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

Abstract

The application relates to the technical field of government service management, in particular to a government service management method and system based on AI intelligent collaboration, which performs probability analysis on problem content based on the similarity degree of similar personnel, performs comprehensive analysis on the problem content based on the problem content probability analysis of the applicant and the problem content probability analysis result of the similar personnel based on the similarity degree of the similar personnel, displays the comprehensive analysis result of the problem content on the client to prompt the customer to select the problem, analyzes the questioning habit of the applicant based on the content portrait of the applicant, and performs quick prediction analysis on the question to be asked by the applicant based on the department popularity of the question and the questioning situation of similar people, so that the language organization time of the applicant is shortened, and the processing efficiency of the government processing department is improved.

Description

Technical Field

[0001] This application relates to the field of government service management technology, and in particular to government service management methods and systems based on AI-powered intelligent collaboration. Background Technology

[0002] Existing government service management systems generally offer two appointment registration methods: online and offline. These two methods cater to different types of people, providing convenience for the public to access government services. However, the appointment registration interface available each day is usually fixed. This necessitates repeated operations to search for the relevant issues during the registration process. The fixed interface cannot meet the registration needs of people with different demographics and lifestyles. Current technology cannot analyze applicants' questioning habits based on their profiles, nor can it quickly predict and analyze the questions applicants might ask by considering the popularity of the departments asking the questions and the questioning patterns of similar groups. This limits the processing efficiency of government departments.

[0003] To address the aforementioned issues, this application proposes an AI-based intelligent collaborative government service management solution. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this application provides an AI-based intelligent collaborative government service management method and system. This method analyzes the probability of questions asked by applicants based on their content profiles and the data on the content they request. It also analyzes the similarity between the content profiles of other applicants and the current applicant's content profile, performs probability analysis on the question content based on the similarity of similar individuals, and then performs a comprehensive analysis of the question content based on the probability analysis results of the applicant's question content and the similarity of similar individuals. The comprehensive analysis results are displayed on the client side, prompting the user to select a question. This application analyzes applicants' questioning habits based on their content profiles and, by combining the popularity of the departments asking questions with the questioning behavior of similar groups, quickly predicts and analyzes the questions applicants may ask, thereby shortening the applicant's language organization time and improving the processing efficiency of government departments.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, this application provides a government service management method based on AI-powered intelligent collaboration, including the following steps:

[0007] S1: Obtain the department data that the applicant needs to apply to, and build a content profile of the applicant based on the applicant's historical application data;

[0008] S2: Based on the content profile of the applicant and the content data that the applicant needs to apply for, obtain the probability analysis of the corresponding question content of the applicant;

[0009] S3: Analyze the similarity between the content profiles of other applicants and the content profile of the current applicant, and perform probability analysis on the question content based on the similarity between similar applicants;

[0010] S4: Based on the probability analysis of the question content corresponding to the applicant and the degree of similarity of similar people, conduct a comprehensive analysis of the question content probability analysis results;

[0011] S5. Display the comprehensive analysis results of the problem content on the client side, prompt the client to select the problem, and prompt them to register.

[0012] In one implementation of this application, step S1 includes the following specific steps:

[0013] S11. Obtain the attribute data of the department that the applicant needs to apply to. The attribute data of the department includes the management direction data of the department and the historical problem data of the department for various personnel applications, which are stored in the first storage component.

[0014] S12. Identify the applicant's identity information, obtain the applicant's historical question data, construct a keyword content profile of the applicant based on the keywords in the applicant's historical question data, and store the applicant's keyword content profile in the second storage component.

[0015] In one implementation of this application, step S2 involves obtaining a probability analysis of the applicant's corresponding question content based on the applicant's content profile and the content data the applicant needs to apply for. This includes the following specific steps:

[0016] S21. Obtain the keyword content profile of the corresponding applicant, and at the same time obtain the keywords of each question, and the number of times each question is asked in this period.

[0017] S22. Based on the keyword content profile of the corresponding applicant, obtain the keywords for each question and the number of times each question has been asked in this period 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 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 profile of the corresponding applicant, ksj is the number of occurrences of the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant, and tsj is the time elapsed since the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant.

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

[0019] In one implementation of this application, the similarity analysis in step S3 includes the following specific steps:

[0020] S31. Obtain historical question data from other applicants, as well as historical question data from the applicants themselves;

[0021] For example, after a question is asked, the applicant's question is stored in the corresponding storage component, and the data is retrieved when needed.

[0022] S32. Obtain historical question data from other applicants and the applicant's historical question data to determine the similarity between the questions from other applicants and the applicant. The formula for calculating the similarity between the questions from the s-th applicant and the applicant is as follows: Where Hs is the number of historical periods, Db is the set of keyword data of the question asked by the s-th applicant in the b-th historical period, Kb is the set of keyword data of the question 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 time units, tb is the duration from the b-th historical period to the current moment.

[0023] S33. Obtain the similarity between all other applicants' questions and the applicants' questions.

[0024] In one implementation of this application, step S3 involves performing a probability analysis on the question content based on the degree of similarity among similar individuals, including the following specific details:

[0025] S34. Obtain the current period's questions from other applicants and the degree of similarity between their questions and those of the applicants;

[0026] S35. Import the current period's questions from other applicants and the similarity of their questions to those of the applicants into the personnel question content probability calculation formula to calculate the personnel question content probability. The formula for calculating the personnel question content probability 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 between the d-th question and the other applicants' questions in the i-th question in the current period.

[0027] In one implementation of this application, step S4 involves a comprehensive analysis of the problem content based on the probability analysis of the problem content corresponding to the applicant and the degree of similarity among similar individuals. This analysis includes the following specific aspects:

[0028] S41. Obtain the content probability of all questions and the content probability of questions asked by individuals. Then, sum the content probabilities of the corresponding questions and the content probabilities of questions asked by individuals in a weighted manner to obtain the final probability of asking the corresponding question.

[0029] S42. Sort the final probability of the corresponding questions in descending order for applicants to choose from, so as to improve the efficiency and speed of applicants in choosing questions.

[0030] Secondly, this application also provides an AI-based intelligent collaborative government service management system, including:

[0031] The content profile building module is used to obtain data on the departments that applicants need to apply to, and to build a content profile of the applicants based on their historical application data.

[0032] The Problem Content Probability Analysis module analyzes the probability of problem content for each applicant based on the applicant's content profile and the data on the content the applicant needs to apply for.

[0033] The personnel probability analysis module analyzes the similarity between the content profiles of other applicants and the content profile of the current applicant, and then performs probability analysis on the question content based on the similarity between similar applicants.

[0034] The comprehensive analysis module for question content performs a comprehensive analysis of the question content based on the probability analysis of the question content for each applicant and the degree of similarity among similar individuals.

[0035] The display module shows the comprehensive analysis results of the problem on the client side, prompting the customer to select the problem and register.

[0036] Thirdly, this application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an AI-based intelligent collaborative government service management method by calling the computer program stored in the memory.

[0037] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an AI-based intelligent collaborative government service management method.

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

[0039] This application analyzes the probability of questions asked by applicants based on their content profiles and the data on the content they request. It also analyzes the similarity between the content profiles of other applicants and the current applicant, and performs a probability analysis of the question content based on this similarity. Finally, it performs a comprehensive analysis of the question content based on the probability analysis of the applicant's question content and the similarity analysis of similar individuals. The comprehensive analysis results are displayed on the client side, prompting the user to select a question. This application analyzes applicants' questioning habits based on their content profiles and, by combining the popularity of the departments asking questions with the questioning behavior of similar groups, quickly predicts and analyzes the questions applicants may ask, thus shortening the time applicants spend organizing their answers and improving the processing efficiency of government departments. Attached Figure Description

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

[0041] Figure 1 This is a schematic diagram of the overall process of the method in this application;

[0042] Figure 2 This is a flowchart of the process of S2 in the method of this application;

[0043] Figure 3 This is a flowchart of the process of S3 in the method of this application;

[0044] Figure 4 This is a schematic diagram of the system structure of this application. Detailed Implementation

[0045] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0046] Example 1

[0047] like Figures 1 to 3As shown, this embodiment provides a government service management method based on AI intelligent collaboration, which specifically includes the following steps:

[0048] S1: Obtain the department data that the applicant needs to apply to, and build a content profile of the applicant based on the applicant's historical application data;

[0049] In one specific embodiment, step S1 includes the following specific steps:

[0050] S11. Obtain the attribute data of the department that the applicant needs to apply to. The attribute data of the department includes the management direction data of the department and the historical problem data of the department for various personnel applications, which are stored in the first storage component.

[0051] For example, for a tax administration department, its corresponding management direction is taxation. The historical question data of various personnel applications of the department is the question data raised by various tax-related personnel in the past. For example, when processing tax refunds, the question raised by personnel in the past was: How much is the tax refund? After keyword extraction, the keywords are tax refund, amount and how much.

[0052] S12. Identify the applicant's identity information, obtain the applicant's historical question data, construct a keyword content profile of the applicant based on the keywords of the applicant's historical question data, and store the applicant's keyword content profile in the second storage component.

[0053] For example, identifying the applicant's identity information is usually required when conducting government administration, which can be done by verifying the applicant's identity information, such as by verifying the ID card or mobile phone number. The stored historical question information corresponding to the applicant's identity information is obtained, and the keywords are extracted. Based on the appearance date of the corresponding keywords, a keyword content profile of the applicant is constructed.

[0054] S2: Based on the content profile of the applicant and the content data that the applicant needs to apply for, obtain the probability analysis of the corresponding question content of the applicant;

[0055] In one specific embodiment, step S2, which involves obtaining a probability analysis of the applicant's corresponding question content based on the applicant's content profile and the content data the applicant needs to request, includes the following specific steps:

[0056] S21. Obtain the keyword content profile of the corresponding applicant, and at the same time obtain the keywords of each question, and the number of times each question is asked in this period.

[0057] S22. Based on the keyword content profile of the corresponding applicant, obtain the keywords for each question and the number of times each question has been asked in this period 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 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 profile of the corresponding applicant, ksj is the number of occurrences of the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant, and tsj is the time elapsed since the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant.

[0058] For example, in a tax scenario, by storing and retrieving customer question data for the current period, the number of questions asked for each question in the current period is as follows: how much is the tax refund amount (5 times), how to apply for a personal tax refund (3 times), and how to handle corporate tax matters (5 times). In this formula, the popularity of the corresponding question keywords and the historical application connections between the keywords and the applicant are comprehensively analyzed to determine the matching between the corresponding question and the applicant.

[0059] S23. Obtain the content probability of all questions asked in this period and store it in the corresponding storage component;

[0060] S3: Analyze the similarity between the content profiles of other applicants and the content profile of the current applicant, and perform probability analysis on the question content based on the similarity between similar applicants;

[0061] In one specific embodiment, the similarity analysis in step S3 includes the following specific steps:

[0062] S31. Obtain historical question data from other applicants, as well as historical question data from the applicants themselves;

[0063] For example, after a question is asked, the applicant's question is stored in the corresponding storage component, and the data is retrieved when needed.

[0064] S32. Obtain historical question data from other applicants and the applicant's historical question data to determine the similarity between the questions from other applicants and the applicant. The formula for calculating the similarity between the questions from the s-th applicant and the applicant is as follows: Where Hs is the number of historical periods, Db is the set of keyword data of the question asked by the s-th applicant in the b-th historical period, Kb is the set of keyword data of the question 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 time units, tb is the duration from the b-th historical period to the current moment.

[0065] For example, by analyzing the similarity of historical question keywords and the time of the corresponding keywords, the similarity of two people's questions can be accurately analyzed to identify people with similar questioning habits to the applicant.

[0066] S33. Obtain the similarity between all other applicants' questions and the applicants' questions.

[0067] Step S3 involves a probability analysis of the question content based on the degree of similarity among similar individuals, including the following specific details:

[0068] S34. Obtain the current period's questions from other applicants and the degree of similarity between their questions and those of the applicants;

[0069] S35. Import the current period's questions from other applicants and the similarity of their questions to those of the applicants into the personnel question content probability calculation formula to calculate the personnel question content probability. The formula for calculating the personnel question content probability 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 between the d-th question and the other applicant's question in the i-th question in the current period;

[0070] For example, in this step, the probability of a person asking a question is analyzed by combining the number of questions asked by other applicants in the current period and the similarity of their questions to those of the applicants;

[0071] S4: Based on the probability analysis of the question content corresponding to the applicant and the degree of similarity of similar people, conduct a comprehensive analysis of the question content probability analysis results;

[0072] In one specific embodiment, step S4 involves a comprehensive analysis of the question content based on the probability analysis of the question content corresponding to the applicant and the degree of similarity among similar individuals. This analysis includes the following specific aspects:

[0073] S41. Obtain the content probability of all questions and the content probability of questions asked by individuals. Then, sum the content probabilities of the corresponding questions and the content probabilities of questions asked by individuals in a weighted manner to obtain the final probability of asking the corresponding question.

[0074] For example, in this step, the weights are obtained experimentally, and the preferred weights for the probability of the question content and the probability of the personnel question content are 0.348 and 0.652, respectively. The acquisition method is as follows: obtain the historical data of the departments that each applicant needs to apply to, and at the same time obtain the question data of the applicant in the current period. Import the historical data of the departments that each applicant needs to apply to into the technical solution to output the historically predicted question data for the current period. Import the historically predicted question data for the current period and the question data of the applicant in the current period into the fitting software, and output the weight values ​​that meet the maximum judgment accuracy.

[0075] S42. Sort the final probability of the corresponding questions in descending order for applicants to choose from, so as to improve the efficiency and speed of applicants in choosing questions;

[0076] S5. Display the comprehensive analysis results of the problem content on the client side, prompt the customer to select the problem, and prompt them to register;

[0077] In one specific embodiment, S5 includes the following steps: the questions are displayed on the registration screen in descending order according to their probability of being asked; the questioner selects the closest question from the registration screen according to the question they want to ask; the registration system prompts the user to register the question according to the category of the selected question; and the registration is made to the specified category.

[0078] In this embodiment, it should be noted that it has the following advantages: Based on the applicant's content profile and the content data the applicant needs to request, a probability analysis of the applicant's corresponding question content is obtained; based on the content profiles of other applicants and the current applicant, a similarity analysis of similar individuals is performed; based on the similarity of similar individuals, a probability analysis of the question content is conducted; based on the probability analysis of the applicant's corresponding question content and the similarity of similar individuals, a comprehensive analysis of the question content is performed; and the comprehensive analysis results of the question content are displayed on the client to prompt the customer to select a question. This application analyzes the applicant's questioning habits based on the applicant's content profile and, by comprehensively considering the departmental popularity of the questions and the questioning situation of similar groups, quickly predicts and analyzes the questions the applicant intends to ask, thereby shortening the applicant's language organization time and improving the processing efficiency of government departments.

[0079] Example 2

[0080] like Figure 4 As shown, this embodiment provides an AI-based intelligent collaborative government service management system, including:

[0081] The content profile building module is used to obtain data on the departments that applicants need to apply to, and to build a content profile of the applicants based on their historical application data.

[0082] The Problem Content Probability Analysis module analyzes the probability of problem content for each applicant based on the applicant's content profile and the data on the content the applicant needs to apply for.

[0083] The personnel probability analysis module analyzes the similarity between the content profiles of other applicants and the content profile of the current applicant, and then performs probability analysis on the question content based on the similarity between similar applicants.

[0084] The comprehensive analysis module for question content performs a comprehensive analysis of the question content based on the probability analysis of the question content for each applicant and the degree of similarity among similar individuals.

[0085] The display module shows the comprehensive analysis results of the problem content on the client side, prompting the customer to select the problem and register.

[0086] The steps and functions of each parameter and unit module in the AI-based intelligent collaborative government service management system of this application can be found in the embodiments of the AI-based intelligent collaborative government service management method, and will not be repeated here.

[0087] Example 3

[0088] An electronic device according to an embodiment of this application includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes an AI-based intelligent collaborative government service management method by calling the computer program stored in the memory. It should be noted that all computer programs for the AI-based intelligent collaborative government service management method are implemented using the C programming language.

[0089] Example 4

[0090] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0091] When a computer program runs on a computer device, it enables the computer device to execute the aforementioned AI-based intelligent collaborative government service management method.

[0092] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0093] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0099] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0101] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A government service management method based on AI-powered intelligent collaboration, characterized in that: Includes the following steps: S1: Obtain the department data that the applicant needs to apply to, and build a content profile of the applicant based on the applicant's historical application content data. The department data includes the management direction data of the department and the historical question data of various personnel applications in the department. S2: Based on the applicant's content profile and the data of the departments the applicant needs to apply to, obtain the probability analysis of the corresponding question content of the applicant; The specific steps include the following: Obtain keyword content profiles for the corresponding applicants, as well as keywords for each question and the number of times each question was asked within the current period; Based on the keyword content profile of the corresponding applicants, the keywords for each question, and the number of times each question was asked in this period, the content probability of each question is calculated. The content probability of the i-th question 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 profile of the corresponding applicant, ksj is the number of occurrences of the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant, and tsj is the time elapsed since the s-th occurrence scenario of the j-th keyword in the keyword content profile of the corresponding applicant. Retrieve the content probability of all questions asked in the current period and store it in the corresponding storage component; S3: Analyze the similarity between the content profiles of other applicants and the current applicant; this includes the following specific steps: Obtain historical question data from other applicants, as well as historical question data from the applicants themselves; To determine the similarity between the questions asked by other applicants over a given period and the questions asked by the applicant over a given period, the similarity between the questions asked by other applicants and the applicant is calculated. The formula for calculating the similarity between the questions asked by the s-th applicant and the applicant is as follows: Where Hs is the number of historical periods, Db is the set of keyword data of the question asked by the s-th applicant in the b-th historical period, Kb is the set of keyword data of the question 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 time units, tb is the duration from the b-th historical period to the current moment. Obtain the similarity between all other applicants and their questions, and perform probability analysis on the question content based on the similarity between similar applicants; S4: Based on the probability analysis of the question content corresponding to the applicant and the degree of similarity of similar people, conduct a comprehensive analysis of the question content probability analysis results; S5. Display the comprehensive analysis results of the problem content on the client side, prompt the client to select the problem, and prompt them to register.

2. The government service management method based on AI intelligent collaboration according to claim 1, characterized in that, The probability analysis of question content based on the similarity of similar individuals includes the following specific content: Obtain the current period's questions from other applicants and the degree of similarity between their questions and those of the applicants; The current period's questions from other applicants and the similarity of their questions to those of the applicants are imported into the formula for calculating the probability of the applicant's question content.

3. The government service management method based on AI intelligent collaboration according to claim 2, characterized in that, The comprehensive analysis of question content based on the probability analysis of question content corresponding to the applicant and the similarity of similar individuals includes the following specific content: Obtain the content probability of all questions and the content probability of questions asked by individuals. Then, sum the content probabilities of the corresponding questions and the content probabilities of questions asked by individuals in a weighted manner to obtain the final probability of asking the corresponding question. The probability of each question being asked will be sorted in descending order for applicants to choose from.

4. The government service management method based on AI intelligent collaboration according to claim 3, characterized in that, The formula for calculating the probability of the personnel question content in 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 between the d-th question and the other applicants' questions in the i-th question in the current period.

5. The government service management method based on AI intelligent collaboration according to claim 4, characterized in that, S1 includes the following specific steps: Obtain the attribute data of the department that the applicant needs to apply to. The attribute data of the department includes the management direction data of the department and the historical problem data of various personnel applications in the department, which are stored in the first storage component. Identify the applicant's identity information, obtain the applicant's historical question data, construct a keyword content profile of the applicant based on the keywords in the applicant's historical question data, and store the applicant's keyword content profile in the second storage component.

6. A government service management system based on AI intelligent collaboration, implemented based on any one of claims 1-5, characterized in that the system... include: The content profile building module is used to obtain data on the departments that applicants need to apply to, and to build a content profile of the applicants based on their historical application data. The Problem Content Probability Analysis module analyzes the probability of problem content for each applicant based on the applicant's content profile and the data on the content the applicant needs to apply for. The personnel probability analysis module analyzes the similarity between the content profiles of other applicants and the content profile of the current applicant, and then performs probability analysis on the question content based on the similarity between similar applicants. The comprehensive analysis module for question content performs a comprehensive analysis of the question content based on the probability analysis of the question content for each applicant and the degree of similarity among similar individuals. The display module shows the comprehensive analysis results of the problem on the client side, prompting the customer to select the problem and register.

7. 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 AI-based intelligent collaborative government service management method as described in any one of claims 1-5 by calling the computer program stored in the memory.

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