A digital intelligent casual labor service platform and method

Abstract

The present invention relates to the technical field of data sharing, and specifically discloses a digital intelligent casual labor service platform and method. The method includes: when receiving a filing request from a user, sending a permission acquisition request to the user and receiving the permission granted by the user; verifying the scenario of the user, and when the verification is passed, counting the work information to generate the work experience of the user; determining the fitness of the user with each field based on the work experience, and when receiving a recruitment requirement containing the fitness input by the recruiter, matching the user according to the fitness and establishing a two-way interaction channel; the platform verifies the work environment of the staff engaged in casual labor, and when the verification is passed, queries the keywords corresponding to the current work environment as the work experience, and based on the work experience, the fitness of the staff engaged in casual labor with each field can be determined, so as to quantify the work ability of the staff engaged in casual labor, which is convenient for both job hunting and recruitment.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and specifically to a digital intelligent casual labor service platform and method. Background Art

[0002] Casual labor refers to workers who sign short-term labor contracts with enterprises or individual customers, engage in various jobs and receive remuneration. Legally, they are not formal employees of the enterprise and can work for multiple enterprises simultaneously. Different from traditional enterprises, enterprises using casual labor no longer rely on internal management systems, wage systems, and labor policies to manage this part of employees.

[0003] For casual labor workers, it is very difficult to have a clear record of their work experience like that of long-term workers. Existing platforms are difficult to evaluate their capabilities, and it is also difficult for recruiters to know the work capabilities of casual labor workers. If the work experience of casual labor workers can be quantified, then an excellent recruitment service platform can be built, which is beneficial to both job seekers and recruiters. Summary of the Invention

[0004] The purpose of the present invention is to provide a digital intelligent casual labor service platform and method to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A digital intelligent casual labor service method, the method includes:

[0007] When receiving a filing request from a user, send a permission acquisition request to the user and receive the permissions granted by the user; the permissions include location sharing permission and camera application permission;

[0008] Obtain the user's location based on the location sharing permission, generate a verification instruction according to the user's location, and conduct a scenario verification on the user;

[0009] After the verification passes, count the work information and generate the user's work experience; the work experience is a set of keywords with time period tags;

[0010] Determine the user's fit with each field based on the work experience. When receiving a recruitment requirement containing the fit degree input by the recruiter, match the user according to the fit degree and establish a two-way interaction channel.

[0011] Further, the step of obtaining the user's location based on the location sharing permission, generating a verification instruction according to the user's location, and conducting a scenario verification on the user includes:

[0012] Receive the working scenario and working period input by the user, and obtain the user's location based on the location sharing permission granted by the user during the working period;

[0013] Statistically analyze the user's location based on time to obtain a location representation map; among them, the update process is to increment a preset value at the location;

[0014] Regularly traverse the location representation map and calculate the data density of each point;

[0015] If the data density of the current location meets the preset conditions, generate a scenario verification request to verify the user's scenario.

[0016] Further, the step of regularly traversing the location representation map and calculating the data density of each point includes:

[0017] Regularly read the location representation map and convert it into a numerical matrix;

[0018] Traverse the numerical matrix, take a preset range centered on each point, and calculate the data density at the center point based on the values within the preset range; the preset range is a circular range;

[0019] The calculation formula for data density is:

[0020] In the formula, M is the data density of a certain point, R is the radius of the preset range, Z θρ is the value at the point (θ, ρ) within the preset range, (θ, ρ) are polar coordinates; S represents the area of the preset range, S = πR 2 .

[0021] Further, the step of if the data density of the current location meets the preset conditions, generating a scenario verification request to verify the user's scenario includes:

[0022] If the current location meets the preset conditions, generate a scenario verification request;

[0023] Record a scenario video based on the camera application permission and convert the scenario video into a group of images;

[0024] Read the working scenario, read the features from the preset feature library according to the working scenario, and traverse the images in the group of images based on the read features;

[0025] During the traversal process, calculate the similarity between the features and each region in the image in real time. When there is any similarity greater than the preset threshold, record the timestamp of the feature and the corresponding image;

[0026] Determine whether the verification is passed according to the recorded features and the timestamps of their corresponding images.

[0027] Further, the step of statistically analyzing work information and generating the user's work experience after verification passes further includes:

[0028] After verification passes, read the work scenarios and work periods input by the user;

[0029] Retrieve keywords based on the work scenarios, count the keywords and their work periods, and update the work experience based on the work periods.

[0030] Further, the step of determining the user's suitability for each field based on the work experience, and when receiving a recruitment requirement containing the suitability input by the recruiter, matching the user according to the suitability and establishing a two-way interaction channel includes:

[0031] Determine the relevance of each keyword in the work experience to each field based on a large language model;

[0032] Query the time difference between the work period corresponding to each keyword and the current time, and determine the weight according to the time difference; the weight is inversely proportional to the time difference;

[0033] For any field, accumulate the relevance of each keyword to the field based on the weight as the user's suitability for the field;

[0034] Receive the required suitability input by the recruiter, match the user based on the required suitability, and establish a two-way interaction channel between the recruiter and the user.

[0035] The technical solution of the present invention also provides a digital intelligent casual labor service platform, and the platform includes:

[0036] A permission acquisition module, configured to send a permission acquisition request to the user when receiving the user's filing request, and receive the permissions granted by the user; the permissions include location sharing permissions and camera application permissions;

[0037] A scenario verification module, configured to obtain the user's location based on the location sharing permission, generate a verification instruction according to the user's location, and perform scenario verification on the user;

[0038] A work experience generation module, configured to statistically analyze work information and generate the user's work experience after verification passes; the work experience is a set of keywords with time period tags;

[0039] A recruitment matching module, configured to determine the user's suitability for each field based on the work experience, and when receiving a recruitment requirement containing the suitability input by the recruiter, match the user according to the suitability and establish a two-way interaction channel.

[0040] Further, the scenario verification module includes:

[0041] A user location acquisition unit, configured to receive a work scenario and a work period input by a user, and acquire the user's location based on the location sharing permission granted by the user within the work period;

[0042] A location statistics unit, configured to statistically analyze the user's location based on time to obtain a location representation graph; wherein, the update process is to increment a preset value at the location;

[0043] A numerical verification unit, configured to periodically traverse the location representation graph and calculate the data density of each point;

[0044] A verification execution unit, configured to generate a scenario verification request to verify the user if the data density of the current location meets a preset condition.

[0045] Further, the work experience generation module includes:

[0046] A data reading unit, configured to read the work scenario and work period input by the user after the verification is passed;

[0047] An experience update unit, configured to retrieve keywords based on the work scenario, statistically analyze the keywords and their work periods, and update the work experience based on the work period.

[0048] Further, the recruitment matching module includes:

[0049] A relevance determination unit, configured to determine the relevance between each keyword in the work experience and each field based on a large language model;

[0050] A weight determination unit, configured to query the time difference between the work period corresponding to each keyword and the current moment, and determine the weight according to the time difference; the weight is inversely proportional to the time difference;

[0051] A fitness calculation unit, configured to, for any field, accumulate the relevance between each keyword and the field based on the weight as the fitness between the user and the field;

[0052] A channel establishment unit, configured to receive the demand fitness input by the recruiter, match the user based on the demand fitness, and establish a two-way interaction channel between the recruiter and the user.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: This platform receives the filing requests uploaded by the workers engaged in odd jobs, analyzes their locations, and then verifies the working environment. After the verification is passed, the keywords corresponding to the current working environment are queried as the work experience. Then, based on the work experience, the fitness between the workers engaged in odd jobs and each field can be determined, thereby quantifying the working ability of the workers engaged in odd jobs, which is convenient for both job hunting and recruitment. Description of the Drawings

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0055] Figure 1 It is a flowchart of a digital intelligent casual labor service method.

[0056] Figure 2 It is a structure diagram of a digital intelligent casual labor service platform. Detailed implementation manners

[0057] For casual labor workers, it is very difficult to have a clear record of their work experience like regular workers. It is difficult for existing platforms to evaluate their capabilities, and it is also difficult for employers to know the work capabilities of casual labor workers.

[0058] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clear, the present invention will be further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] Figure 1 It is a flowchart of a digital intelligent casual labor service method. In an embodiment of the present invention, a digital intelligent casual labor service method includes:

[0060] Step S100: When receiving a filing request from a user, send a permission acquisition request to the user and receive the permissions granted by the user; the permissions include location sharing permission and camera application permission;

[0061] The technical solution of the present invention is generally applicable to job seekers. When a user uses this platform, they will register. After registration, the user will have a dedicated account. During the daily work period, the user will file their work experience. For casual jobs, there is no definite work cycle, and the traditional contract-based filing method has very low flexibility. The platform provided by this application provides a filing method. When receiving a filing request from a user, file the work that the user is currently engaged in, and finally obtain a work experience reflecting the casual work process of the user, which is convenient for employers to recruit, thus building a data sharing platform.

[0062] In the technical solution of the present invention, when creating a work experience, it is necessary to obtain the user's location and the user's scenario. When the user files, they input the work scenario, and then the platform will verify. The verification needs to consider two parameters: the user's location and the user's scenario. Therefore, it is necessary to obtain the following permissions, send a permission acquisition request to the user, and receive the permissions granted by the user.

[0063] Step S200: Obtain the user's location based on the location sharing permission, generate a verification instruction according to the user's location, and conduct a scenario verification on the user;

[0064] After obtaining the location sharing permission, obtain the user's location based on the location sharing permission, conduct a preliminary verification on the user's working status according to the user's location, and determine whether the user is working in the specified area. If the user is working in the specified area, further scenario verification is carried out.

[0065] Step S300: When the verification is passed, count the work information and generate the user's work experience; the work experience is a set of keywords containing time period tags;

[0066] When the verification is passed, it indicates that the record information input by the user is true. At this time, count all the true work information and generate the work experience. The work experience is a set of keywords containing time period tags, that is, which keywords exist in which time period.

[0067] Step S400: Determine the user's fit with each field based on the work experience. When receiving a recruitment requirement containing the fit degree input by the recruiter, match the user according to the fit degree and establish a two-way interaction channel;

[0068] After the work experience is generated, it shows which work the user has done in which time periods, and all are represented by keywords. By identifying these keywords, it can be determined which fields each keyword fits better, represented by the parameter of the fit degree. When receiving a recruitment requirement containing the fit degree input by the recruiter, match the user according to the fit degree and establish a two-way interaction channel.

[0069] It is worth mentioning that from a conventional perspective, each recruiter wants to recruit users with 100% fit degree. However, in fact, the higher the fit degree, the higher the salary. Considering comprehensively, the requirements of different recruiters may be different.

[0070] Regarding step S200, the steps of obtaining the user's location based on the location sharing permission, generating a verification instruction according to the user's location, and conducting a scenario verification on the user include:

[0071] Receive the work scenario and work period input by the user, and obtain the user's location based on the location sharing permission granted by the user during the work period;

[0072] Statistically analyze the user's location based on time to obtain a location representation graph; among them, the update process is to increment a preset value at the location;

[0073] Regularly traverse the location representation graph and calculate the data density of each point;

[0074] If the data density at the current location meets the preset conditions, a scenario verification request is generated to verify the user's scenario.

[0075] In an example of the technical solution of the present invention, the working scenario and working period input by the user are received. The input working scenario and working period indicate where the user works and what the corresponding working period is. Based on the location sharing permission granted by the user within the working period, the user's location is obtained, and the user's location is statistically analyzed based on time to obtain a location representation map. The location representation map indicates which locations the user has appeared at. By analyzing the location representation map, it is possible to determine at which location to generate a scenario verification request, and then verify the user's scenario.

[0076] Regarding the location display map, the following is an explanation:

[0077] After obtaining the location, the corresponding points are marked on the map, and the value of the point is incremented by 1. This process is continuously repeated, and the values of each point will continuously increase. Eventually, a map of the user's historical locations is obtained, which is called a location representation map. The values at each location represent the duration of the user's stay at different locations.

[0078] Further, the step of regularly traversing the location representation map and calculating the data density of each point includes:

[0079] Regularly read the location representation map and convert it into a numerical matrix;

[0080] Traverse the numerical matrix. Taking each point as the center, intercept a preset range, and calculate the data density at the center point based on the values within the preset range; the preset range is a circular range.

[0081] Regularly read the location representation map. The location representation map itself is an image and two-dimensional data, which is essentially a matrix. The process of converting it into a numerical matrix is very simple; traverse each row and column position, calculate the data density and data aggregation degree. The data density represents the duration of the user's stay near it (preset range) with this row and column position as the center; among them, the calculation process of the data density is as follows:

[0082] In the formula, M is the data density of a certain point, R is the radius of the preset range, Z θρ is the value at the point (θ, ρ) within the preset range, and (θ, ρ) is the polar coordinate; S represents the area of the preset range, S = πR 2 .

[0083] The meaning of the location verification process is that only when the user frequently appears within a certain range is it considered that he is working seriously. At this time, the subsequent video verification will be triggered. In fact, the location and the video construct a two-level verification scheme. The location recognition process is used to trigger the video recognition process. Since the video recognition process consumes more resources, this multi-level verification scheme can improve resource utilization.

[0084] Regarding the scenario verification process, the step of generating a scenario verification request if the data density at the current location meets the preset condition and performing scenario verification on the user includes:

[0085] If the current location meets the preset condition, generate a scenario verification request;

[0086] Record a scenario video based on the camera application permission, and convert the scenario video into an image group;

[0087] Read the work scenario, read features from the preset feature library according to the work scenario, and traverse the images in the image group based on the read features;

[0088] During the traversal process, calculate the similarity between the features and each region in the image in real time. When there is any similarity greater than the preset threshold, record the time stamps of the features and the corresponding images;

[0089] Determine whether the verification is passed according to the recorded features and the time stamps of the corresponding images.

[0090] In an example of the technical solution of the present invention, if the current location meets the preset condition, a scenario verification request is generated. Based on the camera application permission pre-granted by the user, the environment where the current user is located can be recorded to obtain a scenario video. By removing the audio in the scenario video, an image group can be obtained. Then, read the work scenario, which is the environment where the user works. For flexible work, the types of environments are limited, and it is also possible to count which scenario features may exist in each environment. The pre-statistical scenario features are stored in the feature library.

[0091] In actual application, read the work scenario, read features from the preset feature library according to the work scenario, and traverse the images in the image group based on the read features. The traversal process is a comparison-based recognition. The meaning of comparison-based recognition is to calculate the similarity between the features and each sub-region in the image. If the similarity is high enough, it is considered a match, that is, the feature appears in the corresponding frame image, and it is determined that the match is successful;

[0092] After each feature has been traversed, according to which features match successfully in the image and how many frames of images each feature matches successfully, combining these two parameters, it can be determined whether the verification is passed.

[0093] In an example of the technical solution of the present invention, one of the conditions for determining whether the verification is passed is that for a certain feature, an image quantity threshold is set. When the number of image frames corresponding to the feature (determined by the time stamp) reaches the image quantity threshold, the feature is marked as a valid feature, and the number of valid features is recorded. Then, a feature quantity threshold is set. When the number of valid features reaches the feature quantity threshold, it is determined that the verification is passed.

[0094] Regarding step S300, the step of, after the verification is passed, counting the work information and generating the work experience of the user further includes:

[0095] After the verification is passed, read the work scenario and work period input by the user;

[0096] Retrieve keywords based on the work scenario, count the keywords and their work periods, and update the work experience based on the work period.

[0097] After the verification is passed, read the work scenario and work period input by the user, and retrieve keywords based on the work scenario. This keyword refers to the field corresponding to the work scenario, such as chemical industry, petroleum, and construction, etc.; some keywords can be queried for each work scenario, and each work scenario also corresponds to a work period. Count all keywords based on the work period to obtain the work experience.

[0098] Regarding step S400, the step of determining the fit degree of the user with each field based on the work experience, and when receiving the recruitment requirement containing the fit degree input by the recruiter, matching the user according to the fit degree and establishing a two-way interaction channel includes:

[0099] Determine the correlation degree between each keyword in the work experience and each field based on the large language model;

[0100] Query the time difference between the work period corresponding to each keyword and the current moment, and determine the weight according to the time difference; the weight is inversely proportional to the time difference;

[0101] For any field, accumulate the correlation degree between each keyword and the field based on the weight as the fit degree of the user with the field;

[0102] Receive the required fit degree input by the recruiter, match the user based on the required fit degree, and establish a two-way interaction channel between the recruiter and the user.

[0103] For any user, based on the large language model, determine the relevance of each keyword in the work experience to each field. For example, using a conventional word vector model, keywords with a stronger relevance generally have a closer vector distance, and the process of obtaining the relevance is very simple; then, query the time difference between the work period corresponding to each keyword and the current time. The weight of the earlier work experience (keyword) is smaller. Calculate the product of the weight and the keyword, and then sum them up to obtain the user's fit with the field. The practical significance of this process is that for any field, query for keywords related to the field in the user's work experience, and read the relevance of the corresponding keywords. The weight of the more recent work experience is higher. Based on the weight, sum up the relevance of the queried keywords to obtain the fit.

[0104] Finally, receive the required fit input by the recruiter, match the users based on the required fit, and establish a two-way interaction channel between the recruiter and the users. In practical applications, when the recruiter inputs the required fit, the platform generally provides a salary level. If the recruiter cannot accept this salary level, at this time, the required fit will be reduced to match more users, and correspondingly, the salary level will also be reduced.

[0105] Figure 2 As shown in the structure diagram of the digital intelligent part-time service platform, in an embodiment of the present invention, a digital intelligent part-time service platform, the platform 10 includes:

[0106] A permission acquisition module 11, configured to send a permission acquisition request to the user when receiving the user's filing request, and receive the permissions granted by the user; the permissions include location sharing permissions and camera application permissions;

[0107] A scenario verification module 12, configured to obtain the user's location based on the location sharing permission, generate a verification instruction according to the user's location, and verify the user's scenario;

[0108] A work experience generation module 13, configured to, after the verification passes, count the work information and generate the user's work experience; the work experience is a set of keywords with time period tags;

[0109] A recruitment matching module 14, configured to determine the fit between the user and each field based on the work experience, and when receiving a recruitment requirement containing the fit input by the recruiter, match the users according to the fit and establish a two-way interaction channel.

[0110] Further, the scenario verification module 12 includes:

[0111] A user location acquisition unit, configured to receive the work scenario and work period input by the user, and obtain the user's location based on the location sharing permission granted by the user within the work period;

[0112] A location statistics unit for statistically analyzing the user's location based on time to obtain a location representation graph; wherein, the update process is to increment a preset value at the location.

[0113] A numerical verification unit for periodically traversing the location representation graph and calculating the data density of each point.

[0114] A verification execution unit for generating a scenario verification request to verify the user if the data density at the current location meets the preset conditions.

[0115] Specifically, the work experience generation module 13 includes:

[0116] A data reading unit for reading the work scenario and work period input by the user after verification passes.

[0117] An experience update unit for retrieving keywords based on the work scenario, counting the keywords and their work periods, and updating the work experience based on the work period.

[0118] Furthermore, the recruitment matching module 14 includes:

[0119] A relevance determination unit for determining the relevance between each keyword in the work experience and each field based on a large language model.

[0120] A weight determination unit for querying the time difference between the work period corresponding to each keyword and the current time, and determining the weight according to the time difference; the weight is inversely proportional to the time difference.

[0121] A fitness calculation unit for, for any field, accumulating the relevance between each keyword and the field based on the weight as the fitness between the user and the field.

[0122] A channel establishment unit for receiving the required fitness input by the recruiter, matching the user based on the required fitness, and establishing a two-way interaction channel between the recruiter and the user.

[0123] The above technical solution provides an intelligent service platform, which is usually applied in the form of an App, and its functions are as follows:

[0124] This platform receives the filing requests uploaded by the workers engaged in odd jobs, analyzes their locations, and then verifies the working environment. After the verification passes, it queries the keywords corresponding to the current working environment as the work experience. Then, based on the work experience, the fitness between the workers engaged in odd jobs and each field can be determined, thereby quantifying the working ability of the workers engaged in odd jobs, which is convenient for both job hunting and recruitment.

[0125] Through the specific descriptions of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0126] Finally, it should be noted that: What is disclosed in an embodiment of a digital intelligent casual labor service platform and method of the present invention is only a preferred embodiment of the present invention, and is only used to illustrate the technical solution of the present invention, rather than limiting it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital intelligent gig service method, characterized in that: The method comprises: Upon receiving a user's filing request, sending a permission acquisition request to the user and receiving the permissions granted by the user; the permissions include location sharing permissions and camera application permissions; Acquire the user's location based on the location sharing authority, generate a verification instruction according to the user's location, and perform scenario verification on the user; When the verification is passed, the work information is counted to generate the user's work experience; the work experience is a set of keywords with time period tags; Determine the compatibility of the user with each field based on the work experience, and upon receiving a recruitment requirement containing the compatibility input by the recruiter, match the user according to the compatibility and establish a two-way interactive channel; The step of determining the compatibility between the user and each field based on the work experience, matching the user according to the compatibility when receiving a recruitment demand containing the compatibility input by the recruiter, and establishing a two-way interactive channel includes: Determine the relevance of each keyword in the work experience to each field based on the large language model; Query the time difference between the working period corresponding to each keyword and the current time, and determine the weight according to the time difference; the weight is inversely proportional to the time difference; For any field, the relevance between each keyword and the field is accumulated based on the weights to serve as the compatibility between the user and the field; Receive the demand fit input by the recruiter, match users based on the demand fit, and establish a two-way interactive channel between the recruiter and the user.

2. The digital intelligent gig service method according to claim 1, characterized in that: The steps of obtaining the user location based on the location sharing authority, generating a verification instruction according to the user location, and performing scene verification on the user include: Receive the work scene and work period input by the user, and obtain the user's location during the work period based on the location sharing permission granted by the user; The user's location is counted based on time to obtain a location representation map; wherein the updating process is to increment a preset value at the location; Regularly traverse the position representation diagram to calculate the data density of each point; If the data density at the current location meets the preset conditions, a scene verification request is generated to perform scene verification on the user.

3. The digital intelligent gig service method according to claim 2, characterized in that: The step of periodically traversing the position representation diagram and calculating the data density of each point comprises: Read the position representation diagram regularly and convert it into a numerical matrix; Traversing the numerical matrix, taking each point as the center, intercepting a preset range, and calculating the data density at the center point based on the numerical value within the preset range; the preset range is a circular range; The calculation formula for data density is: ; In the formula, is the data density at a certain point, is the radius of the preset range, To set the preset range, click The value at are polar coordinates; Indicates the area of ​​the preset range. .

4. The digital intelligent gig service method according to claim 2, characterized in that: If the data density at the current location meets the preset conditions, a scene verification request is generated, and the step of performing scene verification on the user includes: If the current location meets the preset conditions, a scene verification request is generated; Record scene videos based on camera application permissions and convert scene videos into image groups; Reading a working scene, reading features from a preset feature library according to the working scene, and traversing images in the image group based on the read features; During the traversal process, the similarity between the feature and each area in the image is calculated in real time. When any similarity is greater than the preset threshold, the timestamp of the feature and the corresponding image is recorded; The verification is determined based on the recorded features and the timestamp of the corresponding image.

5. The digital intelligent gig service method according to claim 2, characterized in that: After the verification is passed, the step of collecting work information and generating the user's work experience also includes: When the verification is passed, the work scene and work time input by the user are read; Retrieve keywords based on work scenarios, count keywords and their work periods, and update work experience based on work periods.

Images