Candidate geographical location information and retail store-based person and post matching system

Abstract

The invention discloses a person and post matching system based on candidate geographical location information and retail stores, and relates to the technical field of post matching systems. The candidate geographic position information and retail store-based person and post matching system comprises a candidate information management module, a retail store information management module, a commuting data analysis and path planning module, a candidate and store matching module and a result display and feedback adjustment module. By collecting and processing commuting modes and time period preferences of candidates, a commuting path is planned in combination with a map service API, and commuting time considering congestion and congestion influence is calculated, so that the matching accuracy and practicability are improved. In addition, the system also combines factors such as working experience, skills and the like to carry out matching result optimization, so that bidirectional selection is realized, and the matching satisfaction degree is improved. And the result display and feedback adjustment module enables the user to visually see the matching result.

Description

Technical Field

[0001] The present invention relates to the technical field of job matching systems, and in particular to a job matching system based on candidate geographic location information and retail stores. Background Art

[0002] Person-job matching refers to placing the right employees in the right positions to achieve the best combination of employees and positions. This process is crucial to improving the operational efficiency of retail stores and employee satisfaction. When employees are assigned to positions that match their abilities, interests, and expectations, they are more likely to feel satisfied and fulfilled, thereby increasing their motivation and loyalty. The right person-job matching helps employees better realize their talents and potential, thereby improving the overall performance and competitiveness of the store. A high degree of person-job matching can reduce employee turnover, reduce recruitment and training costs, and maintain the stability of store operations.

[0003] When recruiting in retail stores, choosing positions or stores that are suitable for job seekers will increase the employment rate and reduce the turnover rate. Among them, geographical location is a point that job seekers pay more attention to. If the geographical location is not suitable, the job seeker will have a long commuting time and will easily leave even if he / she is temporarily employed.

[0004] Publication No. CN111625733A discloses a method for gathering part-time information based on the geographical location of a business district. The scope of each business district and the attributes of the merchants within the business district are determined according to a map service. Then, the information of part-time employees is collected by using a client based on regional positioning. The information of part-time employees is transmitted to a big data system. The big data system classifies part-time employees into the scope of the business district according to their regional positioning and quantity. Then, part-time employees are matched according to the attributes of the merchants within the business district and the positions, so that the position information and part-time employees are presented in real time. The present invention can gather part-time information according to the geographical location of the business district, effectively match the position information with the part-time employees, greatly reduce the workload of enterprise recruitment, and improve work efficiency.

[0005] As shown in the above prior art, currently retail stores and other companies recruiting employees, as well as job seekers looking for jobs, all look within a business district, but they only take into account the range of straight-line distance. In fact, in addition to distance, commuting time is more practical for job seekers, and commuting time is affected by many factors such as commuting mode and road conditions, all of which will affect the job seekers' inclinations. Simply identifying the distance cannot accurately control the job seekers' needs. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a person-job matching system based on candidate geographic location information and retail stores, which solves the problems existing in the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a person-job matching system based on candidate geographic location information and retail stores, comprising: Candidate information management module: responsible for the collection and processing of candidate geographic location information, providing a user interface for candidates to input address information and personal needs to collect data; Retail store information management module: used to organize store address information into longitude and latitude coordinates, and collect information on transportation facilities around the store; Commute data analysis and route planning module: Based on the candidate's commuting mode and time period preference, the map service API is used to plan the commuting route and calculate the commuting time considering congestion and crowding; Candidate and store matching module: According to the set matching criteria (commuting time threshold T max 、Transfer times limit N max ) and commuting time (considering the impact of congestion and crowding) to match candidates with stores, use the weighted sum method to calculate the matching degree M, combine work experience and skills to optimize the matching results, and allow two-way selection; Result display and feedback adjustment module: Display matching results, including commuting routes, time, transfer information (non-driving mode) and the impact of congestion and crowding on commuting time (driving and non-driving mode), collect user feedback, adjust matching algorithms and parameters, especially the adjustment of congestion and crowding impact coefficient.

[0008] Preferably, the candidate information management module includes: The candidate enters his / her residential address, selects his / her commuting method (subway, walking, cycling, driving), and enters the maximum acceptable commuting time T. max 、N number of transfers max (non-driving methods), commuting time preferences (such as morning and evening peak hours, non-peak hours); at the same time, develop a user input interface to enter information, and use form validation technology to verify the accuracy and completeness of the data.

[0009] Preferably, the retail store information management module uses a crawler or API to obtain data and converts geocoding technology into addresses.

[0010] Preferably, the commuting data analysis and route planning module uses integrated map service API, real-time traffic API, and public transportation data API to plan commuting routes, taking into account commuting modes and time period preferences. Commuting modes are divided into driving modes and non-driving modes, where non-driving modes include subway, walking, and cycling; and congestion impact is refined: (1) Driving mode: Congestion will increase commuting time. The congestion index R is considered in the calculation and a dynamic path planning algorithm is used; The congestion index R is obtained through the real-time traffic API and is a value that reflects the degree of road congestion (e.g. 0-100, the larger the value, the more congested it is); The effect of congestion on commuting time is given by the formula T drive =T free ×(1+α×R) calculation, where T free is the commuting time without congestion, and α is the congestion impact coefficient, which needs to be adjusted according to actual conditions; (2) Non-driving mode: Public transportation congestion level C pub It will affect the comfort and efficiency of commuting; congestion level C pub Obtained through the public transportation data API, it is a numerical value reflecting the crowdedness of the carriage (e.g. 0-100, the larger the value, the more crowded it is); The effect of congestion on commuting time is given by the formula T pub =T base ×(1+β×C pub ) is calculated, where T base is the benchmark commuting time under normal circumstances, and β is the congestion impact coefficient, which needs to be adjusted according to actual conditions.

[0011] Preferably, the benchmark commuting time T base It refers to the commuting time required from the candidate's residential address to the retail store address without considering congestion (for driving mode) or crowding (for non-driving mode); obtain T base The specific steps are as follows: a1. Determine your commuting method and time period: Commute method: Determine the appropriate mode of transportation based on the candidate's chosen commuting method (subway, walking, cycling, driving); Time period: Consider the candidate's preferred commuting time period (e.g. morning and evening peak hours, off-peak hours). Although Tbase itself is not directly affected by the time period (because it is a benchmark time that does not consider congestion and crowding), this information is necessary for selecting the best route and considering time-based changes (e.g. frequency of shifts) when obtaining the actual commuting time; a2. Use the map service API for route planning: API selection: Choose a map service API that can provide route planning functions (such as Google Maps API, Baidu Maps API, etc.); Input parameters: Starting point: the candidate’s residential address (converted to latitude and longitude coordinates); Destination: the address of the retail store (converted to latitude and longitude coordinates); Transportation mode: Choose the appropriate transportation mode based on your commuting method (such as driving, public transportation, walking, cycling); Ignore congestion and crowding: When calling the API, set parameters to ignore real-time traffic conditions and public transportation congestion to obtain a benchmark commute time; a3. Get and process the data returned by the API: API response: After calling the map service API, a response containing benchmark commute time information will be returned; Extract commute time: Extract the benchmark commute time T from the API response base ; This time is the commuting time under ideal conditions without considering congestion and crowding; Verification and storage: Verify the extracted benchmark commute time T base Is it reasonable, including checking whether it is a non-negative value and whether it is within a reasonable range, and then converting the benchmark commuting time T base Stored in the database for subsequent use in calculating actual commuting time.

[0012] Preferably, the congestion index R and the congestion degree C pub It changes with time period, and the parameters of congestion index R and congestion degree C are established accordingly pub The function of the time period, assuming that the time period is represented by a continuous variable t, such as the number of hours in a day (0-23); Congestion index R and time period t change function: The congestion index R(t) is a piecewise function or a fitting function based on historical data. Since congestion usually occurs during peak hours in the morning and evening, a piecewise linear function is assumed to describe this change: ; Among them, R 0 is the congestion index during peak hours, R 1 is the congestion index during off-peak hours, [t1, t2], [t3, t4] are the start and end times of two peak hours (e.g., 7 to 9 a.m., 5 to 7 p.m.); Similarly, set the congestion level C pub The function of change with time period t: ; in, is the congestion index during peak hours, It is the congestion index during non-peak hours.

[0013] Preferably, the calculation formula for the matching degree M of the candidate and store matching module is: Non-driving methods: ; Driving method: ; Among them, T total is the total commuting time, w T 、w N are the weights of commuting time and number of transfers, respectively; For non-driving mode: T total =T pub ; For driving style: T total =T drive .

[0014] Preferably, the two-way selection mechanism of the candidate and store matching module includes: Recommending positions for candidates to stores: Recommending suitable store positions to candidates based on their resume information, skills, expertise, and commuting preferences, and displaying them in order based on the overall calculated matching degree; Store to candidate referrals: Candidate screening: Based on the store's job requirements (such as skill requirements, work experience, expected salary, etc.), select qualified candidates from the candidate database; Matching degree calculation: Combined with the candidate's commuting conditions (whether driving or not), job adaptability, and historical work performance, the matching degree between the candidate and the store position is calculated comprehensively; Sorting and display: The information of candidates with high matching degree will be displayed to the stores first, helping the stores to quickly identify suitable candidates and improve recruitment efficiency.

[0015] Preferably, the acquisition and processing of public transportation data in non-driving commuting, in addition to basic bus routes and station information, also obtains public transportation frequency (including departure intervals during peak hours and off-peak hours), detailed map annotations of station distribution, fare information for different time periods and distances, and multiple transfer options.

[0016] Preferably, the work contents of the result display and feedback adjustment module include: Map visualization technology: Use map APIs (such as Google Maps, Baidu Maps, etc.) to visually display the candidate's commuting routes, store locations, and surrounding facilities information in the form of maps; Chart display: Display the matching degree between candidates and stores (including key indicators such as commuting time, cost, and job matching degree) through bar charts, pie charts, and line charts, making the data more intuitive and easy to understand; Interactive interface: The interactive interface allows candidates and recruiters to view detailed information, filter conditions, and adjust preferences by clicking and sliding, improving the user experience.

[0017] The present invention provides a person-job matching system based on candidate geographic location information and retail stores. Compared with the prior art, it has the following beneficial effects: 1. This job matching system based on the candidate's geographic location information and retail stores achieves accurate matching of the candidate's geographic location information and retail store information. It collects and processes the candidate's commuting mode and time period preferences, plans the commuting route in combination with the map service API, and calculates the commuting time considering congestion and crowding, thereby improving the accuracy and practicality of the matching. In addition, the system also combines work experience, skills and other factors to optimize the matching results, achieving two-way selection and improving matching satisfaction. The result display and feedback adjustment module enables users to intuitively see the matching results, and by collecting user feedback, continuously optimizes the matching algorithm and parameters, thereby improving the system's adaptability and user experience.

[0018] 2. This job matching system based on candidate geographic location information and retail stores ensures the accuracy and completeness of data in the candidate information management module through a detailed user input interface and form verification technology, providing a reliable foundation for subsequent matching. Secondly, in the commuting data analysis and path planning module, by integrating multiple APIs, accurate planning of commuting routes is achieved, and commuting mode and time period preferences, as well as the impact of congestion and crowding on commuting time, improve the accuracy and practicality of matching. In particular, for non-driving methods, detailed information such as the frequency of public transportation, station distribution, and fare information are also obtained, providing users with a more comprehensive commuting plan. In addition, through dynamic path planning algorithms and formula calculations, the impact of congestion and crowding on commuting time is taken into account, further improving the accuracy of matching and user satisfaction. These advances make the system more efficient, accurate, and practical in matching candidate geographic location information with retail stores.

[0019] 3. The job matching system based on the candidate's geographic location information and retail stores is based on the congestion index R and the congestion degree C. pub By establishing a change function with time period t, a dynamic and refined description of the factors affecting commuting time is further realized. This change function can more accurately reflect the actual situation of traffic congestion and crowding in different time periods, thereby improving the accuracy of commuting time calculation. Especially in the morning and evening peak hours, by setting different congestion indexes and congestion levels, the system can be closer to the user's actual commuting experience. This refined processing not only improves the accuracy of matching, but also provides users with more reliable commuting time predictions, enhancing the practicality of the system and user experience.

[0020] 4. This job matching system based on the candidate's geographic location information and retail stores further quantifies and refines the matching process by setting a clear matching degree M calculation formula for the candidate and store matching module. The formula takes into account key factors such as commuting time and number of transfers (non-driving methods) and assigns them corresponding weights w T 、w N , so as to more comprehensively evaluate the degree of match between candidates and stores. This quantitative processing not only improves the accuracy and objectivity of matching, but also provides a reliable basis for the system to optimize matching results. At the same time, the formula also distinguishes between non-driving and driving methods, making the matching process more in line with the user's actual commuting needs.

[0021] 5. This job matching system based on the candidate's geographic location information and retail stores recommends suitable store job information to candidates by combining commuting preferences and job requirements, and displays them in order of matching degree. It also realizes reverse recommendation from stores to candidates, further improving the accuracy and efficiency of recruitment. The result display and feedback adjustment module uses map visualization technology and chart display to make the data more intuitive and easy to understand, and enhances the readability and user-friendliness of the information. The design of the interactive interface improves the user experience, allowing candidates and recruiters to more conveniently view detailed information, filter conditions, and adjust preferences, thereby achieving a more efficient and flexible recruitment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the overall steps of the present invention; Figure 2 It is a schematic diagram of the process of commuting data analysis of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 2 , the present invention provides the following five technical solutions: The first implementation method: a person-job matching system based on candidate geographic location information and retail stores, comprising: Candidate information management module: responsible for the collection and processing of candidate geographic location information, providing a user interface for candidates to input address information and personal needs to collect data; Retail store information management module: used to organize store address information into longitude and latitude coordinates and collect traffic facility information around the store. The retail store information management module uses crawlers or APIs to obtain data and converts geocoding technology into addresses; Commute data analysis and route planning module: Based on the candidate's commuting mode and time period preference, the map service API is used to plan the commuting route and calculate the commuting time considering congestion and crowding; Candidate and store matching module: According to the set matching criteria (commuting time threshold T max 、Transfer times limit N max ) and commuting time (considering the impact of congestion and crowding) to match candidates with stores, use the weighted sum method to calculate the matching degree M, combine work experience and skills to optimize the matching results, and allow two-way selection; Result display and feedback adjustment module: Display matching results, including commuting routes, time, transfer information (non-driving mode) and the impact of congestion and crowding on commuting time (driving and non-driving mode), collect user feedback, adjust matching algorithms and parameters, especially the adjustment of congestion and crowding impact coefficient.

[0025] This system achieves accurate matching of candidate location information and retail store information. It collects and processes candidates' commuting methods and time preferences, plans commuting routes in combination with map service APIs, and calculates commuting times that take into account congestion and crowding, thereby improving the accuracy and practicality of matching. In addition, the system also optimizes matching results by combining work experience, skills and other factors, achieving two-way selection and improving matching satisfaction. The result display and feedback adjustment module enables users to intuitively see the matching results, and by collecting user feedback, continuously optimizes matching algorithms and parameters, thereby improving the system's adaptability and user experience.

[0026] The second implementation mode is mainly different from the first implementation mode in that the candidate information management module includes: The candidate enters his / her residential address, selects his / her commuting method (subway, walking, cycling, driving), and enters the maximum acceptable commuting time T. max 、N number of transfers max (non-driving methods), commuting time preferences (such as morning and evening peak hours, non-peak hours); at the same time, develop a user input interface to enter information, and use form validation technology to verify the accuracy and completeness of the data.

[0027] The commuting data analysis and route planning module uses integrated map service API, real-time traffic API, and public transportation data API to plan commuting routes, taking into account commuting mode and time period preferences. Commuting modes are divided into driving and non-driving modes, where non-driving modes include subway, walking, and cycling; and detailed congestion impact: (1) Driving mode: Congestion will increase commuting time. The congestion index R is considered in the calculation and a dynamic path planning algorithm is used; The congestion index R is obtained through the real-time traffic API and is a value that reflects the degree of road congestion (e.g. 0-100, the larger the value, the more congested it is); The effect of congestion on commuting time is given by the formula T drive =T free ×(1+α×R) calculation, where T free is the commuting time without congestion, and α is the congestion impact coefficient, which needs to be adjusted according to actual conditions; Assume that the driving style includes the following parameter values: The commuting time without congestion is T free :30min; Congestion impact coefficient α: 0.2 (assuming that for every 10 units of congestion increase, commuting time increases by 2%); Congestion index R: obtained in real time (assuming it is 5); The commuting time by car is: T drive = 30min×(1 + 0.2×5) = 60min; (2) Non-driving mode: Public transportation congestion level C pub It will affect the comfort and efficiency of commuting; congestion level C pub Obtained through the public transportation data API, it is a numerical value reflecting the crowdedness of the carriage (e.g. 0-100, the larger the value, the more crowded it is); The effect of congestion on commuting time is given by the formula T pub =T base ×(1+β×C pub ) is calculated, where T base is the benchmark commuting time under normal circumstances, and β is the congestion impact coefficient, which needs to be adjusted according to actual conditions.

[0028] Assume that the non-driving mode includes the following parameter values: Normal base commuting time Tbase: 45min; Congestion impact coefficient β: 0.15 (assuming that for every 10 units of congestion increase, commuting time increases by 1.5%); Crowding level C pub: Real-time acquisition (assuming 6); The non-driving commuting time is; T pub = 45min×(1 + 0.15×6)= 85.5min; Baseline commuting time T base It refers to the commuting time required from the candidate's residential address to the retail store address without considering congestion (for driving mode) or crowding (for non-driving mode); obtain T base The specific steps are as follows: a1. Determine your commuting method and time period: Commute method: Determine the appropriate mode of transportation based on the candidate's chosen commuting method (subway, walking, cycling, driving); Time period: Consider the candidate's preferred commuting time period (e.g. morning and evening peak hours, off-peak hours). Although Tbase itself is not directly affected by the time period (because it is a benchmark time that does not consider congestion and crowding), this information is necessary for selecting the best route and considering time-based changes (e.g. frequency of shifts) when obtaining the actual commuting time; a2. Use the map service API for route planning: API selection: Choose a map service API that can provide route planning functions (such as Google Maps API, Baidu Maps API, etc.); Input parameters: Starting point: the candidate’s residential address (converted to latitude and longitude coordinates); Destination: the address of the retail store (converted to latitude and longitude coordinates); Transportation mode: Choose the appropriate transportation mode based on your commuting method (such as driving, public transportation, walking, cycling); Ignore congestion and crowding: When calling the API, set parameters to ignore real-time traffic conditions and public transportation congestion to obtain a benchmark commute time; a3. Get and process the data returned by the API: API response: After calling the map service API, a response containing benchmark commute time information will be returned; Extract commute time: Extract the benchmark commute time T from the API response base ; This time is the commuting time under ideal conditions without considering congestion and crowding; Verification and storage: Verify the extracted benchmark commute time T base Is it reasonable, including checking whether it is a non-negative value and whether it is within a reasonable range, and then converting the benchmark commuting time T base Stored in the database for subsequent use in calculating actual commuting time.

[0029] The acquisition and processing of public transportation data in non-driving commuting, in addition to basic bus routes and station information, also includes public transportation frequency (including departure intervals during peak and off-peak hours), detailed map annotations of station distribution, fare information for different time periods and distances, and multiple transfer options.

[0030] This embodiment first ensures the accuracy and completeness of the data in the candidate information management module through a detailed user entry interface and form verification technology, providing a reliable basis for subsequent matching. Secondly, in the commuting data analysis and path planning module, by integrating multiple APIs, accurate planning of commuting routes is achieved, and commuting modes and time period preferences, as well as the impact of congestion and crowding on commuting time, are taken into account, thereby improving the accuracy and practicality of matching. In particular, for non-driving modes, detailed information such as the frequency of public transportation, station distribution, and fare information are also obtained, providing users with a more comprehensive commuting plan. In addition, through dynamic path planning algorithms and formula calculations, the impact of congestion and crowding on commuting time is taken into account, further improving the accuracy of matching and user satisfaction. These advances make the system more efficient, accurate, and practical in matching candidate geographic location information with retail stores.

[0031] The third implementation mode is mainly different from the second implementation mode in that the congestion index R and the congestion degree C pub It changes with time period, and the parameters of congestion index R and congestion degree C are established accordingly pub The function of the time period, assuming that the time period is represented by a continuous variable t, such as the number of hours in a day (0-23); Congestion index R and time period t change function: The congestion index R(t) is a piecewise function or a fitting function based on historical data. Since congestion usually occurs during peak hours in the morning and evening, a piecewise linear function is assumed to describe this change: ; Among them, R 0 is the congestion index during peak hours, R 1 is the congestion index during off-peak hours, [t1, t2], [t3, t4] are the start and end times of two peak hours (e.g., 7 to 9 a.m., 5 to 7 p.m.); Similarly, set the congestion level C pub The function of change with time period t: ; in, is the congestion index during peak hours, It is the congestion index during non-peak hours.

[0032] In this embodiment, the congestion index R and the congestion degree C are pub By establishing a change function with time period t, a dynamic and refined description of the factors affecting commuting time is further realized. This change function can more accurately reflect the actual situation of traffic congestion and crowding in different time periods, thereby improving the accuracy of commuting time calculation. Especially in the morning and evening peak hours, by setting different congestion indexes and congestion levels, the system can be closer to the user's actual commuting experience. This refined processing not only improves the accuracy of matching, but also provides users with more reliable commuting time predictions, enhancing the practicality of the system and user experience.

[0033] The fourth implementation mode is mainly different from the first implementation mode in that the calculation formula of the matching degree M of the candidate and store matching module is: Non-driving methods: ; Driving method: ; Among them, T total is the total commuting time, w T 、w N are the weights of commuting time and number of transfers, respectively; For non-driving mode: T total =T pub ; For driving style: T total =T drive .

[0034] Assumption: The maximum commuting time Tmax accepted by the candidate is 60 minutes, and the maximum number of transfers N max 2 times; The actual commuting time T under driving mode drive 40 minutes; Non-driving commuting time T pub The time taken is 50 minutes, and the number of transfers N is 1; Assume the commuting time weight w T is 0.7, and the transfer times weight w N is 0.3; then: Non-driving methods: ; Driving method: ; The matching degree M of the driving mode is 33.33%, and the matching degree M of the non-driving mode is 26.67%, indicating that the matching degree is slightly higher and easier to accept in the driving mode than in the non-driving mode.

[0035] By setting a clear matching degree M calculation formula for the candidate and store matching module, the matching process is further quantified and refined. The formula takes into account key factors such as commuting time and number of transfers (non-driving methods) and assigns them corresponding weights w T 、w N , so as to more comprehensively evaluate the degree of match between candidates and stores. This quantitative processing not only improves the accuracy and objectivity of matching, but also provides a reliable basis for the system to optimize matching results. At the same time, the formula also distinguishes between non-driving and driving methods, making the matching process more in line with the user's actual commuting needs.

[0036] The fifth implementation mode is mainly different from the first implementation mode in that the two-way selection mechanism of the candidate and store matching module includes: Recommending positions for candidates to stores: Recommending suitable store positions to candidates based on their resume information, skills, expertise, and commuting preferences, and displaying them in order based on the overall calculated matching degree; Store to candidate referrals: Candidate screening: Based on the store's job requirements (such as skill requirements, work experience, expected salary, etc.), select qualified candidates from the candidate database; Matching degree calculation: Combined with the candidate's commuting conditions (whether driving or not), job adaptability, and historical work performance, the matching degree between the candidate and the store position is calculated comprehensively; Sorting and display: The information of candidates with high matching degree will be displayed to the stores first, helping the stores to quickly identify suitable candidates and improve recruitment efficiency.

[0037] The work contents of the result display and feedback adjustment module include: Map visualization technology: Use map APIs (such as Google Maps, Baidu Maps, etc.) to visually display the candidate's commuting routes, store locations, and surrounding facilities information in the form of maps; Chart display: Display the matching degree between candidates and stores (including key indicators such as commuting time, cost, and job matching degree) through bar charts, pie charts, and line charts, making the data more intuitive and easy to understand; Interactive interface: The interactive interface allows candidates and recruiters to view detailed information, filter conditions, and adjust preferences by clicking and sliding, improving the user experience.

[0038] By combining commuting preferences and job requirements, suitable store job information is recommended to candidates and displayed in order of matching degree. At the same time, reverse recommendations from stores to candidates are realized, further improving the accuracy and efficiency of recruitment. The result display and feedback adjustment module uses map visualization technology and chart display to make the data more intuitive and easy to understand, and enhance the readability and user-friendliness of the information. The design of the interactive interface improves the user experience, allowing candidates and recruiters to more conveniently view detailed information, filter conditions, and adjust preferences, thereby achieving a more efficient and flexible recruitment process.

[0039] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A person-job matching system based on candidate geographic location information and retail stores, characterized in that: include: Candidate information management module: responsible for the collection and processing of candidate geographic location information, providing a user interface for candidates to input address information and personal needs to collect data; Retail store information management module: used to organize store address information into longitude and latitude coordinates, and collect information on transportation facilities around the store; Commute data analysis and route planning module: Based on the candidate's commuting mode and time period preference, the map service API is used to plan the commuting route and calculate the commuting time considering congestion and crowding; Candidate and store matching module: matches candidates with stores according to the set matching criteria and commuting time, uses the weighted sum method to calculate the matching degree M, optimizes the matching results based on work experience and skills, and allows two-way selection; Result display and feedback adjustment module: displays matching results, including commuting routes, time, transfer information, and the impact of congestion and crowding on commuting time, collects user feedback, and adjusts matching algorithms and parameters.

2. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The candidate information management module includes: The candidate enters his / her residential address, selects his / her commuting method, and enters the maximum acceptable commuting time T. max 、N number of transfers max , commuting time preferences; at the same time, develop a user input interface to enter information, and use form validation technology to verify the accuracy and completeness of the data.

3. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The retail store information management module uses crawlers or APIs to obtain data and converts geocoding technology into addresses.

4. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The commuting data analysis and route planning module uses integrated map service API, real-time traffic API, and public transportation data API to plan commuting routes, taking into account commuting mode and time period preferences. Commuting modes are divided into driving and non-driving modes, where non-driving modes include subway, walking, and cycling; and detailed congestion impact: (1) Driving mode: Congestion will increase commuting time. The congestion index R is considered in the calculation and a dynamic path planning algorithm is used; The congestion index R is obtained through the real-time traffic API and is a value that reflects the degree of road congestion; The effect of congestion on commuting time is given by the formula T drive =T free ×(1+α×R) calculation, where T free is the commuting time without congestion, and α is the congestion impact coefficient, which needs to be adjusted according to actual conditions; (2) Non-driving mode: Public transportation congestion level C pub It will affect the comfort and efficiency of commuting; congestion level C pub Obtained through the public transportation data API, it is a value that reflects the degree of crowdedness in the carriage; The effect of congestion on commuting time is given by the formula T pub =T base ×(1+β×C pub ) is calculated, where T base is the benchmark commuting time under normal circumstances, and β is the congestion impact coefficient, which needs to be adjusted according to actual conditions.

5. The person-job matching system based on candidate geographic location information and retail stores according to claim 4, characterized in that: Baseline commuting time T base It refers to the commuting time required from the candidate's residential address to the retail store address without considering congestion or crowding; get T base The specific steps are as follows: a1. Determine your commuting method and time period: Commuting method: Determine the appropriate mode of transportation based on the candidate's chosen commuting method; Time of day: Consider the candidate’s preferred commuting time; a2. Use the map service API for route planning: API selection: Select a map service API that can provide route planning functions; Input parameters: Starting point: Candidate’s residential address; Destination: the address of the retail store; Transportation mode: Choose the appropriate transportation mode based on your commuting method; Ignore congestion and crowding: When calling the API, set parameters to ignore real-time traffic conditions and public transportation congestion to obtain a benchmark commute time; a3. Get and process the data returned by the API: API response: After calling the map service API, a response containing benchmark commute time information will be returned; Extract commute time: Extract the benchmark commute time T from the API response base ; This time is the commuting time under ideal conditions without considering congestion and crowding; Verification and storage: Verify the extracted benchmark commute time T base Is it reasonable, including checking whether it is a non-negative value and whether it is within a reasonable range, and then converting the benchmark commuting time T base Stored in the database for subsequent use in calculating actual commuting time.

6. The person-job matching system based on candidate geographic location information and retail stores according to claim 4, characterized in that: The congestion index R and congestion degree C pub It changes with time period, and the parameters of congestion index R and congestion degree C are established accordingly pub The function of the change with the time period, assuming that the time period is represented by a continuous variable t; Congestion index R and time period t change function: The congestion index R(t) is a piecewise function or a fitting function based on historical data. Since congestion usually occurs during peak hours in the morning and evening, a piecewise linear function is assumed to describe this change: ； Among them, R0 is the congestion index during peak hours, R1 is the congestion index during non-peak hours, [t1, t2], [t3, t4] are the start and end times of the two peak hours; Similarly, set the congestion level C pub The function of change with time period t: ； in, is the congestion index during peak hours, It is the congestion index during non-peak hours.

7. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The calculation formula for the matching degree M of the candidate and store matching module is: Non-driving methods: ; Driving method: ; Among them, T total is the total commuting time, w T 、w N are the weights of commuting time and number of transfers, respectively; For non-driving mode: T total =T pub ; For driving style: T total =T drive .

8. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The two-way selection mechanism of the candidate and store matching module includes: Recommending positions for candidates to stores: Recommend suitable store positions to candidates based on their resume information, skills, expertise, and commuting preferences, and sort and display them according to the comprehensively calculated matching degree; Store to candidate referrals: Candidate screening: Based on the store's job requirements, select qualified candidates from the candidate database; Matching degree calculation: Combine the candidate's commuting situation, job adaptability, and historical work performance to comprehensively calculate the match between the candidate and the store position; Sorting and display: The information of candidates with high matching degree will be displayed to the stores first.

9. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The acquisition and processing of public transportation data in non-driving commuting, in addition to basic bus routes and station information, also includes public transportation frequency, detailed map annotations of station distribution, fare information for different time periods and distances, and multiple transfer options.

10. The person-job matching system based on candidate geographic location information and retail stores according to claim 1, characterized in that: The work contents of the result display and feedback adjustment module include: Map visualization technology: Using map API, the candidate’s commuting routes, store locations, and surrounding facilities information are intuitively displayed in the form of a map; Chart display: Display the matching degree between candidates and stores through bar charts, pie charts, and line charts; Interactive interface: The interactive interface allows candidates and recruiters to view detailed information, filter conditions, and adjust preferences by clicking and sliding.

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