Target technical field associated industry identification method and device based on science and technology information, computer equipment, storage medium and program product

Abstract

The invention provides a target technical field associated industry identification method and device based on science and technology information, computer equipment, a storage medium and a program product, and the method comprises the steps: obtaining patent data of a target technical field, according to the IPC classification number of the data of each patent, constructing an adjacent matrix representing a technology association relationship; generating an IPC social network diagram reflecting technical relevance based on the adjacent matrix; processing the IPC social network diagram based on the classification association strength index to obtain an association strength value of each IPC classification and the target technical field; and processing the IPC social network graph based on the network co-occurrence strength index, and identifying the IPC classification industry pairs with strong relevance in the target technical field.

Description

Technical Field

[0001] The present invention relates to the field of patent text mining technology, and in particular to a method, device, computer equipment, storage medium and program product for identifying industries associated with a target technology field based on scientific and technological information. Background Art

[0002] During the patent analysis process, each patent is assigned at least one IPC classification number depending on the technology involved. The academic and technology management fields usually regard multiple IPC classification numbers appearing in the same patent as evidence of technology convergence, the so-called co-occurrence relationship. Frequently co-occurring IPC classification numbers reveal the close connection between technologies. Therefore, by analyzing the IPC classification numbers and their relationships in a large number of patent samples, extensive network connections between technologies can be revealed. For example, CN116595197A discloses a link prediction method and system for a patent classification number associated knowledge graph, which can significantly improve the accuracy of link prediction of a patent classification number associated knowledge graph without relying on patent text data.

[0003] Similarly, during the paper analysis process, each paper will be assigned at least one China Library Classification Number according to the different technologies involved. Frequent co-occurrence of China Library Classification Numbers reveals the close connection between technologies. Therefore, by analyzing the China Library Classification Numbers and their relationships in a large number of paper samples, a wide range of network connections between technologies can be revealed. Further analysis of these technology fusion data can construct and understand the connection map between different fields in the technology field, which is crucial to grasping the development context of industry technology.

[0004] Specifically for the tobacco industry, the tobacco industry includes tobacco leaf planting and cultivation, finished product processing, transportation and sales, and other links. Related industries include multiple industries belonging to agriculture and processing and manufacturing. It is crucial to scientifically find technological priorities from several potential growth points in technological development and sort out a foreseen catalog of technologies for priority development.

[0005] In order to solve the above problems, people have been seeking an ideal technical solution. Summary of the invention

[0006] Based on this, it is necessary to provide a method, device, computer equipment, storage medium and program product for identifying related industries in the target technology field based on scientific and technological information to address the above-mentioned technical problems. Based on patent classification information, "point" intelligence and "line" intelligence are combined to analyze and identify tobacco-related industries or disciplines and strongly related industries or disciplines.

[0007] In order to achieve the above-mentioned object, the first aspect of the present invention provides a method for identifying related industries in a target technical field based on scientific and technological information, comprising the following steps: Obtain patent data in the target technology field, and construct an adjacency matrix representing technology associations based on the IPC classification number of each patent data; Generate an IPC social network graph reflecting technology relevance based on the adjacency matrix; The IPC social network graph is processed based on the classification association strength index to obtain the association strength value between each IPC classification and the target technology field; The IPC social network graph is processed based on the network co-occurrence intensity index to identify IPC classification industry pairs with strong correlation in the target technology field.

[0008] In an embodiment of the first aspect, paper data in the target technical field is obtained, and an adjacency matrix representing technical association relationships is constructed according to the Chinese Library Classification Number of each paper data; Generate a Chinese social network graph reflecting technology relevance based on the adjacency matrix; Based on the classification association strength index, the social network diagram of the Chinese Library is processed to obtain the association strength value between each subject classification and the target technical field; The China Library Social Network Graph is processed based on the network co-occurrence intensity index to identify subject classification pairs with strong correlation in the target technology field.

[0009] In an embodiment of the first aspect, the classification association strength index is used to characterize the weighted degree centrality of each node in each social network graph, and the calculation formula is: in, i represents a specific node in a social network graph. j Representation and Node i Nodes with co-occurrence relationships, e ij Indicates the number of co-occurrences between two nodes; x ij Representation Node j With Node i 1 indicates a direct connection, and 0 indicates no direct connection.

[0010] In an embodiment of the first aspect, after obtaining the association strength values ​​of each IPC classification and the target technical field, an IPC classification node whose association strength value is greater than an association threshold is selected, or a front node with the largest association strength value is selected. N IPC classification nodes, which are IPC classification numbers that are strongly associated with the target technology field; After obtaining the association strength values ​​of each subject classification and the target technical field, select the subject classification node with an association strength value greater than the association threshold or select the first subject classification node with the largest association strength value. NA subject classification node is used as a subject classification number that is strongly associated with the target technical field.

[0011] In an embodiment of the first aspect, the network co-occurrence strength index is used to characterize the co-occurrence frequency of two classification nodes in the same scientific and technological information, and the calculation formula is: in, e ij Indicates the number of co-occurrences between two nodes.

[0012] In an embodiment of the first aspect, the step of processing the IPC social network graph based on the network co-occurrence strength index to identify IPC classification industry pairs with strong correlation in the target technology field includes: The IPC social network graph is processed based on the network co-occurrence strength index to obtain the network co-occurrence strength value of any two IPC classification nodes; Select the IPC classification node pair whose network co-occurrence strength value is greater than the co-occurrence threshold or select the top node with the largest network co-occurrence strength value. N IPC classification node pairs as strongly related industry pairs in the target technology field; The steps of processing the Chinese Library Social Network Graph based on the network co-occurrence intensity index and identifying the subject classification pairs with strong correlation in the target technology field include: Based on the network co-occurrence intensity index, the social network graph of the Chinese Library is processed to obtain the network co-occurrence intensity value of any two subject classification nodes; Select the subject classification node pairs whose network co-occurrence intensity value is greater than the co-occurrence threshold or select the top node pairs with the largest network co-occurrence intensity value. N A pair of subject classification nodes is used as a strongly associated subject pair in the target technology field.

[0013] A second aspect of the present invention provides a device for identifying industries related to a target technical field based on scientific and technological information, comprising: The adjacency matrix construction module is used to obtain patent data in various technical fields of the target industry, and construct an adjacency matrix representing the technical association relationship based on the IPC classification number of each patent data; A social network graph generation module is used to generate an IPC social network graph reflecting technical relevance based on an adjacency matrix; A classification association strength acquisition module is used to process the IPC social network diagram based on the classification association strength index to obtain the association strength value between each IPC classification and the target technical field; The network co-occurrence intensity value acquisition module is used to process the IPC social network graph based on the network co-occurrence intensity index and identify the IPC classification industry pairs with strong correlation in the target technology field.

[0014] In order to achieve the above-mentioned purpose, the third aspect of the present invention provides a computer device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor is used to implement the method for identifying related industries in the target technology field based on scientific and technological information as mentioned in the first aspect when executing the program stored in the memory.

[0015] In order to achieve the above objectives, the fourth aspect of the present invention provides a readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the above-mentioned method for identifying related industries in the target technology field based on scientific and technological information.

[0016] In order to achieve the above-mentioned purpose, the fifth aspect of the present invention provides a computer program product, including a computer program, which implements the steps of the method in any one of the above-mentioned embodiments when executed by a processor.

[0017] The beneficial effects of the present invention are: The present invention starts with "point intelligence" information, comprehensively utilizes the IPC classification association network, identifies IPC classification categories with significant co-occurrence intensity, thereby exploring the potential connections between core industries and revealing the deep structure of industrial associations; and comprehensively utilizes the Chinese Library Classification Association Network to identify Chinese Library Classification categories with significant co-occurrence intensity, thereby exploring the potential connections between core disciplines and revealing the deep structure of discipline associations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 1 is a flow chart of a method for identifying industries associated with a target technical field based on scientific and technological information in Embodiment 1 of the present invention; Figure 2 2 is a flow chart of a method for identifying related disciplines in a target technical field based on scientific and technological information in Embodiment 2 of the present invention; Figure 3 is a schematic diagram of the structure of a device for identifying related disciplines in a target technical field in Embodiment 3 of the present invention; Figure 4 It is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0019] The IPC system is a patent classification method widely recognized worldwide. It not only facilitates patent search and management, but also helps analysts identify and classify industries and their characteristics related to economic activities. In addition, the IPC is also an important bridge connecting science and technology with economic data for related statistics and analysis.

[0020] Specifically, each patent is assigned at least one IPC classification number depending on the technology involved. The academic community and the field of intellectual property management usually regard the appearance of multiple IPC classification numbers in the same patent as evidence of technological convergence, the so-called co-occurrence relationship. Frequently co-occurring IPC classification numbers reveal the close connection between the represented technologies. Therefore, by analyzing the IPC classification numbers and their relationships in a large number of patent samples, a network-like relationship between a wide range of technologies can be revealed. Further analysis of these technology fusion data, construction and understanding of the connection map between different classifications in the field of tobacco technology is crucial to grasping the development context of industry technology.

[0021] The technical solution of the present invention is further described in detail below through specific implementation methods.

[0022] Example 1 A first aspect of the present invention provides a method for identifying related industries in a target technical field based on scientific and technological information, comprising the following steps: Step S1, obtain patent data in the target technical field, and construct an adjacency matrix representing the technical association relationship according to the IPC classification number of each patent data.

[0023] Specifically, this embodiment selects the subclass classification number in the IPC system as the target IPC classification number.

[0024] Step S2, generating an IPC social network graph reflecting technical relevance based on the adjacency matrix.

[0025] This step can be performed in a conventional manner to generate an IPC social network graph reflecting technical relevance based on an adjacency matrix.

[0026] Taking new cigarette technology as an example, the categories involved are wide-ranging, and the IPC social network diagram constructed is as follows: Figure 2 As shown, it includes tobacco categories (such as A24F smokers' supplies, A24D cigars, cigarettes, etc.), and electronic device categories (including circuit devices or systems such as H05B electric heating, H02J power supply or distribution, etc.

[0027] In order to further grasp the key related industries, this embodiment further performs subsequent steps to obtain the association strength value between each IPC classification and the target technology field.

[0028] Step S3, processing the IPC social network diagram based on the classification association strength index to obtain the association strength value between each IPC classification and the target technical field.

[0029] It should be noted that the original degree centrality index is the most direct measurement index to characterize the centrality of a node in network analysis. The larger the node degree of a node, the higher the degree centrality of the node, and the more important the node is in the network. The degree centrality index calculation formula is shown in formula 1, where i represents a specific node in a social network graph. j Representation and Node i Nodes with co-occurrence relationship, xij represents the node j With Node i The degree centrality index is the sum of the number of other nodes that have a direct connection with a specific node.

[0030] (1) Although this calculation method is often used to select some nodes with particularly prominent importance from a series of nodes with co-occurrence relationships in the network, this calculation method ignores the difference in co-occurrence frequency. For example, if node A has a direct connection with nodes B and C, and the co-occurrence frequency with B is 5, while the co-line frequency between A and C is 1, the degree centrality index cannot reflect the difference in co-occurrence frequency between B and C.

[0031] The calculation method of weighted degree centrality is then generated: (2) in, e ij Indicates the number of co-occurrences between two nodes.

[0032] Still taking the above node A as an example, it can be seen that the weighted degree centrality of node A is 6=1*5+1*1.

[0033] Further, after obtaining the association strength values ​​of each IPC classification and the target technical field, select the IPC classification node with an association strength value greater than the association threshold or select the top node with the largest association strength value. N IPC classification nodes are used as IPC classification numbers that are strongly associated with the target technology field.

[0034] Verification: Specifically, the domestic patent technology of e-cigarettes in the past 10 years is used as the first verification data set for verification. According to the related industry identification scheme in the previous part, the industrial correlation strength of domestic patent technology of e-cigarettes is calculated, as shown in Table 1.

[0035] Table 1 IPC classification correlation strength of domestic patent technologies for electronic cigarettes (Top 10) IPC IPC meaning Association strength F16B A device, such as a nail, bolt, circlip, clamp, clip, or wedge, used to fasten or secure a component or machine part; a connector or fastener 4510 B65G Transport or storage devices, such as loading or unloading conveyors, shop conveyor systems or pneumatic tube conveyors 986 A24F Smokers' articles; matchboxes; simulated smoking devices (inhalation devices for medical purposes, shaped like cigars, cigarettes or pipes A61M15 / 06) Subclass index Pipes; mouthpieces, general structure A24F1 / 00 Special pipes A24F1 / 28, A24F1 / 30, A24F3 / 00 Tobacco bowls; mouthpieces A24F5 / 00; A24F7 / 00 Accessories; flavoring A24F9 / 00; A24F11 / 00 Utensils for smoking cigars, cigarettes or simulated smoking devices A24F13 / 00, A24F15 / 00, A24F17 / 00 Match containers or boxes A24F27 / 00, A24F29 / 00 Electrically operated smoking devices A24F47 / 00 Simulated smoking devices, not electrically operated A24F42 / 00 Other smokers' articles A24F19 / 00-A24F25 / 00, A24F31 / 00, A24F47 / 00 863 B41F Printing machinery or printing press 513 D21D Treatment of cooking raw materials before paper machine 459 D21J Fiberboard; articles made from suspensions of cellulose fibers or paper 315 D21C Production of cellulose by removing non-cellulose materials from cellulose-containing raw materials; Regeneration of pulping liquor; Apparatus required therefor 300 D21H Pulps or pulp compositions; preparation of pulp compositions not included in subclasses D21C or D21D; impregnation or coating of paper; processing of finished paper not included in class B31 or subclass D21G 293 A24D Cigars; cigarettes; oil filters; cigar or cigarette holders; manufacture of oil filters or cigarette holders 253 C08L Composition of polymer compounds 247 It can be seen that the F16B category has the highest weighted degree centrality value, which includes important electronic cigarette parts such as devices, connectors or connections used to fasten or fix components or machine parts. It has a prominent position among the many IPC categories included in the electronic cigarette industry.

[0036] Similarly, A24D (including the manufacture of cigars, cigarettes, oil filters and oil filters or cigarette holders, etc.), A24F (including various smokers' supplies, matchboxes, simulated smoking devices and other smoking-related devices and supplies, etc.) and B65G (including transportation or storage devices, etc.) all have relatively high weighted degree centrality values. These types of technologies, like F16B, are directly related to the manufacture of electronic cigarette devices, the preparation of electronic cigarettes, and the storage of electronic cigarettes. Among them, A24D includes the design and manufacture of electronic cigarette filters, pots, cigarette holders and accessories, and the design and manufacture of filters; A24F includes the technology of pipes, cigarette holders, pots, match containers or boxes, electric smoking devices, etc.; B65G includes the technology of filling compressed, liquefied or solidified gases into containers or discharging them from containers.

[0037] In summary, the top ten weighted degree centrality indexes are mainly concentrated in the following fields: electronic cigarette processing, electronic cigarette device manufacturing (such as mouthpieces, filters, heating devices, etc.), electronic cigarette transportation and storage technology, etc. The weighted degree centrality values ​​of the above categories are all over 240, occupying a core position in the IPC classification association network.

[0038] Step S4, processing the IPC social network graph based on the network co-occurrence intensity index to identify IPC classification industry pairs with strong correlation in the target technology field.

[0039] Specifically, the network co-occurrence intensity index is used to characterize the co-occurrence frequency of two classification nodes in the same scientific and technological information, and the calculation formula is: in, e ij Indicates the number of co-occurrences between two nodes.

[0040] Further, the steps of processing the IPC social network graph based on the network co-occurrence intensity index and identifying the IPC classification industry pairs with strong correlation in the target technology field include: The IPC social network graph is processed based on the network co-occurrence strength index to obtain the network co-occurrence strength value of any two IPC classification nodes; Select the IPC classification node pair whose network co-occurrence strength value is greater than the co-occurrence threshold or select the top node with the largest network co-occurrence strength value. N IPC classification node pairs as strongly related industry pairs in the target technology field.

[0041] The network co-occurrence strength index not only enhances the understanding of the industry correlation network, but also provides a new perspective for revealing the complex interactions between industries. According to the network co-occurrence strength index, the number of co-occurrences between two IPC categories is counted, so as to identify the IPC industry pairs with the highest IPC classification correlation strength.

[0042] Verification: The domestic patent technology of e-cigarettes in the past 10 years is still used as the first verification data set for verification. According to the related industry identification scheme in the previous part, the strongly related industry pairs of domestic patent technology of e-cigarettes are identified, as shown in Table 2.

[0043] Table 2 Strongly related industry pairs (TOP5) IPC Coding Network co-occurrence intensity Industrial meaning of IPC codes F16B-B65G 715 Devices for fastening or fixing components or machine parts, such as nails, bolts, circlips, clips, clamps or wedges; connectors or connectors - transport or storage devices, such as conveyors for loading or unloading, plant conveyor systems or pneumatic tube conveyors F16B-A24F 376 Devices for fastening or fixing components or parts of machines, such as nails, bolts, circlips, clips, clamps or wedges; fasteners or connections - smokers' articles; matchboxes; simulated smoking devices (inhalation devices for medical purposes, which are shaped like cigars, cigarettes or pipes into A61M15 / 06) Subclass Index Pipes; cigarette holders, general structure A24F1 / 00 Special pipes A24F1 / 28, A24F1 / 30, A24F3 / 00 Tobacco bowls; cigarette holders A24F5 / 00; A24F7 / 00 Accessories; seasonings A24F9 / 00; A24F11 / 00 Equipment for smoking cigars, cigarettes or simulated smoking devices A24F13 / 00, A24F15 / 00, A24F17 / 00 Match containers or boxes A24F27 / 00, A24F29 / 00 Electric smoking devices A24F47 / 00 Non-electrically operated simulated smoking devices A24F42 / 00 Other smokers' articles A24F19 / 00-A24F25 / 00, A24F31 / 00, A24F47 / 00 B41F-A24F 371 Printing machinery or printing presses - articles for smokers; matchboxes; simulated smoking devices (inhalation devices for medical purposes, shaped like cigars, cigarettes or pipes A61M15 / 06) Subclass Index Pipes; cigarette holders, general construction A24F1 / 00 Special pipes A24F1 / 28, A24F1 / 30, A24F3 / 00 Tobacco bowls; cigarette holders A24F5 / 00; A24F7 / 00 Accessories; flavourings A24F9 / 00; A24F11 / 00 Articles for smoking cigars, cigarettes or simulated smoking devices A24F13 / 00, A24F15 / 00, A24F17 / 00 Match containers or boxes A24F27 / 00, A24F29 / 00 Electric smoking devices A24F47 / 00 Simulated smoking devices, non-electrically operated A24F42 / 00 Other smokers' articles A24F19 / 00-A24F25 / 00, A24F31 / 00, A24F47 / 00 D21D - D21J 322 Processing of cooking stock before papermaking - Fiberboard; Articles made from cellulose fiber suspension or paper stock D21D - D21C 213 Processing of cooking raw materials before papermaking machine - production of cellulose by removing non-cellulose materials from cellulose-containing raw materials; regeneration of pulping liquor; required equipment It can be seen that F16B-B65G, F16B-A24F, B41F-A24F, D21D-D21J and D21D-D21C have the highest correlation strength, and the industry categories involved include fasteners, transportation or storage equipment, smoking supplies, printing machinery, fiberboard, etc.

[0044] Example 2 The difference between this embodiment and Embodiment 1 is that a method for identifying related disciplines in a target technical field is provided based on paper data in the target technical field.

[0045] Specifically, the paper data in the target technical field is obtained, and an adjacency matrix representing the technical association relationship is constructed according to the China Library Classification Number of each paper data; specifically, the paper data includes conference papers and journal papers; usually, the paper data includes abstracts, keywords, China Library Classification Number, document identification code and citation format, etc.; among them, the China Library Classification Number is a representative large-scale comprehensive classification system compiled and published after the founding of the People's Republic of China. It is the most widely used classification system in domestic libraries today and can reflect subject categories.

[0046] Generate a Chinese social network graph reflecting technology correlation based on the adjacency matrix.

[0047] The China Library Social Network Diagram was processed based on the classification association strength index to obtain the association strength value between each subject classification and the target technical field.

[0048] The China Library Social Network Graph is processed based on the network co-occurrence intensity index to identify subject classification pairs with strong correlation in the target technology field.

[0049] Specifically, the steps of processing the Chinese Library Social Network Diagram based on the network co-occurrence intensity index and identifying the subject classification pairs with strong correlation in the target technology field include: Based on the network co-occurrence intensity index, the social network graph of the Chinese Library is processed to obtain the network co-occurrence intensity value of any two subject classification nodes; Select the subject classification node pairs whose network co-occurrence intensity value is greater than the co-occurrence threshold or select the top node pairs with the largest network co-occurrence intensity value. N A pair of subject classification nodes is used as a strongly associated subject pair in the target technology field.

[0050] It should be noted that the classification association strength index and the network co-occurrence strength index are as described in Example 1, and will not be described in detail here.

[0051] Example 3 Based on the same inventive concept, the embodiment of the present application also provides a device for identifying industries associated with target technology fields based on scientific and technological information, which is used to implement the above-mentioned method for identifying industries associated with target technology fields based on scientific and technological information. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for identifying industries associated with target technology fields based on scientific and technological information provided below can be referred to the limitations of the method for identifying industries associated with target technology fields based on scientific and technological information above, and will not be repeated here.

[0052] The target technology field-related industry identification device based on scientific and technological information includes: The adjacency matrix construction module is used to obtain patent data in various technical fields of the target industry, and construct an adjacency matrix representing the technical association relationship based on the IPC classification number of each patent data; A social network graph generation module is used to generate an IPC social network graph reflecting technical relevance based on an adjacency matrix; A classification association strength acquisition module is used to process the IPC social network diagram based on the classification association strength index to obtain the association strength value between each IPC classification and the target technical field; The network co-occurrence intensity value acquisition module is used to process the IPC social network graph based on the network co-occurrence intensity index and identify the IPC classification industry pairs with strong correlation in the target technology field.

[0053] Furthermore, it should be noted that when the target technology field-related industry identification device based on scientific and technological information processes the paper data in various technical fields of the target industry, then: The adjacency matrix construction module is used to construct an adjacency matrix representing the technical association relationship based on the Chinese Library Classification Number of each paper data; A social network graph generation module is used to generate a Chinese social network graph reflecting the technical association based on the adjacency matrix; The classification association strength acquisition module is used to process the Chinese social network diagram based on the classification association strength index to obtain the association strength value between each subject classification and the target technical field; The network co-occurrence intensity value acquisition module is used to process the China Library Social Network Diagram based on the network co-occurrence intensity index and identify the subject classification pairs with strong correlation in the target technology field.

[0054] Example 4 This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, the method for identifying industries associated with the target technology field based on scientific and technological information mentioned in Example 1 or Example 2 is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0055] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0056] Example 5 This embodiment provides a readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method for identifying related industries in a target technology field based on scientific and technological information as described in Embodiment 1 or Embodiment 2.

[0057] Example 6 This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for identifying related industries in a target technology field based on scientific and technological information described in Embodiment 1 or Embodiment 2 is implemented.

[0058] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0059] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0060] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A method for identifying related industries in a target technology field based on scientific and technological information, characterized in that: The following steps are involved: Obtain patent data in the target technology field, and construct an adjacency matrix representing technology associations based on the IPC classification number of each patent data; Generate an IPC social network graph reflecting technology relevance based on the adjacency matrix; The IPC social network graph is processed based on the classification association strength index to obtain the association strength value between each IPC classification and the target technology field; The IPC social network graph is processed based on the network co-occurrence intensity index to identify IPC classification industry pairs with strong correlation in the target technology field.

2. According to claim 1, a method for identifying related industries in a target technology field based on scientific and technological information is characterized in that: The following steps are also included: Obtain the paper data in the target technology field, and construct an adjacency matrix representing the technical association relationship based on the Chinese Library Classification Number of each paper data; Generate a Chinese social network graph reflecting technology relevance based on the adjacency matrix; Based on the classification association strength index, the social network diagram of the Chinese Library is processed to obtain the association strength value between each subject classification and the target technical field; The China Library Social Network Graph is processed based on the network co-occurrence intensity index to identify subject classification pairs with strong correlation in the target technology field.

3. A method for identifying related industries in a target technology field based on scientific and technological information according to claim 1 or 2, characterized in that: The classification association strength index is used to characterize the weighted degree centrality of each node in each social network graph, and the calculation formula is: in, i represents a specific node in a social network graph. j Representation and Node i Nodes with co-occurrence relationships, e ij Indicates the number of co-occurrences between two nodes; x ij Representation Node j With Node i 1 indicates a direct connection, and 0 indicates no direct connection.

4. The method for identifying related industries in a target technology field based on scientific and technological information according to claim 3 is characterized in that: After obtaining the association strength values ​​of each IPC classification and the target technical field, select the IPC classification node with an association strength value greater than the association threshold or select the top node with the largest association strength value. N IPC classification nodes, which are IPC classification numbers that are strongly associated with the target technology field; After obtaining the association strength values ​​of each subject classification and the target technical field, select the subject classification node with an association strength value greater than the association threshold or select the first subject classification node with the largest association strength value. N A subject classification node is used as a subject classification number that is strongly associated with the target technical field.

5. A method for identifying related industries in a target technology field based on scientific and technological information according to claim 1 or 2, characterized in that: The network co-occurrence intensity index is used to characterize the co-occurrence frequency of two classification nodes in the same scientific and technological information, and the calculation formula is: in, e ij Indicates the number of co-occurrences between two nodes.

6. A method for identifying related industries in a target technology field based on scientific and technological information according to claim 5, characterized in that: The steps of processing the IPC social network graph based on the network co-occurrence intensity index and identifying the IPC classification industry pairs with strong correlation in the target technology field include: The IPC social network graph is processed based on the network co-occurrence strength index to obtain the network co-occurrence strength value of any two IPC classification nodes; Select the IPC classification node pair whose network co-occurrence strength value is greater than the co-occurrence threshold or select the top node with the largest network co-occurrence strength value. N IPC classification node pairs as strongly related industry pairs in the target technology field; Alternatively, the steps of processing the Chinese Library Social Network Diagram based on the network co-occurrence intensity index and identifying the subject classification pairs with strong correlation in the target technology field include: Based on the network co-occurrence intensity index, the social network graph of the Chinese Library is processed to obtain the network co-occurrence intensity value of any two subject classification nodes; Select the subject classification node pairs whose network co-occurrence intensity value is greater than the co-occurrence threshold or select the top node pairs with the largest network co-occurrence intensity value. N A pair of subject classification nodes is used as a strongly associated subject pair in the target technology field.

7. A device for identifying industries related to a target technology field based on scientific and technological information, characterized in that: include: The adjacency matrix construction module is used to obtain patent data in various technical fields of the target industry, and construct an adjacency matrix representing the technical association relationship based on the IPC classification number of each patent data; A social network graph generation module is used to generate an IPC social network graph reflecting technical relevance based on an adjacency matrix; A classification association strength acquisition module is used to process the IPC social network diagram based on the classification association strength index to obtain the association strength value between each IPC classification and the target technical field; The network co-occurrence intensity value acquisition module is used to process the IPC social network graph based on the network co-occurrence intensity index and identify the IPC classification industry pairs with strong correlation in the target technology field.

8. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the method for identifying related industries in a target technical field based on scientific and technological information as described in any one of claims 1 to 6 when executing the program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying related industries in a target technical field based on scientific and technological information as described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for identifying related industries in a target technical field based on scientific and technological information as described in any one of claims 1 to 6 are implemented.

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