A task matching method and device, computer equipment and readable storage medium

Abstract

The application discloses a task matching method and device, computer equipment and a readable storage medium, comprising: firstly acquiring the fixed attribute of the sending party and the fixed attribute corresponding to each of the plurality of receiving parties, and determining the target receiving party based on the matching result of the fixed attribute. Then, the outsourcing task corresponding to the sending party is assigned to the determined target receiving party. This method can accurately realize the matching between the task and the receiving party, thereby improving the efficiency and quality of task completion.

Description

Technical Field

[0001] This invention relates to the field of task scheduling technology, and more specifically, to a task matching method, apparatus, computer device, and readable storage medium. Background Technology

[0002] Traditional task allocation typically relies on manual selection or simple rules for matching, such as a first-come, first-served principle or selection based on the recipient's historical evaluation. However, these methods fail to consider the specific requirements of the task and the specific capabilities of the recipient, potentially leading to compromised efficiency and quality in task allocation. Therefore, developing a method that can accurately match tasks with recipients has become an urgent need. Summary of the Invention

[0003] The purpose of this invention is to provide a task matching method, apparatus, computer device, and readable storage medium.

[0004] In a first aspect, embodiments of the present invention provide a task matching method, including:

[0005] Obtain the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers;

[0006] Based on the matching results of the first fixed attribute and multiple second fixed attributes, the target recipient corresponding to the target fixed attribute is determined;

[0007] The outsourced tasks corresponding to the outsourcing party are assigned to the target receiving party.

[0008] In one possible implementation, obtaining the first fixed attribute of the data sender and the second fixed attributes corresponding to each of the multiple data receivers includes:

[0009] The first fixed attribute is determined based on the outsourcing task attributes provided by the outsourcing party;

[0010] A second fixed attribute is determined for each recipient based on the personal profile attributes configured for each recipient.

[0011] In one possible implementation, the first fixed attribute includes a first professional field, a first category, and a first task scope, and the second fixed attribute includes a second professional field, a second category, and a second task scope; determining the target recipient corresponding to the target fixed attribute based on the matching results of the first fixed attribute and multiple second fixed attributes includes:

[0012] The first professional field is matched with multiple second professional fields. If a match is successful, the recipient of the package is assigned a first matching value corresponding to the successfully matched second professional field.

[0013] The first category is matched with multiple second categories. If a match is successful, the recipient of the successfully matched second category is assigned a second matching value.

[0014] The first task range is matched with multiple second task ranges. If a match is successful, a third matching value is assigned to the recipient corresponding to the successfully matched second task range. The first matching value is greater than the second matching value, and the second matching value is greater than the third matching value.

[0015] Calculate the sum of the first matching value, the second matching value, and the third matching value corresponding to each of the packet recipients, and select the packet recipient with the highest sum as the target packet recipient.

[0016] In one possible implementation, the method further includes:

[0017] Obtain multiple historical first fixed attributes of the party issuing the package, and multiple task scoring attributes for each of the historical first fixed attributes;

[0018] Based on the first correlation between the multiple historical first fixed attributes and the multiple task rating attributes, a corresponding weight is assigned to each task rating attribute;

[0019] Obtain multiple task processing characteristics of the recipient and determine a second correlation between the multiple task processing characteristics and the multiple task rating attributes;

[0020] Based on the second correlation and the corresponding weights assigned to each of the task scoring attributes, the multiple task processing characteristics are weighted and calculated to obtain the fourth matching value.

[0021] The recipient with the largest fourth matching value is selected as the target recipient.

[0022] In one possible implementation, the plurality of historical first fixed attributes include professional category, gender, age, and customer classification; the plurality of task scoring attributes include timeliness, professionalism, and practicality; the task processing characteristics include processing time, usage of professional vocabulary, and text richness; and the assignment of a corresponding weight to each task scoring attribute based on the first correlation between the plurality of historical first fixed attributes and the plurality of task scoring attributes includes:

[0023] Based on the professional category, gender, age, customer classification, and the first correlation between timeliness, professionalism, and practicality, corresponding weights are assigned to timeliness, professionalism, and practicality.

[0024] The step of obtaining multiple task processing characteristics of the recipient and determining a second correlation between the multiple task processing characteristics and the multiple task rating attributes includes:

[0025] The processing time, the amount of specialized vocabulary used, and the text richness are obtained, and a second correlation between the processing time, the amount of specialized vocabulary used, and the text richness and the timeliness, professionalism, and practicality are determined.

[0026] In one possible implementation, the method further includes:

[0027] The associated data of the target recipient will be used as reference data for determining the first and second associations.

[0028] In one possible implementation, the method further includes:

[0029] Overfitting detection is performed on the target packet receiver according to a preset cycle.

[0030] In a second aspect, embodiments of the present invention provide a task matching device, comprising:

[0031] The acquisition module retrieves the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers.

[0032] The matching module is used to determine the target recipient corresponding to the target fixed attribute based on the matching results of the first fixed attribute and multiple second fixed attributes; and to assign the outsourced task corresponding to the sender to the target recipient.

[0033] Thirdly, embodiments of the present invention provide a computer device, the computer device including a processor and a non-volatile memory storing computer instructions, wherein when the computer instructions are executed by the processor, the computer device performs the task matching method described in the first aspect.

[0034] Fourthly, embodiments of the invention provide a readable storage medium, the readable storage medium including a computer program, which, when executed, controls a computer device containing the readable storage medium to perform the task matching method described in the first aspect.

[0035] Compared to existing technologies, the beneficial effects of this invention include: By employing a task matching method, apparatus, computer device, and readable storage medium disclosed in this invention, the target recipient is determined by acquiring fixed attributes of the task sender and the corresponding fixed attributes of multiple task recipients, and based on the matching results of these fixed attributes. Then, the outsourced task corresponding to the task sender is assigned to the determined target recipient. This method can accurately match tasks with recipients, thereby improving the efficiency and quality of task completion. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the step flow structure of the task matching method provided in the embodiments of the present invention;

[0038] Figure 2 This is a schematic block diagram of the task matching device provided in an embodiment of the present invention;

[0039] Figure 3 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0041] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0042] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0043] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0044] Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0045] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, terms such as "set" and "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0046] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0047] In order to solve the technical problems mentioned in the background art Figure 1 This is a flowchart illustrating the task matching method provided in this embodiment of the disclosure. The task matching method will be described in detail below.

[0048] Step S201: Obtain the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers;

[0049] Step S202: Based on the matching results of the first fixed attribute and multiple second fixed attributes, determine the target packet receiver corresponding to the target fixed attribute;

[0050] Step S203: Assign the outsourced task corresponding to the outsourcing party to the target receiving party.

[0051] In this embodiment of the invention, exemplarily, Xiaoming, as the project issuer, registers and publishes a software development task on an online platform. This task requires an experienced developer, and the technology stack involved includes Java, Python, and database management. The platform obtains Xiaoming's first fixed attribute, namely his description and requirements for the required task, such as "I need a developer with many years of programming experience to complete this software development task, proficient in Java, Python, and database management." The platform also obtains the second fixed attributes of multiple registered project recipients, namely their respective skills and experience. For example, project recipient A states in their profile, "I am a programmer with 5 years of Java development experience," project recipient B states, "I am proficient in Python development and have 2 years of relevant project experience," and project recipient C describes themselves as "a database management expert, familiar with mainstream database systems, and with 3 years of practical work experience." By analyzing the first fixed attribute of the project issuer and the second fixed attributes of multiple project recipients, the platform can determine the most suitable target project recipient to complete the task based on a matching algorithm. In this example, the platform might assign the task to recipient A based on Xiaoming's requirements, as A has Java development experience and his skills match the task requirements. After task matching, the platform automatically assigns Xiaoming's software development task to recipient A. Recipient A will receive a notification or see the task on the platform and begin discussing detailed requirements with Xiaoming to start development work. This design, through this task matching method, allows the platform to more accurately assign tasks from the client to suitable recipients, improving the efficiency and quality of task completion. It also provides a convenient communication and collaboration platform, enabling better cooperation between the client and recipient.

[0052] In one possible implementation, the aforementioned step S201 can be performed in the following manner.

[0053] (1) Determine the first fixed attribute based on the outsourcing task attributes provided by the outsourcing party;

[0054] (2) Determine the second fixed attribute of each recipient based on the personal profile attributes configured for each recipient.

[0055] In this embodiment of the invention, assume that Xiaohong, as the client, posts a graphic design task on an online platform. She needs to find a designer proficient in Photoshop and Illustrator, with creative thinking, to complete the task. The online platform determines Xiaohong's first fixed attribute based on the task attributes provided, such as the requirements description and skill requirements. In this example, the first fixed attribute could be, "I need a designer proficient in Photoshop and Illustrator, with unique insights into creative design." During registration, the platform requires clients to fill in personal information and skill tags, and clients configure their personal profile attributes. For example, client X writes in their profile, "I am an experienced graphic designer, proficient in Photoshop and Illustrator, and have participated in multiple creative projects," while client Y describes themselves as "I am a UI designer, proficient in using various design tools for creative design, and have in-depth research on user experience." With this design, based on the aforementioned task matching method and combined with this technical solution, the platform will match Xiaohong's first fixed attribute with the clients' second fixed attributes to determine the most suitable target client to complete the task. In this example, the task might be assigned to recipient X because he possesses the necessary skills and experience, and his performance aligns well with Xiaohong's task requirements. This method allows the platform to more accurately match suitable recipients to complete tasks based on the task attributes provided by the client and the personal profile attributes configured by the recipient. This satisfies the client's needs, improves the accuracy and efficiency of task allocation, and also allows recipients to receive outsourced tasks that better match their skills and interests.

[0056] In one possible implementation, the first fixed attribute includes a first professional field, a first category, and a first task scope, and the second fixed attribute includes a second professional field, a second category, and a second task scope; the aforementioned step S202 can be implemented in the following manner.

[0057] (1) Match the first professional field with multiple second professional fields. If the match is successful, assign the first matching value to the recipient corresponding to the successfully matched second professional field.

[0058] (2) Match the first category with multiple second categories. If the match is successful, assign the second matching value to the recipient corresponding to the successfully matched second category.

[0059] (3) Match the first task range with multiple second task ranges. If the match is successful, assign a third matching value to the recipient corresponding to the successfully matched second task range. The first matching value is greater than the second matching value, and the second matching value is greater than the third matching value.

[0060] (4) Calculate the sum of the first matching value, the second matching value and the third matching value corresponding to each of the recipients, and take the recipient with the highest sum as the target recipient.

[0061] In this embodiment of the invention, exemplarily, assume that a client issues a software development task with the following first fixed attributes: First professional field: mobile application development; First category: iOS development; First task scope: developing a social media application; There are three clients, A, B, and C, on this platform, whose second fixed attributes are as follows: Client A: Second professional field: mobile application development; Second category: Android development; Second task scope: developing an e-commerce application; Client B: Second professional field: iOS development; Second category: game development; Second task scope: developing a casual game application; Client C: Second professional field: mobile application development; Second category: iOS development; Second task scope: developing an educational application. Matching the first professional field with multiple second professional fields: The client's first professional field is mobile application development, and the second professional fields of clients A and C are also mobile application development. Therefore, clients A and C are assigned a first matching value. Matching the first category with multiple second categories: The client's first category is iOS development, and the second category of clients B and C is also iOS development. Therefore, a second matching value is assigned to recipients B and C. The first task scope is matched against multiple second task scopes: the first task scope of the outsourcing party is developing a social media application, while the second task scopes of recipients B and C are game development and educational applications, respectively, which do not match the outsourcing party's first task scope. This design, combined with the set corresponding matching values, allows for the identification of more suitable target recipients.

[0062] In one possible implementation, the embodiments of the present invention also provide the following methods.

[0063] (1) Obtain multiple historical first fixed attributes of the contract issuer, and multiple task rating attributes for each of the historical first fixed attributes;

[0064] (2) Based on the first correlation between the multiple historical first fixed attributes and the multiple task rating attributes, assign a corresponding weight to each of the task rating attributes;

[0065] (3) Obtain multiple task processing characteristics of the recipient and determine the second correlation between the multiple task processing characteristics and the multiple task rating attributes;

[0066] (4) Based on the second correlation and the corresponding weights assigned to each of the task scoring attributes, the multiple task processing characteristics are weighted and calculated to obtain the fourth matching value;

[0067] (5) The recipient with the largest fourth matching value is taken as the target recipient.

[0068] In this embodiment of the invention, it is assumed that the client publishes a software development task on an online platform, which has the following historical first fixed attributes and task rating attributes:

[0069] Historically, the first fixed attribute 1: First professional field: Artificial Intelligence; First category: Machine Learning Algorithms; First task scope: Image Recognition.

[0070] Task rating attribute 1: Task difficulty rating: 8 / 10; Delivery quality rating: 9 / 10; Communication skills rating: 7 / 10.

[0071] Historical first fixed attribute 2: First professional field: mobile application development; First category: Android development; First task scope: developing a social media application.

[0072] Task rating attribute 2: Task difficulty rating: 6 / 10; Delivery quality rating: 8 / 10; Communication skills rating: 9 / 10.

[0073] There are currently three recipients on the platform, A, B, and C, whose task processing characteristics are as follows:

[0074] Recipient A: Task processing speed: fast; Problem-solving ability: high; Teamwork ability: average.

[0075] Recipient B: Task processing speed: Average; Problem-solving ability: Medium; Teamwork ability: High.

[0076] Recipient C: Task processing speed: fast; Problem-solving ability: high; Teamwork ability: high.

[0077] Follow these steps to match and calculate the matching value:

[0078] Obtain the historical first fixed attribute and task rating attribute of the task sender, and assign corresponding weights to each task rating attribute: For historical first fixed attribute 1, assign weights based on the correlation between multiple task rating attributes 1. For historical first fixed attribute 2, assign weights based on the correlation between multiple task rating attributes 2. Obtain the task processing characteristics of the task recipient and determine the correlation between the task processing characteristics and the task rating attribute. Based on the second correlation and the weight of each task rating attribute, perform a weighted calculation on the task processing characteristics to obtain the fourth matching value. Select the task recipient with the largest fourth matching value as the target task recipient. In this example, the specific weights and correlations need to be determined according to the specific situation. We can assume the following example weights and correlations:

[0079] The weights of the historical first fixed attribute 1 task rating attributes are as follows: task difficulty rating weight: 0.4; delivery quality rating weight: 0.3; communication ability rating weight: 0.3.

[0080] Historically, the first fixed attribute 2 of the task rating has the following weights: Task difficulty rating weight: 0.2; Delivery quality rating weight: 0.4; Communication ability rating weight: 0.4.

[0081] Correlation between task processing characteristics and task rating attributes: Correlation between task processing speed and task difficulty rating: 0.6; Correlation between problem-solving ability and delivery quality rating: 0.8; Correlation between teamwork ability and communication ability rating: 0.7.

[0082] Based on the aforementioned weights and correlations, the fourth matching value for each recipient can be calculated:

[0083] Recipient A:

[0084] The fourth matching value = (task processing speed * 0.6 + problem solving ability * 0.8 + teamwork ability * 0.7) = (fast * 0.6 + high * 0.8 + average * 0.7).

[0085] Recipient B:

[0086] The fourth matching value = (task processing speed * 0.6 + problem solving ability * 0.8 + teamwork ability * 0.7) = (average * 0.6 + medium * 0.8 + high * 0.7).

[0087] Recipient C:

[0088] The fourth matching value = (task processing speed * 0.6 + problem solving ability * 0.8 + teamwork ability * 0.7) = (fast * 0.6 + high * 0.8 + high * 0.7).

[0089] Based on the calculated fourth matching value, the recipient with the maximum value is determined as the target recipient.

[0090] Please note that this is just an example; in practice, the weights and matching calculations need to be determined based on specific requirements and relevance. Furthermore, the accuracy and feasibility of the method must be ensured to meet the needs of the client.

[0091] In this embodiment of the invention, the plurality of historical first fixed attributes include professional category, gender, age, and customer classification; the plurality of task scoring attributes include timeliness, professionalism, and practicality; and the task processing characteristics include processing time, usage of professional terms, and text richness. The aforementioned step of assigning a corresponding weight to each task scoring attribute based on the first correlation between the plurality of historical first fixed attributes and the plurality of task scoring attributes can be implemented through the following example.

[0092] (1) Based on the first correlation between the professional category, gender, age, customer classification and the timeliness, professionalism and practicality, assign corresponding weights to the timeliness, professionalism and practicality;

[0093] The aforementioned steps of obtaining multiple task processing characteristics of the recipient and determining the second correlation between the multiple task processing characteristics and the multiple task rating attributes can be implemented through the following example.

[0094] The processing time, the amount of specialized vocabulary used, and the text richness are obtained, and a second correlation between the processing time, the amount of specialized vocabulary used, and the text richness and the timeliness, professionalism, and practicality are determined.

[0095] In this embodiment of the invention, for example, it is assumed that the party issuing the task publishes a translation task on an online platform, which has the following historical first fixed attributes and task rating attributes:

[0096] Historical first fixed attribute 1: Professional category: Linguistics; Gender: Female; Age: 30 years old; Client category: Corporate client.

[0097] Task rating attribute 1: Timeliness rating: 9 / 10; Professionalism rating: 8 / 10; Practicality rating: 7 / 10.

[0098] Historical first fixed attribute 2: Professional category: Literature; Gender: Male; Age: 35 years old; Client category: Individual client.

[0099] Task rating attribute 2: Timeliness rating: 7 / 10; Professionalism rating: 9 / 10; Practicality rating: 8 / 10.

[0100] There are currently three recipients on the platform, A, B, and C, whose task processing characteristics are as follows:

[0101] Recipient A: Processing time: Fast; Use of technical terms: High; Text richness: Average.

[0102] Recipient B: Processing time: Medium; Use of technical terms: Medium; Text richness: High.

[0103] Recipient C: Processing time: slow; Use of technical terms: low; Text richness: high.

[0104] According to the method requirements, follow these steps to perform matching and calculate the matching value:

[0105] Obtain multiple historical first fixed attributes and task rating attributes from the task issuer, and assign corresponding weights to each task rating attribute:

[0106] For the historical first fixed attribute 1, weights are assigned based on the correlation between multiple task rating attributes 1.

[0107] For the historical first fixed attribute 2, weights are assigned based on the correlation between multiple task rating attributes 2.

[0108] Obtain multiple task processing characteristics of the recipient and determine the correlation between task processing characteristics and task rating attributes.

[0109] Based on the correlation between professional category, gender, age, customer classification, and timeliness, professionalism, and practicality, corresponding weights are assigned to timeliness, professionalism, and practicality.

[0110] Based on the correlation between processing time, the amount of specialized vocabulary used, and text richness with timeliness, professionalism, and practicality, corresponding weights are assigned to processing time, the amount of specialized vocabulary used, and text richness.

[0111] Calculate the fourth matching value for each recipient:

[0112] Recipient A: Fourth Matching Value = (Processing Time * Weight + Number of Professional Terms Used * Weight + Text Richness * Weight).

[0113] Recipient B: Fourth matching value = (processing time * weight + usage of professional terms * weight + text richness * weight).

[0114] Recipient C: Fourth matching value = (processing time * weight + usage of professional terms * weight + text richness * weight).

[0115] Based on the calculated fourth matching value, the recipient with the maximum value is determined as the target recipient.

[0116] Please note that this is just an example. In practice, the weights and matching calculations need to be determined based on specific requirements and relevance. Furthermore, the accuracy and feasibility of the method must be ensured to meet the needs of the client.

[0117] In this embodiment of the invention, the following solutions are also provided.

[0118] The associated data of the target recipient will be used as reference data for determining the first and second associations.

[0119] In this embodiment of the invention, for example, it is assumed that multiple similar software testing tasks have been completed on the platform, and these tasks have been scored and recorded. We can use this historical data to determine the degree of correlation between different historical primary fixed attributes (such as professional category, gender, age, customer classification) and task scoring attributes (such as timeliness, professionalism, and practicality). By analyzing the distribution and scoring of different attributes in the historical data, we can calculate correlation coefficients or other statistical indicators to measure the correlation between the two.

[0120] In addition, user feedback and evaluations can be considered, such as assessments of the recipient's work quality and communication skills. This feedback can be direct user comments, ratings, or other forms of feedback data. By analyzing user feedback and evaluations, we can understand the recipient's performance and user satisfaction under different task rating attributes, thereby further determining the weight of correlations.

[0121] It is worth noting that the embodiments of the present invention also provide the following implementation methods for scoring noise data during the scoring process.

[0122] Imagine an online platform connecting task posters and task recipients. Posters can release task requirements, while recipients can choose suitable tasks based on their skills and interests. The platform uses a matching algorithm to assign tasks to the most suitable recipients.

[0123] During the matching process, we need to consider the historical primary fixed attributes and task rating attributes of the task sender, as well as the task processing characteristics of the task receiver. However, historical rating data (i.e., rating-related data) may contain noise, meaning that the rating results may be inaccurate or subject to significant subjective bias.

[0124] To eliminate this noisy data, we can use regression functions for data cleaning. Here are two potentially effective methods:

[0125] 1. Average Deviation Method: The average deviation method is a commonly used data cleaning method that corrects noisy data by calculating the average deviation between historical rating data and the true ratings. The specific steps are as follows:

[0126] Collect the tasks posted by the outsourcing party and record the actual ratings for each task (e.g., objective evaluations obtained through other means).

[0127] For each party's historical first fixed attribute and task rating attribute, calculate the corresponding average deviation of task rating.

[0128] Historical rating data is cleaned using a regression function, and noisy data is corrected by subtracting the average deviation of task ratings.

[0129] 2. Standard Deviation Test: The standard deviation test can help us identify outliers in historical scoring data. The specific steps are as follows:

[0130] For each party's historical first fixed attribute and task rating attribute, calculate the corresponding task rating standard deviation.

[0131] Identify historical rating data with a standard deviation greater than a certain threshold (e.g., 3 times the standard deviation), as this data may be noisy.

[0132] The identified abnormal rating data is cleaned using regression functions, and the rating data is purified by deleting or replacing these data.

[0133] By using the two methods described above, we can remove noise from historical scoring data, thereby improving the robustness of the matching model. The cleaned, accurate, and objective scores will better reflect the recipient's capabilities and performance, thus helping the platform to allocate tasks more precisely. In this embodiment of the invention, the following solutions are also provided.

[0134] Overfitting detection is performed on the target packet receiver according to a preset cycle.

[0135] In this embodiment of the invention, for example, after determining the target recipient, performing overfit detection at a preset period is to ensure that the selected recipient maintains good performance in long-term operation and avoids overfitting to certain specific tasks. Overfitting refers to a situation where a model performs well on training data but poorly on new data. To avoid overfitting, we can set a period for monitoring and evaluation, such as checking the performance of the target recipient at regular intervals. By comparing with expected results, we can determine whether the target recipient has overfitted to certain tasks or exhibited other anomalies, and take corresponding measures in a timely manner, such as adjusting matching weights or reassessing relevance. Such overfit detection helps the platform maintain the diversity and stability of recipients, ensuring the fairness and efficiency of task allocation. Simultaneously, it also helps ensure that the needs of the task sender are met, improving the overall user experience.

[0136] The following is an overall implementation of an embodiment of the present invention, wherein the contractor is an individual, legal person, or collective that can provide professional services in a certain field, such as legal, medical, forensic, or financial services. The contractor works on a single outsourced task, starting from the acceptance of the contract and ending upon its completion. The specialized client (customer) can be an individual, legal person, or collective that requires temporary professional guidance from specialized personnel during the production process but cannot afford to invest heavily in hiring such personnel. The specific process is as follows:

[0137] (1) Initial Matching

[0138] The roles are divided into outsourcing providers and service recipients. Assignments are made by assigning fixed attribute codes and variable attribute scores to each role. The fixed attribute codes for outsourcing providers are based on the attributes of the outsourced tasks submitted by them, while the fixed attributes for service recipients are based on their personal profile attributes, ensuring rapid initial matching.

[0139] Taking the professional appraisal and consultation as an example, the initial fixed attribute codes of the task package issued by the contracting party are professional field M, professional category N, and specific task scope X1, X2...Xn; the attributes of the personnel of the contracting party are the corresponding professional field m, professional category n, and specific task scope x1, x2...xn.

[0140] The professional attribute M is used to search for the professional field m of the contractor, complete the initial screening, and output the list menu1 that meets the criteria. All personnel in the table are assigned a value of 1000.

[0141] Continue searching for the professional category N in menu1, outputting menu2 that meets the criteria, and assigning 100 to all personnel in the table.

[0142] Specialized work packages have multiple specific requirement tags. Enumerating all requirement tags, the specific task range is X1, X2...Xn. Specialized work packages also have multiple specific requirement tags, with the specific task range being x1, x2...xn.

[0143] Perform Boolean operations; X1 and x1 can output 1 if they correspond, and 0 if they do not correspond.

[0144] Add up all the assignments from the three steps, take the highest score, and the matching is complete.

[0145] (2) Optimize matching

[0146] The automatic retrieval and matching method in step 1 is simple and direct. If you want to achieve more refined matching, you need to add other variable attribute matching.

[0147] The variable attributes are collected dynamically. This part uses statistical attribution to determine the variable attributes that the client is most concerned about, and then matches and links them with the variable attributes of the service provider. The variable attributes are iterated periodically based on the collection of data such as the client's behavior and feedback to ensure the accuracy of the best match.

[0148] The pre-defined sender sends N packets, each packet containing attribute information: professional category N, gender G, age X, customer category C, timeliness of task completion rating T, professionalism Q, practicality E, and custom U.

[0149] Calculate the correlation between each fixed attribute code and each rating to determine if a correlation exists, thereby obtaining the client's ranking of their focus points (timeliness T, professionalism Q, practicality E, and customization U) for a specific type of specialized outsourcing task. Assign values ​​to the factors in descending order of ranking, such as assigning 110% to the first and 0.9% to the lowest.

[0150] The preset processing time t of the receiving party, the amount of professional terminology used q, the richness of text e, and the custom u are compared with the customer's score at the end of the task: timeliness T, professionalism Q, practicality E, and custom U.

[0151] Calculate the actual correlation between the objective situation of the receiving party and the customer's perception rating. Assign a value of 5 to each variable.

[0152] Based on the assigned factors of timeliness (T), professionalism (Q), practicality (E), and custom (U) at the end of the task, the original constants of the receiving party's processing time (t), professional vocabulary usage (q), text richness (e), and custom (u) are assigned a value of 5, multiplied by the assigned factors, and the results are added together. The receiving party with the best score is given the task.

[0153] (3) Dynamic matching

[0154] Subsequent customer reviews and ratings will be continuously collected to dynamically adjust the attribute-focus rating and focus rating-contractor profile, thereby obtaining the best dynamic match.

[0155] (4) Maintenance

[0156] Overfitting occurs during matching learning and is periodically removed.

[0157] Please refer to the following: Figure 2 , Figure 2 A task matching device 110 provided in an embodiment of the present invention includes:

[0158] The module 1101 retrieves the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers.

[0159] The matching module 1102 is used to determine the target recipient corresponding to the target fixed attribute based on the matching results of the first fixed attribute and multiple second fixed attributes; and to assign the outsourced task corresponding to the sender to the target recipient.

[0160] It should be noted that the implementation principle of the aforementioned task matching device 110 can refer to the implementation principle of the aforementioned task matching method, and will not be repeated here. It should be understood that the division of the various modules in the above device is merely a logical functional division; in actual implementation, they can be fully or partially integrated into a single physical entity, or physically separated. Furthermore, these modules can all be implemented in software through processing element calls; they can all be implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the task matching device 110 can be a separately established processing element, or it can be integrated into a chip within the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its functions can be called and executed by a processing element of the aforementioned device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together, or implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0161] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a system-on-a-chip (SOC).

[0162] This invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned task matching device 110. For example... Figure 3As shown, Figure 3 This is a structural block diagram of a computer device 100 provided in an embodiment of the present invention. The computer device 100 includes a task matching device 110, a memory 111, a processor 112, and a communication unit 113.

[0163] To enable data transmission or interaction, the memory 111, processor 112, and communication unit 113 are electrically connected to each other directly or indirectly. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The task matching device 110 includes at least one software function module that can be stored in the memory 111 or embedded in the operating system (OS) of the computer device 100 in the form of software or firmware. The processor 112 is used to execute the task matching device 110 stored in the memory 111, such as the software function module and computer program included in the task matching device 110.

[0164] This invention provides a readable storage medium, which includes a computer program. When the computer program runs, it controls the computer device where the readable storage medium is located to execute the aforementioned task matching method.

[0165] For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the foregoing illustrative discussions are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Numerous modifications and variations are possible in accordance with the foregoing teachings. These embodiments were chosen and described in order to best illustrate the principles of the present disclosure and its practical application, thereby enabling those skilled in the art to best utilize the present disclosure and to employ various embodiments with different modifications to suit a particular intended application. For illustrative purposes, the foregoing description has been made with reference to specific embodiments. However, the foregoing illustrative discussions are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Numerous modifications and variations are possible in accordance with the foregoing teachings. These embodiments were chosen and described in order to best illustrate the principles of the present disclosure and its practical application, thereby enabling those skilled in the art to best utilize the present disclosure and to employ various embodiments with different modifications to suit a particular intended application.

Claims

1. A task matching method, characterized in that, include: Obtain the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers; Based on the matching results of the first fixed attribute and multiple second fixed attributes, the target recipient corresponding to the target fixed attribute is determined; The outsourced tasks corresponding to the outsourcing party are assigned to the target receiving party; The method further includes: Obtain multiple historical first fixed attributes of the party issuing the package, and multiple task scoring attributes for each of the historical first fixed attributes; Based on the first correlation between the multiple historical first fixed attributes and the multiple task rating attributes, a corresponding weight is assigned to each task rating attribute; Obtain multiple task processing characteristics of the recipient and determine a second correlation between the multiple task processing characteristics and the multiple task rating attributes; Based on the second correlation and the corresponding weights assigned to each of the task scoring attributes, the multiple task processing characteristics are weighted and calculated to obtain the fourth matching value. The recipient with the largest fourth matching value is selected as the target recipient. The process of obtaining the first fixed attribute of the sender and the second fixed attributes corresponding to each of the multiple receivers includes: The first fixed attribute is determined based on the outsourcing task attributes provided by the outsourcing party; A second fixed attribute is determined for each recipient based on the personal profile attributes configured for each recipient. The first fixed attribute includes a first professional field, a first category, and a first task scope; the second fixed attribute includes a second professional field, a second category, and a second task scope; determining the target recipient corresponding to the target fixed attribute based on the matching results of the first fixed attribute and multiple second fixed attributes includes: The first professional field is matched with multiple second professional fields. If a match is successful, the recipient of the package is assigned a first matching value corresponding to the successfully matched second professional field. The first category is matched with multiple second categories. If a match is successful, the recipient of the successfully matched second category is assigned a second matching value. The first task range is matched with multiple second task ranges. If a match is successful, a third matching value is assigned to the recipient corresponding to the successfully matched second task range. The first matching value is greater than the second matching value, and the second matching value is greater than the third matching value. Calculate the sum of the first matching value, the second matching value, and the third matching value corresponding to each of the packet recipients, and select the packet recipient with the highest sum as the target packet recipient.

2. The method according to claim 1, characterized in that, The multiple historical first fixed attributes include professional category, gender, age, and customer classification; the multiple task scoring attributes include timeliness, professionalism, and practicality; the task processing characteristics include processing time, usage of professional vocabulary, and text richness; and the assignment of corresponding weights to each task scoring attribute based on the first correlation between the multiple historical first fixed attributes and the multiple task scoring attributes includes: Based on the professional category, gender, age, customer classification, and the first correlation between timeliness, professionalism, and practicality, corresponding weights are assigned to timeliness, professionalism, and practicality. The step of obtaining multiple task processing characteristics of the recipient and determining a second correlation between the multiple task processing characteristics and the multiple task rating attributes includes: The processing time, the amount of specialized vocabulary used, and the text richness are obtained, and a second correlation between the processing time, the amount of specialized vocabulary used, and the text richness and the timeliness, professionalism, and practicality are determined.

3. The method according to claim 1, characterized in that, The method further includes: The associated data of the target recipient will be used as reference data for determining the first and second associations.

4. The method according to claim 1, characterized in that, The method further includes: Overfitting detection is performed on the target packet receiver according to a preset cycle.

5. A task matching device, characterized in that, include: The acquisition module retrieves the first fixed attribute of the sender and the second fixed attribute of each of the multiple receivers. The matching module is used to determine the target recipient corresponding to the target fixed attribute based on the matching results of the first fixed attribute and multiple second fixed attributes; and to assign the outsourced task corresponding to the sender to the target recipient. The matching module is also used for: Obtain multiple historical first fixed attributes of the contract issuer, and multiple task rating attributes for each of the historical first fixed attributes; based on the first correlation between the multiple historical first fixed attributes and the multiple task rating attributes, assign a corresponding weight to each task rating attribute; Obtain multiple task processing characteristics of the recipient and determine a second correlation between the multiple task processing characteristics and the multiple task rating attributes; Based on the second correlation and the corresponding weights assigned to each of the task scoring attributes, the multiple task processing characteristics are weighted and calculated to obtain a fourth matching value; the recipient with the largest fourth matching value is taken as the target recipient. The acquisition module is specifically used for: The first fixed attribute is determined based on the outsourcing task attributes provided by the outsourcing party; the second fixed attribute of each outsourcing party is determined based on the personal profile attributes configured by each outsourcing party. The first fixed attribute includes a first professional field, a first category, and a first task scope; the second fixed attribute includes a second professional field, a second category, and a second task scope; the matching module is specifically used for: The first professional field is matched with multiple second professional fields. If a match is successful, a first matching value is assigned to the recipient corresponding to the successfully matched second professional field. The first category is matched with multiple second categories. If a match is successful, a second matching value is assigned to the recipient corresponding to the successfully matched second category. The first task scope is matched with multiple second task scopes. If a match is successful, a third matching value is assigned to the recipient corresponding to the successfully matched second task scope. The first matching value is greater than the second matching value, and the second matching value is greater than the third matching value. The sum of the first matching value, the second matching value, and the third matching value for each recipient is calculated, and the recipient with the highest sum is selected as the target recipient.

6. A computer device, characterized in that, The computer device includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device performs the task matching method according to any one of claims 1-4.

7. A readable storage medium, characterized in that, The readable storage medium includes a computer program, which, when executed, controls the computer device on which the readable storage medium is located to perform the task matching method according to any one of claims 1-4.

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