WO2022127245A1 - 技术转移办公室通用信息交互方法、终端及介质 - Google Patents

技术转移办公室通用信息交互方法、终端及介质 Download PDF

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WO2022127245A1
WO2022127245A1 PCT/CN2021/119314 CN2021119314W WO2022127245A1 WO 2022127245 A1 WO2022127245 A1 WO 2022127245A1 CN 2021119314 W CN2021119314 W CN 2021119314W WO 2022127245 A1 WO2022127245 A1 WO 2022127245A1
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data
information
analysis
technical
technology
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French (fr)
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池长昀
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上海恒慧知识产权服务有限公司
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Priority to US17/505,546 priority Critical patent/US11847169B2/en
Publication of WO2022127245A1 publication Critical patent/WO2022127245A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/18—Legal services
    • G06Q50/184—Intellectual property management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling

Definitions

  • the present invention relates to the technical field of computer and network communication, and in particular, to a general information exchange method, terminal and medium of a technology transfer office.
  • the operation of the technology transfer office has become an important support for the transfer and transformation of scientific and technological achievements.
  • new technologies can be transferred to the business world.
  • the establishment of a technology transfer office to provide professional services for the transfer and transformation of scientific and technological achievements not only enables researchers to concentrate on scientific research, but also improves the efficiency of achievement transformation and avoids investment risks.
  • the Technology Transfer Office has opened up a channel for the integration of science and technology and economy, and provided a strategic way of "leapfrog" development for the implementation of the overall independent innovation strategy.
  • the publication number is CN108280781A and the publication date is July 13, 2018.
  • the Chinese invention patent application “Business Operation Method of Technology Transfer and Transformation Based on Big Data” includes: the establishment of the big data platform, the acquisition of technology ontology and the acquisition of technology receptors , connect the technology ontology with the technology receptor through the big data platform; complete the transfer or transformation of technology.
  • This method lacks practical and effective data mining tools, and the final results are not optimistic, and the method is not comprehensive in function, which cannot really meet the needs of the effective interaction of general information of the technical patent office and the work of the technical patent office.
  • the present invention provides a general information exchange method, terminal and medium for a technology transfer office.
  • a general information exchange method for a technology transfer office including:
  • data processing instructions are formed, and corresponding information data related to technology transfer is retrieved;
  • a corresponding processing task is executed for the specified data, and a corresponding processing result is obtained, so as to complete the interactive connection between the user and the processing result.
  • the information data related to technology transfer includes:
  • Patent documents and other technical documents are Patent documents and other technical documents
  • Patent metadata and other intellectual property information data
  • the data processing instructions include:
  • Patent information analysis instruction used to obtain patent information in patent documents and form patent competitive intelligence
  • Trend analysis command used to obtain the number of patents, patent lawsuits and patent transactions in different technical fields within a set period of time, form patent trend information, and then discover new fields worth entering;
  • Citation analysis instruction used to generate the citation analysis map of the technology, and obtain the technology source and key nodes in the development process;
  • Geographical analysis instruction used to obtain the distribution of patents in different regions, and then verify the possibility of entering the potential market
  • Technical analysis instruction used to obtain the current technology distribution in the industry, display the technical fields of competitors, and then provide reference for research and development;
  • Litigation risk analysis instruction used to extract patents in the industry and their corresponding legal information, and establish an early warning mechanism
  • Intellectual Property Value Evaluation Directive The value of intellectual property obtained by utilizing the existing value output and future effects of intellectual property
  • Two-way value evaluation instruction of users and information data based on the cooperative relationship between users and technology and the relationship between users and users, two-way scoring of users and technologies;
  • Technical innovation evaluation instruction based on technical keywords, obtain relevant technical information in existing technical literature, and conduct preliminary innovation evaluation of technology;
  • the data query instruction according to the user's search request, obtains the information data required by the user.
  • performing data processing on the information data to obtain required specified data includes:
  • the extracted features are filtered to obtain specified data.
  • the acquisition of the feature data in the information data adopts a time series-based feature data extraction method, including:
  • a feature extraction method is used to extract preliminary feature data from the information data
  • the label motion speed is introduced into the sample gathering area, so that the sliding window of the sample gathering area is adaptively adjusted, and the optimized extraction of the preliminary feature data is completed, and the final feature data is obtained.
  • the feature extraction is performed on the feature data using a method for extracting associated features based on fuzzy hierarchical clustering analysis and semantic similarity, including:
  • Data integration of distributed data ontology is performed on the acquired feature data
  • Cluster analysis is performed on the extracted semantic correlation features, and information fusion of the semantic correlation features is performed to obtain the optimal solution of the feature extraction objective function to realize feature extraction.
  • a string fuzzy matching method based on filtering technology including:
  • a parallel processing method is used to perform fuzzy matching on the filtered string set, and then the specified data is obtained.
  • the corresponding processing task is executed for the specified data, and corresponding processing results are obtained, which is realized by using the established patent network model;
  • the patent network model includes:
  • Patent citation network model based on the patent citation relationship, the model generates the citation analysis map of the technology, and obtains the technology source and key nodes in the development process;
  • the IPC co-occurrence network model based on the patent IPC classification co-occurrence relationship, collects statistics on the information in the patent association network, and then obtains trend analysis, regional analysis, technical analysis and/or litigation risk analysis results;
  • Keyword network model which is based on SAO text mining method to obtain patent information in patent documents and form patent competitive intelligence
  • the patent value evaluation model which is based on the deep learning method, establishes the corresponding relationship between the existing value and future effect of the patent, and then obtains the corresponding value evaluation;
  • a two-way value evaluation model which uses the historical interaction behavior between users and information data to construct a user-technical interaction bipartite network model and a user association network model; wherein: based on the user-technical interaction bipartite network model, users and information are obtained.
  • Cooperation relationship between data based on the user association network model, obtain the association relationship between users; based on the cooperation relationship and the association relationship, score users and information data, and obtain the two-way value of users and information data assessment;
  • the technical innovation evaluation model which is based on the text recognition method, obtains the technical information in the technical literature, and compares it with the technical keywords to be evaluated, and gives the corresponding innovation evaluation according to the comparison result;
  • the data query model which is based on the data search engine, directly queries the required information data.
  • the process of running a corresponding processing task for the specified data and obtaining a corresponding processing result further includes:
  • the obtained corresponding processing results are sorted into indexes.
  • the two-way value evaluation model Preferably, in the two-way value evaluation model, according to the cooperative relationship and the association relationship, user community detection and information data collaborative filtering are performed, and corresponding information data is retrieved on this basis.
  • a text recognition method is used to perform image recognition on technical documents in the relevant technical field, to obtain the recognized text, and to perform text clustering on the recognized text to obtain the text to be used for comparison.
  • Picture compare the technical keyword to be evaluated with the text in the picture to be used for comparison, if the technical keyword to be evaluated and the text in the picture to be used for comparison have an identical rate greater than the judgment threshold, It is judged that the technological innovation is insufficient.
  • the method further includes:
  • the corresponding information data related to the technology transfer is updated.
  • a terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor can be used to execute any one of the above-mentioned programs when the processor executes the program method described in item.
  • the information data is stored in a local storage or the cloud.
  • a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, can be used to perform any of the methods described above.
  • the present invention has the following beneficial effects compared with the prior art:
  • the general information interaction method, system and terminal of the technology transfer office provided by the present invention collect patent information from patent documents, and through processing, sorting and analysis, form patent competitive intelligence, and serve the scientific and technological development strategy of enterprises.
  • the general information exchange method, system and terminal of the technology transfer office provided by the present invention can evaluate the value of intellectual property through the current value output value and future effect of intellectual property, and the result is more accurate.
  • the general information system of the technology transfer office provided by the present invention enables users to effectively understand the risks existing in the process of technology transfer and to recommend business opportunities through two-way evaluation of users and technologies.
  • the general information system of the technology transfer office provided by the present invention can predict the value prospect of the relevant technology in advance through the evaluation of technological innovation.
  • the general information exchange method, system and terminal of the technology transfer office provided by the present invention simultaneously realize the patent information analysis, trend analysis, citation analysis, regional analysis, technical analysis, litigation analysis, value analysis, credit evaluation, innovative evaluation and other functions provide an effective reference for the possibility of entering potential markets and new fields.
  • the general information exchange method, system and terminal of the technology transfer office provided by the present invention improve the recall rate and precision rate of data mining through simple and effective data mining method and data processing model, and have stable and reliable performance.
  • the general information exchange method, system and terminal of the technology transfer office provided by the present invention is an objective and scientifically verified model, which can effectively evaluate technology combinations and identify technical personnel with potential for successful commercialization.
  • the general information exchange method, system and terminal of the technology transfer office provided by the present invention realize the retrieval, viewing, evaluation and batch analysis of patents by patent analysis users, and realize the patent directional recommendation for patent analysis users, and the patent data Global periodic calculation and update, continuous extraction and analysis of patent user and patent related data.
  • FIG. 1 is a work flow diagram of a general information exchange method for a technology transfer office in an embodiment of the present invention
  • Fig. 2 is a working flow chart of obtaining specified data in a preferred embodiment of the present invention
  • Fig. 3 is the working flow chart of acquiring characteristic data in a preferred embodiment of the present invention.
  • FIG. 4 is a working flow chart of feature extraction for feature data in a preferred embodiment of the present invention.
  • Fig. 5 is the working flow chart of obtaining specified data in a preferred embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a patented network model in a preferred embodiment of the present invention.
  • FIG. 7 is a working flow chart of retrieving information data in a preferred embodiment of the present invention.
  • FIG. 1 is a working flowchart of a general information exchange method for a technology transfer office provided by an embodiment of the present invention.
  • the general information exchange method for a technology transfer office may include the following steps:
  • the information data related to technology transfer may include any one or more of the following:
  • Patent documents and other technical documents are Patent documents and other technical documents
  • Patent metadata and other intellectual property information data
  • the data processing instruction may include any one or more of the following:
  • Patent information analysis instruction used to obtain patent information in patent documents and form patent competitive intelligence
  • Trend analysis command used to obtain the number of patents, patent lawsuits and patent transactions in different technical fields within a set period of time, form patent trend information, and then discover new fields worth entering;
  • Citation analysis instruction used to generate the citation analysis map of the technology, and obtain the technology source and key nodes in the development process;
  • Geographical analysis instruction used to obtain the distribution of patents in different regions, and then verify the possibility of entering the potential market
  • Technical analysis instruction used to obtain the current technology distribution in the industry, display the technical fields of competitors, and then provide reference for research and development;
  • Litigation risk analysis instruction used to extract patents in the industry and their corresponding legal information, and establish an early warning mechanism
  • Intellectual Property Value Evaluation Directive The value of intellectual property obtained by utilizing the existing value output and future effects of intellectual property
  • Two-way value evaluation instruction of users and information data based on the cooperative relationship between users and technology and the relationship between users and users, two-way evaluation of users and technologies;
  • Technical innovation evaluation instruction based on technical keywords, obtain relevant technical information in existing technical literature, and conduct preliminary innovation evaluation of technology;
  • the data query instruction according to the user's search request, obtains the information data required by the user.
  • data processing is performed on the information data to obtain the required specified data, as shown in FIG. 2 , which may include the following steps:
  • the feature data in the information data is acquired, and a feature data extraction method based on time series is adopted, as shown in FIG. 3, which may include the following steps:
  • S14 Introduce the label motion speed into the sample gathering area, so that the sliding window of the sample gathering area is adaptively adjusted, and the optimized extraction of the preliminary feature data is completed, and the final feature data is obtained.
  • feature extraction is performed on the feature data, and a method based on fuzzy hierarchical clustering analysis and semantic similarity correlation feature extraction is adopted, as shown in FIG. 4 , which may include the following steps:
  • the extracted features are filtered, and a string fuzzy matching method based on filtering technology is adopted, as shown in FIG. 5, which may include the following steps:
  • a corresponding processing task is executed for the specified data, and corresponding processing results are obtained, which is realized by using the established patent network model;
  • the patent network model as shown in FIG. 6 , may include any of the following One or any of the following:
  • Patent citation network model based on the patent citation relationship, the model generates the citation analysis map of the technology, and obtains the technology source and key nodes in the development process;
  • the IPC co-occurrence network model based on the patent IPC classification co-occurrence relationship, collects statistics on the information in the patent association network, and then obtains trend analysis, regional analysis, technical analysis and/or litigation risk analysis results;
  • Keyword network model which is based on SAO text mining method to obtain patent information in patent documents and form patent competitive intelligence
  • the patent value evaluation model which is based on the deep learning method, establishes the corresponding relationship between the existing value of the patent and the future effect, and then obtains the corresponding value evaluation;
  • a two-way value evaluation model which uses the historical interaction behavior between users and information data to construct a user-technical interaction bipartite network model and a user association network model; wherein: based on the user-technical interaction bipartite network model, users and information are obtained.
  • Cooperation relationship between data based on the user association network model, obtain the association relationship between users; based on the cooperation relationship and the association relationship, score users and information data, and obtain the two-way value of users and information data assessment;
  • the technical innovation evaluation model which is based on the text recognition method, obtains the technical information in the technical literature, and compares it with the technical keywords to be evaluated, and gives the corresponding innovation evaluation according to the comparison result;
  • the data query model which is based on the data search engine, directly queries the required information data.
  • the process of running a corresponding processing task for the specified data and obtaining a corresponding processing result may further include:
  • the corresponding results obtained are organized into indexes and stored as information data related to technology transfer, which can be called directly.
  • the working process of the two-way value evaluation model may include the following steps:
  • S404 based on the cooperative relationship and association relationship, score the user and the information data, and obtain a two-way value evaluation of the user and the information data.
  • S405 according to the cooperative relationship and the association relationship, perform community detection of the user and collaborative filtering of information data, and retrieve corresponding information data on this basis.
  • a graph convolution classification network can be used to predict and obtain a user-related credit score.
  • a logistic regression model can be used to predict and obtain the basic credit score of the user and information data.
  • a linear regression model can be used to take the obtained basic credit score of the user and information data and the user-related credit score as the input of the linear regression model, and output the comprehensive credit score of the user and information data.
  • the technical innovation evaluation model adopts a text recognition method to perform image recognition on technical documents in the related technical field, obtain the recognized text, and perform text clustering on the recognized text to obtain the text to be recognized.
  • the pictures used for comparison compare the technical keywords to be evaluated with the text in the pictures to be used for comparison, if the technical keywords to be evaluated are the same as the text in the pictures to be used for comparison If the rate is greater than the judgment threshold, it is judged that the technological innovation is insufficient.
  • the method may further include the following steps:
  • the corresponding information data related to the technology transfer is updated.
  • a technology for analyzing and mining the basic information of patent documents is proposed.
  • the data structure of patent documents is deconstructed.
  • proposed a multi-angle quantitative statistical analysis method set proposes a patent text mining process from text data modeling, feature representation, feature filtering and keyword analysis based on SAO for unstructured patent text data.
  • a patent complex network modeling and analysis technology using the potential association relationship in patent data is proposed.
  • the patent EPC co-occurrence network, citation network and keyword network are constructed.
  • the main path analysis and heterogeneous information network analysis methods are used to mine the information in the patent complex network.
  • the analysis of the basic information of the patent documents takes the most initial patent data as the object, that is, the stored patent documents and the metadata of the patent documents, and executes the relevant calculation tasks according to the analysis requirements of the user, and converts the results.
  • it mainly includes global data update involving the entire data and OLAP analysis in response to specific analysis scenarios of users.
  • the former is for data expansion and optimization of a single patent document, and the latter is for statistical analysis of patent data extracted under specific filtering conditions. and generate analysis reports.
  • patent network modeling and analysis are aimed at various types of associations included in patent data.
  • the design of this system includes: patent citation network model constructed by patent citation relationship; IPC co-occurrence network model constructed by patent IPC classification co-occurrence relation; keyword network model constructed based on SAO text mining.
  • the modeling of the first two parts is based on the storage of patent document metadata, which is constructed by using the citation relation items and IPC classification items in the patent metadata, while the keyword network is based on the storage of patent documents and is completed in the patent text through natural language processing.
  • the technology association network is constructed and executed by the transformation of heterogeneous information network.
  • citation network analysis and IPC co-occurrence analysis both use presets, perform periodic calculations based on the data granularity level of IPC classification, and present the results on the patent details page.
  • the keyword network analysis is completed at one time when the patent document is imported into the system, and it is no longer calculated when the original text of the patent has not been changed.
  • the two-way value evaluation model uses the historical interaction behavior between users and information data to construct a user-technical interaction bipartite network model and a user association network model; wherein: based on the user-technical interaction bipartite network model, users and information data are obtained. based on the user association network model, obtain the association relationship between users; based on the cooperative relationship and the association relationship, score users and information data, and obtain a two-way value evaluation between users and information data .
  • the technical innovation evaluation model is based on the text recognition method, obtains the technical information in the technical literature, and compares it with the technical keywords to be evaluated, and gives the corresponding innovation evaluation according to the comparison results.
  • the data query model is based on a data search engine, which directly queries the required information data.
  • the technical innovation evaluation model adopts a text recognition method to perform image recognition on technical documents in the relevant technical field, obtain the recognized text (that is, the text as technical information), and perform the recognition on the recognized text.
  • Text clustering to obtain pictures to be used for comparison; compare the technical keywords to be evaluated with the text in the images to be used for comparison, if the technical keywords to be evaluated are compared with the to-be-used comparison If the same rate of text in the pictures is greater than the judgment threshold, it is judged that the technical innovation is insufficient.
  • the bidirectional value evaluation model can use the graph convolution classification network to predict and obtain the associated credit score of the user; the logistic regression model can be used to predict the basic credit score of the user and the information data; the linear regression model can be used to The obtained basic credit score and user-related credit score of the user and information data are used as the input of the linear regression model, and the output is the comprehensive credit score of the user and the information data.
  • the patent value evaluation model based on deep learning has the problem of poor matching error tolerance for most of the existing effect concept map matching methods.
  • LSTM long short-term memory network
  • the method uses the Bi-LSTM-ATT model for training, which has certain usability for determining the effect of patent ownership, and the accuracy rate can reach more than 70%.
  • the feature data extraction method based on time series is adopted, which is a simulation method for optimizing feature data extraction under big data environment.
  • This method optimizes and extracts feature data under big data environment, which can effectively improve the Data quality in the data environment.
  • For the optimal extraction of feature data it is necessary to obtain the density value near each data quality sample, and to give the area where the samples are clustered, so as to complete the optimal extraction of feature data.
  • the traditional method first builds the original transaction data set, and gives the distribution rules of the data, but ignores the area where the data samples are gathered, resulting in low extraction accuracy.
  • This paper proposes an optimal extraction method of feature data in the big data environment based on time series.
  • the method first uses the time series model to identify the time series of each data state quantity, classifies the characteristic data in the time series, and uses the high-density clustering method to obtain the density values near each data quality sample, and gives the area where the samples are clustered. , the label motion speed is introduced into the adaptive adjustment process of the sliding window to complete the optimal extraction of feature data in the big data environment.
  • a method for extracting association features based on fuzzy hierarchical clustering analysis and semantic similarity is adopted, which is a big data mining algorithm based on fuzzy hierarchical clustering analysis.
  • a big data mining algorithm based on fuzzy hierarchical clustering analysis and semantic similarity correlation feature extraction is proposed.
  • the algorithm uses generalization mapping to construct semantic concept tree, combines binary semantic analysis method to construct distributed ontology model of big data, and uses fuzzy analytic hierarchy process to judge the semantic similarity and relevance of big data, and extracts the information flow of big data.
  • Semantic correlation features combined with fuzzy mean algorithm to perform cluster analysis on the extracted features, adaptive uniform traversal learning method to perform information fusion processing of associated features in big data mining, obtain the optimal solution of the mining objective function, and realize big data Optimize mining.
  • the semantic directivity of the algorithm is better, the data focusing performance is better, the recall rate and precision rate of data mining are improved, and the overall performance is stable and reliable.
  • a string fuzzy matching method based on filtering technology is adopted, aiming at the problem of low search efficiency of the string fuzzy matching method based on edit distance, by analyzing the string fuzzy matching process, using parallelization technology Optimize the fuzzy matching process of strings with large amounts of data.
  • an improved method is proposed, which uses string filtering rules to filter the set of strings to be searched and then performs fuzzy matching.
  • a user-technical interaction analysis technology based on swarm intelligence is proposed. Based on the abstract extraction of the interaction behavior between users and patents, the modeling of the user-technical interaction binary network is carried out. Complete the modeling of the user association network. Based on these two networks, the community detection and patent collaborative filtering of patent collaborative analysis users are completed, and the two-way value evaluation of the user-technical evaluation bipartite network is realized at the same time.
  • Another embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the processor executes the program, the processor can be used to execute any one of the foregoing embodiments of the present invention Methods.
  • the information data is stored in the local storage or the cloud.
  • the memory is used to store the program;
  • the memory can include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static random-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc.; memory can also include non-volatile Non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory).
  • the memory is used to store computer programs (such as application programs, functional modules, etc. for implementing the above methods), computer instructions, etc., and the above computer programs, computer instructions, etc. can be stored in one or more memories in partitions. And the above-mentioned computer programs, computer instructions, data, etc. can be called by the processor.
  • the computer programs, computer instructions, etc. described above may be partitioned and stored in one or more memories. And the above-mentioned computer programs, computer instructions, data, etc. can be called by the processor.
  • the processor is configured to execute the computer program stored in the memory, so as to implement each step in the method involved in the above embodiments. For details, refer to the relevant descriptions in the foregoing method embodiments.
  • the processor and memory can be separate structures or integrated structures that are integrated together.
  • the processor and the memory are independent structures, the memory and the processor can be coupled and connected through a bus.
  • a third embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, can be used to execute the method of any one of the foregoing embodiments of the present invention.
  • the general information exchange method, terminal and medium of the technology transfer office provided by the above-mentioned embodiments of the present invention collect patent information from patent documents, and through processing, sorting and analysis, form patent competitive intelligence and serve the scientific and technological development strategy of enterprises. Analysis of patent information. Benefits Through analysis, the isolated information can be transformed into valuable patent competitive intelligence from ordinary information according to different aggregation degrees. According to this intelligence, it is possible to judge companies or countries in related industries and technical fields from the special perspective of patents. The key technology and technology development direction, the technology portfolio and technology investment trend of major competitors, and formulate a patent strategy that matches the overall development strategy for the enterprise.
  • the general information exchange method, terminal and medium of a technology transfer office provided by the above embodiments of the present invention evaluate the value of intellectual property through the current value and future effect of intellectual property.
  • the general information interaction method, terminal and medium of the technology transfer office provided by the above embodiments of the present invention realize multi-dimensional and visualized big data analysis, including but not limited to:
  • Citation analysis Automatically generate a citation analysis map of the technology to find the source of the technology and key nodes in the development process.
  • Litigation risk analysis One-click filtering of high-value patents, litigation history, licenses and other legal information in the industry, and establishing an early warning mechanism in advance.
  • the general information interaction method, terminal and medium of the technology transfer office provided by the above embodiments of the present invention realize the retrieval, viewing, evaluation and batch analysis of patents by the patent analysis user, and realize the patent orientation of the patent analysis user from the perspective of the system. It is recommended that the global periodic calculation and update of patent data, and the continuous extraction and analysis of patent user and patent related data.
  • the system provided by the present invention and its respective devices can be made by logic gates, Switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers are used to achieve the same function. Therefore, the system and its various devices provided by the present invention can be regarded as a kind of hardware components, and the devices for realizing various functions included in the system can also be regarded as structures in the hardware components; The means for implementing various functions can be regarded as either a software module implementing a method or a structure within a hardware component.

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Abstract

一种技术转移办公室通用信息交互方法，包括：根据用户操作指令，形成数据处理指令，调取相应的与技术转移相关的信息数据(S100)；对所述信息数据进行数据处理，获得所需的指定数据(S200)；针对所述指定数据运行相应的处理任务，并得到相应的处理结果，完成用户与处理结果之间的交互对接(S300)。同时提供了一种相应的终端及存储介质。上述方法从专利文献中采集专利信息，经过加工、整理、分析，形成专利竞争情报，为企业的科技发展战略服务；同时实现了对专利技术的专利信息分析、趋势分析、引用分析、地域分析、技术分析、诉讼分析、价值分析等功能，为进入潜在市场、新领域的可能性提供的有效的参考依据。

Description

技术转移办公室通用信息交互方法、终端及介质 技术领域
本发明涉及计算机和网络通信技术领域，具体地，涉及一种技术转移办公室通用信息交互方法、终端及介质。
背景技术
促进科技成果转移转化作为实施创新驱动发展战略的重要任务，是加强科技与经济紧密结合、发挥科技创新在经济转方式调结构重要作用的关键环节。然而由于缺乏从事技术转移工作的高端专业人才通过有效途径来实现研究成果的市场化、产业化，技术转移成果不容乐观。科研成果只有完成从科学研究、实验开发到推广应用，才能真正实现其创新价值。
技术转移办公室的运行成为实现科技成果转移转化的重要支撑，通过技术转移办公室，可以将新技术转移至企业界。建设技术转移办公室为科技成果转移转化提供专业服务，不仅能使研究人员把精力集中于科研，也提高了成果转化效率，避免投资风险。更重要的是，技术转移办公室作为连接政校企三方的桥梁，打通了科技与经济结合的通道，为实施整体自主创新战略提供了“跨越式”发展的战略途径。
经过检索发现：
公开号为CN108280781A、公开日为2018年07月13日的中国发明专利申请《基于大数据的技术转移、转化的商业运作方法》，包括：大数据平台的建立，获取技术本体和获取技术受体，把技术本体通过大数据平台与技术受体进行对接；完成技术的转移或者转化。该方法缺少切实有效的数据挖掘工具，最终获得的成果不容乐观，且该方法功能并不全面，无法真正满足技术专利办公室通用信息的有效交互以及技术专利办公室的工作所需。
综上所述，本领域亟需一个多功能、多维度、一体化、性能稳定且符合科技成果转移转化特点的技术转移办公室通用信息交互技术，目前没有发现同本发明类似技术的说明或报道，也尚未收集到国内外类似的资料。
发明内容
本发明针对现有技术中存在的上述不足，提供了一种技术转移办公室通用信息交互方法、终端及介质。
根据本发明的一个方面，提供了一种技术转移办公室通用信息交互方法，包括：
根据用户操作指令，形成数据处理指令，调取相应的与技术转移相关的信息数据；
对所述信息数据进行数据处理，获得所需的指定数据；
针对所述指定数据运行相应的处理任务，并得到相应的处理结果，完成用户与处理结果之间的交互对接。
优选地，所述与技术转移相关的信息数据，包括：
专利文献以及其他技术文献；
专利元数据以及其他知识产权信息数据；
专利法律状态数据；
项目转化信息数据；
科技成果信息数据；
专家信息数据；
专家科技成果数据；
技术经理人信息数据；
相关企业信息数据；
其他根据需求设置的信息数据。
优选地，所述数据处理指令，包括：
专利信息分析指令：用于获取专利文献中的专利信息，形成专利竞争情报；
趋势分析指令：用于获取不同技术领域在设定时间段内的专利数量、专利诉讼数量以及专利交易数量，形成专利趋势信息，进而发现值得进入的新领域；
引用分析指令：用于生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
地域分析指令：用于获取不同地域的专利分布情况，进而验证潜在市场的进入可能性；
技术分析指令：用于获取行业现有技术分布情况，展示竞争对手的技术领域，进而为研发提供参考；
诉讼风险分析指令：用于提取行业内的专利及其相应的法律信息，建立预警机制；
知识产权价值评估指令：利用知识产权现有的价产值和未来的效应所得到的知识产权价值；
用户与信息数据的双向价值评定指令：基于用户与技术之间的合作关系以及用户与用户之间的关联关系，对用户及技术进行双向评分；
技术创新性评估指令，基于技术关键词，获取现有技术文献中相关技术信息，对技术进行初步创新性评定；
数据查询指令，根据用户搜索请求，获取用户所需信息数据。
优选地，所述对所述信息数据进行数据处理，获得所需的指定数据，包括：
获取所述信息数据中的特征数据；
对所述特征数据进行特征提取；
对提取到的所述特征进行过滤，得到指定数据。
优选地，所述获取所述信息数据中的特征数据，采用基于时间序列的特征数据提取方法，包括：
采用特征提取方法，对所述信息数据提取初步特征数据；
利用时间序列模型，识别出各初步特征数据状态量的时间序列；
将所述时间序列中的初步特征数据进行分类，采用密度聚类方法，得到每个初步特征数据样本附近的密度值，给出样本聚集区域；
在所述样本聚集区域内引入标签运动速度，使得样本聚集区域的滑动窗口自适应调整，完成对初步特征数据的优化提取，得到最终的特征数据。
优选地，所述对所述特征数据进行特征提取，采用基于模糊层次聚类分析和语义相似性关联特征提取方法，包括：
对获取的所述特征数据进行分布式数据本体的数据集成；
对集成的特征数据进行语义相似性和关联性判断，提取特征数据信息流的语义关联特征；
对提取的所述语义关联特征进行聚类分析，并进行语义关联特征的信息融合，求得特征提取目标函数的最优解，实现特征提取。
优选地，所述对提取到的所述特征进行过滤，采用基于过滤技术的字符串模糊匹配方法，包括：
在提取的所述特征中获取待进行匹配的目标字符串，形成字符串集合；
利用正则表达式，对所述字符串集合进行过滤；
采用并行处理方法对过滤后的字符串集合进行模糊匹配，进而得到指定数据。
优选地，所述针对所述指定数据运行相应的处理任务，并得到相应的处理结果，采用建立的专利网络模型实现；其中，所述专利网络模型，包括：
专利引文网络模型，该模型基于专利引用关系，生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
IPC共现网络模型，该模型基于专利IPC分类共现关系，对专利关联网络中的信息进行统计，进而得到趋势分析、地域分析、技术分析和/或诉讼风险分析结果；
关键词网络模型，该模型基于SAO文本挖掘方法，获取专利文献中的专利信息，形成专利竞争情报；
专利价值评估模型，该模型基于深度学习方法，建立专利现有的价产值和未来效应之间的对应关系，进而得到相应的价值评估；
双向价值评定模型，该模型利用用户与信息数据之间的历史交互行为，构建用户-技术交互二分网络模型和用户关联网络模型；其中：基于所述用户-技术交互二分网络模型，获得用户与信息数据之间的合作关系；基于所述用户关联网络模型，获得用户之间的关联关系；基于所述合作关系和所述关联关系，对用户及信息数据进行评分，获得用户与信息数据的双向价值评定；
技术创新性评估模型，该模型基于文字识别方法，获取技术文献中的技术信息，并与待评估的技术关键词进行比对，根据比对结果给出相应的创新性评估；
数据查询模型，该模型基于数据搜索引擎，直接查询所需信息数据。
优选地，针对所述指定数据运行相应的处理任务，并得到相应的处理结果的过程中，还包括：
将得到的所述相应的处理结果整理为索引。
优选地，所述双向价值评定模型，根据所述合作关系和所述关联关系，进行用户的社区检测与信息数据协同过滤，并在此基础上对相应的信息数据进行调取。
优选地，所述技术创新性评估模型，采用文字识别方法，对相关技术领域的技术文献进行图片识别，获得识别后的文字，将识别后的文字进行文字聚类，得到待用于比对的图片；将待评估的技术关键词与待用于比对的图片中的文字进行比对，若待评估的技术关键词与所述待用于比对的图片中的文字相同率大于判断阈值，则判定为技术创新性不足。
优选地，所述方法，还包括：
根据用户操作指令，更新相应的与技术转移相关的信息数据。
根据本发明的另一个方面，提供了一种终端，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时可用于执行上述任一项所述的方法。
优选地，所述信息数据存储于本地存储器或云端。
根据本发明的第三个方面，提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时可用于执行上述任一项所述的方法。
由于采用了上述技术方案，本发明与现有技术相比，具有如下至少一项的有益效果：
1、本发明提供的技术转移办公室通用信息交互方法、系统及终端，从专利文献中采集专利信息，经过加工、整理、分析，形成专利竞争情报，为企业的科技发展战略服务。
2、本发明提供的技术转移办公室通用信息交互方法、系统及终端，通过专利信息分析，将孤立的信息按照不同的聚集度，使它们由普通的信息转化为有价值的专利竞争情报，根据这些情报可以从专利这一特殊的视角研判企业或国家在相关产业和技术领域的重点技术及技术发展方向、主要竞争对手的技术组合和技术投资动向，为企业制定与总体发展战略相匹配的专利战略。
3、本发明提供的技术转移办公室通用信息交互方法、系统及终端，通过知识产权现在的价产值和未来的效应，进行知识产权价值评估，结果更为精准。
4、本发明提供的技术转移办公室通用信息系统，通过对用户及技术的双向评定，使得用户能够有效了解技术转移过程中所存在的风险，并能够完成商机推荐。
5、本发明提供的技术转移办公室通用信息系统，通过对技术创新性的评估，能够提前预估相关技术的价值前景。
6、本发明提供的技术转移办公室通用信息交互方法、系统及终端，同时实现了对专利技术的专利信息分析、趋势分析、引用分析、地域分析、技术分析、诉讼分析、价值分析、信用评价、创新性评估等功能，为进入潜在市场、新领域的可能性提供的有效的参考依据。
7、本发明提供的技术转移办公室通用信息交互方法、系统及终端，通过简单有效的数据挖掘方法和数据处理模型，提高了数据挖掘的查全率和查准率，性能稳定可靠。
8、本发明提供的技术转移办公室通用信息交互方法、系统及终端，是一个客观且 经过科学验证的模型，能够有效评估技术组合并识别具有成功商业化潜力的技术人员。
9、本发明提供的技术转移办公室通用信息交互方法、系统及终端，实现了专利分析用户对专利的检索、查看、评价与批量分析，并实现了对专利分析用户的专利定向推荐，专利数据的全局定期计算与更新、专利用户与专利的相关数据的持续提取与分析。
10、采用本发明提供的技术转移办公室通用信息交互方法、系统及终端，能够为提高商业化成功率、客观评估与分流相应技术、高效投资相关专利、研发和营销预算、避免对不良技术资产的高成本投入、识别和防止在商业化过程中暴露风险的不可预见问题等方法提供切实有效的参考价值。
附图说明
通过阅读参照以下附图对非限制性实施例所作的详细描述，本发明的其它特征、目的和优点将会变得更明显：
图1为本发明一实施例中技术转移办公室通用信息交互方法的工作流程图；
图2为本发明一优选实施例中获得指定数据的工作流程图；
图3为本发明一优选实施例中获取特征数据的工作流程图；
图4为本发明一优选实施例中对特征数据进行特征提取的工作流程图；
图5为本发明一优选实施例中得到指定数据的工作流程图；
图6为本发明一优选实施例中专利网络模型示意图；
图7为本发明一优选实施例中对信息数据进行调取的工作流程图。
具体实施方式
下面对本发明的实施例作详细说明：本实施例在以本发明技术方案为前提下进行实施，给出了详细的实施方式和具体的操作过程。应当指出的是，对本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。
图1为本发明一实施例提供的技术转移办公室通用信息交互方法的工作流程图。
如图1所示，该实施例提供的技术转移办公室通用信息交互方法，可以包括如下步骤：
S100，根据用户操作指令，形成数据处理指令，调取相应的与技术转移相关的信息 数据；
S200，对信息数据进行数据处理，获得所需的指定数据；
S300，针对指定数据运行相应的处理任务，并得到相应的处理结果，完成用户与处理结果之间的交互对接。
作为该实施例的一优选实施例，与技术转移相关的信息数据，可以包括如下任意一项或任意多项：
专利文献以及其他技术文献；
专利元数据以及其他知识产权信息数据；
专利法律状态数据；
项目转化信息数据；
科技成果信息数据；
专家信息数据；
专家科技成果数据；
技术经理人信息数据；
相关企业信息数据；
其他根据需求设置的信息数。
作为该实施例的一优选实施例，数据处理指令，可以包括如下任意一项或任意多项：
专利信息分析指令：用于获取专利文献中的专利信息，形成专利竞争情报；
趋势分析指令：用于获取不同技术领域在设定时间段内的专利数量、专利诉讼数量以及专利交易数量，形成专利趋势信息，进而发现值得进入的新领域；
引用分析指令：用于生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
地域分析指令：用于获取不同地域的专利分布情况，进而验证潜在市场的进入可能性；
技术分析指令：用于获取行业现有技术分布情况，展示竞争对手的技术领域，进而为研发提供参考；
诉讼风险分析指令：用于提取行业内的专利及其相应的法律信息，建立预警机制；
知识产权价值评估指令：利用知识产权现有的价产值和未来的效应所得到的知识产权价值；
用户与信息数据的双向价值评定指令：基于用户与技术之间的合作关系以及用户与 用户之间的关联关系，对用户及技术进行双向评分；
技术创新性评估指令，基于技术关键词，获取现有技术文献中相关技术信息，对技术进行初步创新性评定；
数据查询指令，根据用户搜索请求，获取用户所需信息数据。
作为该实施例的一优选实施例，对信息数据进行数据处理，获得所需的指定数据，如图2所示，可以包括如下步骤：
S1，获取信息数据中的特征数据；
S2，对特征数据进行特征提取；
S3，对提取到的特征进行过滤，得到指定数据。
作为该实施例的一优选实施例，S1中，获取信息数据中的特征数据，采用基于时间序列的特征数据提取方法，如图3所示，可以包括如下步骤：
S11，采用特征提取方法，对信息数据中的数据提取初步特征数据；
S12，利用时间序列模型，识别出各初步特征数据状态量的时间序列；
S13，将时间序列中的初步特征数据进行分类，采用密度聚类方法，得到每个初步特征数据样本附近的密度值，给出样本聚集区域；
S14，在样本聚集区域内引入标签运动速度，使得样本聚集区域的滑动窗口自适应调整，完成对初步特征数据的优化提取，得到最终的特征数据。
作为该实施例的一优选实施例，S2中，对特征数据进行特征提取，采用基于模糊层次聚类分析和语义相似性关联特征提取方法，如图4所示，可以包括如下步骤：
S21，对获取的特征数据进行分布式数据本体的数据集成；
S22，对集成的特征数据进行语义相似性和关联性判断，提取特征数据信息流的语义关联特征；
S23，对提取的语义关联特征进行聚类分析，并进行语义关联特征的信息融合，求得特征提取目标函数的最优解，实现特征提取。
作为该实施例的一优选实施例，S3中，对提取到的特征进行过滤，采用基于过滤技术的字符串模糊匹配方法，如图5所示，可以包括如下步骤：
S31，在提取的特征中获取待进行匹配的目标字符串，形成字符串集合；
S32，利用正则表达式，对字符串集合进行过滤；
S33，采用并行处理方法对过滤后的字符串集合进行模糊匹配，进而得到指定数据。
作为该实施例的一优选实施例，针对指定数据运行相应的处理任务，并得到相应的 处理结果，采用建立的专利网络模型实现；其中，专利网络模型，如图6所示，可以包括如下任意一项或任意多项：
专利引文网络模型，该模型基于专利引用关系，生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
IPC共现网络模型，该模型基于专利IPC分类共现关系，对专利关联网络中的信息进行统计，进而得到趋势分析、地域分析、技术分析和/或诉讼风险分析结果；
关键词网络模型，该模型基于SAO文本挖掘方法，获取专利文献中的专利信息，形成专利竞争情报；
专利价值评估模型，该模型基于深度学习方法，建立专利现有的价产值和未来效应之间的对应关系，进而得到相应的价值评估；
双向价值评定模型，该模型利用用户与信息数据之间的历史交互行为，构建用户-技术交互二分网络模型和用户关联网络模型；其中：基于所述用户-技术交互二分网络模型，获得用户与信息数据之间的合作关系；基于所述用户关联网络模型，获得用户之间的关联关系；基于所述合作关系和所述关联关系，对用户及信息数据进行评分，获得用户与信息数据的双向价值评定；
技术创新性评估模型，该模型基于文字识别方法，获取技术文献中的技术信息，并与待评估的技术关键词进行比对，根据比对结果给出相应的创新性评估；
数据查询模型，该模型基于数据搜索引擎，直接查询所需信息数据。
作为该实施例的一优选实施例，针对指定数据运行相应的处理任务，并得到相应的处理结果的过程中，还可以包括：
将得到的相应结果整理为索引，并作为与技术转移相关的信息数据进行存储，可以直接进行调用。
作为该实施例的一优选实施例，如图7所示，所述双向价值评定模型的工作过程，可以包括如下步骤：
S401，提取用户与信息数据之间的历史交互行为，构建用户-技术交互二分网络模型和用户关联网络模型；
S402，基于用户-技术交互二分网络模型，获得用户与信息数据之间的合作关系；
S403，基于用户关联网络模型，获得用户之间的关联关系；
S404，基于合作关系和关联关系，对用户及信息数据进行评分，获得用户与信息数据的双向价值评定。
作为该实施例的一优选实施例，还可以包括如下步骤：：
S405，根据合作关系和关联关系，进行用户的社区检测与信息数据协同过滤，并在此基础上对相应的信息数据进行调取。
作为该实施例的一优选实施例，可以利用图卷积分类网络预测得到用户关联信用评分。
作为该实施例的一优选实施例，可以利用逻辑回归模型预测得到用户及信息数据的基本信用评分。
作为该实施例的一优选实施例，可以利用线性回归模型，将得到的用户及信息数据的基本信用评分与用户关联信用评分作为线性回归模型的输入，输出得到用户及信息数据的综合信用评分。
作为该实施例的一优选实施例，技术创新性评估模型，采用文字识别方法，对相关技术领域的技术文献进行图片识别，获得识别后的文字，将识别后的文字进行文字聚类，得到待用于比对的图片；将待评估的技术关键词与待用于比对的图片中的文字进行比对，若待评估的技术关键词与所述待用于比对的图片中的文字相同率大于判断阈值，则判定为技术创新性不足。
作为该实施例的一优选实施例，该方法的还可以包括如下步骤：
根据用户操作指令，更新相应的与技术转移相关的信息数据。
在本发明部分实施例中，提出了一种专利文献基本信息分析与挖掘的技术，通过界定专利文献基本信息的数据形式与内容边界，解构了专利文献的数据结构，针对结构化的专利元数据，提出多角度的定量统计分析方法集合，针对非结构化的专利文本数据则提出了从文本数据建模、特征表示、特征过滤以及基于SAO的关键词分析的专利文本挖掘流程。
在本发明部分实施例中，提出了一种利用专利数据中的潜在关联关系的专利复杂网络建模与分析技术，通过提取专利的IPC共现关系、引用/被引关系以及基于SAO的关键词关联关系，构建了专利EPC共现网络、引文网络以及关键词网络，在完成网络指标统计的基础上，利用主路径分析以及异质信息网络分析的方法，对专利复杂网络中的信息加以挖掘。
在本发明部分实施例中，专利文献基本信息的分析以最初始的专利数据为对象，即存储的专利文献和专利文献元数据进行调用，并依据用户的分析要求执行相关的计算任务，将结果更新并存储，并在有调用需求时将对应数据返回到前端展示部件 加以渲染。其中，主要包含涉及数据全体的全局数据更新以及响应用户的特定分析场景的OLAP分析，前者是针对单一专利文献的数据扩展优化，后者则是对特定过滤条件下提取出的专利数据加以统计分析并生成分析报告。
在本发明部分实施例中，专利网络建模与分析针对的是专利数据中包含的各类关联关系。在本系统的设计中包含：以专利引用关系构建的专利引文网络模型；以专利IPC分类共现关系构建的IPC共现网络模型；基于SAO文本挖掘构建的关键词网络模型。其中，前两个部分的建模基于专利文献元数据存储，利用专利元数据中的引证关系项和IPC分类项加以构建，而关键词网络则基于专利文献存储，通过自然语言处理完成专利文本中的技术关联网络构建并执行异质信息网络转化构建而成。由于图计算本身对计算资源的巨大消耗，引文网络分析以及IPC共现分析都采用预置的，基于IPC分类的数据粒度层次执行周期性计算，并将结果呈现于专利详情页面。而关键词网络分析则是在专利文献导入系统中时一次性分析完成，在专利原文未更改的情况下不再进行计算。
在本发明部分实施例中，还包括：双向价值评定模型、技术创新性评估模型和数据查询模型。其中，双向价值评定模型利用用户与信息数据之间的历史交互行为，构建用户-技术交互二分网络模型和用户关联网络模型；其中：基于所述用户-技术交互二分网络模型，获得用户与信息数据之间的合作关系；基于所述用户关联网络模型，获得用户之间的关联关系；基于所述合作关系和所述关联关系，对用户及信息数据进行评分，获得用户与信息数据的双向价值评定。技术创新性评估模型基于文字识别方法，获取技术文献中的技术信息，并与待评估的技术关键词进行比对，根据比对结果给出相应的创新性评估。数据查询模型基于数据搜索引擎，直接查询所需信息数据。
在本发明部分实施例中，技术创新性评估模型，采用文字识别方法，对相关技术领域的技术文献进行图片识别，获得识别后的文字(即作为技术信息的文字)，将识别后的文字进行文字聚类，得到待用于比对的图片；将待评估的技术关键词与待用于比对的图片中的文字进行比对，若待评估的技术关键词与所述待用于比对的图片中的文字相同率大于判断阈值，则判定为技术创新性不足。
在本发明部分实施例中，双向价值评定模型，可以利用图卷积分类网络预测得到用户关联信用评分；可以利用逻辑回归模型预测得到用户及信息数据的基本信用评分；可以利用线性回归模型，将得到的用户及信息数据的基本信用评分与用户关联信用评分作为线性回归模型的输入，输出得到用户及信息数据的综合信用评分。
在本发明部分实施例中，基于深度学习的专利价值评估模型，针对现有的效应概念图匹配方法多数存在匹配容错性差的问题。从大数据的角度提出一种新的挖掘专利与效应对应关系的方法。利用长短期记忆网络(LSTM)与基于attention的双向LSTM相结合形成模型训练专利语料，通过Softmax分类模型进行分类，得到专利所属的效应。该方法利用Bi-LSTM-ATT模型进行训练对判定专利所属效应具有一定的可用性，准确率可以达到70％以上。
在本发明部分实施例中，采用基于时间序列的特征数据提取方法，是一种采用大数据环境下特征数据优化提取仿真方法，该方法对大数据环境下特征数据进行优化提取，能够有效提升大数据环境下的数据质量。对特征数据的优化提取，需要得到每个数据质量样本附近的密度值，给出样本聚集的区域，完成对特征数据的优化提取。传统方法先组建原始交易数据集，给出数据的分布规则，但忽略了给出数据样本聚集的区域，导致提取精度偏低。提出基于时间序列的大数据环境下特征数据优化提取方法。该方法先利用时间序列模型识别出各数据状态量的时间序列，将时间序列中的特征数据进行分类，采用高密度聚类方法得到每个数据质量样本附近的密度值，给出样本聚集的区域，将标签运动速度引入到滑动窗口的自适应调整过程中来完成对大数据环境下特征数据优化提取。
在本发明部分实施例中，采用基于模糊层次聚类分析和语义相似性关联特征提取方法，是一种采用基于模糊层次聚类分析的大数据挖掘算法。在文本大数据挖掘过程中受到语义模糊性因素的影响，导致大数据挖掘查准性不好，故提出了一种基于模糊层次聚类分析和语义相似性关联特征提取的大数据挖掘算法。该算法采用泛化映射构造语义概念树，结合二元语义分析方法进行大数据分布式本体模型构建，并采用模糊层次分析方法进行大数据的语义相似性和关联性判断，提取大数据信息流的语义关联特征，结合模糊均值算法对提取的特征量进行聚类分析，自适应均匀遍历学习方法进行大数据挖掘中关联特征量的信息融合处理，求得挖掘目标函数的最优解，实现大数据优化挖掘。采用该算法的语义指向性较好，数据的聚焦性能较优，提高了数据挖掘的查全率和查准率，总体性能稳定可靠。
在本发明部分实施例中，采用基于过滤技术的字符串模糊匹配方法，针对基于编辑距离的字符串模糊匹配方法搜索效率较低的问题，通过对字符串模糊匹配过程进行分析，利用并行化技术对大数据量的字符串模糊匹配过程进行优化。同时由于计算字符串间编辑距离算法性能较低，提出利用字符串过滤规则对待搜索字符串集合 进行过滤后再进行模糊匹配的改进方法。
在本发明部分实施例中，提出了基于群体智能的用户-技术交互分析技术，在将用户与专利之间的交互行为抽象提取的基础上，进行了用户-技术交互二分网络的建模，并完成用户关联网络的建模。基于这两个网络，完成专利协同分析用户的社区检测与专利协同过滤，同时实现了用户-技术评价二分网络的双向价值评定。
本发明另一实施例提供了一种终端，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行程序时可用于执行本发明上述实施例中任一项的方法。
作为该实施例的一优选实施例，信息数据存储于本地存储器或云端。
可选地，存储器，用于存储程序；存储器，可以包括易失性存储器(英文：volatile memory)，例如随机存取存储器(英文：random-access memory，缩写：RAM)，如静态随机存取存储器(英文：static random-access memory，缩写：SRAM)，双倍数据率同步动态随机存取存储器(英文：Double Data Rate Synchronous Dynamic Random Access Memory，缩写：DDR SDRAM)等；存储器也可以包括非易失性存储器(英文：non-volatile memory)，例如快闪存储器(英文：flash memory)。存储器用于存储计算机程序(如实现上述方法的应用程序、功能模块等)、计算机指令等，上述的计算机程序、计算机指令等可以分区存储在一个或多个存储器中。并且上述的计算机程序、计算机指令、数据等可以被处理器调用。
上述的计算机程序、计算机指令等可以分区存储在一个或多个存储器中。并且上述的计算机程序、计算机指令、数据等可以被处理器调用。
处理器，用于执行存储器存储的计算机程序，以实现上述实施例涉及的方法中的各个步骤。具体可以参见前面方法实施例中的相关描述。
处理器和存储器可以是独立结构，也可以是集成在一起的集成结构。当处理器和存储器是独立结构时，存储器、处理器可以通过总线耦合连接。
本发明第三个实施例提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时可用于执行本发明上述实施例中任一项的方法。
本发明上述实施例提供的技术转移办公室通用信息交互方法、终端及介质，从专利文献中采集专利信息，经过加工、整理、分析，形成专利竞争情报，为企业的科技发展战略服务，这就是所谓的专利信息分析。好处通过分析，将孤立的信息按照不同的聚集度，使它们由普通的信息转化为有价值的专利竞争情报，根据这些情报 可以从专利这一特殊的视角研判企业或国家在相关产业和技术领域的重点技术及技术发展方向、主要竞争对手的技术组合和技术投资动向，为企业制定与总体发展战略相匹配的专利战略。
本发明上述实施例提供的技术转移办公室通用信息交互方法、终端及介质，通过知识产权现在的价产值和未来的效应，对知识产权价值进行评估。
本发明上述实施例提供的技术转移办公室通用信息交互方法、终端及介质，实现多维度、可视化的大数据分析，包括但不限于：
趋势分析：根据不同技术领域近些年的专利数、专利诉讼/交易等趋势，发现值得进入的新领域。
引用分析：自动生成技术的引用分析图谱，找到技术源头和发展过程中的关键节点。
地域分析：根据不同地域的专利分布，验证潜在市场的进入可能性。
技术分析：呈现行业现有技术分布全貌，展示竞争对手的技术强弱领域，为研发提供参考。
诉讼风险分析：一键过滤出行业内的高价值专利、诉讼历史、许可等法律信息，提前建立预警机制。
本发明上述实施例提供的技术转移办公室通用信息交互方法、终端及介质，实现了专利分析用户对专利的检索、查看、评价与批量分析，并从系统的角度实现了对专利分析用户的专利定向推荐，专利数据的全局定期计算与更新、专利用户与专利的相关数据的持续提取与分析。
采用本发明提供的技术转移办公室通用信息交互方法、终端及介质，能够为提高商业化成功率、客观评估与分流相应技术、高效投资相关专利、研发和营销预算、避免对不良技术资产的高成本投入、识别和防止在商业化过程中暴露风险的不可预见问题等方法提供切实有效的参考价值。
本领域技术人员知道，除了以纯计算机可读程序代码方式实现本发明提供的系统及其各个装置以外，完全可以通过将方法步骤进行逻辑编程来使得本发明提供的系统及其各个装置以逻辑门、开关、专用集成电路、可编程逻辑控制器以及嵌入式微控制器等的形式来实现相同功能。所以，本发明提供的系统及其各项装置可以被认为是一种硬件部件，而对其内包括的用于实现各种功能的装置也可以视为硬件部件内的结构；也可以将用于实现各种功能的装置视为既可以是实现方法的软件模块 又可以是硬件部件内的结构。
以上对本发明的具体实施例进行了描述。需要理解的是，本发明并不局限于上述特定实施方式，本领域技术人员可以在权利要求的范围内做出各种变形或修改，这并不影响本发明的实质内容。

Claims (15)

  1. 一种技术转移办公室通用信息交互方法，其特征在于，包括：
    根据用户操作指令，形成数据处理指令，调取相应的与技术转移相关的信息数据；
    对所述信息数据进行数据处理，获得所需的指定数据；
    针对所述指定数据运行相应的处理任务，并得到相应的处理结果，完成用户与处理结果之间的交互对接。
  2. 根据权利要求1所述的技术转移办公室通用信息交互方法，其特征在于，所述与技术转移相关的信息数据，包括：
    专利文献以及其他技术文献；
    专利元数据以及其他知识产权信息数据；
    专利法律状态数据；
    项目转化信息数据；
    科技成果信息数据；
    专家信息数据；
    专家科技成果数据；
    技术经理人信息数据；
    相关企业信息数据；
    其他根据需求设置的信息数据。
  3. 根据权利要求1所述的技术转移办公室通用信息交互方法，其特征在于，所述数据处理指令，包括：
    专利信息分析指令：用于获取专利文献中的专利信息，形成专利竞争情报；
    趋势分析指令：用于获取不同技术领域在设定时间段内的专利数量、专利诉讼数量以及专利交易数量，形成专利趋势信息，进而发现值得进入的新领域；
    引用分析指令：用于生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
    地域分析指令：用于获取不同地域的专利分布情况，进而验证潜在市场的进入可能性；
    技术分析指令：用于获取行业现有技术分布情况，展示竞争对手的技术领域，进而为研发提供参考；
    诉讼风险分析指令：用于提取行业内的专利及其相应的法律信息，建立预警机制；
    知识产权价值评估指令：利用知识产权现有的价产值和未来的效应所得到的知识产权价值；
    用户与信息数据的双向价值评定指令：基于用户与技术之间的合作关系以及用户与用户之间的关联关系，对用户及技术进行双向评分；
    技术创新性评估指令，基于技术关键词，获取现有技术文献中相关技术信息，对技术进行初步创新性评定；
    数据查询指令，根据用户搜索请求，获取用户所需信息数据。
  4. 根据权利要求1所述的技术转移办公室通用信息交互方法，其特征在于，所述对所述信息数据进行数据处理，获得所需的指定数据，包括：
    获取所述信息数据中的特征数据；
    对所述特征数据进行特征提取；
    对提取到的所述特征进行过滤，得到指定数据。
  5. 根据权利要求4所述的技术转移办公室通用信息交互方法，其特征在于，所述获取所述信息数据中的特征数据，采用基于时间序列的特征数据提取方法，包括：
    采用特征提取方法，对所述信息数据提取初步特征数据；
    利用时间序列模型，识别出各初步特征数据状态量的时间序列；
    将所述时间序列中的初步特征数据进行分类，采用密度聚类方法，得到每个初步特征数据样本附近的密度值，给出样本聚集区域；
    在所述样本聚集区域内引入标签运动速度，使得样本聚集区域的滑动窗口自适应调整，完成对初步特征数据的优化提取，得到最终的特征数据。
  6. 根据权利要求4所述的技术转移办公室通用信息交互方法，其特征在于，所述对所述特征数据进行特征提取，采用基于模糊层次聚类分析和语义相似性关联特征提取方法，包括：
    对获取的所述特征数据进行分布式数据本体的数据集成；
    对集成的特征数据进行语义相似性和关联性判断，提取特征数据信息流的语义关联特征；
    对提取的所述语义关联特征进行聚类分析，并进行语义关联特征的信息融合，求得特征提取目标函数的最优解，实现特征提取。
  7. 根据权利要求4所述的技术转移办公室通用信息交互方法，其特征在于，所述 对提取到的所述特征进行过滤，采用基于过滤技术的字符串模糊匹配方法，包括：
    在提取的所述特征中获取待进行匹配的目标字符串，形成字符串集合；
    利用正则表达式，对所述字符串集合进行过滤；
    采用并行处理方法对过滤后的字符串集合进行模糊匹配，进而得到指定数据。
  8. 根据权利要求1所述的技术转移办公室通用信息交互方法，其特征在于，所述针对所述指定数据运行相应的处理任务，并得到相应的处理结果，采用建立的专利网络模型实现；其中，所述专利网络模型，包括：
    专利引文网络模型，该模型基于专利引用关系，生成技术的引用分析图谱，获得技术源头和发展过程中的关键节点；
    IPC共现网络模型，该模型基于专利IPC分类共现关系，对专利关联网络中的信息进行统计，进而得到趋势分析、地域分析、技术分析和/或诉讼风险分析结果；
    关键词网络模型，该模型基于SAO文本挖掘方法，获取专利文献中的专利信息，形成专利竞争情报；
    专利价值评估模型，该模型基于深度学习方法，建立专利现有的价产值和未来效应之间的对应关系，进而得到相应的价值评估；
    双向价值评定模型，该模型利用用户与信息数据之间的历史交互行为，构建用户-技术交互二分网络模型和用户关联网络模型；其中：基于所述用户-技术交互二分网络模型，获得用户与信息数据之间的合作关系；基于所述用户关联网络模型，获得用户之间的关联关系；基于所述合作关系和所述关联关系，对用户及信息数据进行评分，获得用户与信息数据的双向价值评定；
    技术创新性评估模型，该模型基于文字识别方法，获取技术文献中的技术信息，并与待评估的技术关键词进行比对，根据比对结果给出相应的创新性评估；
    数据查询模型，该模型基于数据搜索引擎，直接查询所需信息数据。
  9. 根据权利要求8所述的技术转移办公室通用信息交互方法，其特征在于，所述双向价值评定模型，根据所述合作关系和所述关联关系，进行用户的社区检测与信息数据协同过滤，并在此基础上对相应的信息数据进行调取。
  10. 根据权利要求8所述的技术转移办公室通用信息交互方法，其特征在于，所述技术创新性评估模型，采用文字识别方法，对相关技术领域的技术文献进行图片识别，获得识别后的文字，将识别后的文字进行文字聚类，得到待用于比对的图片；将待评估的技术关键词与待用于比对的图片中的文字进行比对，若待评估的技术关键词与所述待 用于比对的图片中的文字相同率大于判断阈值，则判定为技术创新性不足。
  11. 根据权利要求8所述的技术转移办公室通用信息交互方法，其特征在于，针对所述指定数据运行相应的处理任务，并得到相应的处理结果的过程中，还包括：
    将得到的所述相应的处理结果整理为索引。
  12. 根据权利要求1-11中任一项所述的技术转移办公室通用信息交互方法，其特征在于，还包括：
    根据用户操作指令，更新相应的与技术转移相关的信息数据。
  13. 一种终端，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时可用于执行权利要求1-12中任一项所述的方法。
  14. 根据权利要求1所述的终端，其特征在于，所述信息数据存储于本地存储器或云端。
  15. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时可用于执行权利要求1-12中任一项所述的方法。
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