CN116150483A - Electronic license recommendation method, device and storage medium based on Bayesian network model - Google Patents
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Abstract
The application relates to the technical field of information technology services, in particular to an electronic license recommendation method, device and storage medium based on a Bayesian network model. The method comprises the following steps: collecting government service matters handled by a user and sample data corresponding to the license file; preprocessing sample data; constructing a Bayesian network model according to the sample data; based on government service matters handled by the user, sequencing from high to low according to the probability of the follow-up government service matters in the Bayesian network model, and outputting the forefront government service matters and the corresponding license file set. According to the license file recommendation method and the license file recommendation system, based on the license and time sequence relation associated with the government service matters, the difference of geographic areas of the residents is considered, the license file recommendation model is respectively built, and the preferred follow-up government service matters and the required license file set are recommended for the government service matters handled by the user according to the model, so that the convenience and the intelligent level of application in the government service can be improved.
Description
Technical Field
The application relates to the technical field of information technology services, in particular to an electronic license recommendation method, device and storage medium based on a Bayesian network model.
Background
The electronic license refers to various data telegrams which are produced by national public administration and service institutions in law and do not relate to national secrets, license plates, wholesale, authentication reports, proving materials and the like. In recent years, along with the continuous development of information technology, the degree of electronization of traditional paper certificates is gradually improved, and a foundation is laid for innovative application of electronic certificates. With the continuous deep construction of the digital government in China, the modes of 'one-trip without running', 'one-event' integrated package service, 'no-meeting approval' and the like, which are constructed by depending on the electronic license, become basic supports for promoting the innovation of 'Internet+government service'. Further improving the application level and coverage of electronic certificates is an important way for deepening the innovation of digital government construction.
At present, the government online office platform of the construction and operation of the country and each place records detailed transaction information, including application object names, transaction time, use certificates and the like. The information can help analyze the flow of the business handling of the applicant, comb the association between the matters, find out the electronic license list forming evidence collection used by the same government service matters, and provide support for the intelligent service recommendation method for constructing the electronic license.
The related electronic license intelligent recommendation service is developed mainly based on the relationship between government service matters and certificate lists required by office work, and main application scenes can be divided into two types: (1) In an online office, a set of electronic certificates required for the recommendation of the selected government service matters is searched according to a user. For example: based on the government affair sample scene, the government affair matters and the electronic license set are connected in series by the unique identifier, and the electronic license set required by the applicant identity acquisition is identified based on the service request. (2) And the intelligent recommendation of the electronic license is realized by combining the government affair names. For example: by splitting the application request of the electronic license, the front-back relation of different handling matters is identified, the follow-up handling matters are reminded, and a foundation is laid for possible electronic license recommendation.
The prior art has three main disadvantages: (1) Ignoring the certificate relationship between government service matters, it is difficult to support electronic license recommendation based on the front-to-back relationship of government service matters, for example, electronic license intelligent recommendation is only performed in a single office, and subsequent potential government service matters are not considered. (2) The optimization recommendation is only carried out for the government affair handling flow, the relation between the affair and the certificate is not considered, and the improvement of government affair services such as 'one-trip no-running' and the like is difficult to support deeply. (3) The differences among different areas are not considered, so that more accurate intelligent recommendation is achieved, for example, the differences among electronic certificates and transaction flows for transacting business among the areas with different economic developments and different areas are achieved.
Disclosure of Invention
The invention aims to provide an electronic license recommending method based on a Bayesian network model, which is based on the electronic license and time sequence relation related to government service matters, and simultaneously considers the resident business angle and geographic location difference to construct an electronic license intelligent recommending model, and the electronic license recommending method can recommend the preferable follow-up government service matters and required electronic license file sets for the government service matters handled by a user according to the model, so that the convenience and the intelligent level of application in government service can be improved.
In order to solve the technical problems, the invention provides an electronic license recommendation method based on a Bayesian network model, which comprises the following steps:
step 1, collecting sample data; the sample data comprises government service matters handled by a user and corresponding electronic license file sets;
wherein k1 is a government service item node E i Number of occurrences m1 i λ1 is an expected value of poisson distribution, and is calculated by the duty ratio of corresponding government service matters in all government service matters, wherein the calculation formula is as follows:
n1 is the number of all nodes, namely the number of government service event types;
wherein k2 is item node E i Following is item node E i+1 Is out of (a)Number of occurrences m2 i And lambda 2 is an expected value of poisson distribution, and is calculated by corresponding to the duty ratio of government service matters in all matters, wherein the calculation formula is as follows:
n2 is the number of connecting edges of nodes of all government service matters, namely the number of combination times formed by all government service matters according to the unified user handling sequence;
P(E i ＝1|E i+1 ＝1)＝P(E i ＝1,E i+1 ＝1)/P(E i+1 ＝1)
wherein P (E) i ＝1|E i+1 =1) indicates that the government service event E occurs i+1 In the case of the former government service item is the government service item E i Conditional probability of (2);
P(E i+1 ＝1|E i ＝1)＝P(E i ＝1|E i+1 ＝1)*P(E i+1 ＝1)/P(E i ＝1)
wherein P (E) i ＝1|E i+1 =1) calculation of the posterior probability matrix according to step 324, P (E i =1) and P (E i+1 =1) then calculate according to the prior probability in step 322;
The application is firstly based on the user applicationHistorical sample data of government service matters are requested, a government service matters node forming a Bayesian network model and directed relation edges among the government service matters nodes are constructed, and then the current government service matters E are calculated i Post government service item E i+1 Conditional probability of (2). And recommending proper follow-up government service matters and corresponding electronic license file sets according to the probability when government service matters are managed by the user. On one hand, the characteristics of a Bayesian network model are utilized, the direct front-back handling relation of different government service matters is considered, the preferable post government service object can be recommended, on the other hand, the relation between the government service matters and the electronic license file is considered, and the corresponding electronic license file set is output while the post government service object is recommended.
Further, the electronic license recommendation method based on the Bayesian network model performs preprocessing on sample data, and comprises the following steps:
and step 22, eliminating repeated data.
Further, the electronic license recommendation method based on the Bayesian network model further comprises a step 5 of constructing an accuracy E ac Measuring single recommendation condition, reflecting the level of recommendation algorithm and providing reference for the optimization of the follow-up model, wherein the accuracy rate calculation mode is as follows:
wherein Q represents the number Q of electronic certificate files used by the user based on a subsequent matter of the government service matters in the sample data, and Q represents the number of electronic certificate files used according to the government service matters in the category of the electronic certificate files recommended by the bayesian network model.
According to the technical scheme, the accuracy of the model can be reflected by calculating the statistic accuracy parameters, and a reference can be provided for the optimization of the follow-up model.
Further, the electronic license recommendation method based on the Bayesian network model further comprises the following steps: respectively constructing a local Bayesian network model of each attribution according to the attribution information of the government service matters in the sample data; and selecting a Bayesian network model corresponding to the attribution to which the user handles the government service matters to recommend.
According to the technical scheme, before the Bayesian network model is built, sample data can be continuously divided according to attribution information, recommendation can be carried out by using the Bayesian network model corresponding to attribution when recommendation is carried out, habits of users handling government service matters in different areas can be fully considered, and accurate recommendation results which accord with user behaviors in the areas are achieved.
Accordingly, the present application also provides a computer device comprising: the electronic license recommendation method based on the Bayesian network model comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the steps of the electronic license recommendation method based on the Bayesian network model are realized when the processor executes the computer program.
Accordingly, the present application further provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the electronic license recommendation method based on a bayesian network model as set forth in any one of the preceding claims.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. on one hand, the characteristics of a Bayesian network model are utilized, the direct front-back handling relation of different government service matters is considered, the preferable post government service object can be recommended, on the other hand, the relation between the government service matters and the electronic license file is considered, and the corresponding electronic license file set is output while the post government service object is recommended.
2. Before the Bayesian network model is constructed, sample data can be continuously divided according to attribution information, and recommendation can be performed by using the Bayesian network model corresponding to attribution when recommendation is performed.
Drawings
FIG. 1 is a flowchart of steps of an electronic license recommendation method based on a Bayesian network model.
FIG. 2 is a flowchart illustrating the steps for preprocessing sample data according to the present invention.
FIG. 3 is a flowchart illustrating steps for constructing a Bayesian network model based on sample data.
FIG. 4 is a schematic diagram of the structure of the Bayesian network model of the present invention.
Fig. 5 is a flowchart of the steps for constructing a posterior conditional probability matrix of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 is a flowchart of steps of an electronic license recommendation method based on a Bayesian network model, comprising the following steps:
and step 1, collecting sample data. The sample data is from historical data of government service transaction items handled by a user, and comprises the government service transaction items handled by the user and corresponding electronic license file sets. For example, a "collect accumulation fund" government service transaction is transacted, and an electronic license file corresponding to the transaction includes an identity card, a real estate card, and the like.
and step 21, eliminating abnormal values. For example, data recorded as a null value using a certificate in the data is removed.
And step 22, eliminating repeated data. For example, duplicate data records of the same applicant handling the same government service transaction are identified from the sample data and only the latest government service transaction handling record is maintained.
As described above, the government service matters are used as nodes of the bayesian network model, and the connection edges between the nodes of the government service matters are constructed according to the time sequence before and after the transaction. In the Bayesian network structure diagram, for government service matters with direct dependency relationship, all matters nodes are unidirectional, for example, a newborn needs to transact birth certificate first, then transact matters such as a household opening, medical insurance, social insurance and the like sequentially, and use or generate corresponding certificates, and the edges of transacting birth certificate matters, transacting household opening nodes, transacting medical insurance nodes and transacting social insurance nodes are unidirectional edges correspondingly on the Bayesian network. For government service matters without precedence dependence, the sequence among the matters nodes may be bidirectional, for example, the order among the matters nodes is bidirectional when the two matters types of the public accumulation fund and the social security card information change are extracted. The construction of the Bayesian network structure diagram described in the present application is shown in FIG. 4.
After the Bayesian network structure diagram is established, the connection edge frequency between all the event nodes and the post-node needs to be analyzed and counted, meanwhile, the prior probability and the posterior probability of each event node are obtained according to a probability calculation formula, the complex event structure and the distribution rule are quantized, and the specific calculation formula and the flow are described as follows:
wherein k1 is a government service item node E i Number of occurrences m1 i λ1 is an expected value of poisson distribution, and is calculated by the duty ratio of corresponding government service matters in all government service matters, wherein the calculation formula is as follows:
n1 is the number of all nodes, namely the number of government service event types;
wherein k2 is item node E i Following is item node E i+1 Number of occurrences m2 of (2) i And lambda 2 is an expected value of poisson distribution, and is calculated by corresponding to the duty ratio of government service matters in all matters, wherein the calculation formula is as follows:
n2 is the number of connecting edges of nodes of all government service matters, namely the number of combination times formed by all government service matters according to the unified user handling sequence;
P(E i ＝1|E i+1 ＝1)＝P(E i ＝1,E i+1 ＝1)/P(E i+1 ＝1)
wherein P (E) i ＝1|E i+1 =1) indicates that the government service event E occurs i+1 In the case of the former government service item is the government service item E i In a Bayesian network, the precedence relationship among government service event nodes is determined according to the arrow direction of a connecting edge, and the node from which the arrow is derived is E i The pointed node is E i+1 . The related data can be counted according to the existing historical office information, and a back delay probability matrix is constructed and formed.
P(E i+1 ＝1|E i ＝1)＝P(E i ＝1|E i+1 ＝1)*P(E i+1 ＝1)/P(E i ＝1)
wherein P (E) i ＝1|E i+1 =1) calculation of the posterior probability matrix according to step 324, P (E i =1) and P (E i+1 =1) is calculated based on the prior probability in step 322.
Based on the step, E can be calculated according to the posterior conditional probability matrix and the probability of the current government service event i The government affair service matters are probability conditions of other matters, and a foundation is laid for selecting recommended matters and associated electronic certificates according to probability sizes.
rl i ＝RL 1 ∪RL 2 ∪…∪RL x …∪RL X
For example, when a user handles "handle birth certificate" items, according to the bayesian network model of the present application, the probability of each government service item such as "register" and "collect house deposit" is calculated, several items with the highest probability are selected, and the license corresponding to the relevant item is used as recommended content.
The invention builds the Bayesian network model by using the government affair data, can mine and comb the potential flow of the government affair handling, and simultaneously find out the relation between the government affair and the electronic license. On one hand, the characteristics of a Bayesian network model are utilized, the direct front-back handling relation of different government service matters is considered, the preferable post government service object can be recommended, on the other hand, the relation between the government service matters and the electronic license file is considered, and the corresponding electronic license file set is output while the post government service object is recommended. According to the technical scheme, the government affair handling time can be reduced, the efficiency of residents handling government affair handling is improved, the electronic license scene intelligent application service is effectively supported, and the digital intelligent level of public service, social management and the like is further improved.
In a preferred embodiment, in the electronic license recommendation method based on the bayesian network model, for measuring the accuracy level of the evaluation recommendation result, the accuracy of the recommendation algorithm is comprehensively reflected, the accuracy E can be built through the step 5 by providing a reference for the optimization of the subsequent model ac Measuring single recommendation condition, randomly extracting cases of handling different matters by a unified user, recommending subsequent electronic license sets based on the previous government service matters, and calculating the number Q of the license sets used for subsequent events, wherein the ratio of the number Q of the license sets in a recommended license list is calculated by the method of the invention:
wherein Q represents the number Q of electronic certificate files used by the user based on a subsequent matter of the government service matters in the sample data, and Q represents the number of electronic certificate files used according to the government service matters in the category of the electronic certificate files recommended by the bayesian network model.
In another preferred embodiment, the electronic license recommendation method based on the bayesian network model of the present application further includes the following steps: respectively constructing a local Bayesian network model of each attribution according to the attribution information of the government service matters in the sample data; and selecting a Bayesian network model corresponding to the attribution to which the user handles the government service matters to recommend.
According to the invention, the user searching angle and the difference conditions of different areas are considered in the process of constructing the model, and recommendation is made on the basis of constructing Bayesian network models of different city levels by searching target government matters. The method can fully consider the habit of handling government service matters of users in different areas, so that the method is more in line with the accurate recommendation result of the user behavior in the area, and can realize a more targeted intelligent recommendation method for the electronic license, thereby effectively promoting the optimization of civil service quality and ground government service.
The invention screens data between 4 months and 6 months of 2022 from the office data of the online government affairs hall in certain province in China, and 544275 government affair data records are obtained after data cleaning, and each record comprises information such as the name of the applicant, the name of the application item and the like and is used for analyzing and testing the accuracy of the intelligent recommended service of the method. Wherein the data of 4 months and 5 months are data for establishing a Bayesian network model, and the data of 6 months are reserved data for subsequent verification. The method can effectively mine the relation between government affair handling matters from the original data, find out the corresponding electronic license set, effectively recommend matters in the potential flow needed to be handled subsequently for the applicant based on the to-be-handled matter and government affair matters, recommend the needed electronic license set, and put forward an optimization strategy from the aspects of resident handling and regional difference.
Example two
The present embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including an independent server or a server cluster formed by a plurality of servers) that can execute a program. The computer device of the present embodiment includes at least, but is not limited to: a memory, a processor, and the like, which may be communicatively coupled to each other via a system bus. In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device, or may be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card, etc. provided on the computer device. The processor may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip for controlling the overall operation of the computer device. Specifically, in this embodiment, the processor is configured to execute a computer program stored in the memory, where the processor implements the steps of any one of the electronic license recommendation methods based on the bayesian network model when the processor executes the computer program.
Example III
The present embodiment also provides a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card memory, (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, characterized in that the program when executed by a processor implements the steps of any of the above-mentioned electronic license recommendation methods based on bayesian network models.
The foregoing detailed description will be given for the purpose of illustration only, and the invention is not limited to the above-described embodiments, but is to be construed as merely illustrative of the principles of the invention, as long as they are within the scope of the invention.
Claims (6)
1. The electronic license recommendation method based on the Bayesian network model is characterized by comprising the following steps of:
step 1, collecting sample data; the sample data comprises government service matters handled by a user and corresponding electronic license file sets;
step 2, preprocessing the sample data;
step 3, constructing a Bayesian network model according to the sample data, comprising the following steps:
step 31, constructing a Bayesian network structure diagram, which specifically comprises the following steps: taking government service matters as nodes of a Bayesian network model, establishing directed connection edges between nodes of the Bayesian network model according to the precedence relation of user handling government service matters in the sample data, and simultaneously associating each government service matters node with an electronic license file combination related to handling the government service matters, wherein the expression formula is EP i ＝{E i ,L i }, where EP i Representing government service event nodes i, E in Bayesian network structure diagram i Representing government affair service matters corresponding to node iItems, L i Representing an electronic license file set related to the node i;
step 32, constructing a posterior conditional probability matrix, which comprises the following steps:
step 321, define P (E i =1) represents government service matters E i Probability of occurrence, P (E i =0) represents a government service item E i Probability of non-occurrence and sum of both is 1;
step 322, calculating the prior probability of the government service matters, wherein the calculation formula is as follows:
wherein k1 is a government service item node E i Number of occurrences m1 i λ1 is an expected value of poisson distribution, and is calculated by the duty ratio of corresponding government service matters in all government service matters, wherein the calculation formula is as follows:
n1 is the number of all nodes, namely the number of government service event types;
step 323, calculating the joint distribution probability of government service matters, wherein the calculation formula is as follows:
wherein k2 is item node E i Following is item node E i+1 Number of occurrences m2 of (2) i And lambda 2 is an expected value of poisson distribution, and is calculated by corresponding to the duty ratio of government service matters in all matters, wherein the calculation formula is as follows:
n2 is the number of connecting edges of nodes of all government service matters, namely the number of combination times formed by all government service matters according to the unified user handling sequence;
step 324, calculating a posterior probability matrix of each government service item, wherein the calculation formula is as follows:
P(E i ＝1|E i+1 ＝1)＝P(E i ＝1,E i+1 ＝1)/P(E i+1 ＝1)
wherein P (E) i ＝1|E i+1 =1) indicates that the government service event E occurs i+1 In the case of the former government service item is the government service item E i Conditional probability of (2);
step 325, calculate the occurrence of government service event E i Post government service item E i+1 The conditional probability of (2) is calculated by the following formula:
P(E i+1 ＝1|E i ＝1)＝P(E i ＝1|E i+1 ＝1)*P(E i+1 ＝1)/P(E i ＝1)
wherein P (E) i ＝1|E i+1 =1) calculation of the posterior probability matrix according to step 324, P (E i =1) and P (E i+1 =1) then calculate according to the prior probability in step 322;
step 4, based on government affair service matters handled by the user, sequencing from high to low according to the probability of the follow-up government affair service matters in the Bayesian network model, and outputting the front X government affair service matters { RE } 1 ,RE 2 ,…,RE x ,…,RE X -recommended set of electronic license files rl i That is, RL corresponding to the first X government service matters x Is a union of (a) and (b).
2. The bayesian network model based electronic license recommendation method of claim 1, wherein the preprocessing of the sample data comprises the steps of:
step 21, eliminating abnormal values;
and step 22, eliminating repeated data.
3. The electronic license recommendation method based on the Bayesian network model of claim 1,
also comprises a step 5 of constructing the accuracy E ac Measuring single recommendation condition, reflecting the level of recommendation algorithm and providing reference for the optimization of the follow-up model, wherein the accuracy rate calculation mode is as follows:
wherein Q represents the number Q of electronic certificate files used by the user based on a subsequent matter of the government service matters in the sample data, and Q represents the number of electronic certificate files used according to the government service matters in the category of the electronic certificate files recommended by the bayesian network model.
4. The electronic license recommendation method based on the bayesian network model according to claim 1, further comprising the steps of: respectively constructing a local Bayesian network model of each attribution according to the attribution information of the government service matters in the sample data; and selecting a Bayesian network model corresponding to the attribution to which the user handles the government service matters to recommend.
5. A computer device, the computer device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the bayesian network model based electronic license recommendation method of any one of claims 1 to 4 when the computer program is executed.
6. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of the bayesian network model based electronic license recommendation method according to any of claims 1 to 4.
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