Intelligent order matching and optimizing method and system and medium

Abstract

The invention provides an order intelligent matching and optimizing method and system and a medium. The method comprises the following steps: acquiring order information of all orders of a target platform in a preset time period, extracting and processing to obtain order feature data, processing the order feature data to obtain key feature data, matching corresponding weights, calculating a similarity value between every two orders according to the key feature data and the corresponding weights, and calculating a similarity value between every two orders according to the similarity value. The order matching condition is judged according to the similarity value, clustering processing is conducted on all orders, the target platform is optimized, and therefore the intelligent order matching and optimizing technology is achieved.

Description

Technical Field

[0001] This application relates to the technical field of order matching. Specifically, it relates to an intelligent order matching and optimization method, system, and medium. Background Art

[0002] During the operation of an e-commerce platform, the problem of similar orders is relatively common. Similar orders may involve a single user purchasing similar goods within a short period of time, or different users purchasing the same or similar goods. Existing order processing methods are difficult to accurately detect and process these similar orders, which affects the operation efficiency and user experience of the e-commerce platform.

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

[0004] The purpose of this application is to provide an intelligent order matching and optimization method, system, and medium. It can collect the order information of all orders on the target platform within a preset time period, extract and process it to obtain order feature data, process the order feature data to obtain key feature data, match corresponding weights, calculate the similarity value between every two orders according to the key feature data and the corresponding weights, judge the order matching status according to the similarity value, cluster all orders, and optimize the target platform to achieve the technology of intelligent order matching and optimization.

[0005] This application also provides an intelligent order matching and optimization method, including the following steps:

[0006] Collect the order information of all orders on the target platform within a preset time period, and extract and process it to obtain order feature data;

[0007] Process the order feature data to obtain key feature data, and match corresponding weights;

[0008] Calculate the similarity value between every two orders according to the key feature data and the corresponding weights;

[0009] Judge the order matching status according to the similarity value, cluster all orders, and optimize the target platform.

[0010] Optionally, in the intelligent order matching and optimization method described in this application, the step of collecting the order information of all orders on the target platform within a preset time period, and extracting and processing it to obtain order feature data includes:

[0011] Collect the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information;

[0012] Extract, encode, and normalize the order based on the basic order information, customer information, and product information to obtain order feature data;

[0013] The order feature data includes basic order feature data, product feature data, and customer feature data.

[0014] Optionally, in the order intelligent matching and optimization method described in this application, the processing of the order feature data to obtain key feature data and matching corresponding weights includes:

[0015] Process the basic order feature data, product feature data, and customer feature data through a preset feature selection method to obtain key feature data;

[0016] The key feature data includes order price data, order time data, product category data, and customer purchase frequency data;

[0017] Perform weight allocation processing according to the preset analytic hierarchy process to match corresponding weights to the order price data, order time data, product category data, and customer purchase frequency data.

[0018] Optionally, in the order intelligent matching and optimization method described in this application, the calculation of the similarity value between every two orders according to the key feature data and the corresponding weights includes:

[0019] Select a similarity calculation method according to the key feature data;

[0020] Process the order price data, order time data, product category data, and customer purchase frequency data, combined with the corresponding weights, through the similarity calculation method to obtain the similarity value between every two orders among all the orders.

[0021] Optionally, in the order intelligent matching and optimization method described in this application, the judgment of the order matching status according to the similarity value, the clustering processing of all the orders, and the optimization of the target platform include:

[0022] Compare all the similarity values with a preset similarity threshold respectively to obtain corresponding threshold comparison results;

[0023] Judge the matching status of the order according to the threshold comparison result;

[0024] If the similarity value is less than or equal to the preset similarity threshold, the corresponding two orders belong to low-matching orders;

[0025] If the similarity value is greater than the preset similarity threshold, the corresponding two orders belong to high-matching orders;

[0026] Cluster all the orders according to the matching status, and perform resource optimization processing on the target platform.

[0027] Optionally, in the order intelligent matching and optimization method described in this application, it further includes:

[0028] Monitor the running status of the target platform after optimization, and collect the running information after a preset time period;

[0029] Perform extraction and statistical processing on the running information to obtain order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data;

[0030] Perform weighted processing on the order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data to obtain an order optimization effect coefficient;

[0031] Compare the order optimization effect coefficient with a preset order optimization effect threshold to obtain a second threshold comparison result;

[0032] Evaluate the optimization effect according to the second threshold comparison result;

[0033] If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard, and a second platform optimization is required.

[0034] In a second aspect, this application provides an order intelligent matching and optimization system, which includes: a memory and a processor. The memory includes a program for the order intelligent matching and optimization method. When the program for the order intelligent matching and optimization method is executed by the processor, the following steps are implemented:

[0035] Collect the order information of all orders on the target platform within a preset time period, and extract and process to obtain order feature data;

[0036] Process the order feature data to obtain key feature data and match corresponding weights;

[0037] Calculate the similarity value between every two orders according to the key feature data and the corresponding weights;

[0038] Judge the order matching situation according to the similarity value, cluster all the orders, and optimize the target platform.

[0039] Optionally, in the order intelligent matching and optimization system described in this application, the step of collecting the order information of all orders on the target platform within a preset time period and extracting and processing to obtain order feature data includes:

[0040] Collect the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information;

[0041] Extract, code, and normalize the data according to the order basic information, customer information, and commodity information to obtain order feature data;

[0042] The order feature data includes order basic feature data, commodity feature data, and customer feature data.

[0043] Optionally, in the order intelligent matching and optimization system described in this application, the processing of the order feature data to obtain key feature data and matching corresponding weights includes:

[0044] Process the order basic feature data, commodity feature data, and customer feature data through a preset feature selection method to obtain key feature data;

[0045] The key feature data includes order price data, order time data, commodity type data, and customer purchase frequency data;

[0046] Perform weight allocation processing according to the preset analytic hierarchy process, and match corresponding weights to the order price data, order time data, commodity type data, and customer purchase frequency data.

[0047] In a third aspect, this application also provides a computer-readable storage medium, in which an order intelligent matching and optimization method program is stored. When the order intelligent matching and optimization method program is executed by a processor, the steps of the order intelligent matching and optimization method described in any one of the above are implemented.

[0048] As can be seen from the above, the order intelligent matching and optimization method, system, and medium disclosed in the present invention collect the order information of all orders on the target platform within a preset time period, extract and process to obtain order feature data, process the order feature data to obtain key feature data and match corresponding weights, calculate the similarity value between every two orders according to the key feature data and the corresponding weights, judge the order matching status according to the similarity value, perform clustering processing on all orders, and optimize the target platform, so as to realize the technology of order intelligent matching and optimization.

[0049] Other features and advantages of this application will be described in the subsequent specification, and part of them will become obvious from the specification, or be understood by implementing the embodiments of this application. The purpose and other advantages of this application can be realized and obtained through the structures specifically pointed out in the written specification and the drawings. Description of the Drawings

[0050] 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 therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a flowchart of the order intelligent matching and optimization method provided by the embodiments of the present application;

[0052] Figure 2 It is a flowchart of obtaining order feature data of the order intelligent matching and optimization method provided by the embodiments of the present application;

[0053] Figure 3 It is a flowchart of calculating the similarity value of the order intelligent matching and optimization method provided by the embodiments of the present application;

[0054] Figure 4 It is a flowchart of the optimization target platform of the order intelligent matching and optimization method provided by the embodiments of the present application. Detailed implementation manners

[0055] 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 embodiments of the present application, rather than all embodiments. The components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0056] It should be noted that similar reference numerals and letters indicate 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.

[0057] Please refer to Figure 1 , Figure 1 It is a flowchart of the order intelligent matching and optimization method in some embodiments of the present application. This order intelligent matching and optimization method is used in terminal devices, such as computers, mobile phone terminals, etc. This order intelligent matching and optimization method includes the following steps:

[0058] S11. Collect the order information of all orders on the target platform within a preset time period, and extract and process it to obtain order feature data;

[0059] S12. Process the order feature data to obtain key feature data and match corresponding weights;

[0060] S13. Calculate the similarity value between every two orders according to the key feature data and the corresponding weights;

[0061] S14. Judge the order matching situation according to the similarity value, perform clustering processing on all the orders, and optimize the target platform.

[0062] It should be noted that in the operation process of an e-commerce platform, the problem of similar orders is relatively common. Similar orders may involve the same user purchasing similar goods within a short period of time, or different users purchasing the same or similar goods. The existing order processing methods are difficult to accurately detect and process these similar orders, which affects the operation efficiency and user experience of the e-commerce platform. Therefore, a more intelligent and efficient order matching and optimization method is needed. In this embodiment, first, collect the order information of all orders on the target platform within a preset time period, and extract and process it to obtain order feature data, process the order feature data to obtain key feature data and match corresponding weights, calculate the similarity value between every two orders according to the key feature data and the corresponding weights, judge the order matching situation according to the similarity value, perform clustering processing on all the orders, and optimize the target platform, so as to realize the technology of intelligent order matching and optimization.

[0063] Please refer to Figure 2 , Figure 2 is the flowchart of obtaining order feature data of the order intelligent matching and optimization method in some embodiments of the present application. According to the embodiments of the present invention, the step of collecting the order information of all orders on the target platform within a preset time period, and extracting and processing it to obtain order feature data includes:

[0064] S21. Collect the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information;

[0065] S22. Perform extraction, encoding, and normalization processing according to the order basic information, customer information, and commodity information to obtain order feature data;

[0066] S23. The order feature data includes order basic feature data, commodity feature data, and customer feature data.

[0067] It should be noted that in order to better analyze the order matching situation of the platform, it is necessary to first collect data on all orders, including order basic information, customer information, and commodity information, and then through extraction and processing, obtain the corresponding data, clean and preprocess this data to ensure the accuracy and consistency of the data, encode the categorical data among them, for example, convert non-numerical data such as customer level and commodity category into numerical data for subsequent calculations, and perform standardization or normalization processing on the numerical data to make the data of different features comparable.

[0068] According to an embodiment of the present invention, the processing of the order feature data to obtain key feature data and matching corresponding weights includes:

[0069] Processing the order basic feature data, commodity feature data, and customer feature data through a preset feature selection method to obtain key feature data;

[0070] The key feature data includes order price data, order time data, commodity type data, and customer purchase frequency data;

[0071] Performing weight assignment processing according to the preset analytic hierarchy process, and matching corresponding weights to the order price data, order time data, commodity type data, and customer purchase frequency data.

[0072] It should be noted that the order basic feature data, commodity feature data, and customer feature data are processed through a preset feature selection method to obtain key feature data. Among them, the feature selection method can be selected according to actual needs, and can be one or more of domain knowledge, data analysis, or machine learning. The key feature data refers to the data of features that have an important impact on order matching and optimization, including order price, order time, commodity type, and customer purchase frequency data, and then corresponding weights are assigned to these data according to the analytic hierarchy process.

[0073] Please refer to Figure 3 , Figure 3 which is a flowchart of calculating the similarity value of the order intelligent matching and optimization method in some embodiments of this application. According to an embodiment of the present invention, the calculation of the similarity value between every two orders based on the key feature data and the corresponding weights includes:

[0074] S31. Select a similarity calculation method according to the key feature data;

[0075] S32. According to the order price data, order time data, commodity type data, and customer purchase frequency data, combined with the corresponding weights, perform processing through the similarity calculation method to obtain the similarity value between every two orders among all the orders.

[0076] It should be noted that corresponding similarity calculation methods are selected according to the key feature data. For example, the Euclidean distance can be used for order price data, and the Jaccard similarity can be used for product category data. Then, based on the order price data, order time data, product category data, and customer purchase frequency data, combined with the corresponding weights, the similarity between each pair of orders is calculated through the corresponding similarity calculation method to obtain the similarity values between each pair of orders among all orders, and finally the similarity values between each pair of orders among all orders are obtained.

[0077] Please refer to Figure 4 , Figure 4 is a flowchart of an optimization target platform for an order intelligent matching and optimization method in some embodiments of the present application. According to an embodiment of the present invention, the method for judging the order matching status according to the similarity value, clustering all the orders, and optimizing the target platform includes:

[0078] S41. Compare all the similarity values with a preset similarity threshold respectively to obtain corresponding threshold comparison results;

[0079] S42. Judge the matching status of the orders according to the threshold comparison results;

[0080] S43. If the similarity value is less than or equal to the preset similarity threshold, the corresponding two orders belong to low-matching orders;

[0081] S44. If the similarity value is greater than the preset similarity threshold, the corresponding two orders belong to high-matching orders;

[0082] S45. Cluster all the orders according to the matching status and perform resource optimization processing on the target platform.

[0083] It should be noted that a similarity threshold is set. When the similarity between two orders is greater than this threshold, they are considered similar orders and are matched. The size of the threshold can be adjusted according to business requirements and actual situations. For example, if the threshold is set to 0.8, then order pairs with a similarity greater than 0.8 will be regarded as matching orders. Then, clustering algorithms such as K-means clustering and hierarchical clustering are used to divide the orders into different clusters according to the similarity. Orders within each cluster have a high similarity, while the similarity between orders in different clusters is low. Through clustering, similar orders can be aggregated together, which is convenient for batch processing or resource optimization configuration.

[0084] According to an embodiment of the present invention, it further includes:

[0085] Monitor the running status of the optimized target platform and collect the running information after a preset time period;

[0086] Extract and statistically process according to the operation information to obtain the order matching accuracy rate data, the order processing efficiency improvement rate data, and the customer satisfaction data;

[0087] Perform weighted processing according to the order matching accuracy rate data, the order processing efficiency improvement rate data, and the customer satisfaction data to obtain the order optimization effect coefficient;

[0088] Compare the order optimization effect coefficient with the preset order optimization effect threshold to obtain the second threshold comparison result;

[0089] Evaluate the optimization effect according to the second threshold comparison result;

[0090] If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard, and a second platform optimization is required.

[0091] It should be noted that monitor the operating status of the target platform after optimization, collect the operation information after a preset time period, extract and statistically process according to the operation information to obtain the order matching accuracy rate data, the order processing efficiency improvement rate data, and the customer satisfaction data, further perform weighted processing to obtain the order optimization effect coefficient, then compare with the preset order optimization effect threshold to obtain the second threshold comparison result, evaluate the optimization effect according to the second threshold comparison result, and if the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard, and a second platform optimization is required.

[0092] In a second aspect, the present invention also discloses an order intelligent matching and optimization system, including a memory and a processor. The memory includes an order intelligent matching and optimization method program. When the order intelligent matching and optimization method program is executed by the processor, the following steps are implemented:

[0093] Collect the order information of all orders on the target platform within a preset time period, and extract and process to obtain order feature data;

[0094] Process the order feature data to obtain key feature data and match corresponding weights;

[0095] Calculate the similarity value between every two orders according to the key feature data and the corresponding weights;

[0096] Judge the order matching situation according to the similarity value, perform clustering processing on all the orders, and optimize the target platform.

[0097] It should be noted that during the operation of the e-commerce platform, the problem of similar orders is relatively common. Similar orders may involve the same user purchasing similar goods within a short period of time, or different users purchasing the same or similar goods. The existing order processing methods are difficult to accurately detect and process these similar orders, which affects the operation efficiency and user experience of the e-commerce platform. Therefore, a more intelligent and efficient order matching and optimization method is needed. In this embodiment, first, the order information of all orders on the target platform within a preset time period is collected, and order feature data is obtained through extraction and processing. The order feature data is processed to obtain key feature data, and corresponding weights are assigned. The similarity value between every two orders is calculated based on the key feature data and the corresponding weights. The order matching status is judged according to the similarity value, and all orders are clustered and the target platform is optimized, so as to realize the technology of intelligent order matching and optimization.

[0098] According to an embodiment of the present invention, the collecting the order information of all orders on the target platform within a preset time period, and extracting and processing to obtain order feature data includes:

[0099] Collecting the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information;

[0100] Performing extraction, encoding, and normalization processing according to the order basic information, customer information, and commodity information to obtain order feature data;

[0101] The order feature data includes order basic feature data, commodity feature data, and customer feature data.

[0102] It should be noted that in order to better analyze the order matching situation of the platform, it is necessary to first collect data on all orders, including order basic information, customer information, and commodity information, and then through extraction and processing, obtain the corresponding data. Clean and preprocess these data to ensure the accuracy and consistency of the data. Encode the categorical data among them, for example, convert non-numerical data such as customer level and commodity category into numerical data for subsequent calculation, and perform standardization or normalization processing on the numerical data to make the data of different features comparable.

[0103] According to an embodiment of the present invention, the processing the order feature data to obtain key feature data and matching corresponding weights includes:

[0104] Processing according to the order basic feature data, commodity feature data, and customer feature data through a preset feature selection method to obtain key feature data;

[0105] The key feature data includes order price data, order time data, commodity type data, and customer purchase frequency data;

[0106] Perform weight distribution processing according to the preset analytic hierarchy process, and match corresponding weights to the order price data, order time data, commodity type data, and customer purchase frequency data.

[0107] It should be noted that the order basic feature data, commodity feature data, and customer feature data are processed through a preset feature selection method to obtain key feature data. Among them, the feature selection method can be selected according to actual needs, and can be one or more of domain knowledge, data analysis, or machine learning. The key feature data refers to the data of features that have an important impact on order matching and optimization, including order price, order time, commodity type, and customer purchase frequency data. Then, corresponding weights are assigned to these data according to the analytic hierarchy process.

[0108] According to an embodiment of the present invention, calculating the similarity value between every two orders according to the key feature data and the corresponding weights includes:

[0109] Select a similarity calculation method according to the key feature data;

[0110] According to the order price data, order time data, commodity type data, and customer purchase frequency data, combined with the corresponding weights, process through the similarity calculation method to obtain the similarity value between every two orders among all the orders.

[0111] It should be noted that a corresponding similarity calculation method is selected according to the key feature data. For example, the Euclidean distance can be selected for the order price data, and the Jaccard similarity can be selected for the commodity type data. Then, according to the order price data, order time data, commodity type data, and customer purchase frequency data, combined with the corresponding weights, calculate through the corresponding similarity calculation method to obtain the similarity value between every two orders, and finally obtain the similarity value between every two orders among all the orders.

[0112] According to an embodiment of the present invention, judging the order matching status according to the similarity value, clustering all the orders, and optimizing the target platform includes:

[0113] Compare all the similarity values with a preset similarity threshold respectively to obtain corresponding threshold comparison results;

[0114] Judge the matching status of the order according to the threshold comparison result;

[0115] If the similarity value is less than or equal to the preset similarity threshold, the corresponding two orders belong to low-matching orders;

[0116] If the similarity value is greater than a preset similarity threshold, the corresponding two orders belong to high-matching orders;

[0117] Cluster all the orders according to the matching status, and perform resource optimization processing on the target platform.

[0118] It should be noted that a similarity threshold is set. When the similarity of two orders is greater than this threshold, they are considered similar orders and are matched. The size of the threshold can be adjusted according to business requirements and actual situations. For example, if the threshold is set to 0.8, then order pairs with a similarity greater than 0.8 will be regarded as matching orders. Then, clustering algorithms such as K-means clustering and hierarchical clustering are used to divide the orders into different clusters according to the similarity. Orders within each cluster have a high similarity, while the similarity between different clusters is low. Through clustering, similar orders can be aggregated together, facilitating batch processing or resource optimization allocation.

[0119] According to an embodiment of the present invention, it further includes:

[0120] Monitor the running status of the optimized target platform, and collect the running information after a preset time period;

[0121] Perform extraction and statistical processing according to the running information to obtain order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data;

[0122] Perform weighted processing according to the order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data to obtain an order optimization effect coefficient;

[0123] Compare the order optimization effect coefficient with a preset order optimization effect threshold to obtain a second threshold comparison result;

[0124] Evaluate the optimization effect according to the second threshold comparison result;

[0125] If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard, and a second platform optimization is required.

[0126] It should be noted that monitor the running status of the optimized target platform, collect the running information after a preset time period, perform extraction and statistical processing according to the running information to obtain order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data, further perform weighted processing to obtain an order optimization effect coefficient, then compare it with a preset order optimization effect threshold to obtain a second threshold comparison result, and evaluate the optimization effect according to the second threshold comparison result. If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard, and a second platform optimization is required.

[0127] In a third aspect of the present invention, a readable storage medium is provided. A program for the order intelligent matching and optimization method is stored in the readable storage medium. When the program for the order intelligent matching and optimization method is executed by a processor, the steps of the order intelligent matching and optimization method as described in any one of the above are implemented.

[0128] The order intelligent matching and optimization method, system and medium disclosed in the present invention collect the order information of all orders on the target platform within a preset time period, extract and process the order feature data, process the order feature data to obtain key feature data, match corresponding weights, calculate the similarity value between every two orders according to the key feature data and the corresponding weights, judge the order matching situation according to the similarity value, perform clustering processing on all orders, and optimize the target platform, so as to realize the technology of order intelligent matching and optimization.

[0129] 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 the devices or units can be electrical, mechanical, or other forms.

[0130] 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.

[0131] In addition, each functional unit in the embodiments of the present invention can be all integrated in one processing unit, or each unit can be separately used as one unit, or two or more units can be integrated in one 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.

[0132] Those of ordinary skill in the art can understand that all or part of the steps to implement 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 those of the above method embodiments. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0133] Alternatively, if the above integrated units of the present invention are implemented in the form of software function 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, in essence 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 for causing 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 various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. An intelligent order matching and optimization method, characterized in that It includes the following steps: Collect the order information of all orders on the target platform within a preset time period, and extract and process it to obtain order feature data; Process the order feature data to obtain key feature data and match corresponding weights; Calculate the similarity value between every two orders according to the key feature data and the corresponding weights; Judge the order matching situation according to the similarity value, perform clustering processing on all the orders, and optimize the target platform.

2. The order intelligent matching and optimization method according to claim 1, wherein The step of collecting the order information of all orders on the target platform within a preset time period, and extracting and processing it to obtain order feature data includes: Collect the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information; Perform extraction, coding, and normalization processing according to the order basic information, customer information, and commodity information to obtain order feature data; The order feature data includes order basic feature data, commodity feature data, and customer feature data.

3. The order intelligent matching and optimization method according to claim 2, characterized in that The step of processing the order feature data to obtain key feature data and matching corresponding weights includes: Process according to the order basic feature data, commodity feature data, and customer feature data through a preset feature selection method to obtain key feature data; The key feature data includes order price data, order time data, commodity type data, and customer purchase frequency data; Perform weight distribution processing according to the preset analytic hierarchy process, and match corresponding weights to the order price data, order time data, commodity type data, and customer purchase frequency data.

4. The order intelligent matching and optimization method according to claim 3, characterized in that The step of calculating the similarity value between every two orders according to the key feature data and the corresponding weights includes: Select a similarity calculation method according to the key feature data; Process according to the order price data, order time data, commodity type data, and customer purchase frequency data, combined with the corresponding weights, through the similarity calculation method to obtain the similarity value between every two orders among all the orders.

5. The order intelligent matching and optimization method according to claim 4, wherein The step of judging the order matching situation according to the similarity value, performing clustering processing on all the orders, and optimizing the target platform includes: Perform comparison processing on all the similarity values with a preset similarity threshold respectively to obtain corresponding threshold comparison results; Judge the matching status of the orders according to the threshold comparison results; If the similarity value is less than or equal to the preset similarity threshold, the corresponding two orders belong to low-matching orders; If the similarity value is greater than the preset similarity threshold, the corresponding two orders belong to high-matching orders; Perform clustering processing on all the orders according to the matching status, and perform resource optimization processing on the target platform.

6. The order intelligent matching and optimization method according to claim 5, characterized in that It also includes: Monitor the running status of the optimized target platform, and collect the running information after a preset time period; Perform extraction and statistical processing according to the running information to obtain order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data; Perform weighted processing according to the order matching accuracy data, order processing efficiency improvement rate data, and customer satisfaction data to obtain an order optimization effect coefficient; Compare the order optimization effect coefficient with the preset order optimization effect threshold to obtain the second threshold comparison result; Evaluate the optimization effect according to the second threshold comparison result; If the second threshold comparison result is less than the preset threshold, the optimization effect does not meet the standard and a second platform optimization is required.

7. An order intelligent matching and optimization system, characterized in that The system includes: a memory and a processor. The memory includes a program for the order intelligent matching and optimization method. When the program for the order intelligent matching and optimization method is executed by the processor, the following steps are implemented: Collect the order information of all orders on the target platform within a preset time period, and extract and process to obtain order feature data; Process the order feature data to obtain key feature data and match corresponding weights; Calculate the similarity value between every two orders according to the key feature data and the corresponding weights; Judge the order matching situation according to the similarity value, perform clustering processing on all the orders, and optimize the target platform.

8. The order intelligent matching and optimization system according to claim 7, characterized in that The step of collecting the order information of all orders on the target platform within a preset time period, and extracting and processing to obtain order feature data includes: Collect the order information of all orders on the target platform within a preset time period, including order basic information, customer information, and commodity information; Perform extraction, encoding, and normalization processing according to the order basic information, customer information, and commodity information to obtain order feature data; The order feature data includes order basic feature data, commodity feature data, and customer feature data.

9. The order intelligent matching and optimization system according to claim 8, wherein The step of processing the order feature data to obtain key feature data and matching corresponding weights includes: Process according to the order basic feature data, commodity feature data, and customer feature data through a preset feature selection method to obtain key feature data; The key feature data includes order price data, order time data, commodity type data, and customer purchase frequency data; Perform weight allocation processing according to the preset analytic hierarchy process, and match corresponding weights to the order price data, order time data, commodity type data, and customer purchase frequency data.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for the order intelligent matching and optimization method. When the program for the order intelligent matching and optimization method is executed by the processor, the steps of the order intelligent matching and optimization method as described in any one of claims 1 to 6 are implemented.

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