CN114065058B

CN114065058B - City recommendation method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN114065058B
Application number: CN202210046323.9A
Authority: CN
Inventors: 肖雪松; 严骊; 李慧; 罗桂林; 龙胜海
Original assignee: Tuzi Intelligent Technology Nanjing Co ltd; Chengdu Minto Technology Co ltd
Current assignee: Tuzi Intelligent Technology Nanjing Co ltd; Chengdu Minto Technology Co ltd
Priority date: 2022-01-17
Filing date: 2022-01-17
Publication date: 2022-04-08
Anticipated expiration: 2042-01-17
Also published as: CN114065058A

Abstract

The application provides a city recommendation method, a city recommendation device, electronic equipment and a computer readable storage medium, and belongs to the technical field of computers. The city recommendation method comprises the following steps: acquiring personal data of a user; matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, wherein the city knowledge graph is established based on city policy data and/or city report data; and generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city. Because the city knowledge map is established based on the city policy data and/or the city report data, a user does not need to collect and check the city policy data and the city report data, the user can directly and quickly determine the city suitable for self development according to the city recommendation report, and the efficiency of determining the city suitable for self development by the user is improved.

Description

City recommendation method and device, electronic equipment and computer readable storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a city recommendation method, an apparatus, an electronic device, and a computer-readable storage medium.

Background

At present, an individual user needs to know which city the individual user is suitable for developing, and usually needs to search city information of a target city, such as talent policies, city reports and the like, at each large network station, and then the user needs to compare whether own conditions meet the policies and whether the city reports meet psychological expectations of the user, and if the individual user needs to know the city information of a plurality of cities, the individual user can only search and compare the city information of each city one by one.

At present, policies and reports aiming at cities are generally distributed on websites and media news of all officers, collection and viewing are inconvenient, users can easily ignore the policies and the reports, files related to the policies and the reports are often complex, contents are detailed, processes are more, the users are difficult to quickly and accurately determine whether conditions of the users meet requirements of the policies or not, and report contents are consistent with the users, so that the users are difficult to quickly determine cities suitable for self development.

Disclosure of Invention

The application provides a city recommendation method, a city recommendation device, electronic equipment and a computer-readable storage medium, which are used for solving the problems that the existing policies and reports aiming at cities are inconvenient to collect and view, and a user cannot easily and quickly determine a city suitable for self development.

In a first aspect, the present application provides a city recommendation method, including: acquiring personal data of a user; matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, wherein the matching result represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data; and generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city.

In the embodiment of the application, the personal data are matched with the pre-acquired city knowledge graph of the target city, and the city recommendation report is generated according to the matching result of the personal data and the city knowledge graph of each target city. Because the city knowledge map is established based on the city policy data and/or the city report data, a user does not need to collect and check the city policy data and the city report data, the user can directly and quickly determine the city suitable for self development according to the city recommendation report, and the efficiency of determining the city suitable for self development by the user is improved.

With reference to the technical solution provided by the first aspect, in some possible implementations, before the matching the personal data with the pre-acquired city knowledge graph of the target city, the method further includes: acquiring text data, wherein the text data is city policy data and/or city report data; performing semantic analysis on the text data, dividing the text data into a plurality of modules according to a semantic analysis result, and adding a label to each module, wherein the label added to each module represents the semantic type of the data included in the module; and storing the text data divided into a plurality of modules into the city knowledge graph of the corresponding city.

In the embodiment of the application, the text data is divided into a plurality of modules according to the semantic parsing result, the label is added to each module, and the text data divided into the plurality of modules is stored in the city knowledge graph of the corresponding city, so that when the personal data is matched with the city knowledge graph, only a part of modules in the city policy data or the city report data are matched with the personal data, and the matching efficiency is improved.

With reference to the technical solution provided by the first aspect, in some possible implementations, if the text data is city policy data, the tag includes at least one of a policy name, a release city, a making department, an effective time, a declaration object, a declaration condition, a declaration flow, and a reward content; if the text data is city report data, the label comprises at least one of a report name, a city set, a report issuing organization, a credibility index of the report issuing organization, report content, index ranking and a result display graph.

In the embodiment of the application, the labels of the policy name, the release city, the establishment department, the effective time, the declaration object, the declaration condition, the declaration flow and the reward content can completely reflect the content in the city policy data, so that the semantics of each module after the city policy data is divided can be accurately reflected by setting the labels, and similarly, the credibility indexes, the report content, the index ranking and the result display diagram of the report name, the city set, the release reporting mechanism and the reporting mechanism can completely reflect the content in the city report data, so that the semantics of each module after the city report data is divided can be accurately reflected by setting the labels, and the accuracy of the city knowledge diagram is further improved.

In combination with the technical solution provided by the first aspect, in some possible embodiments, the city knowledge-graph includes the city policy data, and the personal data includes basic information of the user; the matching of the personal data and the pre-acquired city knowledge graph of the target city to acquire the matching result of the personal data and the city knowledge graph of each target city comprises the following steps: aiming at each city knowledge graph, matching the basic information with the declaration conditions of each city policy data in the knowledge graph to obtain condition matching results of the basic information and the declaration conditions of each city policy data in the knowledge graph; and obtaining a first matching result of the personal data and the city knowledge graph according to the condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and the preset weight of each city policy data, wherein the first matching result is used for determining the matching result.

In the embodiment of the application, when the city knowledge graph comprises city policy data, the personal data comprises the basic information of a user, the matching result of the personal data and the city knowledge graph is obtained through the condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and the preset weight of each city policy data, each city policy data is fully considered, the finally obtained matching result is more accurate, meanwhile, the importance degree of different city policy data to the user can be distinguished through the preset weight, and the finally obtained matching result is more suitable for the actual situation.

With reference to the technical solution provided by the first aspect, in some possible implementation manners, the declaring condition of each piece of city policy data includes a plurality of sub-declaring conditions, and the matching, for each city knowledge graph, of the basic information with the declaring condition of each piece of city policy data in the knowledge graph to obtain a condition matching result of the basic information with the declaring condition of each piece of city policy data in the knowledge graph includes: aiming at each sub-declaration condition in the declaration conditions, matching the basic information with the sub-declaration condition to obtain a sub-matching result; and obtaining a condition matching result of the basic information and the declaration condition of the city policy data according to the sub-matching result of the basic information and each sub-declaration condition in the declaration condition.

In the embodiment of the application, the condition matching result of the basic information and the declaration condition of the city policy data is obtained through the sub-matching result of the basic information and each sub-declaration condition in the declaration condition, and the condition matching result of the basic information and the declaration condition of the city policy data is more accurate due to the fact that each sub-declaration condition in the declaration condition is considered in air draft.

With reference to the technical solution provided by the first aspect, in some possible implementations, the personal data further includes a policy priority set by the user for each piece of city policy data, and the method further includes: and acquiring the preset weight of each piece of city policy data in the knowledge graph according to the policy priority set by the user aiming at each piece of city policy data.

In the embodiment of the application, the personal data further comprises the policy priority set by the user for each piece of city policy data, and the weight of each piece of city policy data in the knowledge graph can be acquired according to the policy priority set by each piece of city policy data, so that the attention degree of the user to different city policy data is fully considered, the matching result obtained by calculation by using the weight is enabled to better meet the actual requirements of the user, and the user experience is improved.

With reference to the technical solution provided by the first aspect, in some possible implementations, the city knowledge graph includes the city report data, the matching the personal data with a pre-obtained city knowledge graph of a target city is performed, and a matching result between the personal data and the city knowledge graph of each target city is obtained, further including: aiming at each city knowledge graph, obtaining the score of each city report data in the city knowledge graph; obtaining a second matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and a preset report weight; and determining the matching result according to the first matching result and the second matching result.

In the embodiment of the application, when the city knowledge graph further comprises the city report data, the matching result of the city knowledge graph is obtained through the score and the preset report weight of each piece of city report data in the city knowledge graph, each piece of city report data is fully considered, the finally obtained matching result is more accurate, meanwhile, the importance degree of different city report data to users can be distinguished through the preset weight, and the final matching result is more fit with the actual situation.

With reference to the technical solution provided by the first aspect, in some possible implementations, the personal data further includes a point of interest set by the user for each piece of city report data, and the method further includes: and acquiring the preset report weight of each piece of city report data in the knowledge graph according to the interest points set by the user aiming at each piece of city report data.

In the embodiment of the application, the personal data further comprises interest points set by the user for each piece of city report data, and the weight of each piece of city report data in the knowledge graph can be acquired according to the interest points set by each piece of city report data, so that the attention degree of the user to different city report data is fully considered, the matching result obtained by utilizing the weight calculation is more in line with the actual requirement of the user, and the user experience is improved.

With reference to the technical solution provided by the first aspect, in some possible implementations, the city knowledge graph includes the city report data, and the matching the personal data with the pre-obtained city knowledge graph of the target city to obtain the matching result between the personal data and the city knowledge graph of each target city includes: aiming at each city knowledge graph, obtaining the score of each city report data in the city knowledge graph; and obtaining a matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and the preset report weight.

With reference to the technical solution provided by the first aspect, in some possible implementations, the generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city includes: and sequencing according to the matching result of the city knowledge graph corresponding to each target city and the personal data from high to low to generate a city recommendation report.

In the embodiment of the application, the matching results of the city knowledge graph corresponding to each target city and the personal data are sorted from high to low, so that a user can conveniently check the target city with a high matching result, and then the city suitable for self development can be quickly determined.

With reference to the technical solution provided by the first aspect, in some possible implementations, when the personal data includes a policy priority set by the user for each piece of city policy data, the city recommendation report further includes, for a matching result between the personal data and a city knowledge graph of each target city, matching results between each piece of city policy data and the personal data in the city knowledge graph, which are sequentially arranged according to the policy priority.

According to the embodiment of the application, the matching results of each piece of city policy data and the personal data in the city knowledge graph are sequentially arranged according to the policy priority, so that when a user views a city recommendation report, the user can see the matching result with the high policy priority first, and the user experience is further improved.

In combination with the technical solution provided by the first aspect, in some possible implementations, the city recommendation report further includes a matching result of the personal data and each city policy data and each city report data in the city knowledge graph.

In the embodiment of the application, the city recommendation report further comprises the matching result of the personal data and each city policy data and each city report data in the city knowledge graph, so that a user can check the matching result of the personal data and each city policy data and each city report data in each city knowledge graph through the city recommendation report.

In some possible embodiments, the city recommendation report further includes a classification of matching results between the personal data and each piece of city policy data in each city knowledge graph.

In the embodiment of the application, the personal data and the matching result of each piece of city policy data in each city knowledge graph are classified, so that a user can more quickly judge whether the user accords with the corresponding city policy data, and the speed of checking the city recommendation report by the user is increased.

With reference to the technical solution provided by the first aspect, in some possible implementations, when the matching condition of the personal data and the city policy data is that a policy is satisfied, the method further includes: and receiving the joining operation of the user, and adding the city policy data corresponding to the joining operation into a declaration list based on the joining operation.

In the embodiment of the application, the user can be effectively reminded of declaring the corresponding city policy by adding the city policy data corresponding to the adding operation into the declaration list.

With reference to the technical solution provided by the first aspect, in some possible implementations, after the adding city policy data corresponding to the join operation to a declaration list based on the join operation, the method further includes: and establishing reminding items according to each time node included in the city policy data corresponding to the adding operation.

In the embodiment of the application, the reminding item is established for each time node included in the city policy data corresponding to the joining operation, so that the user can be effectively prevented from missing the declaration time.

In combination with the technical solution provided by the first aspect, in some possible implementations, when the matching condition of the personal data and the city policy data is a partial satisfaction policy, the city recommendation report further includes a condition that the city policy data needs to be declared.

In the embodiment of the application, the city recommendation report further comprises the condition which is required to be met by declaring the city policy data, so that a user can conveniently check the declaration condition which is not met by the user, the user can conveniently judge whether the declaration is successful, the user does not need to search the corresponding city policy data, and the time for the user to determine the city suitable for self development is reduced.

In combination with the technical solution provided by the first aspect, in some possible implementations, the personal data includes at least one type of data of academic calendar, industry, post, certificate, house, and salary.

In the embodiment of the application, personal information of the user can be comprehensively reflected through the study, the industry, the post, the certificate, the house and the salary, so that the city recommendation report generated according to the matching result of the personal data and the city knowledge graph of each target city can better accord with the actual situation of the user.

With reference to the technical solution provided by the first aspect, in some possible implementations, the city recommendation report further includes: comparing data among the same city policy data in the city knowledge graph corresponding to different target cities; or, comparison data between the target city and the same city policy data belonging to a subordinate city of the target city; or the policy treatment comparison data obtained by the same city policy data under different declaration conditions in the target city; or, comparison data between first city policy data and second city policy data, wherein the first city policy data and the second city policy data are different versions of the same city policy data.

In the embodiment of the application, through the comparison data among the same city policy data in the city knowledge maps corresponding to different target cities, a user can intuitively compare the same city policy data among the different target cities; the user can be helped to further select the subordinate city which is suitable for self development in the target city through the comparison data between the target city and the same city policy data belonging to the subordinate city of the target city; through the comparison data of policy treatment obtained by the same city policy data under different declaration conditions in the target city, the user can be helped to select the declaration conditions suitable for the user; through the comparison data between the first city policy data and the second city policy data, the user can be helped to determine whether to select to declare the first city policy data or the second city policy data, and the user experience is improved.

In a second aspect, the application provides a city recommendation device, which includes an acquisition module, a matching module, and a generation module, where the acquisition module is used to acquire personal data of a user; the matching module is used for matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, the matching result represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data; the generation module is used for generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor, the memory and the processor connected; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory to perform the method according to the embodiment of the first aspect and/or any possible implementation manner in combination with the embodiment of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a computer to perform the method as described in the first aspect and/or any one of the possible implementation manners in conjunction with the first aspect.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic flowchart of a city recommendation method according to an embodiment of the present application;

fig. 2 is a schematic diagram illustrating a data structure of personal data including basic information according to an embodiment of the present application;

FIG. 3 is a diagram illustrating a data structure of personal data including basic information and interest tags according to an embodiment of the present application;

fig. 4 is a schematic diagram of a data structure of a city bank according to an embodiment of the present application;

fig. 5 is a schematic data structure diagram of city policy data according to an embodiment of the present application;

fig. 6 is a schematic diagram illustrating a data structure of city report data according to an embodiment of the present application;

FIG. 7 is a data structure diagram of a city knowledge graph according to an embodiment of the present application;

FIG. 8 is a diagram illustrating a data structure including a city knowledge graph of all cities in a city repository, according to an embodiment of the present application;

fig. 9 is a block diagram illustrating a city recommendation apparatus according to an embodiment of the present application;

fig. 10 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, relational terms such as "first," "second," and the like may be used solely in the description herein to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Further, the term "and/or" in the present application is only one kind of association relationship describing the associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone.

Referring to fig. 1, fig. 1 is a schematic flow chart of a city recommendation method according to an embodiment of the present application, and steps included in the method will be described with reference to fig. 1.

S100: personal data of a user is acquired.

The personal data of the user may be obtained in advance and stored in a database or a disk, and may be obtained directly when needed, or may be obtained in real time.

In one embodiment, the personal data of the user may include basic information of the user, for example, at least one of academic calendar, industry, post, certificate, house and salary, and the personal data of the user may be as shown in fig. 2.

In one embodiment, the personal data of the user may further include an interest tag of the user, and optionally, the interest tag may include a city, and at this time, the city included in the interest tag of the user may be set as the target city. Optionally, the interest tag may further include a policy priority set by the user for each piece of city policy data. Optionally, the interest tag may further include a point of interest set by the user for each piece of city report data, for easy understanding, please refer to fig. 3, where fig. 3 is personal data including basic information and the interest tag.

S200: and matching the personal data with the pre-acquired city knowledge graph of the target city to acquire a matching result of the personal data and the city knowledge graph of each target city.

The matching result in the S200 represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data.

In one embodiment, the target city may be one or more cities selected by the user from a pre-established city base (for example, selected by the aforementioned interest tag or selected by the city selection interface), or the target city may be all cities in the city base.

For easy understanding of the above city library, please refer to fig. 4, and fig. 4 is a schematic diagram of a data structure of a city library according to an embodiment of the present application. As shown in fig. 4, a provincial city is taken as a first floor, for example, sichuan, jiangsu, north river, etc. Then, taking city-level cities as a second layer, and storing the city-level cities under corresponding provincial cities, taking Sichuan as an example, wherein the Sichuan comprises city-level cities such as Chengdu, Yibin, Mianyang and the like. And finally, taking the county-level city as a third layer, and storing the county-level city under the corresponding city-level city, wherein the county-level city comprises county-level (district-level) cities such as high and new districts, city rivers and weirs and the like by taking the Chengdu as an example.

Correspondingly, when the target city is determined, the user can select cities of different grades, for example, the grade of the target city can be determined as a provincial city, and at the moment, the personal data is matched with the city knowledge graph of the provincial city; similarly, if the user determines the grade of the target city as the city grade city, matching the personal data with the city knowledge graph of the city grade city; and if the user determines the grade of the target city as a county-level city, matching the personal data with the city knowledge graph of the county-level city.

In one embodiment, the process of establishing the city knowledge graph based on the city policy data and/or the city report data may be that text data is obtained, the text data is the city policy data and/or the city report data, then the text data is subjected to semantic analysis, the text data is divided into a plurality of modules according to a semantic analysis result, a tag is added to each module, wherein the tag added to each module represents a semantic type of data included in the module, and finally the text data divided into the plurality of modules is stored in the city knowledge graph of the corresponding city. The text data may be subjected to semantic analysis by methods such as natural language processing, and the specific manner of performing semantic analysis on the text data is well known to those skilled in the art, and is not described herein again for brief description.

Optionally, the process of storing the text data divided into the plurality of modules into the city knowledge graph corresponding to the city may be that, if the text data is the city policy text data divided into the plurality of modules and a label of one of the modules is a release city, the city policy text data is stored into the city knowledge graph corresponding to the module labeled as the release city, for example, if the content of the module labeled as the release city in the city policy text data is a success, the city policy text data is stored into the city knowledge graph of the success. If the text data is the city report text data divided into a plurality of modules and the label of one module is a city set, the city report text data is stored into the city knowledge graph corresponding to the module labeled as the city set, for example, if the content corresponding to the module labeled as the city set in the city report data is the three cities of Chengdu, Jiangsu and Hebei, the city report data is respectively stored into the city knowledge graphs corresponding to the three cities of Chengdu, Jiangsu and Hebei.

Optionally, the city policy data includes, but is not limited to, city policy data categories such as talent introduction, skill subsidies, residents living, house rentals, vehicle branding, and the like. Each policy category may also include a number of specific city policy data. The city report data includes, but is not limited to, city report data categories such as population conditions, public facilities (e.g., public transportation, public facilities, park greening), industry, post salaries, livability index, air quality, etc., each category may further include a plurality of specific city report data, it is understood that each of the city policy data and the city report data described above is divided into a plurality of modules by corresponding semantic parsing results, and each module is added with a label and then stored in a corresponding city knowledge graph.

If the text data is city policy data, the label comprises at least one of a policy name, a release city, a making department, effective time, a declaration object, a declaration condition, a declaration flow and reward content.

For easy understanding, please refer to fig. 5, as shown in fig. 5, the policy may include city policy data such as talent introduction, skill subsidy, and residency, each city policy data includes a plurality of modules, taking the policy data of talent introduction as an example, the policy data of talent introduction includes modules such as policy name, city for issue, and reward subsidy, and similarly, the city policy data of skill subsidy, residency, and the like also includes a plurality of divided modules (not shown in fig. 5). The above examples are only for ease of understanding, and the specific category of the tag may be set according to the actual semantic result of the city policy data, and the category of the tag is not limited herein.

If the text data is city report data, the label comprises at least one of a report name, a city set, a release reporting organization, a credibility index of the reporting organization, report content, an index ranking and a result display graph.

For easy understanding, please refer to fig. 6, as shown in fig. 6, the report may include city report data such as population situation, public facilities, post wages, etc., each of the city report data includes a plurality of modules, for example, the post wages include modules such as report names, city sets, ranking indexes, etc., and similarly, the city report data such as population situation, public facilities, etc. includes a plurality of modules (not shown in fig. 6). The above examples are only for ease of understanding, and the specific types of tags may be set according to the actual semantic result of the city report data, and the types of tags are not limited herein.

In order to facilitate understanding of the specific structure and content of the city knowledge graph, please refer to fig. 7, which shows that the city knowledge graph corresponding to the assembly includes three contents, namely, a policy, a report and a district and county included therein, as shown in fig. 7, wherein the policy and the report include all city policy data and city report data, and the specific structure and content of the policy and the report are consistent with those in fig. 5 and 6, and are not repeated here for brevity. The contained counties include all counties (county-level cities) contained in the prefecture, such as high-new counties, city rivers and the like, all city policy data and city report data of the counties are stored in each county, and the specific process of storing the city policy data and the city report data is clear from the above description and is not repeated herein for brevity.

In one embodiment, a data structure including a city knowledge graph of all cities in a city base is shown in fig. 8, where the city knowledge graph includes a plurality of provincial cities, such as sichuan, jiangsu, and hebei, and includes all city-level cities under each provincial city, and city policy data, city report data, and prefecture (county-level city) data included in each city-level city. The city policy data, the city report data, and the prefecture data included in the city-level city are consistent with the structure and content of the city knowledge graph corresponding to the city-level city described above, and are not repeated here for brevity.

It should be noted that various city policy data and city report data may be crawled in various websites by means of web crawlers, or may be input by background staff.

In a first implementation manner, when the city knowledge graph includes city policy data and the personal data includes basic information of a user, the specific process of S200 may be, first, matching, for each city knowledge graph, the basic information with a declaration condition of each city policy data in the knowledge graph to obtain a condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph, and then, obtaining a first matching result of the personal data and the city knowledge graph according to a condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and a preset weight of each city policy data, where the first matching result is used to determine the matching result. The basic information of the user can comprise at least one type of data of academic calendar, industry, post, certificate, house and salary.

For example, when the declaration condition of the city policy data is "subject and above", if the subject in the basic information is the subject, it is confirmed that the basic information matches the declaration condition, and if the subject in the basic information is high, it is confirmed that the basic information does not match the declaration condition.

Optionally, when the declaration condition of each piece of city policy data includes a plurality of sub-declaration conditions, the specific process of matching the basic information with the declaration condition of each piece of city policy data in the knowledge graph to obtain the condition matching result of the basic information with the declaration condition of each piece of city policy data in the knowledge graph may be, first, matching the basic information with the sub-declaration condition to obtain a sub-matching result for each sub-declaration condition in the declaration condition, and obtaining the condition matching result of the basic information with the declaration condition of the city policy data according to the sub-matching result of the basic information and each sub-declaration condition in the declaration condition.

When the declaration condition includes a plurality of sub-declaration conditions, the result of condition matching between the basic information and the declaration condition of the city policy data may be represented by a score, for exampleu(j) Representing a userjBasic information of (2) usingsc(i,k) Representing city policy dataiTo (1) akThe conditional clauses are declared by

Representing basic informationu(j) And a firstkConditional statement of sliversc(i,k) The sub-matching result of (1), then

Accordingly, use

Representing basic informationu(j) And city policyiReporting condition ofsc(i) The condition of (1) is matched, then

Wherein the content of the first and second substances,Athe highest score of the declaration condition of a preset city policy data is shown

Is equal toAWhen it is, the basic information is confirmedu(j) Satisfying city policy dataiAll declaration conditions ofsc(i)，AMay be any value, e.g. inAIs 10 as an example, then

In actual useIn the method, the user can set according to actual requirementsAAre not specifically referred to hereinAAre limited by the specific values of (a).

When the city knowledge map comprisesnWhen a city policy data is received, usec(t) Expressing the city corresponding to the city knowledge map by

Representing basic informationu(j) The matching result with the city knowledge map is

Wherein the content of the first and second substances,

for city policy data in the city knowledge mapiThe weight of the received signal is set to a predetermined weight,nis an integer of 1 or more.

Optionally, the personal data further includes a policy priority set by the user for each piece of city policy data, and at this time, the preset weight of each piece of city policy data in the knowledge graph may be obtained according to the policy priority set by the user for each piece of city policy data.

Wherein can be provided withxClass policy priorities, each class policy priority corresponding to a different weight value,xis an integer of 2 or more. For example, 5 types of policy priorities may be set, including special, important, concerned, general, unimportant five types, and special, important, concerned, general, unimportant each correspond to a different weight value, where, for example, the special corresponding weight value may be 1.0, the important corresponding weight value may be 0.8, the concerned corresponding weight value may be 0.6, the general corresponding weight value may be 0.4, and the unimportant corresponding weight value may be 0.2. For convenience of understanding, the specific category of the policy priority and the weight value corresponding to each policy priority may be set according to actual requirements, and no limitation is made herein.

Optionally, a default weight value may be preset, and when the personal data only includes a policy priority set by the user for a part of the city policy data, or the policy priority is not set, the weight value of the city policy data for which the policy priority is not set by the user may be set as the default weight value, and the default weight values are the same. The specific value of the default weight value may be set according to actual requirements, and the specific data is not limited herein.

In a second implementation manner, when the city knowledge graph includes city report data, the personal data is matched with the city knowledge graph of the target city, which is obtained in advance, and a specific process of obtaining a matching result of the personal data and the city knowledge graph of each target city may be that, first, a score of each piece of city report data in the city knowledge graph is obtained for each city knowledge graph, and then, a matching result of the city knowledge graph is obtained according to the score of each piece of city report data in the city knowledge graph and a preset report weight.

Wherein is made ofc(t) Representing a citytBy usingr(v) Represents the second in the city knowledge mapvA city report data, using

Is shown asvStrip city report datar(v) Is scored, then

Wherein the content of the first and second substances,Bfor presetting the maximum score of a piece of city report data,Cis a preset constant and is used as a reference,

for a cityc(t) Reporting data in citiesr(v) Rank of less thanBAndCthe city of the product is scored as

Ranking greater than or equal toBAndCthe city of the product is scored as 0,B、Cmay be any value, e.g. inBIs a number of 10 and is provided with,Cis 3 as an example, then

Wherein, the cityc(t) Reporting data in citiesr(v) The score of less than 30 in

City, cityc(t) Reporting data in citiesr(v) The score of 30 or more of the ranks in (1) is 0. In actual use, the user can set according to actual requirementsBAndCare not specifically referred to hereinBAndCare limited by the specific values of (a).

When the city knowledge map comprisesmWhen reporting data in a city, use

The matching result of the city knowledge graph is represented, then

Wherein the content of the first and second substances,

reporting data for cities in the city knowledge mapr(v) Is set to a predetermined reporting weight of the mobile terminal,mis an integer of 1 or more.

Optionally, the personal data further includes an interest point set by the user for each piece of city report data, and at this time, the preset report weight of each piece of city report data in the knowledge graph may be obtained according to the interest point set by the user for each piece of city report data.

Wherein can be provided withyClass interest points, each class of interest points corresponding to different weight values,yis an integer of 2 or more. For exampleFor example, the weight value corresponding to the special type may be 0.9, the weight value corresponding to the general type may be 0.5, and the weight value corresponding to the unnecessary type may be 0.1. For convenience of understanding, the specific types of the interest points and the weight values corresponding to each type of the interest points may be set according to actual requirements, and no limitation is made herein.

Optionally, a default report weight value may be preset, and when the personal data only includes the interest points set by the user for part of the city report data or the interest points are not set, the weight value of the city policy data for which the user does not set the interest points may be set as the default report weight value, and each default report weight value is the same. The specific value of the default report weight value may be set according to actual requirements, and the specific data thereof is not limited herein.

In a third embodiment, when the city knowledge graph includes city report data and city knowledge graph data, a first matching result of the personal data and the city knowledge graph is obtained according to the personal data and city policy data included in the city knowledge graph, a second matching result of the personal data and the city knowledge graph is obtained according to the personal data and city report policy data included in the city knowledge graph, and then a matching result of the personal data and the city knowledge graph is obtained based on the first matching result and the second matching result, where the first matching result may be a city policy score and the second matching result may be a city report score.

The specific method and principle for obtaining the first matching result according to the personal data and the city policy data included in the city knowledge graph are consistent with the first embodiment, and the specific method and principle for obtaining the second matching result according to the personal data and the city report policy data included in the city knowledge graph are consistent with the second embodiment, and therefore, for the sake of brief description, details are not repeated here.

Optionally, using

Express city policy score by

Express the city report score by

Representing the matching result of the personal data and the city knowledge graph

Wherein the content of the first and second substances,jwhich represents the number of the user,twhich represents the number of the city or the city,iwhich represents the city policy data number and,vindicating a city report data number.

Optionally, a first weight value and a second weight value may also be preset, and a matching result between the personal data and the city knowledge graph is obtained based on the city policy score, the city report score, the first weight value and the second weight value, where the first weight value is a weight value corresponding to the city policy score, and the second weight value is a weight value corresponding to the city report score. For example, byDRepresenting a first weight value byERepresents a second weight value, then

The specific numerical values of the first weight value and the second weight value can be preset and can be directly called when the personal data are needed to be used, or the first weight value and the second weight value can be included in the personal data, and a user can modify the specific numerical values of the first weight value and the second weight value according to the requirements of the user.

S300: and generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city.

In one embodiment, when the target city includes multiple cities, the city recommendation reports may be generated by sorting the matching results of the city knowledge graph and the personal data corresponding to each city from high to low. The target city is a city-level city, namely, the city recommendation report is ranked according to the city-level city.

Optionally, the target city level here is a city level preselected by the user, for example, if the user sets the target city level as a provincial city, the city recommendation report is ranked according to a matching result of the provincial city, if the user sets the target city level as a city level city, the city recommendation report is ranked according to a matching result of the city level city, and if the user sets the target city level as a county level city, the city recommendation report is ranked according to a matching result of the county level city.

In one embodiment, the user may set a matching result threshold, and then only the city corresponding to the city knowledge graph whose matching result of the personal data is greater than or equal to the matching result threshold may be presented in S300 according to the matching result threshold set by the user. The matching result threshold may be set according to actual requirements, and it should be understood that the matching result threshold is less than or equal to the maximum matching result of the personal data and the city knowledge graph of the target city, and specific numerical values thereof are not limited herein.

In an embodiment, a user may set a recommendation area, then in S300, only cities in the recommendation area may be presented in the city recommendation report according to the recommendation area, optionally, the recommendation area may be eight recommendation areas, namely, east, south, west, north, northwest, northeast, southeast, and southwest, and all cities in the city bank are classified into different recommendation areas in advance, where a type of the recommendation area and cities included in different recommendation areas may be set according to actual needs, and here, no limitation is made on the type of the recommendation area and the cities included in different recommendation areas.

In one embodiment, when the personal data includes a policy priority set by the user for each piece of city policy data, the city recommendation report further includes, for the matching result between the personal data and the city knowledge graph of each target city, the matching degree between each piece of city policy data and the personal data in the city knowledge graph arranged in sequence according to the policy priority, and the city policy data with higher policy priority is arranged in the front for the user to view conveniently. For example, when policy priority includes five categories of special, important, concerned, general, and unimportant, the city policy data whose policy priority is special is ranked first, and correspondingly, the city policy data whose policy priority is unimportant is ranked later.

In one embodiment, the city recommendation report further includes a matching result of the personal data and each city policy data and each city report data in the city knowledge graph, that is, the user can check the matching result of the personal data and each city policy data and each city report data in each city knowledge graph through the city recommendation report.

In one embodiment, the city recommendation report further includes a classification of matching results between the personal data and each piece of city policy data in each city knowledge graph, for example, matching situations between the personal data and each piece of city policy data may be divided into three categories, namely, a satisfied policy, a partially satisfied policy, and an unsatisfied policy, where different matching situations correspond to matching results in different ranges.

Wherein is made of

Representing basic informationu(j) And city policyiReporting condition ofsc(i) As a result of the matching ofATo represent

Can then be set

Wherein the content of the first and second substances,Dis less thanAA predetermined constant greater than 0, i.e.

When the city policy number is satisfiedAccording to, in

When it is considered that the city policy data is partially satisfied, the method is characterized in that

Then, the city policy data is considered to be not satisfied.

Optionally, when the matching condition of the personal data and the city policy data is that the policy is satisfied, the city recommendation report further includes contents of an original city policy data, a declaration flow, a declaration required material, declaration time, and the like of the city policy data.

Optionally, when the matching condition of the personal data and the city policy data is that the policy is satisfied, the city recommendation method further includes: and receiving a joining operation of a user, and adding the city policy data into the declaration list based on the joining operation.

For example, whether a join list button is displayed on the presentation interface, and when the user clicks 'yes', the representation is input to join operation.

Optionally, the city recommendation method further includes: and establishing reminding items for the user according to each time node in the city policy data so as to ensure that the user does not miss the declaration time, wherein the city policy data can be the city policy data corresponding to the joining operation.

Optionally, when the matching condition of the personal data and the city policy data is that the policy is partially satisfied, the city recommendation report further includes a condition that the city policy data needs to be satisfied. By the method, the user can clearly and conveniently know which conditions are not met.

It is to be understood that the matching of the personal data with each piece of city policy data may be other classifications besides the satisfied policy, the partially satisfied policy, and the unsatisfied policy described above, and the specific classification manner is not limited herein.

In one embodiment, when the target city includes a plurality of cities, the city recommendation report further includes comparison data between the personal data and a plurality of matching results of the city knowledge maps of the plurality of target cities. The comparison data can be displayed in the form of text, graphics, tables and the like.

Optionally, the comparison data includes the number of the city policy data satisfied by the personal data in each city knowledge graph and the proportion of the city policy data satisfied by the personal data, and the user can visually compare the number and proportion of the city policies satisfied by the user in different target cities, so that the user experience is improved. The comparison data may further include the number of the partially satisfied city policy data and the proportion of the partially satisfied city policy data of the personal data in each city knowledge graph.

Optionally, when the personal data includes target city policy data and/or target city report data, the comparison data includes a matching result of the target city policy data and/or the target city report data and the personal data in each city knowledge map. The user can set the city policy data and the city report data which are regarded by the user as the target city policy data and/or the target city report data, so that the matching result of the target city policy data and/or the target city report data and the personal data in each city knowledge graph can be displayed in the city recommendation report, the user can compare the matching result of the target city policy data and/or the target city report data and the personal data in the city knowledge graphs corresponding to different target cities, and the user can select the target city policy data and/or the target city report data more conveniently.

Optionally, the city recommendation report further includes a total number of subsidies that can be received by the user in each target city, where the total number of subsidies that can be received by the user in each target city can be obtained by calculating a sum of subsidy amounts of all city policy data having subsidies whose personal data satisfy corresponding declaration conditions in a city knowledge graph corresponding to the target city. The city recommendation report may further include a difference between total number of subsidies that the user can receive in different target cities.

Optionally, the user may preset a time period, and correspondingly, the city recommendation report further includes matching results of the personal data and all city policy data and city report data in the time period in the city knowledge graph.

Optionally, the city recommendation report further includes comparison data between the same city policy data in the city knowledge graph corresponding to different target cities, where the comparison data may be presented by using a statistical chart, a table, or the like, for example, when the target city includes a success city and a yippen, and the compared city policy data is a talent drop policy, the comparison data may include the number of drop demographics, the total number of cities, and the drop proportion corresponding to the success city and the yippen in the same time period, and may further include drop requirements and drop procedures corresponding to the success city and the yippen.

Optionally, the city recommendation report further includes comparison data between the target city and the same city policy data of subordinate cities belonging to the target city, and when the target city is a provincial city, the subordinate city of the target city is a city-level city belonging to the provincial city; when the target city is a city-level city, the lower-level city of the target city is a county-level city belonging to the city-level city, wherein the comparison data can be displayed by using a statistical chart, a table and the like. For example, the target city is a capital, the subordinate city of the target city includes county-level cities such as a high-new district and a city river weir, when the compared city policy data is a talent drop policy, the compared data includes compared data between the talent drop policy of the county-level cities such as the high-new district and the city river weir and the talent drop policy of the capital, the compared data may include the number of drops, the total number of cities and the drop proportion of the talents respectively corresponding to the capital and the high-new district … … city river weir in the same time period, and may further include drop requirements and drop flows respectively corresponding to the capital and the high-new district … … river weir.

Optionally, when the user declares the same city policy data, the submitted declaration conditions are different, and the policy treatments obtained by the user are also not completely the same, so that the city recommendation report further includes comparison data of the policy treatments obtained by the same city policy data in the target city under different declaration conditions, wherein the comparison data can be displayed in a statistical chart, a table and the like. For example, the compared city policy data is the talent-falling policy, and the policy treatment obtained by the current discipline claiming talent-falling policy is different from the policy treatment obtained by the previous discipline claiming talent-falling policy, the city recommendation report may include the comparison data between the policy treatment obtained by the current discipline user claiming talent-falling policy and the policy treatment obtained by the previous discipline user claiming talent-falling policy, and the comparison data may include the number of falling users, the total number of cities, and the falling percentage of users corresponding to the current discipline user and the previous discipline user, and may further include the falling requirements and the falling flows corresponding to the current discipline user and the previous discipline user.

Optionally, when the city knowledge graph corresponding to the target city has the first city policy data to be updated to the second city policy data after the target time, and the first city policy data and the second city policy data are different versions of the same city policy data, the city recommendation report may include comparison data between the first city policy data and the second city policy data, where the comparison data may be displayed in a statistical chart, a table, or the like. For example, the compared city policy data is the first-person falling policy and the second-person falling policy, and the compared data may include the falling population number, the total population number of the city, and the falling percentage corresponding to the first-person falling policy and the second-person falling policy, respectively, and may further include the falling requirement and the falling flow corresponding to the first-person falling policy and the second-person falling policy, respectively.

Referring to fig. 9, fig. 9 is a diagram of a city recommendation device 100 according to an embodiment of the present application, where the city recommendation device 100 includes an obtaining module 110, a matching module 120, and a generating module 130.

The obtaining module 110 is configured to obtain personal data of a user.

The matching module 120 is configured to match the personal data with a pre-acquired city knowledge graph of a target city, and acquire a matching result between the personal data and the city knowledge graph of each target city, where the matching result represents a matching degree between the personal data and the city knowledge graph of the target city, and the city knowledge graph is established based on city policy data and/or city report data.

A generating module 130, configured to generate a city recommendation report according to a matching result between the personal data and the city knowledge graph of each target city.

The obtaining module 110 is further configured to obtain text data before the matching of the personal data with the pre-obtained city knowledge graph of the target city, where the text data is city policy data and/or city report data; performing semantic analysis on the text data, dividing the text data into a plurality of modules according to a semantic analysis result, and adding a label to each module, wherein the label added to each module represents the semantic type of the data included in the module; and storing the text data divided into a plurality of modules into the city knowledge graph of the corresponding city.

In one embodiment, if the text data is city policy data, the tag includes at least one of a policy name, a release city, a making department, an effective time, a declaration object, a declaration condition, a declaration process, and a reward content; if the text data is city report data, the label comprises at least one of a report name, a city set, a report issuing organization, a credibility index of the report issuing organization, report content, index ranking and a result display graph.

The city knowledge graph includes the city policy data, the personal data includes basic information of the user, and the matching module 120 is specifically configured to match, for each city knowledge graph, the basic information with a declaration condition of each city policy data in the knowledge graph, so as to obtain a condition matching result of the declaration condition of each city policy data in the knowledge graph and the basic information; and obtaining a first matching result of the personal data and the city knowledge graph according to the condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and the preset weight of each city policy data, wherein the first matching result is used for determining the matching result.

The declaration condition of each piece of city policy data includes a plurality of sub-declaration conditions, and the matching module 120 is specifically configured to match the basic information with each of the sub-declaration conditions in the declaration condition to obtain a sub-matching result; and obtaining a condition matching result of the basic information and the declaration condition of the city policy data according to the sub-matching result of the basic information and each sub-declaration condition in the declaration condition.

The personal data further includes a policy priority set by the user for each piece of city policy data, and the matching module 120 is further configured to obtain the preset weight of each piece of city policy data in the knowledge graph according to the policy priority set by the user for each piece of city policy data.

The city knowledge graph comprises the city report data, and the matching module 120 is further configured to obtain, for each city knowledge graph, a score of each piece of city report data in the city knowledge graph; obtaining a second matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and a preset report weight; and determining the matching result according to the first matching result and the second matching result.

The personal data further includes an interest point set by the user for each piece of city report data, and the matching module 120 is further configured to obtain the preset weight of each piece of city report data in the knowledge graph according to the interest point set by the user for each piece of city report data.

The city knowledge graph includes the city report data, and the matching module 120 is specifically configured to obtain, for each city knowledge graph, a score of each piece of city report data in the city knowledge graph; and obtaining a matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and the preset report weight.

The generating module 130 is specifically configured to sort the matching results of the city knowledge graph corresponding to each target city and the personal data from high to low, and generate a city recommendation report.

In one embodiment, when the personal data includes a policy priority set by the user for each piece of city policy data, the city recommendation report further includes, for matching results of the personal data and city knowledge maps of each target city, matching results of each piece of city policy data and personal data in the city knowledge maps which are arranged in sequence according to the policy priority.

In one embodiment, the city recommendation report further includes a matching result of the personal data with each city policy data and each city report data in the city knowledge graph.

In one embodiment, the city recommendation report further includes a classification of the matching result between the personal data and each city policy data in each city knowledge graph.

The city recommending apparatus 100 further includes an adding module, configured to receive a join operation of a user when the matching condition of the personal data and the city policy data is that a policy is satisfied, and add the city policy data corresponding to the join operation to a declaration list based on the join operation.

The adding module is further configured to establish a reminder item according to each time node included in the city policy data corresponding to the joining operation after the city policy data corresponding to the joining operation is added to a declaration list based on the joining operation.

In one embodiment, when the matching condition of the personal data and the city policy data is a partial satisfaction of the policy, the city recommendation report further includes a condition that is to be satisfied when the city policy data is declared.

In one embodiment, the personal data includes at least one of a scholarly calendar, an industry, a post, a certificate, a house, and a salary.

Under one embodiment, the city recommendation report further includes: comparing data among the same city policy data in the city knowledge graph corresponding to different target cities; or, comparison data between the target city and the same city policy data belonging to a subordinate city of the target city; or the policy treatment comparison data obtained by the same city policy data under different declaration conditions in the target city; or, comparison data between first city policy data and second city policy data, wherein the first city policy data and the second city policy data are different versions of the same city policy data.

The city recommendation apparatus 100 according to the embodiment of the present application has the same implementation principle and technical effect as those of the foregoing city recommendation method embodiment, and for brief description, reference may be made to corresponding contents in the foregoing city recommendation method embodiment where no mention is made in part of the apparatus embodiment.

Please refer to fig. 10, which is an electronic device 200 according to an embodiment of the present disclosure. The electronic device 200 includes: a transceiver 210, a memory 220, a communication bus 230, and a processor 240.

The elements of the transceiver 210, the memory 220, and the processor 240 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, the components may be electrically coupled to each other via one or more communication buses 230 or signal lines. The transceiver 210 is used for transceiving data. The memory 220 is used for storing a computer program, such as a software functional module shown in fig. 9, i.e., the city recommendation device 100. The city recommendation device 100 includes at least one software function module, which may be stored in the memory 220 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the electronic device 200. The processor 240 is configured to execute an executable module stored in the memory 220, such as a software function module or a computer program included in the city recommendation device 100. At this time, a processor 240 for acquiring personal data of the user; matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, wherein the matching result represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data; and generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city.

The Memory 220 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.

The processor 240 may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 240 may be any conventional processor or the like.

The electronic device 200 includes, but is not limited to, a personal computer, a server, and the like.

The embodiment of the present application further provides a computer-readable storage medium (hereinafter, referred to as a storage medium), where a computer program is stored on the storage medium, and when the computer program is run by the electronic device 200 as described above, the city recommendation method described above is executed.

The computer-readable storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A city recommendation method, comprising:

acquiring personal data of a user;

matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, wherein the matching result represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data;

generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city;

wherein, the city knowledge graph comprises the city policy data, the personal data comprises the basic information of the user, the matching of the personal data and the pre-acquired city knowledge graph of the target city is performed, and the acquiring of the matching result of the personal data and the city knowledge graph of each target city comprises:

aiming at each city knowledge graph, matching the basic information with the declaration conditions of each city policy data in the knowledge graph to obtain condition matching results of the basic information and the declaration conditions of each city policy data in the knowledge graph;

and obtaining a first matching result of the personal data and the city knowledge graph according to the condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and the preset weight of each city policy data, wherein the first matching result is used for determining the matching result.

2. The method of claim 1, wherein prior to said matching said personal data to a pre-acquired city knowledge graph of a target city, said method further comprises:

acquiring text data, wherein the text data is city policy data and/or city report data;

performing semantic analysis on the text data, dividing the text data into a plurality of modules according to a semantic analysis result, and adding a label to each module, wherein the label added to each module represents the semantic type of the data included in the module;

and storing the text data divided into a plurality of modules into the city knowledge graph of the corresponding city.

3. The method of claim 2, wherein if the text data is city policy data, the tag comprises at least one of a policy name, a release city, a making department, an effective time, a declaration object, a declaration condition, a declaration process, and a reward content;

if the text data is city report data, the label comprises at least one of a report name, a city set, a report issuing organization, a credibility index of the report issuing organization, report content, index ranking and a result display graph.

4. The method of claim 1, wherein the declaration conditions of each city policy data include a plurality of sub-declaration conditions, and the matching of the basic information with the declaration conditions of each city policy data in the knowledge graph to obtain the condition matching result of the basic information with the declaration conditions of each city policy data in the knowledge graph comprises:

aiming at each sub-declaration condition in the declaration conditions, matching the basic information with the sub-declaration condition to obtain a sub-matching result;

and obtaining a condition matching result of the basic information and the declaration condition of the city policy data according to the sub-matching result of the basic information and each sub-declaration condition in the declaration condition.

5. The method of claim 1, wherein the personal data further includes a policy priority set by a user for each piece of city policy data, the method further comprising:

and acquiring the preset weight of each piece of city policy data in the knowledge graph according to the policy priority set by the user aiming at each piece of city policy data.

6. The method of any one of claims 4-5, wherein the city knowledge graph includes the city report data, the matching the personal data with a pre-obtained city knowledge graph of target cities, and obtaining a matching result of the personal data with the city knowledge graph of each target city, further comprising:

aiming at each city knowledge graph, obtaining the score of each city report data in the city knowledge graph;

obtaining a second matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and a preset report weight;

and determining the matching result according to the first matching result and the second matching result.

7. The method of claim 6, wherein the personal data further includes points of interest set by the user for each piece of city report data, the method further comprising:

and acquiring the preset report weight of each piece of city report data in the knowledge graph according to the interest points set by the user aiming at each piece of city report data.

8. The method of claim 1, wherein the city knowledge graph comprises the city report data, and the matching the personal data with a pre-obtained city knowledge graph of target cities, and the obtaining of the matching result of the personal data with the city knowledge graph of each target city comprises:

and obtaining a matching result of the city knowledge graph according to the score of each piece of city report data in the city knowledge graph and the preset report weight.

9. The method of claim 1, wherein generating a city recommendation report based on the matching of the personal data to the city knowledge graph for each target city comprises:

and sequencing according to the matching result of the city knowledge graph corresponding to each target city and the personal data from high to low to generate a city recommendation report.

10. The method of claim 1, wherein when the personal data includes a policy priority set by the user for each piece of city policy data, the city recommendation report further includes, for the matching result of the personal data and the city knowledge graph of each target city, the matching result of each piece of city policy data and the personal data in the city knowledge graph arranged in sequence according to the policy priority.

11. The method of claim 10, wherein the city recommendation report further comprises a match of the personal data to each city policy data and each city report data in the city knowledge graph.

12. The method of claim 11, wherein the city recommendation report further comprises a classification of the matching of personal data to each city policy data in each city knowledge graph.

13. The method of claim 12, wherein when the personal data matches the city policy data to satisfy a policy, the method further comprises:

and receiving the joining operation of the user, and adding the city policy data corresponding to the joining operation into a declaration list based on the joining operation.

14. The method of claim 13, wherein after the adding city policy data corresponding to the join operation to a declaration list based on the join operation, the method further comprises:

and establishing reminding items according to each time node included in the city policy data corresponding to the adding operation.

15. The method of claim 12 wherein the city recommendation report further includes a condition that is declared that the city policy data further needs to be satisfied when the personal data matches the city policy data in part.

16. The method of claim 1, wherein the personal data includes at least one of academic calendar, industry, post, certificate, house, salary.

17. The method of claim 1, wherein the city recommendation report further comprises:

comparing data among the same city policy data in the city knowledge graph corresponding to different target cities; alternatively, the first and second electrodes may be,

comparison data between a target city and the same city policy data of a subordinate city belonging to the target city; alternatively, the first and second electrodes may be,

the policy treatment comparison data is obtained by the same city policy data in the target city under different declaration conditions; alternatively, the first and second electrodes may be,

comparison data between first city policy data and second city policy data, wherein the first city policy data and the second city policy data are different versions of the same city policy data.

18. A city recommendation device, comprising:

the acquisition module is used for acquiring personal data of a user;

the matching module is used for matching the personal data with a pre-acquired city knowledge graph of a target city to acquire a matching result of the personal data and the city knowledge graph of each target city, the matching result represents the matching degree of the personal data and the city knowledge graph of the city, and the city knowledge graph is established based on city policy data and/or city report data;

the generation module is used for generating a city recommendation report according to the matching result of the personal data and the city knowledge graph of each target city;

the matching module is specifically used for matching the basic information with the declaration conditions of each city policy data in the knowledge graph aiming at each city knowledge graph to obtain the condition matching results of the declaration conditions of the basic information and each city policy data in the knowledge graph; and obtaining a first matching result of the personal data and the city knowledge graph according to the condition matching result of the declaration condition of the basic information and each city policy data in the knowledge graph and the preset weight of each city policy data, wherein the first matching result is used for determining the matching result.

19. An electronic device, comprising: a memory and a processor, the memory and the processor connected;

the memory is used for storing programs;

the processor to invoke a program stored in the memory to perform the method of any of claims 1-17.

20. A computer-readable storage medium, having stored thereon a computer program which, when executed by a computer, performs the method of any one of claims 1-17.