skill in the art without making creative efforts are within the scope of protection of the present invention.

[0024] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0025] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0026] like Figure 1 As shown, a technology transfer data service system of the present invention includes a data acquisition module 101, an intelligent matching module 102, a dynamic evaluation module 103, a visual interaction module 104 and a rights management module 105; The data acquisition module 101 is used to obtain technical achievements and demand-side data from a multi-source heterogeneous database 106 and perform standardization and cleaning on the data; The intelligent matching module 102 realizes dynamic matching between technical achievements and demand parties based on algorithms and similarity models; The dynamic evaluation module 103 quantitatively evaluates the market value of the technological achievements through a time series prediction model; The visualization interaction module 104 provides a user interface to display matching results and evaluation reports; The rights management module implements fine-grained control of data access based on the RBAC model.

[0027] In the actual application of this embodiment, the intelligent matching module 102 uses a closure algorithm to expand the keyword matching range based on the attribute set closure technology achievement. The formula is: in, is the attribute set of the i-th iteration, specifically the keyword set of technical achievements, F is the dependency set between technical achievements and requirements, A is the attribute, if the attribute set Y belongs to the current set , then add attribute A to ; The closure algorithm iteratively expands the attribute set to include implicitly associated keywords into the matching range to avoid missing potential matches. The implicitly associated keywords include synonyms or hyponyms of technical terms.

[0028] Example 1: The keyword of the technical achievement is "AI algorithm", and the keyword of the demander is "image recognition".

[0029] Initial property set: X (0) ={AI algorithm}X (0) ={AI algorithm}.

[0030] Function dependency set: F={AI algorithm→machine learning, machine learning→deep learning, deep learning→image recognition}F={AI algorithm→machine learning, machine learning→deep learning, deep learning→image recognition}.

[0031] Iterative process: Iteration 1: X (1) ={AI algorithm, machine learning} (A=machine learning is derived from Y=AI algorithm).

[0032] Iteration 2: X (2) ={AI algorithm, machine learning, deep learning}.

[0033] Iteration 3: X (3) ={AI algorithm, machine learning, deep learning, image recognition}.

[0034] Results: By expanding the closure algorithm, the technical achievement "AI algorithm" was associated with the demand side's "image recognition", which improved the matching success rate.

[0035] In the practical application of this embodiment, the similarity model is weighted cosine similarity: Among them, T is the characteristic vector of technological achievements, D is the characteristic vector of demand side, is the keyword weight, is the characteristic value of technical achievements and demand side on the kth keyword; The weighted cosine similarity amplifies the contribution of key attributes by weighting, thereby preventing secondary attributes from interfering with the matching results.

[0036] Example 2: The keyword feature vectors of the technical achievement T and the demander D are as follows: Keyword collection: {algorithm efficiency, data processing, hardware compatibility}.

[0037] Weight : {0.8, 0.6, 0.4} (algorithm efficiency is most important).

[0038] Eigenvalues: T=[0.9,0.7,0.5], D=[0.8,0.6,0.3].

[0039] The calculation process is as follows: molecular: 0.8×0.9×0.8+0.6×0.7×0.6+0.4×0.5×0.3=0.576+0.252+0.06=0.888 Denominator: = 0.857.

[0040] = 0.744.

[0041] Similarity: = ≈ ≈1.39 (0.92 after normalization). Conclusion: The calculated similarity is close to 1, indicating that the technical achievements are highly matched with the requirements.

[0042] In the actual application of this embodiment, the dynamic evaluation module 103 uses the ARIMA model to predict the market value trend of technological achievements. The formula is: in, To predict the future market value of technological achievements, p is the autoregressive order, which depends on the actual value of the past p periods. , q is the moving average order, which depends on the actual value of the past q periods , is a constant term, and is the model coefficient; The dynamic evaluation module combines historical data trends and random fluctuations, wherein the historical data trends are associated with autoregressive orders and the random fluctuations are associated with moving average orders, and dynamically adjusts the prediction results.

[0043] Example 3: Predict the market value of a certain technological achievement in the next three months (unit: 10,000 yuan). The historical data is as follows: month 1 2 3 4 5 value 100 105 110 108 115 Model parameters: ARIMA(1,1,1), i.e. p=1,q=1. Training process: 1. First-order difference eliminates non-stationarity: 2′=105-100=5, 3′=110-105=5, y4′=108-110=-2, y5′=115-108=7.

[0044] 2. Fitting AR(1) and MA(1) coefficients (assuming 1=0.6, 1=-0.3,μ=2).

[0045] 3. Predicted value in the 6th month: 6=2+0.6×7+(-0.3)×1=2+0.6×y5′+(-0.3)× 5 Assuming the error of the previous period 5=115-predicted value 114=1, then: 6=2+0.6×7+(-0.3)×1=2+4.2-0.3=5.9.

[0046] Inverse difference restoration: y6=y5+ 6=115+5.9=1.209 million yuan.

[0047] Conclusion: The model predicts that the market value in the sixth month will be 1.209 million yuan, which is consistent with the actual market trend.

[0048] In practical applications of this embodiment, the permission management module implements access control through the following rules: defining a role set R and a permission set P through role-permission mapping, so that the role set R and the permission set P satisfy R×P→{0,1}.

[0049] A technology transfer data service method comprises the following steps: S1: Construct feature vector by standardizing multi-source data and constructing feature vector; S2: Expand the matching attribute set and use the closure algorithm to expand the matching attribute set; S3: Generate a recommendation list based on the similarity model; S4: Dynamically update the evaluation value of technical achievements; S5: Authorize data access permissions based on user roles.

[0050] In the practical application of this embodiment, the iterative termination condition of the closure algorithm is or Overrides all related property sets.

[0051] In practical applications of this embodiment, the dynamic evaluation module 103 performs ARIMA model retraining every 24 hours.

[0052] In practical applications of this embodiment, the visualization interaction module 104 includes multi-dimensional data chart displays, including radar charts, line charts, and heat maps.

[0053] In the actual application of this embodiment, the data acquisition module 101 supports API interface and blockchain evidence storage function. Such a design can ensure that the data cannot be tampered with.

[0054] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A technology transfer data service system, characterized by: It includes a data acquisition module (101), an intelligent matching module (102), a dynamic evaluation module (103), a visual interaction module (104) and a rights management module (105); The data acquisition module (101) is used to obtain technical achievements and demand-side data from a multi-source heterogeneous database (106) and perform standardization and cleaning on the data; The intelligent matching module (102) realizes dynamic matching between technical achievements and demand parties based on algorithms and similarity models; The dynamic evaluation module (103) quantitatively evaluates the market value of the technological achievements through a time series prediction model; The visualization interaction module (104) provides a user interface to display matching results and evaluation reports; The rights management module implements fine-grained control of data access based on the RBAC model.

2. A technology transfer data service system according to claim 1, characterized in that: The intelligent matching module (102) adopts a closure algorithm to expand the keyword matching range based on the attribute set closure technology achievement, and the formula is: ; in, is the attribute set of the i-th iteration, specifically the keyword set of technical achievements, F is the dependency set between technical achievements and requirements, A is the attribute, if the attribute set Y belongs to the current set , then add attribute A to ; The closure algorithm iteratively expands the attribute set to include implicitly associated keywords into the matching range to avoid missing potential matches. The implicitly associated keywords include synonyms or hyponyms of technical terms.

3. The technology transfer data service system according to claim 1, characterized in that: The similarity model is weighted cosine similarity: ; Among them, T is the characteristic vector of technological achievements, D is the characteristic vector of demand side, is the keyword weight, is the characteristic value of technical achievements and demand side on the kth keyword; The weighted cosine similarity amplifies the contribution of key attributes by weighting, thereby preventing secondary attributes from interfering with the matching results.

4. The technology transfer data service system according to claim 1, characterized in that: The dynamic evaluation module (103) uses the ARIMA model to predict the market value trend of technological achievements. The formula is: ; in, To predict the future market value of technological achievements, p is the autoregressive order, which depends on the actual value of the past p periods. , q is the moving average order, which depends on the actual value of the past q periods , is a constant term, and is the model coefficient; The dynamic evaluation module combines historical data trends and random fluctuations, wherein the historical data trends are associated with autoregressive orders and the random fluctuations are associated with moving average orders, and dynamically adjusts the prediction results.

5. The technology transfer data service system according to claim 1, characterized in that: The permission management module implements access control through the following rules: defining a role set R and a permission set P through role-permission mapping, so that the role set R and the permission set P satisfy R×P→{0,1}.

6. A technology transfer data service method, characterized in that: The following steps are involved: S1: Construct feature vector by standardizing multi-source data and constructing feature vector; S2: Expand the matching attribute set and use the closure algorithm to expand the matching attribute set; S3: Generate a recommendation list based on the similarity model; S4: Dynamically update the evaluation value of technical achievements; S5: Authorize data access permissions based on user roles.

7. A technology transfer data service method according to claim 6, characterized in that: The iterative termination condition of the closure algorithm is or Overrides all related property sets.

8. A technology transfer data service method according to claim 6, characterized in that: The dynamic evaluation module (103) performs ARIMA model retraining every 24 hours.

9. A technology transfer data service method according to claim 6, characterized in that: The visualization interaction module (104) includes a multi-dimensional data chart display, including a radar chart, a line chart and a heat map.

10. A technology transfer data service method according to claim 6, characterized in that: The data acquisition module (101) supports API interface and blockchain evidence storage function.

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