One-stop social relief system based on policy matching model

Abstract

The application provides a one-stop social rescue system based on a policy matching model, relates to the field of data processing, and comprises an information acquisition module used for acquiring rescue-related data, an object management module used for determining the portrait of at least one rescued object based on the rescue-related data, a policy management module used for acquiring rescue policy information, analyzing the rescue policy information, and establishing a corresponding matching model, a policy matching module used for determining the rescue policy information matched with the rescued object based on the portrait of the rescued object and the matching model corresponding to the rescue policy information, a project generation module used for generating a rescue project based on the rescue policy information matched with the rescued object, and a work order distribution module used for generating at least one rescue work order based on the rescue project, determining the execution end of each rescue work order, sending the rescue work order to the corresponding execution end, and having the advantages of providing higher-quality and more efficient rescue services for the rescued object.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a one-stop social assistance system based on a policy matching model. Background Technology

[0002] Currently, rescue operations are hampered by numerous departments involved, fragmented and singular content, simplistic and outdated methods, complex and cumbersome procedures, difficulty in applying for assistance, numerous but disorganized policy connections, and low operational efficiency. Furthermore, there are issues such as overlapping and omissions of policy benefits for various rescue recipients, unreasonable resource allocation, information silos, lack of data sharing, and low accuracy in rescue efforts. This fragmented system, coupled with insufficient public awareness of various rescue policies and difficulties in applying for assistance, negatively impacts the timeliness, effectiveness, and efficiency of rescue efforts.

[0003] Therefore, there is a need to provide a one-stop social relief system based on a policy matching model to provide higher quality and more efficient relief services to those in need. Summary of the Invention

[0004] One embodiment of this specification provides a one-stop social rescue system based on a policy matching model, comprising: an information acquisition module for acquiring rescue-related data; an object management module for determining a profile corresponding to at least one rescued object based on the rescue-related data; a policy management module for acquiring rescue policy information, analyzing the rescue policy information, and establishing a matching model corresponding to the rescue policy information; a policy matching module for determining rescue policy information matching the rescued object based on the profile corresponding to the rescued object and the matching model corresponding to the rescue policy information; a project generation module for generating a rescue project corresponding to the rescued object based on the rescue policy information matching the rescued object; and a work order dispatch module for generating at least one rescue work order based on the rescue project corresponding to the rescued object, determining the execution end of each rescue work order, and sending the rescue work order to the corresponding execution end.

[0005] In some embodiments, the information acquisition module acquires rescue-related data, including: acquiring public data from relevant business departments, wherein the public data includes at least medical settlement data, education information, and employment information; acquiring online assistance data from at least one online assistance platform through web crawling; and acquiring offline assistance data from at least one offline assistance platform, wherein the offline assistance platform includes assistance hotlines and rescue publicity stations.

[0006] In some embodiments, obtaining offline assistance data from at least one offline assistance platform includes: obtaining public data based on the relevant business departments to identify at least one first person in need of rescue; obtaining building-related information of a target area, wherein the building-related information includes at least building location, building type, building age, and pedestrian traffic; clustering the buildings in the target area based on the building-related information of the target area to identify multiple sub-areas; for each sub-area, determining the number and location information of rescue publicity stations set up in the sub-area based on the building-related information of the buildings included in the sub-area and the distribution information of the at least one first person in need of rescue; and collecting assistance information of candidate persons in need based on the rescue publicity stations, wherein the offline assistance data includes the assistance information of the candidate persons in need collected by the rescue publicity stations.

[0007] In some embodiments, the object management module determines a profile corresponding to at least one rescued object based on the rescue-related data, including: for each rescued object, determining the rescued object's personal file, family file, and historical rescue file based on the rescue-related data, wherein the profile corresponding to the rescued object includes the rescued object's personal file, family file, and historical rescue file, and the personal file includes at least the type of person in difficulty.

[0008] In some embodiments, the policy management module analyzes the relief policy information and establishes a matching model corresponding to the relief policy information, including: for each piece of relief policy information, extracting keywords from the relief policy information to obtain at least one keyword corresponding to the relief policy information; determining a hardship object type tag applicable to the relief policy information based on the at least one keyword corresponding to the relief policy information; determining a relief condition tag for the relief policy information based on the at least one keyword corresponding to the relief policy information; determining a time tag for the relief policy information based on the at least one keyword corresponding to the relief policy information; and determining a relief measure tag for the relief policy information based on the at least one keyword corresponding to the relief policy information, wherein the matching model corresponding to the relief policy information includes the hardship object type tag applicable to the relief policy information, the relief condition tag, the time tag, and the relief measure tag.

[0009] In some embodiments, the policy matching module determines the rescue policy information matching the rescued object based on the profile corresponding to the rescued object and the matching model corresponding to the rescue policy information. This includes: for rescued objects identified through the public data and the online help platform, determining the rescue policy information matching the rescued object based on the applicable hardship object type tags, rescue condition tags, and time tags included in the matching model corresponding to the rescued object and the rescue policy information; obtaining online help requests through the help hotline, determining the profile corresponding to the rescued object matched by the online help request, and determining the applicable rescue policy information for the rescued object matched by the online help request based on the applicable hardship object type tags, rescue condition tags, and time tags included in the matching model corresponding to the rescued object matched by the online help request; obtaining offline help requests from the rescue publicity station, determining the profile corresponding to the rescued object matched by the offline help request, and determining the applicable rescue policy information for the rescued object matched by the offline help request based on the applicable hardship object type tags, rescue condition tags, and time tags included in the matching model corresponding to the offline help request and the rescue policy information.

[0010] In some embodiments, the project generation module generates a rescue project corresponding to the rescued object based on rescue policy information matching the rescued object, including: for each piece of rescue policy information matching the rescued object, determining the rescue measures for the rescued object based on the rescue measure tags corresponding to the rescue policy information; summarizing the rescue measures for the rescued object corresponding to each piece of rescue policy information matching the rescued object, and generating a rescue project corresponding to the rescued object.

[0011] In some embodiments, the work order dispatch module generates at least one rescue work order based on the rescue project corresponding to the rescued object, including: determining the execution end of each rescue measure included in the rescue project corresponding to the rescued object; and merging the rescue measures included in the rescue project corresponding to the rescued object based on the execution end of each rescue measure to generate the at least one rescue work order.

[0012] In some embodiments, the system further includes: a rescue evaluation module, configured to establish an evaluation system, the evaluation system including multiple evaluation indicators; the rescue evaluation module is also configured to obtain execution-related information of each rescue work order and evaluation information of the rescued object; the rescue evaluation module is also configured to determine the implementation effect of each rescue policy based on the evaluation system, the execution-related information of each rescue work order and the evaluation information of the rescued object.

[0013] In some embodiments, the evaluation system includes a departmental service capacity dimension, a policy relief effect dimension, and a relief recipient happiness index dimension. The departmental service capacity dimension includes indicators such as timely acceptance rate, acceptance rate, and effective settlement rate. The policy relief effect dimension includes indicators such as the number of relief cases, the total number of people assisted, and the total amount of relief funds. The relief recipient happiness index dimension includes indicators such as the effectiveness of relief policies, service satisfaction, and relief coverage.

[0014] Compared to existing technologies, the one-stop social assistance system based on a policy matching model provided in this specification has at least the following beneficial effects:

[0015] 1. Adhere to a problem-oriented and demand-oriented approach, rely on the "V" model, and follow the requirements of "vertical process integration and horizontal business collaboration." Horizontally, connect rescue resources and social rescue forces to achieve resource sharing and data interconnection channels. Vertically, cover districts, townships (streets), villages (communities), and grassroots grids to effectively promote the rational distribution of resources, optimize resource allocation, transform business processes, change the demand discovery and rescue model for rescued individuals, and build a district-wide "precision rescue" system to provide higher quality and more efficient rescue services for rescued individuals.

[0016] 2. By integrating other systems, we obtain information on the families of those receiving assistance, linking individuals to households to gain a deeper understanding of the dynamic information of families in need, and compiling historical data on the number of assistances, amounts received, and policy benefits enjoyed. We statistically analyze assistance efforts on a household basis, providing strong data support for rescue work.

[0017] 3. Utilize data collaboration to proactively identify the needs of those in need, thereby improving work efficiency and service levels. Establish a needs database by aggregating data on four categories of low-income individuals, configuring tags for other individuals facing difficulties, and collecting related data. Collect public data obtained from relevant departments to automatically identify and statistically analyze the urgent needs of eligible individuals, such as children from registered low-income families who are already enrolled in school and have a need for educational assistance. After forming the needs database, the platform's precise matching model intelligently matches individuals based on their basic information (identity, type of need, household registration, age, etc.) and their needs. It then outputs the best matching policy and dispatches corresponding collaborative handling work orders to the collaborative handling center for processing by the appropriate departments. Attached Figure Description

[0018] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0019] Figure 1This is a schematic diagram of a one-stop social assistance system based on a policy matching model, as shown in some embodiments of this specification.

[0020] Figure 2 This is a schematic diagram of the process for obtaining rescue-related data according to some embodiments of this specification;

[0021] Figure 3 This is a schematic diagram of the process for obtaining offline help data according to some embodiments of this specification;

[0022] Figure 4 This is a flowchart illustrating the process of establishing a matching model corresponding to rescue policy information, based on some embodiments of this specification.

[0023] Figure 5 This is a flowchart illustrating the process of determining rescue policy information that matches the rescued object, according to some embodiments of this specification;

[0024] Figure 6 This is a flowchart of Embodiment 1 shown in some embodiments of this specification;

[0025] Figure 7 This is a flowchart of Embodiment 2, which is based on some embodiments of this specification. Detailed Implementation

[0026] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0027] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0028] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0029] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0030] During the current process of assessing the needs of those in need of rescue and providing rescue services in the district, the problems have been identified in the following four aspects:

[0031] 1. The scope of demand identification urgently needs to be expanded.

[0032] Assistance for vulnerable groups primarily relies on the traditional model of self-application by those in need. However, issues remain regarding the timely detection of needs, limited channels for discovery, and unclear baseline data, necessitating an expansion of the reach of needs identification.

[0033] 2. Rescue service resources urgently need to be integrated.

[0034] Rescue resources are still primarily provided by individual government departments or through the purchase of services from social organizations, operating on a "single-point, single-line" basis, without forming an effective synergy. The efficiency of demand-based services is low and fails to meet current needs; therefore, rescue resources urgently need to be integrated to provide one-stop rescue services.

[0035] 3. The standards for rescue entry and exit urgently need improvement.

[0036] The needs of those receiving assistance and the conditions for accepting assistance from various departments are diverse and complex. The level of "policy finding people and coordinated support" is not deep enough, resulting in problems such as those who should receive assistance not doing so, excessive assistance, and untimely and inaccurate supply and demand. The entry and exit standards for assistance to those receiving assistance urgently need to be improved, and the accurate matching of supply and demand urgently needs to be enhanced.

[0037] 4. A multi-dimensional evaluation mechanism urgently needs to be established.

[0038] The effectiveness of rescue policies and projects is unclear, making it difficult to trace rescue efforts, and the senses of those being rescued are not strong enough. There is a lack of multi-dimensional evaluation mechanisms to objectively collect data and provide feedback on rescue effectiveness, assist in policy improvement and correction, and enhance the senses of those in need.

[0039] To address the aforementioned technical challenges, this proposal suggests a one-stop social assistance system based on a policy matching model.

[0040] The one-stop social assistance system network deployment based on the policy matching model uses a cloud platform as its framework, unified data scheduling and access to the IRS as its foundation, and network security as its core to establish supporting infrastructure. It fully supports the integration of government networks, and the infrastructure services include: platform basic server cluster, work order server cluster, big data platform server cluster, data management server cluster, data acquisition strategy and push server cluster, data visualization server cluster, unified portal server cluster, unified organization and user management server cluster, unified identity authentication server cluster, unified monitoring server cluster, unified configuration management server cluster, unified log analysis server cluster, mobile terminal (APP, mini program) services, etc.

[0041] To ensure the continuous improvement of operating system stability, a scheduled task is used to scan for system security risks. Through a comprehensive analysis of the internal security weaknesses of the operating system, it assists the management system in managing security risks.

[0042] To enhance server network security, and in accordance with government network security requirements, an internal network authorization system has been established.

[0043] Implement measures such as identity authentication, access control, boundary protection, and security auditing to ensure healthy cybersecurity.

[0044] Logs and audit logs provide effective intrusion detection and post-incident investigation mechanisms. Basic logging functionality is included to record user and process modifications to important files and access to network resources. Administrators can perform complex queries and analyses on the log system.

[0045] Identity authentication technology is an important means of implementing resource access control and a guarantee for implementing government cybersecurity strategies. By connecting to the Zhejiang Government DingTalk unified user authentication system, user identity verification and access control can be strengthened.

[0046] Fully integrate with the IRS database system, implement clear database access authorization policies, ensure that important information in the database has security, confidentiality, and verification measures, and perform secure backups of critical database information to prepare for unforeseen disasters. Simultaneously, employ database-based scanning and detection technologies. The database scanning system quickly and conveniently scans the database via the network to check for database-specific security vulnerabilities, comprehensively assessing all security vulnerabilities and issues related to authentication, authorization, and integrity.

[0047] Use appropriate backup software to perform comprehensive data backups on all current computer systems, especially operating system backups, file system backups, database system file backups, database data file backups, and related core application backups; establish a sound backup / recovery mechanism and a remote tape storage mechanism.

[0048] By selecting relevant products, the data or applications of each server can be migrated to an external SAN-based array library through technologies such as data snapshots and mirroring, based on certain management and follow-up measures. Unified management can be achieved through a single management interface, shielding the differences between arrays from different vendors.

[0049] The system manages the status of all application systems through appropriate application cluster management software. It enables dual-machine, multi-machine, or single-machine cluster management for existing database systems such as Oracle, SQL Server, DB2, Sybase, and middleware. Applications with the same operating system platform can be integrated to achieve multi-machine clustering. Different database instances function as a single "database service group," running on one of the servers in a multi-machine or dual-machine cluster. "Application service groups" are also established for middleware and other applications and incorporated into the cluster software's management. Furthermore, the system uses centralized software to establish dependencies between "application service groups," "database service groups," or other "application service groups," enabling orderly management of application startup and shutdown.

[0050] Figure 1 This is a schematic diagram of a one-stop social assistance system based on a policy matching model, as shown in some embodiments of this specification. Figure 1 As shown, a one-stop social rescue system based on a policy matching model can include an information acquisition module, an object management module, a policy management module, a policy matching module, a project generation module, a work order dispatch module, and a rescue evaluation module.

[0051] The information acquisition module can be used to acquire rescue-related data.

[0052] Rescue-related data may include information used to locate those being rescued.

[0053] Figure 2 This is a flowchart illustrating the process of obtaining rescue-related data according to some embodiments of this specification, such as... Figure 2 As shown, in some embodiments, the information acquisition module acquires rescue-related data, including:

[0054] Obtain public data from relevant business departments. The public data includes at least medical settlement data, education information, and employment information. For example, public data may include information on registered people, rescue data from the large-scale rescue system, medical insurance settlement data from the medical insurance department, student registration data from the education department, and employee insurance data from the human resources and social security department.

[0055] Obtain online help data from at least one online help platform by web crawling;

[0056] Obtain offline help data from at least one offline help platform, including help hotlines and rescue publicity stations.

[0057] For the public data, online help data and offline help data that are acquired, the information acquisition module can create information collection forms to unify the data format and improve the efficiency of data flow.

[0058] As an example only, the data requirements, data sources, and data tables for public data can be shown in Table 1:

[0059] Table 1

[0060]

[0061]

[0062]

[0063] Figure 3 This is a schematic diagram illustrating the process of obtaining offline help data according to some embodiments of this specification, such as... Figure 3 As shown, in some embodiments, the information acquisition module acquires offline help data from at least one offline help platform, including:

[0064] Based on public data obtained from relevant business departments, at least one first rescue target is identified. The first rescue target can be a rescue target identified based on public data. Specifically, the information acquisition module can collect medical settlement data, student registration information, employment information and other data from the IRS platform and identify individuals in difficulty with large medical expenses, education expenses or employment problems through data rule collision by collecting such data. These individuals in difficulty are then identified as the first rescue target.

[0065] Obtain relevant building information for the target area. This information should include at least the building's location, type (e.g., residential building, hospital, school, commercial building), building age, and pedestrian traffic. Pedestrian traffic can be the building's average daily or monthly pedestrian traffic.

[0066] Based on building-related information in the target area, the buildings in the target area are clustered to determine multiple sub-regions;

[0067] For each sub-region, based on the building-related information of the buildings included in the sub-region and the distribution information of at least one first rescued object, determine the number and location information of rescue publicity stations set up in the sub-region;

[0068] The data is based on requests for help from potential rescue targets collected by rescue publicity stations. The offline request data includes requests for help from potential rescue targets collected by the rescue publicity stations.

[0069] Specifically, the information acquisition module can first determine the valid buildings within the target area based on building type, building age, and pedestrian traffic. For example, buildings built after a preset building age and with pedestrian traffic exceeding a preset first pedestrian traffic threshold are considered valid buildings. Further, the module clusters the buildings in the target area based on the building-related information of the valid buildings, determining multiple sub-regions. For instance, the information acquisition module can determine the distance between any two valid buildings based on their location, and then cluster the buildings in the target area based on the distance between any two valid buildings, building type, building age, and pedestrian traffic, determining multiple sub-regions. As an example only, the information acquisition module can calculate the building similarity between any two valid buildings based on building type, building age, and pedestrian traffic; then, based on the distance between any two valid buildings and the building similarity, cluster the buildings in the target area, determining multiple sub-regions, and grouping two valid buildings with a building similarity greater than a preset building similarity threshold and a distance less than a distance threshold into one sub-region.

[0070] For each sub-region, the information acquisition module can identify key buildings based on pedestrian traffic and the distribution information of at least one first rescued object. Key buildings are those with a high probability of containing the rescued object. Specifically, the information acquisition module can identify buildings associated with the first rescued object within the sub-region based on public data, and designate buildings with pedestrian traffic exceeding a preset second pedestrian traffic threshold and / or with the number of associated first rescued objects exceeding a preset first rescued object number threshold as key buildings.

[0071] For each sub-region, the information acquisition module can generate multiple candidate setup schemes based on the corresponding constraint set. These candidate setup schemes include the locations of multiple rescue publicity stations, and the constraint set can include the maximum and minimum number of rescue publicity stations, the minimum distance between two adjacent rescue publicity stations, and the maximum distance between two adjacent rescue publicity stations. Understandably, the constraint set can be determined based on key building information within the sub-region. For example, the more key buildings in the sub-region, the larger the maximum and minimum number of rescue publicity stations, the smaller the minimum distance between two adjacent rescue publicity stations, and the larger the maximum distance between two adjacent rescue publicity stations.

[0072] For each candidate setup scheme, the information acquisition module can determine the corresponding validity score. Based on this validity score, it selects the target setup scheme from multiple candidate schemes and sets up a rescue and information dissemination station in the sub-area according to the target setup scheme. For example, the candidate setup scheme with the highest validity score is selected as the target setup scheme.

[0073] As an example only, for each candidate deployment scheme, the information acquisition module can determine the overlap area between the coverage areas of two adjacent rescue propaganda stations and the coverage rate of key buildings under that candidate deployment scheme, and calculate the effectiveness score corresponding to the candidate deployment scheme based on the overlap area between the coverage areas of two adjacent rescue propaganda stations and the coverage rate of key buildings. It is understood that if a key building is located within the coverage area of ​​a certain rescue propaganda station, then that key building is covered.

[0074] The information acquisition module can determine the effectiveness score of each candidate deployment scheme based on the overlapping area between the coverage areas of two adjacent rescue propaganda stations and the coverage rate of key buildings, using the following formula:

[0075]

[0076] Among them, S effectiveness Assign a validity score to each candidate scheme, S (i,j) Let R be the overlap area between the coverage areas of the i-th rescue propaganda station and the j-th rescue propaganda station after normalization, where i ≠ j. overage The normalized coverage rate of key buildings corresponding to this candidate setting scheme, where a1 and a2 are preset weights.

[0077] In some embodiments, after acquiring rescue-related data, the information acquisition module can analyze and process it before proceeding to the next step. Specifically, this includes:

[0078] Data cleaning: Cleaning rules need to be defined in advance. Requirements need to be collected while defining cleaning rules. Data cleaning rules can only be defined according to requirements after the requirements are determined.

[0079] Data transformation: Performing data transformation according to given data transformation rules;

[0080] Data filtering: Filtering out data that does not meet requirements, incomplete data, erroneous data, etc.

[0081] Data quality reporting and feedback: This function statistically analyzes, calculates, and stores data that violates cleansing rules during the data governance process. It can also statistically analyze and display frequently occurring problems based on different types of data, generating data quality problem reports.

[0082] The object management module can be used to determine the profile of at least one rescued object based on rescue-related data.

[0083] Specifically, this includes: for each rescued person, based on rescue-related data, determining the rescued person's personal file, family file, and historical rescue file. The profile corresponding to the rescued person includes the rescued person's personal file, family file, and historical rescue file, and the personal file includes at least the type of person in need.

[0084] The personal file of a rescued individual may include the rescued individual's basic information, type of hardship, asset information, hardship information, medical information, and rescue record section. The family file may include the rescued individual's family members' basic information, asset information, hardship information, medical information, and rescue record section. Historical rescue records may include the rescue measures and number of times the rescued individual has received.

[0085] The policy management module for difficult individuals can be used to obtain and analyze rescue policy information and establish a matching model corresponding to the rescue policy information.

[0086] Figure 4 This is a flowchart illustrating the process of establishing a matching model corresponding to rescue policy information based on some embodiments of this specification, such as... Figure 4 As shown, in some embodiments, the policy management module analyzes rescue policy information and establishes a matching model corresponding to the rescue policy information, including:

[0087] For each piece of rescue policy information, keywords are extracted from the rescue policy information to obtain at least one keyword corresponding to the rescue policy information;

[0088] Based on at least one keyword corresponding to the rescue policy information, determine the type of hardship object to which the rescue policy information applies;

[0089] Based on at least one keyword corresponding to the rescue policy information, determine the rescue condition label of the rescue policy information, such as annual income less than 50,000, no repeated rescue, configuration formula, etc. For example only, the configuration formula may include: number of days in hospital = (discharge date - admission date) * number of medical insurance settlement documents;

[0090] Based on at least one keyword corresponding to the relief policy information, determine the time tag corresponding to the relief policy information, for example, the validity period of the relief policy is 2023-2025;

[0091] Based on at least one keyword corresponding to the rescue policy information, the rescue measure label corresponding to the rescue policy information is determined. The matching model corresponding to the rescue policy information includes the label of the type of difficult object to which the rescue policy information applies, the rescue condition label, the time label, and the rescue measure label.

[0092] The policy matching module can be used to determine the rescue policy information that matches the rescued person based on the profile of the rescued person and the matching model corresponding to the rescue policy information.

[0093] Figure 5 This is a flowchart illustrating the process of determining rescue policy information matching the rescued object, as shown in some embodiments of this specification. Figure 5 As shown, in some embodiments, the policy matching module determines the rescue policy information that matches the rescued object based on the profile of the rescued object and the matching model corresponding to the rescue policy information, including:

[0094] For those identified through public data and online assistance platforms, the matching model based on the profile of the rescued person and the rescue policy information includes applicable hardship type tags, rescue condition tags, and time tags to determine the rescue policy information that the rescued person is matched with.

[0095] By obtaining online help requests through the help hotline, the profile of the rescued person matched by the online help request is determined. Based on the profile of the rescued person matched by the online help request and the matching model corresponding to the rescue policy information, including the applicable hardship object type label, rescue condition label and time label, the rescue policy information applicable to the rescued person matched by the online help request is determined.

[0096] We obtain offline requests for help from rescue publicity stations, determine the profile of the rescued individuals matched by the offline requests for help, and based on the profile of the rescued individuals matched by the offline requests for help and the matching model corresponding to the rescue policy information, including applicable hardship type tags, rescue condition tags, and time tags, we determine the rescue policy information applicable to the rescued individuals matched by the offline requests for help.

[0097] For example, the policy matching module can match basic information such as age, household registration, and hardship information based on the profile of the person being rescued and the matching model corresponding to the rescue policy information; it can also match demand keywords, outputting demand keywords through data cleaning, and then obtaining the final matching result after template matching. It also supports matching multiple demands from a single source, thereby ensuring that the needs of people in difficulty are met to the greatest extent and improving rescue efficiency. When the demand source is a social rescue project within the Visiting and Care program, the final demand matching result is determined by matching conditions such as the timeliness of the social rescue project, the type of personnel, and the project requirements.

[0098] It is understandable that proactive and reactive rescue efforts can be achieved through public data, helplines, and offline assistance.

[0099] Figure 6 This is a flowchart of Embodiment 1 shown in some embodiments of this specification, such as... Figure 6As shown, based on the profile of the person being rescued and the matching model corresponding to the rescue policy information, the rescue policy information matching the person being rescued can be determined, which may include:

[0100] Decision 0: Match the individual's ID number with the registered information of the four categories of low-income individuals. If the match is found, proceed to Decision 1.

[0101] Decision 1: Is the applicant a resident of XX district? If yes, proceed to Decision 2.

[0102] Judgment 2.1: If the medical settlement statement from the designated institution includes out-of-pocket expenses, proceed to Judgment 2.

[0103] Judgment 2.2: (Discharge date - Admission date) * Number of matching serial numbers within 1 day; if correct, proceed to Judgment 3.

[0104] Decision 3: If the person is eligible for this policy for 100 days, then the match is successful.

[0105] Matching result: Zhang San, ID number 158****0345, resident of X Village, X Town, has accumulated 100 days of hospitalization this year, has already received subsidies for 50 days of hospitalization, and will receive subsidies for another 50 days of hospitalization this time. Estimated relief amount: 4000 yuan.

[0106] Figure 7 This is a flowchart of Embodiment 2 shown in some embodiments of this specification, such as... Figure 7 As shown, based on the profile of the person being rescued and the matching model corresponding to the rescue policy information, the rescue policy information matching the person being rescued can be determined, which may include:

[0107] Decision 0: Match the individual's ID number with the registered information of the four categories of low-income individuals. If the match is found, proceed to Decision 1.

[0108] Verification 1.1: Is the applicant a resident of XX district? If yes, proceed to Verification 1.2.

[0109] Judgment 1.2: Is the age < 8 years old? If yes, proceed to Judgment 1.3.

[0110] Judgment 1.3: Has the student obtained kindergarten enrollment for the current year?

[0111] Judgment 2: Confirm enrollment status based on clues or by phone inquiry; if correct, proceed to Judgment 3.

[0112] Decision 3: If the person is entitled to 5,500 yuan for the number of days under this policy, then the match is successful.

[0113] Matching result: Zhang San, ID number, contact number 158****0345, is from X Village, X Town, enrolled in X School, X Class this year, and has already received a subsidy of 3000 yuan this year. Estimated relief amount: 2500 yuan.

[0114] The project generation module can be used to generate rescue projects corresponding to the rescued individuals based on rescue policy information that matches the rescued individuals.

[0115] Specifically, it includes:

[0116] For each rescued person matched with rescue policy information, the rescue measures for the rescued person are determined based on the rescue measure tags corresponding to the rescue policy information. The rescue measures may include the rescue policy on which they are based and the specific rescue method (e.g., how much financial assistance is provided).

[0117] The rescue measures corresponding to the rescue policy information matched for each rescued person are summarized to generate the rescue project corresponding to the rescued person.

[0118] The work order dispatch module can be used to generate at least one rescue work order based on the rescue project corresponding to the rescued object, determine the execution end of each rescue work order, and send the rescue work order to the corresponding execution end.

[0119] Specifically, it includes:

[0120] Identify the execution point for each rescue measure within the rescue program corresponding to the rescued individual;

[0121] Based on the execution end of each rescue measure, the rescue measures included in the rescue project corresponding to the rescued object are merged to generate at least one rescue work order. For example, rescue measures with consistent execution ends among the rescue measures included in the rescue project corresponding to the rescued object are summarized to generate a single rescue work order.

[0122] In some embodiments, to prevent the leakage of information about the rescued object, the work order dispatch module can encrypt each rescue work order and send it to the corresponding execution terminal.

[0123] Specifically, for each rescue work order, the project generation module can first encrypt the rescue work order based on the public key of the execution end of the rescue work order, and then encrypt the rescue work order a second time based on a random number and the device identifier (e.g., ID) of the execution end. The module can also generate a random number identifier based on the random number and send the second encrypted rescue work order and the random number identifier to the execution end corresponding to the rescue work order.

[0124] As an example only, the project generation module can generate random number identifiers based on the following formula:

[0125]

[0126] Where, N identification This is the identifier for the random number. P, r, and t are all preset parameters, and n is the generated random number.

[0127] In some embodiments, after completing the verification and rescue of relevant individuals, the processing specialist at the execution end feeds back information to the system to close the work order. The system supports viewing the work order list and detailed query functions for relevant departments based on role permissions, and provides functions for work order acceptance, closure, return, termination, and reassignment. Work order acceptance is supported; if a rescue order is incorrectly assigned, or the rescue unit believes there is an error in the rescue content, the work order can be returned. The rescue platform backend can terminate or reassign work orders that do not require processing. Work order closure and rescue feedback are supported, allowing users to fill in the rescue content, rescue amount (annual estimate), and upload relevant vouchers. Batch work order processing is supported; when a department has a system but the data is not integrated, or when batch rescues need to be processed, a pre-set batch rescue template is provided. Users can fill in the template, import rescue orders, process them uniformly, and provide feedback on rescue data.

[0128] The rescue evaluation module can be used to determine the effectiveness of each rescue policy.

[0129] Specifically, it includes:

[0130] Establish an evaluation system, which includes multiple evaluation indicators;

[0131] Obtain execution-related information and evaluation information of the rescued individuals for each rescue work order;

[0132] Based on the evaluation system, the execution information of each rescue work order, and the evaluation information of the rescued individuals, the effectiveness of each rescue policy is determined.

[0133] In some embodiments, the evaluation system includes a departmental service capacity dimension, a policy relief effect dimension, and a relief recipient happiness index dimension. The departmental service capacity dimension includes indicators such as the timeliness of acceptance, the acceptance rate, and the effective settlement of cases. The policy relief effect dimension includes indicators such as the number of relief cases, the total number of people assisted, and the total amount of relief funds. The relief recipient happiness index dimension includes indicators such as the effectiveness of relief policies, the service satisfaction evaluation, and the relief coverage.

[0134] The timeliness of acceptance rate indicates the percentage of collaborative rescue orders that exceed the required timeframe from issuance to acceptance. Acceptance rate indicates the percentage of collaborative rescue orders accepted by a department out of the total dispatched orders; effective completion rate indicates the percentage of collaborative rescue orders accepted by a department that are successfully completed and used for rescue out of all rescue orders; number of rescue cases (person-times) indicates the total number of cases (person-times) rescued under each rescue policy; total number of rescued persons indicates the total number of people rescued under each policy; total rescue amount indicates the actual total rescue amount under each policy; partial service satisfaction evaluation indicates the timeliness, effectiveness, and service quality of acceptance; rescue coverage indicates the percentage of those who should have received assistance but did not, and the percentage of those who should have received assistance overall.

[0135] As an example only, for each evaluation indicator, the rescue evaluation module can determine the score for each rescue work order based on the evaluation system, the execution information of each rescue work order, and the evaluation information of the rescued object. It then performs a weighted sum of the scores for each rescue work order for that indicator to generate the total score for that indicator. Furthermore, by performing a weighted sum of the total scores for multiple evaluation indicators, the effectiveness of each rescue policy can be determined.

[0136] In some embodiments, the rescue evaluation module may also include a rescue effectiveness early warning model, which performs data analysis on dimensions such as rescue coverage and effectiveness, and economic changes of rescue recipients. It provides early warnings for situations such as poor rescue effectiveness and inadequate coverage of rescue recipients, conducts policy deduction and optimization, imports pre-adjusted policies into the rescue resource project database for precise pre-matching, and finally releases rescue policies formulated based on big data support.

[0137] Newly proposed policies can be implemented by creating rules in the precise matching module. By default, the system uses data from the demand database and personal profile information database over the past year to calculate rescue effectiveness based on test model rules, including estimated rescue trips, rescue amounts, and the number of people rescued. The system supports selecting the trial calculation period, such as six months, one year, or one quarter.

[0138] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0139] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0140] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0141] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0142] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A one-stop social assistance system based on a policy matching model, characterized in that, include: The information acquisition module is used to acquire rescue-related data, including: acquiring offline assistance data from at least one offline assistance platform; The object management module is used to determine a profile of at least one rescued object based on the rescue-related data. The policy management module is used to acquire rescue policy information, analyze the rescue policy information, and establish a matching model corresponding to the rescue policy information; The policy matching module is used to determine the rescue policy information that matches the rescued object based on the profile of the rescued object and the matching model corresponding to the rescue policy information. The project generation module is used to generate a rescue project corresponding to the rescued object based on the rescue policy information that matches the rescued object; The work order dispatch module is used to generate at least one rescue work order based on the rescue project corresponding to the rescued object, determine the execution terminal of each rescue work order, and send the rescue work order to the corresponding execution terminal. The information acquisition module acquires offline help data from at least one offline help platform, including: Based on the public data obtained by the relevant business departments, at least one first rescued person is identified, wherein the public data includes at least medical settlement data, education information and employment information; Based on building type, building age, and pedestrian traffic, valid buildings within the target area are identified. Based on building location, the distance between any two valid buildings is determined. Based on the distance between any two valid buildings, building type, building age, and pedestrian traffic, the buildings in the target area are clustered to determine multiple sub-regions. Two valid buildings with a similarity greater than a preset building similarity threshold and a distance less than a distance threshold are grouped into one sub-region. Based on public data, buildings within a sub-region associated with the first rescued person are identified. Buildings with pedestrian traffic greater than a preset second pedestrian traffic threshold and / or with the number of associated first rescued persons greater than a preset first rescued person number threshold are designated as key buildings. For each sub-region, multiple candidate setup schemes are generated based on the corresponding constraint set. These candidate setup schemes include multiple rescue information stations. The location of the rescue propaganda station is determined by a set of constraints, including the maximum number of rescue propaganda stations, the minimum number of rescue propaganda stations, the minimum distance between two adjacent rescue propaganda stations, and the maximum distance between two adjacent rescue propaganda stations. For each candidate setting scheme, the information acquisition module determines the validity score corresponding to the candidate setting scheme. Based on the validity score, the target setting scheme is determined from multiple candidate setting schemes. According to the target setting scheme, rescue propaganda stations are set up in the sub-area. For each candidate setting scheme, the overlapping area between the coverage areas of two adjacent rescue propaganda stations and the coverage rate of key buildings are determined. The validity score corresponding to the candidate setting scheme is calculated based on the overlapping area between the coverage areas of two adjacent rescue propaganda stations and the coverage rate of key buildings. If a key building is located within the coverage area of ​​a certain rescue propaganda station, then the key building is covered. The offline request data is based on the request information of the candidate rescue targets collected by the rescue publicity station.

2. The one-stop social assistance system based on a policy matching model according to claim 1, characterized in that, The information acquisition module acquires rescue-related data, including: acquiring public data from relevant business departments; Obtain online help data from at least one online help platform by web crawling; Offline help data is obtained from at least one offline help platform, which includes help hotlines and rescue publicity stations.

3. The one-stop social assistance system based on a policy matching model according to claim 1, characterized in that, Based on the rescue-related data, the object management module determines a profile corresponding to at least one rescued object, including: For each rescued person, based on the rescue-related data, a personal file, family file, and historical rescue file for the rescued person are determined. The profile corresponding to the rescued person includes the personal file, family file, and historical rescue file for the rescued person, and the personal file includes at least the type of person in difficulty.

4. A one-stop social assistance system based on a policy matching model according to claim 3, characterized in that, The policy management module analyzes the relief policy information and establishes a matching model corresponding to the relief policy information, including: For each piece of rescue policy information, keywords are extracted from the rescue policy information to obtain at least one keyword corresponding to the rescue policy information; Based on at least one keyword corresponding to the rescue policy information, determine the type of hardship object to which the rescue policy information applies; Based on at least one keyword corresponding to the rescue policy information, determine the rescue condition tag of the rescue policy information; Based on at least one keyword corresponding to the rescue policy information, determine the time tag corresponding to the rescue policy information; Based on at least one keyword corresponding to the rescue policy information, rescue measure tags corresponding to the rescue policy information are determined. The matching model corresponding to the rescue policy information includes tags for the type of difficult object to which the rescue policy information applies, rescue condition tags, time tags, and rescue measure tags.

5. A one-stop social assistance system based on a policy matching model according to claim 4, characterized in that, The policy matching module determines the rescue policy information that matches the rescued person based on the profile of the rescued person and the matching model corresponding to the rescue policy information, including: For the rescued individuals identified through the public data and the online help platform, the rescue policy information matched to the rescued individuals is determined based on the profile corresponding to the rescued individuals and the matching model corresponding to the rescue policy information, which includes applicable hardship type tags, rescue condition tags, and time tags. By obtaining online help requests through the help hotline, determining the profile of the rescued person matched by the online help request, and based on the profile of the rescued person matched by the online help request and the matching model corresponding to the rescue policy information, including the applicable hardship object type label, rescue condition label and time label, determining the rescue policy information applicable to the rescued person matched by the online help request; The system obtains offline requests for help from the rescue publicity station, determines the profile of the rescued person matched by the offline request for help, and determines the applicable rescue policy information for the rescued person matched by the offline request for help based on the profile of the rescued person matched by the offline request for help and the matching model of the rescue policy information, which includes applicable hardship object type tags, rescue condition tags and time tags.

6. A one-stop social assistance system based on a policy matching model according to claim 4, characterized in that, The project generation module generates a rescue project corresponding to the rescued person based on rescue policy information matching the rescued person, including: For each piece of rescue policy information matching the rescued object, the rescue measures for the rescued object are determined based on the rescue measure tags corresponding to the rescue policy information; The rescue measures corresponding to the rescue policy information matched for each rescued object are summarized to generate the rescue project corresponding to the rescued object.

7. A one-stop social assistance system based on a policy matching model according to claim 6, characterized in that, The work order dispatch module generates at least one rescue work order based on the rescue project corresponding to the rescued object, including: Determine the execution point for each rescue measure included in the rescue project corresponding to the rescued object; Based on the execution end of each rescue measure, the rescue measures included in the rescue project corresponding to the rescued object are merged to generate at least one rescue work order.

8. A one-stop social assistance system based on a policy matching model according to any one of claims 1-4, characterized in that, Also includes: The rescue evaluation module is used to establish an evaluation system, which includes multiple evaluation indicators. The rescue evaluation module is also used to obtain execution-related information for each rescue work order and evaluation information for the rescued object; The rescue evaluation module is also used to determine the effectiveness of each rescue policy based on the evaluation system, the execution information of each rescue work order, and the evaluation information of the rescued object.

9. A one-stop social assistance system based on a policy matching model according to claim 8, characterized in that, The evaluation system includes dimensions of departmental service capacity, policy relief effectiveness, and relief recipient happiness index. The departmental service capacity dimension includes indicators of timely acceptance rate, acceptance rate, and effective settlement. The policy relief effectiveness dimension includes indicators of the number of relief cases, the total number of people assisted, and the total amount of relief funds. The relief recipient happiness index dimension includes indicators of relief policy effectiveness, service satisfaction, and relief coverage.

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