Intelligent Matching Transaction Method, System and Medium Based on Order Information

Abstract

The present application provides an intelligent matching transaction method, system and medium based on order information. The method includes: obtaining the business scope data of a merchant and business operation evaluation data including accurate matching evaluation data, matching ability evaluation data and matching response evaluation data, processing them respectively to obtain a registration index, an energy matching index and an efficiency matching index, extracting the order category keywords and order analysis data of the order information, matching to obtain a first merchant list, processing according to the order analysis data in combination with the registration index, the energy matching index and the efficiency matching index to obtain an overall order matching evaluation index, obtaining a second merchant list through threshold comparison, sorting and sending it to the user side for display, obtaining the user's order placement instruction, and obtaining and processing the user's risk assessment data to obtain a risk index, obtaining the order risk level through threshold comparison and sending it to the merchant side for display; thereby realizing the intelligent matching of orders and merchants based on order information.

Description

Technical Field

[0001] This application relates to the technical field of e-commerce technology. Specifically, it relates to an intelligent matching transaction method, system, and medium based on order information. Background Art

[0002] The effective connection between orders and merchants has become a key link affecting transaction fluency and user experience. However, the existing matching models have drawbacks. On the one hand, the traditional manual-dominated matching process is inefficient. Facing a large number of orders and numerous merchants, it is difficult for staff to comprehensively consider various factors in a short time and accurately find the suitable combinations, resulting in insufficient matching accuracy. On the other hand, there is a problem of lagging information update. Existing systems often cannot capture these dynamic information in a timely manner, making the matching results based on outdated data, unable to reflect the current real situation, with a long response time and affecting transaction timeliness. At the same time, there is a lack of an intelligent recommendation mechanism, and the laws and potential connections contained in historical order data are not fully explored, making it difficult to help merchants discover potential risks.

[0003] In view of the above problems, there is an urgent need for effective technical solutions. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent matching transaction method, system, and medium based on order information. By using the business operation evaluation data, order category keywords, and order analysis data of merchants, the first merchant can be obtained through matching, the second merchant and the corresponding order matching comprehensive evaluation index can be obtained through analysis and calculation, and the order risk level of the order can be obtained through analysis, calculation, and threshold comparison, thereby realizing the intelligent matching of orders and merchants based on order information.

[0005] This application also provides an intelligent matching transaction method based on order information, including the following steps:

[0006] Obtain the business scope data and business operation evaluation data of merchants within a preset time period. The business operation evaluation data includes matching accuracy evaluation data, matching ability evaluation data, and matching response evaluation data;

[0007] Process according to the matching accuracy evaluation data to obtain a registration index, process according to the matching ability evaluation data to obtain an energy matching index, and process according to the matching response evaluation data to obtain an efficiency matching index;

[0008] Obtain order information, extract order category keywords and order analysis data, and perform matching according to the order category keywords to obtain a list of the first merchants;

[0009] Process according to the order analysis data in combination with the registration index, energy matching index, and efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant;

[0010] Compare the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, and sort the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest and send them to the user side for display;

[0011] Obtain the user's order placement instruction, obtain the risk assessment data corresponding to the order placement instruction for the user, and process the risk assessment data to obtain the risk index corresponding to the order;

[0012] Compare the risk index with a preset risk level evaluation threshold to obtain the order risk level and send it to the merchant side for display.

[0013] Optionally, in the intelligent matching transaction method based on order information described in this application, the obtaining of the business scope data and business evaluation data of merchants within a preset time period, where the business evaluation data includes matching accuracy evaluation data, matching ability evaluation data, and matching response evaluation data, includes:

[0014] Obtain the business scope data and business evaluation data of merchants within a preset time period, where the business evaluation data includes matching accuracy evaluation data, matching ability evaluation data, and matching response evaluation data;

[0015] The matching accuracy evaluation data includes the repurchase rate, the first return rate, and the complaint rate;

[0016] The matching ability evaluation data includes the average score, the order rate above the average, the sales volume, the delayed delivery rate, and the favorable comment rate;

[0017] The matching response evaluation data includes the average response duration data and the average stock preparation duration data.

[0018] Optionally, in the intelligent matching transaction method based on order information described in this application, the processing according to the matching accuracy evaluation data to obtain a registration index, the processing according to the matching ability evaluation data to obtain an energy matching index, and the processing according to the matching response evaluation data to obtain an efficiency matching index includes:

[0019] Process the repurchase rate, the first return rate, and the complaint rate to obtain a registration index;

[0020] Process the average score, the order rate above the average, the sales volume, the delayed delivery rate, and the favorable comment rate to obtain an energy matching index;

[0021] Process the average response duration data and the average stock preparation duration data to obtain an efficiency matching index.

[0022] Optionally, in the intelligent matching transaction method based on order information described in this application, the steps of obtaining order information, extracting order category keywords and order analysis data, and matching according to the order category keywords to obtain a first merchant list include:

[0023] Obtain order information, extract order category keywords and order analysis data, where the order analysis data includes order timeliness data, order budget data, and the user credit value corresponding to the order;

[0024] Match the order category keywords with the business scope data to obtain a first merchant list.

[0025] Optionally, in the intelligent matching transaction method based on order information described in this application, the steps of processing the order analysis data in combination with the registration index, energy matching index, and efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant include:

[0026] Process the order timeliness data and order budget data in combination with the preset timeliness weight value and preset budget weight value to obtain the order demand index;

[0027] Query the preset weight value database according to the order demand index to obtain the matching demand weight values, including the registration weight value corresponding to the registration index, the energy matching weight value corresponding to the energy matching index, and the efficiency matching weight value corresponding to the efficiency matching index;

[0028] Process the registration weight value, energy matching weight value, and efficiency matching weight value in combination with the registration index, energy matching index, and efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant.

[0029] Optionally, in the intelligent matching transaction method based on order information described in this application, the steps of comparing the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, sorting the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest, and sending them to the user terminal for display include:

[0030] Compare the order matching comprehensive evaluation index with the preset matching degree threshold;

[0031] If the order matching comprehensive evaluation index is less than the preset matching degree threshold, eliminate the corresponding merchant;

[0032] If the order matching comprehensive evaluation index is greater than or equal to the preset matching degree threshold, retain the corresponding merchant and generate a second merchant list;

[0033] Sort the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest and send them to the user terminal for display.

[0034] Optionally, in the intelligent matching transaction method based on order information described in this application, the steps of obtaining the user's order placement instruction, obtaining the risk assessment data of the user corresponding to the order placement instruction, and processing according to the risk assessment data to obtain the risk index corresponding to the order include:

[0035] Obtain the user's order placement instruction, and obtain the risk assessment data of the user corresponding to the order placement instruction, including user risk assessment data and payment environment risk assessment data;

[0036] The user risk assessment data includes the second return rate, credit arrears amount, and order placement frequency within a preset time period, and the payment environment risk assessment data includes payment device security data and network environment security data;

[0037] Process according to the second return rate, credit arrears amount, and order placement frequency to obtain the user risk index;

[0038] Process according to the payment device security data and network environment security data to obtain the payment environment risk index;

[0039] Process according to the user risk index and the payment environment risk index in combination with the user credit value to obtain the risk index corresponding to the order.

[0040] Optionally, in the intelligent matching transaction method based on order information described in this application, the steps of comparing the risk index with a preset risk level evaluation threshold to obtain the order risk level and sending it to the merchant side for display include:

[0041] Compare the risk index with a preset risk level evaluation threshold to obtain the order risk level;

[0042] If the risk index is less than or equal to the preset risk level evaluation threshold, determine that the order is a low risk level;

[0043] If the risk index is greater than the preset risk level evaluation threshold, determine that the order is a high risk level;

[0044] Send the low risk level or high risk level to the merchant side for display.

[0045] In a second aspect, this application provides an intelligent matching transaction system based on order information. The system includes: a memory and a processor. The memory includes a program of the intelligent matching transaction method based on order information. When the program of the intelligent matching transaction method based on order information is executed by the processor, the following steps are implemented:

[0046] Obtain the business scope data and business evaluation data of the merchant within a preset time period. The business evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data;

[0047] Process the data accurately evaluated according to the matching to obtain a registration index, process the data evaluated according to the matching ability to obtain an energy matching index, and process the data evaluated according to the matching response to obtain an efficiency matching index;

[0048] Obtain order information, extract order category keywords and order analysis data, perform matching according to the order category keywords to obtain a list of the first merchants;

[0049] Process according to the order analysis data in combination with the registration index, the energy matching index and the efficiency matching index to obtain an overall evaluation index of order matching corresponding to the first merchant;

[0050] Compare the overall evaluation index of order matching with a preset matching degree threshold to obtain a list of the second merchants, and sort the overall evaluation index of order matching corresponding to the second merchants from largest to smallest and send it to the user side for display;

[0051] Obtain the user's order placement instruction, obtain the risk assessment data corresponding to the order placement instruction, and process according to the risk assessment data to obtain the risk index corresponding to the order;

[0052] Compare the risk index with a preset risk level evaluation threshold to obtain the order risk level and send it to the merchant side for display.

[0053] In a third aspect, the present application also provides a computer-readable storage medium, in which a program for an intelligent matching transaction method based on order information is stored. When the program for the intelligent matching transaction method based on order information is executed by a processor, the steps of the intelligent matching transaction method based on order information as described in any one of the above are implemented.

[0054] As can be seen from the above, the intelligent matching transaction method, system and medium based on order information provided by the present application match to obtain the first merchant according to the business evaluation data of the merchant, the order category keywords and the order analysis data, analyze and calculate to obtain the second merchant and the corresponding overall evaluation index of order matching, and obtain the order risk level of the order through analysis, calculation and threshold comparison, thereby realizing the intelligent matching of orders and merchants based on order information.

[0055] Other features and advantages of the present application will be described in the subsequent specification, and part of them will become obvious from the specification, or will be understood by implementing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings. Description of the Drawings

[0056] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.

[0057] Figure 1 It is a flowchart of the intelligent matching transaction method based on order information provided by the embodiments of the present application;

[0058] Figure 2 It is a flowchart of obtaining the comprehensive evaluation index of order matching corresponding to the first merchant in the intelligent matching transaction method based on order information provided by the embodiments of the present application;

[0059] Figure 3 It is a flowchart of obtaining the risk index corresponding to the order in the intelligent matching transaction method based on order information provided by the embodiments of the present application. Detailed implementation manners

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of them. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the drawings below is not intended to limit the scope of the claimed present application, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0062] Please refer to Figure 1 , Figure 1 which is a flowchart of the intelligent matching transaction method based on order information in some embodiments of the present application. This intelligent matching transaction method based on order information is used in terminal devices, such as computers, mobile phone terminals, etc. This intelligent matching transaction method based on order information includes the following steps:

[0063] S11. Obtain the business scope data and business operation evaluation data of a merchant within a preset time period. The business operation evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data;

[0064] S12. Process according to the accurate matching evaluation data to obtain a registration index, process according to the matching ability evaluation data to obtain an energy matching index, and process according to the matching response evaluation data to obtain an efficiency matching index;

[0065] S13. Obtain order information, extract order category keywords and order analysis data, and perform matching according to the order category keywords to obtain a first merchant list;

[0066] S14. Process according to the order analysis data in combination with the registration index, energy matching index, and efficiency matching index to obtain an overall order matching evaluation index corresponding to the first merchant;

[0067] S15. Compare the overall order matching evaluation index with a preset matching degree threshold to obtain a second merchant list, and sort the overall order matching evaluation index corresponding to the second merchant from largest to smallest and send it to the user terminal for display;

[0068] S16. Obtain the user's placing order instruction, obtain the risk assessment data of the user corresponding to the placing order instruction, and process according to the risk assessment data to obtain a risk index corresponding to the order;

[0069] S17. Compare the risk index with a preset risk level assessment threshold to obtain an order risk level and send it to the merchant terminal for display.

[0070] It should be noted that, in order to achieve intelligent matching of orders and merchants, by obtaining business operation evaluation data including accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data within a preset time period, and processing them respectively to obtain a registration index, an energy matching index, and an efficiency matching index. According to the order information, order category keywords and order analysis data are extracted. The order category keywords are used to match with the business scope data of the obtained merchants, and the unqualified merchants are excluded to obtain a first merchant list, reducing the matching calculation resources. The order analysis data is used to determine the weight values corresponding to the registration index, the energy matching index, and the efficiency matching index, and through processing in combination with the registration index, the energy matching index, and the efficiency matching index, an overall order matching evaluation index corresponding to the first merchant is obtained. By comparing with a threshold, the merchants with an overall order matching evaluation index less than the preset matching degree threshold in the first merchant list are excluded to obtain a second merchant list, and the overall order matching evaluation indexes corresponding to the second merchants are sorted from large to small and sent to the user terminal for display. After the user places an order, risk assessment is introduced to reduce the risk of false transactions. By obtaining the risk assessment data of the user and processing it, a risk index corresponding to the order is obtained. Finally, by comparing with a preset risk level evaluation threshold, the order risk level is obtained and sent to the merchant terminal for display.

[0071] According to an embodiment of the present invention, the obtaining of the business scope data and business operation evaluation data of merchants within a preset time period, where the business operation evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data, includes:

[0072] Obtain the business scope data and business operation evaluation data of merchants within a preset time period, where the business operation evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data;

[0073] The accurate matching evaluation data includes a repurchase rate, a first return rate, and a complaint rate;

[0074] The matching ability evaluation data includes an average score, an order rate above the average, sales volume, a delayed delivery rate, and a favorable comment rate;

[0075] The matching response evaluation data includes an average response duration data and an average stock preparation duration data.

[0076] It should be noted that by obtaining business operation evaluation data and business scope data including accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data, it is used to accurately evaluate the supply accuracy, supply ability, and supply timeliness of merchants. Among them, the repurchase rate refers to the ratio of the number of users who place repeated orders within a preset time period to the total number of users who place orders with the merchant. The first return rate refers to the ratio of the number of order return orders of the merchant within a preset time period to the total number of orders of the merchant. The order rate above the average refers to the ratio of the total order volume of the merchant minus the industry average order volume to the industry average order volume.

[0077] According to an embodiment of the present invention, processing the accurately evaluated matching data to obtain a registration index, processing the matching ability evaluation data to obtain an energy matching index, and processing the matching response evaluation data to obtain an efficiency matching index, including:

[0078] Processing according to the repurchase rate, the first return rate, and the complaint rate to obtain a registration index;

[0079] Processing according to the average score, the order rate exceeding the average, the sales amount, the delayed delivery rate, and the favorable comment rate to obtain an energy matching index;

[0080] Processing according to the average response duration data and the average stock preparation duration data to obtain an efficiency matching index.

[0081] It should be noted that processing according to the repurchase rate, the first return rate, and the complaint rate to obtain a registration index, and the registration index is used to evaluate the matching accuracy of the merchant;

[0082] The calculation formula of the registration index is:

[0083] ;

[0084] Wherein, is the registration index, , , are the repurchase rate, the first return rate, and the complaint rate respectively, , are preset characteristic coefficients (the characteristic coefficients are obtained by querying a preset intelligent matching trading platform);

[0085] Processing according to the average score, the order rate exceeding the average, the sales amount, the delayed delivery rate, and the favorable comment rate to obtain an energy matching index, and the energy matching index is used to evaluate the matching ability of the merchant;

[0086] The calculation formula of the energy matching index is:

[0087] ;

[0088] Wherein, is the energy matching index, , , , , are the average score, the order rate exceeding the average, the sales amount, the delayed delivery rate, and the favorable comment rate respectively, , , are preset characteristic coefficients (the characteristic coefficients are obtained by querying a preset intelligent matching trading platform);

[0089] Based on the average response duration data and the average stock preparation duration data, processing is performed to obtain a matching efficiency index, which is used to evaluate the matching efficiency of merchants. Among them, the average response duration data refers to the average value of the time taken to receive confirmation for multiple orders within a preset time period, and the average stock preparation duration data refers to the average value of the time taken from order confirmation to order shipment within a preset time period;

[0090] The calculation formula for the matching efficiency index is as follows:

[0091] ;

[0092] Wherein, is the matching efficiency index, 、 are the average response duration data and the average stock preparation duration data respectively, is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset intelligent matching trading platform).

[0093] According to an embodiment of the present invention, obtaining order information, extracting order category keywords and order analysis data, and performing matching according to the order category keywords to obtain a first merchant list, including:

[0094] Obtaining order information, and extracting order category keywords and order analysis data. The order analysis data includes order timeliness data, order budget data, and the user credit value corresponding to the order;

[0095] Performing matching according to the order category keywords and the business scope data to obtain a first merchant list.

[0096] It should be noted that, in order to improve the accuracy of order matching and reduce unnecessary matching calculations, order category keywords and order analysis data are extracted according to order information, and matching is performed according to the extracted order category keywords and the business scope data of the obtained merchants. Merchants that do not match are excluded, and merchants that match are generated into a first merchant list. The order analysis data is used to determine the weight ratios of the registration index, energy matching index, and matching efficiency index of the merchants.

[0097] Please refer to Figure 2 , Figure 2 is a flowchart of obtaining the order matching comprehensive evaluation index corresponding to the first merchant in the intelligent matching trading method based on order information in some embodiments of the present application. According to an embodiment of the present invention, processing the order analysis data in combination with the registration index, energy matching index, and matching efficiency index to obtain the order matching comprehensive evaluation index corresponding to the first merchant includes:

[0098] S21. Processing the order timeliness data and the order budget data in combination with a preset timeliness weight value and a preset budget weight value to obtain an order demand index;

[0099] S22. Query the preset weight value database according to the order demand index to obtain the matching demand weight values, including the registration weight value corresponding to the registration index, the energy matching weight value corresponding to the energy matching index, and the efficiency matching weight value corresponding to the efficiency matching index;

[0100] S23. Process according to the registration weight value, the energy matching weight value, and the efficiency matching weight value in combination with the registration index, the energy matching index, and the efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant.

[0101] It should be noted that in order to comprehensively evaluate the matching situation between the merchant and the order, first process the extracted order timeliness data and order budget data in combination with the preset timeliness weight value and the preset budget weight value to obtain the order demand index. The preset timeliness weight value and the preset budget weight value are obtained by querying through the preset intelligent matching trading platform;

[0102] The calculation formula of the order demand index is:

[0103] ;

[0104] Wherein, is the order demand index, , are the order timeliness data and the order budget data respectively, , are the timeliness weight value and the budget timeliness weight value respectively, , are preset feature coefficients (the feature coefficients are obtained by querying through the preset intelligent matching trading platform);

[0105] Then query the preset weight value database according to the obtained order demand index to obtain the matching demand weight values, including the registration weight value corresponding to the registration index, the energy matching weight value corresponding to the energy matching index, and the efficiency matching weight value corresponding to the efficiency matching index. Among them, the preset weight value database is obtained by querying through the preset intelligent matching trading platform;

[0106] Process according to the registration weight value, the energy matching weight value, and the efficiency matching weight value in combination with the registration index, the energy matching index, and the efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant;

[0107] The calculation formula of the order matching comprehensive evaluation index is:

[0108] ;

[0109] Wherein, is the order matching comprehensive evaluation index, , , are the registration weight value, the energy matching weight value, and the efficiency matching weight value respectively, , , They are the registration index, energy matching index, and efficiency matching index respectively.

[0110] According to an embodiment of the present invention, comparing the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, and sorting the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest and sending them to the user side for display includes:

[0111] Comparing the order matching comprehensive evaluation index with a preset matching degree threshold;

[0112] If the order matching comprehensive evaluation index is less than the preset matching degree threshold, the corresponding merchant is excluded;

[0113] If the order matching comprehensive evaluation index is greater than or equal to the preset matching degree threshold, the corresponding merchant is retained, and a second merchant list is generated;

[0114] Sorting the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest and sending them to the user side for display.

[0115] It should be noted that in order to further improve the matching efficiency, first compare the order matching comprehensive evaluation index with the preset matching degree threshold, exclude the merchants with an order matching comprehensive evaluation index less than the preset matching degree threshold, retain the merchants with an order matching comprehensive evaluation index greater than or equal to the preset matching degree threshold, generate a second merchant list, sort the order matching comprehensive evaluation indexes corresponding to the second merchants from largest to smallest, and send them to the user side for display for the user to screen and place orders with merchants.

[0116] Please refer to Figure 3 , Figure 3 , which is a flowchart of obtaining the risk index corresponding to an order for the intelligent matching transaction method based on order information in some embodiments of the present application. According to an embodiment of the present invention, obtaining a placing order instruction of a user, obtaining risk assessment data corresponding to the placing order instruction, and processing the risk assessment data to obtain the risk index corresponding to the order includes:

[0117] S31. Obtaining the placing order instruction of the user and obtaining the risk assessment data corresponding to the placing order instruction, including user risk assessment data and payment environment risk assessment data;

[0118] S32. The user risk assessment data includes the second return rate, credit arrears amount, and order placing frequency within a preset time period, and the payment environment risk assessment data includes payment device security data and network environment security data;

[0119] S33. Processing according to the second return rate, credit arrears amount, and order placing frequency to obtain the user risk index;

[0120] S34. Process according to the payment device security data and the network environment security data to obtain a payment environment risk index;

[0121] S35. Process according to the user risk index and the payment environment risk index in combination with the user credit value to obtain a risk index corresponding to the order.

[0122] It should be noted that in order to improve the security of the transaction between the order and the merchant, risk assessment is introduced. By obtaining the risk assessment data of the ordering user, including user risk assessment data and payment environment risk assessment data, and processing them respectively, a user risk index and a payment environment risk index are obtained. Among them, the second return rate refers to the ratio of the number of user returns to the number of orders placed by the user within a preset time period, the credit arrears amount refers to the amount of the user's credit payment that has not been repaid in time within a preset time period, the order placement frequency refers to the number of orders placed by the user within a preset time period, the payment device security data is obtained by those skilled in the art through analyzing server security data, router security data and switch security data, and the network environment security data is obtained by those skilled in the art through analyzing network scanner security data, vulnerability scanner security data and application scanner security data;

[0123] The formula for calculating the user risk index is:

[0124] ;

[0125] Wherein, is the user risk index, , , are the second return rate, the credit arrears amount and the order placement frequency respectively, , , are preset feature coefficients (the feature coefficients are obtained by querying through a preset intelligent matching trading platform);

[0126] The formula for calculating the payment environment risk index is:

[0127] ;

[0128] Wherein, is the payment environment risk index, , are the payment device security data and the network environment security data respectively, , are preset feature coefficients (the feature coefficients are obtained by querying through a preset intelligent matching trading platform);

[0129] Process according to the obtained user risk index and payment environment risk index to obtain a risk index corresponding to the order;

[0130] The risk index calculation formula is as follows:

[0131] ;

[0132] wherein, is the risk index, , are the user risk index and the payment environment risk index respectively, is the user credit value, , are preset feature coefficients (the feature coefficients are obtained by querying through a preset intelligent matching trading platform).

[0133] According to the embodiment of the present invention, the threshold comparison of the risk index with a preset risk level evaluation threshold to obtain the order risk level and send it to the merchant side for display includes:

[0134] Performing threshold comparison of the risk index with a preset risk level evaluation threshold to obtain the order risk level;

[0135] If the risk index is less than or equal to the preset risk level evaluation threshold, it is determined that the order is of a low risk level;

[0136] If the risk index is greater than the preset risk level evaluation threshold, it is determined that the order is of a high risk level;

[0137] Sending the low risk level or high risk level to the merchant side for display.

[0138] It should be noted that by performing threshold comparison of the obtained risk index with a preset risk level evaluation threshold to obtain the order risk level, in this embodiment, the risk level evaluation threshold is set to (0, 0.7], (0.7, 1], corresponding to the low risk level and the high risk level respectively; for example, if the obtained risk index is 0.6, which is less than the preset risk level evaluation threshold, it is determined that the order is of a low risk level, and if the obtained risk index is 0.8, which is greater than the preset risk level evaluation threshold, it is determined that the order is of a high risk level, and the order risk level is sent to the merchant side for display to reduce the transaction risk.

[0139] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0140] Processing the order timeliness data in combination with the industry order timeliness average data within a preset time period to obtain the order timeliness deviation rate;

[0141] Processing the order budget data in combination with the industry order price average data within a preset time period to obtain the order price deviation rate;

[0142] Process according to the order timeliness deviation rate and the order price deviation rate to obtain a risk assessment impact factor;

[0143] Revise the risk index according to the risk assessment impact factor to obtain a risk revised index.

[0144] It should be noted that in order to more accurately evaluate the risk index, the obtained risk index should be revised in combination with the order analysis data of the order. First, process the order timeliness data in combination with the industry order timeliness average data within a preset time period to obtain the order timeliness deviation rate. The order timeliness deviation rate refers to the ratio of the absolute value of the difference between the order timeliness data and the industry order timeliness average to the preset industry order timeliness average. Then, process the order budget data in combination with the industry order price average data within a preset time period to obtain the order price deviation rate. The order price deviation rate refers to the ratio of the absolute value of the difference between the order budget data and the industry order price average data to the industry order budget average data. Process according to the obtained order timeliness deviation rate and order price deviation rate to obtain a risk assessment impact factor;

[0145] The calculation formula of the risk assessment impact factor is:

[0146] ;

[0147] Wherein, is the risk assessment impact factor, , are the order timeliness deviation rate and the order price deviation rate respectively, , are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset intelligent matching trading platform);

[0148] Revise the risk index according to the obtained risk assessment impact factor to obtain a risk revised index;

[0149] The calculation formula of the risk revised index is:

[0150] ;

[0151] Wherein, is the risk revised index, , are the risk assessment impact factor and the risk index respectively, is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset intelligent matching trading platform).

[0152] It is worth mentioning that according to the embodiments of the present invention, it further includes:

[0153] Obtain the historical purchase record data of the user within a preset time period, including shopping category data, corresponding purchase time point data, average purchase interval data, and average purchase price data;

[0154] Obtain the time point data when the user opens the shopping platform, and process it according to the time point data and the purchase time point data in combination with the average purchase interval data to obtain the purchase time deviation rate;

[0155] If the purchase time deviation rate is less than or equal to the purchase time deviation rate threshold, obtain the merchant price data corresponding to the shopping category data, and process it according to the average purchase price data and the merchant price data to obtain the actual price deviation rate;

[0156] Push the merchants with the actual price deviation rate less than the preset actual price deviation rate threshold to the user side for display.

[0157] It should be noted that in order to improve the user's shopping experience and reduce the search difficulty, by obtaining the historical purchase record data including shopping category data, corresponding purchase time point data, average purchase interval data, and average purchase price data, the shopping pattern of the user is understood. For example, the user buys shampoo once a month on average, and the average price is 25 yuan. When the user opens the shopping platform, first judge the deviation of the time point when the shopping platform is opened from the last purchase time. Specifically, it is processed according to the time point data and the purchase time point data in combination with the average purchase interval data to obtain the purchase time deviation rate. The purchase time deviation rate refers to the ratio of the difference between the time point data and the purchase time point data to the average purchase interval data. Compare the obtained purchase time deviation rate with the purchase time deviation rate threshold. If it is greater than the purchase time deviation rate threshold, such products will not be pushed. If it is less than or equal to the purchase time deviation rate threshold, further obtain the merchant price data corresponding to the shopping category data, and process it in combination with the average purchase price data to obtain the actual price deviation rate. The actual price deviation rate refers to the ratio of the absolute value of the difference between the merchant price data and the average purchase price data to the average purchase price data, and then compare it with the preset actual price deviation rate threshold, and push the merchants with the actual price deviation rate less than the preset actual price deviation rate threshold to the user side for display.

[0158] The present invention also discloses an intelligent matching trading system based on order information, including a memory and a processor. The memory includes an intelligent matching trading method program based on order information. When the intelligent matching trading method program based on order information is executed by the processor, the following steps are implemented:

[0159] Obtain the business scope data and business evaluation data of the merchant within a preset time period. The business evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data;

[0160] Process according to the accurately evaluated data of the matching to obtain a registration index, process according to the evaluated data of the matching ability to obtain an energy matching index, and process according to the evaluated data of the matching response to obtain an efficiency matching index;

[0161] Obtain order information, extract order category keywords and order analysis data, match according to the order category keywords, and obtain a list of the first merchants;

[0162] Process according to the order analysis data in combination with the registration index, the energy matching index and the efficiency matching index to obtain an overall evaluation index of order matching corresponding to the first merchant;

[0163] Compare the overall evaluation index of order matching with a preset matching threshold to obtain a list of the second merchants, sort the overall evaluation index of order matching corresponding to the second merchants from largest to smallest, and send it to the user terminal for display;

[0164] Obtain the user's order placement instruction, obtain the risk assessment data of the user corresponding to the order placement instruction, and process according to the risk assessment data to obtain the risk index corresponding to the order;

[0165] Compare the risk index with a preset risk level evaluation threshold to obtain the order risk level, and send it to the merchant terminal for display.

[0166] It should be noted that in order to achieve intelligent matching of orders and merchants, by obtaining business evaluation data including accurately evaluated data of matching, evaluated data of matching ability and evaluated data of matching response within a preset time period, and processing them respectively to obtain a registration index, an energy matching index and an efficiency matching index. Extract order category keywords and order analysis data according to the order information. The order category keywords are used to match the business scope data of the obtained merchants, and the inconsistent merchants are eliminated to obtain a list of the first merchants, reducing the matching calculation resources. The order analysis data is used to determine the weight values corresponding to the registration index, the energy matching index and the efficiency matching index, and process in combination with the registration index, the energy matching index and the efficiency matching index to obtain an overall evaluation index of order matching corresponding to the first merchant. By threshold comparison, eliminate the merchants in the list of the first merchants whose overall evaluation index of order matching is less than the preset matching threshold to obtain a list of the second merchants, and sort the overall evaluation index of order matching corresponding to the second merchants from largest to smallest and send it to the user terminal for display. After the user places an order, introduce risk assessment to reduce the risk of false transactions. By obtaining the user's risk assessment data and processing it, obtain the risk index corresponding to the order. Finally, by comparing with the preset risk level evaluation threshold, obtain the order risk level and send it to the merchant terminal for display.

[0167] According to an embodiment of the present invention, obtaining the business scope data and business operation evaluation data of a merchant within a preset time period, where the business operation evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data, includes:

[0168] Obtaining the business scope data and business operation evaluation data of a merchant within a preset time period, where the business operation evaluation data includes accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data;

[0169] The accurate matching evaluation data includes the repurchase rate, the first return rate, and the complaint rate;

[0170] The matching ability evaluation data includes the average score, the order rate above the average, the sales volume, the delayed delivery rate, and the favorable comment rate;

[0171] The matching response evaluation data includes the average response time data and the average stock preparation time data.

[0172] It should be noted that by obtaining the business operation evaluation data and business scope data including accurate matching evaluation data, matching ability evaluation data, and matching response evaluation data, it is used to accurately evaluate the supply accuracy, supply ability, and supply timeliness of the merchant. Among them, the repurchase rate is the ratio of the number of users who place repeated orders to the total number of users who place orders with the merchant within a preset time period, the first return rate is the ratio of the number of returned orders of the merchant to the total number of orders of the merchant within a preset time period, and the order rate above the average is the ratio of the total order volume of the merchant minus the industry average order volume to the industry average order volume.

[0173] According to an embodiment of the present invention, processing according to the accurate matching evaluation data to obtain a registration index, processing according to the matching ability evaluation data to obtain an energy matching index, and processing according to the matching response evaluation data to obtain an efficiency matching index, includes:

[0174] Processing according to the repurchase rate, the first return rate, and the complaint rate to obtain a registration index;

[0175] Processing according to the average score, the order rate above the average, the sales volume, the delayed delivery rate, and the favorable comment rate to obtain an energy matching index;

[0176] Processing according to the average response time data and the average stock preparation time data to obtain an efficiency matching index.

[0177] It should be noted that processing according to the repurchase rate, the return rate, and the complaint rate to obtain a registration index, and the registration index is used to evaluate the matching accuracy of the merchant;

[0178] The calculation formula of the registration index is:

[0179] ;

[0180] Among them, is the registration index, , , are the repurchase rate, return rate, and complaint rate respectively, , are preset feature coefficients (the feature coefficients are obtained by querying a preset intelligent matching trading platform);

[0181] According to the average score, order over-average rate, sales volume, delayed delivery rate, and favorable comment rate, processing is performed to obtain the energy matching index, and the energy matching index is used to evaluate the matching ability of the merchant;

[0182] The calculation formula for the energy matching index is:

[0183] ;

[0184] Among them, is the energy matching index, , , , , are the average score, order over-average rate, sales volume, delayed delivery rate, and favorable comment rate respectively, , , are preset feature coefficients (the feature coefficients are obtained by querying a preset intelligent matching trading platform);

[0185] According to the average response time data and average stock preparation time data, processing is performed to obtain the efficiency matching index, and the efficiency matching index is used to evaluate the matching efficiency of the merchant. Among them, the average response time data refers to the average value of the time taken to receive confirmation for multiple orders within a preset time period, and the average stock preparation time data refers to the average value of the time from order confirmation to order shipment for multiple orders within a preset time period;

[0186] The calculation formula for the efficiency matching index is:

[0187] ;

[0188] Among them, is the efficiency matching index, , are the average response time data and average stock preparation time data respectively, is a preset feature coefficient (the feature coefficient is obtained by querying a preset intelligent matching trading platform).

[0189] According to the embodiments of the present invention, obtaining order information, extracting order category keywords and order analysis data, and performing matching according to the order category keywords to obtain a first merchant list, including:

[0190] Obtain order information, and extract order category keywords and order analysis data. The order analysis data includes order timeliness data, order budget data, and the user credit value corresponding to the order.

[0191] Match the order category keywords with the business scope data to obtain a first list of merchants.

[0192] It should be noted that, in order to improve the accuracy of order matching and reduce unnecessary matching calculations, order category keywords and order analysis data are extracted according to the order information. The extracted order category keywords are matched with the business scope data of the obtained merchants, and the unqualified merchants are excluded. The qualified merchants generate a first list of merchants. The order analysis data is used to determine the weight ratios of the registration index, energy matching index, and efficiency matching index of the merchants.

[0193] According to an embodiment of the present invention, the processing of the order analysis data in combination with the registration index, energy matching index, and efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant includes:

[0194] Process the order timeliness data and order budget data in combination with a preset timeliness weight value and a preset budget weight value to obtain an order demand index.

[0195] Query a preset weight value database according to the order demand index to obtain matching demand weight values, including a registration weight value corresponding to the registration index, an energy matching weight value corresponding to the energy matching index, and an efficiency matching weight value corresponding to the efficiency matching index.

[0196] Process the registration weight value, energy matching weight value, and efficiency matching weight value in combination with the registration index, energy matching index, and efficiency matching index to obtain the order matching comprehensive evaluation index corresponding to the first merchant.

[0197] It should be noted that, in order to comprehensively evaluate the matching situation between the merchant and the order, first, the extracted order timeliness data and order budget data are processed in combination with a preset timeliness weight value and a preset budget weight value to obtain an order demand index. The preset timeliness weight value and preset budget weight value are obtained by querying a preset intelligent matching trading platform.

[0198] The calculation formula of the order demand index is:

[0199] ;

[0200] Wherein, is the order demand index, , are the order timeliness data and order budget data respectively, , are the timeliness weight value and budget timeliness weight value respectively, , is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset intelligent matching trading platform);

[0201] Then, according to the obtained order demand index, query the preset weight value database to obtain the matching demand weight values, including the registration weight value corresponding to the registration index, the energy matching weight value corresponding to the energy matching index, and the efficiency matching weight value corresponding to the efficiency matching index, where the preset weight value database is obtained by querying through a preset intelligent matching trading platform;

[0202] Process according to the registration weight value, the energy matching weight value, and the efficiency matching weight value in combination with the registration index, the energy matching index, and the efficiency matching index to obtain the order matching comprehensive evaluation index of the first merchant;

[0203] The calculation formula of the order matching comprehensive evaluation index is:

[0204] ;

[0205] Among them, is the order matching comprehensive evaluation index, , , are respectively the registration weight value, the energy matching weight value, and the efficiency matching weight value, , , are respectively the registration index, the energy matching index, and the efficiency matching index.

[0206] According to the embodiment of the present invention, comparing the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, and sorting the order matching comprehensive evaluation indexes of the second merchants from large to small and sending them to the user terminal for display includes:

[0207] Comparing the order matching comprehensive evaluation index with a preset matching degree threshold;

[0208] If the order matching comprehensive evaluation index is less than the preset matching degree threshold, the corresponding merchant is excluded;

[0209] If the order matching comprehensive evaluation index is greater than or equal to the preset matching degree threshold, the corresponding merchant is retained, and a second merchant list is generated;

[0210] Sort the order matching comprehensive evaluation indexes of the second merchants from large to small and send them to the user terminal for display.

[0211] It should be noted that, in order to further improve the matching efficiency, first, the comprehensive evaluation index of order matching is compared with the preset matching threshold. Merchants with a comprehensive evaluation index of order matching less than the preset matching threshold are excluded, and merchants with a comprehensive evaluation index of order matching greater than or equal to the preset matching threshold are retained to generate a second merchant list. Then, the comprehensive evaluation index of order matching corresponding to the second merchant list is sorted from largest to smallest and sent to the user terminal for display, which is used for the user to screen the merchants for placing orders.

[0212] According to an embodiment of the present invention, obtaining a placing order instruction of a user, obtaining risk assessment data of the user corresponding to the placing order instruction, and processing the risk assessment data to obtain a risk index corresponding to the order includes:

[0213] Obtaining a placing order instruction of a user, and obtaining risk assessment data of the user corresponding to the placing order instruction, including user risk assessment data and payment environment risk assessment data;

[0214] The user risk assessment data includes a second return rate, a credit arrears amount, and an order placing frequency within a preset time period, and the payment environment risk assessment data includes payment device security data and network environment security data;

[0215] Processing according to the second return rate, the credit arrears amount, and the order placing frequency to obtain a user risk index;

[0216] Processing according to the payment device security data and the network environment security data to obtain a payment environment risk index;

[0217] Processing according to the user risk index and the payment environment risk index in combination with the user credit value to obtain a risk index corresponding to the order.

[0218] It should be noted that, in order to improve the security of transactions between orders and merchants, risk assessment is introduced. By obtaining risk assessment data of the placing order user, including user risk assessment data and payment environment risk assessment data, and processing them respectively to obtain a user risk index and a payment environment risk index. Among them, the second return rate refers to the ratio of the number of user returns to the number of placing orders by the user within a preset time period, the credit arrears amount refers to the amount of credit payment not repaid in time by the user within a preset time period, the order placing frequency refers to the number of placing orders by the user within a preset time period, the payment device security data is obtained by those skilled in the art through analyzing server security data, router security data, and switch security data, and the network environment security data is obtained by those skilled in the art through analyzing network scanner security data, vulnerability scanner security data, and application scanner security data;

[0219] The calculation formula of the user risk index is:

[0220] ;

[0221] Among them, is the user risk index, , , are respectively the second return rate, the credit arrears amount, and the order placement frequency, , , are preset feature coefficients (the feature coefficients are obtained by querying a preset intelligent matching trading platform);

[0222] The calculation formula for the payment environment risk index is:

[0223] ;

[0224] Among them, is the payment environment risk index, , are respectively the payment device security data and the network environment security data, , are preset feature coefficients (the feature coefficients are obtained by querying a preset intelligent matching trading platform);

[0225] Process the obtained user risk index and payment environment risk index to obtain the risk index corresponding to the order;

[0226] The calculation formula for the risk index is:

[0227] ;

[0228] Among them, is the risk index, , are respectively the user risk index and the payment environment risk index, is the user credit value, , are preset feature coefficients (the feature coefficients are obtained by querying a preset intelligent matching trading platform).

[0229] According to the embodiment of the present invention, the comparing the risk index with a preset risk level evaluation threshold to obtain the order risk level and sending it to the merchant side for display includes:

[0230] Compare the risk index with a preset risk level evaluation threshold to obtain the order risk level;

[0231] If the risk index is less than or equal to the preset risk level evaluation threshold, it is determined that the order is a low risk level;

[0232] If the risk index is greater than the preset risk level evaluation threshold, it is determined that the order is a high risk level;

[0233] Send the low-risk level or high-risk level to the merchant side for display.

[0234] It should be noted that the obtained risk index is compared with the preset risk level evaluation threshold to obtain the order risk level. In this embodiment, the risk level evaluation threshold is set to (0, 0.7] and (0.7, 1], corresponding to the low-risk level and high-risk level respectively. For example, if the obtained risk index is 0.6, which is less than the preset risk level evaluation threshold, the order is determined to be a low-risk level. If the obtained risk index is 0.8, which is greater than the preset risk level evaluation threshold, the order is determined to be a high-risk level, and the order risk level is sent to the merchant side for display to reduce transaction risks.

[0235] It is worth mentioning that according to the embodiment of the present invention, it further includes:

[0236] Process the order timeliness data in combination with the industry order timeliness average data within a preset time period to obtain the order timeliness deviation rate;

[0237] Process the order budget data in combination with the industry order price average data within a preset time period to obtain the order price deviation rate;

[0238] Process the order timeliness deviation rate and the order price deviation rate to obtain the risk evaluation impact factor;

[0239] Correct the risk index according to the risk evaluation impact factor to obtain the risk correction index.

[0240] It should be noted that in order to more accurately evaluate the risk index, the obtained risk index should be corrected in combination with the order analysis data of the order. First, process the order timeliness data in combination with the industry order timeliness average data within a preset time period to obtain the order timeliness deviation rate. The order timeliness deviation rate is the ratio of the absolute value of the difference between the order timeliness data and the industry order timeliness average to the preset industry order timeliness average. Then, process the order budget data in combination with the industry order price average data within a preset time period to obtain the order price deviation rate. The order price deviation rate is the ratio of the absolute value of the difference between the order budget data and the industry order price average data to the industry order budget average data. Process the obtained order timeliness deviation rate and order price deviation rate to obtain the risk evaluation impact factor;

[0241] The calculation formula of the risk evaluation impact factor is:

[0242] ;

[0243] Wherein, is the risk evaluation impact factor, 、 They are the order timeliness deviation rate and the order price deviation rate respectively. 、 are preset feature coefficients (the feature coefficients are obtained by querying through a preset intelligent matching trading platform);

[0244] The risk index is corrected according to the obtained risk evaluation influencing factors to obtain a risk correction index;

[0245] The calculation formula of the risk correction index is:

[0246] ;

[0247] Among them, is the risk correction index, 、 are the risk evaluation influencing factor and the risk index respectively, is a preset feature coefficient (the feature coefficient is obtained by querying through a preset intelligent matching trading platform).

[0248] It is worth mentioning that according to the embodiments of the present invention, it further includes:

[0249] Obtain the historical purchase record data of the user within a preset time period, including shopping category data, corresponding purchase time point data, average purchase interval data, and average purchase price data;

[0250] Obtain the time point data when the user opens the shopping platform, and process it according to the time point data and the purchase time point data in combination with the average purchase interval data to obtain the purchase time deviation rate;

[0251] If the purchase time deviation rate is less than or equal to the purchase time deviation rate threshold, obtain the merchant price data corresponding to the shopping category data, and process it according to the average purchase price data and the merchant price data to obtain the actual price deviation rate;

[0252] Push the merchants with the actual price deviation rate less than the preset actual price deviation rate threshold to the user side for display.

[0253] It should be noted that, in order to improve the user shopping experience and reduce the search difficulty, historical purchase record data including shopping category data, corresponding purchase time point data, average purchase interval data, and average purchase price data are obtained to understand the user's shopping pattern. For example, the user purchases shampoo once a month on average, and the average price is 25 yuan. When the user opens the shopping platform, first, the deviation between the time point when the shopping platform is opened and the last purchase time is judged. Specifically, it is processed according to the time point data, the purchase time point data, and the average purchase interval data to obtain the purchase time deviation rate. The purchase time deviation rate refers to the ratio of the difference between the time point data and the purchase time point data to the average purchase interval data. The obtained purchase time deviation rate is compared with the purchase time deviation rate threshold. If it is greater than the purchase time deviation rate threshold, such products are not pushed. If it is less than or equal to the purchase time deviation rate threshold, the merchant price data corresponding to the shopping category data is further obtained and processed in combination with the average purchase price data to obtain the actual price deviation rate. The actual price deviation rate refers to the ratio of the absolute value of the difference between the merchant price data and the average purchase price data to the average purchase price data, and then it is compared with the preset actual price deviation rate threshold. The merchants with an actual price deviation rate less than the preset actual price deviation rate threshold are pushed to the user side for display.

[0254] The third aspect of the present invention provides a readable storage medium, in which a program for an intelligent matching transaction method based on order information is stored. When the program for the intelligent matching transaction method based on order information is executed by a processor, the steps of the intelligent matching transaction method based on order information as described in any one of the above are implemented.

[0255] The intelligent matching transaction method, system, and medium based on order information disclosed in the present invention realize the intelligent matching of orders and merchants based on order information by matching to obtain a first merchant according to the business evaluation data, order category keywords, and order analysis data of the merchant, analyzing and calculating to obtain a second merchant and the corresponding order matching comprehensive evaluation index, and obtaining the order risk level of the order through analysis, calculation, and threshold comparison.

[0256] 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 only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or 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 the devices or units can be electrical, mechanical, or other forms.

[0257] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed across 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.

[0258] In addition, each functional unit in the embodiments of the present invention may be all integrated in a processing unit, or each unit may be separately regarded as a unit, or two or more units may be integrated in one unit; the above-mentioned integrated units may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0259] 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 readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0260] Alternatively, if the above-mentioned 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 readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention essentially or the part that contributes to the prior art can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to enable a computer device (which may 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 discs and other various media that can store program codes.

Claims

1. An intelligent matching transaction method based on order information, characterized in that: The following steps are involved: Obtaining the business scope data and business evaluation data of the merchant within a preset time period, the business evaluation data including matching accuracy evaluation data, matching ability evaluation data and matching response evaluation data; Processing the matching accuracy evaluation data to obtain a registration index, processing the matching ability evaluation data to obtain a matching ability index, and processing the matching response evaluation data to obtain a matching effectiveness index; Obtain order information, extract order category keywords and order analysis data, match according to order category keywords, and obtain the first merchant list; Processing the order analysis data in combination with the registration index, matching index and matching efficiency index to obtain an order matching comprehensive evaluation index corresponding to the first merchant; Compare the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, sort the order matching comprehensive evaluation indexes corresponding to the second merchants from large to small, and send the list to the user end for display; Obtain the user's order instruction and the risk assessment data of the user corresponding to the order instruction, process the risk assessment data, and obtain the risk index corresponding to the order; The risk index is compared with a preset risk level assessment threshold to obtain the order risk level, which is then sent to the merchant for display.

2. The intelligent matching transaction method based on order information according to claim 1 is characterized in that: The business scope data and business evaluation data of the merchant within the preset time period are obtained, and the business evaluation data includes matching accuracy evaluation data, matching ability evaluation data and matching response evaluation data, including: Obtaining the business scope data and business evaluation data of the merchant within a preset time period, the business evaluation data including matching accuracy evaluation data, matching ability evaluation data and matching response evaluation data; The matching accurate evaluation data includes repurchase rate, first return rate and complaint rate; The matching capability evaluation data includes the average score, order excess rate, sales volume, delayed delivery rate and favorable comment rate; The matching response evaluation data includes response time average data and stock preparation time average data.

3. The intelligent matching transaction method based on order information according to claim 2 is characterized in that: The processing according to the matching accuracy evaluation data to obtain the registration index, the processing according to the matching ability evaluation data to obtain the matching ability index, and the processing according to the matching response evaluation data to obtain the matching effectiveness index include: Processing is performed according to the repurchase rate, the first return rate and the complaint rate to obtain a registration index; The allocation index is obtained by processing the score average, order excess rate, sales volume, delayed delivery rate and favorable comment rate; The response time average data and the stocking time average data are processed to obtain a matching efficiency index.

4. The intelligent matching transaction method based on order information according to claim 3 is characterized in that: The obtaining of order information, extracting order category keywords and order analysis data, matching according to the order category keywords, and obtaining a first merchant list includes: Obtain order information and extract order category keywords and order analysis data. The order analysis data includes order timeliness data, order budget data, and the user credit value corresponding to the order; The order category keywords are matched with the business scope data to obtain a first merchant list.

5. The intelligent matching transaction method based on order information according to claim 4 is characterized in that: The step of processing the order analysis data in combination with the registration index, the matching index, and the matching efficiency index to obtain the order matching comprehensive evaluation index corresponding to the first merchant includes: The order demand index is obtained by processing the order timeliness data and the order budget data in combination with a preset timeliness weight value and a preset budget weight value; According to the order demand index, a preset weight value database is queried to obtain a matching demand weight value, including a registration weight value corresponding to the registration index, an energy weight value corresponding to the energy index, and an efficiency weight value corresponding to the efficiency index; The order matching comprehensive evaluation index corresponding to the first merchant is obtained by processing the registration weight value, the energy matching weight value and the efficiency matching weight value in combination with the registration index, the energy matching index and the efficiency matching index.

6. The intelligent matching transaction method based on order information according to claim 5 is characterized in that: The order matching comprehensive evaluation index is compared with a preset matching degree threshold to obtain a second merchant list, and the order matching comprehensive evaluation indexes corresponding to the second merchants are sorted from large to small, and sent to the user terminal for display, including: Comparing the order matching comprehensive evaluation index with a preset matching degree threshold; If the order matching comprehensive evaluation index is less than the preset matching threshold, the corresponding merchant is eliminated; If the order matching comprehensive evaluation index is greater than or equal to the preset matching threshold, the corresponding merchant is retained and a second merchant list is generated; The order matching comprehensive evaluation indexes corresponding to the second merchant are sorted from large to small and sent to the user end for display.

7. The intelligent matching transaction method based on order information according to claim 6 is characterized in that: The obtaining of the user's order instruction, obtaining the risk assessment data of the user corresponding to the order instruction, and performing processing according to the risk assessment data to obtain the risk index corresponding to the order include: Obtain the user's order instruction and the risk assessment data of the user corresponding to the order instruction, including the user risk assessment data and the payment environment risk assessment data; The user risk assessment data includes the second return rate, credit arrears amount and order frequency within a preset time period, and the payment environment risk assessment data includes payment device security data and network environment security data; Processing is performed according to the second return rate, the amount of credit arrears and the order frequency to obtain a user risk index; Processing the payment device security data and the network environment security data to obtain a payment environment risk index; The risk index corresponding to the order is obtained by processing the user risk index and the payment environment risk index in combination with the user credit value.

8. The intelligent matching transaction method based on order information according to claim 7 is characterized in that: The risk index is compared with a preset risk level assessment threshold to obtain the order risk level, and sent to the merchant for display, including: Compare the risk index with a preset risk level assessment threshold to obtain the order risk level; If the risk index is less than or equal to the preset risk level assessment threshold, the order is judged to be of low risk level; If the risk index is greater than the preset risk level assessment threshold, the order is judged to be at a high risk level; The low risk level or high risk level is sent to the merchant end for display.

9. Intelligent matching trading system based on order information, characterized by: The invention comprises a memory and a processor, wherein the memory comprises a program of an intelligent matching transaction method based on order information, and when the program of the intelligent matching transaction method based on order information is executed by the processor, the following steps are implemented: Obtaining the business scope data and business evaluation data of the merchant within a preset time period, the business evaluation data including matching accuracy evaluation data, matching ability evaluation data and matching response evaluation data; Processing the matching accuracy evaluation data to obtain a registration index, processing the matching ability evaluation data to obtain a matching ability index, and processing the matching response evaluation data to obtain a matching effectiveness index; Obtain order information, extract order category keywords and order analysis data, match according to order category keywords, and obtain the first merchant list; Processing the order analysis data in combination with the registration index, matching index and matching efficiency index to obtain an order matching comprehensive evaluation index corresponding to the first merchant; Compare the order matching comprehensive evaluation index with a preset matching degree threshold to obtain a second merchant list, sort the order matching comprehensive evaluation indexes corresponding to the second merchants from large to small, and send the list to the user end for display; Obtain the user's order instruction and the risk assessment data of the user corresponding to the order instruction, process the risk assessment data, and obtain the risk index corresponding to the order; The risk index is compared with a preset risk level assessment threshold to obtain the order risk level, which is then sent to the merchant for display.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for an intelligent matching transaction method based on order information. When the program for an intelligent matching transaction method based on order information is executed by a processor, the steps of the intelligent matching transaction method based on order information as described in any one of claims 1 to 8 are implemented.

Images