Government affair service management method and system based on AI intelligent collaboration

Abstract

The invention relates to the technical field of government affair service management, in particular to a government affair service management method and system based on AI intelligent collaboration. And based on the problem content probability analysis corresponding to the applicant and the similarity degree of the similar personnel, carrying out comprehensive analysis of the problem content on a problem content probability analysis result, and displaying the comprehensive analysis result of the problem content on a client to prompt a client to carry out problem selection. According to the method, the questioning habit of the applicant is analyzed based on the content portrait of the applicant, and the question to be put forward by the applicant is quickly predicted and analyzed by integrating the department popularity condition of the questioning question and the questioning condition of the similar crowd, so that the language organization time of the applicant is shortened, and the processing efficiency of the government affair processing department is improved.

Description

Technical Field

[0001] This application relates to the technical field of government service management, and particularly to a government service management method and system based on AI intelligent collaboration. Background Art

[0002] The existing appointment and registration methods of government service management systems generally include online appointment and offline registration. These two appointment and registration methods are suitable for different types of people, providing convenience for the public to enjoy government services. However, the appointment and registration interfaces opened by existing government service management systems every day are usually fixed, resulting in the need to repeatedly operate the registration system to search for problem classifications to be solved when registering. The fixed appointment and registration interfaces cannot meet the registration needs of people with different population structures and different living habits. The prior art cannot analyze the question-asking habits of applicants based on their content portraits, and cannot quickly predict and analyze the questions to be raised by applicants by comprehensively considering the department popularity of the questions and the question-asking situations of similar people, thus restricting the processing efficiency of government affairs processing departments.

[0003] In response to the above problems, this application proposes a government service management solution based on AI intelligent collaboration. Summary of the Invention

[0004] In order to overcome the defects and deficiencies of the prior art, this application provides a government service management method and system based on AI intelligent collaboration. By obtaining the probability analysis of the problem content corresponding to the applicant based on the content portrait of the applicant and the content data to be applied by the applicant, analyzing the similarity degree of similar people based on the content portraits of other applicants and the content portrait of the current applicant, performing probability analysis on the problem content based on the similarity degree of similar people, comprehensively analyzing the problem content probability analysis results based on the probability analysis of the problem content corresponding to the applicant and the similarity degree of similar people, and displaying the comprehensive analysis result of the problem content on the client to prompt the customer to select the problem. This application analyzes the question-asking habits of applicants based on their content portraits, and quickly predicts and analyzes the questions to be raised by applicants by comprehensively considering the department popularity of the questions and the question-asking situations of similar people, so as to shorten the language organization time of applicants and improve the processing efficiency of government affairs processing departments.

[0005] To achieve the above object, this application adopts the following technical solutions:

[0006] In the first aspect, this application provides a government service management method based on AI intelligent collaboration, including the following steps:

[0007] S1: Obtain the department data to be applied by the applicant, and construct a content portrait of the applicant based on the historical application content data of the applicant;

[0008] S2: Obtain the probability analysis of the problem content corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for.

[0009] S3: Conduct an analysis of the similarity degree of similar personnel based on the content portraits of other applicants and the content portrait of the current applicant, and conduct a probability analysis of the problem content based on the similarity degree of similar personnel.

[0010] S4: Conduct a comprehensive analysis of the problem content based on the probability analysis of the problem content corresponding to the applicant and the similarity degree of similar personnel for the result of the probability analysis of the problem content.

[0011] S5: Display the comprehensive analysis result of the problem content on the client to prompt the customer to select the problem and prompt for registration.

[0012] In an implementation manner of the present application, step S1 includes the following specific steps:

[0013] S11: Obtain the attribute data of the department that the applicant needs to apply for. Among them, the attribute data of the department includes the management direction data of the department and the historical problem data of various personnel applications corresponding to the department, and store them in the first storage component.

[0014] S12: Identify the identity information of the applicant, obtain the historical problem data of the corresponding applicant, construct the keyword content portrait of the corresponding applicant based on the keywords of the historical problem data of the corresponding applicant, and store the keyword content portrait of the corresponding applicant in the second storage component.

[0015] In an implementation manner of the present application, in step S2, obtaining the probability analysis of the problem content corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for includes the following specific steps:

[0016] S21: Obtain the keyword content portrait of the corresponding applicant, simultaneously obtain the keywords of each corresponding problem, and simultaneously obtain the number of times each problem is asked within this period.

[0017] S22: Based on the keyword content portrait of the corresponding applicant, the keywords of each corresponding problem, and the number of times each problem is asked within this period, conduct the content probability of the corresponding problem. Among them, the content probability of the i-th problem is: , where fi is the number of times the i-th question is asked in this period, m is the total number of questions asked in this period, Ni is the number of keywords corresponding to the i-th question, bj is the number of occurrence scenarios of the j-th keyword of the i-th question in the keyword content portrait of the corresponding applicant, ksj is the number of occurrences of the j-th keyword in the s-th occurrence scenario in the keyword content portrait of the corresponding applicant, and tsj is the duration from the occurrence time of the s-th occurrence scenario of the j-th keyword in the keyword content portrait of the corresponding applicant to the current moment;

[0018] S23. Obtain the content probability of all the questions asked in this period; and store it in the corresponding storage component.

[0019] In an implementation manner of the present application, the similarity analysis in step S3 includes the following specific steps:

[0020] S31. Obtain the question data of the historical periods of other applicants, and the question data of the historical periods of the applicant;

[0021] Exemplarily, after the applicant's question is asked, it is stored in the corresponding storage component, and the data is retrieved when needed;

[0022] S32. Obtain the question data of the historical periods of other applicants and the question data of the historical periods of the applicant, and obtain the question similarity degree between other applicants and the applicant. Among them, the calculation formula for the question similarity degree between the s-th applicant and the applicant is: , where Hs is the number of historical periods, Db is the set composed of the keyword data of the questions asked by the s-th applicant in the b-th historical period, Kb is the set composed of the keyword data of the questions asked by the applicant in the b-th historical period, s() is the number of parameters in the set, tm is the standard duration, and in order to eliminate the time unit, tb is the duration from the b-th historical period to the current moment;

[0023] S33. Obtain the question similarity degrees between all other applicants and the applicant.

[0024] In an implementation manner of the present application, the probability analysis of the question content based on the similarity degree of similar persons in step S3 includes the following specific contents:

[0025] S34. Obtain the questions asked by other applicants in the current period and the question similarity degree with the applicant;

[0026] S35. Import the questions asked by other applicants in the current period and the question similarity degree with the applicant into the formula for calculating the content probability of the person's question to calculate the content probability of the person's question. Among them, the calculation formula for the content probability of the person's question of the i-th question is: , where Di is the number of people asking the i-th question in the current period, and Pdi is the similarity degree of the d-th other applicant's question to the i-th question in the current period.

[0027] In an implementation manner of the present application, in step S4, based on the analysis of the probability of the question content corresponding to the applicant and the similarity degree of similar personnel, a comprehensive analysis of the question content is performed on the analysis result of the question content probability, including the following specific contents:

[0028] S41. Obtain the content probability of all the asked questions and the content probability of the applicant's questions, and perform a weighted sum of the content probability of the corresponding questions and the content probability of the applicant's questions to obtain the final probability of asking the corresponding questions;

[0029] S42. Sort the obtained final probability of asking the corresponding questions in descending order for the applicant to select, so as to improve the question selection efficiency and selection speed of the applicant.

[0030] In a second aspect, the present application also provides a government service management system based on AI intelligent collaboration, including:

[0031] A content portrait construction module, which is used to obtain the department data that the applicant needs to apply for, and construct a content portrait of the applicant based on the historical application content data of the applicant;

[0032] A question content probability analysis module, which obtains the question content probability analysis corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for;

[0033] A personnel probability analysis module, which analyzes the similarity degree of similar personnel based on the content portraits of other applicants and the content portrait of the current applicant, and performs probability analysis on the question content based on the similarity degree of the similar personnel;

[0034] A question content comprehensive analysis module, which performs a comprehensive analysis of the question content on the analysis result of the question content probability based on the question content probability analysis corresponding to the applicant and the similarity degree of the similar personnel;

[0035] A display module, which displays the comprehensive analysis result of the question content on the client to prompt the customer to select questions and prompt for registration.

[0036] In a third aspect, an electronic device provided by the present application includes: a processor and a memory, where a computer program that can be called by the processor is stored in the memory, and the processor executes the government service management method based on AI intelligent collaboration by calling the computer program stored in the memory.

[0037] Fourthly, a computer-readable storage medium provided by this application stores instructions, which, when run on a computer, cause the computer to execute the government service management method based on AI intelligent collaboration.

[0038] Compared with the prior art, this application has the following advantages and beneficial effects:

[0039] This application obtains the probability analysis of the problem content corresponding to the applicant based on the content portrait of the applicant and the content data to be applied by the applicant, analyzes the similarity degree of similar personnel based on the content portraits of other applicants and the current applicant's content portrait, conducts probability analysis on the problem content based on the similarity degree of similar personnel, conducts comprehensive analysis of the problem content on the basis of the probability analysis of the problem content corresponding to the applicant and the similarity degree of similar personnel, and displays the comprehensive analysis result of the problem content on the client to prompt the customer to select the problem. This application analyzes the applicant's questioning habits based on the applicant's content portrait, and conducts rapid prediction analysis on the questions to be raised by the applicant by comprehensively considering the department popularity of the questions and the questioning situations of similar groups, so as to shorten the applicant's language organization time and improve the processing efficiency of the government affairs processing department. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of this application will become more apparent:

[0041] Figure 1 It is the overall flow diagram of the method of this application;

[0042] Figure 2 It is the working flow diagram of S2 in the method of this application;

[0043] Figure 3 It is the working flow diagram of S3 in the method of this application;

[0044] Figure 4 It is the structural diagram of the system of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solution of this application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features in the embodiments of this application and the embodiments are detailed descriptions of the technical solution of this application, rather than limitations on the technical solution of this application. Without conflict, the technical features in the embodiments of this application and the embodiments can be combined with each other.

[0046] Embodiment 1 As Figures 1 to 3 shown, this embodiment provides a government service management method based on AI intelligent collaboration, which specifically includes the following steps:

[0047] S1: Obtain the department data that the applicant needs to apply for, and construct a content portrait of the applicant based on the historical application content data of the applicant.

[0048] In a specific embodiment, step S1 includes the following specific steps:

[0049] S11: Obtain the attribute data of the department that the applicant needs to apply for. Among them, the attribute data of the department includes the management direction data of the department and the historical problem data of various personnel applications corresponding to the department, and store them in the first storage component.

[0050] Exemplarily, for the tax management department, its corresponding management direction is tax, and the historical problem data of various personnel applications corresponding to the department is the problem data proposed by various historical tax handling personnel. For example, when applying for a tax refund, the historical problem raised by the personnel is: How much is the tax refund amount? After keyword extraction, the keywords are tax refund, amount, and how much.

[0051] S12: Identify the identity information of the applicant, obtain the historical problem data of the corresponding applicant, construct a keyword content portrait of the corresponding applicant based on the keywords of the historical problem data of the corresponding applicant, and store the keyword content portrait of the corresponding applicant in the second storage component.

[0052] Exemplarily, identify the identity information of the applicant. When conducting government affairs management, it is usually necessary to verify the identity information of the applicant, which is carried out by verifying the ID card or mobile phone number. Obtain the stored historical question information corresponding to the identity information of the personnel, obtain the keywords therein, and construct a keyword content portrait of the corresponding applicant based on the occurrence dates of the corresponding keywords.

[0053] S2: Obtain the analysis of the problem content probability corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for.

[0054] In a specific embodiment, in step S2, obtaining the analysis of the problem content probability corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for includes the following specific steps:

[0055] S21: Obtain the keyword content portrait of the corresponding applicant, simultaneously obtain the keywords of each corresponding problem, and simultaneously obtain the number of times each problem is asked in this period.

[0056] S22: Based on the keyword content portrait of the corresponding applicant, the keywords of each corresponding problem, and the number of times each problem is asked in this period, conduct the content probability of the corresponding problem. Among them, the content probability of the i-th problem is: , where fi is the number of times the i-th question is asked in this period, m is the total number of questions asked in this period, Ni is the number of keywords corresponding to the i-th question, bj is the number of occurrence scenarios of the j-th keyword of the i-th question in the keyword content portrait of the corresponding applicant, ksj is the number of occurrences of the j-th keyword in the s-th occurrence scenario in the keyword content portrait of the corresponding applicant, and tsj is the duration from the occurrence time of the s-th occurrence scenario of the j-th keyword in the keyword content portrait of the corresponding applicant to the current moment;

[0057] Exemplarily, for example, in a tax scenario, by storing the question data of customers in this period, the number of times each question is asked in this period is as follows: "What is the tax refund amount" is asked 5 times, "How to conduct individual tax refund" is asked 3 times, and "How to handle enterprise taxes" is asked 5 times. In this formula, the matching situation between the corresponding question and the applicant is comprehensively analyzed by considering the popularity of the keywords corresponding to the question and the historical application connection between the keywords and the applicant;

[0058] S23. Obtain the content probability of all questions asked in this period and store it in the corresponding storage component;

[0059] S3: Analyze the similarity degree of similar personnel based on the content portraits of other applicants and the current applicant, and analyze the probability of the question content based on the similarity degree of similar personnel;

[0060] In a specific embodiment, the similarity degree analysis in step S3 includes the following specific steps:

[0061] S31. Obtain the question data of other applicants in historical periods and the question data of the applicant in historical periods;

[0062] Exemplarily, after the applicant asks a question, store the question in the corresponding storage component and retrieve the data when needed;

[0063] S32. Obtain the question data of other applicants in historical periods and the question data of the applicant in historical periods to obtain the question similarity degree between other applicants and the applicant. Among them, the formula for the question similarity degree between the s-th applicant and the applicant is: , where Hs is the number of historical periods, Db is the set composed of the keyword data of the questions asked by the s-th applicant in the b-th historical period, Kb is the set composed of the keyword data of the questions asked by the applicant in the b-th historical period, s() is the number of parameters in the set, tm is the standard duration, and to eliminate the time unit, tb is the duration from the b-th historical period to the current moment;

[0064] Exemplarily, the similarity degree of the questions of two persons is accurately analyzed by the similarity of historical question keywords and the time of corresponding keywords, so as to obtain the persons whose question habits are similar to those of the applicant;

[0065] S33. Obtain the similarity degree of the questions of all other applicants and the applicant.

[0066] In step S3, probability analysis is performed on the question content based on the similarity degree of similar persons, including the following specific contents:

[0067] S34. Obtain the questions asked by other applicants in the current period and the similarity degree of their questions with those of the applicant.

[0068] S35. Import the questions asked by other applicants in the current period and the similarity degree of their questions with those of the applicant into the formula for calculating the probability of the question content of the person. Among them, the formula for calculating the probability of the question content of the person for the i-th question is: , where Di is the number of people asking the i-th question in the current period, and Pdi is the similarity degree of the question of the d-th other applicant for the i-th question in the current period;

[0069] Exemplarily, in this step, the probability of a person asking a question is jointly analyzed by the number of questions asked by other applicants in the current period and the similarity degree of their questions with those of the applicant.

[0070] S4: Based on the analysis of the probability of the question content corresponding to the applicant and the similarity degree of similar persons, a comprehensive analysis of the question content is performed on the analysis result of the probability of the question content.

[0071] In a specific embodiment, in step S4, a comprehensive analysis of the question content is performed on the analysis result of the probability of the question content based on the analysis of the probability of the question content corresponding to the applicant and the similarity degree of similar persons, including the following specific contents:

[0072] S41. Obtain the content probability of all the questions asked and the probability of the question content of the person. After performing weighted summation on the content probability and the probability of the question content of the corresponding question, obtain the final probability of asking the corresponding question;

[0073] Exemplarily, in this step, the weights are obtained through an experimental method. The weights of the content probability of the corresponding question and the content probability of the personnel question are preferably 0.348 and 0.652 respectively. The acquisition method is as follows: Obtain the department data question data that each historical applicant needs to apply for, and at the same time obtain the question data of the applicant in the current period. Import the department data question data that each historical applicant needs to apply for into the technical solution to output the question data of the predicted current period in history. Import the question data of the predicted current period in history and the question data of the applicant in the current period into the fitting software to output the weight value that meets the maximum judgment accuracy rate.

[0074] S42. Sort the final probabilities of the questions obtained for the corresponding questions in descending order for the applicant to select, so as to improve the question selection efficiency and selection speed of the applicant.

[0075] S5. Display the comprehensive analysis result of the question content on the client side to prompt the customer to select a question and prompt for registration.

[0076] In one specific embodiment, S5 includes the following steps: Sort and display the final probabilities of the questions obtained for the corresponding questions in descending order on the registration screen. The questioner selects a question closest to the one he wants to ask from the registration screen. The registration system prompts for registration according to the type category of the selected question and registers to the specified category.

[0077] In this embodiment, it should be noted that this embodiment has the following advantages. Based on the content portrait of the applicant and the content data that the applicant needs to apply for, analyze the probability of the corresponding question content of the applicant. Analyze the similarity degree of similar personnel based on the content portraits of other applicants and the content portrait of the current applicant. Analyze the probability of the question content based on the similarity degree of similar personnel. Based on the analysis of the probability of the question content corresponding to the applicant and the similarity degree of similar personnel, conduct a comprehensive analysis of the question content. Display the comprehensive analysis result of the question content on the client side to prompt the customer to select a question. This application analyzes the applicant's question-asking habits based on the applicant's content portrait, and comprehensively analyzes the department popularity of the question to be asked and the question-asking situation of similar people to quickly predict and analyze the question to be asked by the applicant, so as to shorten the applicant's language organization time and improve the processing efficiency of the government affairs processing department.

[0078] Embodiment 2 As Figure 4 shown, this embodiment provides a government service management system based on AI intelligent collaboration, including:

[0079] The content image construction module is used to obtain the department data that the applicant needs to apply for, and construct a content image of the applicant based on the historical application content data of the applicant;

[0080] The problem content probability analysis module obtains the problem content probability analysis corresponding to the applicant based on the content image of the applicant and the content data that the applicant needs to apply for;

[0081] The personnel probability analysis module analyzes the similarity degree of similar personnel based on the content images of other applicants and the content image of the current applicant, and performs probability analysis on the problem content based on the similarity degree of the similar personnel;

[0082] The problem content comprehensive analysis module comprehensively analyzes the problem content based on the problem content probability analysis corresponding to the applicant and the similarity degree of the similar personnel for the problem content probability analysis result;

[0083] The display module displays the comprehensive analysis result of the problem content on the client to prompt the customer to select the problem and prompt for registration;

[0084] For the parameters and steps of each unit module in the above-mentioned government service management system based on AI intelligent collaboration of the present application to implement corresponding functions, as well as the corresponding functions, reference can be made to the parameters and steps in the embodiment of the government service management method based on AI intelligent collaboration in the method embodiment, which will not be elaborated here.

[0085] Embodiment 3 An electronic device according to an embodiment of the present application includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes the government service management method based on AI intelligent collaboration by calling the computer program stored in the memory. It should be noted that: all computer programs of the government service management method based on AI intelligent collaboration are implemented using the C language.

[0086] Embodiment 4 This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored;

[0087] When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned government service management method based on AI intelligent collaboration.

[0088] Each embodiment in the present application is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0089] The system, medium, and method provided by the embodiments of the present application correspond one by one. Therefore, the system and medium also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be elaborated here.

[0090] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0094] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0095] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0096] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0097] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A government service management method based on AI intelligent collaboration, characterized in that: The steps include: S1: Obtain the department data that the applicant needs to apply for, and build a content profile of the applicant based on the applicant's historical application content data; S2: Based on the content profile of the applicant and the content data that the applicant needs to apply for, the probability analysis of the corresponding question content of the applicant is obtained; S3: Perform similarity analysis on similar persons based on the content portraits of other applicants and the content portrait of the current applicant, and perform probability analysis on the question content based on the similarity of similar persons; S4: Conduct a comprehensive analysis of the question content based on the probability analysis of the question content corresponding to the applicants and the similarity of similar persons; S5. Display the comprehensive analysis results of the question content on the client to prompt the customer to select the question and register.

2. The government service management method based on AI intelligent collaboration according to claim 1 is characterized in that: The method of obtaining the probability analysis of the question content corresponding to the applicant based on the content portrait of the applicant and the content data that the applicant needs to apply for includes the following specific steps: Obtain the keyword content portrait of the corresponding applicant, obtain the keywords corresponding to each question, and obtain the number of times each question has been asked in this cycle; Based on the keyword content portrait of the corresponding applicant, the keywords corresponding to each question and the number of times each question was asked in this cycle are obtained to calculate the content probability of the corresponding question, where the content probability of the i-th question is: , where fi is the number of times the ith question is asked in this cycle, m is the total number of questions asked in this cycle, Ni is the number of keywords corresponding to the ith question, bj is the number of occurrences of the jth keyword of the ith question in the keyword content portrait of the corresponding applicant, ksj is the number of occurrences of the jth keyword in the sth occurrence scene in the keyword content portrait of the corresponding applicant, and tsj is the duration from the appearance time of the jth keyword in the sth occurrence scene in the keyword content portrait of the corresponding applicant to the current moment; Obtain the content probability of all questions asked in this cycle; and store them in the corresponding storage component.

3. The government service management method based on AI intelligent collaboration according to claim 2 is characterized in that: The similarity analysis includes the following specific steps: Obtain historical periodic question data of other applicants and historical periodic question data of applicants; Obtain the historical periodic question data of other applicants and the historical periodic question data of the applicant to obtain the similarity between the questions of other applicants and the applicant. The similarity between the questions of the sth applicant and the applicant is calculated as follows: , where Hs is the number of historical periods, Db is the set of keyword data of the questions asked by the s-th applicant in the b-th historical period, Kb is the set of keyword data of the questions asked by the applicant in the b-th historical period, s() is the number of parameters in the set, tm is the standard time, and in order to eliminate the time unit, tb is the time from the b-th historical period to the current moment; Get the similarity of all other applicants' problems to the applicant's.

4. The government service management method based on AI intelligent collaboration according to claim 3 is characterized in that: The probability analysis of the question content based on the similarity of similar persons includes the following specific contents: Obtain the questions asked by other applicants in the current cycle and the similarity between them and the applicants; The questions asked by other applicants in the current cycle and the degree of similarity between them and the applicant's questions are imported into the personnel question content probability calculation formula to calculate the personnel question content probability.

5. The government service management method based on AI intelligent collaboration according to claim 4 is characterized in that: The comprehensive analysis of the question content based on the probability analysis of the question content corresponding to the applicant and the similarity of similar persons on the question content probability analysis results includes the following specific contents: Obtain the content probability of all questions asked and the content probability of personnel questions, and perform weighted summation of the content probability of the corresponding question and the content probability of the personnel question to obtain the final probability of the corresponding question; The final probability of asking the corresponding questions is sorted in descending order for the applicants to choose.

6. The government service management method based on AI intelligent collaboration according to claim 5 is characterized in that: The formula for calculating the probability of the personnel question content of the i-th question is: , where Di is the number of people who asked the i-th question in the current cycle, and Pdi is the similarity of the questions of the d-th other applicants for the i-th question in the current cycle.

7. The government service management method based on AI intelligent collaboration according to claim 6 is characterized in that: The S1 comprises the following specific steps: Acquire attribute data of the department to which the applicant needs to apply, wherein the attribute data of the department includes management direction data of the department and historical problem data of the department corresponding to various personnel applications, and store them in the first storage component; Identify the identity information of the applicant, obtain the historical question data of the corresponding applicant, build a keyword content portrait of the corresponding applicant based on the keywords of the historical question data of the corresponding applicant, and store the keyword content portrait of the corresponding applicant in the second storage component.

8. A government service management system based on AI intelligent collaboration, which is implemented based on the government service management method based on AI intelligent collaboration as described in any one of claims 1 to 7, characterized in that the system include: The content profile building module is used to obtain the department data that the applicant needs to apply for, and build a content profile of the applicant based on the applicant's historical application content data; The question content probability analysis module obtains the question content probability analysis corresponding to the applicant based on the applicant's content portrait and the content data that the applicant needs to apply for; The personnel probability analysis module performs similarity analysis on similar personnel based on the content portraits of other applicants and the content portrait of the current applicant, and performs probability analysis on the question content based on the similarity of similar personnel; The question content comprehensive analysis module conducts a comprehensive analysis of the question content based on the probability analysis of the question content corresponding to the applicant and the similarity of similar persons; The display module displays the comprehensive analysis results of the question content on the client to prompt the customer to select the question and register.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the government service management method based on AI intelligent collaboration as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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