Free employee management platform based on online technology

Abstract

The invention discloses a free employee management platform based on an online technology, which can convert unstructured text data into structured semantic vectors by performing natural language analysis and semantic embedding coding on project requirements and free employee resumes. The semantic vectors can be used to calculate a matching degree and a comprehensive score between a project demand and a resume of a freestyle, thereby utilizing the comprehensive score to decide whether to recommend the freestyle to an employer. According to the free employee management platform based on the online technology, a more efficient, accurate and flexible recruitment solution is provided through an automatic and intelligent mode, and the defects of a traditional recruitment mode are effectively overcome. The platform not only improves the recruitment efficiency of enterprises, but also provides more opportunities for freestyle employees, and promotes further development of the freestyle employee market.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and more specifically, to a freelancer management platform based on online technology. Background Art

[0002] With the development of globalization and digitalization, the freelance market has expanded rapidly, and more and more companies and individuals choose to use freelancers to complete projects. This trend has not only changed the traditional working model, but also provided companies with greater flexibility and cost-effectiveness. However, how to efficiently recruit and match freelancers has become an important challenge.

[0003] Traditional recruitment usually requires multiple steps such as posting job advertisements, screening resumes, and arranging interviews. The entire process may take weeks or even months. For projects that need to be completed urgently, this inefficient recruitment method cannot meet the needs of modern enterprises for rapid response and accurate matching. In addition, traditional recruitment methods rely on resumes and interviews, but this information is often difficult to objectively screen out freelancers who meet the project needs of employers. In other words, recruitment decisions are often affected by the subjective judgment of interviewers, which can easily lead to biased and unfair recruitment results.

[0004] Therefore, a freelancer management platform based on online technology is expected. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the present application is proposed. The embodiment of the present application provides a freelancer management platform based on online technology, which can convert unstructured text data into structured semantic vectors by performing natural language parsing and semantic embedding coding on project requirements and freelancer resumes. These semantic vectors can be used to calculate the matching degree and comprehensive score between project requirements and freelancer resumes, so as to use this comprehensive score to decide whether to recommend freelancers to employers. This freelancer management platform based on online technology provides a more efficient, accurate and flexible recruitment solution through automation and intelligent means, effectively solving the shortcomings of traditional recruitment methods. This platform not only improves the recruitment efficiency of enterprises, but also provides more opportunities for freelancers, promoting the further development of the freelancer market.

[0006] According to one aspect of the present application, there is provided a freelancer management platform based on online technology, which includes: The project requirements collection module is used to receive project requirements uploaded by employers; A candidate freelancer extraction module is used to extract resumes of candidate freelancers from a freelancer database; A natural language parsing and structuring module, configured to perform semantic embedding coding on the project requirements to obtain a semantic embedding coding vector of the project requirements and perform natural language processing on the resumes of the candidate freelancers to obtain a semantic embedding coding vector of the resumes of the candidate freelancers; A skill matching degree measurement module, used to determine the skill matching degree based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; An experience value measurement module, used to determine the experience value based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A budget compatibility analysis module, configured to determine budget compatibility based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A response efficiency analysis module, used to determine the response efficiency based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; An additional score analysis module, used to determine additional scores based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A comprehensive scoring module, used for calculating the weighted sum of the skill matching degree, the experience value, the budget compatibility, the response efficiency and the additional score based on preset weights to obtain a comprehensive score; The recommendation result output module is used to recommend the candidate freelancer to the employer in response to the comprehensive score being greater than or equal to a preset threshold.

[0007] Compared with the prior art, the freelancer management platform based on online technology provided by the present application can convert unstructured text data into structured semantic vectors by performing natural language parsing and semantic embedding coding on project requirements and freelancer resumes. These semantic vectors can be used to calculate the matching degree and comprehensive score between project requirements and freelancer resumes, so as to use this comprehensive score to decide whether to recommend freelancers to employers. This freelancer management platform based on online technology provides a more efficient, accurate and flexible recruitment solution through automation and intelligent means, effectively solving the shortcomings of traditional recruitment methods. This platform not only improves the recruitment efficiency of enterprises, but also provides more opportunities for freelancers, promoting the further development of the freelancer market. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0009] Figure 1 A block diagram of a freelancer management platform based on online technology according to an embodiment of the present application; Figure 2 A data flow diagram of a freelancer management platform based on online technology according to an embodiment of the present application; Figure 3 It is a block diagram of an experience value measurement module in a freelancer management platform based on online technology according to an embodiment of the present application; Figure 4 It is a block diagram of a semantic collaborative association analysis unit in a freelancer management platform based on online technology according to an embodiment of the present application. DETAILED DESCRIPTION

[0010] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0011] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0012] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0013] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0014] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0015] The freelance market covers a wide range of skills and fields, including software development, design, writing, marketing, etc. Companies need to quickly find freelancers with the corresponding skills and experience according to the specific needs of the project. However, due to the large number and wide distribution of freelancers, companies often find it difficult to find suitable candidates through traditional methods (such as recruitment websites or intermediaries). With the development of artificial intelligence (AI), natural language processing (NLP) and big data technology, freelancer management platforms based on online technology have emerged. They help companies quickly screen and match freelancers in an automated and intelligent way, greatly improving recruitment efficiency.

[0016] In the technical solution of the present application, a freelancer management platform based on online technology is proposed. Figure 1 It is a block diagram of a freelancer management platform based on online technology according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a freelancer management platform based on online technology according to an embodiment of the present application. Figure 1 and Figure 2As shown, according to an embodiment of the present application, a freelancer management platform 300 based on online technology includes: a project demand collection module 310, which is used to receive project requirements uploaded by an employer; a candidate freelancer extraction module 320, which is used to extract the resumes of candidate freelancers from a freelancer database; a natural language parsing and structuring module 330, which is used to perform semantic embedding encoding on the project requirements to obtain a project requirement semantic embedding encoding vector and to perform natural language processing on the resumes of the candidate freelancers to obtain a semantic embedding encoding vector of the candidate freelancer resume; a skill matching degree measurement module 340, which is used to determine the skill matching degree based on the project requirement semantic embedding encoding vector and the candidate freelancer resume semantic embedding encoding vector; an experience value measurement module 350, which is used to measure the experience value based on the project requirement semantic embedding encoding vector and the candidate freelancer resume semantic embedding encoding vector. coding vector, determining the experience value; a budget compatibility analysis module 360, for determining the budget compatibility based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; a response efficiency analysis module 370, for determining the response efficiency based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; an additional score analysis module 380, for determining the additional score based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; a comprehensive scoring module 390, for calculating the weighted sum of the skill matching degree, the experience value, the budget compatibility, the response efficiency and the additional score based on preset weights to obtain a comprehensive score; a recommendation result output module 400, for recommending the candidate freelancer to the employer in response to the comprehensive score being greater than or equal to a preset threshold.

[0017] In particular, the project requirements collection module 310 is used to receive project requirements uploaded by employers. In one example, a user-friendly interface can be set up to allow employers to describe their project requirements in detail, including but not limited to the type of project, required skills, expected experience level, budget range, project deadline, and any other specific requirements. In order to ensure that the collected information is as detailed and accurate as possible, the platform will provide some auxiliary tools or prompts. For example, for the skill requirements section, the platform will provide a preset list of skills to choose from, and also allow users to add custom skills. This approach not only helps to standardize the input data format, but also facilitates subsequent data processing and analysis. In addition, for some complex or special project requirements, the platform supports the upload attachment function, allowing employers to upload documents, links or other forms of reference materials to more comprehensively convey project details.

[0018] In particular, the candidate freelancer extraction module 320 is used to extract the resumes of candidate freelancers from the freelancer database. That is, after the platform receives the project requirement information uploaded by the employer, the resumes of candidate freelancers are extracted from the freelancer database to preliminarily screen out the candidates with the highest matching degree with the project requirements.

[0019] In particular, the natural language parsing and structuring module 330 is used to perform semantic embedding encoding on the project requirements to obtain a semantic embedding encoding vector of the project requirements and to perform natural language processing on the resumes of the candidate freelancers to obtain semantic embedding encoding vectors of the resumes of the candidate freelancers. It should be understood that in the above-mentioned freelancer management platform based on online technology, since project requirements and resumes usually contain a large amount of unstructured text data, these data are expressed in various ways and may have semantic similarities, but are not completely consistent in form. For example, the project requirements may mention "machine learning experience is required", while the freelancer's resume may say "good at deep learning algorithms". Although "machine learning" and "deep learning" are not exactly the same in words, they are highly related in semantics. Traditional keyword matching methods may ignore this semantic association, resulting in matching failure. Therefore, in order to semantically match the project requirements and the resumes of candidate freelancers for talent recruitment and precise matching, in the technical solution of the present application, the project requirements are further semantically embedded and encoded to obtain a semantic embedding encoding vector of the project requirements, and the resumes of the candidate freelancers are subjected to natural language processing to obtain a semantic embedding encoding vector of the resume of the candidate freelancer. In particular, by means of semantic embedding coding and natural language processing, a deep learning model (such as BERT) can be used to embed and map project requirements and resumes of candidate freelancers into a common space to represent them in vector form, and understand their context and extract the deep meaning of the text, rather than just relying on keyword matching. For example, it can identify the association between "machine learning" and "deep learning", thereby improving the accuracy of matching. In other words, even if the descriptions in the project requirements and resumes are different, as long as the semantics are similar, the platform can still identify the matching relationship.

[0020] In particular, the skill matching degree measurement module 340 is used to determine the skill matching degree based on the semantic embedding coding vector of the project requirement and the semantic embedding coding vector of the candidate freelancer resume. In a specific example, the cosine similarity between the semantic embedding coding vector of the project requirement and the semantic embedding coding vector of the candidate freelancer resume can be calculated. This is an indicator to measure the angular difference between vectors, and its value is between -1 and 1. The larger the value, the more similar the two vectors are. In the technical solution of the present application, a higher cosine similarity means that the project requirement is more matched with the freelancer's skills.

[0021] In particular, the experience value measurement module 350 is used to determine the experience value based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector. It should be understood that in the process of matching suitable freelancers based on project requirements, it is necessary to evaluate the experience of freelancers. Traditional experience value evaluation methods usually rely on explicit information in the resume (such as years of work, job title, etc.), but this information often cannot fully reflect the actual ability of freelancers, especially in cross-domain or cross-industry projects. Therefore, in order to more accurately evaluate whether freelancers have the experience level that matches the project requirements, in the technical solution of the present application, the experience value is further determined based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector. Specifically, in a specific example of the present application, such as Figure 3 As shown, the experience value measurement module 350 includes: a semantic collaborative association analysis unit 351, which is used to perform semantic collaborative association analysis on the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector to obtain a project requirement-resume semantic joint anchoring experience value coding vector; an experience value determination unit 352, which is used to determine the experience value based on the project requirement-resume semantic joint anchoring experience value coding vector.

[0022] Specifically, the semantic collaborative association analysis unit 351 is used to perform semantic collaborative association analysis on the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector to obtain the project requirement-resume semantic joint anchoring experience value coding vector. It should be understood that since the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector contain a large amount of high-dimensional data, redundancy and noise. Traditional methods are difficult to effectively extract core information. For example, when an enterprise needs to recruit a freelancer with "more than 5 years of machine learning experience", traditional keyword matching may miss candidates because the resume only mentions "4 years of deep learning project experience". Therefore, in the technical solution of the present application, the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector are further subjected to semantic collaborative association analysis to obtain the project requirement-resume semantic joint anchoring experience value coding vector. It is worth mentioning that the process of semantic collaborative association analysis can filter redundancy and focus on key semantic information through autocorrelation decoupling and core anchor point extraction. For example, although the "machine learning experience" in the project requirements and the "deep learning project" in the resume are expressed differently, their core semantics are related. The engine can capture this association and accurately quantify the experience matching degree through multi-granularity interaction modeling (semantic coupling at the feature level and numerical dependence of feature values). This overcomes the limitations of traditional methods that rely on explicit keywords and manual judgment, and improves the accuracy and efficiency of automated matching. Specifically, semantic collaborative association analysis uses autocorrelation decoupling technology to extract core anchor information (such as the semantic association between "machine learning" and "deep learning") from the high-dimensional semantic vectors of project requirements and resumes, filter redundant noise (such as irrelevant skill descriptions), and further quantify the deep association between the two at the experience and ability level through feature granularity interaction modeling (such as the coupling of semantic units "data processing capabilities" and "model development experience") and feature value granularity interaction modeling (such as the numerical dependence between project complexity and candidate experience years). This process can not only identify the core experience matching points across expressions at the semantic level (such as associating "tens of millions of data modeling" with "large-scale data processing needs"), but also generate structured, low-noise joint anchored experience value encoding vectors through multi-granular interactive fusion, thereby overcoming the defects of traditional methods that rely on manual rules and ignore implicit semantic associations, and achieving more accurate, automated and explainable experience value evaluation, and ultimately recommending freelancers who meet both explicit skill requirements and implicit experience adaptation to enterprises, significantly improving talent matching efficiency and project success rate. More specifically, in a specific example of this application, such as Figure 4As shown, the semantic collaborative association analysis unit 351 includes: a semantic autocorrelation association matrix construction subunit 3511, which is used to construct a semantic autocorrelation association matrix of the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector to obtain a project requirement semantic autocorrelation association matrix and a candidate freelancer resume semantic autocorrelation association matrix; a feature response interaction analysis subunit 3512, which is used to perform feature granularity response interaction analysis and feature value granularity response interaction analysis based on the project requirement semantic autocorrelation association matrix and the candidate freelancer resume semantic autocorrelation association matrix to obtain a project requirement-resume semantic feature granularity response interaction coding vector and a project requirement-resume semantic feature granularity response interaction coding vector. Semantic feature value granularity response interaction coding vector; a feature perturbation correction subunit 3513, used to perform feature perturbation correction on the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector to obtain a corrected project requirement-resume semantic feature granularity response interaction coding vector and a corrected project requirement-resume semantic feature value granularity response interaction coding vector; a fusion subunit 3514, used to fuse the corrected project requirement-resume semantic feature granularity response interaction coding vector and the corrected project requirement-resume semantic feature value granularity response interaction coding vector to obtain the project requirement-resume semantic joint anchoring experience value coding vector.

[0023] More specifically, the semantic autocorrelation association matrix construction subunit 3511 is used to construct the semantic autocorrelation association matrix of the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector to obtain the project requirement semantic autocorrelation association matrix and the alternative freelancer resume semantic autocorrelation association matrix. Specifically, by analyzing the complex interactive relationship between project requirements and freelancer resumes, it is possible to more accurately identify candidates who are highly adaptable in specific fields or tasks. This method takes into account the similarity and complementarity at the semantic level, helps to eliminate the bias and unfairness caused by subjective judgment, and provides strong support for employers to recommend the most suitable freelancers. In a specific example, the semantic autocorrelation association matrix of the project requirement semantic embedding coding vector and the alternative freelancer resume semantic embedding coding vector is constructed with the following formula to obtain the project requirement semantic autocorrelation association matrix and the alternative freelancer resume semantic autocorrelation association matrix; wherein, the formula is:

[0024]

[0025] in, and are the semantic embedding coding vector of the project requirements and the semantic embedding coding vector of the candidate freelancer resume, respectively. and is a feature mapping function, such as a linear mapping or a nonlinear kernel function, and They are respectively the semantic autocorrelation matrix of project requirements and the semantic autocorrelation matrix of the candidate freelancer resumes.

[0026] More specifically, the feature response interaction analysis subunit 3512 is used to perform feature granularity response interaction analysis and feature value granularity response interaction analysis based on the project requirement semantic autocorrelation association matrix and the alternative freelancer resume semantic autocorrelation association matrix to obtain the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector. That is, in an embodiment of the present application, first, the project requirement semantic autocorrelation association matrix and the alternative freelancer resume semantic autocorrelation association matrix are anchored with core information to obtain the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector; that is, in the technical solution of the present application, the project requirement semantic autocorrelation association matrix and the alternative freelancer resume semantic autocorrelation association matrix are respectively input into the core information anchoring network based on autocorrelation decoupling to obtain the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector. It should be understood that the needs and capability descriptions of enterprises and freelancers often contain a large amount of complex and unstructured text data. Simply relying on the information on the surface of these texts cannot accurately reflect the deep-level match between the two. Through core information anchoring, irrelevant or redundant information can be filtered out, focusing on those parts that can truly reflect the core competitiveness of freelancers such as skills and experience. In this way, the accuracy of matching is improved, the decision-making bias caused by information overload is reduced, and it is ensured that the candidates recommended to the company are the most suitable for the needs. This not only helps to improve the generalization ability of the model so that it still performs well when facing different types of task requirements, but also effectively constrains the semantic consistency of feature representation in the compressed space. In a specific example, the project requirement semantic autocorrelation association matrix and the candidate freelancer resume semantic autocorrelation association matrix are anchored with core information using the following core information anchoring formula to obtain the project requirement semantic feature core information anchoring encoding vector and the candidate freelancer resume semantic feature core information anchoring encoding vector; wherein, the core information anchoring formula is:

[0027]

[0028] in, Indicates feature decoupling, They represent the first, second, and third items of the semantic autocorrelation matrix of project requirements. and The row vector is used as the project requirement semantic autocorrelation vector, and are the project requirement semantic weight matrix and the project requirement semantic bias vector respectively, is the semantic modulation vector of project requirements, Anchor factor for the semantic core information of project requirements. express function, Anchor the weight of the semantic core information of project requirements, is the number of row vectors in the semantic autocorrelation matrix of the project requirements, Anchor the encoding vector for the core information of the semantic features of project requirements, They represent the first, second, and third semantic autocorrelation matrix of the candidate freelancer resumes. and row vectors as candidate semantic autocorrelation vectors of freelancer resumes, and are the semantic weight matrix and semantic bias vector of the candidate freelancer resume, respectively. Semantic modulation vector for candidate freelancer resumes, To select the semantic core information anchoring factor of freelancer resumes, express function, Anchor the weights of the semantic core information of the candidate freelancer resumes. is the number of row vectors in the semantic autocorrelation matrix of the candidate freelancer resumes, Anchor the encoding vector for the core information of semantic features of candidate freelancer resumes.

[0029] Furthermore, the feature granularity response interaction features and feature value granularity response interaction features between the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector are calculated respectively to obtain the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector. That is, in the technical solution of the present application, the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector are input into the feature granularity response interaction encoder to obtain the project requirement-resume semantic feature granularity response interaction coding vector; and the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector are input into the feature value granularity response interaction encoder to obtain the project requirement-resume semantic feature value granularity response interaction coding vector. That is, after completing the core information anchoring, the feature interaction response analysis engine enters the feature interaction analysis stage, i.e., the modeling of feature granularity interaction and feature value granularity interaction. This deep interaction analysis method greatly improves the platform's calculation accuracy of the match between freelancers and project requirements, enabling the system to quickly screen out the most suitable candidates from a large number of candidates and further optimize the quality of recommendation results. The feature granularity response interaction encoder focuses on exploring the response relationship between two vectors in semantic units, which helps capture the semantic coupling at the micro level and provides a detailed assessment of the fit between freelancer skills and project requirements. At the same time, the feature value granularity response interaction encoder regresses to numerical operations, characterizes the interdependence between feature values ​​through element-by-element operations or nonlinear mapping functions, connects the semantic abstraction layer with the data numerical layer, and enriches the representation dimension of interaction modeling. This dual-dimensional interaction analysis not only enhances the generalization ability of the model, but also improves the relevance and practicality of the recommendation results, promotes the efficient operation of the recruitment process, and also provides freelancers with more possibilities to demonstrate their abilities and obtain job opportunities, promoting the healthy development of the freelance market. In a specific example, the feature granularity response interaction feature and the feature value granularity response interaction feature between the project requirement semantic feature core information anchor coding vector and the candidate freelancer resume semantic feature core information anchor coding vector are respectively calculated using the following feature response interaction formula to obtain the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector; wherein the feature response interaction formula is:

[0030] in, For positional addition, and are the feature interaction weight matrix and feature interaction bias vector respectively, for function, Encode the vector for the project requirement-resume semantic feature granular response interaction, Encode vector for item requirement-resume semantic feature value granular response interaction.

[0031] More specifically, the feature perturbation correction subunit 3513 is used to perform feature perturbation correction on the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector to obtain the corrected project requirement-resume semantic feature granularity response interaction coding vector and the corrected project requirement-resume semantic feature value granularity response interaction coding vector. Considering the differences between the modeling representations of the project requirement-resume semantic feature granularity interaction and the project requirement-resume semantic feature value granularity interaction, this difference may cause unstable perturbations in the feature manifold interface of the fused semantic joint coding vector with multi-granularity information expression. For example, the project requirement-resume semantic feature granularity interaction representation and the project requirement-resume semantic feature value granularity interaction representation may produce different feature distributions and gradient diffusion patterns during the modeling process. This inconsistency will affect the accuracy of the subsequent comprehensive score and recommendation results. Therefore, in order to ensure the stability and reliability of the feature interaction coding vector, it is necessary to perform feature perturbation correction on these two granularity interaction representations.

[0032] Specifically, the granular interaction representation of project requirements and resume semantic feature values ​​is used as the representation of scalability constraints. Based on the growth index representation under scalability constraints, the overall distribution of the interface shape perturbation deviation of the interaction of project requirements and resume semantic feature granularity is modeled for the growth of the overall feature interaction distribution under the eigenvalue diffusion process. That is, the interaction interface gradient under the eigenvalue granularity is used as the perturbation contribution factor to determine the growth index stabilization contribution of the gradient diffusion under the eigenvalue mutual dependence, so as to achieve the gradient correction of the growth mode under the dominance of perturbation stabilization and improve the manifold interface stability of the response interaction encoding vector between project requirements and resume semantic features. It is worth mentioning that the corrected project requirements and resume semantic feature granular response interaction encoding vector and the corrected project requirements and resume semantic feature value granular response interaction encoding vector can better reflect the matching degree and comprehensive score between project requirements and freelancer resumes. This stability not only helps to improve the accuracy of recommendation results, but also enhances the overall performance and user experience of the platform. In this way, the platform can match freelancers and project requirements more efficiently, accurately and flexibly, thereby promoting the further development of the freelancer market.

[0033] In a specific example of the present application, the specific steps of performing feature perturbation correction on the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector are as follows: first, after calculating the square of each eigenvalue in the project requirement-resume semantic feature value granularity response interaction coding vector, it is positionally subtracted from the double of each eigenvalue in the project requirement-resume semantic feature granularity response interaction coding vector to obtain a project requirement-resume semantic difference value response representation vector; then, after calculating the square root of each eigenvalue in the project requirement-resume semantic difference value response representation vector, it is positionally divided by the project requirement-resume semantic feature granularity response interaction coding vector to obtain the corrected project requirement-resume semantic feature value granularity. Response interaction coding vector; then, calculate the exponential function value with the natural constant e as the base and each eigenvalue in the project demand-resume semantic feature granularity response interaction coding vector as the exponential power to obtain the project demand-resume semantic feature granularity response interaction exponential modulation coding vector; then, calculate the cosine function value of each eigenvalue in the corrected project demand-resume semantic feature value granularity response interaction coding vector to obtain the corrected project demand-resume semantic feature granularity response interaction cosine modulation coding vector; further, calculate the point-by-point multiplication between the project demand-resume semantic feature granularity response interaction exponential modulation coding vector and the corrected project demand-resume semantic feature granularity response interaction cosine modulation coding vector to obtain the corrected project demand-resume semantic feature granularity response interaction coding vector.

[0034] In other words, in this specific example, the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector are subjected to feature perturbation correction using the following correction formula to obtain a corrected project requirement-resume semantic feature granularity response interaction coding vector and a corrected project requirement-resume semantic feature value granularity response interaction coding vector; wherein the correction formula is:

[0035] in, and They are and The corresponding eigenvalues ​​of The eigenvalues ​​representing the corrected item requirement-resume semantic feature granularity response interaction encoding vector, The eigenvalues ​​of the corrected item requirement-resume semantic eigenvalue granularity response interaction encoding vector, Represents the cosine function.

[0036] More specifically, the fusion subunit 3514 is used to fuse the corrected project demand-resume semantic feature granularity response interaction coding vector and the corrected project demand-resume semantic feature value granularity response interaction coding vector to obtain the project demand-resume semantic joint anchoring experience value coding vector. It should be understood that the corrected project demand-resume semantic feature granularity response interaction coding vector and the corrected project demand-resume semantic feature value granularity response interaction coding vector respectively provide different levels of information about semantic coupling, but it is difficult to fully capture the complex relationship between the two by relying on only a single level of information. By fusing these two different dimensional coding vectors, a semantic joint coding vector with multi-granularity information expression can be constructed, which can more comprehensively and meticulously reflect the matching between project requirements and freelancers. In this way, it not only helps to eliminate matching errors caused by insufficient or biased information on a single aspect, but also enhances the model's adaptability to diverse task requirements, further making the final matching results more stable and reliable, thereby providing users with more accurate recommendation services. In a specific example, the corrected project requirement-resume semantic feature granularity response interaction coding vector and the corrected project requirement-resume semantic feature value granularity response interaction coding vector are fused by the following fusion formula to obtain the project requirement-resume semantic joint anchoring experience value coding vector; wherein the fusion formula is:

[0037] in, represents feature fusion, Represents the project requirement-resume semantic joint anchored experience value encoding vector.

[0038] Specifically, the experience value determination unit 352 is used to determine the experience value based on the project requirement-resume semantic joint anchoring experience value encoding vector. In the technical solution of the present application, the project requirement-resume semantic joint anchoring experience value encoding vector is input into the decoder-based experience value estimation module to obtain the experience value. It should be understood that although the project requirement-resume semantic joint anchoring experience value encoding vector has captured the deep semantic association between project requirements and resumes (such as skill matching, project complexity adaptation, etc.) through semantic collaborative association analysis, this information still exists in the form of a high-dimensional vector and cannot be directly used for comparison or sorting. The role of the decoder-based experience value estimation module is to decode these complex semantic information into a scalar value (such as an experience value score between 0 and 1) through a deep learning model (such as a neural network), thereby converting abstract semantic matching into a specific quantitative result. In this way, it is possible to automatically match and recommend suitable freelancers based on project requirements, effectively solving the shortcomings of traditional recruitment methods.

[0039] In particular, the budget compatibility analysis module 360 ​​is used to determine budget compatibility based on the semantic embedding coding vector of the project requirements and the semantic embedding coding vector of the candidate freelancer resume. That is, the budget compatibility is used to quantify the fit between the project budget and the expected remuneration of the freelancer. In one example, a scoring model can be constructed to score each pair of project-freelancer combinations based on factors such as the difference between the project budget and the freelancer's quotation, the consistency of payment conditions, etc. For example, if the budget cap of a project is slightly lower than the minimum expected remuneration of a freelancer, but the project provides the possibility of long-term cooperation or has additional benefits, that is, in this case, the algorithm may consider that the pair has a certain budget compatibility. In addition, in order to improve the accuracy of matching, a machine learning method can also be introduced to train the model to predict which factors are most likely to affect budget compatibility based on historical data, and adjust the scoring mechanism accordingly. This method can not only consider direct financial indicators, but also incorporate the influence of more indirect factors, such as industry trends, regional economic conditions, etc., so as to provide a more comprehensive and accurate budget compatibility assessment.

[0040] In particular, the response efficiency analysis module 370 is used to determine the response efficiency based on the semantic embedding coding vector of the project requirements and the semantic embedding coding vector of the candidate freelancer resume. That is, the potential response speed of the freelancer to the requirements of a specific type of project is quantified by the response efficiency score. For example, the performance of the freelancer in the current project can be predicted by comparing the freelancer's previous experience in handling similar urgent or time-sensitive projects. If a freelancer is often able to respond to customer inquiries in a short time and has a history of quickly starting and efficiently completing projects, the system may consider the freelancer to have a high response efficiency. In addition, in order to further improve the accuracy of the evaluation, the system can also combine other data sources, such as freelancers' interaction records on the platform, evaluation feedback, etc., to supplement and improve the portrait of their response efficiency. In this way, it not only helps employers find suitable candidates faster, but also improves the efficiency and success rate of the entire recruitment process, and promotes the healthy development of the freelance market.

[0041] In particular, the additional score analysis module 380 is used to determine the additional score based on the semantic embedding coding vector of the project requirements and the semantic embedding coding vector of the candidate freelancer resume. That is, to identify factors that are not listed as the main evaluation criteria but are still crucial to the success of the project. For example, assuming that the project requirements mention "looking for team members who can quickly adapt to new technologies", and a freelancer has had many successful experiences in learning and applying emerging technologies in his career, even if these experiences are not directly mentioned as prerequisites, the system will regard them as bonus items. In addition, the system may also combine other data sources to supplement and improve the evaluation of additional points. For example, the freelancer's historical evaluation on the platform, the quality rating of the completed project, and the interaction record with the customer can all be used as a reference. If a freelancer often receives good reviews from customers, especially in terms of the speed and effect of solving problems, then these positive feedbacks will also affect his final additional score. It is worth mentioning that the additional score analysis is not limited to personal professional skills or experience, but can also include considerations of soft skills. For example, communication skills, teamwork spirit, leadership, etc. are all important factors affecting project success. Through comprehensive analysis of the description in the resume and past work performance, the system can identify freelancers with excellent soft skills and give them appropriate additional points. This not only makes the evaluation more comprehensive and accurate, but also provides employers with more diversified choices.

[0042] In particular, the comprehensive scoring module 390 is used to calculate the weighted sum of the skill matching, the experience value, the budget compatibility, the response efficiency and the additional points based on preset weights to obtain a comprehensive score. That is, by integrating information from multiple dimensions such as skill matching, experience value, budget compatibility, response efficiency and additional points, a comprehensive score that fully reflects the degree of matching between freelancers and project requirements is calculated. This process not only relies on the accurate evaluation of each individual indicator, but also requires the weighted summation of these indicators according to preset weights to ensure that the final score can accurately reflect the overall suitability of each candidate. In one example, first, after receiving the project requirements uploaded by the employer, the system will analyze key indicators such as skill matching, experience value, budget compatibility and response efficiency in turn, and generate corresponding scores for each indicator. For example, skill match may be determined based on the overlap between the skills required for the project and the skills in the freelancer's resume; experience value considers the candidate's past relevant work experience and his achievements in the field; budget compatibility focuses on whether the freelancer's quote fits the project's budget range; responsiveness evaluates how quickly the candidate responds to new opportunities and how efficiently they complete tasks; and bonus points are awarded based on additional positive factors (such as innovation, problem-solving skills, etc.). Next, the system needs to set the weights for each indicator. These weights are pre-set based on the needs and importance of a specific project to ensure that the importance of different aspects is appropriately reflected. For example, for certain technology-intensive projects, skill match and experience may be given higher weights; while for time-sensitive projects, the weight of responsiveness may be increased accordingly. These weights reflect the uniqueness and priority of the project's needs, helping to ensure that the final recommended candidate is the best fit for the project. Once the specific scores and corresponding weights for each indicator are determined, the system will perform a weighted sum operation to calculate the overall score. This process involves multiplying the score of each indicator by its corresponding weight, and then adding all the results to obtain a single value, the overall score. In particular, the system can also be designed to support dynamic weight adjustment in consideration of changes in actual conditions. This means that as the project progresses or requirements change, users can flexibly adjust the weights of each indicator to adapt to new requirements or priorities. This flexibility enables the platform to better serve various types of projects and customer needs, improving the accuracy and practicality of matching.

[0043] In particular, the recommendation result output module 400 is used to recommend the candidate freelancer to the employer in response to the comprehensive score being greater than or equal to a preset threshold. That is, after completing the multi-dimensional evaluation of skill matching, experience value, budget compatibility, response efficiency and additional points of each candidate freelancer and calculating the comprehensive score, the system will compare these scores with a preset threshold. This threshold is pre-set by the platform based on historical data, industry standards and the characteristics of specific projects, and is intended to screen out those candidates who are most likely to successfully complete the project. For example, for some projects with particularly high criticality or technical requirements, a higher threshold may need to be set to ensure the quality of the recommendation; while for other types of projects, a relatively low threshold may be used to expand the range of candidates. In one example, when the comprehensive score of a freelancer reaches or exceeds this preset threshold, the system will start the recommendation process. Specifically, the process can be implemented by generating a detailed recommendation report, which not only includes the basic information of the freelancer (such as name, contact information, etc.), but also lists the scores of the freelancer in each evaluation dimension and the final comprehensive score.

[0044] As described above, the freelancer management platform 300 based on online technology according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with a freelancer management algorithm based on online technology. In a possible implementation, the freelancer management platform 300 based on online technology according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the freelancer management platform 300 based on online technology can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the freelancer management platform 300 based on online technology can also be one of the many hardware modules of the wireless terminal.

[0045] Alternatively, in another example, the freelancer management platform 300 based on online technology and the wireless terminal may also be separate devices, and the freelancer management platform 300 based on online technology may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0046] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A freelancer management platform based on online technology, characterized by: include: The project requirements collection module is used to receive project requirements uploaded by employers; A candidate freelancer extraction module is used to extract resumes of candidate freelancers from a freelancer database; A natural language parsing and structuring module, configured to perform semantic embedding coding on the project requirements to obtain a semantic embedding coding vector of the project requirements and perform natural language processing on the resumes of the candidate freelancers to obtain a semantic embedding coding vector of the resumes of the candidate freelancers; A skill matching degree measurement module, used to determine the skill matching degree based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; An experience value measurement module, used to determine the experience value based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A budget compatibility analysis module, configured to determine budget compatibility based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A response efficiency analysis module, used to determine the response efficiency based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; An additional score analysis module, used to determine additional scores based on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector; A comprehensive scoring module, used for calculating the weighted sum of the skill matching degree, the experience value, the budget compatibility, the response efficiency and the additional score based on preset weights to obtain a comprehensive score; The recommendation result output module is used to recommend the candidate freelancer to the employer in response to the comprehensive score being greater than or equal to a preset threshold.

2. The freelancer management platform based on online technology according to claim 1, characterized in that: The experience value measurement module includes: A semantic collaborative association analysis unit, configured to perform semantic collaborative association analysis on the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector to obtain a project requirement-resume semantic joint anchoring experience value coding vector; An experience value determination unit is used to determine the experience value based on the project requirement-resume semantic joint anchoring experience value encoding vector.

3. The freelancer management platform based on online technology according to claim 2, characterized in that: The semantic collaborative association analysis unit comprises: A semantic autocorrelation association matrix construction subunit, used to construct a semantic autocorrelation association matrix of the project requirement semantic embedding coding vector and the candidate freelancer resume semantic embedding coding vector to obtain a project requirement semantic autocorrelation association matrix and a candidate freelancer resume semantic autocorrelation association matrix; A feature response interaction analysis subunit is used to perform feature granularity response interaction analysis and feature value granularity response interaction analysis based on the project requirement semantic autocorrelation association matrix and the candidate freelancer resume semantic autocorrelation association matrix to obtain a project requirement-resume semantic feature granularity response interaction coding vector and a project requirement-resume semantic feature value granularity response interaction coding vector; A feature perturbation correction subunit is used to perform feature perturbation correction on the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector to obtain a corrected project requirement-resume semantic feature granularity response interaction coding vector and a corrected project requirement-resume semantic feature value granularity response interaction coding vector; A fusion subunit is used to fuse the corrected project requirement-resume semantic feature granularity response interaction coding vector and the corrected project requirement-resume semantic feature value granularity response interaction coding vector to obtain the project requirement-resume semantic joint anchoring experience value coding vector.

4. The freelancer management platform based on online technology according to claim 3, characterized in that: The feature response interaction analysis subunit includes: The core information anchoring secondary subunit is used to perform core information anchoring on the project requirement semantic autocorrelation association matrix and the candidate freelancer resume semantic autocorrelation association matrix to obtain a project requirement semantic feature core information anchoring coding vector and a candidate freelancer resume semantic feature core information anchoring coding vector; The response interaction feature calculation secondary sub-unit is used to respectively calculate the feature granularity response interaction features and feature value granularity response interaction features between the project requirement semantic feature core information anchor coding vector and the alternative freelancer resume semantic feature core information anchor coding vector to obtain the project requirement-resume semantic feature granularity response interaction coding vector and the project requirement-resume semantic feature value granularity response interaction coding vector.

5. The freelancer management platform based on online technology according to claim 4, characterized in that: The core information anchoring secondary sub-unit is used to: input the project requirement semantic autocorrelation association matrix and the candidate freelancer resume semantic autocorrelation association matrix into the core information anchoring network based on autocorrelation decoupling respectively to obtain the project requirement semantic feature core information anchoring coding vector and the candidate freelancer resume semantic feature core information anchoring coding vector.

6. The freelancer management platform based on online technology according to claim 5, characterized in that: The response interaction feature calculation secondary subunit is used for: Input the project requirement semantic feature core information anchor coding vector and the candidate freelancer resume semantic feature core information anchor coding vector into a feature granularity response interactive encoder to obtain the project requirement-resume semantic feature granularity response interactive coding vector; The project requirement semantic feature core information anchor coding vector and the candidate freelancer resume semantic feature core information anchor coding vector are input into the feature value granularity response interactive encoder to obtain the project requirement-resume semantic feature value granularity response interactive coding vector.

7. The freelancer management platform based on online technology according to claim 6, characterized in that: The characteristic disturbance correction subunit is used for: After calculating the square of each eigenvalue in the project requirement-resume semantic feature value granularity response interactive coding vector, subtract it from the double of each eigenvalue in the project requirement-resume semantic feature granularity response interactive coding vector by position to obtain a project requirement-resume semantic difference value response representation vector; After calculating the square root of each eigenvalue in the project requirement-resume semantic difference response representation vector, divide it by the project requirement-resume semantic feature granularity response interaction coding vector by position to obtain the corrected project requirement-resume semantic feature value granularity response interaction coding vector; Calculate the exponential function value with the natural constant e as the base and each eigenvalue in the project demand-resume semantic feature granularity response interaction coding vector as the exponential power to obtain the project demand-resume semantic feature granularity response interaction exponential modulation coding vector; Calculate the cosine function value of each eigenvalue in the corrected project demand-resume semantic feature value granularity response interaction coding vector to obtain the corrected project demand-resume semantic feature granularity response interaction cosine modulation coding vector; Calculate the point-by-point multiplication between the project requirement-resume semantic feature granularity response interaction index modulation coding vector and the corrected project requirement-resume semantic feature granularity response interaction cosine modulation coding vector to obtain the corrected project requirement-resume semantic feature granularity response interaction coding vector.

8. The freelancer management platform based on online technology according to claim 7, characterized in that: The experience value determination unit is used to: input the project requirement-resume semantic joint anchored experience value encoding vector into a decoder-based experience value estimation module to obtain the experience value.

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