Inter-event influence assessment method, system and device

Abstract

The invention discloses an inter-event influence assessment method, system and device, and the method comprises the following steps: fusing multi-source data, and constructing a causal event-event graph; the multi-source data at least comprises patent data and paper data; calculating the sum of the weights of the edges in the technical evolution path of the event atlas through a dynamic planning method, and extracting and quantifying the technical evolution path; calculating the weighting influence degree through the event weight and weighting degree centrality, and quantifying the influence between technical events in the evolution path; the inter-event influence in the technical evolution path is expressed in a weighting matrix form; according to the method, the causal relationship in the technology evolution process is quantified, and the technology evolution rule is deeply analyzed.

Description

Technical Field

[0001] The present invention relates to a method, system and device for evaluating influence between events. Background Art

[0002] By evaluating the influence of events in the technology evolution path, we can have a deeper understanding of which technology events have a key impact on innovation and development, such as which technology events are key nodes, which events are turning points, and which events promote the evolution of technology. This is a useful exploration for deepening the research on technology evolution paths.

[0003] Current research mainly identifies technology evolution paths by building relationships such as common categories, co-occurrences, citations, and semantic similarities through patent classification, patent citations, patent texts, and the integration of multi-source data such as patents and papers. However, this identification method lacks the ability to judge technology evolution paths from a causal logic perspective, and does not analyze the impact of scientific and technological events in the path. Therefore, it is difficult to deeply understand the laws of technology evolution and cannot accurately evaluate the influence of events. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method, system and device for evaluating the influence of events, which deeply analyzes the laws of technological evolution and quantifies the cause-effect relationship in the process of technological evolution.

[0005] A method for evaluating the impact of events, the method comprising the following steps: Integrate multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map; Calculating the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, extracting and quantifying the technology evolution path; The weighted influence is calculated using the event's causal weight and weighted degree centrality to quantify the influence between technical events in the evolutionary path; the influence between technical events in the technical evolutionary path is represented in the form of a weighted matrix.

[0006] In some implementations, the weighted influence is calculated by the event's event weight and weighted degree centrality to quantify the influence between technical events in the evolution path, including: According to the weighted in-degree centrality and weighted out-degree centrality of events in the technology evolution path, the weighted centrality of each event is calculated; Combining the event weights and event weighted centrality, we get the weighted influence. A weighted matrix is ​​constructed according to the weighted influence of events in the technology evolution path, and each element in the weighted matrix represents the weighted influence of events in the technology evolution path.

[0007] In some embodiments, the weighted influence is calculated using the event's event weight and weighted degree centrality:

[0008] in, and The events and The value of the weighted degree centrality of For events About the event The weight of reason.

[0009] In some implementations, the weighted centrality of each event is calculated as follows:

[0010] in, is the weight parameter, 0< <1; For events The weighted in-degree centrality of For events The weighted out-degree centrality of .

[0011] In some embodiments, constructing a causal event-reasoning graph includes: Extracting the multi-source data, screening and identifying event pairs with causal relationships, and forming a data set containing causal event pairs; Generate an event graph according to the causal event pair; in the event graph, nodes are events, and each edge in the event graph is a causal relationship between events; Event clustering is generalized to construct a cause-and-effect graph; in the cause-and-effect graph, nodes are causes and each edge in the cause-and-effect graph is a causal relationship between causes and effects.

[0012] In some embodiments, the sum of the weights of the edges in the technology evolution path of the event graph is calculated by a dynamic programming method, including: Starting from the source point of each edge, the event graph is traversed in a breadth-first manner along the direction of technological evolution; Each time an event node is visited, the sum of the weights of the edges from the source point to the node is calculated and recorded as the first path strength; Taking the visited event node as the starting point, continue the breadth traversal; During the breadth traversal process, if the event node has been visited, the strength of the currently visited path is recorded as the second path strength, and the first path strength is compared with the second path strength, and the larger value of the two is taken as the current path strength; Until all technology evolution paths are iteratively traversed.

[0013] In some embodiments, quantifying the influence of technology events in an evolutionary path includes: According to the row order analysis of the weighted matrix, the influence of events in the technology evolution path is determined according to the size of each element in the weighted matrix.

[0014] The present invention also provides an event-to-event influence assessment system, comprising: A causal event-matter map construction module integrates multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map; A weight calculation module, which calculates the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, and extracts and quantifies the technology evolution path; The event influence calculation module calculates the weighted influence through the event's causal weight and weighted degree centrality, and quantifies the influence between events in the evolution path; the influence between events in the technical evolution path is in the form of a weighted matrix.

[0015] The present invention also provides an event-to-event influence assessment device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for evaluating the influence between events as described above is implemented.

[0016] Beneficial effects: The present invention provides a method, system and device for evaluating the influence of events, which can calculate the weighted influence of events, construct a weighted matrix, quantify the influence of events, help identify key nodes and turning points in technological development, provide a scientific basis for technological decision-making and planning, deeply analyze the law of technological evolution, realize the quantification of causal relationships in the process of technological evolution, and improve the accuracy and reliability of technological evolution path analysis. The application of this method can promote the innovative development of the technology field, and by identifying events with strong influence, technology research and development and innovation can be carried out more targetedly. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of an event-to-event influence assessment method provided by the present invention; Figure 2 A schematic flow chart of step S1 of a method for evaluating the influence of events provided by the present invention; Figure 3 This is a flow chart of step S3 of a method for evaluating influence between events provided by the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0019] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products or apparatus.

[0020] like Figure 1 As shown, the present invention provides a method for evaluating the influence between events, deeply analyzes the laws of technological evolution, and quantifies the cause-effect relationship in the process of technological evolution.

[0021] Specifically, the method comprises the following steps: Step S1, integrating multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map.

[0022] like Figure 2 As shown, step S1 also includes: Step S11: extract the multi-source data, screen and identify event pairs with causal relationships, and form a data set containing causal event pairs.

[0023] Exemplarily, the method of screening and identifying causal event pairs can be implemented using template matching or a large language model. Using the Jieba word segmentation tool, with the help of a self-built scientific paper keyword thesaurus, preliminarily screen patents and scientific paper texts, retain sentences with event entities, and remove sentences that have no significance for subsequent analysis. The screened sentences use Stanfordparser to generate a syntactic tree to analyze and reveal the deep structure of the sentences. Match the syntactic analysis results to the rule template to identify and extract event pairs with obvious causal relationships. More specifically, the syntactic analysis results can be imported into Excel, and the if statement can be used to match the rule template. For the remaining sentences, Excel is used to screen high-frequency words, and semantic judgment is used to identify and extract events containing fuzzy causal relationships.

[0024] Alternatively, the causal event and relationship extraction can be converted into a question-answering task through the large language model ChatGLM. A concise and practical Prompt instruction template is designed to maximize the extraction potential of the large model, control the model illusion, and enable it to perform better in the One-Shot task. Technical words are marked as causes, and functional words and effect words are marked as effects. Technology-function / effect is a causal relationship. This marking method allows the large model to clearly know the meaning of causal relationships.

[0025] Step S12: generating an event graph according to the causal event pairs; the nodes in the event graph are events, and each edge in the graph is a causal relationship between events.

[0026] The causal event pairs extracted in step S11 are imported into the graph tool to generate an event graph. In the event graph, nodes represent events, edges represent causal relationships between events, and the weights of edges are determined by the normalized number of occurrences of the causal event pairs.

[0027] Step S13, clustering and generalizing the events to construct a cause-and-effect graph; in the cause-and-effect graph, the nodes are causes and each edge in the cause-and-effect graph is a causal relationship between causes and effects.

[0028] The Bert model is combined with the K-means algorithm to generalize event clustering and construct a cause-and-effect graph. In the clustering process, several groups of comparative experiments with different configurations are usually conducted. The performance indicators such as accuracy, recall, and F1 value after clustering with different numbers of clusters are calculated to find the optimal number of clusters. In the cause-and-effect graph, each node represents a cause and effect, and each edge represents the causal relationship between causes and effects. The edge weight calculation method is the same as the event graph, which is calculated based on the frequency of occurrence of the causal relationship to reveal the strength of the causal logic between different causes and effects.

[0029] Step S2: Calculate the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, and extract and quantify the technology evolution path.

[0030] The dynamic programming method is used to calculate the sum of the weights of the edges in the technology evolution path to extract and quantify the technology evolution path. The specific method of dynamic programming is to start from the source point of each edge and perform a breadth-first traversal of the event graph along the direction of technology evolution; each time an event node is visited, the sum of the weights of the edges between the source point and the node is calculated and recorded as the first path strength; starting from the visited event node, the breadth traversal continues; during the breadth traversal process, if the event node has been visited, the current path strength is recorded as the second path strength, and the first path strength is compared with the second path strength, and the larger value of the two is taken as the current path strength; until all technology evolution paths are iteratively traversed. This method extracts and quantifies the technology evolution path, which facilitates the sorting of technology evolution paths.

[0031] Step S3, using the event's causal weight and weighted degree centrality to calculate the weighted influence, quantifying the influence between technical events in the evolution path; the influence between technical events in the technical evolution path is represented in the form of a weighted matrix.

[0032] like Figure 3 As shown, step S3 also includes: Step S31, calculating the weighted centrality of each event according to the weighted in-degree centrality and weighted out-degree centrality of the event in the technology evolution path; Calculate the weighted centrality of each event, specifically:

[0033] in, is the weight parameter, 0< <1; For events The weighted in-degree centrality of For events The weighted out-degree centrality of .

[0034] Weight Parameters is a weight parameter between 0 and 1, Different values ​​make the event in-degree or out-degree have different effects on weighted degree centrality. For example, the weight parameter can be assigned 0.3, 0.5, and 0.7. When it is found that there is no impact on the event sorting, it means that the in-degree or out-degree is equally important, and α takes the value of 0.5.

[0035] In multi-source data such as patent data and paper data, it can be understood that the out-degree is the number of events that a certain event is the cause of other events. The out-degree reflects the number of causal relationships emanating from the event, that is, how many events are caused by the event. For example, if event A causes event B and event C, then the out-degree of event A is 2. The in-degree indicates the number of other events that a certain event is caused by. The in-degree reflects the number of causal relationships to the event, that is, how many events cause the event. For example, if event D is caused by event A and event C, then the in-degree of event D is 2. Out-degree centrality and in-degree centrality The out-degree and in-degree are used for calculation respectively. Among them, the event in-degree centrality is calculated by the following formula:

[0036] m is the total number of events in the network, that is, the total number of network nodes, m-1 is the maximum possible degree of the event, The number of in-degree connections a node has.

[0037] Similarly, the event out-degree centrality is calculated by the following formula:

[0038] The number of out-degree connections a node has.

[0039] Out-degree centrality indicates that an event, as a "cause" in a causal chain, affects other events in the network. Therefore, the higher the out-degree centrality, the more influence the event has on other events in the network, reflecting its importance as a "cause" or "driving factor". In-degree centrality means that an event is cited or influenced by other events as a "result" in a causal chain. The higher the in-degree centrality, the more influence the event has on other events.

[0040] Step S32: Combining the event weights between events with the weighted centrality of the events to obtain the weighted influence; The weighted influence is

[0041] in, and The events and The value of the weighted degree centrality of For events About the event The value of the event weight is determined by referring to the node relationship strength in the complex system theory, which is the same as the calculation method of the weight of causal events in the event map. According to the complex system theory proposed by scholar S. Boccaletti, the strength of the relationship between network nodes is determined by the degree of interaction and dependence between them. Therefore, in this application, it is believed that the event weight between events depends on the weight between the event logic associated with these events. Specifically, the event weight is calculated by the following formula:

[0042] In the logic diagram, Indicates cause and effect The frequency of causal relationships in the data set reflects the specific reasoning. As a cause, reason As a result, the number of associations. It should be noted that if two events belong to the same cause, the cause weight between them is set to the maximum value of 1. This is because events within the same cause have close internal connections and share the same technical basis or goals. The influence and connection between them are usually stronger than between events of different causes. Therefore, giving the events within the same cause the maximum cause relationship weight helps to accurately reflect the close connection and importance of events in the technological evolution path.

[0043] Step S33: construct a weighted matrix according to the weighted influence of events in the technology evolution path, wherein each element in the weighted matrix represents the weighted influence of events in the technology evolution path.

[0044] Schematically, the weighted influence The constructed weighting matrix for:

[0045] The rows and columns in the weighted matrix represent the events in the technology evolution path. , , …, , the elements in the matrix Indicates an event About the event According to the row order analysis of the weighted matrix, the influence between events in the technology evolution path is determined according to the size of each element in the weighted matrix.

[0046] It is worth noting that the elements of the weighting matrix With elements There is an unequal situation, that is, the weighted influence of event A on event B and event B on event A are different. The weighted matrix is asymmetric. This is because in the calculation of weighted influence, the weight of the event represents and Representative events About the event ,event About the event Since the interaction between technical events is usually directional, the impact of one event on another may be greater than the reverse impact, so the weights of events may be asymmetric.

[0047] The quantitative results of the influence of events in the technological evolution path are presented in the form of a weighted matrix, providing an in-depth interpretation of the implicit correlation between events in the technological evolution path.

[0048] Instructively, for example, path A contains five events, namely A1, A2, A3, A4, and A5. After calculations are completed through steps S1 to S3, the influence between any two events forms a two-dimensional weighted matrix. According to the analysis in the order of the matrix rows, for example, the influence value between A1 and A2, A3, and A4 technical events is significantly larger, and the influence value between A1 and A5 is smaller. In this case, it is believed that A1 may have driven the development of A2, A3, and A4, while having a relatively small impact on A5, thereby interpreting the implicit correlation between technical events.

[0049] The quantification results of the influence of events in the technical evolution path of this application are explained in conjunction with the following examples: Table 1 Weight of affairs

[0050] Table 2 Event weighted degree centrality

[0051] As shown in Tables 1 and 2, these are the calculation results of event weights and event weighted centrality.

[0052] Take the path with a larger weight "fingerprint recognition → fingerprint collection → optical equipment and technology → microscope technology and type → microscope" as an example and split it into fingerprint recognition , fingerprint collection , Optical Equipment and Technology , Microscopy Techniques and Types ,microscope Wait for 5 events.

[0053] Fingerprint recognition To fingerprint collection The weight of the cause and effect is the method and weight, that is, the cause and effect → the result and effect are the method and weight, such as No. 7 in Table 1, the weight of the cause and effect is 0.11. To fingerprint recognition It is number 2 in Table 1, and its weight is 0.46. Fingerprint recognition To Microscope Techniques and Types It is the serial number 11 in Table 1, and the event weight is 0.1. According to the above method, the event weights between events are determined in turn.

[0054] The event weighted centrality is obtained from Table 2. The event weight and event weighted centrality are used as parameters, and the weighted influence between events is calculated based on the weighted influence. The events are regarded as rows and columns, and the weighted influence between events is used as an element to form a weighted matrix .

[0055]

[0056] Through the weighting matrix It can be concluded that there are significant differences in the impact of events. "Mainly driven by "fingerprint collection "Optical Equipment and Technology ” and “Microscopy Techniques and Types The development of "microscope "The impact is relatively small. "Fingerprint collection " has a certain degree of influence on other events in the path, and on "microscope technology and type "The impact is particularly significant." Optical Equipment and Technology "It plays the role of bridging the middle of the path," Microscope technology and types "Only for "Fingerprint Collection "Make an Impact," Microscope " has relatively little impact on other events in the path. On this basis, combined with factors such as technology maturity, market demand and R&D investment, the formation mechanism of the influence between path events is explored, and the influence of each event on other events in the process of technological evolution is comprehensively and accurately interpreted, so as to understand the deep reasons behind these differences.

[0057] Based on the same inventive concept, the present application provides an event-to-event influence assessment system, including: A causal event-matter map construction module integrates multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map; A weight calculation module, which calculates the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, and extracts and quantifies the technology evolution path; The event influence calculation module calculates the weighted influence through the event's causal weight and weighted degree centrality, and quantifies the influence between events in the evolution path; the influence between events in the technical evolution path is in the form of a weighted matrix.

[0058] Based on the same inventive concept, the present application also provides an event-to-event influence assessment device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an event-to-event influence assessment method as described above is implemented.

[0059] The present invention provides a method, system and device for evaluating the influence of events, which helps to identify key nodes and turning points in technological development by quantifying the influence of events, and provides a scientific basis for technological decision-making and planning. It deeply analyzes the law of technological evolution, realizes the quantification of causal relationships in the process of technological evolution, and improves the accuracy and reliability of technological evolution path analysis. The application of this method can promote the innovative development of the technology field. By identifying events with strong influence, technological research and development and innovation can be carried out in a more targeted manner.

[0060] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the described method for assessing the influence between events.

[0061] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the described method for assessing the influence between events.

[0062] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment. Those of ordinary skill in the art may understand and implement the embodiments without creative work.

[0063] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0064] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for evaluating the impact of events. It is characterized in that The method comprises the following steps: Integrate multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map; Calculating the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, extracting and quantifying the technology evolution path; The weighted influence is calculated using the event's causal weight and weighted degree centrality to quantify the influence between technical events in the evolutionary path; the influence between technical events in the technical evolutionary path is represented in the form of a weighted matrix.

2. According to claim 1, a method for evaluating the influence of events, It is characterized in that The weighted influence is calculated by the event’s event weight and weighted degree centrality to quantify the influence between technical events in the evolution path, including: According to the weighted in-degree centrality and weighted out-degree centrality of events in the technology evolution path, the weighted centrality of each event is calculated; Combining the event weights and event weighted centrality, we get the weighted influence. A weighted matrix is ​​constructed according to the weighted influence of events in the technology evolution path, and each element in the weighted matrix represents the weighted influence of events in the technology evolution path.

3. According to the method for evaluating the influence of events between events, It is characterized in that The weighted influence is calculated using the event's logical weight and weighted degree centrality: in, and The events and The value of the weighted degree centrality of For events About Events The weight of reason.

4. The method for evaluating the influence of events according to claim 3, It is characterized in that The weighted centrality of each event is calculated as follows: in, is the weight parameter, 0< <1; For events The weighted in-degree centrality of For events The weighted out-degree centrality of .

5. The method for evaluating the influence of events according to claim 2, It is characterized in that Construct a causal event-reasoning map, including: Extracting the multi-source data, screening and identifying event pairs with causal relationships, and forming a data set containing causal event pairs; Generate an event graph according to the causal event pair; in the event graph, nodes are events, and each edge in the event graph is a causal relationship between events; Event clustering is generalized to construct a cause-and-effect graph; in the cause-and-effect graph, nodes are causes and each edge in the cause-and-effect graph is a causal relationship between causes and effects.

6. A method for evaluating the influence of events according to claim 5, It is characterized in that The sum of the weights of the edges in the technical evolution path of the event graph is calculated by a dynamic programming method, including: Starting from the source point of each edge, the event graph is traversed in a breadth-first manner along the direction of technological evolution; Each time an event node is visited, the sum of the weights of the edges from the source point to the node is calculated and recorded as the first path strength; Taking the visited event node as the starting point, continue the breadth traversal; During the breadth traversal process, if the event node has been visited, the strength of the currently visited path is recorded as the second path strength, and the first path strength is compared with the second path strength, and the larger value of the two is taken as the current path strength; Until all technology evolution paths are iteratively traversed.

7. The method for evaluating the influence of events according to claim 2, It is characterized in that Quantify the influence of technological events in the evolution path, including: According to the row order analysis of the weighted matrix, the influence size between events in the technology evolution path is determined according to the size of each element in the weighted matrix.

8. An event-to-event impact assessment system, It is characterized in that include: A causal event-matter map construction module integrates multi-source data to construct a causal event-matter map; the multi-source data includes at least patent data and paper data; the causal event-matter map includes an event map and a matter map; A weight calculation module, which calculates the sum of the weights of the edges in the technology evolution path of the event graph by a dynamic programming method, and extracts and quantifies the technology evolution path; The module for calculating the influence between events calculates the weighted influence through the event weight and weighted degree centrality, and quantifies the influence between events in the evolution path; The influence between events in the technology evolution path is in the form of a weighted matrix.

9. An event-to-event influence assessment device, It is characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the influence between events as described in any one of claims 1 to 7 when executing the computer program.

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