A policy recommendation method and device based on a policy atlas, and an electronic device

Abstract

A policy recommendation method and device based on a policy graph and an electronic device, relating to the field of big data technology. The method comprises: obtaining policy information input by a user; the policy key information comprises a first policy keyword and a second keyword; obtaining a first policy graph corresponding to the first policy keyword in a preset database according to the first policy keyword; obtaining a second policy graph corresponding to the second keyword in the preset database according to the second keyword; obtaining enterprise data of the user; obtaining a first correlation degree value of the first policy graph and the enterprise data, and obtaining a second correlation degree value of the second policy graph and the enterprise data; determining the size relationship between the first correlation degree value and the second correlation degree value, and if the first correlation degree value is greater than the second correlation degree value, the first policy graph is preferentially displayed. The technical solution provided in the application solves the problem that the user cannot accurately understand the adaptation degree of the policy application and the enterprise.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of software testing, in particular to a policy recommendation method and device based on a policy atlas and an electronic device. BACKGROUND

[0002] Nowadays, as the welfare of the policy is getting higher and higher, various enterprises pay more and more attention to the application of the policy, therefore, the policy recommendation according to the policy atlas has become a reliable method for policy recommendation.

[0003] However, the policy recommendation according to the policy atlas at the present stage generally only lists the policy atlas when the user queries, and the user cannot accurately understand the adaptation degree of the policy application to the enterprise.

[0004] Therefore, a policy recommendation method and device based on a policy atlas and an electronic device are urgently needed. SUMMARY

[0005] The policy recommendation method and device based on a policy atlas and the electronic device solve the problem that the user cannot accurately understand the adaptation degree of the policy application to the enterprise.

[0006] In a first aspect of the present application, a policy recommendation method based on a policy atlas is provided, the method being applied to a server, and the specific steps of the method are as follows: obtaining policy text input by a user; the policy text comprising a plurality of policy keywords; the plurality of policy keywords comprising a first policy keyword and a second keyword; the plurality of policy keywords comprising the first policy keyword and the second keyword; obtaining a first policy atlas corresponding to the first policy keyword in a preset database according to the first policy keyword; obtaining a second policy atlas corresponding to the second policy keyword in the preset database according to the second policy keyword; the preset database being used to store policy keywords, policy atlases, and the corresponding relationship between the policy keywords and the policy atlases; obtaining enterprise data of the user; obtaining a first correlation degree value of the first policy atlas and the enterprise data, and obtaining a second correlation degree value of the second policy atlas and the enterprise data; determining the size relationship between the first correlation degree value and the second correlation degree value, and if the first correlation degree value is greater than the second correlation degree value, the first policy atlas is preferentially displayed.

[0007] By adopting the above technical solution, the policy atlas corresponding to the keyword can be pushed to the user according to the plurality of policy keywords input by the user, and the pushing priority of the policy atlas is determined according to the correlation degree between the policy atlas and the user's enterprise, on the one hand, the user can understand the content of the policy atlas at a glance, and on the other hand, the user can accurately understand the adaptation degree of the application of different policies to the enterprise through the relationship of the priority.

[0008] Optionally, the third policy keyword is obtained after a preset time interval, and the policy text data corresponding to the third policy keyword is obtained; the policy text data can be obtained from a preset website, and the preset website includes a policy website publishing page of the same region, a policy website publishing page of the same department, and a policy belonging to the same key field; policy information data associated with the third policy keyword in the policy text data is obtained; a third policy graph corresponding to the third policy keyword is constructed according to the policy information data; a corresponding relationship between the third policy keyword and the third policy graph is constructed, and the third policy keyword, the third policy graph, and the corresponding relationship between the third policy keyword and the third policy graph are saved to a preset database.

[0009] Through the above technical solution, by setting a preset time, the server updates the policy keywords and policy graphs in the preset library every preset time interval. When a new policy keyword is published, the corresponding publishing website is obtained, and the corresponding policy graph is constructed for the policy keyword, so that the server can provide the latest policy recommendation in a timely manner.

[0010] Optionally, the policy graph includes a plurality of nodes and edges, and the nodes and edges are connected according to a preset corresponding relationship to form the policy graph; wherein the node is policy information, and the policy information includes one or more of application conditions, policy names, and time limits; and the edge is edge information, and the edge information is used to represent the association information between two policy information.

[0011] Through the above technical solution, the policy information is used as the node, and the association relationship of the policy information is used as the edge, so that the detailed policy graph of a certain policy is constructed, and the user can intuitively understand all information associated with the policy.

[0012] Optionally, the data to be matched is obtained, and the enterprise data includes a plurality of data to be matched; the number of matched data is counted by using a preset correlation degree model; the number of matched data is the number of data to be matched that meets the preset condition with the first policy graph, and the number of matched data is configured as the first correlation degree value.

[0013] Through the above technical solution, all data in the enterprise data, i.e., the data to be matched, is compared with all nodes in a certain policy graph one by one, and the number of data to be matched that meets the preset condition is counted by using a preset correlation degree model. The number of data to be matched that meets the condition is used as the correlation degree value of the policy graph, and the correlation degree values of each policy graph and the enterprise information are obtained, so as to facilitate the server to determine the priority when recommending the policy graph to the user.

[0014] Optionally, the association level of the first policy graph and the enterprise data is acquired; the association level is divided into weak association and strong association; the number of matched data is compared with a preset threshold; if the number of matched data is less than the preset threshold, the association level is determined as weak association; if the number of matched data is greater than or equal to the preset threshold, the association level is determined as strong association; and the association level is displayed to the user.

[0015] By using the above technical solution, the association level is set to show the user the association degree of the policy graph and the enterprise information. The association level can be divided into two levels, and the association degree of each policy graph is determined. It should be noted that the division of the association level in the present application can be divided into multiple levels, which can be divided according to actual conditions. For example, four preset thresholds can be set from small to large, so that the association level is divided into five levels, which is not limited in the present application.

[0016] Optionally, in response to the user's operation of clicking the edge, the edge information is displayed on the display interface; and in response to the user's operation of clicking the node, the node information is displayed on the display interface.

[0017] By using the above technical solution, the user can view the detailed information of the nodes and edges in the policy graph through the clicking operation, so as to facilitate the user to clearly understand the overall situation of the policy information corresponding to the related policy.

[0018] Optionally, the display page includes a first area and a second area, the first area is an area for displaying the first policy graph and the second policy graph, and the second area is an area for displaying the edge information and the node information.

[0019] By using the above technical solution, the display area can be divided into two sub-areas, one of which displays the policy graph, and the other displays the detailed information that the user wants to view. It should be noted that the display area in the present application can also be divided into more than two areas. For example, the third area can be used to display the above-mentioned association level to the user, which is not limited in the present application.

[0020] In a second aspect of the present application, a policy recommendation device based on a policy graph is provided. The device is a server, and the device includes an acquisition module and a display module.

[0021] The acquisition module is configured to acquire policy text input by a user, the policy text comprising a plurality of policy keywords, the plurality of policy keywords comprising a first policy keyword and a second policy keyword, acquire, according to the first policy keyword, a first policy graph corresponding to the first policy keyword from a preset database, acquire, according to the second policy keyword, a second policy graph corresponding to the second policy keyword from the preset database, and store the policy keywords, the policy graphs, and the corresponding relationship between the policy keywords and the policy graphs in the preset database.

[0022] The display module is configured to determine the size relationship between the first correlation degree value and the second correlation degree value, and display the first policy graph preferentially if the first correlation degree value is greater than the second correlation degree value.

[0023] Optionally, the acquisition module is configured to acquire policy text data corresponding to a third policy keyword at intervals of a preset time, acquire policy information data associated with the third policy keyword from the policy text data, construct a third policy graph corresponding to the third policy keyword according to the policy information data, and save the third policy keyword, the third policy graph, and the corresponding relationship between the third policy keyword and the third policy graph in the preset database.

[0024] Optionally, the acquisition module is configured to acquire enterprise data, and the enterprise data comprises a plurality of pieces of to-be-matched data, count the number of matched data by using a preset correlation degree model, and configure the number of matched data as the first correlation degree value, where the number of matched data refers to the number of pieces of to-be-matched data that satisfy a preset condition in terms of correlation degree value.

[0025] Optionally, the acquisition module is configured to acquire the correlation level of the first policy graph and the enterprise data, and determine the size relationship between the number of matched data and a preset threshold value, where the correlation level is divided into weak correlation and strong correlation, the correlation level is determined to be weak correlation if the number of matched data is less than the preset threshold value, and the correlation level is determined to be strong correlation if the number of matched data is greater than or equal to the preset threshold value.

[0026] Optionally, the display module is configured to display edge information on a display interface in response to a user's operation of clicking an edge, and display node information on the display interface in response to a user's operation of clicking a node.

[0027] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method according to any one of the preceding aspects.

[0028] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is configured to enable a processor to perform the method according to any one of the preceding aspects.

[0029] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0030] 1. According to the present application, a plurality of policy keywords input by a user are used to push a policy graph corresponding to the keywords to the user, and the priority of pushing the policy graph is determined according to the relevance of the policy graph and the user's enterprise, so that the user can understand the content of the policy graph at a glance, and the user can accurately understand the adaptation degree of different policies to the user's enterprise through the priority.

[0031] 2. By setting a preset time, the server updates the policy keywords and the policy graph in the preset library every preset time, which can be used to obtain the policy keywords from the corresponding publishing website when new policy keywords are published, and construct the corresponding policy graph for the policy keywords, so that the server can provide the latest policy recommendation in time.

[0032] 3. In the first aspect, the relevance of the policy graph and the enterprise information can be displayed to the user, the relevance level can be divided into two levels, and the relevance of each policy graph is determined; in the second aspect, the user can view the detailed information of the nodes and edges in the policy graph by clicking operation, so that the user can more clearly understand the overall situation of the policy information corresponding to the related policies; in the third aspect, the display area can be divided into two sub-areas, one of which displays the policy graph, and the other displays the detailed information that the user wants to view. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of a policy recommendation method based on a policy graph provided by the embodiments of the present application.

[0034] Figure 2a is a schematic diagram of a policy graph provided by the embodiments of the present application.

[0035] Figure 2bis another policy atlas schematic diagram of a policy recommendation method based on a policy atlas provided by an embodiment of the present application.

[0036] Figure 3 is a display interface schematic diagram of a policy recommendation method based on a policy atlas provided by an embodiment of the present application.

[0037] Figure 4 is a structural diagram of a policy recommendation device based on a policy atlas provided by an embodiment of the present application.

[0038] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0039] Legend: 41, acquisition module; 42, display module; 500, electronic device; 501, processor; 502, memory; 503, user interface; 504, network interface; 505, communication bus. DETAILED DESCRIPTION

[0040] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0041] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be limiting to the present application. As used in the specification of the present application, the singular expression "one", "a", "said", "the above", "the" and "this" are intended to also include the plural expression, unless there is clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application means any or all possible combinations of one or more listed items.

[0042] Hereinafter, the terms "first", "second" are only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" is two or more.

[0043] Before introducing the embodiments of the present application, first define and explain some terms involved in the embodiments of the present application.

[0044] Business: In the present application, business refers to an interface collection encapsulated by multiple interfaces.

[0045] In order to make the technical personnel in the art better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the drawings.

[0046] Please refer to Figure 1 It shows a flowchart of a policy recommendation method based on a policy atlas provided by an embodiment of the present application, and the method is applied to a server. The flowchart mainly includes the following steps: S101 to S105.

[0047] Step S101, obtaining a policy text input by a user; the policy text includes a plurality of policy keywords; the plurality of policy keywords include a first policy keyword and a second keyword.

[0048] Specifically, the user can input a policy text that wants to query in a search bar. The policy text can include a plurality of keywords. The server obtains the policy keywords in the policy information through the policy information input by the user.

[0049] For example, the user can input the policy text as: high-tech, special and new, and obtain the policy keywords in the policy information as "high-tech" and "special and new". In the following embodiments, the policy keywords "high-tech" and "special and new" will be explained.

[0050] Step S102, obtaining a first policy atlas corresponding to the first policy keyword in a preset database according to the first policy keyword; obtaining a second policy atlas corresponding to the second policy keyword in the preset database according to the second policy keyword; the preset database is used to store the policy keywords, the policy atlas and the corresponding relationship between the policy keywords and the policy atlas.

[0051] Specifically, a preset database is established. The corresponding policy atlas of the policy keyword is queried in the user database through the obtained policy keyword. In the present application, the policy atlas is not an overall atlas combined by a plurality of policy information, but a detailed information atlas of each policy. Therefore, the policy atlas and the policy keyword need to be one-to-one corresponding.

[0052] For example, the first policy atlas corresponding to the policy keyword "high-tech" is queried in the preset database, and the policy atlas is uniquely corresponding to the policy keyword "high-tech". The second policy atlas corresponding to the policy keyword "special and new" is queried in the preset database, and the policy atlas is uniquely corresponding to the policy keyword "high-tech".

[0053] In a possible implementation, the step S102 further includes: obtaining policy text data corresponding to the third policy keyword at intervals of a preset time; the policy text data can be obtained from a preset website, and the preset website includes: a policy website publishing page of a same region, a policy website publishing page of a same department, and a policy belonging to a same key field; obtaining policy information data associated with the third policy keyword in the policy text data; constructing a third policy graph corresponding to the third policy keyword according to the policy information data; and constructing a corresponding relationship between the third policy keyword and the third policy graph, and saving the third policy keyword, the third policy graph, and the corresponding relationship between the third policy keyword and the third policy graph to a preset database.

[0054] Specifically, a preset time is set, and the server obtains a new policy keyword through each policy publishing website at intervals of the preset time. This step can be implemented by a crawler system to crawl relevant data from the policy publishing website. The server extracts policy information data related to the policy keyword from the relevant data, constructs data of a policy graph uniquely corresponding to the policy keyword according to the newly obtained policy keyword and the policy information data, generates the policy graph according to the data of the policy graph, and saves the policy keyword, the policy graph, and the corresponding relationship between the policy keyword and the policy graph to a database for future use. The generation of the policy graph can be generated by a preset learning model, and the preset learning model is trained by a large amount of actual data, and can accurately generate the policy graph corresponding to different policy keywords. It should be noted that in the present embodiment, the updating method of the policy keyword further includes another method, when each website changes the information of the policy keyword, the changed information of the policy keyword is obtained, and the corresponding policy graph is updated according to the changed information of the policy keyword. The above two updating methods can both update the policy keyword in the preset database, and the present application does not limit the description.

[0055] For example, a certain policy text published by a certain policy website is converted into policy data, and it can be determined that the policy data is related to the policy keyword "high-tech" through a preset training model. The policy information related to "high-tech" in the policy data can be extracted, for example, through a learning model, such as "national high-tech enterprise", "registered for more than one year", "enterprise innovation capability evaluation requirement", and the like. The policy graph of "high-tech" and the policy information such as "national high-tech enterprise", "registered for more than one year", "enterprise innovation capability evaluation requirement", and the like is established.

[0056] In a possible implementation, the step S102 further includes: the policy graph includes a plurality of nodes and edges, the nodes and the edges are connected according to a preset correspondence relationship to form the policy graph; wherein: the node is policy information, the policy information includes one or more of an application condition, a policy name and a time limit; and the edge is edge information, the edge information is used to represent association information between two policy information.

[0057] Specifically, taking the policy information as the node and the association relationship of the policy information as the edge, a detailed policy graph of a certain policy is constructed, so that the user can intuitively understand all information associated with the policy.

[0058] For example, refer to Figure 2a Fig. 2 illustrates a first policy graph corresponding to the policy keyword "high-tech" according to an embodiment of the present application. The edges with the same association relationship can be one or more, for example, in Fig. 2, "high-tech" can be taken as a starting node, "name" can be taken as edge information of the first policy graph, and "high-tech enterprise" can be taken as node information of a child node of the first policy graph, so that the association relationship between the child node "high-tech enterprise" and the starting node is a "name" relationship; in Fig. 2, "region" can be taken as edge information of the first policy graph, and "national high-tech enterprise", "provincial high-tech enterprise" and "municipal high-tech enterprise" can be taken as first node information of the first policy graph, so that the association relationship between the child nodes "national high-tech enterprise", "provincial high-tech enterprise" and "municipal high-tech enterprise" and the starting node is a "region" relationship. The association relationship between the child node and a lower child node thereof can also be established, for example, in Fig. 2, "application condition" can be taken as the association relationship, and "registered for more than one year", "the proportion of scientific and technological personnel engaged in R&D and related technological innovation activities in the total number of employees of the enterprise in the current year is not less than 10%", "the proportion of enterprises with sales revenue less than 5,000 million yuan (including) in the same period is not less than 5%", "the proportion of high-tech product (service) income in the total income of the enterprise in the same period is not less than 60%", and "the enterprise innovation ability evaluation should reach the corresponding requirements" can be taken as lower child nodes of "national high-tech enterprise".

[0059] In step S103, the enterprise data of the user is acquired.

[0060] Specifically, the enterprise data of the user is data stored locally on the server, which can be registration information data input by the user during account registration, or information data manually input into the server locally by relevant staff through online search of relevant information of the enterprise. The enterprise data includes registered capital of the enterprise, establishment date of the enterprise, administrative penalty record, etc. The application does not limit the acquisition method of the enterprise.

[0061] In step S104, a first correlation degree value of the first policy graph and the enterprise data is obtained, and a second correlation degree value of the second policy graph and the enterprise data is obtained.

[0062] Specifically, each enterprise data is associated with each node information in the same policy graph for multiple-to-multiple correlation degree judgment, so as to obtain the correlation degree value of the enterprise information and the policy graph.

[0063] In one possible implementation, step S104 further includes: obtaining the to-be-matched data, the enterprise data including a plurality of to-be-matched data; counting the number of matched data by using a preset correlation degree model; the number of matched data being the number of to-be-matched data and the correlation degree value of the first policy graph satisfying a preset condition, and the number of matched data being configured as the first correlation degree value.

[0064] Specifically, the server inputs the enterprise data and the policy graph into a preset correlation degree model, and performs multiple-to-multiple correlation degree judgment on each enterprise data, i.e., the un-matched data, and each node information in the policy graph by using the preset correlation degree model. When the preset model judges that the correlation degree between a certain enterprise data and a single node information in a certain policy graph satisfies a preset condition, the enterprise data is recorded as a successful pairing, i.e., the enterprise data is matched data. The number of matched data obtained by performing correlation degree judgment on the node information in the same policy graph is counted, and the number of matched data is used as the node information of the policy graph.

[0065] For example, by the preset correlation degree model, it is judged that there are 5 enterprise data meeting the preset correlation degree condition with the node information in the first policy graph, and the 5 matched enterprise data are taken as the correlation degree value of the first policy graph. It should be noted that the preset correlation degree model improves the accuracy of the judgment through a large amount of experimental data, and the judgment standard of the preset correlation degree model can be determined according to the actual situation when judging the correlation degree of a certain enterprise data and a certain node. For example, there is “registration time” in the enterprise data of a certain company, and if the correlation degree calculation is performed with the policy graph corresponding to “high-tech”, the node closest to “registration time”, i.e. the node “registered for more than one year”, is first judged. At this time, the preset correlation degree model judges whether “registration time” meets “registered for more than one year”, and if it meets, the enterprise data “registration time” is recorded as the matched data.

[0066] In step S105, the size relationship between the first correlation degree value and the second correlation degree value is judged. If the first correlation degree value is greater than the second correlation degree value, the first policy graph is preferentially displayed.

[0067] Specifically, the first correlation degree value of the same enterprise data and the first policy graph, the second correlation degree value of the same enterprise data and the second policy graph are obtained, and the size relationship is compared, and the policy graph with a larger correlation degree value is preferentially displayed to the user.

[0068] For example, by the preset correlation degree model, it is judged that there are 5 enterprise data meeting the preset correlation degree condition with the node information in the first policy graph, and the 5 matched enterprise data are taken as the correlation degree value of the first policy graph. It should be noted that the preset correlation degree model improves the accuracy of the judgment through a large amount of experimental data, and the judgment standard of the preset correlation degree model can be determined according to the actual situation when judging the correlation degree of a certain enterprise data and a certain node. For example, there is “registration time” in the enterprise data of a certain company, and if the correlation degree calculation is performed with the policy graph corresponding to “high-tech”, the node closest to “registration time”, i.e. the node “registered for more than one year”, is first judged. At this time, the preset correlation degree model judges whether “registration time” meets “registered for more than one year”, and if it meets, the enterprise data “registration time” is recorded as the matched data.

[0069] In a possible implementation, step S105 further includes: obtaining an association level of the first policy graph and the enterprise data; the association level is divided into weak association and strong association; determining the quantity of the matched data and the size relationship with the preset threshold; if the quantity of the matched data is less than the preset threshold, determining that the association level is weak association; if the quantity of the matched data is greater than or equal to the preset threshold, determining that the association level is strong association; and showing the association level to the user.

[0070] Specifically, an association level is set, the association degree of the policy graph and the enterprise information can be shown to the user, the association level can be divided into two levels, and the association degree of each policy graph is determined. It should be noted that the division of the association level in the present application can be divided into multiple levels, which can be divided according to actual conditions, for example, four preset thresholds can be set from small to large, thereby dividing the association level into five levels, which is not limited in the present application.

[0071] For example, four preset thresholds can be set and the association level can be divided into 1-5 stars, the four preset thresholds are 1, 3, 5, 7 and 9 in order from small to large, assuming that the quantity of the enterprise data satisfying the preset association degree condition is denoted as α (α≥0), when α<1, the association level of the enterprise data and the first policy graph is 1 star; when 3>α≥1, the association level of the enterprise data and the first policy graph is 2 stars; when 5>α≥3, the association level of the enterprise data and the first policy graph is 3 stars; when 7>α≥5, the association level of the enterprise data and the first policy graph is 4 stars; and when 9>α≥7, the association level of the enterprise data and the first policy graph is 5 stars. The higher the star level is, the higher the association degree of the first policy graph and the enterprise information is.

[0072] In a possible implementation, step S105 further includes: in response to the operation of clicking the edge by the user, displaying the edge information on the display interface; and in response to the operation of clicking the node by the user, displaying the node information on the display interface.

[0073] Specifically, the server can display specific information that the user wants to know through the display page according to the operation of the user.

[0074] For example, taking the node "national high-tech enterprise" as an example, when the user clicks the national high-tech enterprise node, the content information related to the "national high-tech enterprise" is displayed; please refer to Figure 2b which shows another policy graph diagram of a policy recommendation method based on a policy graph according to an embodiment of the present application, taking the edge "name" as an example, before the user clicks the edge, the edge information "name" is in a hidden state, that is, Figure 2bThe pre-click state shown in FIG. 1, when the user clicks the edge, the hidden state is cancelled, i.e. Figure 2b The post-click state shown in FIG. 1, the edge information of the edge is displayed to the user.

[0075] In a possible implementation, the step S105 further includes: the display page includes a first region and a second region, the first region is a region for displaying the first policy graph and the second policy graph, and the second region is a region for displaying the edge information and the node information.

[0076] Specifically, the display region is divided into two sub-regions, one of which displays the policy graph, and the other displays the detailed information that the user wants to view. It should be noted that the display region can also be divided into more than two regions in the present application, for example, a third region can be used to display the association level to the user, which is not limited in the present application.

[0077] For example, please refer to Figure 3 which shows a display interface schematic diagram of a policy recommendation method based on a policy graph according to an embodiment of the present application. According to the description Figure 3 , the left part of the display interface is the display region of the first policy graph and the second policy graph, the upper right part of the display interface is the display region for displaying the node information or the edge information in the graph after the user clicks the first policy graph, and the lower right part of the display interface is the display part of the association level. The user can also select the second policy graph below the first policy graph to view, and the omitted information of the second policy graph is displayed in the figure. After the user clicks the second policy graph, the server displays the detailed information of the second policy graph at the current position of the first policy, and displays the omitted information of the first policy graph at the current position of the second policy graph. Then, the user can select the policy graph that the user wants to view between the first policy graph and the second policy graph, and the server displays the policy graph selected by the user.

[0078] The present application also provides a policy recommendation device based on a policy graph, which includes an acquisition module 41 and a display module 42.

[0079] The acquisition module 41 is configured to acquire a policy text input by a user, the policy text comprising a plurality of policy keywords, the plurality of policy keywords comprising a first policy keyword and a second policy keyword, acquire, according to the first policy keyword, a first policy graph corresponding to the first policy keyword from a preset database, acquire, according to the second policy keyword, a second policy graph corresponding to the second policy keyword from the preset database, and store the policy keywords, the policy graphs, and the corresponding relationship between the policy keywords and the policy graphs in the preset database, acquire enterprise data of the user, acquire a first correlation degree value of the first policy graph and the enterprise data, and acquire a second correlation degree value of the second policy graph and the enterprise data.

[0080] The display module 42 is configured to determine a size relationship between the first correlation degree value and the second correlation degree value, and display the first policy graph preferentially if the first correlation degree value is greater than the second correlation degree value.

[0081] In a possible implementation, the acquisition module 41 is configured to acquire, at intervals of a preset time, policy text data corresponding to a third policy keyword, acquire, from a preset website, the policy text data, the preset website comprising a policy website publishing page of a same region, a policy website publishing page of a same department, and a policy belonging to a same key field, acquire policy information data associated with the third policy keyword from the policy text data, construct a third policy graph corresponding to the third policy keyword according to the policy information data, and store the third policy keyword, the third policy graph, and the corresponding relationship between the third policy keyword and the third policy graph in the preset database.

[0082] In a possible implementation, the acquisition module 41 is configured to acquire the enterprise data, the enterprise data comprising a plurality of to-be-matched data, count a number of matched data by using a preset correlation degree model, the number of matched data being a number of to-be-matched data that satisfies a preset condition in terms of correlation degree value, and configure the number of matched data as the first correlation degree value.

[0083] In a possible implementation, the acquisition module 41 is configured to acquire a correlation level of the first policy graph and the enterprise data, the correlation level being divided into weak correlation and strong correlation, determine a size relationship between the number of matched data and a preset threshold, determine that the correlation level is weak correlation if the number of matched data is less than the preset threshold, determine that the correlation level is strong correlation if the number of matched data is greater than or equal to the preset threshold, and display the correlation level to the user.

[0084] In a possible implementation, the display module 42 is configured to display edge information on a display interface in response to a user operation of clicking an edge, and display node information on the display interface in response to a user operation of clicking a node.

[0085] It should be noted that the apparatus provided by the above-mentioned embodiments is only exemplified by the above-mentioned division of functional modules when realizing its functions, and in actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the apparatus and method embodiments provided by the above-mentioned embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0086] The present application also discloses an electronic device. Referring to Figure 5 , Figure 5 is a structural schematic diagram of an electronic device disclosed by an embodiment of the present application. The electronic device 500 can include at least one processor 501, a memory 502, a user interface 503, at least one network interface 504, and at least one communication bus 505.

[0087] The communication bus 505 is used to realize the connection and communication between the components.

[0088] The user interface 503 can include a display screen (Display) and a camera (Camera), and the optional user interface 503 can further include a standard wired interface and a wireless interface.

[0089] The network interface 504 can include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0090] The processor 501 can include one or more processing cores. The processor 501 connects various parts within the recall server through various interfaces and lines, performs various functions of the recall server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 502, and calling data stored in the memory 502. Optionally, the processor 501 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 501 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content required to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 501, but can be realized by a separate chip.

[0091] The memory 502 can include a random access memory (RAM) and a read-only memory (ROM). Optionally, the memory 502 includes a non-transitory computer-readable storage medium. The memory 502 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 502 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 502 can also be at least one storage device located away from the aforementioned processor 501. Referring to Figure 5 The memory 502 as a kind of computer storage medium can include an operating system, a network communication module, a user interface module and a policy recommendation application based on policy atlas.

[0092] In Figure 5The user interface 503 is mainly used to provide an interface for user input, and obtain user input data. The processor 501 can be used to call a policy recommendation application based on a policy graph stored in the memory 502, and when executed by one or more processors 501, the electronic device 500 can perform the method described in one or more of the above embodiments. It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the application is not limited by the action sequence described, because according to the application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the application.

[0093] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0094] In the several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of units is only a logical function division. There can be another division during actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between units can be indirect coupling or communication connection through some services interface, device or unit, and can be electrical or other form.

[0095] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0096] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0097] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable memory. Based on such understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a recall server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0098] In the above embodiments, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting" according to the context. Similarly, the phrase "upon determining" or "if detecting (the stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)" according to the context.

[0099] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, recall server, or data center to another website, computer, recall server, or data center through wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a recall server, data center, etc. that includes one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state disk), etc.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned method embodiments can be implemented by a computer program instructing relevant hardware to complete, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The aforementioned storage medium includes ROM or random storage memory RAM, magnetic disk or optical disk, and various storage program code media.

[0101] The above description is only used to illustrate the technical solutions of the present application, and is not a limitation thereof. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents. These modifications or replacements do not change the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A policy recommendation method based on policy graphs, characterized in that, The method is applied to a server, and the method includes: The system obtains policy text input by the user; the policy text includes multiple policy keywords; the multiple policy keywords include a first policy keyword and a second policy keyword. Based on the first policy keyword, a first policy map corresponding to the first policy keyword is obtained from a preset database; based on the second policy keyword, a second policy map corresponding to the second policy keyword is obtained from the preset database; the preset database is used to store policy keywords, policy maps, and the correspondence between the policy keywords and the policy maps; Obtain the user's enterprise data; Obtain a first correlation value between the first policy map and the enterprise data, and obtain a second correlation value between the second policy map and the enterprise data; Determine the relationship between the first correlation value and the second correlation value. If the first correlation value is greater than the second correlation value, then the first policy map will be displayed first.

2. The method according to claim 1, characterized in that, Before obtaining the first policy map corresponding to the first policy keyword from a preset database based on the first policy keyword, and obtaining the second policy map corresponding to the second policy keyword from the preset database based on the second policy keyword, the method further includes: At preset intervals, obtain policy text data corresponding to the third policy keyword; the policy text data can be obtained from preset websites, including: policy website pages of the same region, policy website pages of the same department, and policies belonging to the same key areas. Obtain policy information data from the policy text data that is related to the third policy keyword; Construct a third policy map corresponding to the third policy key based on the policy information data; Construct the correspondence between the third policy keywords and the third policy map, and save the third policy keywords, the third policy map, and the correspondence between the third policy keywords and the third policy map to the preset database.

3. The method according to claim 2, characterized in that, The policy graph includes multiple nodes and edges, and the nodes and edges are connected according to a preset correspondence to form the policy graph; in; The node represents the policy information, which includes one or more of the following: application requirements, policy name, and time limit. The edge is edge information, which is used to represent the association information between two policy information items.

4. The method according to claim 1, characterized in that, The step of obtaining the first correlation value between the first policy map and the enterprise data specifically includes: Obtain the data to be matched, wherein the enterprise data includes multiple data items to be matched; By using a preset correlation model, the number of matched data is counted; the number of matched data is the number of data to be matched whose correlation value with the first policy map meets preset conditions, and the number of matched data is configured as the first correlation value.

5. The method according to claim 4, characterized in that, After determining the relationship between the first correlation value and the second correlation value, and if the first correlation value is greater than the second correlation value, then the first policy map is displayed first, the method further includes: Obtain the association level between the first policy map and the enterprise data; the association level is divided into weak association and strong association; determine the relationship between the number of matched data and a preset threshold. If the number of matched data is less than the preset threshold, then the association level is determined to be the weak association. If the number of matched data is greater than or equal to the preset threshold, then the association level is determined to be the strong association. The association level is displayed to the user.

6. The method according to claim 3, characterized in that, After determining the relationship between the first correlation value and the second correlation value, and if the first correlation value is greater than the second correlation value, then the first policy map is displayed first, the method further includes: In response to the user's click on the edge, the edge information is displayed on the display interface; In response to the user's click on the node, the node information is displayed on the display interface.

7. The method according to claim 6, characterized in that, The display interface includes a first area and a second area, wherein the first area is the area for displaying the first policy map and the second policy map; The second region is the area that displays the edge information and the node information.

8. A policy recommendation device based on policy graphs, characterized in that, The device is a server, and the device includes an acquisition module (41) and a display module (42), wherein... The acquisition module (41) is used to acquire policy information input by the user; the policy information includes multiple policy keywords; The plurality of policy keywords includes a first policy keyword and a second policy keyword; and, based on the first policy keyword, a first policy graph corresponding to the first policy keyword is obtained from a preset database; based on the second policy keyword, a second policy graph corresponding to the second policy keyword is obtained from the preset database; the preset database is used to store policy keywords, policy graphs, and the correspondence between the policy keywords and the policy graphs; and, the user's enterprise data is obtained; and, a first correlation value between the first policy graph and the enterprise data is obtained, and a second correlation value between the second policy graph and the enterprise data is obtained; The display module (42) is used to determine the relationship between the first correlation value and the second correlation value. If the first correlation value is greater than the second correlation value, the first policy map is displayed first.

9. An electronic device, characterized in that, The device includes a processor (501), a memory (502), a user interface (503), and a network interface (504). The memory (502) is used to store instructions. The user interface (503) and the network interface (504) are used to communicate with other devices. The processor (501) is used to execute the instructions stored in the memory (502) to cause the electronic device (500) to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the steps of the method as described in any one of claims 1 to 7.

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