Logistics price data processing method, device and electronic equipment

Abstract

The present application provides a logistics price data processing method, device, and electronic device, which relate to the field of logistics data technology and solve the current technical problem of low efficiency in logistics data processing for logistics platforms. The method includes: pre-processing the price data, service data, and time efficiency data of multiple logistics platforms using a specified pre-processing method to obtain target data; training an initial prediction model based on the target data using an artificial intelligence algorithm to obtain a logistics platform data prediction model; responding to a selection request for logistics services corresponding to multiple target logistics platforms, using the logistics platform data prediction model to compare the logistics prices, logistics services, and logistics time efficiency of the multiple target logistics platforms; automatically matching the multiple target logistics platforms based on the comparison results and a dynamic programming algorithm according to the selection request, and determining the first target logistics platform among the multiple target logistics platforms whose matching degree with the selection request is higher than a preset matching degree.

Description

Technical Field

[0001] The present application relates to the field of logistics data technology, and in particular to a logistics price data processing method, device and electronic equipment. Background Art

[0002] Currently, in the traditional logistics industry, due to the differences in pricing, service, and timeliness among various express delivery companies and other logistics platforms, users often need to conduct comprehensive comparisons when selecting a logistics platform. However, this process is often manual, time-consuming, labor-intensive, and error-prone. Users are unable to determine the most appropriate logistics platform, resulting in low efficiency in logistics data processing for logistics platforms. Summary of the Invention

[0003] The purpose of the present invention is to provide a logistics price data processing method, device and electronic equipment to solve the current technical problem of low logistics data processing efficiency for logistics platforms.

[0004] In a first aspect, an embodiment of the present application provides a method for processing logistics price data, the method comprising:

[0005] Obtain price data, service data, and timeliness data from multiple logistics platforms;

[0006] Preprocessing the price data, the service data, and the timeliness data using a specified preprocessing method to obtain target data; wherein the specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers;

[0007] Based on the target data, an initial prediction model is trained using an artificial intelligence algorithm to obtain a logistics platform data prediction model;

[0008] In response to a selection request for logistics services corresponding to a plurality of target logistics platforms, the logistics prices, logistics services, and logistics timeliness of the plurality of target logistics platforms are compared using the logistics platform data prediction model to obtain a comparison result, and according to the selection request, the plurality of target logistics platforms are automatically matched using the comparison result and a dynamic programming algorithm to determine a first target logistics platform among the plurality of target logistics platforms whose degree of matching with the selection request is higher than a preset matching degree; wherein the selection request includes a user price requirement, a user service requirement, and a user timeliness requirement;

[0009] In response to an addition operation and / or a deletion operation on the logistics project conditions corresponding to the plurality of first target logistics platforms, screening the plurality of first target logistics platforms according to the addition operation and / or the deletion operation to select a final first target logistics platform corresponding to the selection request;

[0010] In response to the feedback data of the client corresponding to the selection request on the final first target logistics platform, the logistics platform data prediction model is adjusted and optimized according to the feedback data.

[0011] In one possible implementation, the logistics platform data prediction model is used to compare the logistics prices of the multiple target logistics platforms to obtain a comparison result, including:

[0012] Acquiring historical logistics and transportation data of the multiple target logistics platforms;

[0013] Analyzing the historical logistics and transportation data through the AI ​​system corresponding to the logistics platform data prediction model to obtain the logistics and transportation speed, logistics and transportation routes, the excellence of the transit warehouse storage environment, and the logistics and transportation safety of each of the multiple target logistics platforms;

[0014] Based on the logistics transportation speeds, the logistics transportation routes, the storage environment of the transit warehouse, and the logistics transportation safety levels of each of the multiple target logistics platforms, the logistics prices of the multiple target logistics platforms are compared to obtain a comparison result; wherein, in the comparison result, the slower the logistics transportation speed, the longer the logistics transportation route, the better the storage environment of the transit warehouse and / or the lower the logistics transportation safety level, the lower the logistics price of the target logistics platform.

[0015] In one possible implementation, it also includes:

[0016] Obtaining item property data and historical sales data for target type goods in the logistics platform; the item property data includes item storage temperature and / or item storage humidity;

[0017] Determining, based on the historical sales data, a first region from among the multiple regions to which the target type of goods are sold, where the historical sales volume is greater than a first preset sales volume;

[0018] Determine, by the AI ​​system, a location for a logistics transfer warehouse for the target type of goods based on the historical sales data of the target type of goods and the item storage temperature and / or the item storage humidity, so that the logistics transfer warehouse is located as close to the first region as possible while maintaining storage conditions that meet the item storage temperature and / or the item storage humidity;

[0019] The distribution logistics price of the target type of goods in the logistics platform is adjusted according to the setting location of the logistics transit warehouse to obtain the adjusted price data of the logistics platform.

[0020] In a possible implementation, after obtaining historical sales data for target type goods in the logistics platform, the method further includes:

[0021] Determining, based on the historical sales data, a sales peak time period during which the historical sales volume of the target type of goods is greater than a second preset sales volume;

[0022] Determining, from the plurality of regions to which the target type of goods are sold, a second region where the peak sales period is greater than a preset period, and a third region where the peak sales period is less than or equal to the preset period;

[0023] Adjusting the delivery area priority of the target type of goods to obtain an adjusted delivery area priority result, so that the delivery priority of the target type of goods to the second area is greater than the delivery priority to the third area;

[0024] The distribution logistics price of the target type of goods in the logistics platform is adjusted according to the distribution area priority result to obtain the adjusted price data of the logistics platform; the higher the distribution area priority result, the higher the distribution logistics price.

[0025] In a possible implementation, the number of locations where the logistics transfer warehouse is set includes multiple; and adjusting the distribution logistics price of the target type of goods according to the location where the logistics transfer warehouse is set includes:

[0026] Obtaining the warehouse usage price of each logistics transit warehouse location;

[0027] determining a warehouse storage cost of the target type of goods based on the storage temperature and / or the storage humidity of the goods;

[0028] Determine the comprehensive total logistics cost corresponding to the location of the logistics transfer warehouse based on the warehouse usage price, the warehouse storage cost, and the transportation distance logistics cost based on the location of the logistics transfer warehouse;

[0029] Comparing the multiple comprehensive total logistics costs corresponding to the multiple logistics transfer warehouse setting locations to obtain a comparison result, and determining the multiple first logistics transfer warehouse setting locations whose comprehensive total logistics costs are lower than the preset logistics costs in the comparison results as candidate logistics transfer warehouse setting locations for the target type of goods;

[0030] Acquire first historical sales data and user feedback data of the target type of goods within a preset distance range around the location where the alternative logistics transfer warehouse is set;

[0031] Determine a target alternative logistics transfer warehouse setting location with the highest first historical sales data and the highest user feedback data from the multiple alternative logistics transfer warehouse setting locations, and determine the target alternative logistics transfer warehouse setting location as the final logistics transfer warehouse setting location;

[0032] According to the final comprehensive total logistics cost corresponding to the final logistics transit warehouse setting location, the distribution logistics price of the target type of goods in the logistics platform is adjusted to obtain the adjusted price data of the logistics platform.

[0033] In one possible implementation, it also includes:

[0034] Obtaining an actual return rate of logistics goods on the logistics platform by a first user belonging to a fourth region;

[0035] If the actual return rate is greater than or equal to the preset return rate, obtaining the regional environment data of the fourth region and the user habit data of the first user;

[0036] Adjusting logistics configuration items for delivery to the fourth region based on the regional environment data and the user habit data to obtain logistics configuration results for the adjusted logistics configuration items; wherein the logistics configuration items include any one or more of a storage temperature configured during the logistics process, an adjustable preference type corresponding to the logistics object, and an adjustable quality type corresponding to the logistics object; and the preference type includes any one or more of a taste preference, a temperature preference, a color preference, a shape preference, and a size preference;

[0037] The logistics price of the logistics platform for delivering to the fourth region is adjusted according to the logistics configuration result to obtain adjusted price data of the logistics platform.

[0038] In one possible implementation, it also includes:

[0039] Obtaining repurchase rates and user evaluation data for logistics goods on the logistics platform by users in multiple regions;

[0040] Analyze the user's tolerance for goods through the AI ​​system based on the repurchase rate and user evaluation data;

[0041] For the fifth region corresponding to the second user whose cargo tolerance is lower than the first preset tolerance, the shipping priority of the logistics cargo delivered to the fifth region is increased; and for the sixth region corresponding to the third user whose cargo tolerance is higher than the second preset tolerance, the shipping priority of the logistics cargo delivered to the sixth region is decreased;

[0042] The distribution logistics prices for the logistics platform delivered to the fifth region and the sixth region are adjusted according to the adjusted delivery priority level to obtain the adjusted price data of the logistics platform; the higher the adjusted delivery priority level, the higher the distribution logistics price, and the lower the adjusted delivery priority level, the lower the distribution logistics price.

[0043] In a second aspect, a logistics price data processing device is provided, the device comprising:

[0044] The acquisition module is used to obtain price data, service data, and timeliness data from multiple logistics platforms;

[0045] a preprocessing module, configured to preprocess the price data, the service data, and the timeliness data using a specified preprocessing method to obtain target data; wherein the specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers;

[0046] A training module, configured to train the initial prediction model using an artificial intelligence algorithm based on the target data to obtain a logistics platform data prediction model;

[0047] a matching module for responding to a selection request for corresponding logistics services of multiple target logistics platforms, comparing the logistics prices, logistics services, and logistics timeliness of the multiple target logistics platforms using the logistics platform data prediction model to obtain a comparison result, and automatically matching the multiple target logistics platforms according to the selection request using the comparison result and a dynamic programming algorithm to determine a first target logistics platform among the multiple target logistics platforms whose degree of matching with the selection request is higher than a preset matching degree; wherein the selection request includes user price requirements, user service requirements, and user timeliness requirements;

[0048] a screening module, configured to respond to an addition operation and / or a deletion operation on the logistics project conditions corresponding to the plurality of first target logistics platforms, and screen, according to the addition operation and / or the deletion operation, a final first target logistics platform corresponding to the selection request among the plurality of first target logistics platforms;

[0049] An adjustment module is used to respond to the feedback data of the client corresponding to the selection request on the final first target logistics platform, and adjust and optimize the logistics platform data prediction model according to the feedback data.

[0050] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0051] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method described in the first aspect above.

[0052] The embodiments of the present application bring the following beneficial effects:

[0053] The embodiments of the present application provide a logistics price data processing method, device and electronic device, which can obtain price data, service data and time-efficiency data of multiple logistics platforms; pre-process the price data, service data and time-efficiency data by a specified pre-processing method to obtain target data; wherein the specified pre-processing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers; based on the target data, an initial prediction model is trained using an artificial intelligence algorithm to obtain a logistics platform data prediction model; in response to a selection request for logistics services corresponding to multiple target logistics platforms, the logistics prices, logistics services and logistics time-efficiency of the multiple target logistics platforms are compared using the logistics platform data prediction model to obtain a comparison result, and According to the selection request, the plurality of target logistics platforms are automatically matched through the comparison results and a dynamic programming algorithm to determine a first target logistics platform among the plurality of target logistics platforms that matches the selection request more than a preset matching degree; wherein the selection request includes a user price requirement, a user service requirement, and a user timeliness requirement; in response to adding and / or deleting operations on the logistics project conditions corresponding to the plurality of first target logistics platforms, the plurality of first target logistics platforms are screened according to the adding and / or deleting operations to select the final first target logistics platform corresponding to the selection request; in response to the feedback data of the client corresponding to the selection request on the final first target logistics platform, the logistics platform data prediction model is adjusted and optimized according to the feedback data. In this solution, the logistics platform data prediction model is used to compare and match the relevant data of the logistics platforms, which can provide users with a simple, intuitive, and efficient express delivery selection solution, while ensuring service quality and meeting the personalized needs of users, improving the user experience, helping to distinguish between existing comparison solutions and logistics platform selection solutions, improving user experience and satisfaction, and enabling users to quickly and accurately find a logistics platform that meets their personalized needs, thereby solving the current technical problem of low logistics data processing efficiency for logistics platforms.

[0054] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flow chart of the logistics price data processing method provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of a graphical user interface for logistics platform recommendation results in the logistics price data processing method provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of a graphical user interface in which a user can perform add / modify operations in the logistics price data processing method provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of another graphical user interface in which a user can perform add / modify operations in the logistics price data processing method provided in an embodiment of the present application;

[0060] Figure 5 A schematic diagram of another graphical user interface in which a user can perform add / modify operations in the logistics price data processing method provided in an embodiment of the present application;

[0061] Figure 6 A schematic diagram of another graphical user interface in which a user can perform add / modify operations in the logistics price data processing method provided in an embodiment of the present application;

[0062] Figure 7 A schematic diagram of another graphical user interface in which a user can perform add / modify operations in the logistics price data processing method provided in an embodiment of the present application;

[0063] Figure 8 A schematic diagram of the structure of a logistics price data processing device provided in an embodiment of the present application;

[0064] Figure 9 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0066] The terms "including," "having," and any variations thereof, as used in the embodiments of this application, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0067] Currently, users often struggle to determine the most suitable courier because factors such as price, service, and delivery time may differ from user to user. Specifically, due to the varying pricing, service, and delivery timelines across courier companies, users often need to conduct extensive comparisons when choosing a courier. This process is both time-consuming and labor-intensive, as well as prone to errors, as it is difficult for users to fully understand the service quality and delivery timelines of all courier companies. Furthermore, each user may place varying weights on factors such as price, service, and delivery time, making it difficult for users to find a courier that fully meets their individual needs, leading to unmet needs. Furthermore, due to inconsistent pricing and service standards across various courier companies, users struggle to establish a unified standard for comparing the pros and cons of various courier companies, resulting in a lack of a unified comparison standard. This demonstrates the low efficiency of logistics data processing on current logistics platforms.

[0068] Based on this, the embodiments of the present application provide a logistics price data processing method, device and electronic device, which can solve the current technical problem of low logistics data processing efficiency for logistics platforms.

[0069] The embodiments of the present invention are further described below with reference to the accompanying drawings.

[0070] Figure 1 This is a flow chart of a logistics price data processing method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0071] Step S110, obtaining price data, service data and time efficiency data of multiple logistics platforms.

[0072] As an optional implementation, data collection can be used to obtain price, service, and time efficiency data from multiple logistics platforms. Specifically, by establishing partnerships with major express delivery companies, relevant data on prices, services, and time efficiency can be collected. This data includes, but is not limited to, the delivery companies' freight rates, delivery times, network coverage, and complaint rates.

[0073] To obtain price data, the average price, price range, and price change trends of each courier company can be obtained through the courier company's official website, third-party price comparison platforms, or market research. To obtain service data, user evaluation platforms, social media, questionnaires, etc. can be used to collect user evaluations of courier companies' services, including service attitude, professionalism, and problem-solving capabilities. To obtain delivery speed data, the average delivery time and punctuality rate of each courier company can be calculated through the timeliness data provided by the courier company, user feedback, and data from third-party monitoring agencies. To obtain complaint rate data, the number of complaints and complaint rates of each courier company can be obtained from channels such as consumer associations, courier company customer service departments, and complaint platforms, and the types of complaints and their resolution can be analyzed.

[0074] Step S120 , pre-processing the price data, service data, and timeliness data using a specified pre-processing method to obtain target data.

[0075] The specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers.

[0076] In one possible implementation, the data preprocessing process can utilize big data technologies to process the collected data, including operations such as data cleaning and preprocessing, to ensure data accuracy and usability. During data preprocessing, operations such as filtering, deduplication, filling missing values, and removing outliers can be performed on the data to improve model training effectiveness. Data cleaning can include removing duplicate, erroneous, or invalid data to ensure data accuracy and consistency.

[0077] During data preprocessing, since data from different dimensions may have different dimensions and magnitudes, the data can be standardized to facilitate unified comparison and analysis. The collected data can also be categorized and organized by dimensions such as courier company and time period to facilitate subsequent analysis.

[0078] Step S130: Using artificial intelligence algorithms to train the initial prediction model based on the target data to obtain a logistics platform data prediction model.

[0079] Regarding the model training process, it should be noted that artificial intelligence algorithms (such as neural networks, decision trees, etc.) can be used to train the processed data to derive a prediction model between the price, service, and timeliness of each express company. During the model training process, different features can be combined and optimized to obtain the best prediction effect. As an example, for the distribution of weights in the model, different weights can be assigned to price, service evaluation, delivery speed, complaint rate, etc. according to the importance of each dimension of data. The distribution of weights can be based on expert scoring, user surveys, or historical data analysis. For the calculation of the comprehensive score in the model, the weighted average method or other appropriate mathematical models can be used to weight the data of each dimension and sum them to obtain the comprehensive score of each express company.

[0080] Step S140, in response to a selection request for logistics services corresponding to multiple target logistics platforms, the logistics prices, logistics services and logistics timeliness of the multiple target logistics platforms are compared using the logistics platform data prediction model to obtain a comparison result, and according to the selection request, the comparison result and the dynamic programming algorithm are used to automatically match the multiple target logistics platforms to determine the first target logistics platform among the multiple target logistics platforms whose matching degree with the selection request is higher than the preset matching degree.

[0081] The selection request includes the user's price requirements, service requirements, and time requirements. In one possible implementation, when the user selects a logistics platform (express delivery service), the system can automatically match the most suitable logistics platform (express delivery company) based on the user's personalized requirements (such as price, service, timeliness, etc.) through the price comparison module. Figure 2 As shown, the logistics platform recommendation results after price comparison are displayed to the user. The price comparison module can be implemented based on a predictive model and a dynamic programming algorithm, and can provide the optimal matching solution based on the user's personalized needs and the actual situation of the logistics platform (express company). For example, it is also possible to rank and compare express companies based on the comprehensive score, and analyze the advantages and disadvantages of each company. For example, it is also possible to observe the performance changes of each express company in different time periods and analyze its development trends through methods such as time series analysis. It is also possible to use visualization tools such as charts and maps to display the evaluation results in an intuitive way, realize visual display, and facilitate user understanding and use.

[0082] In one optional implementation, the most suitable courier can be automatically matched to the user's personalized needs. For example, based on user needs analysis and multi-dimensional data evaluation results, an intelligent matching algorithm is developed. This algorithm automatically selects the most suitable courier options from a large number of courier companies based on the user's personalized needs and ranks them according to their degree of compatibility. Furthermore, given that user needs and market conditions may change at any time, this intelligent matching algorithm should be capable of dynamic adjustment. By real-time monitoring of user behavior and market data, and regularly updating courier company evaluation results, dynamic adjustment strategies can be implemented to ensure the algorithm's timeliness and accuracy.

[0083] For example, in the process of analyzing various aspects of user needs, a comprehensive user needs analysis system can be established. This system can identify and analyze user behavior on the platform, including search history, filter conditions, purchase history, etc., to gain a deep understanding of user preferences and needs in terms of price, service, timeliness, etc. For example, in the process of analyzing the specific logic of user needs, the following steps and principles can be followed to ensure that the system can comprehensively and accurately capture and understand user needs:

[0084] First, clarify the goals and scope of the needs analysis. First, determine the project objectives. This means clarifying the overall goals of the project or product, specifically the problem it will solve and the user needs it will meet. Define the scope of the needs analysis: Based on the project objectives, define the specific scope of the needs analysis, including functional modules, business processes, and user groups.

[0085] Next, gather user needs. This can be done through user interviews, covering users with diverse backgrounds, roles, and needs. Alternatively, a detailed questionnaire can be designed and distributed online or offline to the target user group to gather extensive user feedback and opinions. During competitive product analysis, analyze the features, strengths and weaknesses, and user reviews of similar products on the market to understand industry trends and user preferences. During data analysis, leverage existing data (such as user behavior data and transaction data) to mine and analyze potential user needs and issues.

[0086] Next, requirements analysis and organization are conducted. First, requirements classification involves categorizing and organizing the collected requirements according to dimensions such as functional modules, business processes, and user groups. Next, requirements prioritization involves prioritizing requirements based on factors such as urgency, importance, and implementation difficulty. Requirements verification then involves verifying and revising the requirements through methods such as prototyping and user testing to ensure their accuracy and feasibility.

[0087] During the development of the requirements specification, the requirements are written, which means that the analyzed and organized requirements are recorded in a documented form, including the requirements description, functional requirements, performance indicators, constraints, etc. During the review and confirmation, relevant personnel are organized to review and confirm the requirements specification to ensure that all parties have a consistent understanding of the requirements.

[0088] Tracking and change management of user requirements. During the project development process, we continuously track the implementation of requirements to ensure that the development results are consistent with the requirements specifications. For changes in requirements, we establish a strict change management process, including change application, evaluation, approval, implementation, and verification.

[0089] As an example, technical and tool support can also be introduced. For example, utilizing specialized requirements analysis software (such as client-server software) can improve the efficiency and accuracy of requirements analysis. This software typically includes functional modules such as system management, project views, requirement type views, and requirement views. Automated tools (such as data mining tools and user behavior analysis tools) can be used to assist with requirements analysis, improving the efficiency and accuracy of data processing. For continuous optimization and iteration, user feedback can be collected first. After the product is launched, user feedback and opinions can be continuously collected to understand user usage and evolving needs. Then, requirements iteration can be performed. Based on user feedback and market changes, product features can be continuously optimized and iterated to meet evolving user needs. By following these steps and principles, a comprehensive user needs analysis system can be established to ensure that a project or product accurately captures and meets user needs, improving product competitiveness and user satisfaction.

[0090] In one possible implementation, data from multiple dimensions (including price, service evaluation, delivery speed, complaint rate, etc.) can be integrated to conduct a comprehensive evaluation of major express delivery companies, thereby achieving multi-dimensional data evaluation. These data can be obtained through cooperation with express delivery companies, or collected through user feedback, third-party evaluations, and other channels.

[0091] Step S150, in response to the adding operation and / or deleting operation on the logistics project conditions corresponding to the multiple first target logistics platforms, the final first target logistics platform corresponding to the selected request is screened and selected from the multiple first target logistics platforms according to the adding operation and / or deleting operation.

[0092] like Figure 2As shown in Figure 1, based on the user's browsing history and purchasing behavior, the system can recommend the courier company (first target logistics platform) that best meets the user's needs. The implementation of the recommendation module is based on the collaborative filtering algorithm, which can recommend the most suitable courier company (first target logistics platform) based on the user's interests and behavior data. Then, the user can also manually add or delete certain conditions to filter among multiple first target logistics platforms, such as Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 and Figure 7 As shown, further filtering can be performed by adding operations and / or removing operations.

[0093] In an optional implementation, technical means can be used to track the user's browsing history and purchasing behavior on the platform, including the courier companies viewed, service types, price ranges, etc., as well as the final purchase decisions, to achieve user behavior tracking. Based on user behavior data, a user portrait can be constructed. By analyzing the user's browsing and purchasing habits, identifying the user's preferences and needs, as well as potential changes in needs, the construction of the user portrait can be achieved. Based on the user portrait and historical data, a personalized recommendation algorithm can be designed. The algorithm can predict which courier companies the user may be interested in and the user's possible choices in specific situations. When displaying personalized recommendation results on the user interface, the recommendation results may include courier companies similar to the user's historical choices, high-quality options that meet the user's current needs, etc. At the same time, flexible filtering and sorting functions are provided so that users can adjust according to their needs to achieve personalized recommendation display.

[0094] Specifically, the implementation process of the above-mentioned user behavior tracking may include the following: The system can track shipping behavior. For ordering methods, it tracks whether users place orders through official websites, apps, mini-programs, or third-party platforms. For shipping frequency, it analyzes users' shipping frequency to understand their shipping habits and needs. For shipping type, it distinguishes whether it is personal or corporate shipping, as well as the type and characteristics of the items being shipped. Furthermore, the system can also track inquiry behavior. For inquiry methods, it tracks whether users inquire about logistics information through official websites, apps, text messages, phone calls, or third-party platforms. For inquiry frequency, it analyzes how often users inquire about logistics information to understand the level of user interest in their packages. For inquiry result feedback, it collects user satisfaction and feedback on the inquiry results. In addition, the system can also track complaint behavior. For complaint channels, it tracks whether users file complaints through official customer service, social media, complaint platforms, or other channels. For complaint reasons, it analyzes the specific reasons for users' complaints, such as package loss, delays, damage, etc. For complaint handling, it records the process and results of complaint handling and evaluates the efficiency and effectiveness of complaint handling.

[0095] Specifically, the detailed process for constructing the aforementioned user profile can include the following steps: First, data collection. For user data, basic user information (such as age, gender, and location), historical behavior data (such as browsing history, purchase history, and click history), and user feedback data (such as ratings and reviews) are collected. For item data, item attribute information (such as price, brand, category, and description) as well as inter-item correlation information (such as similarity and complementarity) are collected. Next, data preprocessing is performed. Data cleaning removes duplicate, erroneous, or invalid data to ensure data accuracy and consistency. Data standardization involves unified processing of data from different sources and formats for subsequent analysis. Feature extraction extracts features from the raw data that are useful for recommendations, such as user interests and item popularity. Next, algorithm selection and design is performed. Based on the user's historical behavior data, other users or items with similar interests to the target user are identified to recommend similar items to the target user. Collaborative filtering algorithms include user-based and item-based collaborative filtering. By analyzing item content (such as text descriptions and tags) and user preferences, content-based recommendation algorithms recommend items with similar content to those users have previously liked. This implements a content-based recommendation algorithm. Combining the strengths of collaborative filtering and content-based recommendation algorithms, weighting and fusion methods are used to improve the accuracy and diversity of recommendations, resulting in a hybrid recommendation algorithm. Deep learning techniques, such as deep neural networks, automatically extract user and item features and model complex nonlinear relationships, enabling more accurate recommendations and implementing a deep learning recommendation algorithm. Next comes model training and optimization. Training data partitioning involves dividing the collected data into training and test sets for model training and evaluation. Model training uses the training set data to train the recommendation algorithm model and adjust model parameters to optimize recommendation performance. Model evaluation uses the test set data to evaluate the trained model, measuring recommendation performance through metrics such as recall, precision, coverage, and novelty. Model optimization involves iteratively optimizing the model based on the evaluation results, including adjusting algorithm parameters, refining feature extraction methods, and introducing new data sources. Finally, the recommendation results are generated and displayed. For recommendation list generation, a recommendation list is generated for the user based on the trained recommendation algorithm model, including recommended items, reasons for recommendation, etc.; for recommendation result sorting, the items in the recommendation list are sorted, and the items that the user is most likely to be interested in are placed in front.

[0096] Specifically, for the flexible filtering and sorting functions mentioned above, we first conduct a demand analysis to clarify the user's specific needs for filtering and sorting. This usually includes: filtering function, that is, users may need to filter express services according to different conditions (such as express companies, estimated delivery time, price range, whether cash on delivery is supported, etc.); sorting function, that is, users may want to arrange the filtering results according to a certain order (such as price from low to high, estimated delivery time from short to long, evaluation score from high to low, etc.). Then comes the database and data model design. For database design, ensure that the database can store all necessary express service information, including express companies, prices, estimated delivery times, user reviews, etc.; for data models, build a reasonable data model to support efficient query and filtering operations. For example, you can use indexes to speed up queries, or store commonly used filtering conditions as separate fields to reduce the amount of calculation. For back-end development, in the API design phase, a RESTful API or other interface is designed to receive filtering and sorting requests from the front-end and return the corresponding results. For query optimization, data is retrieved using SQL or NoSQL query languages ​​based on the query conditions. To improve query efficiency, query statements can be optimized using techniques such as indexes and join queries. For sorting algorithms, a sorting algorithm is implemented on the back-end to sort data based on the user-selected sort fields and order. This can be achieved using built-in database sorting functionality (such as the SQL ORDER BY clause), or by sorting the data in memory before returning it to the front-end. For front-end development, the user interface design involves designing an intuitive and easy-to-use interface, including filter input fields and sorting option drop-down menus. For interaction logic, JavaScript code is written to handle user filtering and sorting operations. When users change filter conditions or sorting options, the front-end sends a corresponding request to the back-end and updates the page to display the new results. For data presentation, tables, lists, or other suitable controls are used to display the filtered and sorted express service information. To enhance the user experience, features such as paging and loading indicators can be added. Finally, in terms of testing and optimization, functional testing is a comprehensive test of the filtering and sorting functions to ensure that they can correctly respond to user operations and return expected results; performance testing: testing the performance of the system when processing large amounts of data, including response time, throughput, etc.; if the performance is poor, optimization is required; in terms of user feedback, user feedback is collected and the functions are iterated and optimized based on user feedback.

[0097] Step S160 , in response to the feedback data of the client corresponding to the selection request to the final first target logistics platform, the logistics platform data prediction model is adjusted and optimized according to the feedback data.

[0098] Users can evaluate and provide feedback on recommended courier companies, allowing the platform to continuously optimize its recommendation algorithm and improve service quality. The feedback module is based on user evaluation and feedback data, enabling timely collection of user opinions and suggestions, and adjustments and optimization of the recommendation algorithm, achieving continuous optimization and feedback. Specifically, user feedback collection is first implemented, regularly collecting user feedback on evaluation results to understand user needs and expectations. Next, model optimization is performed, continuously optimizing and improving the evaluation model based on user feedback and actual conditions to improve the accuracy and reliability of the evaluation. Finally, data updates are performed, regularly updating data in various dimensions to ensure the timeliness and accuracy of the evaluation results.

[0099] Comparing and matching data across logistics platforms (express delivery companies) using a logistics platform data prediction model can provide users with a simple, intuitive, and efficient express delivery selection solution, while ensuring service quality and meeting users' personalized needs. This improves the user experience and helps differentiate between existing comparison solutions and logistics platform selection solutions, enhancing user experience and satisfaction. This allows users to quickly and accurately find a logistics platform that meets their personalized needs, while also increasing the logistics platform's business volume and reputation. This feature not only enhances the user experience but also improves the service quality of express delivery companies, resolving the current technical issue of low logistics data processing efficiency for logistics platforms.

[0100] In the embodiments of the present application, users can search for services from multiple courier companies simultaneously on a single platform, eliminating the need to log into the websites or apps of different courier companies separately. This saves time and effort, and provides convenient and efficient user experience. Furthermore, a wider range of courier options is provided, allowing users to select the most suitable courier company based on their needs and preferences. This allows users to make informed choices based on their individual circumstances. Different courier companies may vary in terms of speed, price, and service coverage. Furthermore, a comparison function is provided for courier fees, allowing users to directly compare prices from different courier companies on a single page and select the service with the best value for money. This not only helps users save costs but also creates more room for competition among courier companies. Furthermore, based on the user's browsing history and purchasing behavior, the system can recommend the courier company that best meets their personalized needs. This personalized recommendation function can enhance the user experience and make it easier for users to choose the courier service that best suits their needs. Furthermore, users can also provide evaluation and feedback on recommended courier companies, allowing the system to continuously optimize its recommendation algorithm and improve service quality. This feedback mechanism can increase user trust and satisfaction with the platform. The price comparison function and personalized recommendations can attract more users to the platform and select the appropriate courier company, thereby increasing the courier company's business volume and reputation. To sum up, the system's comparison function for logistics prices and other aspects has achieved convenient and fast inquiries, more diverse choices, price comparison advantages, personalized recommendations, user feedback, and improved express company business volume and reputation, bringing more convenience and value to users and logistics platforms such as express companies.

[0101] The above steps are described in detail below.

[0102] In some embodiments, the logistics platform data prediction model is used to compare the logistics prices of multiple target logistics platforms in step S140 to obtain a comparison result, which may specifically include the following steps:

[0103] Obtain historical logistics and transportation data for multiple target logistics platforms; analyze this historical logistics and transportation data through the AI ​​system corresponding to the logistics platform data prediction model to obtain the logistics and transportation speed, logistics and transportation routes, the excellence of the transit warehouse storage environment, and the logistics and transportation safety level of each of the multiple target logistics platforms;

[0104] Based on the logistics transportation speeds, logistics transportation routes, transit warehouse storage environments, and logistics transportation safety levels of multiple target logistics platforms, the logistics prices of multiple target logistics platforms are compared to obtain comparison results; among which, the slower the logistics transportation speed, the longer the logistics transportation route, the better the transit warehouse storage environment and / or the lower the logistics transportation safety level in the comparison results, the lower the logistics price of the target logistics platform.

[0105] For example, the price adjustment process for each courier company (logistics platform) can be based on warehouse location, shipping distance, and the main road conditions of the transportation route. For example, through a detailed comparison by the AI ​​system, if a courier company's delivery speed is slow, the price of that courier company can be lowered; if a courier company's delivery speed is fast, the price of that courier company can be raised. This logistics price processing method makes the logistics price processing results more reasonable and improves the user experience.

[0106] In some embodiments, the method may further include the following steps:

[0107] Obtaining item property data and historical sales data for target type goods from the logistics platform; the item property data including item storage temperature and / or item storage humidity; and determining, based on the historical sales data, a first region from among multiple regions to which the target type goods were sold, where the historical sales volume exceeds a first preset sales volume;

[0108] Based on the historical sales data of the target type of goods and the storage temperature and / or storage humidity of the items, the AI ​​system is used to determine the location of the logistics transit warehouse for the target type of goods, so that the location of the logistics transit warehouse is as close to the first region as possible under storage conditions that meet the storage temperature and / or storage humidity of the items; the distribution logistics price of the target type of goods in the logistics platform is adjusted according to the location of the logistics transit warehouse to obtain adjusted price data of the logistics platform.

[0109] As an optional implementation, a logistics and warehouse quotation integration system can be used to select transit warehouses and shipment priorities in other regions based on the local nature and sales of a particular type of goods through an AI system. The system then adjusts logistics prices based on the transit warehouse's location. This logistics pricing approach makes the processing of logistics prices more reasonable and improves the user experience.

[0110] In some embodiments, after obtaining historical sales data for target type goods in the logistics platform, the method may further include the following steps:

[0111] Determining, based on historical sales data, a peak sales period during which the historical sales volume of the target type of goods exceeds a second preset sales volume; determining, from the plurality of regions to which the target type of goods are sold, a second region having a peak sales period exceeding the preset period, and a third region having a peak sales period equal to or less than the preset period;

[0112] Adjusting the delivery area priority of the target type of goods to obtain an adjusted delivery area priority result, so that the delivery priority of the target type of goods to the second area is greater than the delivery priority to the third area;

[0113] The distribution logistics price of the target type of goods in the logistics platform is adjusted according to the distribution area priority result to obtain the adjusted price data of the logistics platform; the higher the distribution area priority result, the higher the distribution logistics price.

[0114] As an example, based on the local sales of a certain type of goods, the AI ​​system selects transit warehouses and delivery priorities in other regions. For example, for goods such as ice cream that sell in large quantities during hot weather, the preferred delivery area is a southern city; during periods of large climate differences between different regions (such as September when it snows in the Northeast and it is hot in Sichuan), ice cream is preferably shipped from warehouses in the Northeast to avoid long-term accumulation of goods due to local sales conditions, and finally the corresponding goods storage and logistics prices are calculated. This logistics price processing method makes the logistics price processing results more reasonable and improves user experience.

[0115] In some embodiments, the number of locations where the logistics transfer warehouse is set includes multiple; the process of adjusting the distribution logistics price of the target type of goods according to the location where the logistics transfer warehouse is set may specifically include the following steps:

[0116] Obtain the warehouse usage price for each logistics transit warehouse location; determine the warehouse storage cost for the target type of goods based on the item storage temperature and / or item storage humidity; determine the comprehensive total logistics cost corresponding to the logistics transit warehouse location based on the warehouse usage price, warehouse storage cost, and the transportation logistics cost based on the logistics transit warehouse location;

[0117] Comparing the multiple comprehensive total logistics costs corresponding to the multiple logistics transfer warehouse setting locations to obtain a comparison result, and determining the multiple first logistics transfer warehouse setting locations with the comprehensive total logistics costs lower than the preset logistics costs in the comparison results as the alternative logistics transfer warehouse setting locations for the target type of goods;

[0118] Obtaining first historical sales data and user feedback data of target type goods within a preset distance range around the alternative logistics transfer warehouse setting location; determining a target alternative logistics transfer warehouse setting location with the highest first historical sales data and user feedback data from multiple alternative logistics transfer warehouse setting locations, and determining the target alternative logistics transfer warehouse setting location as the final logistics transfer warehouse setting location;

[0119] According to the final comprehensive total logistics cost corresponding to the final logistics transit warehouse setting location, the distribution logistics price of the target type of goods in the logistics platform is adjusted to obtain the adjusted price data of the logistics platform.

[0120] For example, first determine whether it is fresh cold chain goods (special item storage temperature requirements). If so, confirm to set up ordinary warehouses in low temperature climate areas, instead of low temperature functional warehouses in other areas, to reduce warehouse storage costs. Furthermore, it is also possible to compare and comprehensively judge through the AI ​​system: search the quotes of local warehouses through the system (set up a system interface with the local housing rental system for data communication to realize the inquiry and quotation of warehouse rental in a certain place), calculate the first comprehensive logistics total cost based on the warehouse quote, warehouse storage cost and transportation distance logistics cost, and compare the second comprehensive logistics total cost of warehousing and storing goods in other regions through the AI ​​system, select the goods storage location corresponding to the lower comprehensive logistics total cost as the final goods storage method, and calculate the logistics price corresponding to the final goods storage method. In the embodiment of the present application, it is also possible to combine the type of goods, regional warehouse data, logistics transportation data, goods sales data and user feedback data, perform data comparison through IA, perform data balancing algorithm through IA based on logistics cost and user experience results, and select the optimal logistics method and final logistics price for the goods. Through this logistics price processing method, the adjustment result of the logistics price is more reasonable and the user experience is improved.

[0121] In some embodiments, the method may further include the following steps:

[0122] Obtaining an actual return rate of goods on the logistics platform by a first user in a fourth region; if the actual return rate is greater than or equal to a preset return rate, obtaining regional environment data of the fourth region and user habit data of the first user;

[0123] The logistics configuration items for delivery to the fourth region are adjusted based on the regional environmental data and the user habit data to obtain the logistics configuration results of the adjusted logistics configuration items; wherein the logistics configuration items include any one or more of the storage temperature configured in the logistics process, the adjustable preference type corresponding to the logistics object, and the adjustable quality type corresponding to the logistics object; the preference type includes any one or more of the taste preference, temperature preference, color preference, shape preference, and size preference; the delivery logistics price for delivery to the fourth region in the logistics platform is adjusted based on the logistics configuration results to obtain the adjusted price data of the logistics platform.

[0124] As an optional implementation method, shipments are made based on the regional environmental data, user habit data, and user return rate of each region. For example, if the user return rate of a region is higher than a preset value (e.g., the user return rate of users in a certain region is relatively high), then priority will be given to sending high-quality goods of the same type to users in that region; if the user return rate of a region is lower than or equal to a preset value (e.g., the user return rate of users in a certain region is relatively low), then priority will be given to sending low-quality goods of the same type to users in that region. This logistics price processing method makes the adjustment results of logistics prices more reasonable, improving user experience.

[0125] In some embodiments, the method may further include the following steps:

[0126] Obtain repurchase rates and user evaluation data for goods on the logistics platform from users in multiple regions; use the AI ​​system to analyze users' tolerance for goods based on the repurchase rates and user evaluation data;

[0127] For the fifth region corresponding to the second user whose cargo tolerance is lower than the first preset tolerance, the shipping priority of the logistics goods delivered to the fifth region is increased. For the sixth region corresponding to the third user whose cargo tolerance is higher than the second preset tolerance, the shipping priority of the logistics goods delivered to the sixth region is decreased.

[0128] According to the adjusted delivery priority level, the delivery logistics prices to the fifth and sixth regions in the logistics platform are adjusted to obtain the adjusted price data of the logistics platform; the higher the adjusted delivery priority level, the higher the delivery logistics price, and the lower the adjusted delivery priority level, the lower the delivery logistics price.

[0129] For example, the AI ​​system first reads the user's repurchase rate and reviews, and then uses these to determine the user's tolerance for goods. If the tolerance is low, they will be given priority for shipment, meaning that the delivery speed will be adjusted faster. If the tolerance is high, the shipment priority will be lowered, the delivery speed will be adjusted slower, and a gift will be sent to the user after a preset period of time. This balance is then used to calculate the corresponding storage and logistics prices. This logistics price processing method makes the logistics price adjustment results more reasonable and improves the user experience.

[0130] Figure 8 A structural diagram of a logistics price data processing device is provided. Figure 8 As shown, the logistics price data processing device 800 includes:

[0131] Acquisition module 801, used to obtain price data, service data and time efficiency data of multiple logistics platforms;

[0132] A preprocessing module 802 is configured to preprocess the price data, the service data, and the timeliness data using a specified preprocessing method to obtain target data; wherein the specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers;

[0133] A training module 803 is configured to train the initial prediction model using an artificial intelligence algorithm based on the target data to obtain a logistics platform data prediction model;

[0134] Matching module 804 is configured to respond to a selection request for corresponding logistics services of multiple target logistics platforms, compare the logistics prices, logistics services, and logistics timeliness of the multiple target logistics platforms using the logistics platform data prediction model, obtain a comparison result, and automatically match the multiple target logistics platforms based on the selection request using the comparison result and a dynamic programming algorithm to determine a first target logistics platform among the multiple target logistics platforms that has a higher degree of matching with the selection request than a preset degree of matching; wherein the selection request includes a user's price requirement, a user's service requirement, and a user's timeliness requirement;

[0135] A screening module 805 is configured to respond to an addition operation and / or a deletion operation on the logistics project conditions corresponding to the plurality of first target logistics platforms and, based on the addition operation and / or the deletion operation, screen the plurality of first target logistics platforms for a final first target logistics platform that is finally selected and corresponds to the selection request;

[0136] The adjustment module 806 is used to respond to the feedback data of the client corresponding to the selection request on the final first target logistics platform, and adjust and optimize the logistics platform data prediction model according to the feedback data.

[0137] The logistics price data processing device provided in the embodiment of the present application has the same technical features as the logistics price data processing method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0138] An electronic device provided in an embodiment of the present application is as follows: Figure 9 As shown, the electronic device 900 includes a processor 902 and a memory 901 , wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0139] See also Figure 9The electronic device further includes: a bus 903 and a communication interface 904, a processor 902, a communication interface 904 and a memory 901 connected via the bus 903; the processor 902 is used to execute executable modules stored in the memory 901, such as computer programs.

[0140] The memory 901 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 904 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0141] The bus 903 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 9 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0142] Among them, the memory 901 is used to store programs, and the processor 902 executes the program after receiving the execution instruction. The method executed by the process definition device disclosed in any embodiment of the present application can be applied to the processor 902 or implemented by the processor 902.

[0143] The processor 902 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 902 or by instructions in the form of software. The above-mentioned processor 902 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 901, and processor 902 reads the information in memory 901 and, in conjunction with its hardware, completes the steps of the above method.

[0144] Corresponding to the above-mentioned logistics price data processing method, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned logistics price data processing method.

[0145] The logistics price data processing device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, for the parts not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0146] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. 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. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0148] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0149] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0150] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the logistics price data processing method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0151] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0152] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. However, these modifications, changes, or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. They should all be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A logistics price data processing method, characterized in that: The method comprises: Obtain price data, service data, and timeliness data from multiple logistics platforms; Preprocessing the price data, the service data, and the timeliness data using a specified preprocessing method to obtain target data; wherein the specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers; Based on the target data, an initial prediction model is trained using an artificial intelligence algorithm to obtain a logistics platform data prediction model; In response to a selection request for logistics services corresponding to a plurality of target logistics platforms, the logistics prices, logistics services, and logistics timeliness of the plurality of target logistics platforms are compared using the logistics platform data prediction model to obtain a comparison result, and according to the selection request, the plurality of target logistics platforms are automatically matched using the comparison result and a dynamic programming algorithm to determine a first target logistics platform among the plurality of target logistics platforms whose degree of matching with the selection request is higher than a preset matching degree; wherein the selection request includes a user price requirement, a user service requirement, and a user timeliness requirement; In response to an addition operation and / or a deletion operation on the logistics project conditions corresponding to the plurality of first target logistics platforms, screening the plurality of first target logistics platforms according to the addition operation and / or the deletion operation to select a final first target logistics platform corresponding to the selection request; In response to feedback data from the client corresponding to the selection request on the final first target logistics platform, adjusting and optimizing the logistics platform data prediction model according to the feedback data; Also includes: Obtaining item property data and historical sales data for target type goods in the logistics platform; the item property data includes item storage temperature and / or item storage humidity; Determining, based on the historical sales data, a first region from among the multiple regions to which the target type of goods are sold, where the historical sales volume is greater than a first preset sales volume; Determine, by the AI ​​system, a location for a logistics transfer warehouse for the target type of goods based on the historical sales data of the target type of goods and the item storage temperature and / or the item storage humidity, so that the logistics transfer warehouse is located as close to the first region as possible while maintaining storage conditions that meet the item storage temperature and / or the item storage humidity; Adjusting the distribution logistics price of the target type of goods in the logistics platform according to the location of the logistics transit warehouse to obtain adjusted price data of the logistics platform; It also includes: through the logistics and warehouse quotation integration system, according to the local nature and sales of a certain type of goods, the AI ​​system selects transit warehouses in other regions and delivery priorities, and then adjusts logistics prices according to the location of the transit warehouse; After obtaining the historical sales data of the target type of goods in the logistics platform, the method further includes: Determining, based on the historical sales data, a sales peak time period during which the historical sales volume of the target type of goods is greater than a second preset sales volume; Determining, from the plurality of regions to which the target type of goods are sold, a second region where the peak sales period is greater than a preset period, and a third region where the peak sales period is less than or equal to the preset period; Adjusting the delivery area priority of the target type of goods to obtain an adjusted delivery area priority result, so that the delivery priority of the target type of goods to the second area is greater than the delivery priority to the third area; Adjusting the delivery logistics price of the target type of goods in the logistics platform according to the delivery area priority result to obtain adjusted price data of the logistics platform; the higher the delivery area priority result, the higher the delivery logistics price; The number of the logistics transfer warehouse setting locations includes multiple; and the adjusting of the distribution logistics price of the target type of goods according to the logistics transfer warehouse setting locations includes: Obtaining the warehouse usage price of each logistics transit warehouse location; determining a warehouse storage cost of the target type of goods based on the storage temperature and / or the storage humidity of the goods; Determine the comprehensive total logistics cost corresponding to the location of the logistics transfer warehouse based on the warehouse usage price, the warehouse storage cost, and the transportation distance logistics cost based on the location of the logistics transfer warehouse; Comparing the multiple comprehensive total logistics costs corresponding to the multiple logistics transfer warehouse setting locations to obtain a comparison result, and determining the multiple first logistics transfer warehouse setting locations whose comprehensive total logistics costs are lower than the preset logistics costs in the comparison results as candidate logistics transfer warehouse setting locations for the target type of goods; Acquire first historical sales data and user feedback data of the target type of goods within a preset distance range around the location where the alternative logistics transfer warehouse is set; Determine a target alternative logistics transfer warehouse setting location with the highest first historical sales data and the highest user feedback data from the multiple alternative logistics transfer warehouse setting locations, and determine the target alternative logistics transfer warehouse setting location as the final logistics transfer warehouse setting location; Adjusting the distribution logistics price of the target type of goods in the logistics platform according to the final comprehensive total logistics cost corresponding to the final logistics transit warehouse setting location to obtain adjusted price data of the logistics platform; It also includes: comprehensive judgment through AI system comparison: searching for quotations from local warehouses through the system, calculating a first comprehensive logistics total cost based on the warehouse quotations, warehouse storage costs, and transportation logistics costs, and comparing the second comprehensive logistics total cost of storing goods in other regions other than the local warehouse through the AI ​​system, selecting the goods storage location corresponding to the comprehensive logistics total cost lower than the specified cost as the final goods storage method, and calculating the logistics price corresponding to the final goods storage method; It also includes: combining the type of goods, regional warehouse data, logistics and transportation data, goods sales data and user feedback data, performing data comparison through AI, and performing data balancing algorithms through AI based on logistics costs and user experience results to select the optimal logistics method and final logistics price for the goods.

2. The method according to claim 1, characterized in that The logistics platform data prediction model is used to compare the logistics prices of the multiple target logistics platforms to obtain comparison results, including: Acquiring historical logistics and transportation data of the multiple target logistics platforms; Analyzing the historical logistics and transportation data through the AI ​​system corresponding to the logistics platform data prediction model to obtain the logistics and transportation speed, logistics and transportation routes, the excellence of the transit warehouse storage environment, and the logistics and transportation safety of each of the multiple target logistics platforms; Based on the logistics transportation speeds, the logistics transportation routes, the storage environment of the transit warehouse, and the logistics transportation safety levels of each of the multiple target logistics platforms, the logistics prices of the multiple target logistics platforms are compared to obtain a comparison result; wherein, in the comparison result, the slower the logistics transportation speed, the longer the logistics transportation route, the better the storage environment of the transit warehouse and / or the lower the logistics transportation safety level, the lower the logistics price of the target logistics platform.

3. The method according to claim 1, characterized in that Also includes: Obtaining an actual return rate of logistics goods on the logistics platform by a first user belonging to a fourth region; If the actual return rate is greater than or equal to the preset return rate, obtaining the regional environment data of the fourth region and the user habit data of the first user; Adjusting logistics configuration items for delivery to the fourth region based on the regional environment data and the user habit data to obtain logistics configuration results for the adjusted logistics configuration items; wherein the logistics configuration items include any one or more of a storage temperature configured during the logistics process, an adjustable preference type corresponding to the logistics object, and an adjustable quality type corresponding to the logistics object; and the preference type includes any one or more of a taste preference, a temperature preference, a color preference, a shape preference, and a size preference; The logistics price of the logistics platform for delivering to the fourth region is adjusted according to the logistics configuration result to obtain adjusted price data of the logistics platform.

4. The method according to claim 1, wherein Also includes: Obtaining repurchase rates and user evaluation data for logistics goods on the logistics platform by users in multiple regions; Analyze the user's tolerance for goods through the AI ​​system based on the repurchase rate and user evaluation data; For the fifth region corresponding to the second user whose cargo tolerance is lower than the first preset tolerance, the shipping priority of the logistics cargo delivered to the fifth region is increased; and for the sixth region corresponding to the third user whose cargo tolerance is higher than the second preset tolerance, the shipping priority of the logistics cargo delivered to the sixth region is decreased; The distribution logistics prices for the logistics platform delivered to the fifth region and the sixth region are adjusted according to the adjusted delivery priority level to obtain the adjusted price data of the logistics platform; the higher the adjusted delivery priority level, the higher the distribution logistics price, and the lower the adjusted delivery priority level, the lower the distribution logistics price.

5. A logistics price data processing device, characterized in that: The device comprises: The acquisition module is used to obtain price data, service data, and timeliness data from multiple logistics platforms; a preprocessing module, configured to preprocess the price data, the service data, and the timeliness data using a specified preprocessing method to obtain target data; wherein the specified preprocessing method includes any one or more of data cleaning, data screening, data deduplication, data filling of missing values, and data removal of outliers; A training module, configured to train the initial prediction model using an artificial intelligence algorithm based on the target data to obtain a logistics platform data prediction model; a matching module for responding to a selection request for corresponding logistics services of multiple target logistics platforms, comparing the logistics prices, logistics services, and logistics timeliness of the multiple target logistics platforms using the logistics platform data prediction model to obtain a comparison result, and automatically matching the multiple target logistics platforms according to the selection request using the comparison result and a dynamic programming algorithm to determine a first target logistics platform among the multiple target logistics platforms whose degree of matching with the selection request is higher than a preset matching degree; wherein the selection request includes user price requirements, user service requirements, and user timeliness requirements; a screening module, configured to respond to an addition operation and / or a deletion operation on the logistics project conditions corresponding to the plurality of first target logistics platforms, and screen, according to the addition operation and / or the deletion operation, a final first target logistics platform corresponding to the selection request among the plurality of first target logistics platforms; an adjustment module, configured to respond to feedback data from the client corresponding to the selection request on the final first target logistics platform, and adjust and optimize the logistics platform data prediction model according to the feedback data; It also includes a determination module, which is used to: obtain item property data and historical sales data for the target type of goods in the logistics platform; the item property data includes item storage temperature and / or item storage humidity; determine a first region with a historical sales quantity greater than a first preset sales quantity from multiple regions to which the target type of goods are sold based on the historical sales data; determine a logistics transit warehouse setting location for the target type of goods through an AI system based on the historical sales data of the target type of goods and the item storage temperature and / or the item storage humidity, so that the logistics transit warehouse setting location is as close to the first region as possible under storage conditions that meet the item storage temperature and / or the item storage humidity; adjust the distribution logistics price of the target type of goods in the logistics platform based on the logistics transit warehouse setting location to obtain adjusted price data of the logistics platform; It also includes a selection module for: using the logistics and warehouse quotation integration system, based on the local nature and sales of a certain type of goods, the AI ​​system selects transit warehouses in other regions and the priority of shipments, and then adjusts logistics prices based on the location of the transit warehouse; It also includes a second determination module, which is used to: determine, based on the historical sales data, a sales peak time period when the historical sales quantity of the target type of goods is greater than a second preset sales quantity; determine, from multiple regions to which the target type of goods are sold, a second region where the sales peak time period is greater than the preset time period, and a third region where the sales peak time period is less than or equal to the preset time period; adjust the delivery region priority level of the target type of goods to obtain an adjusted delivery region priority level result, so that the delivery priority level of the target type of goods to the second region is greater than the delivery priority level to the third region; adjust the delivery logistics price of the target type of goods in the logistics platform according to the delivery region priority level result to obtain adjusted price data of the logistics platform; the higher the delivery region priority level result, the higher the delivery logistics price; The number of the logistics transfer warehouse setting locations includes multiple; the first determination module is specifically used to: obtain the warehouse usage price of each of the logistics transfer warehouse setting locations; determine the warehouse storage cost of the target type of goods according to the item storage temperature and / or the item storage humidity; determine the comprehensive total logistics cost corresponding to the logistics transfer warehouse setting location according to the warehouse usage price, the warehouse storage cost and the transportation distance logistics cost based on the logistics transfer warehouse setting location; compare the multiple comprehensive total logistics costs corresponding to the multiple logistics transfer warehouse setting locations to obtain a comparison result, and select the multiple first logistics transfer warehouse setting locations whose comprehensive total logistics cost in the comparison result is lower than the preset logistics cost. Determine an alternative logistics transfer warehouse setting location for the target type of goods; obtain first historical sales data and user feedback data of the target type of goods within a preset distance range around the alternative logistics transfer warehouse setting location; determine the target alternative logistics transfer warehouse setting location with the highest first historical sales data and the highest user feedback data from multiple alternative logistics transfer warehouse setting locations, and determine the target alternative logistics transfer warehouse setting location as the final logistics transfer warehouse setting location; adjust the distribution logistics price of the target type of goods in the logistics platform according to the final comprehensive total logistics cost corresponding to the final logistics transfer warehouse setting location to obtain adjusted price data of the logistics platform; The system further includes a judgment module for: performing a comprehensive judgment through comparison using an AI system: searching for quotations from local warehouses through the system, calculating a first comprehensive logistics total cost based on the warehouse quotations, warehouse storage costs, and transportation logistics costs, and comparing the second comprehensive logistics total cost of goods stored in warehouses other than the local warehouse through the AI ​​system, selecting a goods storage location corresponding to a comprehensive logistics total cost lower than a specified cost as the final goods storage method, and calculating a logistics price corresponding to the final goods storage method; It also includes a comparison module, which is used to: combine the type of goods, regional warehouse data, logistics and transportation data, goods sales data and user feedback data, perform data comparison through AI, and use AI to perform data balancing algorithms based on logistics costs and user experience results to select the optimal logistics method and final logistics price for the goods.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 4.

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