Vehicle-cargo matching method and device

Abstract

The present application belongs to the field of data processing technology, and specifically discloses a vehicle-cargo matching method and device. The present application establishes a portrait of the vehicle to be matched based on the historical freight data of the vehicle to be matched, and generates a vehicle recommendation sequence that matches the cargo owner's cargo source information based on the vehicle model, freight behavior analysis information, historical frequently transported cargo types, and vehicle attendant information in the portrait of the vehicle to be matched, thereby achieving accurate matching of cargo and vehicles. At the same time, the cargo-vehicle matching process does not require the addition of a third-party agency and does not rely on the communication between the cargo owner and the vehicle, which invisibly reduces the logistics cost of cargo transportation and improves the efficiency of cargo transportation.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and more specifically, relates to a vehicle-cargo matching method and device. Background Art

[0002] At present, most freight drivers are engaged in freight transportation as private vehicle owners, and often need some third-party agencies to introduce cargo information to establish a communication link between cargo owners and truck drivers, or link cargo owners and vehicle owners through online transactions provided by third-party platforms. However, these methods still have the following problems: The cargo information introduced by third-party agencies is often applicable to a small group of "acquaintance economy". For cargo owners and vehicle owners, the addition of a third-party agency in the middle invisibly increases the cost of freight.

[0003] The method of matching cargo demand and truck drivers provided by third-party platforms can only meet the needs of registered users. Moreover, this matching model usually adopts a comprehensive analysis of cargo information, vehicle model, vehicle condition, driver evaluation and other information. It is relatively simple, and the overall matching success rate depends more on the communication between cargo owners and vehicle owners, and the efficiency is relatively low.

[0004] It is difficult to achieve accurate matching of cargo demand and truck owners through intelligent matching that combines cargo demand and truck drivers in modes such as grabbing orders. Summary of the invention

[0005] In view of the defects of the prior art, the purpose of this application is to solve the problems of poor vehicle-cargo matching accuracy, low efficiency and high freight cost in the prior art.

[0006] To achieve the above objectives, in a first aspect, the present application provides a vehicle-cargo matching method, comprising: Based on the historical freight data of the vehicle to be matched, a profile of the vehicle to be matched is established, including the vehicle model, freight behavior analysis information, historically frequently transported cargo types, and vehicle attendant information; Based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched, a vehicle recommendation sequence that matches the cargo owner's cargo source information is generated.

[0007] In some embodiments, based on the historical freight data of the vehicle to be matched, establishing a profile of the vehicle to be matched including the vehicle type, freight behavior analysis information, historical frequently transported cargo types and accompanying personnel information includes: Analyze historical freight data to obtain the length information and vehicle type information of the vehicle to be matched, and determine the vehicle model based on the length information and vehicle information; Determine freight behavior analysis information based on the delivery round-trip information and empty vehicle round-trip information of the to-be-matched vehicle within a fixed period in the historical freight data; Determine the types of cargo that have been frequently transported in history based on the weight information of the cargo carried by the to-be-matched vehicle in the historical freight data; Obtain the information of the accompanying personnel according to the frequency of changes in the accompanying personnel information within a fixed period in the historical freight data; Create a profile of the vehicle to be matched based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information.

[0008] In some embodiments, according to the weight information of the cargo types pulled by the to-be-matched vehicle in the historical freight data, determining the cargo types frequently pulled in history includes: Obtain the weight information sequence of the cargo carried by the to-be-matched vehicle in the historical freight data; Obtain a sequence of the proportion of the weight of each type of cargo in the weight information sequence to the total weight of the cargo pulled by the to-be-matched vehicle; For the first and second commodities in the sequence, if the proportion of the first commodity is within the first preset range, the historically frequently pulled commodity is determined to be the first commodity, the first commodity is the commodity with the largest proportion in the sequence, and the second commodity is the commodity with a proportion only less than the first commodity in the sequence; In the case where the first commodity is within the second preset range, if the second commodity is within the third preset range, the historically frequently pulled commodities are determined to be the first commodity and the second commodity; otherwise, the historically frequently pulled commodity is determined to be the first commodity; When the proportion of the first type of cargo is within the fourth preset range, the historically frequently transported type of cargo is determined to be any type of cargo among the types of cargo transported by the to-be-matched vehicle.

[0009] In some embodiments, according to the frequency of changes in the accompanying personnel information within a fixed period in the historical freight data, obtaining the accompanying personnel information includes: Different values ​​are assigned to the accompanying personnel information according to the frequency of changes in the accompanying personnel information within a fixed period.

[0010] In some embodiments, generating a vehicle recommendation sequence that matches the cargo source information of the cargo owner based on the vehicle model, freight behavior analysis information, historical frequently transported cargo types, and vehicle attendant information in the portrait of the to-be-matched vehicle includes: Obtain cargo source information including at least cargo type, cargo weight and vehicle type requirements; Generate a first portrait information queue corresponding to the vehicle to be matched according to the portrait of the vehicle to be matched; According to the vehicle model requirement, the vehicle model of the to-be-matched vehicle in the first portrait information queue and the delivery round-trip information of the to-be-matched vehicle, the first portrait information queue is updated to obtain the second portrait information queue; Combined with the cargo type, assign values ​​to the historically frequently transported cargo types in the portraits of the vehicles to be matched in the second portrait information queue to obtain a third portrait information queue; Calculate the recommendation factor of the vehicle to be matched in the third portrait information queue according to the freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched in the third portrait information queue; According to the size of the recommendation factor of the to-be-matched vehicles in the third portrait information queue, the recommendation order of the to-be-matched vehicles in the third portrait information queue is sorted to generate a vehicle recommendation sequence.

[0011] In some embodiments, the method further comprises: Get the shipping destination and shipping time; In the recommended order, the cargo source information including cargo type, cargo weight, vehicle type requirement, transportation destination and transportation time will be notified to the owners of the vehicles to be matched in the vehicle recommendation sequence.

[0012] In a second aspect, the present application provides a vehicle-cargo matching device, comprising: The acquisition module is used to establish a profile of the vehicle to be matched, including the vehicle model, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information, based on the historical freight data of the vehicle to be matched; The matching module is used to generate a vehicle recommendation sequence that matches the cargo owner's cargo source information based on the vehicle model, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched.

[0013] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the method described in the first aspect or any embodiments of the first aspect.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any embodiments of the first aspect.

[0015] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any embodiments of the first aspect.

[0016] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: The vehicle-cargo matching method and device provided in the present application establish a portrait of the vehicle to be matched based on the historical freight data of the vehicle to be matched, and generate a vehicle recommendation sequence that matches the cargo owner's cargo source information based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched, thereby achieving accurate matching of cargo and vehicles. At the same time, the cargo-vehicle matching process does not require the addition of a third-party agency and does not rely on the communication between the cargo owner and the vehicle, which invisibly reduces the logistics cost of cargo transportation and improves the efficiency of cargo transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the vehicle-cargo matching method provided in an embodiment of the present application; Figure 2 is a structural schematic diagram of a vehicle-cargo matching device provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0020] The terms "first", "second", etc. in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of objects. For example, the first type of goods and the second type of goods are used to distinguish different types of goods rather than to describe a specific order of the types of goods.

[0021] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0022] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0023] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0024] See also Figure 1 , an embodiment of the present application provides a vehicle-cargo matching method, including: step 110 and step 120.

[0025] Step 110: Based on the historical freight data of the vehicle to be matched, a profile of the vehicle to be matched is established, including the vehicle type, freight behavior analysis information, historical frequently transported cargo types, and vehicle attendant information; Step 120 generates a vehicle recommendation sequence that matches the cargo source information of the cargo owner based on the vehicle model, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched.

[0026] In the embodiment of the present application, the to-be-matched vehicle may include multiple vehicles, and the to-be-matched vehicle may be one or more vehicles used for transporting goods. For example, the to-be-matched vehicle may be a truck.

[0027] The historical freight data may be freight data within a historical time period, and may include round-trip delivery information of the vehicle to be matched (such as the frequency of round-trip delivery, round-trip delivery time, types of goods delivered, etc.), vehicle model, accompanying personnel, and other information.

[0028] By analyzing the historical freight data of the vehicle to be matched, the vehicle model, freight behavior analysis information, historical cargo types and vehicle attendant information of the vehicle to be matched are obtained, and a portrait of the vehicle to be matched is established based on the obtained vehicle model, freight behavior analysis information, historical cargo types and vehicle attendant information.

[0029] For example, it is assumed that the image of the vehicle to be matched is Indicates that the model is Indicates that freight behavior analysis information is used Indicates that historically, the most common type of cargo used Indicates that the accompanying personnel information is used If the vehicle to be matched has For any vehicle to be matched , and its corresponding portrait can be expressed as: , .

[0030] In the embodiment of the present application, the cargo source information of the cargo owner may include the cargo type (which can be Indicated), cargo weight (can be Indicates), vehicle model requirements (can be used Indicates), transportation destination (can be used ) and shipping time (which can be express).

[0031] Based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information in the above-obtained portrait of the vehicle to be matched, a vehicle recommendation sequence matching the cargo source information of the cargo owner is obtained.

[0032] The vehicle recommendation sequence refers to the owner information, vehicle information and recommendation factors of all recommended vehicles. The vehicle recommendation sequence can be Indicates that the owner's information can be used Indicates vehicle information , the recommendation factor can be used express.

[0033] For example, assuming that the vehicle recommendation sequence includes recommended vehicles, then there are , to Respectively represent the first to The owner information of the recommended vehicles, to Respectively represent the first to Vehicle information of recommended vehicles, to Respectively represent the first to The recommendation factor of the recommended vehicle.

[0034] It should be noted that the above steps 110 and 120 can be abstracted into an automated vehicle-cargo matching model, the input of which includes the historical freight data of the vehicle to be matched and the cargo owner and cargo source information, and the output includes a vehicle recommendation sequence.

[0035] In practical applications, the vehicle-cargo matching method provided in the embodiment of the present application can be applied to transportation operations in Province A, City B, and Strait B.

[0036] Strait B is the "life channel" for people's livelihood development and the "golden waterway" for economic connectivity. More than 90% of the daily necessities, 30% of the passengers and all vehicles in Province B enter and leave the island through Strait B. The main means of transporting materials is by trucks. Every year, there are nearly 2 million trucks entering and leaving Strait B through Island C. The main goods include crops, steel, wood, textiles, waste products, express delivery, etc.

[0037] By obtaining the historical data of trucks crossing the sea (i.e. freight data) and the cargo source information of the cargo owners, and inputting them into the automated vehicle-cargo matching model, the statistics of truck portraits are completed by analyzing the historical data of trucks crossing the sea, mainly including the models of trucks , freight behavior analysis information , the most common cargo types in history , information of the accompanying personnel The truck recommendation sequence is output through the automated truck-cargo matching model. The historical truck crossing data may be all truck historical freight data in the past three or five years, such as inbound and outbound data information, which may include round-trip delivery information, vehicle type, and accompanying personnel. For example, the round-trip delivery information may include inbound and outbound time, inbound and outbound frequency, inbound and outbound cargo type, and other information.

[0038] Based on the unique inbound and outbound truck information of Island C, the automatic truck screening and vehicle-cargo matching technology is realized. Through this method, the cargo demand information and inbound and outbound truck portrait information can be combined to generate an available truck sequence and give recommendation factors and reasons for recommendation, so as to achieve accurate matching of outbound cargo and trucks, reduce the overall logistics cost of the island, and improve the efficiency of cargo transportation.

[0039] The vehicle-cargo matching method provided in the embodiment of the present application establishes a portrait of the vehicle to be matched based on the historical freight data of the vehicle to be matched, and generates a vehicle recommendation sequence that matches the cargo owner's cargo source information based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched, thereby achieving accurate matching of cargo and vehicles. At the same time, the cargo-vehicle matching process does not require the addition of a third-party agency and does not rely on the communication between the cargo owner and the vehicle, which invisibly reduces the logistics cost of cargo transportation and improves the efficiency of cargo transportation.

[0040] Furthermore, in some embodiments, based on the historical freight data of the to-be-matched vehicle, establishing a portrait of the to-be-matched vehicle including the vehicle type, freight behavior analysis information, historical frequently-hauled cargo types, and vehicle attendant information includes: Analyze historical freight data to obtain the length information and vehicle type information of the vehicle to be matched, and determine the vehicle model based on the length information and vehicle information; Determine freight behavior analysis information based on the delivery round-trip information and empty vehicle round-trip information of the to-be-matched vehicle within a fixed period in the historical freight data; Determine the types of cargo that have been frequently transported in history based on the weight information of the cargo carried by the to-be-matched vehicle in the historical freight data; Obtain the information of the accompanying personnel according to the frequency of changes in the accompanying personnel information within a fixed period in the historical freight data; Create a profile of the vehicle to be matched based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information.

[0041] In the embodiment of the present application, the historical freight data of the vehicle to be matched is preprocessed as follows to establish a profile of the vehicle to be matched, specifically: Count the length information and vehicle type information of the vehicles to be matched in the above historical freight data, and use the length information and vehicle type information as the vehicle model in the portrait of the vehicle to be matched .

[0042] The delivery round-trip information and empty truck round-trip information of the to-be-matched vehicles within a fixed period in the above historical freight data are analyzed to obtain freight behavior analysis information, as follows: Taking the transportation operation of Province A and City B as an example, assuming that the vehicle to be matched is a truck, the delivery round-trip information (such as the frequency of delivery round-trips and delivery round-trip conditions) and empty truck round-trip information (such as the frequency of empty truck round-trips) of the truck in the past year are comprehensively analyzed, and the freight behavior analysis information is calculated based on the following calculation method:

[0043]

[0044]

[0045] In the formula, Analyze information for freight behavior, For the delivery frequency, in the embodiment of the present application, It can be the monthly average frequency of entering the island, For the shipment frequency, in the embodiment of the present application, It can be the monthly average frequency of leaving the island, is the empty vehicle round trip frequency. In the embodiment of the present application, It can be the frequency of entering and leaving the island.

[0046] By analyzing the weight information of the cargo types transported by the to-be-matched vehicles in the above historical freight data, the cargo types that were frequently transported in history can be determined.

[0047] Furthermore, in some embodiments, determining the historically frequently transported cargo types according to the weight information of the cargo types transported by the to-be-matched vehicle in the historical freight data includes: Obtain the weight information sequence of the cargo carried by the to-be-matched vehicle in the historical freight data; Obtain a sequence of the proportion of the weight of each type of cargo in the weight information sequence to the total weight of the cargo pulled by the to-be-matched vehicle; For the first and second commodities in the sequence, if the proportion of the first commodity is within the first preset range, the historically frequently pulled commodity is determined to be the first commodity, the first commodity is the commodity with the largest proportion in the sequence, and the second commodity is the commodity with a proportion only less than the first commodity in the sequence; In the case where the first commodity is within the second preset range, if the second commodity is within the third preset range, the historically frequently pulled commodities are determined to be the first commodity and the second commodity; otherwise, the historically frequently pulled commodity is determined to be the first commodity; When the proportion of the first type of cargo is within the fourth preset range, the historically frequently transported type of cargo is determined to be any type of cargo among the types of cargo transported by the to-be-matched vehicle.

[0048] In the present application embodiment, the historically frequently pulled goods It refers to the types of goods that trucks often carry in and out of the island. Since the goods that leave the island from C Island have a certain periodicity, the types of goods that are often carried in different seasons will change to a certain extent. Therefore, it is generally sufficient to only count the types of goods that are often carried in half a year or a year. The statistical method is as follows: Obtain all the information about the cargo types carried by the matching vehicle (for example, the cargo types carried by the truck entering and leaving the island), accumulate each cargo type separately, and obtain the weight information sequence of all cargo types (assuming that For example, assuming that the types of goods carried by the vehicles to be matched are If there are, ,in, to Respectively represent the vehicles to be matched Types of goods.

[0049] Calculate the proportion of all cargo types, that is, divide the weight of each cargo type by the total weight of all cargo types to obtain the proportion of the weight of each cargo type to the total weight. The calculation method is as follows:

[0050] In the formula, For the The weight of each type of cargo accounts for the total weight. The first Types of goods.

[0051] Generate a sequence of the weight of each type of goods as a percentage of the total weight of all types of goods (assuming that ), and arrange them in descending order. Assume that the weight of each type of goods accounts for the total weight. In this sequence, the type of goods with the largest proportion is the first type of goods, and the type of goods with a proportion only less than the first type of goods is the second type of goods. In the embodiment of this application, the first type of goods is The second type of goods is .

[0052] In the first category If the truck is within the first preset range, the first cargo type is determined to be the cargo type that the truck has frequently transported in the past. , then you can Assign a value to the commodity. In the embodiment of the present application, the first preset range is greater than 60%.

[0053] In the first category In the second preset range, if the second commodity If the truck is within the third preset range, it is determined that the type of cargo that the truck has frequently transported in the past is the first type of cargo. And the second , then you can Assign values ​​to the two cargo types with the highest proportion. Otherwise, determine that the cargo type that the truck has frequently transported in history is the first cargo type. The value is assigned to the type of goods with the highest proportion. In the embodiment of the present application, the second preset range is greater than 40% and less than 60%, and the third preset range is greater than 30%.

[0054] In the first category If it is in the fourth preset range, it is considered that the truck has no fixed cargo type, that is, it is determined that the cargo type that the truck has often transported in the past is any of the cargo types that the truck has transported. Assign a value to any of the cargo types carried by the truck. In the embodiment of the present application, the fourth preset range is less than 40%.

[0055] In an embodiment of the present application, the accompanying personnel information can be obtained by analyzing the frequency of changes in the accompanying personnel information of the to-be-matched vehicle within a fixed period in the historical freight data.

[0056] Further, in some embodiments, according to the frequency of changes in the accompanying personnel information within a fixed period in the historical freight data, obtaining the accompanying personnel information includes: Different values ​​are assigned to the accompanying personnel information according to the frequency of changes in the accompanying personnel information within a fixed period.

[0057] In the embodiment of the present application, the accompanying personnel information , mainly refers to the information of the accompanying personnel of the truck within a fixed period (for example, the past 1 year), which can be obtained based on the frequency of changes in the accompanying personnel information within the fixed period.

[0058] If the frequency of changes in the accompanying personnel information is high, for example, the frequency of changes in the accompanying personnel information is higher than a preset value, then If the frequency of changes in the accompanying personnel information is not high, for example, the frequency of changes in the accompanying personnel information is lower than the preset value, then ; If the information of the accompanying personnel remains basically unchanged, then .

[0059] Based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information obtained above, a profile of the vehicle to be matched can be established.

[0060] The vehicle-cargo matching method provided in the embodiment of the present application comprehensively analyzes the vehicle model information, freight behavior analysis information, historical cargo information, and vehicle attendant information of the vehicle to be matched, and cross-calculates it with the cargo owner's cargo source information to form a vehicle recommendation sequence, thereby improving the accuracy and efficiency of vehicle-cargo matching.

[0061] Furthermore, in some embodiments, generating a vehicle recommendation sequence that matches the cargo source information of the cargo owner according to the vehicle model, freight behavior analysis information, historical frequently transported cargo types, and vehicle attendant information in the portrait of the to-be-matched vehicle includes: Obtain cargo source information including at least cargo type, cargo weight and vehicle type requirements; Generate a first portrait information queue corresponding to the vehicle to be matched according to the portrait of the vehicle to be matched; According to the vehicle model requirement, the vehicle model of the to-be-matched vehicle in the first portrait information queue and the delivery round-trip information of the to-be-matched vehicle, the first portrait information queue is updated to obtain the second portrait information queue; Combined with the cargo type, assign values ​​to the historically frequently transported cargo types in the portraits of the vehicles to be matched in the second portrait information queue to obtain a third portrait information queue; Calculate the recommendation factor of the vehicle to be matched in the third portrait information queue according to the freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched in the third portrait information queue; According to the size of the recommendation factor of the to-be-matched vehicles in the third portrait information queue, the recommendation order of the to-be-matched vehicles in the third portrait information queue is sorted to generate a vehicle recommendation sequence.

[0062] In this embodiment of the present application, the cargo source information is imported, including the cargo type , Cargo weight 、Model requirements , Transportation destination and shipping time .

[0063] According to the obtained portrait of the vehicle to be matched , generate its corresponding portrait information queue for (i.e. the first portrait information queue).

[0064] According to vehicle model requirements and the first portrait information queue The models of the vehicles to be matched are compared and screened, that is, Keep the image of the vehicle Otherwise, remove it from the first portrait information queue After completing the data screening, update the first portrait information queue .

[0065] Combine the delivery round-trip information of the vehicle to be matched (for example, the truck entering the island and the vehicle leaving the island) to determine the first portrait information queue If the truck has left the island, remove it from the first portrait information queue. , and update the first portrait information queue, and the updated first portrait information queue is the second portrait information queue.

[0066] Combined cargo type , compare the historical frequently transported cargo types in the portraits of each to-be-matched vehicle in the second portrait information queue , divided into the following situations Re-assign the value and update the second portrait information queue. The obtained portrait information queue is the third portrait information queue: like If the value is a commodity, then equal When The value is assigned to 3; otherwise, The value is assigned to 1; like The value is two kinds of goods, then when When equal to any kind of goods, The value is assigned to 2; otherwise, The value is assigned to 1; like If the value is any type of cargo, the historical cargo types of the truck are obtained. Within the range of cargo types that the truck has historically carried, Assign a value of 2.5; otherwise, Assign a value of 1.

[0067] According to the freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information of the to-be-matched vehicles in the third portrait information queue, the recommendation factor of each to-be-matched vehicle is calculated through the following aspects:

[0068] According to the size of the recommendation factor of the vehicles to be matched in the third portrait information queue, the recommendation order of the vehicles to be matched in the third portrait information queue is sorted from large to small to generate a vehicle recommendation sequence .

[0069] in, is the maximum recommendation factor, is the owner information of the vehicle to be matched corresponding to the maximum recommendation factor, is the basic information of the vehicle to be matched corresponding to the maximum recommendation factor, is only less than The recommendation factor, is only less than The owner information of the vehicle to be matched corresponding to the recommendation factor, is only less than The basic information of the vehicle to be matched corresponding to the recommendation factor.

[0070] Furthermore, in some embodiments, the above method further includes: Get the shipping destination and shipping time; In the recommended order, the cargo source information including cargo type, cargo weight, vehicle type requirement, transportation destination and transportation time will be notified to the owners of the vehicles to be matched in the vehicle recommendation sequence.

[0071] In the embodiment of the present application, combined with the cargo source information and shipping time , inform the owner of the vehicle to be matched in the third portrait information queue of its cargo source information, including cargo type, through robot calls, text messages, phone calls, etc. in the recommended order , Cargo weight 、Model requirements , Transportation destination and shipping time , and a message notification will be sent. If the first recommended car owner rejects the request, the second car owner will be notified until a suitable car owner is found to accept the order.

[0072] The vehicle-cargo matching method provided in the embodiment of the present application solves the complexity of the current method that relies more on cargo owners and drivers to actively report information on third-party platforms. By obtaining information on trucks entering and leaving the island, it automatically screens matching needs and forms recommendation factors, which is conducive to reducing comprehensive logistics costs. It can realize the automatic screening and recommendation of all trucks on the island, and the information screening range covers all trucks. For truck drivers who are not registered on various third-party platforms, accurate and complete information push can be achieved, which is conducive to new truck drivers to accept orders and has high social value.

[0073] The vehicle-cargo matching device provided in the present application is described below. The vehicle-cargo matching device described below and the vehicle-cargo matching method described above can be referenced to each other.

[0074] See also Figure 2 An embodiment of the present application provides a vehicle-cargo matching device, including: an acquisition module 210 and a matching module 220.

[0075] The acquisition module 210 is used to establish a profile of the vehicle to be matched, including the vehicle type, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information, based on the historical freight data of the vehicle to be matched; The matching module 220 is used to generate a vehicle recommendation sequence that matches the cargo source information of the cargo owner based on the vehicle model, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched.

[0076] The vehicle-cargo matching device provided in the embodiment of the present application establishes a portrait of the vehicle to be matched based on the historical freight data of the vehicle to be matched, and generates a vehicle recommendation sequence that matches the cargo owner's cargo source information based on the vehicle model, freight behavior analysis information, historically frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched, thereby achieving accurate matching of cargo and vehicles. At the same time, the cargo-vehicle matching process does not require the addition of a third-party agency and does not rely on the communication between the cargo owner and the vehicle, which invisibly reduces the logistics cost of cargo transportation and improves the efficiency of cargo transportation.

[0077] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0078] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0079] Based on the method in the above embodiment, the present application embodiment provides an electronic device, see Figure 3 The electronic device may include: a processor (Processor) 310, a communication interface (Communications Interface) 320, a memory (Memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the method in the above embodiment.

[0080] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. 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, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0081] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0082] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0083] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0084] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0085] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0086] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A vehicle-cargo matching method, characterized in that: include: Based on the historical freight data of the vehicle to be matched, a profile of the vehicle to be matched is established, including the vehicle model, freight behavior analysis information, historically frequently transported cargo types, and vehicle attendant information; A vehicle recommendation sequence matching the cargo owner's cargo source information is generated based on the vehicle model, the freight behavior analysis information, the historically frequently transported cargo types and the vehicle attendant information in the portrait of the vehicle to be matched.

2. The vehicle-cargo matching method according to claim 1, characterized in that: The process of establishing a profile of the vehicle to be matched, including vehicle type, freight behavior analysis information, historical frequently transported cargo types, and vehicle attendant information, based on the historical freight data of the vehicle to be matched, includes: Analyze the historical freight data to obtain length information and vehicle type information of the vehicle to be matched, and determine the vehicle type according to the length information and vehicle information; Determine the freight behavior analysis information according to the delivery round-trip information and empty vehicle round-trip information of the to-be-matched vehicle within a fixed period in the historical freight data; Determine the historically frequently transported cargo types according to the weight information of the cargo types transported by the to-be-matched vehicle in the historical freight data; Acquire the accompanying personnel information according to the frequency of changes in the accompanying personnel information within a fixed period in the historical freight data; A profile of the vehicle to be matched is established based on the vehicle model, the freight behavior analysis information, the historically frequently transported cargo types and the accompanying personnel information.

3. The vehicle-cargo matching method according to claim 2, characterized in that: Determining the historical frequently transported cargo types according to the weight information of the cargo types transported by the to-be-matched vehicle in the historical freight data includes: Obtaining a weight information sequence of the cargo type carried by the to-be-matched vehicle in the historical freight data; Obtaining a sequence of the proportion of the weight of each type of cargo in the weight information sequence to the total weight of the cargo pulled by the to-be-matched vehicle; For the first commodity and the second commodity in the sequence, when the proportion of the first commodity is within a first preset range, it is determined that the historically frequently pulled commodity is the first commodity, the first commodity is the commodity with the largest proportion in the sequence, and the second commodity is the commodity with a proportion in the sequence that is only less than the first commodity; In the case where the first commodity type is within the second preset range, if the second commodity type is within the third preset range, the historically frequently pulled commodities are determined to be the first commodity type and the second commodity type; otherwise, the historically frequently pulled commodities are determined to be the first commodity type; When the proportion of the first cargo type is within the fourth preset range, the cargo type frequently transported in history is determined to be any cargo type among the cargo types transported by the to-be-matched vehicle.

4. The vehicle-cargo matching method and device according to claim 2, characterized in that: The acquiring the accompanying personnel information according to the changing frequency of the accompanying personnel information within a fixed period in the historical freight data includes: Different values ​​are assigned to the accompanying personnel information according to the frequency of changes in the accompanying personnel information within the fixed period.

5. The vehicle-cargo matching method according to claim 1, characterized in that: The generating of a vehicle recommendation sequence matching the cargo source information of the cargo owner according to the vehicle model, the freight behavior analysis information, the historical frequently transported cargo types and the accompanying personnel information in the portrait of the to-be-matched vehicle comprises: Obtain cargo source information including at least cargo type, cargo weight and vehicle type requirements; Generating a first portrait information queue corresponding to the vehicle to be matched according to the portrait of the vehicle to be matched; According to the vehicle model requirement, the vehicle model of the to-be-matched vehicle in the first portrait information queue and the delivery round-trip information of the to-be-matched vehicle, the first portrait information queue is updated to obtain a second portrait information queue; In combination with the cargo type, assign values ​​to the historically frequently transported cargo types in the portraits of the vehicles to be matched in the second portrait information queue to obtain a third portrait information queue; Calculate the recommendation factor of the vehicle to be matched in the third portrait information queue according to the freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information in the portrait of the vehicle to be matched in the third portrait information queue; According to the size of the recommendation factor of the to-be-matched vehicles in the third portrait information queue, the recommendation order of the to-be-matched vehicles in the third portrait information queue is sorted to generate the vehicle recommendation sequence.

6. The vehicle-cargo matching method according to claim 5, characterized in that: The method further comprises: Get the shipping destination and shipping time; The cargo source information including the cargo type, the cargo weight, the vehicle type requirement, the transportation destination and the transportation time is notified to the owners of the vehicles to be matched in the vehicle recommendation sequence in the recommended order.

7. A vehicle-cargo matching device, characterized in that: include: The acquisition module is used to establish a profile of the vehicle to be matched, including the vehicle model, freight behavior analysis information, historical frequently transported cargo types and vehicle attendant information, based on the historical freight data of the vehicle to be matched; The matching module is used to generate a vehicle recommendation sequence that matches the cargo source information of the cargo owner based on the vehicle model, the freight behavior analysis information, the historically frequently transported cargo types and the accompanying personnel information in the portrait of the to-be-matched vehicle.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the vehicle-cargo matching method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor executes the vehicle-cargo matching method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor executes the vehicle-cargo matching method as described in any one of claims 1 to 6.

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