Data matching method and device, storage medium and electronic equipment

Abstract

The invention discloses a data matching method and device, a storage medium and electronic equipment. The method relates to the field of data processing, and comprises the following steps: acquiring volunteer sequences of a plurality of objects and candidate sequences of a plurality of posts; based on the volunteer sequences of the multiple objects and the candidate sequences of the multiple posts, data portraits of multiple matching pairs are determined, and each matching pair comprises any post and one object filling the post; based on the data portrait of each matching pair, scoring the matching degree of the post and the object in the matching pair to obtain the comprehensive matching degree of each matching pair; based on the comprehensive matching degree of the multiple matching pairs, a target matching result is determined, and the target matching result comprises posts and objects which are successfully matched between the multiple posts and the multiple objects. According to the invention, the technical problem of low matching precision of the voluntary data of the employee and the post in the related technology is solved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to a data matching method, apparatus, storage medium, and electronic device. Background Technology

[0002] In the operation and management of modern enterprises, the optimal allocation of internal human resources is a continuous and crucial core activity. Among them, job rotation, promotion or internal recruitment through mutual selection between employees and hiring departments (such as branch offices and project teams) has become a common means to stimulate organizational vitality and improve employee satisfaction.

[0003] In the early days, the matching of employee volunteers with job positions was done manually, which was inefficient, had a high error rate, and lacked transparency.

[0004] In related technologies, some software systems have been introduced to replace manual operations, but their underlying logic and functional design often have significant limitations, failing to meet the increasingly refined and intelligent management needs, as detailed below:

[0005] (1) Rigid rules, lacking intelligence: Based on a rigid matching rule of "first choice priority". For example, if employee A's first choice is position X, and position X's first choice is employee A, then a direct match is made. This "black and white" rule ignores the differences in the "degree" of matching. It cannot answer complex questions such as "employee A is the first choice for position X, but he is also the 'perfect candidate' for position Y, and position Y is more in need of people". Therefore, this mechanism can only achieve a locally optimal solution.

[0006] (2) Information silos and blind decision-making: During the application stage, the information asymmetry between employees and department heads leads to a great deal of blindness in the application behavior. It is easy for a large number of people to flock to popular positions and both of them to be rejected. At the same time, some second-best positions are left unfilled, resulting in a waste of resources and increasing the cost of multiple rounds of coordination.

[0007] (3) Simple process, unable to reflect complex intentions: It is difficult to handle complex intentions in the real world. For example, a position plans to recruit 3 people. The department head may have 5 ideal candidates in mind and want to prioritize these 5 people (such as the first 3 as "preferred" and the last 2 as "alternatives"). Related technologies can often only perform simple "select" or "not select" operations, which cannot bear and process such complex intentions with hierarchy and order, so that the matching results cannot accurately reflect the true thoughts of the hiring department.

[0008] There is currently no effective solution to the above problems. Summary of the Invention

[0009] This invention provides a data matching method, apparatus, storage medium, and electronic device to at least solve the technical problem of low matching accuracy in matching employee volunteer data with job positions in related technologies.

[0010] According to one aspect of the present invention, a data matching method is provided, comprising: acquiring a volunteer sequence of multiple objects and a candidate sequence of multiple positions, wherein the volunteer sequence of each object includes a sequence of positions applied for by the object, and the candidate sequence of each position includes multiple objects that have applied for the position in the volunteer sequence; determining a data profile of multiple matching pairs based on the volunteer sequences of multiple objects and the candidate sequences of multiple positions, wherein the matching pair includes any position and an object that applied for the position; scoring the matching degree of the position and object in the matching pair based on the data profile of each matching pair to obtain a comprehensive matching degree of each matching pair; and determining a target matching result based on the comprehensive matching degree of the multiple matching pairs, wherein the target matching result includes the positions and objects that are successfully matched between the multiple positions and the multiple objects.

[0011] Furthermore, the data profile of each matching pair includes: the volunteer ranking of the matching pair, the candidate ranking of the matching pair, and the fit of the matching pair. The volunteer ranking of the matching pair includes: the order of the position in the matching pair within the volunteer sequence of the object in the matching pair. The candidate ranking of the matching pair includes: the order of the object in the matching pair within the candidate sequence of the position in the matching pair. The fit of each matching pair includes: the degree of matching between the position and the object in the matching pair as determined by natural language processing technology. Based on the data profile of each matching pair, the degree of matching between the position and the object in the matching pair is scored to obtain the comprehensive matching degree of each matching pair, including: determining a first score value for each matching pair based on the volunteer ranking of each matching pair; determining a second score value for each matching pair based on the candidate ranking of each matching pair; determining a third score value for each matching pair based on the fit of each matching pair; and determining the comprehensive matching degree of each matching pair based on the first score value, the second score value, the third score value, and a preset weight.

[0012] Further, based on the first score, second score, and third score of each matching pair and a preset weight, the comprehensive matching degree of each matching pair is determined, including: obtaining the skill data of the objects in each matching pair and the job requirements of the positions in the matching pair; calculating the similarity between the skill data and job requirements associated with each matching pair to obtain the target similarity of each matching pair; determining the fourth score of each matching pair based on the target similarity of each matching pair; and determining the comprehensive matching degree of each matching pair based on the first score, second score, third score, fourth score and the preset weight.

[0013] Further, determining the target matching result based on the comprehensive matching degree of multiple matching pairs includes: constructing a weighted bipartite graph based on the comprehensive matching degree of multiple matching pairs, wherein the weighted bipartite graph includes: nodes representing objects in each matching pair, nodes representing job positions in each matching pair, and edges with the comprehensive matching degree as weight; and determining the target matching result based on the weighted bipartite graph.

[0014] Further, determining the target matching result based on the weighted bipartite graph includes: obtaining target constraints, wherein the target constraints include: each object is matched at most once, and each job position is matched at most once; based on the target constraints and the weighted bipartite graph, using a maximum weight matching strategy to match multiple objects and multiple job positions to obtain a first matching result; and determining the target matching result based on the first matching result.

[0015] Further, determining the target matching result based on the first matching result includes: based on the first matching result, filtering out objects from a plurality of objects that do not match a job position to obtain a plurality of target objects; based on the first matching result, filtering out jobs from a plurality of jobs that do not match an object to obtain a plurality of target jobs; adjusting preset weights to obtain adjusted preset weights, wherein the preset weights include: weights used to calculate the overall matching degree of each matching pair; based on the adjusted preset weights, matching the plurality of target objects and the plurality of target jobs again to obtain a second matching result; and determining the target matching result based on the first matching result and the second matching result.

[0016] Furthermore, after determining the target matching result based on the comprehensive matching degree of multiple matching pairs, the process further includes: generating a matching list and an analysis report based on the target matching result, wherein the matching list and the analysis report include: the reason for the unsuccessful matching of each object that did not find a job; and storing the matching list and the analysis report.

[0017] According to another aspect of the present invention, a data matching apparatus is also provided, comprising: an acquisition unit, configured to acquire a volunteer sequence of multiple objects and a candidate sequence of multiple positions, wherein the volunteer sequence of each object includes a sequence of positions applied for by the object, and the candidate sequence of each position includes multiple objects that have applied for the position in the volunteer sequence; a first determination unit, configured to determine a data profile of multiple matching pairs based on the volunteer sequence of multiple objects and the candidate sequence of multiple positions, wherein the matching pair includes any position and an object that applied for the position; a scoring unit, configured to score the matching degree of the position and object in the matching pair based on the data profile of each matching pair, to obtain a comprehensive matching degree of each matching pair; and a second determination unit, configured to determine a target matching result based on the comprehensive matching degree of the multiple matching pairs, wherein the target matching result includes positions and objects that are successfully matched between the multiple positions and the multiple objects.

[0018] Furthermore, the data profile of each matching pair includes: the volunteer ranking of the matching pair, the candidate ranking of the matching pair, and the fit of the matching pair. The volunteer ranking of the matching pair includes: the order of the position in the matching pair within the volunteer sequence of the object in the matching pair. The candidate ranking of the matching pair includes: the order of the object in the matching pair within the candidate sequence of the position in the matching pair. The fit of each matching pair includes: the degree of matching between the position and the object in the matching pair as determined by natural language processing technology. The scoring unit includes: a first determining subunit, used to determine a first score value for each matching pair based on the volunteer ranking of each matching pair; a second determining subunit, used to determine a second score value for each matching pair based on the candidate ranking of each matching pair; a third determining subunit, used to determine a third score value for each matching pair based on the fit of each matching pair; and a fourth determining subunit, used to determine the comprehensive matching degree of each matching pair based on the first score value, the second score value, the third score value, and a preset weight.

[0019] Further, the determining subunit includes: a first acquisition module, used to acquire skill data of the objects in each matching pair and job requirements of the positions in the matching pair; a calculation module, used to calculate the similarity between the skill data and job requirements associated with each matching pair to obtain the target similarity of each matching pair; a first determining module, used to determine a fourth score value for each matching pair based on the target similarity of each matching pair; and a second determining module, used to determine the comprehensive matching degree of each matching pair based on the first score value, second score value, third score value, and the preset weight of each matching pair.

[0020] Further, the second determining unit includes: a construction subunit, used to construct a weighted bipartite graph based on the comprehensive matching degree of multiple matching pairs, wherein the weighted bipartite graph includes: nodes representing objects in each matching pair, nodes representing positions in each matching pair, and edges with the comprehensive matching degree as weights; and a fifth determining subunit, used to determine the target matching result based on the weighted bipartite graph.

[0021] Further, the fifth determining subunit includes: a second acquisition module, used to acquire target constraints, wherein the target constraints include: each object is matched at most once, and each job position is matched at most once; a matching module, used to match multiple objects and multiple job positions based on the target constraints and the weighted bipartite graph using a maximum weight matching strategy to obtain a first matching result; and a third determining module, used to determine the target matching result based on the first matching result.

[0022] Further, the third determining module includes: a first filtering submodule, used to filter out objects that do not match a job position from among the multiple objects based on the first matching result, to obtain multiple target objects; a second filtering submodule, used to filter out jobs that do not match objects from among the multiple jobs based on the first matching result, to obtain multiple target jobs; an adjustment submodule, used to adjust preset weights to obtain adjusted preset weights, wherein the preset weights include: a weight for calculating the comprehensive matching degree of each matching pair; a matching submodule, used to match the multiple target objects and the multiple target jobs again based on the adjusted preset weights, to obtain a second matching result; and a determining submodule, used to determine the target matching result based on the first matching result and the second matching result.

[0023] Furthermore, the data matching device also includes: a generation unit, used to generate a matching list and an analysis report based on the target matching result after determining the target matching result based on the comprehensive matching degree of multiple matching pairs, wherein the matching list and the analysis report include: the reason for the unsuccessful matching of each object that was not matched with a job; and a storage unit, used to store the matching list and the analysis report.

[0024] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the data matching method of any of the above-mentioned methods by executing the executable instructions.

[0025] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the data matching method described above when it is running.

[0026] In this invention, the following method is adopted: Multiple object volunteer sequences and multiple job candidate sequences are obtained. Each object's volunteer sequence includes a sequence of jobs applied for by that object, and each job candidate sequence includes objects that applied for that job in multiple volunteer sequences. Based on the multiple object volunteer sequences and multiple job candidate sequences, multiple matching pairs are identified, where each matching pair includes any job and an object that applied for that job. Based on the data profile of each matching pair, the matching degree between the job and the object in that matching pair is scored to obtain a comprehensive matching degree for each matching pair. Based on the comprehensive matching degree of multiple matching pairs, a target matching result is determined, where the target matching result includes the jobs and objects that are successfully matched between multiple jobs and multiple objects. This solves the technical problem of low matching accuracy in matching employee volunteer data with jobs in related technologies. In this invention, by comprehensively scoring the matching pairs based on the data profile of the job and object matching pairs, and matching the object's volunteer data with the job based on the comprehensive scoring result, the technical effect of improving the matching accuracy of employee volunteer data with jobs is achieved. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0028] Figure 1 This is a flowchart of an optional data matching method according to an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of an optional Comprehensive Matching Score (CMS) calculation according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of an optional data matching system according to an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of an optional data matching process according to an embodiment of the present invention;

[0032] Figure 5 This is a schematic diagram of an optional data matching device according to an embodiment of the present invention;

[0033] Figure 6This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] It should be noted that the user information (including but not limited to user device information, user personal information, etc.), the collected information and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation portals for users to choose to authorize or refuse.

[0037] Example 1

[0038] According to an embodiment of the present invention, an alternative data matching method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0039] Figure 1 This is a flowchart of an optional data matching method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0040] Step S101: Obtain the volunteer sequence of multiple objects and the candidate sequence of multiple positions. The volunteer sequence of each object includes the sequence of positions applied for by the object, and the candidate sequence of each position includes the objects that have applied for the position in the multiple volunteer sequences.

[0041] For each candidate's application sequence, a user interface can be provided for employees (corresponding to the candidate) to log in. Employees can browse all open positions and their detailed job descriptions. Employees can submit at least one ordered application sequence (e.g., first choice, second choice, third choice, etc.). In an optional example, employees can also be allowed to select from a preset skills library or manually enter skill tags related to the selected position (e.g., "proficient in data analysis," "cross-departmental communication and coordination," "Python programming"), or directly upload and update their resumes.

[0042] For each position's candidate sequence, the system provides an interface for department heads (such as branch office leaders). Heads can view a list of all employees who have expressed interest in joining their department's positions (i.e., those who have applied for the position in the preference sequence). For each recruitment position (including positions recruiting multiple people), an ordered preference sequence can be selected from the candidate pool (i.e., those who have applied for the position). For example, for a position recruiting N people, M candidates can be selected (M≥N), and they can be labeled as "first priority," "second priority," ..., "Mth priority." The first N priorities can be defined by the system as the "preferred sequence," while priorities N+1 to M can be defined as the "alternative sequence."

[0043] Step S102: Based on the volunteer sequence of multiple objects and the candidate sequence of multiple positions, determine the data profiles of multiple matching pairs, wherein the matching pair includes: any position and an object that applied for that position.

[0044] In this embodiment, for each candidate sequence of a position, a matching pair can be formed by the position and each object in the candidate sequence. For each matching pair, a data profile of each matching pair can be determined based on the volunteer sequence corresponding to the object in the matching pair and the candidate sequence of the position. The data profile of each matching pair can be a structured data object, which includes the volunteer priority of the object (e.g., employee), the department preference priority, and preliminary ability matching information extracted and matched from the employee resume / tags and job description through NLP (Natural Language Processing) technology.

[0045] Step S103: Based on the data profile of each matching pair, score the matching degree of the job and the object in the matching pair to obtain the comprehensive matching degree of each matching pair.

[0046] In this embodiment, the matching degree between the job and the object in each matching pair can be scored based on the data profile of each matching pair to obtain a comprehensive matching pair for each matching pair.

[0047] Step S104: Based on the comprehensive matching degree of multiple matching pairs, determine the target matching result, wherein the target matching result includes: the positions and objects that are successfully matched between multiple positions and multiple objects.

[0048] In this embodiment, multiple objects and multiple job positions can be matched based on the comprehensive matching degree of multiple matching pairs to obtain the target matching result.

[0049] Through the above steps, the technical problem of low matching accuracy in matching employee volunteer data with job positions in related technologies is thus solved. In this embodiment, based on the data profile of the matching pair consisting of job position and candidate, the matching pair is comprehensively scored, and based on the comprehensive score result, the candidate's volunteer data is matched with the job position, thereby achieving the technical effect of improving the matching accuracy of employee volunteer data with job positions.

[0050] Optionally, the data profile of each matching pair includes: the volunteer ranking of the matching pair, the candidate ranking of the matching pair, and the fit of the matching pair. The volunteer ranking of the matching pair includes: the order of the position in the matching pair within the volunteer sequence of the object in the matching pair. The candidate ranking of the matching pair includes: the order of the object in the matching pair within the candidate sequence of the position in the matching pair. The fit of each matching pair includes: the degree of matching between the position and the object in the matching pair as determined by natural language processing technology. Based on the data profile of each matching pair, the degree of matching between the position and the object in the matching pair is scored to obtain the comprehensive matching degree of each matching pair. This includes: determining a first score value for each matching pair based on the volunteer ranking of each matching pair; determining a second score value for each matching pair based on the candidate ranking of each matching pair; determining a third score value for each matching pair based on the fit of each matching pair; and determining the comprehensive matching degree of each matching pair based on the first score value, the second score value, the third score value, and a preset weight.

[0051] In this embodiment, a multi-dimensional willingness profile can be automatically generated for each potential "employee-job" pairing. This is a structured data object that includes the employee's willingness ranking (i.e., the order / ranking of the job in the candidate sequence of the employee's willingness), the department willingness ranking (i.e., the order / ranking of the employee in the candidate sequence of the job), and preliminary ability matching information extracted and matched from the employee's resume / tags and job description through NLP technology.

[0052] In this embodiment, for each matching pair, the comprehensive matching pair can be determined based on the data profile of that matching pair. Multiple score values ​​can be determined in the following ways:

[0053] S_e (Employee Willingness Score) (corresponding to the first score): The score assigned based on the employee's preference order, for example, 100 points for the first preference and 60 points for the second preference.

[0054] S_d (Department Preference Score) (corresponding to the second score): The score assigned based on the department's preference ranking. For example, the first priority in the "preferred sequence" is 100 points, and the second priority is 90 points; the "alternative sequence" is 50 points.

[0055] OV (Organizational Value Score) (corresponding to the third score): Administrators can set a fixed bonus for specific strategic key positions or high-potential talents to reflect organizational orientation, or determine the score value based on the fit of the matching pair.

[0056] Then, the overall matching degree of each matching pair can be determined based on the first, second, and third scores of each matching pair and the preset weights.

[0057] By identifying the overall match for each pair, the degree of matching between employee aspirations and job requirements can be accurately assessed.

[0058] Optionally, based on the first score, second score, third score, and preset weight of each matching pair, the comprehensive matching degree of each matching pair is determined, including: obtaining the skill data of the objects in each matching pair and the job requirements of the positions in the matching pair; calculating the similarity between the skill data and job requirements associated with each matching pair to obtain the target similarity of each matching pair; determining the fourth score of each matching pair based on the target similarity of each matching pair; and determining the comprehensive matching degree of each matching pair based on the first score, second score, third score, fourth score, and preset weight.

[0059] For example, S_s (ability matching score) (corresponding to the fourth score value): a score of 0-100 automatically generated by the ability profile matching module (which can use algorithms such as cosine similarity to calculate the similarity between employee skill vectors and job requirement vectors). For example, the cosine similarity algorithm can be used to calculate the similarity between the skill data associated with each matching pair and the job requirements to obtain the target similarity of each matching pair. A score of 0-100 can be automatically generated based on the target similarity of each matching pair to obtain the fourth score value.

[0060] The aforementioned preset weights may include the weight coefficients for each of the following: the first score value S_e, the second score value S_d, the third score value OV, and the fourth score value S_s.

[0061] Figure 2This is a flowchart of an optional Comprehensive Matching Score (CMS) calculation according to an embodiment of the present invention, such as... Figure 2 As shown, for each pairing, its Comprehensive Match Score (CMS) can be calculated according to a preset weighted formula that can be configured by the administrator. An example of an optional CMS calculation formula is as follows: CMS = (w_e S_e)+(w_d S_d)+(w_s S_s)+OV, where w_e, w_d, w_s are the weight coefficients of each item, which can be adjusted by the administrator according to the emphasis of the double selection in different batches (e.g., w_e=0.4, w_d=0.4, w_s=0.2).

[0062] By using the weight coefficients of each of the first score value S_e, the second score value S_d, the third score value OV, and the fourth score value S_s, the technical effect of accurately calculating the comprehensive matching degree of each matching pair is achieved.

[0063] Optionally, the target matching result is determined based on the comprehensive matching degree of multiple matching pairs, including: constructing a weighted bipartite graph based on the comprehensive matching degree of multiple matching pairs, wherein the weighted bipartite graph includes: nodes representing objects in each matching pair, nodes representing job positions in each matching pair, and edges with the comprehensive matching degree as weight; and determining the target matching result based on the weighted bipartite graph.

[0064] In this embodiment, all calculated CMS values ​​(comprehensive matching degree) can be constructed into a weighted bipartite graph (or a more complex network flow model), where one side of the weighted bipartite graph consists of all object nodes (e.g., employee nodes), and the other side consists of all job "slot" nodes (e.g., a job that recruits 3 people has 3 nodes), and the weight of the edge is the CMS.

[0065] Subsequently, based on the weighted bipartite graph, the volunteers and job positions of multiple objects can be matched to obtain the target matching results, thus achieving the technical effect of accurately matching job positions and employees.

[0066] Optionally, determining the target matching result based on the weighted bipartite graph includes: obtaining target constraints, wherein the target constraints include: each object is matched at most once and each job position is matched at most once; based on the target constraints and the weighted bipartite graph, using the maximum weight matching strategy to match multiple objects and multiple job positions to obtain a first matching result; and based on the first matching result, determining the target matching result.

[0067] In this embodiment, the core algorithm engine can be started to run a maximum weight matching algorithm (e.g., the Kuhn-Munkres algorithm or a network flow-based algorithm). The goal of this algorithm is to find a set of matches that maximizes the sum of the weights (CMS) of all matching edges, under the constraints that "each object (e.g., an employee) is matched at most once" and "each job position is matched at most once". After the algorithm finishes running, the first round of "pre-matching" successful list (corresponding to the first matching result) can be output.

[0068] Based on the first matching result, the target matching result can be determined, thus achieving the technical effect of improving the matching degree between job positions and employee aspirations.

[0069] Optionally, based on the first matching result, determining the target matching result includes: based on the first matching result, filtering out objects from multiple objects that do not match any job postings to obtain multiple target objects; based on the first matching result, filtering out jobs from multiple job postings that do not match any objects to obtain multiple target job postings; adjusting preset weights to obtain adjusted preset weights, wherein the preset weights include: weights used to calculate the overall matching degree of each matching pair; based on the adjusted preset weights, matching the multiple target objects and multiple target job postings again to obtain a second matching result; and based on the first matching result and the second matching result, determining the target matching result.

[0070] In this embodiment, after obtaining the first matching result, if there are still unmatched objects and positions, a phased attenuation supplementary recruitment phase (second round matching) can be carried out: to provide a second chance for personnel and positions that were not successfully matched in the first round, and to reflect humanistic care. Specifically, the system identifies positions that are still vacant after the first round, as well as employees who have not been matched. A supplementary recruitment mode can be entered, and the weights of the CMS calculation formula are dynamically adjusted. A typical adjustment strategy is to increase the weight of employee willingness and ability scores, and decrease the weight of department willingness scores (e.g., w_e=0.5, w_d=0.2, w_s=0.3). The purpose of this is to prioritize those with strong personal willingness or highly matched abilities who may have failed in the first round due to a lower department ranking. Using the adjusted preset weights, the CMS is recalculated for the remaining "employee-position" pairs, and the maximum weight matching algorithm is run again to supplement vacant positions. This phase can be repeated as needed, or it can be set to end after two rounds.

[0071] Finally, based on the objects and positions that were successfully matched in the first matching result and the objects and positions that were successfully matched in the second matching result, the target matching result is obtained.

[0072] Optionally, after determining the target matching result based on the comprehensive matching degree of multiple matching pairs, the method further includes: generating a matching list and an analysis report based on the target matching result, wherein the matching list analysis report includes: the reason for the unsuccessful matching of each object that did not match a job position; and storing the matching list and the analysis report.

[0073] In this embodiment, the matching results from all rounds (i.e., the target matching results) can be integrated to generate a final matching list, which will be notified to relevant employees and department heads via system messages, emails, etc. For employees who are not successfully matched, the system can automatically generate an analysis report, which may include: "Analysis of the competitiveness of the position you selected", "Assessment of the match between your ability profile and the position", "It is recommended that you pay attention to the next open XX type of position", etc., to provide a reference for the employee's future development.

[0074] By introducing and applying "global optimal matching algorithms" (such as maximum weight matching), the decision-making objective is no longer simply to satisfy as many "first-choice" candidates as possible, but to find a globally optimal solution that maximizes the sum of the "overall matching degree" of all successful pairings. The aim is to ensure, at the system level, that every two-way selection activity brings maximum human capital allocation benefits to the organization, achieving a synergistic effect of "1+1>2".

[0075] To address the pain point of "blind decision-making" caused by information asymmetry, a "competition intensity analysis" mechanism is proposed. During the application period, this mechanism can display real-time, anonymous data on the competitive landscape of key positions to both participants and deputies. Its aim is to break down information barriers, transforming the system from a passive "data collector" into a proactive "decision navigator," empowering employees and departments to make wiser and more strategic choices, effectively reducing the risk of rejection, and improving participant satisfaction and experience.

[0076] To address the deep-seated problem of "person-job mismatch," this embodiment expands the matching dimension from a single "willingness" to a two-dimensional space of "willingness + ability." By integrating "ability profiling matching" technology, it proactively analyzes employees' skill profiles and job requirements, using this as one of the core factors in calculating the matching degree. The aim is to ensure that the final matching result not only reflects the mutual willingness of both parties but also a match of objective abilities, thereby significantly improving employee efficiency and job output quality after placement.

[0077] Example 2

[0078] Embodiment 2 of the present invention provides an optional data matching system, which can be used to execute the data matching method provided in Embodiment 1 of the present invention.

[0079] Figure 3This is a schematic diagram of an optional data matching system according to an embodiment of the present invention, such as... Figure 3 As shown, its functional module architecture diagram includes:

[0080] User interaction and data collection module (corresponding to S1): Provides a front-end interface for employees and department heads to fill in volunteer information and sort their preferences.

[0081] Data profiling (corresponding to S1) and scoring engine modules (corresponding to S2): The core components are the NLP engine and the scoring rule engine. They are responsible for parsing unstructured data (resumes), building profiles, and calculating CMS based on configurable formulas.

[0082] The core algorithm engine module (corresponding to S3 and S4) embeds the maximum weight matching algorithm and includes logic for dynamic weight adjustment in stages. This is the system's "decision-making brain".

[0083] Data Analysis and Reporting Module (corresponding to S5): Responsible for generating "competition heat" data during the process and generating a final matching report and personal analysis report after completion.

[0084] System Management and Configuration Module: Allows administrators to configure weight coefficients, add or delete positions, and manage users.

[0085] Data persistence module: Uses a database to store all user, job, volunteer, profile, matching results and other data.

[0086] Figure 4 This is a schematic diagram of an optional data matching process according to an embodiment of the present invention, such as... Figure 4 As shown, it includes:

[0087] S1. Willingness Data Collection and Profile Building Stage: This step aims to capture the complex intentions of all parties in the two-way selection in a structured and digital manner, and to build a matching foundation.

[0088] S1-1, Employee-side Preference Collection: The system provides a user interface for employees to log in. Employees can browse all open positions and their detailed job descriptions. Employees need to fill in at least one ordered preference sequence (e.g., first choice, second choice, third choice, etc.). As a preferred embodiment, the system also allows employees to select from a preset skill library or manually enter ability tags related to the selected position (e.g., "proficient in data analysis," "cross-departmental communication and coordination," "Python programming"), or directly upload and update their resumes.

[0089] S1-2. Departmental Willingness Collection: The system provides an interface for department heads (such as branch office leaders). Heads can view a list of all employees who have expressed a desire to join their department. For each recruitment position (including positions recruiting multiple people), the head can select an ordered sequence of willingness from the candidate pool. For example, for a position recruiting N people, the head can select M candidates (M≥N) and label them as "first priority," "second priority," ..., "Mth priority." The first N priorities are defined as the "preferred sequence," while priorities N+1 to M are defined as the "alternative sequence."

[0090] S1-3. Generation of Multi-Dimensional Willingness Profiles: After receiving the above data, the system backend will automatically generate a multi-dimensional willingness profile for each potential "employee-job" pairing. This is a structured data object that includes the employee's willingness ranking, department willingness ranking, and preliminary ability fit information extracted and matched from the employee's resume / tags and job description through NLP technology.

[0091] S2, Comprehensive Matching Score (CMS) Dynamic Calculation Stage: This step is the core quantification step of the present invention, which aims to assign a scientific and comparable score to each possible match.

[0092] The system calls the matching score engine to traverse all "employee-position" pairs that have mutual intention (i.e., the employee has selected a certain position, and the department to which the position belongs has also selected the employee).

[0093] For each pairing, the engine calculates its Comprehensive Match Score (CMS) according to a preset, administrator-configurable weighted formula. A typical example of the calculation formula is as follows:

[0094] CMS=(w_e S_e)+(w_d S_d)+(w_s S_s)+OV

[0095] S_e (Employee Preference Score): The score assigned based on the employee's preference order, for example, 100 points for the first preference and 60 points for the second preference.

[0096] S_d (Department Preference Score): The score assigned based on the department's preference ranking. For example, the first priority in the "Preferred Sequence" is 100 points, the second priority is 90 points, and the "Alternative Sequence" is 50 points.

[0097] S_s (Ability Matching Score): A score of 0-100 automatically generated by the ability profile matching module (which can use algorithms such as cosine similarity to calculate the similarity between employee skill vectors and job requirement vectors).

[0098] OV (Organizational Value Score): Administrators can set a fixed bonus for specific strategic key positions or high-potential talents to reflect organizational orientation.

[0099] w_e, w_d, and w_s are the weighting coefficients for each item, which can be adjusted by the administrator according to the emphasis of the double selection in different batches (e.g., w_e=0.4, w_d=0.4, w_s=0.2).

[0100] S3, Global Optimal Matching Execution Phase (First Round): This step aims to find the global optimal solution through advanced algorithms.

[0101] The system constructs all the calculated CMS values ​​into a weighted bipartite graph (or a more complex network flow model), where one side of the graph consists of all employee nodes and the other side consists of all job "slot" nodes (for example, a job that hires 3 people has 3 nodes), and the weight of the edge is the CMS.

[0102] The system launches its core algorithm engine and runs a maximum weight matching algorithm (e.g., the Kuhn-Munkres algorithm or a network flow-based algorithm). The goal of this algorithm is to find a set of matches that maximizes the sum of the weights (CMS) of all matching edges, while satisfying the constraints that "each employee is matched at most once" and "each job position is matched at most once".

[0103] After the algorithm finishes running, it outputs the list of successful "pre-matches" in the first round.

[0104] S4. Phased attenuation and supplementary recruitment phase (second round): This step aims to provide a second chance for personnel and positions that were not successfully matched in the first round, and reflects humanistic care.

[0105] The system identified positions that remained vacant after the first round, as well as employees who were not matched.

[0106] The system enters supplementary recruitment mode and dynamically adjusts the weights of the CMS calculation formula. A typical adjustment strategy is to increase the weight of employee willingness and ability scores, and decrease the weight of department willingness scores (e.g., w_e=0.5, w_d=0.2, w_s=0.3). The purpose of this is to prioritize candidates with strong personal willingness or highly matched abilities who may have been disadvantaged in the first round due to lower department rankings, in order to fill vacant positions.

[0107] The system uses the adjusted weights to recalculate the CMS for the remaining "employee-position" pairs and runs the maximum weight matching algorithm again to fill vacant positions.

[0108] This stage can be repeated as needed, or it can be set to end after two rounds.

[0109] S5. Results Publication and Analysis Phase:

[0110] The system integrates the matching results from all rounds, generates the final matching list, and notifies relevant employees and department heads via system messages, emails, and other means.

[0111] For employees who are ultimately not matched, the system can automatically generate an analysis report, which may include: "Analysis of the competitiveness of the position you selected", "Assessment of the match between your skills profile and the position", "Suggestions for you to pay attention to the next open positions of type XX", etc., to provide a reference for the employee's future development.

[0112] In this embodiment, a unified quantification method is used to address various subjective and objective factors in the two-way selection process (employee preference, departmental preference, objective ability fit, and organizational strategic orientation). This goes beyond simply collecting information; instead, it uses a configurable weighted formula to generate a precise and comparable comprehensive score for each potential "employee-job" pairing. This fundamentally solves the problems of "crude rules and ambiguous judgments" in matching methods of related technologies. The matching decision is elevated from a binary "yes / no" logic to a continuous and refined score evaluation dimension. This enables the system to scientifically and fairly measure the "quality" of any two matching combinations, providing a solid data foundation and possibility for achieving globally optimal resource allocation.

[0113] In this embodiment, the priority-based greedy matching strategy (such as "first choice first") is abandoned in the matching execution stage. Instead, a maximum weight matching algorithm aimed at finding the global optimum is adopted. The decision goal of this algorithm is to find a matching scheme that maximizes the sum of the CMS scores of all successfully paired individuals. This ensures that the matching result can maximize the overall human resource allocation utility of the organization, effectively avoiding missing the "globally optimal" opportunity to let an employee create greater value by satisfying a "local optimum" (such as a high-scoring employee and a general position being each other's first choice). This has significant value for improving the overall operational efficiency and competitiveness of the organization.

[0114] By introducing artificial intelligence technology into human resource matching, an NLP engine automatically parses unstructured text such as employee resumes and skill tags, and performs semantic comparison with job requirements to generate an objective competency matching score. This score is then used as one of the core weighting factors in calculating the CMS (Company Management System), significantly improving the depth and accuracy of person-job matching. This ensures that the matching result is not only a match of "willingness" but also a fit of "competence," reducing training costs and the risk of underperformance due to mismatched abilities from the outset. This truly evolves the matching process from "forced pairing" to "precise matching."

[0115] In this embodiment, a multi-round matching process is designed. In subsequent supplementary rounds, the system can dynamically adjust the weighting coefficients in the CMS calculation model. For example, it can appropriately "de-weight" the subjective will of departments while "up-weighting" the personal will and objective abilities of employees. This "weight decay" strategy combines efficiency with humanistic care. While ensuring that the first round efficiently matches the optimal combination, it provides a second chance for those outstanding employees who are not ranked at the top of their departments, and also prioritizes employees with strong desires. This increases the success rate of matching and the sense of accomplishment for employees, making the entire matching process more flexible, humane, and adaptable.

[0116] Example 3

[0117] Embodiment 3 of the present invention provides an optional data matching device, wherein each implementation unit in the data matching device corresponds to each implementation step in Embodiment 1.

[0118] Figure 5 This is a schematic diagram of an optional data matching device according to an embodiment of the present invention, such as... Figure 5 As shown, the data matching device includes: an acquisition unit 51, a first determination unit 52, a scoring unit 53, and a second determination unit 54.

[0119] The acquisition unit 51 is used to acquire the volunteer sequences of multiple objects and the candidate sequences of multiple positions. The volunteer sequence of each object includes a sequence of positions applied for by the object, and the candidate sequence of each position includes objects that have applied for the position in multiple volunteer sequences.

[0120] The first determining unit 52 is used to determine the data profiles of multiple matching pairs based on the volunteer sequence of multiple objects and the candidate sequence of multiple positions, wherein the matching pair includes: any position and an object that filled in that position;

[0121] Scoring unit 53 is used to score the matching degree of the job and the object in each matching pair based on the data profile of each matching pair, and to obtain the comprehensive matching degree of each matching pair;

[0122] The second determining unit 54 is used to determine the target matching result based on the comprehensive matching degree of multiple matching pairs, wherein the target matching result includes: the positions and objects that are successfully matched between multiple positions and multiple objects.

[0123] According to another aspect of the present invention, a data matching apparatus is also provided, comprising: an acquisition unit 51, configured to acquire a volunteer sequence of multiple objects and a candidate sequence of multiple positions, wherein the volunteer sequence of each object includes a sequence of positions applied for by the object, and the candidate sequence of each position includes objects that have applied for that position in the multiple volunteer sequences; a first determining unit 52, configured to determine a data profile of multiple matching pairs based on the volunteer sequences of multiple objects and the candidate sequences of multiple positions, wherein a matching pair includes any position and an object that applied for that position; a scoring unit 53, configured to score the matching degree of the position and object in the matching pair based on the data profile of each matching pair, thereby obtaining a comprehensive matching degree for each matching pair; and a second determining unit 54, configured to determine a target matching result based on the comprehensive matching degree of multiple matching pairs, wherein the target matching result includes positions and objects that are successfully matched between multiple positions and multiple objects. This solves the technical problem of low matching accuracy in matching employee volunteer data with positions in related technologies. In this embodiment, the matching pairs are comprehensively scored based on the data profiles of the matching pairs consisting of job positions and candidates. Based on the comprehensive score results, the candidates' preferences are matched with job positions, thereby achieving the technical effect of improving the accuracy of matching employee preference data with job positions.

[0124] Optionally, the data profile of each matching pair includes: the volunteer ranking of the matching pair, the candidate ranking of the matching pair, and the fit of the matching pair. The volunteer ranking of the matching pair includes: the order of the position in the matching pair within the volunteer sequence of the object in the matching pair. The candidate ranking of the matching pair includes: the order of the object in the matching pair within the candidate sequence of the position in the matching pair. The fit of each matching pair includes: the degree of matching between the position and the object in the matching pair as determined by natural language processing technology. The scoring unit includes: a first determining subunit, used to determine a first score value for each matching pair based on the volunteer ranking of each matching pair; a second determining subunit, used to determine a second score value for each matching pair based on the candidate ranking of each matching pair; a third determining subunit, used to determine a third score value for each matching pair based on the fit of each matching pair; and a fourth determining subunit, used to determine the comprehensive matching degree of each matching pair based on the first score value, the second score value, the third score value, and a preset weight.

[0125] Optionally, the determining subunit includes: a first acquisition module, used to acquire skill data of objects in each matching pair and job requirements of positions in the matching pair; a calculation module, used to calculate the similarity between the skill data and job requirements associated with each matching pair to obtain the target similarity of each matching pair; a first determining module, used to determine a fourth score value for each matching pair based on the target similarity of each matching pair; and a second determining module, used to determine the comprehensive matching degree of each matching pair based on the first score value, second score value, third score value, fourth score value, and preset weights of each matching pair.

[0126] Optionally, the second determining unit includes: a construction subunit for constructing a weighted bipartite graph based on the comprehensive matching degree of multiple matching pairs, wherein the weighted bipartite graph includes: nodes representing objects in each matching pair, nodes representing positions in each matching pair, and edges with the comprehensive matching degree as weight; and a fifth determining subunit for determining the target matching result based on the weighted bipartite graph.

[0127] Optionally, the fifth determining subunit includes: a second acquisition module, used to acquire target constraints, wherein the target constraints include: each object is matched at most once and each job position is matched at most once; a matching module, used to match multiple objects and multiple jobs based on the target constraints and the weighted bipartite graph, using a maximum weight matching strategy to obtain a first matching result; and a third determining module, used to determine the target matching result based on the first matching result.

[0128] Optionally, the third determining module includes: a first filtering submodule, used to filter out objects that do not match a job position from multiple objects based on the first matching result, to obtain multiple target objects; a second filtering submodule, used to filter out jobs that do not match objects from multiple jobs based on the first matching result, to obtain multiple target jobs; an adjustment submodule, used to adjust preset weights to obtain adjusted preset weights, wherein the preset weights include: a weight used to calculate the comprehensive matching degree of each matching pair; a matching submodule, used to match multiple target objects and multiple target jobs again based on the adjusted preset weights, to obtain a second matching result; and a determining submodule, used to determine the target matching result based on the first matching result and the second matching result.

[0129] Optionally, the data matching device further includes: a generation unit, used to generate a matching list and an analysis report based on the target matching result after determining the target matching result based on the comprehensive matching degree of multiple matching pairs, wherein the matching list analysis report includes: the reason for the unsuccessful matching of each object that was not matched with a job; and a storage unit, used to store the matching list and the analysis report.

[0130] The data matching device described above may also include a processor and a memory. The acquisition unit 51, the first determination unit 52, the scoring unit 53, and the second determination unit 54 described above are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0131] The aforementioned processor contains a kernel that retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, a comprehensive score is calculated based on the data profile of the matching pairs (jobs and candidates). Based on the comprehensive score, the candidate's preferences are matched with the job, thereby improving the accuracy of matching employee preferences with job requirements.

[0132] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0133] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the data matching method of any of the above-mentioned methods by executing the executable instructions.

[0134] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the data matching method described above when it is running.

[0135] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of the present invention, such as... Figure 6 As shown, an embodiment of the present invention provides an electronic device 60, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements any of the above-mentioned data matching methods.

[0136] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0137] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0142] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data matching method, characterized in that, include: Obtain a volunteer sequence of multiple objects and a candidate sequence of multiple positions. Each object's volunteer sequence includes a sequence of positions applied for by that object, and each position's candidate sequence includes multiple objects that applied for that position in the volunteer sequence. Based on the volunteer sequence of multiple objects and the candidate sequence of multiple positions, a data profile of multiple matching pairs is determined, wherein the matching pair includes: any of the positions and an object that applied for the position; Based on the data profile of each matching pair, the matching degree between the job and the object in the matching pair is scored to obtain the comprehensive matching degree of each matching pair; Based on the overall matching degree of multiple matching pairs, a target matching result is determined, wherein the target matching result includes: multiple job positions and multiple objects that are successfully matched.

2. The data matching method according to claim 1, characterized in that, Each matching pair's data profile includes: the pair's volunteer ranking, the pair's candidate ranking, and the pair's fit. The volunteer ranking includes the order of the job within the volunteer sequence of the person in the matching pair. The candidate ranking includes the order of the person in the matching pair within the candidate sequence of the job within the matching pair. The fit of each matching pair includes the degree of match between the job and the person in the matching pair, determined by natural language processing technology. Based on the data profile of each matching pair, the matching degree between the job and the target in the matching pair is scored to obtain the comprehensive matching degree of each matching pair, including: Based on the volunteer ranking of each of the matching pairs, a first score value is determined for each of the matching pairs; Based on the candidate ranking of each of the matching pairs, a second score value is determined for each of the matching pairs; A third score value is determined for each of the matching pairs based on the fit of each pair. The overall matching degree of each matching pair is determined based on the first score, second score, third score, and preset weight of each matching pair.

3. The data matching method according to claim 2, characterized in that, Based on the first score, second score, and third score of each matching pair, and a preset weight, the overall matching degree of each matching pair is determined, including: Obtain the skill data of the objects in each matching pair and the job requirements of the positions in that matching pair; Calculate the similarity between the skill data associated with each matching pair and the job requirements to obtain the target similarity of each matching pair; A fourth score value is determined for each of the matching pairs based on the target similarity of each matching pair; Based on the first score, second score, third score, and fourth score of each matching pair, as well as the preset weight, the overall matching degree of each matching pair is determined.

4. The data matching method according to claim 1, characterized in that, Based on the overall matching degree of multiple matching pairs, the target matching result is determined, including: Based on the comprehensive matching degree of multiple matching pairs, a weighted bipartite graph is constructed, wherein the weighted bipartite graph includes: nodes representing objects in each matching pair, nodes representing job positions in each matching pair, and edges with the comprehensive matching degree as weights; The target matching result is determined based on the weighted bipartite graph.

5. The data matching method according to claim 4, characterized in that, Based on the weighted bipartite graph, the target matching result is determined, including: Obtain target constraints, wherein the target constraints include: each object is matched at most once, and each job position is matched at most once; Based on the target constraints and the weighted bipartite graph, a maximum weight matching strategy is used to match multiple objects and multiple job positions to obtain a first matching result; Based on the first matching result, the target matching result is determined.

6. The data matching method according to claim 5, characterized in that, Based on the first matching result, the target matching result is determined, including: Based on the first matching result, objects that do not match a job position are filtered out from the multiple objects to obtain multiple target objects; Based on the first matching result, positions that did not match an object are filtered out from the multiple positions to obtain multiple target positions; Adjust the preset weights to obtain the adjusted preset weights, wherein the preset weights include: weights used to calculate the overall matching degree of each matching pair; Based on the adjusted preset weights, multiple target objects and multiple target positions are matched again to obtain a second matching result; Based on the first matching result and the second matching result, the target matching result is determined.

7. The data matching method according to claim 1, characterized in that, After determining the target matching result based on the comprehensive matching degree of multiple matching pairs, the process further includes: Based on the target matching results, a matching list and an analysis report are generated. The matching list and the analysis report include the reasons for the unsuccessful matching of each candidate who did not find a job. The matching list and the analysis report are stored.

8. A data matching device, characterized in that, include: The acquisition unit is used to acquire the volunteer sequences of multiple objects and the candidate sequences of multiple positions. Each object's volunteer sequence includes a sequence of positions applied for by the object, and each position's candidate sequence includes multiple objects that have applied for that position in the volunteer sequences. The first determining unit is used to determine a data profile of multiple matching pairs based on the volunteer sequence of multiple objects and the candidate sequence of multiple positions, wherein the matching pair includes: any of the positions and an object that filled in the position; The scoring unit is used to score the matching degree of the job and the object in each matching pair based on the data profile of each matching pair, so as to obtain the comprehensive matching degree of each matching pair; The second determining unit is used to determine the target matching result based on the comprehensive matching degree of the multiple matching pairs, wherein the target matching result includes: the multiple job positions and the multiple objects that are successfully matched.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the data matching method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the data matching method according to any one of claims 1 to 7.

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