Learning method and system based on order issuing and order grabbing mode

Abstract

According to the learning method and system based on the order issuing and grabbing mode disclosed by the invention, a learning user can freely issue a learning demand and construct a learning order according to the learning demand; the knowledge sharing end authenticated in the platform sends a task undertaking invitation to the learning user through an order grabbing mode, and the learning user can check data information of the knowledge sharing end according to the task undertaking invitation and then confirm and select the required knowledge sharing end; according to the invention, through bidirectional confirmation, the willingness and enthusiasm of the learning user and the knowledge sharing terminal are improved; and through an order issuing and grabbing mode, a decentralized learning market is realized, and the education cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of online education technology, and more specifically, to a learning method and system based on an order-issuing and order-grabbing mode. Background Art

[0002] Existing online education mainly relies on recorded course libraries or fixed live class schedules. Students passively select content, which makes it difficult to meet personalized and timely learning needs. This is especially true for niche subjects, where users are scattered and it is difficult to form classes, or for advanced subjects, it is too expensive for a single user to purchase.

[0003] Therefore, the prior art has defects and needs to be improved urgently. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a learning method and system based on the order-issuing and order-grabbing mode, which can improve the willingness and enthusiasm of learning users and knowledge sharing terminals through two-way confirmation; through the order-issuing and order-grabbing mode, a decentralized learning market can be realized, reducing the cost of education.

[0005] The first aspect of the present invention provides a learning method based on the order issuance and order grabbing mode, comprising: Obtain learning needs of learning users; Build learning orders for corresponding learning users according to their learning needs; Classify the learning orders according to the learning needs and obtain the corresponding learning order types; Determine whether there is a group discount activity for the type of the learning order. If so, group the learning order into similar groups to obtain group orders, and extract conditions in the group orders. If the conditions in the group orders meet the preset conditions, publish the group orders; if not, directly publish the corresponding learning orders; When the knowledge sharing end receives the order release information, if it accepts the order, it determines the acceptance priority value of the corresponding knowledge sharing end; The knowledge sharing end is sent to the learning user end in order of the acceptance priority value from large to small for display; The group preferential activities include group buying preferential activities and crowdfunding group study preferential activities.

[0006] In this solution, the step of determining the acceptance priority value of the corresponding knowledge sharing terminal specifically includes: Obtain the evaluation score of the knowledge sharing terminal and the response time value of the corresponding order; When the evaluation score of the knowledge sharing terminal is greater than or equal to the preset benchmark evaluation score, the preset evaluation weight coefficient is multiplied by 1 to obtain the evaluation priority score; When the evaluation score of the knowledge sharing end is less than the preset benchmark evaluation score, the evaluation score is divided by the preset benchmark evaluation score, and the quotient is multiplied by the preset evaluation weight coefficient to obtain the evaluation priority score; When the response time of an order is less than or equal to the preset benchmark response time, the response time of the order is subtracted from the preset benchmark response time, the difference is divided by the preset benchmark response time, the quotient is multiplied by the preset response time weight coefficient, and the response time priority value is obtained; When the response time of an order is greater than the preset benchmark response time, the response time priority value is set to zero; The evaluation priority score and the response time priority value are accumulated to obtain the acceptance priority value of the corresponding knowledge sharing end.

[0007] In this solution, the formula for obtaining the evaluation score of the knowledge sharing terminal is specifically: , where S represents the evaluation score of the knowledge sharing end, , and represents the corresponding weight coefficient, represents the teaching quality score of the knowledge sharing end, C represents the number of times the knowledge sharing end participates in taking orders within the preset first time period; when the response time of the order is greater than the preset benchmark response time, Set to zero, when the order response time is less than or equal to the preset benchmark response time, ,in Indicates the preset benchmark reaction time, Indicates the response time of the order.

[0008] In this solution, the step of grouping the learning orders into similar groups specifically includes: Get the learning order collection that participates in group discount activities; Extract the learning requirement keywords and the timestamp of the learning order in the learning order; Sort the learning orders by timestamp, and take the first-ranked learning order as the benchmark learning order; Extract the learning demand keywords in other learning orders and the learning demand keywords in the benchmark learning order in turn for comparative analysis to obtain similarity values; If the similarity value is greater than or equal to a preset first similarity threshold, the two learning orders corresponding to the similarity value are grouped into similar groups; If the similarity values ​​between the learning requirement features in other learning orders and the learning requirement keywords in all benchmark learning orders are less than a preset first similarity threshold, the other learning orders corresponding to the similarity values ​​are set as benchmark learning orders.

[0009] This plan also includes: Obtain the set of feature vectors of the learning user, where the set of feature vectors of the learning user at least includes a learning objective feature vector, an ability level feature vector, and a preference label feature vector; Based on the type of the learning order, match the corresponding set of knowledge sharing terminals; Extract the set of feature vectors of any knowledge sharing terminal in the corresponding set of knowledge sharing terminals, where the set of feature vectors of the knowledge sharing terminal at least includes a content label feature vector, a difficulty coefficient feature vector, and a teaching style feature vector; Perform a comparative analysis on the set of feature vectors of the learning user and the set of feature vectors of the knowledge sharing terminal to obtain a difference value; If the difference value is greater than the preset difference threshold, then block the knowledge sharing terminal corresponding to the difference value; if the difference value is less than or equal to the preset difference threshold, then send the order corresponding to the difference value to the corresponding knowledge sharing terminal for display.

[0010] In this solution, the formula for obtaining the difference value is specifically: ; where represents the difference value, represents the corresponding difference degree weight, where and respectively represent the ability level feature vector and the difficulty coefficient feature vector, respectively represent the preference label feature vector and the teaching style feature vector, respectively represent the learning objective feature vector and the content label feature vector, represents the coverage function of the learning objective of the learning user and the knowledge content.

[0011] In this solution, it further includes: Obtain the set of teaching feature score values of the knowledge sharing terminal within a preset second time period; Calculate the average value of the values in the set of initial teaching quality score values of the knowledge sharing terminal within a preset second time period to obtain the first average value; Subtract the preset reference score value from the first average value, divide the obtained difference by the preset reference score value, multiply the obtained quotient by the corresponding adjustment coefficient to obtain the difference degree weight adjustment value of the corresponding teaching feature; Accumulate the difference degree weight and the difference degree weight adjustment value of the teaching feature in the previous preset second time period to obtain the difference degree weight of the teaching feature in the current preset second time period.

[0012] A second aspect of the present invention provides a learning system based on the order-issuing and order-grabbing mode, comprising a memory and a processor, wherein the memory stores a learning method program based on the order-issuing and order-grabbing mode, and when the learning method program based on the order-issuing and order-grabbing mode is executed by the processor, the following steps are implemented: Obtain learning needs of learning users; Build learning orders for corresponding learning users according to their learning needs; Classify the learning orders according to the learning needs and obtain the corresponding learning order types; Determine whether there is a group discount activity for the type of the learning order. If so, group the learning order into similar groups to obtain group orders, and extract conditions in the group orders. If the conditions in the group orders meet the preset conditions, publish the group orders; if not, directly publish the corresponding learning orders; When the knowledge sharing end receives the order release information, if it accepts the order, it determines the acceptance priority value of the corresponding knowledge sharing end; The knowledge sharing end is sent to the learning user end in order of the acceptance priority value from large to small for display; The group preferential activities include group buying preferential activities and crowdfunding group study preferential activities.

[0013] In this solution, the step of determining the acceptance priority value of the corresponding knowledge sharing terminal specifically includes: Obtain the evaluation score of the knowledge sharing terminal and the response time value of the corresponding order; When the evaluation score of the knowledge sharing terminal is greater than or equal to the preset benchmark evaluation score, the preset evaluation weight coefficient is multiplied by 1 to obtain the evaluation priority score; When the evaluation score of the knowledge sharing end is less than the preset benchmark evaluation score, the evaluation score is divided by the preset benchmark evaluation score, and the quotient is multiplied by the preset evaluation weight coefficient to obtain the evaluation priority score; When the response time of an order is less than or equal to the preset benchmark response time, the response time of the order is subtracted from the preset benchmark response time, the difference is divided by the preset benchmark response time, the quotient is multiplied by the preset response time weight coefficient, and the response time priority value is obtained; When the response time of an order is greater than the preset benchmark response time, the response time priority value is set to zero; The evaluation priority score and the response time priority value are accumulated to obtain the acceptance priority value of the corresponding knowledge sharing end.

[0014] In this solution, the formula for obtaining the evaluation score of the knowledge sharing terminal is specifically: , where S represents the evaluation score of the knowledge sharing end, , and represents the corresponding weight coefficient, represents the teaching quality score value of the knowledge sharing side, C represents the number of times the knowledge sharing side participates in receiving orders within a preset first time period; when the response time of the order is greater than the preset reference response time, is set to zero, and when the response time of the order is less than or equal to the preset reference response time, , where represents the preset reference response time, represents the response time of the order.

[0015] A learning method and system based on an order - placing and order - grabbing mode disclosed by the present invention, where learning users can freely publish individual learning requirements and construct learning orders according to the learning requirements; the certified knowledge sharing sides in the platform send invitation to undertake tasks to learning users through the order - grabbing mode, and learning users can view the material information of the knowledge sharing sides according to the invitation to undertake tasks, and then confirm and select the required knowledge sharing sides; through two - way confirmation, the present invention improves the willingness and enthusiasm of learning users and knowledge sharing sides; through the order - placing and order - grabbing mode, a decentralized learning market is realized, and the education cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 shows a flowchart of a learning method based on an order - placing and order - grabbing mode of the present invention; Figure 2 shows a flowchart of calculating the acceptance priority value of the knowledge sharing side of the present invention; Figure 3 shows a block diagram of a learning system based on an order - placing and order - grabbing mode of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to more clearly understand the above - mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0018] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0019] Figure 1 shows a flowchart of a learning method based on an order - placing and order - grabbing mode of the present invention.

[0020] As Figure 1 shown, the present invention discloses a learning method based on an order - placing and order - grabbing mode, including: S101, obtaining learning needs of learning users; S102, constructing a learning order corresponding to the learning user according to the learning needs of the learning user; S103, classifying the learning orders according to the learning requirements to obtain the types of corresponding learning orders; S104, determining whether there is a group discount activity for the type of the learning order, if so, grouping the learning order into similar groups to obtain group orders, and extracting conditions in the group orders, and publishing the group orders if the conditions in the group orders meet the preset conditions; if not, directly publishing the corresponding learning orders; S105, after receiving the order release information, if the knowledge sharing terminal accepts the order, the acceptance priority value of the corresponding knowledge sharing terminal is determined; S106, sending the knowledge sharing end in order of the acceptance priority value from large to small to the learning user end for display.

[0021] It should be noted that the learning needs of the learning users include the subjects studied, learning time, learning quotation value, etc. The group preferential activities include group purchase preferential activities and crowdfunding group learning preferential activities. Group purchase preferential activities refer to activities in which each person in the group purchases one order to achieve a discount, and crowdfunding group learning preferential activities refer to activities in which multiple people buy one order together. When the learning user does not want to participate in the group preferential activities, the learning user can choose to exit the group and directly publish the learning order. The knowledge sharing end is a teacher, academic master or skilled person in the corresponding field certified by the platform. Group orders or learning orders are published through the learning platform. The order is published and broadcasted. When the knowledge sharing end sees the group order or the learning order, it confirms whether it is willing to accept the order according to the customized situation. If it is willing, it clicks to accept the order. The learning platform calculates the acceptance priority value of the knowledge sharing end through a preset algorithm, and sends the acceptance priority value of the knowledge sharing end to the learning user end for display. The learning user then confirms and selects the appropriate knowledge sharing end. After both parties confirm, the order will no longer be broadcast. The order includes group orders and learning orders; the preset conditions include that the number of people in the group reaches the preset number requirement, the cost in the group reaches the preset cost requirement, etc.

[0022] Figure 2 A flow chart of calculating the acceptance priority value of the knowledge sharing terminal according to the present invention is shown.

[0023] like Figure 2 As shown, according to an embodiment of the present invention, the step of determining the acceptance priority value of the corresponding knowledge sharing terminal specifically includes: S201, obtaining the evaluation score of the knowledge sharing terminal and the response time value of accepting the corresponding order; S202, when the evaluation score of the knowledge sharing terminal is greater than or equal to the preset benchmark evaluation score, multiply the preset evaluation weight coefficient by 1 to obtain the evaluation priority score; S203, when the evaluation score of the knowledge sharing terminal is less than the preset benchmark evaluation score, the evaluation score is divided by the preset benchmark evaluation score, and the quotient is multiplied by the preset evaluation weight coefficient to obtain the evaluation priority score; S204, when the reaction time of the order is less than or equal to the preset benchmark reaction time, the preset benchmark reaction time is subtracted from the reaction time of the order, the difference is divided by the preset benchmark reaction time, the quotient is multiplied by the preset reaction time weight coefficient, and the reaction time priority value is obtained; S205, when the response time of the order is greater than the preset reference response time, the response time priority value is set to zero; S206, accumulating the evaluation priority score and the response time priority value to obtain the acceptance priority value of the corresponding knowledge sharing terminal.

[0024] According to an embodiment of the present invention, the reaction time value for accepting a corresponding order is the time node when the knowledge sharing end accepts the corresponding order minus the time node when the learning user publishes the order. The acceptance priority value of the knowledge sharing end and the reaction time value for accepting a corresponding order are inversely proportional within a certain range.

[0025] According to an embodiment of the present invention, the formula for obtaining the evaluation score of the knowledge sharing terminal is specifically: , where S represents the evaluation score of the knowledge sharing end, , and represents the corresponding weight coefficient, represents the teaching quality score of the knowledge sharing end, C represents the number of times the knowledge sharing end participates in taking orders within the preset first time period; when the response time of the order is greater than the preset benchmark response time, Set to zero, when the order response time is less than or equal to the preset benchmark response time, ,in Indicates the preset benchmark reaction time, Indicates the response time of the order.

[0026] It should be noted that the more times the knowledge sharing end participates in order reception within the preset first time period, the more active the corresponding knowledge sharing end is, and thus the higher the evaluation score of the corresponding knowledge sharing end. The initial teaching quality score value of the knowledge sharing end is determined by the score values of the historical students taught by the corresponding knowledge sharing end, for example, the average value of the score values of the historical students. After the learning user completes this learning, the learning user rates the knowledge sharing end of this teaching, and at the same time, the knowledge sharing end also rates the learning user of this learning, that is, the mutual evaluation between the learning user and the knowledge sharing end.

[0027] Furthermore, the steps for obtaining the teaching quality score value of the knowledge sharing end specifically include: Obtain the historical teaching quality score values of the historical learning users for this knowledge sharing end, and divide them according to the preset time nodes to obtain the historical score values before the time node and the historical score values after the time node; Calculate the average value of the historical score values before the time node to obtain the first historical score average value; calculate the average value of the historical score values after the time node to obtain the second historical score average value; According to the first historical score average value and the second historical score average value, obtain the teaching quality score value of the corresponding knowledge sharing end, and its formula is , where , represents the first historical score average value, represents the second historical score average value.

[0028] It should be noted that taking the preset time node as the dividing line, the historical score values are divided into two equal parts. For example, if the preset time node is 30 days, taking 30 days as the boundary, the average value of the historical score values before 30 days is the first historical score average value, and the average value of the historical score values after 30 days is the second historical score average value. By , the teaching quality score value of the corresponding knowledge sharing end is more inclined to the historical score values after the preset time node, improving the accuracy of the corresponding teaching quality score value.

[0029] According to the embodiment of the present invention, the steps for grouping the learning orders into similar groups specifically include: Obtain the set of learning orders participating in the group discount activity; Extract the learning requirement keywords and the time stamps of the learning orders in the learning orders; Sort the learning orders according to the time stamps, and take the learning order ranked first as the reference learning order; Successively extract the learning requirement keywords in other learning orders and compare and analyze them with the learning requirement keywords in the reference learning order to obtain the similarity value; If the similarity value is greater than or equal to a preset first similarity threshold, group the two learning orders corresponding to the similarity value into a similarity group; If the similarity value between the learning requirement features in other learning orders and the learning requirement keywords in all benchmark learning orders is less than the preset first similarity threshold, set the other learning orders corresponding to the similarity value as benchmark learning orders.

[0030] It should be noted that the learning order with the first timestamp is set as the benchmark learning order. For example, if the similarity value between the learning requirement keywords in the learning order with the second timestamp in other learning orders and the learning requirement keywords in the benchmark learning order is greater than or equal to the preset first similarity threshold, group the learning order with the second timestamp and the benchmark learning order into a similarity group. When the similarity value is less than the preset first similarity threshold, set the learning order with the second timestamp in other learning orders as the benchmark learning order as well. Then, compare and analyze the subsequent other learning orders with the benchmark learning order with the first timestamp first. If the similarity value is less than the preset first similarity threshold, continue to compare and analyze with the benchmark learning order with the second timestamp. When the similarity value between the learning requirement features in other learning orders and the learning requirement keywords in all benchmark learning orders is less than the preset first similarity threshold, set the other learning orders corresponding to the similarity value as benchmark learning orders.

[0031] According to an embodiment of the present invention, it further includes: Obtain the feature vector set of the learning user, where the feature vector set of the learning user at least includes a learning objective feature vector, an ability level feature vector, and a preference label feature vector; Based on the type of the learning order, match the corresponding type of knowledge sharing end set; Extract the feature vector set of any knowledge sharing end in the corresponding type of knowledge sharing end set, where the feature vector set of the knowledge sharing end at least includes a content label feature vector, a difficulty coefficient feature vector, and a teaching style feature vector; Compare and analyze the feature vector set of the learning user and the feature vector set of the knowledge sharing end to obtain a difference value; If the difference value is greater than a preset difference threshold, block the knowledge sharing end corresponding to the difference value; if the difference value is less than or equal to the preset difference threshold, send the order corresponding to the difference value to the corresponding knowledge sharing end for display.

[0032] According to an embodiment of the present invention, the formula for obtaining the difference value is specifically: ; where represents the difference value, represents the corresponding difference degree weight, where 、 respectively represent the ability level feature vector and the difficulty coefficient feature vector, respectively represent the preference label feature vector and the teaching style feature vector, respectively represent the learning objective feature vector and the content label feature vector, represents the coverage function of the learning objective of the learning user and the knowledge content.

[0033] It should be noted that the knowledge sharing side is screened according to the difference value to find an accurate knowledge sharing side for the learning order of the user's learning, improving the efficiency of order sending and order grabbing; when it is a group order, the difference value between the knowledge sharing side receiving the group order and the set of feature vectors of any one learning user in the group order is less than or equal to the difference threshold of the factor.

[0034] Further, the where represents the average score of the learning user in the historical test process. When the learning user does not have an average score in the historical test process, represents the average score of the historical tests of other learning users in the corresponding learning order type, represents the full score in the historical test process. When the full scores in the historical test process are different, the scores in each historical test process are first converted according to the preset full score system and then calculated; represents the historical average course completion rate of the learning user, n represents the historical average number of abandoned courses of the learning user. When the learning user does not have a historical average course completion rate and a historical average number of abandoned courses, the calculation is carried out according to the historical average course completion rate and the historical average number of abandoned courses of other learning users in the corresponding learning order type; where 、 respectively represent the average learning hours and the course standard duration of the historical learning users under the teaching of the corresponding knowledge sharing side, represents the average pass rate of the historical learning users under the teaching of the knowledge sharing side; the preference label feature vector and the teaching style feature vector are determined according to the set scores. For example, the preference labels include live teaching, graphic materials, interactive experiments, etc. Different preference labels are set with different scores, and the scores of all preference labels are accumulated to obtain the corresponding preference label feature vector; the teaching styles include humorous, theoretical derivation, case analysis, etc. Different teaching styles are set with different scores, and the scores corresponding to all teaching styles are accumulated to obtain the corresponding teaching style feature vector; represents the coverage function of the learning objective of the learning user and the knowledge content, and its formula is where represents the number of keywords in the learning requirement, represents the number of knowledge labels, Represents the number of intersections of keywords and knowledge tags in the learning requirements, which is the corresponding coefficient.

[0035] According to an embodiment of the present invention, it further includes: Obtain the set of teaching feature score values of the knowledge sharing end within a preset second time period; Calculate the average value of the numerical values in the initial teaching quality score value set of the knowledge sharing end within a preset second time period to obtain the first average value; Subtract the preset reference score value from the first average value, divide the obtained difference by the preset reference score value, multiply the obtained quotient by the corresponding adjustment coefficient to obtain the difference degree weight adjustment value of the corresponding teaching feature; Accumulate the difference degree weight and the difference degree weight adjustment value of the teaching feature in the previous preset second time period to obtain the difference degree weight of the teaching feature in the current preset second time period.

[0036] It should be noted that taking the preset second time period as the time period, for example, if the preset second time period is 10 days, then taking 10 days as a time period, and each adjustment is optimized based on the previous time period. By continuously superimposing and adjusting, the difference degree weight coefficients of different knowledge sharing ends are dynamically adjusted to improve the accuracy of the difference degree.

[0037] According to an embodiment of the present invention, it further includes: Obtain the course completion rate of the learning user, and determine the payment ratio of the learning user according to the range in which the course completion rate of the learning user falls.

[0038] It should be noted that the course completion rate is divided into different range intervals, and different range intervals correspond to different payment ratios. For example, the payment ratio in the range interval where the course completion rate is less than 20% is 20%, and the payment ratio in the range interval where the course completion rate is greater than or equal to 20% and less than 40% is 40%; by the principle of hierarchical payment, the teaching quality of the knowledge sharing end is improved.

[0039] According to an embodiment of the present invention, after obtaining the type of the corresponding learning order, it further includes: Extract the historical transaction order database of the type of this learning order; Compare and analyze this learning order with the historical transaction orders in the historical transaction order database of the corresponding type to obtain the order similarity value; When the order similarity value is greater than the preset second similarity threshold, send the corresponding historical transaction order to the preset similar order library for storage; Extract the historical orders in the preset similar order library, extract the historical transaction prices in the historical orders, and extract the reported value of the current learning order; According to the historical transaction price in the historical order and the quoted value of the current learning order, the price evaluation index F of the corresponding order is determined, and the formula is: ;in Preset parameters, Represents the standard deviation of historical transaction prices, Represents the average value of historical transaction prices. Indicates the quoted value of the current learning order. Represents the standard normal distribution CDF; When the price evaluation index of an order is greater than or equal to a preset price evaluation index threshold, corresponding order abnormality information is triggered, and the order abnormality information is sent to the corresponding learning user terminal for prompting.

[0040] It should be noted that, for example, if the average value of the historical transaction price in the corresponding historical order is 100, the standard deviation of the historical transaction price is 10, the preset parameter is 0.05, and the current learning quotation value is 130, then the price evaluation index of the corresponding order is , if the preset price evaluation index threshold is 40, then , triggering the corresponding order exception information.

[0041] Figure 3 A block diagram of a learning system based on the order issuing and order grabbing mode of the present invention is shown.

[0042] like Figure 3 As shown, the second aspect of the present invention provides a learning system 3 based on the order-issuing and order-grabbing mode, comprising a memory 31 and a processor 32, wherein the memory stores a learning method program based on the order-issuing and order-grabbing mode, and when the learning method program based on the order-issuing and order-grabbing mode is executed by the processor, the following steps are implemented: Obtain learning needs of learning users; Build learning orders for corresponding learning users according to their learning needs; Classify the learning orders according to the learning needs and obtain the corresponding learning order types; Determine whether there is a group discount activity for the type of the learning order. If so, group the learning order into similar groups to obtain group orders, and extract conditions in the group orders. If the conditions in the group orders meet the preset conditions, publish the group orders; if not, directly publish the corresponding learning orders; When the knowledge sharing end receives the order release information, if it accepts the order, it determines the acceptance priority value of the corresponding knowledge sharing end; The knowledge sharing end is sent to the learning user end in order of the acceptance priority value from large to small for display; The group preferential activities include group buying preferential activities and crowdfunding group study preferential activities.

[0043] In this solution, the steps of determining the acceptance priority value corresponding to the knowledge sharing end specifically include: Obtain the evaluation score of the knowledge sharing end and the response time value for accepting the corresponding order; When the evaluation score of the knowledge sharing end is greater than or equal to the preset benchmark evaluation score, multiply the preset evaluation weight coefficient by 1 to obtain the evaluation priority score; When the evaluation score of the knowledge sharing end is less than the preset benchmark evaluation score, divide the evaluation score by the preset benchmark evaluation score, multiply the quotient by the preset evaluation weight coefficient, and obtain the evaluation priority score; When the response time of the order is less than or equal to the preset benchmark response time, subtract the response time of the order from the preset benchmark response time, divide the obtained difference by the preset benchmark response time, multiply the quotient by the preset response time weight coefficient, and obtain the response time priority value; When the response time of the order is greater than the preset benchmark response time, set the response time priority value to zero; Accumulate the evaluation priority score and the response time priority value to obtain the acceptance priority value corresponding to the knowledge sharing end.

[0044] In this solution, the formula for obtaining the evaluation score of the knowledge sharing end is specifically: , where S represents the evaluation score of the knowledge sharing end, , and represent the corresponding weight coefficients, represents the teaching quality score value of the knowledge sharing end, C represents the number of times the knowledge sharing end participates in accepting orders within the preset first time period; when the response time of the order is greater than the preset benchmark response time, is set to zero, when the response time of the order is less than or equal to the preset benchmark response time, , where represents the preset benchmark response time, represents the response time of the order.

[0045] A learning method and system based on an order - posting and order - grabbing mode disclosed by the present invention. Learning users can freely post their learning needs and construct learning orders according to the learning needs; the certified knowledge sharing ends in the platform send invitation to accept tasks to learning users through the order - grabbing mode. Learning users can view the material information of the knowledge sharing ends according to the invitation to accept tasks, and then confirm and select the required knowledge sharing ends; through two - way confirmation, the present invention improves the willingness and enthusiasm of learning users and knowledge sharing ends; through the order - posting and order - grabbing mode, a decentralized learning market is realized, and the education cost is reduced.

[0046] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0047] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0048] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0049] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0050] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

Claims

1. A learning method based on an order - issuing and order - snatching mode, characterized in that, Including: Obtain the learning needs of the learning user; Construct a learning order corresponding to the learning user according to the learning needs of the learning user; Classify the learning order according to the learning needs to obtain the type of the corresponding learning order; Determine whether there is a group discount activity for the type of the learning order. If so, group the similar learning orders to obtain a group order, and extract the conditions in the group order. After the conditions in the group order meet the preset conditions, publish the group order; If not, directly publish the corresponding learning order; When the knowledge sharing end receives the order release information, if it undertakes the order, determine the acceptance priority value of the corresponding knowledge sharing end; Send the knowledge sharing ends to the learning user end in descending order of the acceptance priority value for display; The group discount activity includes a joint order discount activity and a crowd-funding joint learning discount activity.

2. The learning method based on an order - issuing and order - snatching mode according to claim 1, characterized in that, The steps of determining the acceptance priority value of the corresponding knowledge sharing end specifically include: Obtain the evaluation score of the knowledge sharing end and the response time value for undertaking the corresponding order; When the evaluation score of the knowledge sharing end is greater than or equal to the preset benchmark evaluation score, multiply the preset evaluation weight coefficient by 1 to obtain the evaluation priority score; When the evaluation score of the knowledge sharing end is less than the preset benchmark evaluation score, divide the evaluation score by the preset benchmark evaluation score, multiply the quotient by the preset evaluation weight coefficient to obtain the evaluation priority score; When the response time of the order is less than or equal to the preset benchmark response time, subtract the response time of the order from the preset benchmark response time, divide the obtained difference by the preset benchmark response time, multiply the quotient by the preset response time weight coefficient to obtain the response time priority value; When the response time of the order is greater than the preset benchmark response time, set the response time priority value to zero; Accumulate the evaluation priority score and the response time priority value to obtain the acceptance priority value of the corresponding knowledge sharing end.

3. The learning method based on an order - issuing and order - snatching mode according to claim 2, characterized in that, The formula for obtaining the evaluation score of the knowledge sharing end is specifically: , where S represents the evaluation score of the knowledge sharing side, , and represent the corresponding weight coefficients, represents the teaching quality score value of the knowledge sharing side, C represents the number of times the knowledge sharing side participates in receiving orders within a preset first time period; when the response time of the order is greater than the preset reference response time, is set to zero, and when the response time of the order is less than or equal to the preset reference response time, , where represents the preset reference response time, represents the response time of the order.

4. The learning method based on an order - issuing and order - snatching mode according to claim 1, characterized in that, The steps of grouping the similar learning orders specifically include: Obtain the set of learning orders participating in the group discount activity; Extract the learning need keywords and the time stamp of the learning order in the learning order; Sort the learning orders according to the time stamp, and take the learning order ranked first as the benchmark learning order; Extract the learning need keywords in other learning orders and the learning need keywords in the benchmark learning order in turn for comparative analysis to obtain a similarity value; If the similarity value is greater than or equal to the preset first similarity threshold, group the two learning orders corresponding to the similarity value; If the similarity value between the learning need characteristics in other learning orders and the learning need keywords in all benchmark learning orders is less than the preset first similarity threshold, set the other learning orders corresponding to the similarity value as the benchmark learning order.

5. The learning method based on an order - issuing and order - snatching mode according to claim 1, characterized in that, Also including: Obtain the set of feature vectors of the learning user, and the set of feature vectors of the learning user at least includes a learning target feature vector, an ability level feature vector, and a preference label feature vector; Match the set of knowledge sharing ends of the corresponding type based on the type of the learning order; Extract the feature vector set of any knowledge sharing terminal in the knowledge sharing terminal set of the corresponding type. The feature vector set of the knowledge sharing terminal includes at least a content label feature vector, a difficulty coefficient feature vector, and a teaching style feature vector; Compare and analyze the feature vector set of the learning user and the feature vector set of the knowledge sharing terminal to obtain a difference value; If the difference value is greater than the preset difference threshold, block the knowledge sharing terminal corresponding to the difference value; if the difference value is less than or equal to the preset difference threshold, send the order corresponding to the difference value to the corresponding knowledge sharing terminal for display.

6. The learning method based on an order - issuing and order - snatching mode according to claim 5, characterized in that, The formula for obtaining the difference value is specifically: ; wherein is represented as a difference value, is represented as the corresponding difference degree weight, wherein , respectively represent the ability level feature vector and the difficulty coefficient feature vector, respectively represent the preference label feature vector and the teaching style feature vector, respectively represent the learning objective feature vector and the content label feature vector, represents the coverage function of the learning objective of the learning user and the knowledge content.

7. A learning method based on the order - issuing and order - grabbing mode, characterized in that, It also includes: Obtain the set of teaching feature score values of the knowledge sharing terminal within a preset second time period; Calculate the average value of the numerical values in the initial teaching quality score value set of the knowledge sharing terminal within a preset second time period to obtain the first average value; Subtract the preset reference score value from the first average value, divide the obtained difference by the preset reference score value, multiply the obtained quotient by the corresponding adjustment coefficient to obtain the difference degree weight adjustment value of the corresponding teaching feature; Accumulate the difference degree weight and the difference degree weight adjustment value of the teaching feature in the previous preset second time period to obtain the difference degree weight of the teaching feature in the current preset second time period.

8. A learning system based on the order - issuing and order - grabbing mode, characterized in that, It includes a memory and a processor. A learning method program based on the order-issuing and order-grabbing mode is stored in the memory. When the learning method program based on the order-issuing and order-grabbing mode is executed by the processor, the following steps are implemented: Obtain the learning needs of the learning user; Construct a learning order corresponding to the learning user according to the learning needs of the learning user; Classify the learning orders according to the learning needs to obtain the types of the corresponding learning orders; Judge whether there is a group discount activity for the type of the learning order. If so, group the learning order to obtain a group order, and extract the conditions in the group order. After the conditions in the group order meet the preset conditions, publish the group order; If not, directly publish the corresponding learning order; When the knowledge sharing terminal receives the order release information, if it undertakes the order, determine the acceptance priority value of the corresponding knowledge sharing terminal; Send the knowledge sharing terminals to the learning user terminal in descending order of the acceptance priority value for display; The group discount activities include a joint order discount activity and a crowdfunding joint learning discount activity.

9. A learning system based on the order - issuing and order - grabbing mode according to claim 8, characterized in that, The steps for determining the acceptance priority value of the corresponding knowledge sharing terminal specifically include: Obtain the evaluation score value of the knowledge sharing terminal and the response time value for undertaking the corresponding order; When the evaluation score value of the knowledge sharing terminal is greater than or equal to the preset reference evaluation score value, multiply the preset evaluation weight coefficient by 1 to obtain the evaluation priority score value; When the evaluation score value of the knowledge sharing terminal is less than the preset reference evaluation score value, divide the evaluation score value by the preset reference evaluation score value, multiply the obtained quotient by the preset evaluation weight coefficient to obtain the evaluation priority score value; When the response time of an order is less than or equal to the preset benchmark response time, the response time of the order is subtracted from the preset benchmark response time, the difference is divided by the preset benchmark response time, the quotient is multiplied by the preset response time weight coefficient, and the response time priority value is obtained; When the response time of an order is greater than the preset benchmark response time, the response time priority value is set to zero; The evaluation priority score and the response time priority value are accumulated to obtain the acceptance priority value of the corresponding knowledge sharing end.

10. A learning system based on the order - issuing and order - grabbing mode according to claim 9, characterized in that, The formula for obtaining the evaluation score of the knowledge sharing terminal is specifically: , where S represents the evaluation score of the knowledge sharing end, , and represents the corresponding weight coefficient, represents the teaching quality score of the knowledge sharing end, and C represents the number of times the knowledge sharing end participates in taking orders within the preset first time period; when the response time of the order is greater than the preset benchmark response time, Set to zero, when the order response time is less than or equal to the preset benchmark response time, ,in Indicates the preset benchmark reaction time, Indicates the response time of the order.

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