Full-chain closed-loop accurate relieving method and system based on big data driving

Abstract

The invention belongs to the field of big data processing, and provides a full-chain closed-loop accurate relieving method and system based on big data driving. The method comprises the following steps: screening out employment difficulty key groups from a real-name system information database according to ages and employment states, and constructing an employment difficulty key group database; dynamically adjusting an employment difficulty key group database based on the help willingness of the employment difficulty key group; obtaining corresponding skill features, behavior features and attribution features by using the dynamically adjusted employment difficulty key group database; according to the skill characteristics and behavior characteristics of the employment difficulty key group, intelligently recommending relieving information; and according to the location characteristics of the employment difficulty key group, forming an assistance task and automatically distributing the assistance task to a related management and control department. And the employment state and the assistance willingness of the employment difficulty key group are regularly acquired, the employment difficulty key group database is dynamically adjusted again, and subsequent intelligent recommendation of the remission information and the assistance task distribution processing are executed.

Description

Technical Field

[0001] The present invention belongs to the field of big data processing, and in particular to a full-chain closed-loop precise relief method and system driven by big data. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Under the current model, human resources and social security staff mainly rely on inefficient door-to-door visits to collect data on people with employment difficulties and unemployed people. This work is time-consuming and laborious, and it is difficult to quickly and accurately obtain the required information. Moreover, the entire work process is not transparent, and it is difficult to obtain the data in a timely manner, making it difficult to conduct effective monitoring and generate precise relief plans. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a full-chain closed-loop precise relief method and system driven by big data, which can realize the full-chain closed-loop monitoring of precise identification of key groups with employment difficulties, generation of personalized assistance plans, efficient task distribution and execution feedback.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] The first aspect of the present invention provides a full-chain closed-loop precision relief method driven by big data.

[0007] In one or more embodiments, a full-chain closed-loop precision relief method driven by big data is provided, including:

[0008] Obtain the real-name information of individuals in the set area to form a real-name information database;

[0009] According to age and employment status, key groups with employment difficulties are screened from the real-name information database to build a database of key groups with employment difficulties;

[0010] Dynamically adjust the database of key groups with employment difficulties based on their willingness to be helped;

[0011] Using the dynamically adjusted database of key groups with employment difficulties, label the key groups with employment difficulties and extract the corresponding skill characteristics, behavioral characteristics and territorial characteristics;

[0012] Intelligently recommend relief information based on the skill and behavior characteristics of key groups with employment difficulties; and form assistance tasks based on the territorial characteristics of key groups with employment difficulties and automatically distribute them to relevant control departments;

[0013] Regularly obtain updated information on the employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties, and carry out subsequent intelligent recommendation of relief information and distribution and processing of assistance tasks.

[0014] As an implementation method, a similarity matching recommendation algorithm is used to calculate the similarity between the skill characteristics and behavioral characteristics of key groups with employment difficulties and the characteristics of the relief information to be recommended, and the content with the highest similarity is selected as the recommendation result.

[0015] As an implementation method, a collaborative filtering recommendation algorithm and the behavioral characteristics of key groups with employment difficulties are used, and the Pearson correlation coefficient is used to calculate the similarity between key groups. Based on the behaviors of similar key groups, the content that the target key group may be interested in is predicted and recommended.

[0016] As an implementation method, a cluster analysis recommendation algorithm is used to cluster the skill characteristics and behavioral characteristics of key groups with difficulty in finding employment, and then matching relief information is recommended for each cluster type.

[0017] As an implementation method, an intelligent task distribution algorithm based on geographic location and the territorial characteristics of key groups with difficulty in finding employment are used to form assistance tasks and automatically distribute them to relevant management and control departments.

[0018] As an implementation method, the relief information includes job information, training information, subsidy information and interest information.

[0019] The second aspect of the present invention provides a full-chain closed-loop precise relief plan generation system driven by big data.

[0020] In one or more embodiments, a full-chain closed-loop precise relief plan generation system driven by big data includes:

[0021] A real-name information database construction module, which is used to obtain the real-name information of individuals in a set area to form a real-name information database;

[0022] A key group database construction module is used to select key groups with employment difficulties from the real-name information database according to age and employment status, and to construct a database for key groups with employment difficulties;

[0023] A dynamic adjustment module for the database of key groups with employment difficulties, which is used to dynamically adjust the database of key groups with employment difficulties based on the willingness of key groups with employment difficulties to help;

[0024] A key group feature extraction module is used to use the dynamically adjusted database of key groups with employment difficulties to label the key groups with employment difficulties and extract the corresponding skill features, behavioral features and territorial features;

[0025] Intelligent recommendation and task distribution module, which is used to intelligently recommend relief information based on the skill characteristics and behavioral characteristics of key groups with employment difficulties; and to form assistance tasks and automatically distribute them to relevant management and control departments based on the territorial characteristics of key groups with employment difficulties;

[0026] The information update feedback module is used to regularly obtain updated employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties, and execute subsequent intelligent recommendation of relief information and distribution and processing of assistance tasks.

[0027] A third aspect of the present invention provides a computer-readable storage medium.

[0028] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the full-chain closed-loop precision relief method driven by big data as described above.

[0029] A fourth aspect of the present invention provides a computer program product.

[0030] A computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the full-chain closed-loop precision relief method driven by big data as described above.

[0031] A fifth aspect of the present invention provides an electronic device.

[0032] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the full-chain closed-loop precise relief method driven by big data as described above are implemented.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention regularly updates the employment status and assistance willingness of key groups with employment difficulties, dynamically adjusts the database of key groups with employment difficulties, and performs subsequent intelligent recommendation of relief information and distribution of assistance tasks. It can effectively and comprehensively understand and accurately locate groups with employment difficulties, intelligently recommend matching resources, meet the personalized needs of the assisted targets, form a complete closed loop from identification to assistance to feedback, continuously optimize the assistance effect, provide more personalized, efficient and sustainable solutions for the distressed targets, and improve the assistance efficiency and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 It is a flowchart of a full-chain closed-loop precision relief method driven by big data according to an embodiment of the present invention;

[0037] Figure 2 It is a structural diagram of a full-chain closed-loop precise relief plan generation system based on big data drive in an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] Figure 1 is a flow chart of a full-chain closed-loop precision relief method driven by big data in an embodiment of the present invention, such as Figure 1 The full-chain closed-loop precision relief method based on big data driven in this embodiment shown may include:

[0043] S101, obtaining the real-name information of individuals in a set area to form a real-name information database.

[0044] It should be noted here that the real-name information of individuals in the set area is obtained through legal channels and with authorization.

[0045] Among them, real-name information includes but is not limited to age, gender, place of residence, education, skills, employment status and other information.

[0046] A real-name information database is formed through data preprocessing measures such as removing duplicate data, processing missing values, correcting erroneous data, and data standardization.

[0047] S102, according to age and employment status, select key groups with employment difficulties from the real-name information database, and build a database for key groups with employment difficulties.

[0048] For example, using the age screening algorithm, we can select groups between the ages of 16 and 65;

[0049] Use employment status screening algorithms to screen out groups such as "unemployed for more than 6 months" or "multiple unemployed and unable to find stable employment";

[0050] Using the entrepreneurial screening algorithm, exclude corporate legal persons, shareholders or partners, and exclude groups that apply for or enjoy entrepreneurial-related policies (such as entrepreneurial guarantee loans);

[0051] Using the education status screening algorithm, we excluded people who were attending college or receiving higher education, and also excluded people with high education who had already graduated;

[0052] By using a multi-dimensional screening algorithm model to intelligently screen out key groups with employment difficulties, and using a clustering algorithm to group people, we can identify groups with similar characteristics that have employment difficulties, and form an accurate database of key groups with employment difficulties.

[0053] It should be noted here that the age screening algorithm, the entrepreneurship situation screening algorithm, the entrepreneurial situation screening algorithm and the education status screening algorithm are all corresponding conditional screening algorithms. By configuring the corresponding screening conditions, the corresponding screening algorithm can be formed.

[0054] S103, dynamically adjust the database of key groups with difficulties in finding employment based on their willingness to be helped.

[0055] The willingness to help key groups with employment difficulties here can be collected through online questionnaires to collect information such as employment intentions, training needs, and expectations for help of key groups;

[0056] It is also possible to combine big models, voice processing, machine learning and other technologies to automatically collect and analyze the willingness to help key groups through multiple rounds of online interactive question-and-answer sessions using "virtual digital humans".

[0057] S104, using the dynamically adjusted database of key groups with employment difficulties, label the key groups with employment difficulties, and extract corresponding skill characteristics, behavioral characteristics and territorial characteristics.

[0058] S105, intelligently recommends relief information based on the skill characteristics and behavioral characteristics of key groups with employment difficulties; and forms assistance tasks and automatically distributes them to relevant management and control departments based on the territorial characteristics of key groups with employment difficulties.

[0059] In some optional embodiments, a similarity matching recommendation algorithm is used to calculate the similarity between the skill characteristics and behavioral characteristics of the key groups with employment difficulties and the characteristics of the relief information to be recommended, and the content with the highest similarity is selected as the recommendation result.

[0060] Employment-related feature values ​​are extracted using data from key groups. Different features (such as age, education, skills, etc.) have different degrees of influence on the recommendation results. The model is trained and the weight of each feature is adjusted. For example, the weights of skills and education are relatively high.

[0061] At the same time, factors such as time are introduced. Time factors may have an important impact on the recommendation results. For example, newer positions may be more in line with current market demand. A time decay function is introduced in the similarity calculation to consider the impact of time factors on the recommendation results.

[0062] The time decay function is:

[0063] λ is the attenuation coefficient. The larger λ is, the more significant the impact of time factors on recommendation results; the smaller λ is, the weaker the impact of time factors is.

[0064] Timestamp t represents the current time, timestamp t0 represents the time of a certain position or data record, and t-t0 represents the time difference, that is, the difference between the current time t and the time t0 of a certain position or data record.

[0065] δ represents the time attenuation factor, and its value is between 0 and 1. When t-t0 is small (i.e., the time is close), δ is close to 1, indicating that the time factor has little influence on the recommendation result; conversely, when t-t0 is large (i.e., the time is far), δ is close to 0, indicating that the time factor has a greater influence on the recommendation result.

[0066] In other optional embodiments, a collaborative filtering recommendation algorithm and the behavioral characteristics of key groups with difficulty finding employment (such as previous work experience, training course participation, policy subsidy receipt records, etc.) are used to calculate the similarity between key groups using the Pearson correlation coefficient. Based on the behaviors of similar key groups (such as job preferences, evaluations of training courses, etc.), content that may be of interest to the target key group is predicted and recommendations are made.

[0067] In some other embodiments, cluster analysis recommendation algorithms are used to cluster the skill characteristics and behavioral characteristics of key groups with employment difficulties, and then matching relief information is recommended for each cluster type, such as average age, occupational distribution, skill level, etc., and suitable jobs, training courses, policy subsidies, etc. are recommended for each group based on its characteristics.

[0068] In some optional embodiments, an intelligent task distribution algorithm based on geographic location and the territorial characteristics of key groups with difficulty in finding employment are used to generate assistance tasks and automatically distribute them to relevant management and control departments.

[0069] Among them, the territorial feature is text information, which can be parsed into province, city, district, county, town and street information through natural language word segmentation algorithm, and the number data can be parsed into address information through relevant number rules.

[0070] The relief information described in the present invention includes but is not limited to job information, training information, subsidy information and information of interest.

[0071] S106, regularly obtain updated employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties again, and perform subsequent intelligent recommendation of relief information and distribution and processing of assistance tasks.

[0072] In other embodiments, the entire key group assistance process is also managed in an information-based manner, including but not limited to overall coordination, progress tracking, follow-up supervision and performance evaluation.

[0073] This embodiment regularly updates the employment status and assistance willingness of key groups with employment difficulties, dynamically adjusts the database of key groups with employment difficulties, and performs subsequent intelligent recommendation of relief information and distribution of assistance tasks. It can effectively and comprehensively understand and accurately locate groups with employment difficulties, intelligently recommend matching resources, meet the personalized needs of the assisted targets, form a complete closed loop from identification to assistance to feedback, continuously optimize the assistance effect, provide more personalized, efficient and sustainable solutions for the distressed targets, and improve the efficiency and satisfaction of assistance.

[0074] Figure 2 is a schematic diagram of the structure of a full-chain closed-loop precision relief system driven by big data in an embodiment of the present invention. Figure 1 The full-chain closed-loop precise relief method based on big data is corresponding, such as Figure 2 As shown, the full-chain closed-loop precision relief system driven by big data in this embodiment may include:

[0075] A real-name information database construction module 201 is used to obtain the real-name information of individuals in a set area to form a real-name information database;

[0076] A key group database construction module 202 is used to select key groups with employment difficulties from the real-name information database according to age and employment status, and to construct a database for key groups with employment difficulties;

[0077] A key group database dynamic adjustment module 203, which is used to dynamically adjust the key group database with employment difficulties based on the assistance willingness of the key group with employment difficulties;

[0078] The key group feature extraction module 204 is used to utilize the dynamically adjusted database of key groups with employment difficulties to label the key groups with employment difficulties and extract the corresponding skill features, behavior features and territorial features;

[0079] Intelligent recommendation and task distribution module 205, which is used to intelligently recommend relief information based on the skill characteristics and behavioral characteristics of key groups with employment difficulties; and to form assistance tasks and automatically distribute them to relevant control departments based on the territorial characteristics of key groups with employment difficulties;

[0080] The information update feedback module 206 is used to regularly obtain updated employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties again, and perform subsequent intelligent recommendation of relief information and distribution of assistance tasks.

[0081] It should be noted here that Figure 2 The modules in the full-chain closed-loop precision relief system driven by big data are Figure 1 The various steps in the full-chain closed-loop precise relief method driven by big data correspond one to another, and the specific implementation process is the same, so I will not repeat it here.

[0082] This embodiment regularly updates the employment status and assistance willingness of key groups with employment difficulties, dynamically adjusts the database of key groups with employment difficulties, and performs subsequent intelligent recommendation of relief information and distribution of assistance tasks. It can effectively and comprehensively understand and accurately locate groups with employment difficulties, intelligently recommend matching resources, meet the personalized needs of the assisted targets, form a complete closed loop from identification to assistance to feedback, continuously optimize the assistance effect, provide more personalized, efficient and sustainable solutions for the distressed targets, and improve the efficiency and satisfaction of assistance.

[0083] Reference Figure 3 , a schematic diagram of an electronic device is given. It should be noted that, Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0084] like Figure 3As shown, electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage part 308 into a random access memory (RAM) 303. In RAM 303, various programs and data required for system operation are also stored. Central processing unit 301, ROM 302 and RAM 303 are connected to each other via a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0085] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0086] When the central processing unit 301 in the electronic device of this embodiment executes the program, the following is achieved: Figure 1 The steps shown are based on a full-chain closed-loop precision relief method driven by big data.

[0087] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 1 In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit 301, various functions defined in the apparatus of the present application are executed.

[0088] in, Figure 1 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A full-chain closed-loop precision relief system driven by big data, characterized by: include: Obtain the real-name information of individuals in the set area to form a real-name information database; According to age and employment status, key groups with employment difficulties are screened from the real-name information database to build a database of key groups with employment difficulties; Dynamically adjust the database of key groups with employment difficulties based on their willingness to be helped; Using the dynamically adjusted database of key groups with employment difficulties, label the key groups with employment difficulties and extract the corresponding skill characteristics, behavioral characteristics and territorial characteristics; Intelligently recommend relief information based on the skill and behavior characteristics of key groups with employment difficulties; and form assistance tasks based on the territorial characteristics of key groups with employment difficulties and automatically distribute them to relevant control departments; Regularly obtain updated information on the employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties, and carry out subsequent intelligent recommendation of relief information and distribution and processing of assistance tasks.

2. The full-chain closed-loop precision relief method based on big data drive as claimed in claim 1 is characterized in that: Using the similarity matching recommendation algorithm, the skill and behavioral characteristics of key groups with employment difficulties are combined with the characteristics of the relief information to be recommended for similarity calculation, and the content with the highest similarity is selected as the recommendation result.

3. The full-chain closed-loop precision relief method based on big data drive as claimed in claim 1 is characterized in that: By utilizing collaborative filtering recommendation algorithm and the behavioral characteristics of key groups with employment difficulties, the Pearson correlation coefficient is used to calculate the similarity between key groups. Based on the behaviors of similar key groups, the content that the target key groups may be interested in is predicted and recommended.

4. The full-chain closed-loop precision relief method based on big data drive as claimed in claim 1 is characterized in that: The cluster analysis recommendation algorithm is used to cluster the skill characteristics and behavioral characteristics of key groups with difficulty in finding employment, and then matching relief information is recommended for each cluster type.

5. The full-chain closed-loop precision relief method based on big data drive as claimed in claim 1 is characterized in that: By utilizing an intelligent task distribution algorithm based on geographic location and the territorial characteristics of key groups with difficulties in finding employment, assistance tasks are generated and automatically distributed to relevant management and control departments.

6. The full-chain closed-loop precision relief method based on big data drive as claimed in claim 1 is characterized in that: The relief information includes job information, training information, subsidy information and interest information.

7. A full-chain closed-loop precision relief system driven by big data, characterized in that: include: A real-name information database construction module, which is used to obtain the real-name information of individuals in a set area to form a real-name information database; A key group database construction module is used to select key groups with employment difficulties from the real-name information database according to age and employment status, and to construct a database for key groups with employment difficulties; A dynamic adjustment module for the database of key groups with employment difficulties, which is used to dynamically adjust the database of key groups with employment difficulties based on the willingness of key groups with employment difficulties to help; A key group feature extraction module is used to use the dynamically adjusted database of key groups with employment difficulties to label the key groups with employment difficulties and extract the corresponding skill features, behavioral features and territorial features; Intelligent recommendation and task distribution module, which is used to intelligently recommend relief information based on the skill characteristics and behavioral characteristics of key groups with employment difficulties; and to form assistance tasks and automatically distribute them to relevant management and control departments based on the territorial characteristics of key groups with employment difficulties; The information update feedback module is used to regularly obtain updated employment status and assistance willingness of key groups with employment difficulties, dynamically adjust the database of key groups with employment difficulties, and execute subsequent intelligent recommendation of relief information and distribution and processing of assistance tasks.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the full-chain closed-loop precision relief method driven by big data are implemented as described in any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps in the full-chain closed-loop precision relief method driven by big data as described in any one of claim 16 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the full-chain closed-loop precision relief method driven by big data are implemented as described in any one of claims 1-6.

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