Freight information recommendation method and device, computer readable storage medium and equipment

Abstract

The invention is suitable for the field of data processing, and provides a freight information recommendation method and device, a computer readable storage medium and computer equipment, and the freight information recommendation method comprises the steps: obtaining corresponding multi-dimensional information of different freight objects in each time period; according to the multi-dimensional information and a preset user demand, configuring a recommendation strategy of each freight object; the recommendation strategies are tested, and the recommendation strategies meeting preset conditions are stored in a database; when a freight request of a user is received, the freight information is recommended to the user according to the freight request and the recommendation strategy meeting the preset condition, and the scheme can improve the individuation degree of a freight information recommendation scheme.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, apparatus, computer-readable storage medium, and computer equipment for recommending freight information. Background Art

[0002] In the online freight sector, the dynamic nature of time, space, and supply creates complex and ever-changing freight demands from diverse users. For example, faced with special scenarios such as multi-batch distribution and transportation, precise cold chain temperature control requirements, and compliance restrictions for dangerous goods transport, as well as the need for dynamic route adjustments due to traffic restrictions and sudden weather changes, traditional freight platforms rely on a crude model of manual scheduling and simple keyword matching. This model is unable to dynamically adjust product configurations based on the ever-changing demands and conditions of actual operations, resulting in a decline in user experience and operational efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a freight information recommendation method, apparatus, computer-readable storage medium and computer equipment, aiming to provide a freight information recommendation solution with high user experience and operational efficiency.

[0004] In a first aspect, the present application provides a method for recommending freight information, comprising: Obtain multi-dimensional information corresponding to different freight objects in each time period; Configuring a recommendation strategy for each of the freight objects based on the multi-dimensional information and preset user needs; Testing the recommended strategies and storing the recommended strategies that meet the preset conditions in a database; When a shipping request from a user is received, shipping information is recommended to the user based on the shipping request and a recommendation strategy that meets preset conditions.

[0005] In a second aspect, the present application provides a freight information recommendation device, comprising: The acquisition module is used to obtain multi-dimensional information corresponding to different freight objects in different time periods; A configuration module, configured to configure a recommendation strategy for each of the freight objects based on the multi-dimensional information and preset user needs; A testing module, used to test the recommendation strategy and store the recommendation strategy that meets the preset conditions in a database; The recommendation module is configured to, upon receiving a freight request from a user, recommend freight information to the user based on the freight request and a recommendation strategy that meets preset conditions.

[0006] Optionally, in some embodiments of the present application, the configuration module includes: A determination unit, configured to determine time requirement information, location requirement information, and vehicle type requirement information corresponding to a preset user requirement; The first configuration unit is used to configure a recommendation strategy for each of the freight objects according to the multi-dimensional information, time requirement information, location requirement information, and vehicle type requirement information.

[0007] Optionally, in some embodiments of the present application, the configuration unit is specifically configured to: Determine the time demand information, location demand information, and vehicle model demand information, and prioritize them according to the preset user needs; Extract target information from the multi-dimensional information of each freight object according to the determined priority; A recommendation strategy is configured for each of the freight objects based on the target information and the determined priority.

[0008] Optionally, in some embodiments of the present application, the testing module includes: The second configuration unit is used to configure the control group and the experimental group; a testing unit, configured to test the control group using a default strategy and the experimental group using a recommended strategy; A comparison unit, used to compare the indicators of the control group and the experimental group after the test; The storage unit is used to store the recommended strategy that meets the preset conditions in the database when the indicators after the test meet the preset conditions.

[0009] Optionally, in some embodiments of the present application, the second configuration unit is specifically configured to: Obtaining information of multiple users participating in the experiment and the freight objects corresponding to the user information; The multiple freight objects are configured into a control group and an experimental group according to the user information and / or the geographical location corresponding to the user information.

[0010] Optionally, in some embodiments of the present application, the recommendation module includes: A parsing unit, configured to parse a freight request received from a user; A matching unit, configured to match a recommendation strategy corresponding to the parsing result to a target recommendation strategy; A recommendation unit is used to recommend the freight information of the target recommendation strategy to the user.

[0011] Optionally, in some embodiments of the present application, the recommendation unit is specifically configured to: Display the recommendation interface; The freight object, freight time and freight advantages corresponding to the target recommendation strategy are displayed in the recommendation interface.

[0012] In a third aspect, the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0013] In a fourth aspect, the present application also provides a computer device comprising one or more processors; a memory; and one or more computer programs, wherein the processor and the memory are connected via a bus, wherein the one or more computer programs are stored in the memory and are configured to be executed by the one or more processors, wherein the processor executes the program as in any of the steps of the method described above.

[0014] The embodiment of the present application uses the same freight information recommendation method, device, storage medium and computer equipment. After obtaining the multi-dimensional information corresponding to different freight objects in each time period, it configures the recommendation strategy for each freight object according to the multi-dimensional information and the preset user needs. Then, the recommendation strategy is tested. When the recommendation strategy meets the preset conditions, the recommendation strategy that meets the preset conditions is stored in the database. When the user's freight request is received, the freight information is recommended to the user according to the freight request and the recommendation strategy that meets the preset conditions. It can be seen that in the freight information recommendation scheme of the present application, differentiated recommendation strategies can be configured in advance according to the multi-dimensional information of different freight objects in each time period; when the user's freight request is received, the system can intelligently match the request content with the pre-configured recommendation strategy and recommend suitable freight information to the user. In this way, the recommendation logic can be flexibly adjusted according to the user's real-time freight request, which changes the rigid mode of the traditional platform that relies on fixed rules or manual scheduling, thereby improving user experience and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of a freight information recommendation method provided by an embodiment of the present application.

[0016] Figure 2 This is another flow chart of the freight information recommendation method provided by one embodiment of the present application.

[0017] Figure 3 This is another flow chart of the freight information recommendation method provided by an embodiment of the present application.

[0018] Figure 4 This is a functional module block diagram of a freight information recommendation device provided in one embodiment of the present application.

[0019] Figure 5 This is a specific structural block diagram of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purposes, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application.

[0021] In order to illustrate the technical solutions described in the present application, the following will be described by specific embodiments.

[0022] Please refer to Figure 1 FIG. 1 is a flowchart of a freight information recommendation method provided by an embodiment of the present application. The embodiment mainly takes an example of the freight information recommendation method applied to a computer device. The freight information recommendation method provided by an embodiment of the present application includes the following steps. S101, obtaining multi-dimensional information corresponding to different freight objects in each time period.

[0023] The freight object is a tool for realizing the space transfer of goods. The freight object can be a van, a refrigerated vehicle or a flatbed truck, etc. The multi-dimensional information of the freight object refers to a data set described and measured from multiple interrelated angles around the freight commodity. The multi-dimensional information of the freight object can include the following information: Position dimension information: the geographical position of the vehicle will change at different time periods. The real-time position information obtained by the GPS positioning system can be accurate to the specific latitude and longitude or detailed map position, which is used to monitor the driving track of the vehicle in real time, judge whether the vehicle is driving according to the planned route, and predict the time when the vehicle arrives at the destination.

[0024] Driving state dimension information: the driving speed, driving direction, whether in driving, etc. of the vehicle at different time periods.

[0025] Vehicle health state dimension information: with the passage of time, the state of each component of the vehicle will change. The data of the key components of the vehicle at different time periods, such as engine water temperature, oil pressure, tire pressure, etc. can reflect the health condition of the vehicle.

[0026] Loading state dimension information: during the transportation process, the loading condition of the vehicle will change with the loading and unloading links. The information of the loading weight, volume, type of goods, etc. of the vehicle at different time periods.

[0027] Operating cost dimension information: in each time period, the operating cost of the vehicle is also continuously accumulated. This includes fuel consumption, road toll expenditure, driver's salary, etc. By recording the fuel consumption data at different time periods, the fuel economy of the vehicle can be analyzed, and the influence of the driver's driving habits on fuel consumption can be evaluated; while the road toll expenditure is related to the driving route and driving time period. There may be different charging standards for certain sections at certain time periods.

[0028] For example, various sensors and onboard terminals installed on freight vehicles can be used to obtain vehicle location information at different times. Transportation management systems can also be used to obtain the stops, estimated and actual arrival times, and the routes traveled by freight vehicles. Data from third-party platforms, such as road condition information platforms, can also be used to identify congestion and construction along freight routes, allowing analysis of their impact on travel speed and time.

[0029] S102: Configure a recommendation strategy for each freight object based on multi-dimensional information and preset user needs.

[0030] Pre-set user needs refer to various pre-defined user requirements for freight services that the freight system is expected to meet, based on factors such as the business characteristics, market environment, and user behavior in the online freight sector. For example, from a temporal perspective, consider the differences between peak and off-peak periods in freight traffic. For example, during peak periods, requirements might be set to quickly match vehicles and prioritize timeliness; during off-peak periods, cost control might be prioritized, with price sensitivity being the primary focus. From a spatial perspective, based on the origin and destination locations of a shipment, such as between cities or counties, requirements might be set for vehicles familiar with local road conditions and capable of providing specialized regional transportation services. Based on the business scenario, for example, for fresh produce transport, requirements might be set for refrigerated vehicles with temperature monitoring capabilities; for large equipment transport, requirements might be set for flatbed trucks with professional loading and unloading services. Furthermore, historical user order data can be collected and analyzed, including selected freight vehicle types, preferred routes, and acceptable price and timeliness levels. If a user frequently selects a certain type of vehicle or route, requirements might be set to prioritize recommendations for that type of vehicle or similar routes. If a user is sensitive to price fluctuations, requirements might be set to prioritize price and provide price fluctuation alerts. Specific settings can be tailored to the specific needs.

[0031] Furthermore, based on temporal and spatial information, suitable freight carriers are recommended for different origins, destinations, and time periods. During peak hours, freight carriers familiar with local road conditions and able to avoid congested routes are recommended. When transporting goods between specific areas, freight carriers who frequently travel that route and are familiar with the road conditions and loading and unloading locations are recommended. For example, during the Spring Festival, when transporting New Year's goods from a city to surrounding rural areas, freight carriers familiar with rural roads and able to adapt to complex road conditions are recommended.

[0032] Vehicle recommendations can also be made based on business attributes such as the order port, order version, and national standard vehicle model ID. For users placing orders on the logistics platform and transporting large equipment (corresponding to specific national standard models), vehicles with specialized transport capabilities, professional loading and unloading tools, and experienced drivers are recommended. Recommendation strategies are optimized based on user historical behavior data and preferences for different business attributes. For example, if a user frequently orders a specific type of cargo through a specific port, vehicles they have previously used and have received good reviews may be prioritized.

[0033] Optionally, in some embodiments of the present application, S102 can specifically include: determining time demand information, location demand information and vehicle model demand information corresponding to the preset user demand; configuring the recommendation strategy of each freight object according to the multi-dimensional information, the time demand information, the location demand information and the vehicle model demand information.

[0034] Specifically, for the transportation demand with urgent time requirement, the freight object with short distance to the loading location, fast driving speed and short estimated arrival time is preferentially recommended. For example, when transporting emergency medical supplies, the vehicle with short distance to the departure point and smooth driving route (avoiding congested road sections) is screened, and the driving speed and estimated arrival time of the vehicle are checked, and the vehicle meeting the requirements is ranked in the front row of the recommendation. If the transportation time is relatively loose, the cost factor of the vehicle can be considered, and the vehicle with low operation cost, such as good fuel economy and low road toll, is selected. When the transportation starting point or terminal is in the congested area of the city center, the small and flexible freight object familiar with the local road conditions is recommended, such as a small van, which can drive more conveniently in narrow streets and congested road sections, reducing the transportation time. In addition, after determining the vehicle model according to the type of goods, the vehicle can be further screened. For example, after determining to select a refrigerated vehicle for transporting fresh goods, the performance of the refrigeration equipment of the refrigerated vehicle, the heat preservation effect of the vehicle compartment and other parameters are checked, and the vehicle with advanced equipment and good heat preservation is preferentially recommended to ensure the quality of the goods during transportation.

[0035] Optionally, in some embodiments of the present application, the step of “configuring the recommendation strategy of each freight object according to the multi-dimensional information, the time demand information, the location demand information and the vehicle model demand information” can specifically include: determining the time demand information, the location demand information and the vehicle model demand information according to the preset priority of the user demand; extracting target information from the multi-dimensional information of each freight object according to the determined priority; configuring the recommendation strategy of each freight object according to the target information and the determined priority.

[0036] For example, when transporting time-sensitive goods such as fresh goods and urgent parts, the priority of the time demand information is high. If the starting point or terminal of transportation is in a special area, such as the destination of transportation of flammable and explosive goods being a chemical industry park, the priority of the location demand information is high due to strict requirements on transportation safety and vehicle qualification.

[0037] If time demand information has a high priority, the focus is on extracting information such as the vehicle's real-time location, driving speed, and estimated time of arrival. When location demand information has a high priority, information such as the vehicle's operating area, familiarity with the destination's road conditions, and qualifications for transporting to special areas is extracted. If time demand information has the highest priority, vehicles with short estimated arrival times are prioritized based on the extracted time-related target information. For example, in the fresh produce transport scenario, vehicles that are close to the loading point, have stable driving speeds, and can reach the destination quickly are prioritized. When location demand information has the highest priority, recommendations are made based on location-related target information.

[0038] It's important to note that in practice, it's often necessary to prioritize multiple requirements. When time, location, and vehicle type are all prioritized, we first filter out vehicles that meet the vehicle type requirements. Then, we select vehicles that meet the location requirements (e.g., special area qualifications and road familiarity). Finally, we select vehicles that meet the time requirements (e.g., shortest ETA) for recommendation.

[0039] S103: Test the recommendation strategies, and store the recommendation strategies that meet the preset conditions in the database.

[0040] In this application, preconditions are set based on system goals and user needs. The system's goal is to optimize freight resource allocation, improve user experience, and enhance operational efficiency, and the preconditions are structured around these goals. If increasing user order rates is a key objective, the precondition might be set as, "When a certain recommendation strategy is adopted, the user order rate increases by more than 15% during the test period compared to the previous average level." If the focus is on transport service quality, the precondition might be set as, "The recommended vehicle's cargo damage rate during transport is 5% lower than the industry average, and user satisfaction is at least 80%."

[0041] Specifically, determine the recommendation strategy variables to test, such as different sorting rules for freight items (prioritizing distance, price, or shipping time), different default freight item combinations, and different sales presentation styles. For example, you might want to test the impact of sorting rules "prioritizing shipping time" and "prioritizing price" on user freight selection. Then, apply the new recommendation strategy to the experimental group, such as recommending freight vehicles based on shipping time. The control group uses an existing or traditional recommendation strategy, such as recommending freight items based on price. When a recommendation strategy meets the pre-set conditions, it is stored in the database.

[0042] Optionally, in some embodiments of the present application, S103 may specifically include: Configure the control group and experimental group; A control group was tested using the default strategy, and; The recommended strategy was tested on the experimental group; Comparing the post-test indicators of the control group and the experimental group; When the indicators after testing meet the preset conditions, the recommended strategies that meet the preset conditions are stored in the database.

[0043] For example, specifically, users with similar freight demands (such as transporting similar goods from the same city to another city) in the same time period are randomly divided into two groups (i.e., a control group and an experimental group).

[0044] The control group was tested using the default strategy: The default strategy is typically the recommendation strategy currently used by the system or commonly used in the industry. In a freight scenario, this might recommend freight vehicles in ascending order of price, or prioritize vehicle suppliers with whom one has extensive experience and familiarity. During testing, various behavioral data from the control group users using the default strategy were recorded, including the length of time spent browsing recommended vehicles, the number of clicks on recommended vehicles, the proportion of vehicles ultimately selected (conversion rate), and the number of orders placed. This data reflects the performance of the default strategy in real-world applications and serves as a baseline for comparison with the experimental group.

[0045] A recommendation strategy was tested on the experimental group: A recommendation strategy is a new, yet-to-be-verified strategy designed based on multi-dimensional information and pre-defined user needs. For example, a strategy could recommend vehicles from fastest to slowest based on transport time, location, and vehicle type requirements. Or, a strategy could recommend the most suitable vehicle type with high service ratings for a specific transport route and cargo type. During the test, behavioral data from the experimental group's users, including the same monitoring metrics as the control group, was collected simultaneously. By observing the experimental group's responses to the recommended vehicles, the effectiveness of the new recommendation strategy was understood.

[0046] Finally, the collected data of various indicators during the test process of the control group and the experimental group will be compared and analyzed. For example, the differences in key indicators such as conversion rate, order volume, and user satisfaction will be compared. If the conversion rate of the experimental group is higher than that of the control group by a certain percentage, or the order volume increases significantly, it means that the new recommendation strategy is more in line with user needs and can improve the user's willingness to choose recommended vehicles; if the user satisfaction of the experimental group is higher, it means that the new strategy has an advantage in improving user experience. By comparing, the pros and cons of the recommendation strategy compared to the default strategy can be intuitively judged. When the conversion rate of the experimental group is improved by more than 20% compared to the control group, or the order volume increases by more than 30%, and the user satisfaction reaches more than 80 points (out of 100), it is considered that the recommendation strategy meets the preset conditions. When the test indicators of the recommendation strategy reach these preset standards, the detailed information of the recommendation strategy (including the specific rules of the strategy, the configuration of the multi-dimensional information involved, the key data in the test process, etc.) is stored in the database.

[0047] Optionally, in some embodiments of the present application, the step of "configuring the control group and the experimental group" can specifically include: Obtaining user information participating in the experiment and freight objects corresponding to the user information; According to the user information and / or the geographical location corresponding to the user information, the plurality of freight objects are configured into the control group and the experimental group.

[0048] It should be noted that on the freight platform, users will fill in some basic information (such as name, contact information, enterprise name, etc.) when registering, and more detailed information will be generated when conducting freight transactions, such as transportation demand (cargo type, weight, volume, origin, destination), transportation time requirement, payment record, etc. By screening and organizing these records, user information participating in the experiment can be obtained. For example, users who have recently had transportation needs for electronic products and whose transportation routes are in a certain specific area are selected. Therefore, the freight platform can be used to obtain a plurality of user information participating in the experiment, and the obtained user information can be analyzed in depth, and then the user features obtained through analysis are used to distribute the plurality of freight objects to the control group and the experimental group. For example, the freight objects corresponding to high-frequency transportation users are randomly distributed to the two groups, so that the two groups have similarity in transportation frequency. In this way, it can be ensured that the experimental results can reflect the influence of different recommendation strategies on users with different transportation frequencies.

[0049] Optionally, in some embodiments of the present application, the geographical location of the user can be determined, such as a city's central business district, an industrial area, a logistics park, etc. The characteristics of freight demand in different geographical locations are analyzed. For example, a city's central business district may have a greater demand for small freight vehicles and higher requirements for transportation timeliness; an industrial area may require large-duty trucks and be more sensitive to transportation costs. Based on the characteristics of the geographical location, multiple freight objects are assigned to a control group and an experimental group. For example, the freight objects corresponding to users from the city's central business district are evenly distributed to the two groups so that the two groups are consistent in their geographical location attributes. In this way, the influence of geographical location factors on the experimental results can be controlled, and the effectiveness of the recommendation strategy can be evaluated more accurately.

[0050] Optionally, in some embodiments of the present application, multiple freight objects are grouped to ensure that the control group and the experimental group have similarities in user characteristics and geographic location attributes, so as to improve the accuracy and reliability of the experiment.

[0051] S104: When receiving a shipping request from a user, recommend shipping information to the user based on the shipping request and a recommendation strategy that meets preset conditions.

[0052] When a user submits a freight request, the system first analyzes the request in detail, extracting key information such as cargo type (fragiles, fresh produce, electronics, or general cargo), cargo weight and volume, origin and destination (down to the city, district, or even specific address), and desired delivery time (whether there are strict time constraints, such as urgent transportation or timeframes). For example, a user requests the transport of a batch of high-value precision electronics from Beijing's Haidian District to Shanghai's Pudong New Area within 24 hours. Based on the parsed user freight request information, the system then matches the corresponding content from the multi-dimensional information database. In the temporal dimension, the system examines freight resources for the current time period and the user's desired delivery period, including the estimated availability of different vehicles and the traffic conditions along the route during that time. In the spatial dimension, the system determines the optimal route between the origin and destination, as well as the available freight vehicle resources for that route. In the business attribute dimension, the system considers information such as the user's order port and order version, as well as the national standard vehicle model ID that matches the cargo type, to select the vehicle type that meets the requirements. For example, for the above-mentioned request to transport electronic products, vehicles that can complete the transportation within 24 hours are screened out in the time dimension; the optimal route that avoids congested roads is planned in the spatial dimension, and vehicles that can be deployed on this route are found; in the business attribute dimension, vehicles suitable for transporting electronic products are selected, such as vehicles with special devices such as shockproof and anti-static devices.

[0053] Optionally, in some embodiments of the present application, S104 may specifically include: When receiving a freight request from a user, parsing the freight request; The recommended strategy corresponding to the matching analysis result is the target recommended strategy; Recommend freight information to users with target recommendation strategy.

[0054] When a user submits a freight request, the request is parsed to obtain the corresponding parsed information, including the cargo attributes, the starting and ending locations, the desired transportation time, the budget range, and any special requirements for transportation services. For example, a user may request the safe delivery of a batch of valuable and fragile porcelain from Hangzhou's Xihu District to Shenzhen's Nanshan District within 48 hours, with insurance coverage. After obtaining the detailed parsed results of the freight request, this information can be used to filter and match freight requests from a variety of pre-defined and tested recommendation strategies. Once a target recommendation strategy is determined, matching freight requests will be selected based on that strategy and recommended to the user. Recommended freight requests may include various aspects, such as basic vehicle information (model, load capacity, volume, etc.), the reputation and service rating of the vehicle company, the driver's qualifications and experience, the estimated transportation cost, the estimated arrival time, and any additional services offered (such as insurance and special packaging).

[0055] Optionally, in some embodiments of the present application, the step of “recommending the freight information of the target recommendation strategy to the user” may specifically include: Display the recommendation interface; The freight objects, freight time and freight advantages corresponding to the target recommendation strategy are displayed in the recommendation interface.

[0056] The recommendation interface can be presented as a webpage or a page within the freight platform app. For example, in a mobile freight app, clicking the "Get Recommendations" button will bring users to a new page, the recommendation interface. This interface displays detailed information about freight targets (such as freight vehicles and freight companies) selected based on the targeted recommendation strategy. For freight vehicles, this information includes the vehicle type (van, flatbed, refrigerated truck, etc.), brand, age, load capacity, and volume. For freight companies, this information includes the company name, registered address, business license information, company size (number of vehicles, number of employees, etc.), and business scope. For example, for fresh produce transport, the recommendation interface might display a refrigerated truck of a certain brand, indicating its load capacity of 5 tons, volume of 20 cubic meters, and age of 3 years, giving users a clear understanding of the vehicle's basic characteristics.

[0057] Freight delivery time is an important indicator that users are very concerned about. The system will calculate and display the estimated freight delivery time based on the target recommendation strategy and relevant data. This includes the driving time from the loading point to the unloading point, the loading and unloading time, etc. When calculating the driving time, factors such as the distance of the transportation route, road conditions (whether it is congested, road construction, etc.), and the average driving speed of the vehicle are taken into account. The loading and unloading time will refer to the type and quantity of the goods, as well as the equipment and manpower conditions at the loading and unloading locations. For example, for a freight task from Beijing to Shanghai, the recommendation interface will show that the estimated driving time is 18 hours, the loading and unloading time is 2 hours in total, and the total estimated freight time is 20 hours, so that users can clearly know the approximate transportation time of the goods and arrange subsequent work.

[0058] To help users better understand the value of a recommended freight solution, the recommendation interface will highlight its advantages. These advantages can include various aspects, such as timeliness (faster delivery than other solutions), cost advantages (relatively lower shipping costs), service quality advantages (professional cargo protection measures, 24-hour customer support, etc.), and safety advantages (vehicles equipped with advanced safety equipment, experienced drivers, etc.). For example, for a recommended solution for transporting high-value electronic products, the solution will emphasize that the vehicle is equipped with anti-static and shockproof packaging, the driver is professionally trained and has extensive experience in transporting electronic products, and real-time monitoring services are provided throughout the process to ensure the safety of the goods. This will demonstrate the safety advantages of the solution and strengthen user confidence in the recommended solution.

[0059] By fully displaying information such as freight objects, freight time, and freight advantages in the recommendation interface, users can have a more comprehensive understanding of the recommended freight plans, making it easier to make choices that meet their needs and improve the satisfaction and efficiency of freight services.

[0060] To further understand the freight information recommendation scheme of this application, please refer to the following Figure 2After a user initiates an operation, they access the system homepage. The request first reaches uapi (User API, used to receive and forward user requests), which forwards it to the business processing module. The business processing module processes the configured sorting information, such as abtest (A / B testing, a testing method used to compare the effectiveness of different solutions). After encapsulating the data, it either queries the database (database) or reads the cache to obtain relevant information. The processed content is then returned to uapi, which then returns the content to the user. Backend personnel perform configuration operations (add / delete / check / modify) on the console (Ark) using uadmin (UserAdmin, a user management module used for system configuration and other operations). uadmin passes the configuration information to ubase_setting (User Base Setting, a module used to record and process basic configuration operations). ubase_setting records the database operations and returns the results to uadmin. Uadmin also uses xxl-job (a distributed task scheduling platform used for executing scheduled tasks, etc.) to obtain product information from the product center and district, county, or fence information from the map. The relevant modules then return the corresponding content. xxl-job tracks the validity and expiration dates of configurations every minute and updates the status of configurations that have exceeded their validity period. ubase_setting then handles the update accordingly.

[0061] Also, see Figure 3 , the business processing module can also respond to data conversion requests from backend personnel, as follows: A user initiates a system access request. The request first reaches the User API (UAPI), which receives and forwards user requests. The UAPI forwards the request to the business processing module. The business processing module converts the longitude and latitude coordinates to fences and converts the request data. The business processing module then retrieves configuration information from the relevant module (by querying the database or reading from the cache).

[0062] After obtaining the configuration, the business processing module obtains the configured sorting information, encapsulates the data, and returns the processed content to the uapi, which then returns the final content to the user. Backend personnel select the county configuration through uadmin (User Admin, the user management module used for system configuration and other operations) on the Ark console. uadmin sends a request to the map module for county information, which is returned to uadmin. uadmin then passes the configuration information to ubase_setting (User Base Setting, the module used to record and process basic configuration operations), which records the database and other operations. On the Ark console, uadmin selects the fence configuration and sends a request to the map module for fence information. The map module returns fence information to uadmin, which is passed to ubase_setting to record the relevant operation. uadmin can also retrieve a list of fences based on fence ID and city ID, and the map module returns the corresponding content.

[0063] The above is the recommended process for freight information provided in this application.

[0064] The present application provides a method for recommending freight information. After obtaining the multi-dimensional information corresponding to different freight objects in each time period, a recommendation strategy for each freight object is configured according to the multi-dimensional information and preset user needs. Then, the recommendation strategy is tested, and the recommendation strategy that meets the preset conditions is stored in the database. When a freight request from the user is received, freight information is recommended to the user according to the freight request and the recommendation strategy that meets the preset conditions. It can be seen that in the freight information recommendation scheme of the present application, differentiated recommendation strategies can be configured in advance according to the multi-dimensional information of different freight objects in each time period; when a freight request from the user is received, the system can intelligently match the request content with the pre-configured recommendation strategy and recommend suitable freight information to the user. In this way, the recommendation logic can be flexibly adjusted according to the user's real-time freight request, which changes the rigid mode of the traditional platform that relies on fixed rules or manual scheduling, thereby improving user experience and operational efficiency.

[0065] See also Figure 4 The freight information recommendation device provided in one embodiment of the present application can be a computer program or a piece of program code running on a computer device. For example, the freight information recommendation device is an application software. The freight information recommendation device can be used to execute the corresponding steps of the freight information recommendation method provided in the embodiment of the present application. The freight information recommendation device provided in one embodiment of the present application includes an acquisition module 201, a configuration module 202, a testing module 203, and a recommendation module 204, which are specifically as follows: Acquisition module 201, used to obtain multi-dimensional information corresponding to different freight objects in each time period; Configuration module 202, configured to configure a recommendation strategy for each freight object based on multi-dimensional information and preset user needs; The testing module 203 is used to test the recommendation strategy and store the recommendation strategy that meets the preset conditions in the database; The recommendation module 204 is configured to recommend freight information to the user based on the freight request and a recommendation strategy that meets preset conditions when receiving the freight request from the user.

[0066] Optionally, in some embodiments of the present application, the configuration module 202 may specifically include: A determination unit, configured to determine time requirement information, location requirement information, and vehicle type requirement information corresponding to a preset user requirement; The first configuration unit is used to configure a recommendation strategy for each freight object based on multi-dimensional information, time demand information, location demand information, and vehicle type demand information.

[0067] Optionally, in some embodiments of the present application, the configuration unit may be specifically configured to: Determine the time demand information, location demand information, and vehicle model demand information, and prioritize them according to the preset user needs; Extract target information from the multi-dimensional information of each freight object according to the determined priority; Configure the recommended strategy for each freight object based on the target information and the determined priority.

[0068] Optionally, in some embodiments of the present application, the testing module 203 may specifically include: The second configuration unit is used to configure the control group and the experimental group; The test unit is used to test the control group using the default strategy and the experimental group using the recommended strategy; A comparison unit, used to compare the indicators of the control group and the experimental group after the test; The storage unit is used to store the recommended strategy that meets the preset conditions in the database when the indicators after the test meet the preset conditions.

[0069] Optionally, in some embodiments of the present application, the second configuration unit may be specifically used to: Obtaining information of multiple users participating in the experiment and the freight objects corresponding to the user information; According to the user information and / or the geographical location corresponding to the user information, multiple freight objects are configured into a control group and an experimental group.

[0070] Optionally, in some embodiments of the present application, the recommendation module 204 can specifically include: a parsing unit, configured to parse the freight request when receiving the freight request of the user; a matching unit, configured to match the recommendation strategy corresponding to the parsing result as the target recommendation strategy; a recommendation unit, configured to recommend the freight information of the target recommendation strategy to the user.

[0071] Optionally, in some embodiments of the present application, the recommendation unit is specifically configured to: display a recommendation interface; display the freight object, the freight time and the freight advantage corresponding to the target recommendation strategy in the recommendation interface.

[0072] The freight information recommendation device provided by an embodiment of the present application and the freight information recommendation method provided by an embodiment of the present application belong to the same concept, and the specific implementation process is described in detail in the full text of the specification, which is not repeated here.

[0073] The present application provides a freight information recommendation device, after the acquisition module 201 acquires the multi-dimensional information of different freight objects in each period, the configuration module 202 configures the recommendation strategy of each freight object according to the multi-dimensional information and the preset user demand, then the test module 203 tests the recommendation strategy, and stores the recommendation strategy meeting the preset condition to the database, and the recommendation module 204 recommends the freight information to the user according to the freight request and the recommendation strategy meeting the preset condition when receiving the freight request of the user. It can be seen that in the freight information recommendation scheme of the present application, the differentiated recommendation strategy can be configured in advance according to the multi-dimensional information of different freight objects in each period; when receiving the freight request of the user, the system can intelligently match the request content with the pre-configured recommendation strategy, and recommend the adaptive freight information to the user. Therefore, the recommendation logic can be flexibly adjusted according to the real-time freight request of the user, which changes the rigid mode of the traditional platform relying on fixed rules or manual scheduling, and thus improves the user experience and the operation efficiency.

[0074] An embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the freight information recommendation method provided by an embodiment of the present application.

[0075] Figure 5The specific structural block diagram of a computer device provided in an embodiment of the present application is shown. The computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processor 101 and the memory 102 are connected via a bus, the one or more computer programs are stored in the memory 102, and are configured to be executed by the one or more processors 101, and when the processor 101 executes the computer program, the steps of the freight information recommendation method provided in an embodiment of the present application are implemented. The computer device includes a server and a terminal, etc. The computer device can be a desktop computer, a mobile terminal, or an in-vehicle device, and the mobile terminal includes at least one of a mobile phone, a tablet computer, a personal digital assistant, or a wearable device.

[0076] It should be understood that each step in each embodiment of the present application is not necessarily performed in sequence according to the order indicated by the step numbers. Unless clearly stated herein, the execution of these steps does not have strict order restrictions, and these steps can be performed in other orders. Moreover, in each embodiment, at least a portion of steps may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or the sub-steps or stages of other steps.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0078] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered to be within the scope of the present disclosure.

[0079] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled persons in the art, some modifications and improvements can be made without departing from the concept of the present application, and these are within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A freight information recommendation method, characterized in that: include: Obtain multi-dimensional information corresponding to different freight objects in each time period; Configuring a recommendation strategy for each of the freight objects based on the multi-dimensional information and preset user needs; Testing the recommended strategies and storing the recommended strategies that meet the preset conditions in a database; When a shipping request from a user is received, shipping information is recommended to the user based on the shipping request and a recommendation strategy that meets preset conditions.

2. The freight information recommendation method according to claim 1, characterized in that: The configuration of a recommendation strategy for each freight object based on the multi-dimensional information and preset user needs includes: Determine the time requirement information, location requirement information, and vehicle model requirement information corresponding to the preset user requirements; A recommendation strategy for each of the freight objects is configured based on the multi-dimensional information, time requirement information, location requirement information, and vehicle type requirement information.

3. The freight information recommendation method according to claim 2, characterized in that: The configuration of a recommendation strategy for each freight object based on the multi-dimensional information, time requirement information, location requirement information, and vehicle type requirement information includes: Determine the time demand information, location demand information, and vehicle model demand information, and prioritize them according to the preset user needs; Extract target information from the multi-dimensional information of each freight object according to the determined priority; A recommendation strategy is configured for each of the freight objects based on the target information and the determined priority.

4. The freight information recommendation method according to claim 1, characterized in that: The testing of the recommendation strategy and storing the recommendation strategy that meets the preset conditions in the database includes: Configure the control group and experimental group; The control group was tested using the default strategy, and; The experimental group was tested using the recommended strategy; Comparing the indicators of the control group and the experimental group after the test; When the indicators after testing meet the preset conditions, the recommended strategies that meet the preset conditions are stored in the database.

5. The freight information recommendation method according to claim 4, characterized in that: The configuration of the control group and the experimental group includes: Obtaining information of multiple users participating in the experiment and the freight objects corresponding to the user information; The multiple freight objects are configured into a control group and an experimental group according to the user information and / or the geographical location corresponding to the user information.

6. The freight information recommendation method according to claim 1, characterized in that: When a shipping request is received from a user, the shipping information recommended to the user based on the shipping request and a recommendation strategy that meets preset conditions includes: When receiving a shipping request from a user, parsing the shipping request; The recommended strategy corresponding to the matching analysis result is the target recommended strategy; Recommending freight information of the target recommendation strategy to the user.

7. The freight information recommendation method according to claim 6, characterized in that: The freight information of the target recommendation strategy recommended to the user includes: Display the recommendation interface; The freight object, freight time and freight advantages corresponding to the target recommendation strategy are displayed in the recommendation interface.

8. A freight information recommendation device, characterized in that: include: The acquisition module is used to obtain multi-dimensional information corresponding to different freight objects in different time periods; A configuration module, configured to configure a recommendation strategy for each of the freight objects based on the multi-dimensional information and preset user needs; A testing module, used to test the recommendation strategy and store the recommendation strategy that meets the preset conditions in a database; The recommendation module is configured to, upon receiving a freight request from a user, recommend freight information to the user based on the freight request and a recommendation strategy that meets preset conditions.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the freight information recommendation method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising: one or more processors; Memory; as well as One or more computer programs, the processor and the memory are connected via a bus, wherein the one or more computer programs are stored in the memory and are configured to be executed by the one or more processors, wherein the processor implements the steps of the freight information recommendation method according to any one of claims 1 to 7 when executing the computer program.

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