Entity analysis method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to an entity analysis method, electronic equipment and a storage medium, the method comprises the following steps: calculating the data similarity between a first to-be-analyzed entity and a second to-be-analyzed entity, if the name data of the two to-be-analyzed entities are the same, when the data similarity is greater than a first preset similarity threshold, determining that the first to-be-analyzed entity is the same as the second to-be-analyzed entity; when the data similarity is not greater than a first preset similarity threshold value and is greater than a second preset similarity threshold value, determining that the two entities to be analyzed are the same entity; obtaining a plurality of first target devices corresponding to the first to-be-analyzed entity and a plurality of second target devices corresponding to the second to-be-analyzed entity based on the historical activity areas and historical activity time periods corresponding to the two to-be-analyzed entities; determining that the two entities to be analyzed are the same entity; according to the invention, the same entity can be accurately identified from the knowledge graph.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an entity analysis method, electronic device, and storage medium. Background Technology

[0002] With the development of information technology, the scale of data generated by various web scraping, business registration, and public disclosure has become increasingly massive, forming a super-large-scale, multimodal entity information database. This database contains intricate networks of relationships between various entities such as enterprises, platforms, and target users. To improve usability, this data is often presented in the form of knowledge graphs. However, due to the large volume of data and the varying collection times and sources, the same entity may appear repeatedly in the knowledge graph under the same or different names. Therefore, accurately identifying the same entity from a large amount of data is crucial for data retrieval and statistical management. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an entity analysis method, electronic device, and storage medium, which can accurately identify the same entity from a knowledge graph, thus playing a positive role in subsequent knowledge graph updates and data statistical management.

[0004] According to a first aspect of the present invention, a solid analysis method is provided, comprising the following steps: Based on the obtained first entity to be analyzed, second entity to be analyzed, and the preset attribute parameter set corresponding to each entity to be analyzed, the data similarity between the preset attribute parameter sets corresponding to the first entity to be analyzed and the second entity to be analyzed is calculated.

[0005] If the name data corresponding to the first entity to be analyzed is the same as the name data corresponding to the second entity to be analyzed, and the data similarity is greater than the first preset similarity threshold, the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

[0006] When the data similarity is not greater than the first preset similarity threshold but greater than the second preset similarity threshold, the historical activity areas and corresponding historical activity periods of the first entity to be analyzed and the second entity to be analyzed within a preset time period before the current time are crawled from the given platform.

[0007] Obtain the first set of target devices of the first entity to be analyzed within the corresponding historical activity area and the corresponding historical activity period, and the second set of target devices of the second entity to be analyzed within the corresponding historical activity area and the corresponding historical activity period.

[0008] If it is determined that several first target devices in the first target device set and several second target devices in the second target device set have the same devices, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

[0009] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the entity analysis method described above.

[0010] According to a third aspect of the present invention, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0011] The present invention has at least the following beneficial effects: This invention provides an entity analysis method. First, it calculates the data similarity between preset attribute parameter sets corresponding to a first entity to be analyzed and a second entity to be analyzed. Then, it determines whether the names of the two entities to be analyzed are the same. If the names of the two entities to be analyzed are the same, and the data similarity is greater than a first preset similarity threshold, the two entities to be analyzed are determined to be the same entity. By comprehensively judging the two dimensions of name data and data similarity, the accuracy of the judgment result is improved. When the data similarity is not greater than the first preset similarity threshold but greater than the second preset similarity threshold, based on the historical activity areas and historical activity time periods corresponding to the two entities to be analyzed, several first target devices corresponding to the first entity to be analyzed and several second target devices corresponding to the second entity to be analyzed are obtained respectively. That is, when the data similarity is not high enough, further judgment is made. If it is determined that the first target device and the second target device have the same device, the two entities to be analyzed are determined to be the same entity. By judging whether there is a device in the same device, the determination of the same entity can provide another possibility for judging the same entity while ensuring privacy. Furthermore, judging the two entities to be analyzed as the same entity based on the data similarity being greater than the second preset similarity threshold also improves the credibility of the two entities to be analyzed being the same entity. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of the entity analysis method provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] This invention provides an entity analysis method, such as... Figure 1 As shown, the method includes the following steps: S100: Based on the acquired first entity to be analyzed, second entity to be analyzed, and the preset attribute parameter set corresponding to each entity to be analyzed, calculate the data similarity between the preset attribute parameter sets corresponding to the first entity to be analyzed and the second entity to be analyzed, respectively. This can be understood as: extracting parameter values ​​corresponding to several preset attribute indicators from each preset feature text, based on the acquired preset attribute feature text corresponding to the first entity to be analyzed and the second entity to be analyzed, respectively. For example, when the entity to be analyzed is a legal person or a platform user, several preset attribute indicators include, but are not limited to, basic personal information such as height, gender, education level, graduating institution, date of birth, and zodiac sign. Data similarity can be understood as semantic data similarity.

[0016] Preferably, the first and second texts to be analyzed are obtained through the following steps: S101, based on the preset attribute parameter set corresponding to each preset entity in the target knowledge graph, transform each preset attribute parameter set into a corresponding attribute parameter vector; this can be understood as: the preset entity is any entity belonging to the target type in the target knowledge graph in the preset database. For example, when the target knowledge graph includes information such as company name and legal person name, the preset entity is any legal person name.

[0017] S102, a pre-defined clustering model is used to cluster several attribute parameter vectors to obtain several attribute parameter vector clusters. For example, a k-means clustering model or an adaptive iterative clustering model can be used. Those skilled in the art are familiar with the specific implementation methods of these two clustering models, and will not be described in detail here.

[0018] S103, take any two attribute parameter vectors in any attribute parameter vector cluster as the first entity to be analyzed and the second entity to be analyzed.

[0019] As mentioned above, since there may be cases in the target knowledge graph where the same entity has different names due to different data acquisition sources, it is necessary to identify the same entity. However, through the clustering process described above, entities with similar information can be grouped into a cluster. Only entities within the same cluster need to be analyzed, without having to judge whether an entity is the same as all other entities, which greatly reduces the amount of data computation.

[0020] S200, if the name data corresponding to the first entity to be analyzed is the same as the name data corresponding to the second entity to be analyzed, and the data similarity is greater than a first preset similarity threshold, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity. Those skilled in the art can set the first preset similarity threshold according to actual needs, for example, 90%.

[0021] As mentioned above, since a node is usually used to represent an entity or object in a knowledge graph, when the name data of two entities to be analyzed are the same, it indicates that there is a certain probability that they are the same entity. However, in order to avoid coincidences, further judgment is required. Based on this, a comparison of the data similarity and threshold of the preset attribute parameter set is introduced. When the data similarity is high, the two entities to be analyzed are considered to be the same entity, which improves the accuracy of the judgment results.

[0022] S300, when the data similarity is not greater than a first preset similarity threshold but greater than a second preset similarity threshold, the historical activity areas and corresponding historical activity time periods of the first and second entities to be analyzed within a preset time period before the current moment are crawled from the given platform. For example, historical activity areas and historical activity time periods are searched from web pages or a given information platform. The historical activity area can be specific location information such as the address of an office building of a company, a hotel, or a factory. Those skilled in the art set the second preset similarity threshold according to actual needs.

[0023] As mentioned above, when the obtained preset attribute parameter information is incomplete or the data changes due to different data uploaded at different times, there may be cases where the data similarity is not high enough. In this case, further judgment is required, which improves the reliability of the judgment result.

[0024] Furthermore, the method also includes: When the data similarity is not greater than the second preset similarity threshold, it is determined that the first entity to be analyzed and the second entity to be analyzed are not the same entity.

[0025] S400, obtain the first set of target devices of the first entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period, and the second set of target devices of the second entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period.

[0026] When acquiring target devices in historical activity areas and historical activity periods, the system obtains all devices that were in the historical activity area during the historical activity period based on the anonymous location information uploaded by the devices.

[0027] As mentioned above, since the location information of the entity to be analyzed cannot be directly obtained, when the historical activity area and historical activity period are known, the first target device set contains the device corresponding to the first entity to be analyzed, and the second target device set contains the device corresponding to the second entity to be analyzed. By determining whether there is a device in the same set, another possibility can be provided for determining the same entity while ensuring privacy.

[0028] S500, if it is determined that some first target devices in the first target device set and some second target devices in the second target device set have the same devices, the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

[0029] In a preferred embodiment, the process for determining whether there are identical devices among a plurality of first target devices in the first target device set and a plurality of second target devices in the second target device set is as follows: S501, based on the unique identification codes corresponding to the first target device and the second target device respectively, if there is a common unique identification code between the unique identification codes corresponding to the first target device and the second target device, output the judgment result that there are several first target devices and several second target devices with the same device. For example, the unique identification code is any one of IMEI identification code, OAID identifier, and IDFV identifier, and the types of the unique identification codes corresponding to the first target device and the second target device are the same.

[0030] S502, when no judgment result is obtained indicating that several first target devices and several second target devices have the same device, the trajectory data of each first target device and each second target device in a preset historical time period is obtained. For example, due to insufficient permissions, it is impossible to obtain the unique identification code of all devices, or due to different systems, the unique identification code is displayed in a different way.

[0031] S503, calculate the similarity between the trajectory data of each first target device in the preset historical time period and the trajectory data of each second target device in the preset historical time period, and generate a trajectory similarity matrix.

[0032] The similarity calculation of trajectory data includes the following steps: S5031, calculate the spatial distance between the location points at corresponding time points of two trajectory data.

[0033] S5032, Based on the time series of the spatial distance, calculate the trajectory similarity between the two trajectory data. For example, the trajectory similarity between the two trajectories can be calculated using a dynamic time warping algorithm or a Fraser distance algorithm.

[0034] S504, based on a preset first trajectory similarity threshold, select target device pairs from the trajectory similarity matrix whose trajectory similarity is higher than the first trajectory similarity threshold; the first trajectory similarity threshold is set according to the duration of a preset historical period and the average device density corresponding to the preset historical period.

[0035] When setting the first trajectory similarity threshold, considering that the shorter the preset historical period (i.e., the observation time), the more likely the two trajectories are to be similar, the higher the probability of similarity, the more likely the first trajectory similarity threshold should be. Therefore, the first trajectory similarity threshold should be inversely proportional to the preset historical period. In densely populated areas such as city centers or train stations, the probability of two devices sharing the same trajectory is higher. To reduce the possibility of misjudgment, the first trajectory similarity threshold should be increased. Therefore, the first trajectory similarity threshold should be directly proportional to the average device density. Thus, normalization and other methods can be used to convert the duration of the preset historical period and the average device density to the same data volume, and then a weighted sum can be calculated based on the weights corresponding to the duration of the preset historical period and the average device density to obtain the first trajectory similarity threshold.

[0036] S505: Divide the preset historical time period into preset time windows. If the trajectory similarity of the first target device and the second target device in the target device pair is greater than a preset second trajectory similarity threshold in each time window, output the judgment result that several first target devices and several second target devices have the same device. Those skilled in the art can set the second trajectory similarity threshold according to actual needs, which will not be elaborated here.

[0037] As described above, when determining whether the same device exists in different areas at different times, the first consideration is to accurately identify the unique identification code of the device. If not all unique identification codes of the devices can be identified, another method is used, namely the trajectory data reported by the device. Device pairs with high trajectory similarity are first filtered out, and then the trajectory is segmented and the similarity of each small segment is compared separately. This improves the accuracy of trajectory similarity calculation and thus improves the reliability of the judgment result. Since the probability that the device trajectory is continuously similar to the activity location and activity time of a certain entity is very low, the identified identical devices are considered to be devices corresponding to two entities to be analyzed, which improves the credibility that the two entities to be analyzed are the same entity.

[0038] Furthermore, the confidence level of the judgment result output by step S505 is lower than that of the judgment result output by step S501. Staff can decide whether to conduct further verification based on the accuracy requirements. By identifying two entities to be analyzed that have the same judgment result, a data basis is also provided for further verification.

[0039] In another embodiment, the method further includes the following steps: S10, if the name data corresponding to the first entity to be analyzed is different from the name data corresponding to the second entity to be analyzed, and the semantics of the preset attribute parameter set corresponding to the first entity to be analyzed and the preset attribute parameter set corresponding to the second entity to be analyzed are completely consistent, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

[0040] S20, when the semantics of two preset attribute parameter sets are not completely consistent and the data similarity between the two preset attribute parameter sets is greater than the first preset similarity threshold, crawl the historical activity areas and corresponding historical activity periods of the first entity to be analyzed and the second entity to be analyzed within a preset time period before the current time from the given platform.

[0041] Furthermore, the method also includes: When the data similarity between the preset attribute parameter set corresponding to the first entity to be analyzed and the preset attribute parameter set corresponding to the second entity to be analyzed is not greater than the first preset similarity threshold, it is determined that the first entity to be analyzed and the second entity to be analyzed are not the same entity.

[0042] S30, obtain the first set of target devices of the first entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period, and the second set of target devices of the second entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period.

[0043] S40, if it is determined that some first target devices in the first target device set and some second target devices in the second target device set have the same devices, the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

[0044] The above analysis addresses the case where the names of two entities are different. When they are different, the probability of them belonging to the same entity is relatively low. Therefore, this increases the strictness of the judgment condition compared to the case where the names are the same, ensuring the reliability of the judgment result.

[0045] Furthermore, the method also includes the following steps: S1, if the first entity to be analyzed and the second entity to be analyzed are the same entity, retrieve several objects that have connection relationships with the first entity to be analyzed and the second entity to be analyzed from the target knowledge graph stored in the preset database. These objects include multiple types, and the first entity to be analyzed and the second entity to be analyzed are objects of the same type.

[0046] S2, associate and integrate several objects that are connected to the first entity to be analyzed and several objects that are connected to the second entity to be analyzed, and store them in a preset database.

[0047] By finding and storing related data for the same entity, positive effects have been achieved on updating the target knowledge graph and managing the statistical data.

[0048] Based on this, the method further includes the following steps: S3, set the association integration result in a preset hidden box in the preset presentation page; the association integration result includes several objects that are connected to the first entity to be analyzed, several objects that are connected to the second entity to be analyzed, and an association identifier.

[0049] S4: When a user clicks on a preset hidden box, the associated and integrated results will be presented according to the preset display area.

[0050] As mentioned above, when identifying entities with the same name or different names in the target knowledge graph as the same entity, while ensuring an intuitive display effect, the data complexity of the knowledge graph displayed on the page is limited and constrained by spatial arrangement. Therefore, it is not necessary to convert two entities into one entity on the page and connect the objects connecting the two entities to the converted entity. It is only necessary to store the relationship and pre-set it in a preset hidden box so that users can query according to their needs. This not only obtains the related data but also ensures the page presentation effect and user convenience.

[0051] In another embodiment, prior to analyzing entities in the target knowledge graph, a reasonable layout of nodes in the target knowledge graph can improve the clarity and readability of the target knowledge graph. Therefore, the method further includes the following steps: F100: Obtain several key nodes from a given knowledge graph; the key nodes refer to nodes in the given knowledge graph whose number of corresponding neighbor nodes is not less than n; this can be understood as: taking nodes whose number of connected neighbor nodes exceeds a certain threshold as the main position adjustment nodes.

[0052] Specifically, a given knowledge graph refers to nodes that include several predefined entity types. For example, when constructing a knowledge graph of corporate financing relationships or bidding relationships, there are two predefined entity types: enterprises and legal persons.

[0053] F200, based on the number of key nodes, divides the preset presentation page into an initial node grid containing several grid units and obtains the grid unit corresponding to each key node in the initial node grid; it can be understood that the preset presentation page refers to a page that is pre-built to present a given knowledge graph.

[0054] Specifically, dividing the preset presentation page into an initial node grid containing several grid units includes the following steps: F201 calculates the aspect ratio R of the preset page.

[0055] F202, set the number of rows and columns of the initial node grid as a and b respectively, traverse all values ​​of a from 1 to L, and calculate the value of b for each value; where b = roundup(L / a), roundup() is the round-up function, and L is the number of key nodes.

[0056] F203 obtains the values ​​of a and b that minimize the absolute difference between the mesh aspect ratio b / a and R, in order to obtain the initial node mesh after partitioning; this can be understood as: using the values ​​of a and b that minimize the absolute difference between the mesh aspect ratio b / a and R as the number of rows and columns of the initial node mesh after partitioning.

[0057] As mentioned above, when dividing the preset presentation page, the number of grid units was guaranteed to ensure that all key nodes could be assigned to the corresponding grid units. In addition, aspect ratio calculation was introduced to make the aspect ratio of the divided grid units as close as possible to the aspect ratio of the preset presentation page, thereby making the divided grid units more reasonable and improving the space utilization of the preset presentation page.

[0058] Furthermore, obtaining the corresponding mesh cell for each key node in the initial node mesh includes the following steps: F210 calculates the association strength between every two key nodes and constructs an association matrix. Those skilled in the art are familiar with how to calculate the association strength between any two nodes, so it will not be elaborated here.

[0059] F220, based on the aforementioned correlation matrix, uses a force-directed layout algorithm to calculate the two-dimensional coordinates corresponding to each key node. Those skilled in the art will understand the specific implementation steps for calculating the two-dimensional coordinates of nodes using the force-directed layout algorithm, and will not elaborate further here.

[0060] F230 normalizes the two-dimensional coordinates of each key node to the coordinate space of the initial node grid, and obtains the grid cell corresponding to each key node in the initial node grid; it can be understood as: assigning each key node to the grid cell where its normalized coordinates are located.

[0061] As described above, the grid cell that matches each key node is found by calculating the correlation strength between each pair of key nodes. The presentation positions of key nodes are distributed based on maximizing the use of the preset presentation page. Since key nodes are nodes with a large number of neighboring nodes, the presentation position of each key node is more reasonable by distributing the key node positions. Furthermore, key nodes with more connected nodes are preferentially assigned to the center of the initial node grid, thus optimizing the presentation layout of the knowledge graph.

[0062] F300 calculates the area allocation score for each key node based on the number of neighboring nodes of different preset entity types connected to each key node and the preset presentation area corresponding to each preset entity type node.

[0063] In one implementation, the preset display area corresponding to the node of the preset entity type is proportional to the importance of the preset entity type; this can be understood as: assigning importance to each preset entity type, the higher the importance, the larger the corresponding preset display area, and the preset display area corresponding to the node is generally a solid circle.

[0064] In another implementation, the preset display area corresponding to the node of the preset entity type is proportional to the average number of characters in the name of the node corresponding to the preset entity type. For example, company names are generally longer, while legal person names are generally shorter, so a larger preset display area is set for the node corresponding to the company.

[0065] Preferably, the area allocation score corresponding to the key node meets the following conditions: Where Q represents the area allocation score corresponding to any key node, m is the number of preset entity types, and S i N represents the preset rendering area corresponding to the i-th preset entity type node. i The number of nodes of the i-th preset entity type connected to the key node.

[0066] As mentioned above, when calculating the area allocation score of key nodes, since the preset presentation area corresponding to different preset entity types is inconsistent, the proportion of the preset presentation area corresponding to each preset entity type and the number of nodes connecting different preset entity types are introduced to make the obtained area allocation score more reasonable. That is, the more nodes with larger preset presentation areas, the larger the corresponding area allocation score, which is conducive to optimizing the overall page layout.

[0067] F400 adjusts the area of ​​the grid cells corresponding to each key node in the initial node grid based on the area allocation score of each key node to obtain the target node grid.

[0068] In one specific embodiment, step F400 includes the following steps: F401, for any critical node, calculates the proportion of the area allocation score corresponding to the critical node to the total area allocation score of all critical nodes.

[0069] F402, the product of the total rendering area of ​​the initial node mesh and the aforementioned ratio is used as the area of ​​the mesh cell corresponding to the key node, so as to adjust the area of ​​the mesh cell corresponding to the key node in the initial node mesh to obtain the target node mesh. In one embodiment, during adjustment, the key node closest to the center of the initial node mesh is used as the adjustment reference. While keeping the center of the mesh cell corresponding to the key unchanged, the area of ​​the mesh cell is expanded, and the area of ​​each mesh cell is adjusted outward in sequence. The center point of the outer mesh can be shifted outward accordingly, and the shift distance decreases sequentially according to the preset shift distance. It should be noted that mesh cell overlap may occur during the adjustment process.

[0070] As mentioned above, since the larger the area allocation score of the key node, the more neighboring nodes the key node has, it should be allocated a larger display cell. This is beneficial for the subsequent setting of the preset local area, so that when optimizing the position of key nodes and non-key nodes, it can ensure that the key node does not leave its corresponding cell, and that strongly related non-key nodes are reasonably displayed in the preset local area near the key node.

[0071] F500, based on the target node grid, adjusts the presentation positions of each key node and non-key node to obtain the final target knowledge graph; where a non-key node refers to any node in the given knowledge graph other than the key nodes.

[0072] In one specific embodiment, the F500 step includes the following steps: F501, for any critical node, moves the critical node within its own grid cell in the direction of the average coordinates based on the average coordinates of several neighboring nodes connected to the critical node, with a preset offset step size. This can be understood as the average coordinates of the neighboring nodes connected to the critical node referring to the centroids of the several neighboring nodes connected to the critical node. Those skilled in the art can set the preset offset step size according to actual needs, which will not be elaborated here.

[0073] F502 initially assigns each non-critical node to a preset local region containing a critical node with the highest correlation strength with itself. Then, a force-directed layout algorithm is used, centered on the corresponding critical node, to calculate the local position of each non-critical node. It should be noted that during the calculation process, a global repulsive force is applied to prevent overlap between non-critical nodes in different local regions, and edge constraints are applied to ensure that all nodes are within the preset rendering page.

[0074] F503, iteratively execute the above steps F501 and F502 until the average position change of all nodes is less than the preset change threshold or the maximum number of iterations is reached, to obtain the final presentation positions of key nodes and non-key nodes; it can be understood that the average position change can be the average of several position change distances.

[0075] As described above, with the positions of key nodes already preliminarily determined, the connection strength between each non-key node and the key node, as well as the distribution center of neighboring nodes, are then considered to fine-tune the positions of key and non-key nodes, making the final knowledge graph more reasonable and clear.

[0076] In another embodiment, accurately and comprehensively identifying the potential objects associated with each node before acquiring a given knowledge graph is crucial for the accurate construction of the knowledge graph and subsequent data analysis. Based on this, the method further includes the following steps: P100, based on the information of the object to be processed uploaded by the server to be processed, obtains the parameter value corresponding to each of the preset processing parameters; this can be understood as: the server to be processed is the server corresponding to the object to be processed. For example, the object to be processed can be an enterprise or a platform.

[0077] Specifically, the parameters to be processed include several technical capability parameters and several preset basic information parameters.

[0078] In practical implementation, when the target is an enterprise, the technical capability information includes the number of technical documents generated by the enterprise annually, and the preset basic information includes average education level, average salary, office space, years of establishment, and number of employees covered by social security. When the target is a platform, the technical capability information includes the number of technical documents generated by the platform annually, and the preset basic information includes the total number of target users corresponding to the platform identifier, the number of target users annually, the average usage period of target users, and the longest usage period. The number of technical documents can be the number of authorized patent documents.

[0079] Furthermore, the parameter values ​​corresponding to several technical capability parameters include the total number of technical texts generated by the object to be processed, the technical text quality score, the technical development trend score within a preset time period, and the current degree of influence in the technical community; this can be understood as: the current degree of influence in the technical community refers to the current degree of influence in the same technical field or the same industry. Those skilled in the art can set the preset time period according to actual needs, such as 5-10 years.

[0080] In one specific embodiment, the evaluation dimensions of the technical text quality score include the total number of patent application texts and the authorization rate. For example, the total number of patent application texts is normalized to a range of 0-1, and the technical text quality score is obtained by weighted summation using preset weights corresponding to the total number of patent application texts and the authorization rate.

[0081] In a specific embodiment, the technological development trend score within a preset time period is obtained through the following steps: P101, obtain the number of technical texts per year within a preset time period, and determine the fluctuation of the number of technical texts; the fluctuation is any one of rising, falling, stable, and wave-like.

[0082] P102, based on the fluctuations in the number of technical texts, several preset fitting curve models corresponding to the fluctuations are determined, and the model with the lowest degree of dispersion among these preset fitting curve models is taken as the target fitting curve model; this can be understood as: a preset database stores several preset fitting curve models corresponding to each fluctuation. Each preset fitting curve model is used to fit the number of technical texts to obtain the preset fitting curve model with the lowest degree of dispersion.

[0083] P103, find the preset importance level corresponding to the target fitting curve model; the preset importance level decreases sequentially from high to low according to the development trend of the characterization technology. For example, when the target fitting curve model is a power function, it indicates that the technology development trend is very high, and the corresponding preset importance level is also higher.

[0084] P104. Data preprocessing is performed on the preset importance level and the total number of technical texts within the preset time period corresponding to the target fitting curve model. The preset weights corresponding to the preset importance level and the total number of technical texts are combined to calculate the weighted sum to obtain the technology development trend score. Data preprocessing refers to normalizing the preset importance level and the total number of technical texts to values ​​of the same order of magnitude, such as both being between 0 and 1.

[0085] As mentioned above, the technological development trend score of the object to be processed can characterize the development capability of the object. When calculating the technological development trend score, the most accurate target fitting curve model is selected by analyzing the fluctuation of the number of technical texts. The technological development trend score is obtained by comprehensively considering the importance of the target fitting curve model and the total number of technical texts. This makes the obtained technological development trend score more reasonable and accurate, which is conducive to accurately evaluating the development capability of the object to be processed.

[0086] In a specific embodiment, the current level of influence of the object to be processed in the technical community is obtained through the following steps: P110: Extract technical keywords from each technical text within a preset time period and form a first keyword vector.

[0087] During implementation, to avoid having too many keywords, core keywords that meet preset conditions are extracted based on semantics, or keywords that are consistent with business operations are selected from the extracted keywords.

[0088] P120, obtain the second keyword vector corresponding to each preset object in the preset database; it can be understood that the second keyword vector is obtained in the same way as the first keyword vector, and will not be repeated here.

[0089] Specifically, the preset object refers to any object whose attribute tag has a similarity greater than a preset similarity threshold with the attribute tag of the object to be processed. For example, when the object to be processed is a company, the attribute tag is any industry category tag to which the object to be processed belongs.

[0090] On page 130, clustering is performed on the first keyword vector and several second keyword vectors. Based on the clustering results, the first target cluster corresponding to the object to be processed is obtained; this can be understood as: the first target cluster is the cluster where the object to be processed belongs. In specific implementations, the k-means clustering algorithm can be used.

[0091] P140, based on the distance between the object to be processed and the centroid of the first target cluster and the maximum distance corresponding to the first target cluster, the current influence level S of the object to be processed in the technical community is calculated; the current influence level S meets the following conditions: S = 1 - (d / r), where d is the distance between the object to be processed and the centroid of the first target cluster, and r is the maximum distance corresponding to the first target cluster.

[0092] As described above, calculating the current influence of the object to be processed in the technical community can characterize the importance of the object in this technical field. When calculating the current influence, the object to be processed is clustered with several preset objects with the same attribute labels. Objects clustered into a cluster indicate that they are objects with similar development potential or scale. Since the total distance from the cluster centroid to each vector is optimal, and the closer the distance to the cluster centroid, the higher the importance of the object, the current influence of the object to be processed in the technical community obtained by the above method is more reliable.

[0093] Furthermore, the preset basic information parameters include several parameters used to characterize the scale and resource configuration of the object to be processed. In one implementation, the preset basic information parameters may be values ​​obtained by performing data standardization processing on any of the aforementioned preset basic information parameters.

[0094] P200 merges the parameter values ​​corresponding to each parameter to be processed and transforms them into feature vectors corresponding to the objects to be processed. It then clusters these feature vectors with the feature vectors corresponding to each preset object obtained from the preset database. Based on the clustering results, it determines the reference objects corresponding to the objects to be processed.

[0095] Preferably, the reference object corresponding to the object to be processed is determined through the following steps: P201. Both the feature vectors corresponding to the object to be processed and the feature vectors corresponding to the preset object are standardized to ensure that each feature in the feature vector is on the same scale. For example, the Z-score standardization algorithm or the Min-Max normalization method can be used. Those skilled in the art are familiar with the specific implementation methods of the above two standardization methods, and will not be described in detail here.

[0096] P202, cluster several standardized feature vectors based on a preset number of clusters K, and determine the second target cluster corresponding to the object to be processed from the K clusters obtained.

[0097] In one implementation, those skilled in the art can set the K value according to actual needs, for example, K can be set to 3 based on the importance of high, medium and low, or the K value can be obtained by using the profile coefficient method. Those skilled in the art are familiar with the implementation of the profile coefficient method, and it will not be described in detail here.

[0098] P203, the Z preset objects whose distances are closest to the feature vectors of the objects to be processed within the second target cluster are all taken as reference objects.

[0099] Preferably, Z is proportional to the number of samples in the second target cluster.

[0100] As described above, by obtaining the parameter values ​​of the object to be processed under multiple indicators and merging them into a feature vector, the overall situation of the object to be processed can be reflected. Then, by clustering the feature vectors of preset objects with the same type of label, several preset objects similar to the overall situation of the object to be processed can be accurately obtained.

[0101] P300 retrieves several key objects associated with the reference object from a preset database, and retrieves the feature labels corresponding to the reference object and each key object respectively; it can be understood that: key objects and reference objects refer to having a relationship that meets preset requirements. For example, when it is necessary to find the investment object of the object to be processed, the key object can be the object that has been obtained after investing in the reference object.

[0102] Specifically, the step of retrieving several key objects associated with the reference object from the preset database includes the following steps: P301, based on the target knowledge graph stored in the preset database, several objects that are connected to the reference object obtained from the target knowledge graph are used as initial objects; it can be understood that the target knowledge graph is a pre-constructed knowledge graph that includes a large number of objects and the relationships between them.

[0103] On page 302, obtain the type of each initial object and designate the initial object whose type matches that of the reference object as the key object. For example, when the reference object is an enterprise, the initial object of type enterprise is designated as the key object. If the reference object also connects to legal person information, then the legal person information is not designated as the key object.

[0104] As mentioned above, since the reference objects are selected objects that are similar to the overall situation of the object to be processed, the key objects that are associated with the reference objects can also be considered to have a strong relationship with the object to be processed. This is conducive to expanding the associated tags of the object to be processed, finding more potential objects or potential users that are related to the object to be processed, and improving the reliability of the potential objects or potential users found.

[0105] P400 inputs several feature labels corresponding to the reference object and each key object, along with several pre-acquired feature labels of the objects to be processed, into a pre-trained large model to obtain several expanded feature labels corresponding to the objects to be processed. This expanded feature label list is then used to obtain the target object list corresponding to the objects to be processed. This can be understood as: when expanding the feature labels using the pre-trained large model, semantic expansion of the labels is also included. These feature labels include technical field labels and business labels, etc.

[0106] For ease of understanding, an example is provided below: The original feature tags of the object to be processed include: lithium battery, energy storage system; the feature tags of the reference object and the key object include: lithium battery, power battery, automotive industry, battery materials and recycling. The expanded output tags include: lithium battery, energy storage system, power battery, battery production equipment, automotive industry, battery materials and recycling.

[0107] Specifically, obtaining the target object list corresponding to the object to be processed based on the expanded feature labels includes the following steps: P401, obtain several feature labels corresponding to each preset object.

[0108] P402. For any preset object, if there are consistent labels between the feature labels corresponding to the preset object and the extended feature labels, the preset object will be used as the target object corresponding to the object to be processed.

[0109] P403 integrates the acquired target objects to obtain a list of target objects corresponding to the objects to be processed.

[0110] As mentioned above, since both reference objects and key objects are potential objects that are highly related to the object to be processed, by integrating and expanding the feature tags of the three, we can find more and more reliable potential objects of the object to be processed from the database based on the expanded tags, providing reliable data references for subsequent operations of the object to be processed.

[0111] Furthermore, the method also includes the following steps: P10: Obtain several original objects associated with the object to be processed from the target knowledge graph stored in the preset database.

[0112] P20, compare the consistency of several target objects in the target object list corresponding to the object to be processed with several original objects associated with the object to be processed, and identify target objects that are different from each original object from the several target objects.

[0113] P30 associates the identified target objects with the objects to be processed in the target knowledge graph in order to update the target knowledge graph.

[0114] As described above, when the associated objects corresponding to the object to be processed are obtained, updating the associated objects of the object to be processed in the target knowledge graph ensures the timeliness and availability of the data, so that there is a more accurate and comprehensive data foundation for the next calculation, and ensures the reliability of the calculation results.

[0115] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the entity analysis method provided in the above embodiments.

[0116] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0117] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. It should also be understood that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A solid analysis method, characterized in that, The method includes the following steps: Based on the first entity to be analyzed, the second entity to be analyzed, and the preset attribute parameter set corresponding to each entity to be analyzed, the data similarity between the preset attribute parameter sets corresponding to the first entity to be analyzed and the second entity to be analyzed is calculated. If the name data corresponding to the first entity to be analyzed is the same as the name data corresponding to the second entity to be analyzed, when the data similarity is greater than the first preset similarity threshold, the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity. When the data similarity is not greater than the first preset similarity threshold and is greater than the second preset similarity threshold, the historical activity areas and corresponding historical activity periods of the first entity to be analyzed and the second entity to be analyzed within a preset time period before the current time are crawled from the given platform. Obtain the first set of target devices of the first entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period, and the second set of target devices of the second entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period; If it is determined that several first target devices in the first target device set and several second target devices in the second target device set have the same devices, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

2. The entity analysis method according to claim 1, characterized in that, The method further includes: When the data similarity is not greater than the second preset similarity threshold, it is determined that the first entity to be analyzed and the second entity to be analyzed are not the same entity.

3. The entity analysis method according to claim 1, characterized in that, The method further includes the following steps: If the name data corresponding to the first entity to be analyzed is different from the name data corresponding to the second entity to be analyzed, and the semantics of the preset attribute parameter set corresponding to the first entity to be analyzed and the preset attribute parameter set corresponding to the second entity to be analyzed are completely consistent, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity. When the semantics of two preset attribute parameter sets are not completely consistent and the data similarity between the two preset attribute parameter sets is greater than the first preset similarity threshold, crawl the historical activity areas and corresponding historical activity periods of the first entity to be analyzed and the second entity to be analyzed within the preset time before the current time from the given platform. Obtain the first set of target devices of the first entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period, and the second set of target devices of the second entity to be analyzed in the corresponding historical activity area and the corresponding historical activity period; If it is determined that several first target devices in the first target device set and several second target devices in the second target device set have the same devices, then the first entity to be analyzed and the second entity to be analyzed are determined to be the same entity.

4. The entity analysis method according to claim 3, characterized in that, The method further includes: When the data similarity between the preset attribute parameter set corresponding to the first entity to be analyzed and the preset attribute parameter set corresponding to the second entity to be analyzed is not greater than the first preset similarity threshold, it is determined that the first entity to be analyzed and the second entity to be analyzed are not the same entity.

5. The entity analysis method according to claim 1, characterized in that, The process for determining whether there are identical devices among several first target devices in the first target device set and several second target devices in the second target device set is as follows: Based on the unique identification codes corresponding to the first target device and the second target device respectively, if there is a common unique identification code in the unique identification code corresponding to the first target device and the unique identification code corresponding to the second target device, output the judgment result that there are several first target devices and several second target devices with the same device. When no judgment result is obtained that several first target devices and several second target devices have the same device, the trajectory data of each first target device and each second target device in the preset historical time period is obtained respectively. The trajectory data of each first target device in a preset historical time period is similar to the trajectory data of each second target device in a preset historical time period, and a trajectory similarity matrix is ​​generated. Based on a preset first trajectory similarity threshold, target device pairs with trajectory similarity higher than the first trajectory similarity threshold are selected from the trajectory similarity matrix; The first trajectory similarity threshold is set based on the duration of a preset historical period and the average device density within the preset historical period. The preset historical time period is divided into preset time windows. If the trajectory similarity of the first target device and the second target device in the target device pair is greater than the preset second trajectory similarity threshold in each time window, the judgment result that there are several first target devices and several second target devices are the same device is output.

6. The entity analysis method according to claim 1, characterized in that, The first and second texts to be analyzed are obtained through the following steps: Based on the preset attribute parameter set corresponding to each preset entity in the target knowledge graph, each preset attribute parameter set is transformed into a corresponding attribute parameter vector; Several attribute parameter vectors are clustered using a pre-defined clustering model to obtain several attribute parameter vector clusters; The preset entities corresponding to any two attribute parameter vectors in any attribute parameter vector cluster are respectively taken as the first entity to be analyzed and the second entity to be analyzed.

7. The entity analysis method according to claim 1, characterized in that, The method further includes the following steps: If the first entity to be analyzed and the second entity to be analyzed are the same entity, obtain several objects that have connection relationships with the first entity to be analyzed and the second entity to be analyzed respectively from the target knowledge graph stored in the preset database; Several objects that are connected to the first entity to be analyzed and several objects that are connected to the second entity to be analyzed are associated, integrated, and stored in a preset database.

8. The entity analysis method according to claim 7, characterized in that, The method further includes the following steps: The association integration results are set in a preset hidden box in the preset presentation page; the association integration results include several objects that are connected to the first entity to be analyzed, several objects that are connected to the second entity to be analyzed, and association identifiers; When a user clicks on a preset hidden box, the associated and integrated results are presented according to the preset display area.

9. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the entity analysis method as described in any one of claims 1-8.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

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