Intelligent optimization management system for e-government system

Abstract

The application relates to the technical field of government affair information management, in particular to an intelligent optimization management system of an e-government system, which comprises the following steps: target customers and target businesses are identified, historical service data of the target businesses are acquired according to the target businesses and classified according to ID identity information, a handling process is identified, and inertia service information is integrated; inertia service information in a case closing state is selected, same handling processes are combined and counted, the frequency of the handling processes is calculated, and the handling processes are divided into characteristic business units and conventional business units; the basic information of the target customers and the target businesses are combined, the business units are analyzed, local tasks are determined, the business windows corresponding to the local tasks are identified, and the tasks are transmitted, so that the business personnel of the corresponding business windows can remotely process; the application improves the efficiency of business handling, reduces the time cost of the public, strengthens the cooperation between the business windows, and improves the overall administrative efficiency and service quality of the government.

Description

Technical Field

[0001] This invention relates to the field of e-government information management technology, and in particular to an intelligent optimization management system for e-government systems. Background Technology

[0002] As the core infrastructure and key support platform for the digital transformation of government, the main value of government cloud lies in its precise response to the needs of a comprehensive perspective and integrated cloud infrastructure.

[0003] The existing technology CN118394987A discloses a government information allocation and management system based on big data. This system includes classifying government information according to data type, implementing different operations for different data types to ensure good storage or transmission effects, classifying government information according to its application scenarios to achieve classified allocation and distribution, ensuring the streamlining of government information, avoiding a large amount of useless redundant information from slowing down different information use platforms, actively generating information allocation signals, and using these signals to call up information, while storing and analyzing the information being called up.

[0004] However, current e-government systems face numerous challenges in actual operation. On the one hand, the complex procedures for handling different services and the lack of unified standardized management result in low efficiency, requiring citizens to visit multiple service windows, wasting a lot of time and energy. On the other hand, existing systems struggle to effectively integrate and analyze historical service data, making it difficult to accurately identify user needs and patterns in service handling, thus hindering the realization of precise and personalized government services. Furthermore, insufficient coordination among various stages of the service process and untimely information sharing affect the government's administrative efficiency and service quality. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing an intelligent optimization management system for e-government systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An intelligent optimization management system for e-government systems, comprising:

[0008] The business identification module is used to identify target customers and their corresponding target businesses;

[0009] The service analysis module is used to obtain the corresponding historical service data based on the target business, divide the historical service data into several individual information according to the ID identity information, identify the processing process in the individual information, and integrate the processing process in the individual information according to independent inertial service information.

[0010] The business analysis module is used to acquire inertial service information, identify the corresponding case tags, select inertial service information with the case status of closed, merge and count the same processing procedures, calculate the process frequency of the corresponding processing procedures, and divide the processing procedures into characteristic business units and regular business units based on the process frequency.

[0011] The integrated processing module is used to acquire basic information and target business of target customers, and at the same time analyze customer information of characteristic business units and regular business units to determine the local tasks of target customers in the e-government system.

[0012] The collaborative processing module is used to identify the business window corresponding to a local task and transmit the local task to the corresponding business window, where the business personnel in the corresponding window can perform remote operation and processing.

[0013] As a further aspect of the present invention, the method for dividing individual information includes:

[0014] S1: Obtain historical data from the government system, simultaneously obtain the target business, use the target business as key retrieval information, perform information retrieval in the historical data, and mark the obtained retrieval results as historical service data;

[0015] S2: Obtain historical service data and identify customer ID information from the historical service data, where the ID information includes the customer's name and ID card number;

[0016] Historical service data is categorized based on customer ID information to obtain multiple information subsets. Each information subset is then labeled as a single piece of information, with each single piece of information corresponding to an ID.

[0017] As a further aspect of the present invention, the method for integrating inertial service information includes:

[0018] Each individual piece of information is sequentially labeled as a target analysis subset, and the processing flow within the target analysis subset is identified. Specifically, a business processed at the same time in one business window is labeled as one processing flow, while a business processed at different times in the same business window is labeled as a different processing flow.

[0019] The processing procedures in the target analysis subset are arranged in chronological order to obtain the process sequence, and case tags are detected in the target analysis subset at the same time.

[0020] When a case tag is detected in the target analysis subset, the number of case tags is identified. If the number of case tags is 1, it means that this customer has only handled the business requirement related to the target business once in the government affairs system. Then, the process sequence corresponding to this target analysis subset is obtained and this process sequence is marked as a separate inertial service information.

[0021] Conversely, when the number of case tags in the target analysis subset is greater than 1, it indicates that this customer has repeatedly handled business needs related to the target business in the government affairs system. Then, the corresponding process sequence is obtained, the process in which the case tag is located in the process sequence is identified, and this process is marked as a terminated process. The first process in the process sequence is identified and used as the starting process. Starting from the starting process, the most recent terminated process is identified in positional order. The process between the starting process and the terminated process is combined into an inertial service information. Then, the process after this terminated process is remarked as the starting process, and the above processing method is repeated until the process sequence is divided into multiple inertial service information.

[0022] As a further aspect of the present invention, the case tag refers to a tag indicating the status of the business terminal, including the case closed status and the suspension status. The case closed status indicates that the customer's comprehensive business needs have been completed, and the suspension status indicates that the customer's comprehensive business needs have been stopped midway through the process.

[0023] As a further aspect of the present invention, the method for calculating the process frequency includes:

[0024] All inertial service information is acquired, the case tags corresponding to the inertial service information are identified, and the inertial service information with the case tag being suspended is deleted. At this time, the case tags corresponding to the remaining inertial service information are all in the closed state.

[0025] Obtain the remaining inertial service information, identify all processing procedures in the inertial service information, merge the same processing procedures, count the number of procedures under the same processing procedure, and mark the obtained number as the processing value Bi, where i represents different processing procedures;

[0026] At the same time, the inertial service information in the case-closed state is statistically analyzed and the statistical quantity is marked as RT. Then, the processing value Bi of the processing process i is divided by RT, and the resulting value is marked as the process frequency Fi of the processing process i, that is, Fi = Bi ÷ RT.

[0027] The frequency Fi of each processing process i is compared with the frequency threshold Fy. If Fi < Fy, the corresponding processing process i is marked as a characteristic business unit; otherwise, if Fi ≥ Fy, the corresponding processing process is marked as a regular business unit.

[0028] As a further aspect of the present invention, in a single processing flow contained in an inertial service information, all processing flows are in a normal processing state. If a processing flow fails due to staff or customer error, the corresponding processing flow is deleted from the inertial service information. That is, the process in the failed processing state does not participate in the unit segmentation process.

[0029] As a further aspect of the present invention, the method for obtaining local tasks includes:

[0030] Extract the regular business units from the business units and set them directly as local tasks. Then, obtain the characteristic business units and arbitrarily select one of the characteristic business units as the target unit. Taking the target unit as an example, obtain the customer corresponding to each business transaction in the target unit and mark this customer as a reference customer. At the same time, obtain the basic information of each reference customer and use natural language processing algorithms to identify common features in the basic information of the reference customers and mark these common features as the feature information of the target unit.

[0031] The basic information of the target customer is obtained. The cosine similarity processing algorithm is used to process the basic information of the target customer with the feature information of the target unit to obtain the information similarity value. Then, the information similarity value is compared with the similarity threshold. If the information similarity value is less than the similarity threshold, the corresponding target unit is marked as a hidden unit. Conversely, if the information similarity value is greater than or equal to the similarity threshold, the corresponding target unit is marked as an explicit unit.

[0032] Identify explicit units within characteristic business units and mark them as local tasks of the target customer.

[0033] As a further embodiment of the present invention, it also includes an information storage module and a terminal processing module;

[0034] The information storage module is used to store data information from various service departments in the government system. The information storage module has a one-way communication connection with the service analysis module and the collaborative processing module.

[0035] The terminal processing module is used to obtain the partially processed information transmitted by the collaborative processing module and display the partially processed information on the terminal display device. The target customer then performs operations and confirms based on the information displayed on the terminal display device.

[0036] Compared with existing technologies, the advantages of this invention are:

[0037] This invention identifies target customers and their corresponding target services, identifies historical service data based on the target services, processes this historical service data, integrates the processing flow according to inertial service information, and then analyzes this inertial service information to distinguish between characteristic business units and routine business units. This provides data support for government departments to optimize resource allocation, enabling resources to be more rationally allocated to different types of services. Then, by combining the basic information of target customers and the analysis of target services, local tasks are determined, achieving precise service positioning and better meeting customers' personalized needs. Finally, the collaborative processing module identifies the business window corresponding to the local task and performs remote operation processing, thereby breaking the spatial limitations of traditional business processing, greatly improving the efficiency of business processing, reducing the time cost for the public, and strengthening the collaboration between various business windows, thus improving the overall administrative efficiency and service quality of the government. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0040] Reference Figure 1 An intelligent optimization management system for e-government systems includes an information storage module, a business identification module, a service analysis module, a business analysis module, a comprehensive processing module, a collaborative processing module, and a terminal processing module.

[0041] The information storage module is used to store the data information of each service department in the e-government system. Furthermore, in the e-government system, each service department sets up a separate data information database, and when accessing the data information database, authentication is required to obtain the corresponding access permissions. After that, the information storage module has a one-way communication connection with the service analysis module and the collaborative processing module respectively.

[0042] The business identification module is used to identify the business needs of target customers. When a business to be processed is detected, it marks the business to be processed as a target business and transmits the target business to the service analysis module. At the same time, when the business identification module detects a target business, it also collects the basic information of the corresponding target customer and transmits the basic information of the target customer to the service analysis module. Furthermore, the basic information of the target customer includes information such as name, ID card number and mobile phone number. The target customer refers to the customer who is handling business in the government system at the current time.

[0043] The service analysis module is used to acquire the target business of the target customer, collect corresponding historical service data based on the target business, and analyze the historical service data to determine the inertial service information of the target business. Furthermore, the specific methods for determining the inertial service information include:

[0044] S1: Obtain historical data from the government system, simultaneously obtain the target business, use the target business as key retrieval information, perform information retrieval in the historical data, and mark the obtained retrieval results as historical service data;

[0045] It should be further explained that when a customer handles a comprehensive service, they need to go back and forth between multiple service windows, and each service window corresponds to a different task process and a different department. At the same time, the data information databases between departments operate independently. Therefore, in the historical service data, each task process is stored independently. So when searching the historical data with the target service as the key information, it means searching separately in the data information database corresponding to each department to obtain the historical service information.

[0046] S2: Obtain historical service data and identify customer ID information from the historical service data, where the ID information includes the customer's name and ID card number;

[0047] Historical service data is categorized based on customer ID information to obtain multiple information subsets. Each information subset is then labeled as a single piece of information, with each single piece of information corresponding to an ID.

[0048] S3: Randomly select a single piece of information and mark this single piece of information as a target analysis subset. Taking this target analysis subset as an example, identify the processing flow in the target analysis subset. Among them, the business processed at the same time in one business window is marked as one processing flow. If the business processed at different times in the same business window is marked as different processing flows, for example, in business window A, it is detected that a customer has a processing flow on June 7 and a processing flow on August 13. At this time, this customer is marked as different processing flows on June 7 and August 13 respectively.

[0049] The processing flow in the target analysis subset is arranged in chronological order to obtain the process sequence. At the same time, case tags are detected in the target analysis subset. Case tags refer to tags that indicate the status of the business terminal, including case closure status and suspension status. Case closure status indicates that the customer's comprehensive business needs have been completed, and suspension status indicates that the customer's comprehensive business needs have been stopped in the middle of the process.

[0050] When a case tag is detected in the target analysis subset, the number of case tags is first identified. If the number of case tags is 1, it means that this customer has only handled the business related to the target business once in the government affairs system. Then, the process sequence corresponding to this target analysis subset is obtained and this process sequence is marked as a single inertial service information. Conversely, when the number of case tags in the target analysis subset is greater than 1, it means that this customer has handled the business related to the target business multiple times in the government affairs system. Then, the corresponding process sequence is obtained, the handling process in which the case tag is located in the process sequence is identified, and this handling process is marked as a terminated process. The first handling process in the process sequence is identified and used as the starting process. Starting from the starting process, the nearest terminated process is identified in positional order. The handling processes between the starting process and the terminated process are combined into an inertial service information. Then, the handling processes after this terminated process are remarked as the starting process, and the above processing method is repeated until the process sequence is divided into multiple inertial service information.

[0051] The remaining individual information is set as target analysis subsets in sequence and processed according to the above method. Then, each target analysis subset is divided according to inertial service information, where one inertial service information corresponds to one case tag. After that, the service analysis module transmits all inertial service information to the business analysis module.

[0052] The business analysis module is used to acquire inertial service information and segment it into multiple business units. These business units include regular business units and special business units, and each business unit corresponds to a service department. Specifically, the methods for segmenting inertial service information into units include:

[0053] All inertial service information is acquired, the case tags corresponding to the inertial service information are identified, and the inertial service information with the case tag being suspended is deleted. At this time, the case tags corresponding to the remaining inertial service information are all in the closed state.

[0054] Obtain the remaining inertial service information, identify all processing procedures in the inertial service information, merge the same processing procedures, count the number of procedures under the same processing procedure, and mark the obtained number as the processing value Bi, where i represents different processing procedures;

[0055] It should be noted that in a single processing flow contained in an inertial service information, all processing flows are in a normal processing state. If a processing flow fails due to staff or customer error, the corresponding processing flow will be deleted from the inertial service information. That is, a process in a failed processing state will not participate in the unit segmentation process.

[0056] At the same time, the inertial service information in the case-closed state is statistically analyzed and the statistical quantity is marked as RT. Then, the processing value Bi of the processing process i is divided by RT, and the resulting value is marked as the process frequency Fi of the processing process i, that is, Fi = Bi ÷ RT.

[0057] The frequency Fi of each processing process i is compared with the frequency threshold Fy. If Fi < Fy, the corresponding processing process i is marked as a characteristic business unit. Otherwise, if Fi ≥ Fy, the corresponding processing process is marked as a regular business unit. The specific value of the frequency threshold Fy is set by those skilled in the art based on big data experience. In this embodiment, the frequency threshold Fy is set to 0.75.

[0058] The business analysis module then transmits the business units to the comprehensive processing module;

[0059] The integrated processing module is used to acquire business units, as well as basic information about target businesses and corresponding target customers. It then performs aggregate analysis of the target business and target customer information with the business units to determine the local tasks under the current target business of the target customer. The specific methods for determining these local tasks include:

[0060] Extract the regular business units from the business units and set them directly as local tasks. Then, obtain the characteristic business units and arbitrarily select one of the characteristic business units as the target unit. Taking the target unit as an example, obtain the customer corresponding to each business transaction in the target unit and mark this customer as a reference customer. At the same time, obtain the basic information of each reference customer and use natural language processing algorithms to identify common features in the basic information of the reference customers and mark these common features as the feature information of the target unit.

[0061] The basic information of the target customer is obtained, and a similarity processing algorithm is used to perform similarity processing between the basic information of the target customer and the feature information of the target unit to obtain an information similarity value. Then, the information similarity value is compared with a similarity threshold. If the information similarity value is less than the similarity threshold, the corresponding target unit is marked as a hidden unit. Conversely, if the information similarity value is greater than or equal to the similarity threshold, the corresponding target unit is marked as an explicit unit. The specific value of the similarity threshold is obtained by those skilled in the art based on big data calculations. The similarity processing algorithm used in this embodiment is the cosine similarity algorithm, which is an existing technology and will not be described in detail here.

[0062] Then, all the remaining feature business units are set as target units in sequence and processed according to the above method, thereby dividing the feature business units into explicit units and implicit units.

[0063] Identify the explicit units in the characteristic business units and mark the explicit units as local tasks of the target customer. Then, obtain all local tasks of the target customer and transmit them from the comprehensive processing module to the collaborative processing module.

[0064] The collaborative processing module is used to acquire all the local tasks of the target customer, identify the business window corresponding to each local task, and transmit the local task to the corresponding business window. The business personnel at the corresponding window will then perform remote operation and processing, and transmit the processed business information to the terminal processing module.

[0065] The terminal processing module is used to obtain the partially processed information and display it on the terminal display device. Then, the target customer performs operations and confirmations based on the information displayed on the terminal display device.

[0066] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An intelligent optimization management system for e-government systems, characterized in that, include: The business identification module is used to identify target customers and their corresponding target businesses; The service analysis module is used to obtain the corresponding historical service data based on the target business, divide the historical service data into several individual information according to the ID identity information, identify the processing process in the individual information, and integrate the processing process in the individual information according to independent inertial service information. Inertial service information integration methods include: Historical service data is categorized based on customer ID information to obtain multiple information subsets, and each information subset is labeled as a single piece of information. Each individual piece of information is sequentially labeled as a target analysis subset, and the processing flow within the target analysis subset is identified. Specifically, a business processed at the same time in one business window is labeled as one processing flow, while a business processed at different times in the same business window is labeled as a different processing flow. The processing procedures in the target analysis subset are arranged in chronological order to obtain the process sequence, and case tags are detected in the target analysis subset at the same time. When a case tag is detected in the target analysis subset, the number of case tags is identified. If the number of case tags is 1, it means that this customer has only handled the business requirement related to the target business once in the government affairs system. Then, the process sequence corresponding to this target analysis subset is obtained and this process sequence is marked as a separate inertial service information. Conversely, when the number of case tags in the target analysis subset is greater than 1, it indicates that this customer has repeatedly handled business needs related to the target business in the government affairs system. Then, the corresponding process sequence is obtained, the process where the case tag is located in the process sequence is identified, and this process is marked as a terminated process. The first process in the process sequence is identified and used as the starting process. Starting from the starting process, the nearest terminated process is identified in positional order. The process between the starting process and the terminated process is combined into an inertial service information. Then, the process after this terminated process is remarked as the starting process, and the above processing method is repeated until the process sequence is divided into multiple inertial service information. The business analysis module is used to acquire inertial service information, identify the corresponding case tags, select inertial service information with the case status of closed, merge and count the same processing procedures, calculate the process frequency of the corresponding processing procedures, and divide the processing procedures into characteristic business units and regular business units based on the process frequency. Specifically, the frequency Fi of each processing process i is compared with the frequency threshold Fy. If Fi < Fy, the corresponding processing process i is marked as a characteristic business unit; otherwise, if Fi ≥ Fy, the corresponding processing process is marked as a regular business unit. The integrated processing module is used to acquire basic information and target business of target customers, and at the same time analyze customer information of characteristic business units and regular business units to determine the local tasks of target customers in the e-government system. Methods for determining local tasks include: Extract the regular business units from the business units and set them directly as local tasks. Then, obtain the characteristic business units and arbitrarily select one of the characteristic business units as the target unit. Taking the target unit as an example, obtain the customer corresponding to each business transaction in the target unit and mark this customer as a reference customer. At the same time, obtain the basic information of each reference customer and use natural language processing algorithms to identify common features in the basic information of the reference customers and mark these common features as the feature information of the target unit. The basic information of the target customer is obtained. The cosine similarity processing algorithm is used to process the basic information of the target customer with the feature information of the target unit to obtain the information similarity value. Then, the information similarity value is compared with the similarity threshold. If the information similarity value is less than the similarity threshold, the corresponding target unit is marked as a hidden unit. Conversely, if the information similarity value is greater than or equal to the similarity threshold, the corresponding target unit is marked as an explicit unit. Identify explicit units within characteristic business units and mark these explicit units as local tasks of the target customer; The collaborative processing module is used to identify the business window corresponding to a local task and transmit the local task to the corresponding business window, where the business personnel in the corresponding window can perform remote operation and processing.

2. The intelligent optimization management system for e-government systems according to claim 1, characterized in that, Methods for partitioning individual entity information include: S1: Obtain historical data from the government system, simultaneously obtain the target business, use the target business as key retrieval information, perform information retrieval in the historical data, and mark the obtained retrieval results as historical service data; S2: Obtain historical service data and identify customer ID information from the historical service data, where the ID information includes the customer's name and ID card number; Historical service data is categorized based on customer ID information to obtain multiple information subsets. Each information subset is then labeled as a single piece of information, with each single piece of information corresponding to an ID.

3. The intelligent optimization management system for e-government systems according to claim 1, characterized in that, Case tags refer to tags that indicate the status of a business terminal, including closed and suspended status. Closed status indicates that the customer's comprehensive business needs have been completed, while suspended status indicates that the customer's comprehensive business needs have been stopped midway through the process.

4. The intelligent optimization management system for e-government systems according to claim 1, characterized in that, The methods for calculating process frequency include: All inertial service information is acquired, the case tags corresponding to the inertial service information are identified, and the inertial service information with the case tag being suspended is deleted. At this time, the case tags corresponding to the remaining inertial service information are all in the closed state. Obtain the remaining inertial service information, identify all processing procedures in the inertial service information, merge the same processing procedures, count the number of procedures under the same processing procedure, and mark the obtained number as the processing value Bi, where i represents different processing procedures; At the same time, the inertial service information in the case-closed state is statistically analyzed, and the statistical quantity is marked as RT. Then, the processing value Bi of the processing process i is divided by RT, and the resulting value is marked as the process frequency Fi of the processing process i, that is, Fi=Bi÷RT.

5. The intelligent optimization management system for e-government systems according to claim 4, characterized in that, In a single processing flow contained in an inertial service information, all processing flows are in a normal processing state. If a processing flow fails due to staff or customer error, the corresponding processing flow will be deleted from the inertial service information. That is, processes in a failed processing state will not participate in the unit segmentation process.

6. The intelligent optimization management system for e-government systems according to claim 1, characterized in that, It also includes an information storage module and a terminal processing module; The information storage module is used to store data information from various service departments in the government system. The information storage module establishes a one-way communication connection with the service analysis module and the collaborative processing module respectively. The terminal processing module is used to obtain the partially processed information transmitted by the collaborative processing module and display the partially processed information on the terminal display device. The target customer then performs operations and confirms based on the information displayed on the terminal display device.

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