Cargo logistics strategy determination method and device, equipment, storage medium and program product

Abstract

The invention provides a cargo logistics strategy determination method and device, equipment, a storage medium and a program product, which can be applied to the fields of carbon neutralization, intelligent logistics and intelligent supply chains. The method comprises the following steps: obtaining logistics information of a target cargo and vehicle logistics attributes of a target vehicle, wherein the logistics information comprises logistics route information and cargo quantity; and processing the logistics route information, the cargo quantity and the vehicle logistics attributes by using a logistics cost optimization function and a logistics constraint condition to obtain target vehicle logistics information of the target vehicle, the logistics cost optimization function being used for optimizing the logistics cost of transporting the target cargo, and the logistics constraint condition being used for optimizing the logistics cost of transporting the target cargo. The logistics constraint condition is used for constraining the logistics information to be matched with the target vehicle logistics information and constraining the target vehicle logistics information of the new energy vehicle in the target vehicles to meet the carbon emission condition; and determining a cargo logistics strategy according to the target vehicle logistics information.

Description

Technical Field

[0001] The present disclosure relates to the fields of smart logistics and intelligent supply chain, and more specifically, to a method, device, equipment, storage medium and program product for determining a cargo logistics strategy. Background Art

[0002] Energy conservation and carbon reduction are important measures to promote the comprehensive green transformation of economic and social development, and transport carbon reduction is one of the key areas of carbon emission reduction. Transportation, automobiles and energy constitute a carbon chain that supports and constrains each other. Transportation demand will affect the number of cars and energy consumption in the transportation sector, thereby affecting carbon emissions; the terminal energy consumption structure and energy consumption level of automobiles in turn affect carbon emissions in the energy and transportation sectors.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related technology: in the transportation carbon reduction strategy, it is generally considered to introduce new energy vehicles or optimize model algorithms for energy-saving and carbon reduction, but this will generate a large overall logistics and transportation cost. Summary of the invention

[0004] In view of this, the present disclosure provides a method, apparatus, device, storage medium and program product for determining a cargo logistics strategy.

[0005] One aspect of the present disclosure provides a method for determining a cargo logistics strategy, comprising:

[0006] The logistics information of the target cargo and the vehicle logistics attributes of the target vehicle are obtained, wherein the logistics information includes the logistics route information and the cargo quantity; the logistics route information, the cargo quantity and the vehicle logistics attributes are processed using the logistics cost optimization function and the logistics constraint conditions to obtain the target vehicle logistics information of the target vehicle, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting the target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraint conditions are used to constrain the logistics information to be compatible with the target vehicle logistics information, and are used to constrain the target vehicle logistics information of the new energy vehicle in the target vehicle to meet the carbon emission conditions; and the cargo logistics strategy is determined according to the target vehicle logistics information.

[0007] According to an embodiment of the present disclosure, the logistics cost optimization function includes a logistics cost optimization function determined based on the vehicle transportation cost and the target vehicle logistics route information, and the vehicle logistics cost information includes the vehicle transportation cost; wherein, the logistics route information, the cargo volume and the vehicle logistics attributes are processed by the logistics cost optimization function and the logistics constraint conditions to obtain the target vehicle logistics information of the target vehicle, including:

[0008] The logistics cost optimization function and logistics constraints are used to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost and vehicle transportation capacity attributes, to obtain the target vehicle logistics route information and target vehicle transportation volume of the target vehicle, wherein the vehicle logistics attributes also include vehicle transportation capacity attributes, and the target vehicle logistics information includes the target vehicle logistics route information and the target vehicle transportation volume.

[0009] According to an embodiment of the present disclosure, the logistics constraint condition includes a new energy vehicle allocation condition, and the new energy vehicle constraint condition includes at least one of the following:

[0010] The new energy vehicle transportation trip condition indicates that the number of target new energy vehicles meets the preset trip number threshold, and the target vehicles include the target new energy vehicles; the new energy vehicle transportation mileage condition indicates that the transportation mileage of the target new energy vehicle for cargo transportation based on the target vehicle logistics route information meets the preset transportation mileage threshold.

[0011] According to an embodiment of the present disclosure, the logistics constraint condition includes a logistics cargo quantity constraint condition for constraining the cargo quantity to be compatible with the target vehicle transport quantity of multiple target vehicles, and the logistics cargo quantity constraint condition includes at least one of the following:

[0012] The first logistics cargo volume constraint condition represents that the transport volume of multiple target vehicles is greater than or equal to the cargo volume; the second logistics cargo volume constraint condition represents that the transport volume of the target vehicle is less than or equal to the preset transport capacity of the target vehicle; the third logistics cargo volume constraint condition represents that the cumulative sum of the preset transport capacities of multiple target vehicles assigned the first logistics route identifier is greater than or equal to the cargo volume; wherein, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the vehicle transportation capacity attributes include the preset transportation capacity.

[0013] According to an embodiment of the present disclosure, the logistics constraint condition includes a route information constraint condition for constraining the multiple logistics route information to be compatible with the multiple target vehicle logistics route information, and the route information constraint condition includes at least one of the following:

[0014] The first route constraint condition indicates that the route transportation mileage included in the logistics route information of the target vehicle is less than or equal to the preset transportation mileage of the target vehicle; the second route constraint condition indicates that the route transportation time included in the logistics route information is greater than or equal to the ratio between the preset vehicle speed and the route transportation mileage of the target vehicle, wherein the vehicle transportation capacity attribute includes the preset vehicle speed, and the logistics route information also includes the preset transportation mileage.

[0015] According to an embodiment of the present disclosure, the logistics constraint conditions include a vehicle number constraint condition, which is used to constrain the target vehicle number of vehicles to match the preset logistics vehicle number of vehicles. The logistics route information includes the preset logistics vehicle number of vehicles, and the target vehicle number of vehicles is determined based on the logistics route information of the target vehicle.

[0016] According to an embodiment of the present disclosure, the logistics cost optimization function also includes a logistics carbon emission cost optimization function determined based on the vehicle carbon emission cost and the target vehicle logistics route information, and the vehicle logistics cost information also includes the vehicle carbon emission cost, wherein the logistics cost optimization function and the logistics constraint conditions are used to process multiple logistics route information and cargo quantities, as well as the vehicle transportation cost and vehicle transportation capacity attributes, including:

[0017] A logistics cost optimization function, a logistics carbon emission cost optimization function and logistics constraints are used to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost, vehicle carbon emission cost and vehicle transportation capacity attributes, wherein the logistics constraints also include a logistics cost constraint for constraining the cumulative sum of the vehicle transportation cost of the target vehicle assigned a first logistics route identifier to be less than or equal to a preset logistics cost, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the logistics information also includes a preset logistics cost.

[0018] According to an embodiment of the present disclosure, the vehicle carbon emission cost includes at least one of the following:

[0019] Fuel consumption information, electric energy consumption information of target new energy vehicles, fuel density information, electric emission factor, carbon oxidation rate information, fuel calorific value information, and fuel calorific value carbon content information.

[0020] According to an embodiment of the present disclosure, the target vehicle logistics information includes the target vehicle logistics route information and the target vehicle transportation volume of the target vehicle, and the method further includes:

[0021] Based on the target vehicle logistics route information and the target vehicle transportation volume, the logistics carbon emission information of transporting the target goods is determined.

[0022] Another aspect of the present disclosure provides a cargo logistics strategy determination device, comprising:

[0023] An acquisition module is used to acquire the logistics information of the target goods and the vehicle logistics attributes of the target vehicle, wherein the logistics information includes the logistics route information and the quantity of goods;

[0024] a processing module, used for processing logistics route information, cargo volume and vehicle logistics attributes by using logistics cost optimization function and logistics constraint conditions to obtain target vehicle logistics information of target vehicles, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraint conditions are used to constrain the logistics information to be compatible with the target vehicle logistics information, and are used to constrain the target vehicle logistics information of new energy vehicles in the target vehicles to meet carbon emission conditions;

[0025] The determination module is used to determine the cargo logistics strategy according to the target vehicle logistics information.

[0026] Another aspect of the present disclosure provides an electronic device, comprising:

[0027] one or more processors;

[0028] a memory for storing one or more programs,

[0029] When one or more programs are executed by one or more processors, the one or more processors implement the above cargo logistics strategy determination method.

[0030] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the above method for determining a cargo logistics strategy.

[0031] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and the instructions are used to implement the above cargo logistics strategy determination method when executed.

[0032] According to the cargo logistics strategy determination method and apparatus, equipment, storage medium and program product provided by the present invention, since the optimization goal of the logistics cost optimization function is to optimize the logistics cost of transporting the target cargo, by utilizing the logistics cost optimization function and logistics constraints to process the logistics route information, cargo volume and vehicle logistics attributes, it is possible to achieve the conditions of matching the logistics information with the target vehicle logistics information, and the target vehicle logistics information of the new energy vehicle meets the carbon emission conditions, while optimizing the logistics cost of each target vehicle transporting the target cargo. As a result, the cargo logistics strategy determined based on the target vehicle logistics information can meet the carbon emission conditions of the new energy vehicles participating in logistics transportation while reducing the logistics cost of transporting the target cargo, thereby improving the decision-making efficiency of logistics strategy decisions for the target cargo, improving the degree of adaptation between the cargo logistics strategy and the carbon emission constraints, and reducing the overall logistics cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0034] Figure 1 The application scenario of the cargo logistics strategy determination method according to the embodiment of the present disclosure is schematically shown;

[0035] Figure 2 A flowchart of a method for determining a cargo logistics strategy according to an embodiment of the present disclosure is schematically shown;

[0036] Figure 3 An exemplary system architecture for implementing a method and device for determining a cargo logistics strategy according to an embodiment of the present disclosure is schematically shown;

[0037] Figure 4 A block diagram schematically shows a device for determining a cargo logistics strategy according to an embodiment of the present disclosure; and

[0038] Figure 5 A block diagram of an electronic device suitable for implementing a method for determining a cargo logistics strategy according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0039] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0040] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0041] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0042] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0043] In the process of realizing the present disclosure, the inventors found that energy conservation and carbon reduction are important measures to promote the comprehensive green transformation of economic and social development, and transportation carbon reduction is one of the key areas of carbon emission reduction. Transportation, automobiles and energy constitute a carbon chain that supports and constrains each other, which can increase the substitution effect of new energy vehicles on traditional fuel vehicles, thereby effectively improving carbon emissions in the use of automobiles. In the transportation carbon reduction strategy, it is generally considered to introduce new energy vehicles or optimize model algorithms for energy conservation and carbon reduction transformation, but it often generates a large overall cost of logistics and transportation.

[0044] In view of this, the embodiments of the present disclosure provide a method and apparatus, device, storage medium and program product for determining a cargo logistics strategy. The method includes: obtaining the logistics information of the target cargo and the vehicle logistics attributes of the target vehicle, the logistics information including the logistics route information and the cargo volume; using the logistics cost optimization function and the logistics constraint conditions to process the logistics route information, the cargo volume and the vehicle logistics attributes to obtain the target vehicle logistics information of the target vehicle, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting the target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraint conditions are used to constrain the logistics information to be compatible with the target vehicle logistics information, and are used to constrain the target vehicle logistics information of the new energy vehicle in the target vehicle to meet the carbon emission conditions; and determining the cargo logistics strategy according to the target vehicle logistics information.

[0045] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0046] Figure 1 The application scenario of the cargo logistics strategy determination method according to an embodiment of the present disclosure is schematically illustrated.

[0047] like Figure 1As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a vehicle 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the vehicle 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0048] The user may use at least one of the first terminal device 101, the second terminal device 102, and the vehicle 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the vehicle 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0049] The first terminal device 101 and the second terminal device 102 may be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc. The vehicle 103 may be various types such as fuel transport vehicles and new energy transport vehicles.

[0050] The server 105 may be a server that provides various services, such as a background management server that provides support for users to use the first terminal device 101, the second terminal device 102, and the websites browsed (only as an example). The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device. The server 105 may also exchange information with the vehicle 103 to facilitate the delivery of cargo logistics strategies to the vehicle 103, or obtain vehicle logistics attribute information such as the current location and cargo capacity from the vehicle 103.

[0051] It should be noted that the cargo logistics strategy determination method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the cargo logistics strategy determination device provided in the embodiment of the present disclosure can generally be set in the server 105. The cargo logistics strategy determination method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the vehicle 103 and / or the server 105. Correspondingly, the cargo logistics strategy determination device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the vehicle 103 and / or the server 105.

[0052] Alternatively, the cargo logistics strategy determination method provided in the embodiment of the present disclosure may also be executed by the first terminal device 101, the second terminal device 102 or the vehicle 103, or may also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102 or the vehicle 103. Accordingly, the cargo logistics strategy determination device provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the vehicle 103, or may be arranged in other terminal devices different from the first terminal device 101, the second terminal device 102 or the vehicle 103.

[0053] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0054] Figure 2 The flowchart of the cargo logistics strategy determination method according to an embodiment of the present disclosure is schematically shown.

[0055] like Figure 2 As shown, the method 200 includes operations S210 to S230.

[0056] In operation S210, logistics information of target cargo and vehicle logistics attributes of a target vehicle are acquired.

[0057] According to an embodiment of the present disclosure, the target cargo may represent cargo waiting to be transported, and the logistics information of the target cargo and the vehicle logistics attributes of the target vehicle may be extracted from the transportation system through a database query operation.

[0058] According to an embodiment of the present disclosure, logistics information includes logistics route information and cargo volume. Logistics route information represents the path information of the target cargo, such as the route mileage of the target cargo, the transportation volume to complete the transportation task within the specified time, etc. The cargo volume can represent the cargo volume, cargo weight, cargo quantity, etc. of the target cargo to be transported.

[0059] According to an embodiment of the present disclosure, the target vehicle represents a vehicle to be selected that is capable of transporting target goods, and the vehicle logistics attributes include the vehicle type for transporting the target goods, the proportion threshold of each vehicle type, and the mileage threshold, cargo capacity threshold, speed, etc. of each type of vehicle.

[0060] In operation S220, the logistics route information, the cargo volume and the vehicle logistics attributes are processed using the logistics cost optimization function and the logistics constraint conditions to obtain the target vehicle logistics information of the target vehicle.

[0061] According to an embodiment of the present disclosure, a logistics cost optimization function is used to optimize the logistics cost of transporting target goods. The logistics cost is determined based on vehicle logistics cost information. Vehicle logistics attributes include vehicle logistics cost information. Logistics constraints are used to constrain logistics information to match target vehicle logistics information, and to constrain target vehicle logistics information of new energy vehicles in target vehicles to meet carbon emission conditions.

[0062] According to an embodiment of the present disclosure, the vehicle logistics cost information represents the transportation cost, carbon emission cost, time cost, etc. generated by the vehicle transporting the target goods in the logistics transportation task. The logistics cost can represent the total transportation cost or total carbon emission generated by all vehicles transporting the target goods on the logistics route. For example, the logistics cost optimization function can take the minimum total transportation cost or the minimum total carbon emission as the optimization goal.

[0063] According to an embodiment of the present disclosure, the target vehicle logistics information may include logistics route information of the target vehicle transporting the target goods, as well as cargo volume, cargo weight, cargo quantity and other information of the target vehicle transporting the target goods.

[0064] According to an embodiment of the present disclosure, constraining the target vehicle logistics information of new energy vehicles among the target vehicles to meet the carbon emission conditions can be achieved by the proportion of new energy vehicles among the target vehicles.

[0065] According to an embodiment of the present disclosure, the logistics constraint condition is used to constrain the comparison relationship between the logistics information and the target vehicle logistics information. For example, the target vehicle can travel a distance of not less than the distance of the logistics route where the target vehicle is located when transporting the target goods.

[0066] In operation S230, a cargo logistics strategy is determined according to the target vehicle logistics information.

[0067] According to an embodiment of the present disclosure, the target vehicle logistics information includes information such as the target vehicle number, the logistics route of the target vehicle, and the transportation volume of the target vehicle. The cargo logistics strategy is determined based on the target vehicle logistics information and the target total cargo volume. The cargo logistics strategy includes the target vehicle number corresponding to each logistics route and the cargo volume transported by the target vehicle.

[0068] According to the embodiments of the present disclosure, since the optimization goal of the logistics cost optimization function is to optimize the logistics cost of transporting the target goods, by utilizing the logistics cost optimization function and logistics constraints to process the logistics route information, cargo volume and vehicle logistics attributes, it is possible to achieve the conditions of matching the logistics information with the target vehicle logistics information, and the target vehicle logistics information of the new energy vehicle meets the carbon emission conditions, while optimizing the logistics cost of each target vehicle transporting the target goods. As a result, the cargo logistics strategy determined based on the target vehicle logistics information can meet the carbon emission conditions of new energy vehicles participating in logistics transportation while reducing the logistics cost of transporting the target goods, thereby improving the decision-making efficiency of logistics strategy decisions for target goods, improving the degree of adaptation between the cargo logistics strategy and the carbon emission constraints, and reducing the overall logistics cost.

[0069] According to an embodiment of the present disclosure, the logistics cost optimization function includes a logistics cost optimization function determined based on the vehicle transportation cost and the target vehicle logistics route information, and the vehicle logistics cost information includes the vehicle transportation cost.

[0070] According to an embodiment of the present disclosure, the vehicle transportation cost represents the transportation fee expenditure of the vehicle transporting the target goods.

[0071] In one embodiment, the logistics cost optimization function is shown in formula (1):

[0072] (1);

[0073] in, Characterizes the collection of logistics routes, A set representing the target vehicle type, A set of numbers representing the target vehicles, is the logistics route information of the target vehicle, Characterization Types of vehicles on logistics routes Target vehicle number The corresponding target vehicle, Characterization Types of vehicles on logistics routes The corresponding vehicle transportation cost.

[0074] According to an embodiment of the present disclosure, logistics route information, cargo volume and vehicle logistics attributes are processed using a logistics cost optimization function and logistics constraints to obtain target vehicle logistics information of a target vehicle, including: processing multiple logistics route information and cargo volume, as well as vehicle transportation cost and vehicle transportation capacity attributes using a logistics cost optimization function and logistics constraints to obtain target vehicle logistics route information and target vehicle transportation volume of the target vehicle, wherein the vehicle logistics attributes also include vehicle transportation capacity attributes, and the target vehicle logistics information includes target vehicle logistics route information and target vehicle transportation volume.

[0075] According to an embodiment of the present disclosure, vehicle logistics attributes include vehicle transport capacity attributes, which characterize the ability of a target vehicle to transport target goods, for example, respective ratio thresholds of vehicle types and mileage thresholds, cargo capacity thresholds, speed, etc. of the target vehicle.

[0076] According to the embodiments of the present disclosure, a solver can be called or a single-objective optimization solution algorithm can be used to process multiple logistics route information, cargo quantities and vehicle transportation capacity attributes based on logistics constraints, and the logistics route information and vehicle transportation cost of multiple target vehicles can be processed based on the logistics cost optimization function to obtain an optimal solution that simultaneously satisfies the logistics cost optimization function and logistics constraints. The optimal solution includes the target vehicle logistics route information and target vehicle transportation volume of the target vehicle.

[0077] According to an embodiment of the present disclosure, the target vehicle logistics route information represents the logistics route information of the target vehicle transporting the target goods, for example, the target vehicle numbered 100 transports the target goods on the first logistics route, etc. The target vehicle transportation volume represents the cargo volume, cargo weight, cargo quantity, etc. of the target vehicle transporting the target goods.

[0078] According to the embodiment of the present disclosure, the target vehicle logistics route information with a value of 1 in the optimal solution Characterization Types of vehicles on logistics routes Vehicle number The corresponding target vehicle, for example, The vehicle number representing the target vehicle is 3, the vehicle type is a new energy vehicle, and the logistics route is 1.

[0079] According to an embodiment of the present disclosure, the logistics constraint condition includes a new energy vehicle allocation condition, and the new energy vehicle constraint condition includes at least one of the following:

[0080] The new energy vehicle transportation trip condition indicates that the number of target new energy vehicles meets the preset trip number threshold, and the target vehicles include the target new energy vehicles; the new energy vehicle transportation mileage condition indicates that the transportation mileage of the target new energy vehicle for cargo transportation based on the target vehicle logistics route information meets the preset transportation mileage threshold.

[0081] In one example, the preset trip number threshold may be based on the preset target new energy vehicle ratio lower limit threshold. and the number of target vehicles Sure.

[0082] For example, the new energy vehicle transportation trip condition represents the number of target new energy vehicles. Less than or equal to the preset train number threshold, Characterizes the set of target new energy vehicles.

[0083] According to an embodiment of the present disclosure, the new energy vehicle transportation mileage condition can be used to constrain the transportation mileage of the target new energy vehicle for cargo transportation using the target vehicle logistics route information to be less than a preset transportation mileage threshold.

[0084] According to the embodiments of the present disclosure, based on the new energy vehicle transportation number conditions and the new energy vehicle transportation mileage conditions, the upper limit of the transportation mileage of the target new energy vehicle and the lower limit of the proportion of the number of target new energy vehicles to the total number of target vehicles are constrained, thereby optimizing the greenhouse gas emissions in the transportation link of the logistics company.

[0085] According to an embodiment of the present disclosure, the logistics constraint condition includes a logistics cargo quantity constraint condition for constraining the cargo quantity to be compatible with the target vehicle transport quantity of multiple target vehicles, and the logistics cargo quantity constraint condition includes at least one of the following:

[0086] The first logistics cargo volume constraint condition represents that the transport volume of multiple target vehicles is greater than or equal to the cargo volume; the second logistics cargo volume constraint condition represents that the transport volume of the target vehicle is less than or equal to the preset transport capacity of the target vehicle; the third logistics cargo volume constraint condition represents that the cumulative sum of the preset transport capacities of multiple target vehicles assigned the first logistics route identifier is greater than or equal to the cargo volume; wherein, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the vehicle transportation capacity attributes include the preset transportation capacity.

[0087] According to an embodiment of the present disclosure, the preset transport capacity represents the maximum transport capacity carried by the target vehicle in the transport task.

[0088] In one example, the first logistics cargo volume constraint condition may constrain the relationship between the sum of the target vehicle transportation volumes of multiple target vehicles on each logistics route and the cargo volume allocated to the logistics route.

[0089] For example, the first logistics cargo volume constraint condition may constrain the sum of the target vehicle transport volumes of multiple target vehicles on each logistics route (for example, expressed as ) is greater than or equal to the amount of goods allocated to the logistics route ,in, A set representing the target vehicle type, Characterization Types of vehicles on logistics routes Vehicle number The corresponding target vehicle transport volume, Characterization The volume of cargo on a logistics route.

[0090] In one example, the second logistics cargo volume constraint condition may constrain the upper limit of the target vehicle transportation volume of each target vehicle in each vehicle type. Types of vehicles on logistics routes Vehicle number The corresponding target vehicle and vehicle type Corresponding preset transport capacity The default unit of transport capacity is cubic meter.

[0091] For example, the second logistics cargo quantity constraint can constrain the first Types of vehicles on logistics routes Vehicle number Corresponding target vehicle transport volume Less than or equal to the upper limit of the target vehicle transport volume corresponding to the target vehicle.

[0092] According to an embodiment of the present disclosure, the third logistics cargo quantity constraint condition can ensure that the target cargo on each logistics route is transported.

[0093] For example, for each logistics route, the third logistics cargo volume constraint condition can constrain the upper limit of the target vehicle transportation volume of all target vehicles to be greater than or equal to the third The volume of goods on the logistics route .

[0094] In one example, the upper limit of the target vehicle transportation volume of all target vehicles corresponding to each logistics route can be determined according to the upper limits of the target vehicle transportation volumes of each of the multiple target vehicles. For example, the upper limit of the target vehicle transportation volume of all target vehicles corresponding to each logistics route represents the cumulative sum of the upper limits of the target vehicle transportation volumes of each of the multiple target vehicles (for example, represented as )

[0095] According to an embodiment of the present disclosure, the target vehicle logistics route information Can be 1 or 0, the first logistics route identification representation The first logistics route logo indicates the vehicle type. Vehicle number The target vehicle participates in Transportation of target goods along logistics routes.

[0096] According to an embodiment of the present disclosure, the logistics constraint condition includes a route information constraint condition for constraining the multiple logistics route information to be compatible with the multiple target vehicle logistics route information, and the route information constraint condition includes at least one of the following:

[0097] The first route constraint condition indicates that the route transportation mileage contained in the logistics route information is less than or equal to the preset transportation mileage of the target vehicle; the second route constraint condition indicates that the route transportation time contained in the logistics route information is greater than or equal to the ratio between the preset vehicle speed and the route transportation mileage of the target vehicle, wherein the vehicle transportation capacity attribute includes the preset vehicle speed, and the logistics route information also includes the preset transportation mileage.

[0098] According to an embodiment of the present disclosure, the route transportation mileage represents the mileage information of the logistics route. The preset transportation mileage represents the upper limit of the mileage of the target vehicle transporting the target goods. The route transportation duration represents the duration limit of the logistics route.

[0099] According to an embodiment of the present disclosure, the first route constraint condition can ensure that the mileage of the target vehicle transporting the target goods is not less than the route transportation mileage of the logistics route where the target vehicle is located.

[0100] In one example, the mileage of the target vehicle transporting the target goods represents the vehicle type Corresponding preset transportation mileage The route transportation mileage of the logistics route where the target vehicle is located is based on the Types of vehicles on logistics routes Vehicle number The corresponding target vehicle and The transport mileage of the logistics route Sure.

[0101] According to an embodiment of the present disclosure, the second route constraint condition can ensure that the time taken by the target vehicle to transport the target goods does not exceed the route transportation time of the logistics route where the target vehicle is located.

[0102] In one example, the time taken by the target vehicle to transport the target goods may be based on the Types of vehicles on logistics routes Vehicle number The corresponding target vehicle , No. The transport mileage of the logistics route and vehicle type Corresponding preset speed The target vehicle is located in The transportation time of the logistics route represents the The transportation time on the logistics route .

[0103] According to an embodiment of the present disclosure, the logistics constraint conditions include a vehicle number constraint condition, which is used to constrain the target vehicle number of vehicles to match the preset logistics vehicle number of vehicles. The logistics route information includes the preset logistics vehicle number of vehicles, and the target vehicle number of vehicles is determined based on the logistics route information of the target vehicle.

[0104] According to an embodiment of the present disclosure, the target number of trains represents the actual total number of target vehicles, and the target number of trains can be obtained by counting the sum of target vehicles on all logistics routes according to the logistics route information of the target vehicles. The preset logistics number of trains represents the preset total number upper limit of the target vehicles.

[0105] According to an embodiment of the present disclosure, the vehicle quantity constraint condition can limit the upper limit of the total number of target vehicles per day. The upper limit of the total number of vehicle quantities represents the preset number of logistics vehicles. .

[0106] According to an embodiment of the present disclosure, the upper limit of the number of trips of target vehicles in each vehicle type may also be limited by a number of trips constraint condition.

[0107] For example, for vehicle type V, constraint The number of target vehicles on the logistics route No more than the preset number of logistics trips corresponding to vehicle type V .

[0108] According to an embodiment of the present disclosure, the logistics cost optimization function also includes a logistics carbon emission cost optimization function determined based on the vehicle carbon emission cost and logistics route information, and the vehicle logistics cost information also includes the vehicle carbon emission cost.

[0109] In one embodiment, the logistics cost optimization function is shown in formula (2):

[0110] (2);

[0111] in, Represents the total vehicle carbon emission cost calculated based on the target vehicle.

[0112] According to an embodiment of the present disclosure, the vehicle carbon emission cost represents the greenhouse gas emissions calculated based on the target vehicle transporting the target goods. The vehicle carbon emission cost can be calculated based on the carbon emissions generated by different types of target vehicles, for example, it can be calculated based on the fuel consumption information and fuel density information of fuel vehicles and the power consumption information and electric emission factor of new energy vehicles.

[0113] According to an embodiment of the present disclosure, using a logistics cost optimization function and logistics constraints to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost and vehicle transportation capacity attributes includes:

[0114] A logistics cost optimization function, a logistics carbon emission cost optimization function and logistics constraints are used to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost, vehicle carbon emission cost and vehicle transportation capacity attributes, wherein the logistics constraints also include a logistics cost constraint for constraining the cumulative sum of the vehicle transportation cost of the target vehicle assigned a first logistics route identifier to be less than or equal to a preset logistics cost, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the logistics information also includes a preset logistics cost.

[0115] According to an embodiment of the present disclosure, the logistics carbon emission cost optimization function may aim to minimize logistics carbon emissions.

[0116] According to an embodiment of the present disclosure, the cumulative sum of the vehicle transportation costs of the target vehicles assigned the first logistics route identifier on all logistics routes is counted based on the target vehicle logistics route information, and then the vehicle transportation cost of the target vehicle is limited by using the preset logistics cost and logistics constraint conditions. The preset logistics cost represents the preset upper limit value of the total vehicle transportation cost.

[0117] According to the embodiments of the present disclosure, a solver can be called or a multi-objective optimization solution algorithm can be used to process multiple logistics route information, cargo volume and vehicle transportation capacity attributes based on logistics constraints, process the logistics route information and vehicle transportation cost of multiple target vehicles based on the logistics cost optimization function, and process the logistics route information and vehicle carbon emission cost of multiple target vehicles based on the logistics carbon emission cost optimization function to obtain an optimal solution that simultaneously satisfies the logistics cost optimization function, logistics constraints and logistics carbon emission cost optimization function. The optimal solution includes the target vehicle and the target vehicle logistics route information, target vehicle transportation volume, total logistics carbon emission cost value and total logistics transportation cost cost value of the target vehicle.

[0118] According to an embodiment of the present disclosure, the multi-objective optimization solution algorithm may be a multi-objective optimization algorithm based on a genetic algorithm, a multi-objective optimization algorithm based on an evolutionary strategy, or the like.

[0119] According to an embodiment of the present disclosure, the solver performs solving based on a solving algorithm. For example, the solving algorithm may be an operations research optimization algorithm, a linear programming algorithm, and the like.

[0120] According to an embodiment of the present disclosure, the vehicle carbon emission cost includes at least one of the following:

[0121] Fuel consumption information, electric energy consumption information of target new energy vehicles, fuel density information, electric emission factor, carbon oxidation rate information, fuel calorific value information, and fuel calorific value carbon content information.

[0122] According to an embodiment of the present disclosure, the fuel calorific value carbon content information represents the carbon content per unit calorific value, the fuel calorific value information represents the average low calorific value, the electric energy consumption information represents the vehicle's electric consumption per 100 kilometers, and the fuel consumption information represents the fuel consumption information of the vehicle's electric consumption per 100 kilometers.

[0123] In one embodiment, the total vehicle carbon emission cost As shown in formula (3):

[0124] (3);

[0125] in, Characterizes the set of target new energy vehicles, A collection representing vehicle types, A set of numbers representing the target vehicle, Characterization Types of vehicles on logistics routes Vehicle number The corresponding target vehicle transport volume, Characterizing vehicle types The corresponding preset transport capacity, Characterizing vehicle types The corresponding power consumption information, the unit of power consumption information is , Characterizing vehicle types The corresponding electricity emission factor, the unit of electricity emission factor is , Characterize the set of target fuel vehicles, Characterization Vehicle types on logistics routes Vehicle number The corresponding target vehicle transport volume, Characterizing vehicle types The corresponding preset transport capacity, Characterizing vehicle types The corresponding fuel consumption information, Represents fuel density information. The unit of fuel density information is , Represents the fuel calorific value information. The unit of fuel calorific value information is , Characterizes the fuel calorific value carbon content information. The unit of fuel calorific value carbon content information is , Characterizes carbon oxidation rate information.

[0126] According to the embodiments of the present disclosure, new energy vehicle models, carbon emission factors, electricity consumption information, etc. are introduced to obtain an optimized total logistics carbon emission cost value, which is shared with goods suppliers as a basis for emission reduction, thereby realizing greenhouse gas emission calculation and emission reduction calculation within the scope of goods suppliers, so as to provide sharing of greenhouse gas emission and emission reduction inventory / verification data.

[0127] According to an embodiment of the present disclosure, the target vehicle logistics information includes the target vehicle logistics route information and the target vehicle transportation volume of the target vehicle, and the cargo logistics strategy determination method further includes:

[0128] Based on the target vehicle logistics route information and the target vehicle transportation volume, the logistics carbon emission information of transporting the target goods is determined.

[0129] According to an embodiment of the present disclosure, after the introduction of new energy vehicle types, the target vehicle logistics route information, target vehicle transportation volume and optimized total vehicle carbon emission cost value are obtained according to the logistics cost optimization function, logistics constraint conditions and logistics carbon emission cost optimization function.

[0130] According to the embodiments of the present disclosure, before the introduction of new energy vehicle types, the existing method generates a total vehicle carbon emission cost value before optimization based on the transportation of target goods by fuel vehicle types, which is greater than the total logistics carbon emission cost value after optimization. The logistics carbon emission information includes greenhouse gas emission reduction, and the greenhouse gas emission reduction can be obtained based on the difference between the total vehicle carbon emission cost value before optimization and the total logistics carbon emission cost value after optimization.

[0131] According to the embodiments of the present disclosure, the greater the greenhouse gas emission reduction, the better the emission reduction effect, and the smaller the carbon emission cost value of the optimized total logistics.

[0132] In one embodiment, the total vehicle carbon emission cost before optimization is Can be based on inclusion , , , , , , , The octet is determined by

[0133] in, Characterization before optimization Vehicle types on logistics routes Vehicle number The corresponding target vehicle transport volume.

[0134] In one example, greenhouse gas emissions reductions It can be based on the total vehicle carbon emission cost before optimization and the optimized total vehicle carbon emission cost Sure.

[0135] According to the embodiments of the present disclosure, a target optimization solution model is constructed according to the above-mentioned logistics cost optimization function and logistics constraints, and then the logistics information of the target goods and the vehicle logistics attributes of the target vehicle and other relevant model parameters are input for solution to obtain the target vehicle logistics route information and the target vehicle transportation volume, the total logistics transportation cost value, and the optimized total vehicle carbon emission value, and then the greenhouse gas emission reduction is calculated, but it is not limited to this. The embodiments of the present disclosure do not limit the construction method of the above-mentioned target optimization solution model.

[0136] Figure 3 An exemplary system architecture for implementing a method and apparatus for determining a cargo logistics strategy according to an embodiment of the present disclosure is schematically shown.

[0137] like Figure 3 As shown, the system architecture includes an input system 310, a target optimization solution system 320, and an output system 330. The logistics information of the target goods and the vehicle logistics attributes of the target vehicle are obtained from the database and input into the input system 310. The input information in the input system 310 includes a preset vehicle number threshold 311, a preset transportation mileage threshold 312, a cargo volume 313, power consumption information 314, a preset vehicle speed 315, a preset transportation capacity 316, carbon oxidation rate information 317, an electric emission factor 318, fuel calorific value information 319, etc. The target optimization solution system 320 constructs a target optimization solution model based on the new energy vehicle constraint 321, the logistics cargo volume constraint 322, the route information constraint 323, the vehicle number constraint 324, the logistics cost optimization function 325, and the logistics carbon emission cost optimization function 326. The target optimization solution model solves the input information in the input system 310 to obtain the output results in the output system 330. The output results include the total logistics transportation cost value 331, the optimized total logistics carbon emission cost value 332, the target vehicle logistics route information 333, the target vehicle transportation volume 334, the total logistics carbon emission cost value 335 before optimization, and the greenhouse gas emission reduction 336.

[0138] Based on the above cargo logistics strategy determination method, the present disclosure also provides a cargo logistics strategy determination device. Figure 4 The device is described in detail.

[0139] Figure 4 A structural block diagram of a device for determining a cargo logistics strategy according to an embodiment of the present disclosure is shown.

[0140] like Figure 4 As shown, the cargo logistics strategy determination device 400 of this embodiment includes an acquisition module 410, a processing module 420 and a determination module 430.

[0141] The acquisition module 410 is used to acquire the logistics information of the target goods and the vehicle logistics attributes of the target vehicle, wherein the logistics information includes logistics route information and the quantity of goods. In one embodiment, the acquisition module 410 can be used to perform the operation S210 described above, which will not be described in detail here.

[0142] The processing module 420 is used to process the logistics route information, the cargo volume and the vehicle logistics attributes using the logistics cost optimization function and the logistics constraint conditions to obtain the target vehicle logistics information of the target vehicle, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting the target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraint conditions are used to constrain the logistics information to be compatible with the target vehicle logistics information, and to constrain the target vehicle logistics information of the new energy vehicle in the target vehicle to meet the carbon emission conditions. In one embodiment, the processing 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0143] The determination module 430 is used to determine the cargo logistics strategy according to the target vehicle logistics information. In one embodiment, the determination module 430 can be used to perform the operation S230 described above, which will not be described in detail here.

[0144] According to an embodiment of the present disclosure, the processing module 420 includes a first processing submodule.

[0145] The first processing submodule is used to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost and vehicle transportation capacity attributes using logistics cost optimization functions and logistics constraints to obtain target vehicle logistics route information and target vehicle transportation volume of the target vehicle, wherein the vehicle logistics attributes also include vehicle transportation capacity attributes, and the target vehicle logistics information includes target vehicle logistics route information and target vehicle transportation volume.

[0146] According to an embodiment of the present disclosure, the processing module 420 includes a second processing submodule.

[0147] The second processing submodule is used to process multiple logistics route information and cargo quantities, as well as vehicle transportation cost, vehicle carbon emission cost and vehicle transportation capacity attributes using a logistics cost optimization function, a logistics carbon emission cost optimization function and logistics constraints, wherein the logistics constraints also include a logistics cost constraint for constraining the cumulative sum of the vehicle transportation cost of the target vehicle assigned a first logistics route identifier to be less than or equal to a preset logistics cost, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the logistics information also includes a preset logistics cost.

[0148] According to an embodiment of the present disclosure, the cargo logistics strategy determination device 400 also includes a generation module.

[0149] The generation module is used to determine the logistics carbon emission information of transporting the target goods based on the logistics route information of the target vehicle and the transportation volume of the target vehicle.

[0150] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0151] For example, any multiple of the acquisition module 410, the processing module 420, and the determination module 430 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the acquisition module 410, the processing module 420, and the determination module 430 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 410 , the processing module 420 , and the determination module 430 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0152] Figure 5 A block diagram of an electronic device suitable for implementing a method for determining a cargo logistics strategy according to an embodiment of the present invention is shown.

[0153] Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0154] like Figure 5 As shown, the computer electronic device 500 according to an embodiment of the present invention includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an on-board memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0155] In RAM 503, various programs and data required for the operation of electronic device 500 are stored. Processor 501, ROM 502 and RAM 503 are connected to each other via bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in one or more memories.

[0156] Optionally, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0157] Optionally, the method flow according to the embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present invention are executed. Optionally, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0158] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the cargo logistics strategy determination method according to the embodiment of the present invention is implemented.

[0159] Optionally, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0160] For example, optionally, the computer-readable storage medium may include the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503 .

[0161] An embodiment of the present invention also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present invention. When the computer program product is running on an electronic device, the program code is used to enable the electronic device to implement the cargo logistics strategy determination method provided by the embodiment of the present invention.

[0162] When the computer program is executed by the processor 501, the above functions defined in the system / device of the embodiment of the present invention are performed. Optionally, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0163] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0164] Optionally, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages, specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, such as Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0165] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0166] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining a cargo logistics strategy, comprising: Obtaining logistics information of target goods and vehicle logistics attributes of target vehicles, wherein the logistics information includes logistics route information and cargo quantity; The logistics route information, the cargo volume and the vehicle logistics attributes are processed by using a logistics cost optimization function and logistics constraints to obtain the target vehicle logistics information of the target vehicle, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting the target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraints are used to constrain the logistics information to be compatible with the target vehicle logistics information, and are used to constrain the target vehicle logistics information of the new energy vehicle among the target vehicles to meet the carbon emission conditions; as well as A cargo logistics strategy is determined based on the target vehicle logistics information.

2. The method according to claim 1, wherein: The logistics cost optimization function includes a logistics cost optimization function determined based on the vehicle transportation cost and the target vehicle logistics route information, and the vehicle logistics cost information includes the vehicle transportation cost; Wherein, the logistics route information, the cargo volume and the vehicle logistics attributes are processed by using the logistics cost optimization function and the logistics constraint conditions to obtain the target vehicle logistics information of the target vehicle, including: The logistics cost optimization function and the logistics constraint conditions are used to process the multiple logistics route information and the cargo quantities, as well as the vehicle transportation cost and the vehicle transportation capacity attributes, to obtain the target vehicle logistics route information and target vehicle transportation volume of the target vehicle, wherein the vehicle logistics attributes also include the vehicle transportation capacity attributes, and the target vehicle logistics information includes the target vehicle logistics route information and the target vehicle transportation volume.

3. The method according to claim 2, wherein: The logistics constraint condition includes a new energy vehicle allocation condition, and the new energy vehicle constraint condition includes at least one of the following: The new energy vehicle transportation trip condition indicates that the number of trips of the target new energy vehicle meets a preset trip number threshold, and the target vehicle includes the target new energy vehicle; The new energy vehicle transportation mileage condition represents that the transportation mileage of the target new energy vehicle for cargo transportation based on the target vehicle logistics route information meets the preset transportation mileage threshold.

4. The method according to claim 2, wherein: The logistics constraint condition includes a logistics cargo quantity constraint condition for constraining the cargo quantity to be compatible with the target vehicle transportation quantity of the plurality of target vehicles, and the logistics cargo quantity constraint condition includes at least one of the following: A first logistics cargo volume constraint condition, indicating that the transport volume of the plurality of target vehicles is greater than or equal to the cargo volume; A second logistics cargo volume constraint condition, indicating that the transport volume of the target vehicle is less than or equal to the preset transport capacity of the target vehicle; The third logistics cargo volume constraint condition represents that the cumulative sum of the preset transportation capacities of multiple target vehicles assigned the first logistics route identifier is greater than or equal to the cargo volume; wherein, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the vehicle transportation capacity attribute includes the preset transportation capacity.

5. The method according to claim 2, wherein: The logistics constraint condition includes a route information constraint condition for constraining the plurality of logistics route information to be compatible with the plurality of target vehicle logistics route information, and the route information constraint condition includes at least one of the following: The first route constraint condition indicates that the route transportation mileage included in the logistics route information of the target vehicle is less than or equal to the preset transportation mileage of the target vehicle; The second route constraint condition characterizes that the route transportation time contained in the vehicle logistics route information is greater than or equal to the ratio between the preset speed of the target vehicle and the route transportation mileage, wherein the vehicle transportation capacity attribute includes the preset speed, and the logistics route information also includes the preset transportation mileage.

6. The method according to claim 2, wherein: The logistics constraint conditions include a vehicle quantity constraint condition, which is used to constrain the target vehicle quantity of the target vehicle to match the preset logistics vehicle quantity. The logistics route information includes the preset logistics vehicle quantity, and the target vehicle quantity is determined based on the logistics route information of the target vehicle.

7. The method according to any one of claims 2 to 6, wherein: The logistics cost optimization function also includes a logistics carbon emission cost optimization function determined based on the vehicle carbon emission cost and the target vehicle logistics route information, and the vehicle logistics cost information also includes the vehicle carbon emission cost. Wherein, using the logistics cost optimization function and the logistics constraint condition to process the plurality of logistics route information and the cargo volume, as well as the vehicle transportation cost and the vehicle transportation capacity attributes includes: The logistics cost optimization function, the logistics carbon emission cost optimization function and the logistics constraint conditions are used to process multiple logistics route information and the cargo volume, as well as the vehicle transportation cost, the vehicle carbon emission cost and the vehicle transportation capacity attribute, wherein the logistics constraint conditions also include a logistics cost constraint condition for constraining the cumulative sum of the vehicle transportation cost of the target vehicle assigned the first logistics route identifier to be less than or equal to the preset logistics cost, the first logistics route identifier indicates the logistics route information in which the target vehicle participates in the transportation, the first logistics route identifier is determined based on the logistics route information of the target vehicle, and the logistics information also includes the preset logistics cost.

8. The method according to claim 7, wherein: The vehicle carbon emission cost includes at least one of the following: Fuel consumption information, electric energy consumption information of target new energy vehicles, fuel density information, electric emission factor, carbon oxidation rate information, fuel calorific value information, and fuel calorific value carbon content information.

9. The method according to claim 1, wherein: The target vehicle logistics information includes the target vehicle logistics route information and the target vehicle transportation volume of the target vehicle. The method further comprises: Based on the target vehicle logistics route information and the target vehicle transportation volume, the logistics carbon emission information of transporting the target cargo is determined.

10. A cargo logistics strategy determination device, comprising: An acquisition module, used to acquire logistics information of target goods and vehicle logistics attributes of target vehicles, wherein the logistics information includes logistics route information and cargo quantity; A processing module, used to process the logistics route information, the cargo volume and the vehicle logistics attributes using a logistics cost optimization function and logistics constraints to obtain target vehicle logistics information of the target vehicle, wherein the logistics cost optimization function is used to optimize the logistics cost of transporting the target cargo, the logistics cost is determined based on the vehicle logistics cost information, the vehicle logistics attributes include the vehicle logistics cost information, the logistics constraints are used to constrain the logistics information to be compatible with the target vehicle logistics information, and are used to constrain the target vehicle logistics information of the new energy vehicle among the target vehicles to meet the carbon emission conditions; The determination module is used to determine the cargo logistics strategy according to the target vehicle logistics information.

11. An electronic device, comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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