Multi-factor logistics price generation method and system, terminal and storage medium
Through the multi-factor logistics price generation method, data analysis and particle swarm algorithm are used to solve the single-dimensional and complex problems of traditional freight calculation methods, achieve scientific and accurate freight calculation, and improve logistics operation efficiency and service quality.
Patent Information
- Application Number
- CN202411372458.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-29
- Publication Date
- 2025-01-24
AI Technical Summary
The traditional method of calculating labor freight costs is single-dimensional and complicated, resulting in a lack of uniformity and transparency in billing standards, affecting logistics efficiency and customer satisfaction.
Adopt multi -factor logistics price generation methods, collect historical logistics data, use hierarchical analysis method and cluster algorithm to extract the core influencing factors and influencing factors, build a target function of minimizing costs, and use the particle group algorithm to solve the best transportation solution to generate generate Transport price.
It achieves a scientific and accurate freight calculation process, comprehensively considers multiple factors, and improves logistics operation efficiency and service quality.
Smart Images
Figure CN119359203A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of logistics and transportation, and specifically relates to a multi-factor logistics price generation method, system, terminal and storage medium. Background Art
[0002] With the rapid development of the logistics and transportation industry, the traditional manual freight calculation method has become increasingly inadequate and difficult to meet the efficient and accurate management needs of the modern logistics industry. These traditional methods often rely on a single dimension such as the weight of the goods and the transportation mileage to estimate the cost, which not only leads to a lack of uniformity and transparency in the billing standards, but also makes the calculation process cumbersome and complex, and is prone to human errors, thus affecting logistics efficiency and customer satisfaction.
[0003] In view of the above challenges, the modern logistics industry urgently needs a more intelligent and comprehensive freight calculation solution to cope with the growing logistics demand, ensure that the billing process is both fast and accurate, and improve overall operational efficiency and service quality. Summary of the invention
[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a multi-factor logistics price generation method, system, terminal and storage medium to solve the above-mentioned technical problems.
[0005] In a first aspect, the present invention provides a multi-factor logistics price generation method, comprising: Collect historical logistics data and use the analytic hierarchy process to extract the core factors affecting logistics costs and the influencing factors of the core factors from the historical logistics data; Use clustering algorithms to process historical logistics data to obtain basic information; Construct an objective function to minimize costs based on basic information and the impact factors of core influencing factors; The objective function is used as the fitness function of the particle swarm algorithm, the particle swarm algorithm is used to solve the optimal transportation plan, and a transportation price is generated for the optimal transportation plan.
[0006] In an optional implementation, historical logistics data is collected, and the core influencing factors and influencing factors of the core influencing factors that affect logistics costs are extracted from the historical logistics data using the analytic hierarchy process, including: Collect historical logistics data, including the type of transportation vehicle, the type of goods carried by the transportation vehicle, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; Establishing a hierarchical model, wherein the hierarchical model includes a lowest level, a middle level, and a highest level; Constructing a judgment matrix, and comparing the influencing factors pairwise by using the judgment matrix; The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is normalized and recorded as W. The elements of W are the ranking weights of the relative importance of the factors at the same level to the factors at the previous level. Perform consistency check on sorting weights; Calculate the relative importance of all factors at each level to the highest level from the highest level to the lowest level; Multiple influencing factors whose weights reach a set threshold are screened out as core influencing factors, and the weights of the core influencing factors are extracted as influencing factors.
[0007] In an optional implementation, historical logistics data is processed using a clustering algorithm to obtain basic information, including: Group the transportation types of historical logistics data to obtain sample groups corresponding to each transportation type; The data in the sample group are clustered according to the transportation cost to obtain data classes with multiple cost ranges; Count the starting and ending points involved in each data class, and establish the corresponding relationship between cost range, transportation type and path starting and ending points; Extract the average path distance based on the paths between the starting and ending points of the paths that match the transportation type; Calculate the unit distance cost range based on the average path distance and the corresponding cost range, and establish the corresponding relationship between the type of transportation and the unit distance cost range; The unit weight cost and unit volume cost are calculated based on the rated cargo weight and rated cargo volume corresponding to the vehicle type, and the correspondence between the vehicle type and the unit distance cost range.
[0008] In an optional implementation, an objective function for minimizing costs is constructed based on basic information and the influencing factors of core influencing factors, including: ; Among them, T() is the time cost, O() is the transportation cost, X i A transportation plan, which includes transfer nodes between the starting point and the end point, and the types of transportation used for the sections between the transfer nodes; and is the set weight factor; ; in, is the distance of the jth road section in the i-th transportation plan; is the influencing factor for selecting the kth means of transportation; is the transportation cost per kilometer per unit weight of the k-th means of transport, and x is the weight of the cargo; is the transportation cost per kilometer per unit volume of the k-th means of transportation, and y is the volume of the goods; is the influencing factor of cargo weight; is the influencing factor of cargo volume; The insurance amount for the k-th mode of transport when the cargo type is g.
[0009] In an optional implementation, the objective function is used as the fitness function of the particle swarm algorithm, the particle swarm algorithm is used to solve the optimal transportation plan, and a transportation price is generated for the optimal transportation plan, including: Use particle swarm algorithm to solve the best transportation plan and calculate the time cost and transportation cost of the best transportation plan; Preset the expected profit per unit time, calculate the product of the expected profit per unit time and the time cost, and get the expected profit; Calculate the expected profit and the transportation cost to get the transportation price using the best transportation plan.
[0010] In a second aspect, the present invention provides a multi-factor logistics price generation system, comprising: AHP module is used to collect historical logistics data and extract the core influencing factors and influencing factors of the core influencing factors from the historical logistics data using AHP; The data processing module is used to process historical logistics data using clustering algorithms to obtain basic information; Function building module, used to build the objective function of minimizing cost based on basic information and the impact factors of core influencing factors; The scheme optimization module is used to use the objective function as the fitness function of the particle swarm algorithm, use the particle swarm algorithm to solve the best transportation scheme, and generate a transportation price for the best transportation scheme.
[0011] In an optional implementation, the hierarchical analysis module includes: A data collection unit, used to collect historical logistics data, wherein the historical logistics data includes the type of transportation vehicle, the type of goods carried by the transportation vehicle, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; A hierarchical construction unit, used to establish a hierarchical structure model, wherein the hierarchical structure model includes a lowest layer, a middle layer and a highest layer; A matrix construction unit, used to construct a judgment matrix, and compare the influencing factors in pairs through the judgment matrix; The intra-layer sorting unit is used to correspond to the eigenvector of the maximum eigenroot of the judgment matrix, which is recorded as W after normalization. The elements of W are the sorting weights of the relative importance of the factors at the same level to the factors at the previous level. A consistency check unit, used to check the consistency of the sorting weights; The inter-layer sorting unit is used to calculate the relative importance of all factors in each layer to the highest layer from the highest layer to the lowest layer; The factor screening unit is used to screen out multiple influencing factors whose weights reach a set threshold as core influencing factors, and extract the weights of the core influencing factors as influencing factors.
[0012] In an optional embodiment, the data processing module includes: The first processing unit is used to group the transportation tool types of the historical logistics data to obtain a sample group corresponding to each transportation tool type; The second processing unit is used to perform clustering processing on the data in the sample group according to the transportation cost to obtain data classes with multiple cost ranges; The third processing unit is used to count the starting point and end point involved in each data class, and establish the corresponding relationship between the cost range, the type of transportation tool and the starting point and end point of the path; a fourth processing unit, configured to extract an average path distance based on the path between the starting point and the end point of the path that matches the type of transportation; a fifth processing unit, configured to calculate a unit distance cost range based on the average path distance and the corresponding cost range, and establish a corresponding relationship between the type of transportation and the unit distance cost range; The sixth processing unit is used to calculate the unit weight cost and the unit volume cost based on the rated cargo weight and the rated cargo volume corresponding to the vehicle type, and the corresponding relationship between the vehicle type and the unit distance cost range.
[0013] In an optional embodiment, the function building module includes: ; Among them, T() is the time cost, O() is the transportation cost, X i A transportation plan, which includes transfer nodes between the starting point and the end point, and the types of transportation used for the sections between the transfer nodes; and is the set weight factor; ; in, is the distance of the jth road section in the i-th transportation plan; is the influencing factor for selecting the kth means of transportation; is the transportation cost per kilometer per unit weight of the k-th means of transport, and x is the weight of the cargo; is the transportation cost per kilometer per unit volume of the k-th means of transportation, and y is the volume of the goods; is the influencing factor of cargo weight; is the influencing factor of cargo volume; The insurance amount for the k-th mode of transport when the cargo type is g.
[0014] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.
[0015] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.
[0016] The beneficial effect of the present invention is that the multi-factor logistics price generation method, system, terminal and storage medium provided by the present invention can comprehensively consider the impact of multiple factors such as cargo attributes, transportation conditions, market supply and demand, vehicle and driver costs on freight pricing by constructing a multi-factor comprehensive evaluation model, thereby making the pricing process more scientific and accurate.
[0017] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0019] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.
[0020] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.
[0021] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0022] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.
[0024] The multi-factor logistics price generation method provided in the embodiment of the present invention is executed by a computer device, and accordingly, the multi-factor logistics price generation system runs in the computer device.
[0025] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject can be a multi-factor logistics price generation system. According to different requirements, the order of the steps in the flow chart can be changed, and some can be omitted.
[0026] like Figure 1 As shown, the method includes: Step 110, collecting historical logistics data, and using the analytic hierarchy process to extract the core influencing factors and influencing factors of the core influencing factors that affect logistics costs from the historical logistics data; Step 120, using a clustering algorithm to process historical logistics data to obtain basic information; Step 130, constructing an objective function for minimizing costs based on the basic information and the influencing factors of the core influencing factors; Step 140: Using the objective function as the fitness function of the particle swarm algorithm, solving the optimal transportation plan using the particle swarm algorithm, and generating a transportation price for the optimal transportation plan.
[0027] To facilitate understanding of the present invention, the multi-factor logistics price generating method provided by the present invention is further described below based on the principle of the multi-factor logistics price generating method of the present invention and the process of generating logistics prices based on multiple factors in the embodiment.
[0028] Specifically, the multi-factor logistics price generation method includes: S1. Collect historical logistics data and use the analytic hierarchy process to extract the core influencing factors and influencing factors of the core influencing factors that affect logistics costs from the historical logistics data.
[0029] Collect historical logistics data, the historical logistics data includes the type of transportation, the type of goods carried by the transportation, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; establish a hierarchical model, the hierarchical model includes the lowest level, the middle level and the highest level; construct a judgment matrix, and compare the influencing factors in pairs through the judgment matrix; the eigenvector corresponding to the maximum eigenroot of the judgment matrix is recorded as W after normalization, and the elements of W are the ranking weights of the relative importance of the factors at the same level to the factors at the previous level; perform a consistency check on the ranking weights; calculate the weights of the relative importance of all factors at each level to the highest level from the highest level to the lowest level; screen out multiple influencing factors whose weights reach the set threshold as core influencing factors, and extract the weights of the core influencing factors as influencing factors.
[0030] The core influencing factors in this embodiment are the weight and volume of the goods and the type of transportation.
[0031] Specifically, they include: The logistics data for the past year were collected, including: Type of transport: truck, train, plane; Type of goods: electronic products, food, daily necessities; Number, weight and volume of cargo; Starting and ending cities; Transportation distance (km); Transportation cost (yuan); Total transaction amount (RMB).
[0032] The following hierarchical model is constructed: the highest level: overall logistics cost; the middle level: transportation cost, time cost, cargo characteristics (weight, volume), and vehicle characteristics; the lowest level: specific data items (such as the transportation cost of each vehicle, the weight and volume of each cargo, etc.).
[0033] Through expert evaluation and data analysis, a judgment matrix was constructed and the weight of each factor was calculated. For example, for the comparison between transportation cost and time cost, it may be concluded that transportation cost is slightly more important.
[0034] The judgment matrix was checked for consistency to ensure that the weight distribution was reasonable.
[0035] By calculating from the highest level to the lowest level, the relative importance of each factor to the overall logistics cost is determined, and the core influencing factors such as the weight, volume and type of transportation of the goods are screened out.
[0036] S2. Use clustering algorithm to process historical logistics data to obtain basic information.
[0037] The transportation types of historical logistics data are grouped to obtain sample groups corresponding to each transportation type; the data in the sample group are clustered according to the transportation cost to obtain data classes with multiple cost ranges; the starting points and end points involved in each data class are counted to establish the corresponding relationship between the cost range, transportation type and the starting and ending points of the path; the average path distance is extracted based on the path between the starting and ending points of the path that matches the transportation type; the unit distance cost range is calculated based on the average path distance and the corresponding cost range, and the corresponding relationship between the transportation type and the unit distance cost range is established; the unit weight cost and unit volume cost are calculated based on the rated cargo weight and rated cargo volume corresponding to the transportation type, and the corresponding relationship between the transportation type and the unit distance cost range.
[0038] For example: Grouping by transportation type: The data is divided into three groups according to the type of transportation: truck group, train group and airplane group.
[0039] Cost clustering: Within each group, we clustered the transportation costs and divided them into different cost ranges (such as low cost, medium cost, and high cost).
[0040] Path and cost analysis: For each cost range, the starting and ending cities involved were counted, and the average path distance was calculated based on historical data. Combined with the cost range, the unit distance cost range was obtained.
[0041] Unit cost calculation: The unit weight cost and unit volume cost are calculated by combining the rated cargo capacity (weight and volume) of the vehicle and the unit distance cost range.
[0042] S3. Construct an objective function to minimize costs based on basic information and the impact factors of core influencing factors.
[0043] ; Among them, T() is the time cost, which is calculated based on the road section distance and vehicle speed in the transportation plan; O() is the transportation cost, which is calculated based on the route distance, transportation mode selection, cargo weight, cargo volume, unit weight cost, unit volume cost and insurance amount in the transportation plan.
[0044] X i A transportation plan, which includes transfer nodes between the starting point and the end point, and the types of transportation used for the sections between the transfer nodes; and A weight factor is set to balance the importance of time cost and transportation cost.
[0045] ; in, is the distance of the jth road section in the i-th transportation plan; is the influencing factor for selecting the kth means of transportation; is the transportation cost per kilometer per unit weight of the k-th means of transport, and x is the weight of the cargo; is the transportation cost per kilometer per unit volume of the k-th means of transportation, and y is the volume of the goods; is the influencing factor of cargo weight; is the influencing factor of cargo volume; The insurance amount for the k-th mode of transport when the cargo type is g.
[0046] S4. Using the objective function as the fitness function of the particle swarm algorithm, solving the optimal transportation plan using the particle swarm algorithm, and generating a transportation price for the optimal transportation plan.
[0047] The above objective function is used as the fitness function of the particle swarm algorithm to initialize a group of particles, each particle represents a possible transportation plan. By iteratively updating the position and speed of the particles, we look for the particle that minimizes the fitness function value, that is, the best transportation plan.
[0048] Calculate the time cost and transportation cost of the best transportation plan; pre-set the expected profit per unit time (such as 100 yuan per day), calculate the product of the expected profit per unit time and the time cost to get the expected profit; calculate the sum of the expected profit and the transportation cost to get the transportation price using the best transportation plan.
[0049] In some embodiments, the multi-factor logistics price generation system may include multiple functional modules composed of computer program segments. The computer program of each program segment in the multi-factor logistics price generation system may be stored in the memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function for generating multi-factor logistics prices.
[0050] In this embodiment, the multi-factor logistics price generation system can be divided into multiple functional modules according to the functions it performs, such as Figure 2As shown. The functional modules of the system 200 may include: a hierarchical analysis module 210, a data processing module 220, a function construction module 230 and a solution optimization module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.
[0051] AHP module is used to collect historical logistics data and extract the core influencing factors and influencing factors of the core influencing factors from the historical logistics data using AHP; The data processing module is used to process historical logistics data using clustering algorithms to obtain basic information; Function building module, used to build the objective function of minimizing cost based on basic information and the impact factors of core influencing factors; The scheme optimization module is used to use the objective function as the fitness function of the particle swarm algorithm, use the particle swarm algorithm to solve the best transportation scheme, and generate a transportation price for the best transportation scheme.
[0052] Optionally, as an embodiment of the present invention, the hierarchical analysis module includes: A data collection unit, used to collect historical logistics data, wherein the historical logistics data includes the type of transportation vehicle, the type of goods carried by the transportation vehicle, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; A hierarchical construction unit, used for establishing a hierarchical structure model, wherein the hierarchical structure model includes a lowest layer, a middle layer and a highest layer; A matrix construction unit, used to construct a judgment matrix, and compare the influencing factors in pairs through the judgment matrix; The intra-layer sorting unit is used to correspond to the eigenvector of the maximum eigenroot of the judgment matrix, which is recorded as W after normalization. The elements of W are the sorting weights of the relative importance of the factors at the same level to the factors at the previous level. A consistency check unit, used to check the consistency of the sorting weights; The inter-layer sorting unit is used to calculate the relative importance of all factors in each layer to the highest layer from the highest layer to the lowest layer; The factor screening unit is used to screen out multiple influencing factors whose weights reach a set threshold as core influencing factors, and extract the weights of the core influencing factors as influencing factors.
[0053] Optionally, as an embodiment of the present invention, the data processing module includes: The first processing unit is used to group the transportation tool types of the historical logistics data to obtain a sample group corresponding to each transportation tool type; The second processing unit is used to perform clustering processing on the data in the sample group according to the transportation cost to obtain data classes with multiple cost ranges; The third processing unit is used to count the starting point and end point involved in each data class, and establish the corresponding relationship between the cost range, the type of transportation tool and the starting point and end point of the path; a fourth processing unit, configured to extract an average path distance based on the path between the starting point and the end point of the path that matches the type of transportation; a fifth processing unit, configured to calculate a unit distance cost range based on the average path distance and the corresponding cost range, and establish a corresponding relationship between the type of transportation and the unit distance cost range; The sixth processing unit is used to calculate the unit weight cost and the unit volume cost based on the rated cargo weight and the rated cargo volume corresponding to the vehicle type, and the corresponding relationship between the vehicle type and the unit distance cost range.
[0054] Optionally, as an embodiment of the present invention, the function building module includes: ; Among them, T() is the time cost, O() is the transportation cost, X i A transportation plan, which includes transfer nodes between the starting point and the end point, and the types of transportation used for the sections between the transfer nodes; and is the set weight factor; ; in, is the distance of the jth road section in the i-th transportation plan; is the influencing factor for selecting the kth means of transportation; is the transportation cost per kilometer per unit weight of the k-th means of transport, and x is the weight of the cargo; is the transportation cost per kilometer per unit volume of the k-th means of transportation, and y is the volume of the goods; is the influencing factor of cargo weight; is the influencing factor of cargo volume; The insurance amount for the k-th mode of transport when the cargo type is g.
[0055] Figure 3 The present invention provides a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the multi-factor logistics price generation method provided in an embodiment of the present invention.
[0056] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.
[0057] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.
[0058] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.
[0059] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.
[0060] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).
[0061] Therefore, the present invention, by constructing a multi-factor comprehensive evaluation model, can comprehensively consider the impact of multiple factors such as cargo attributes, transportation conditions, market supply and demand, vehicle and driver costs on freight pricing, making the pricing process more scientific and accurate. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.
[0062] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.
[0063] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.
[0064] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.
[0065] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0066] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
[0067] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.
Claims
1. A multi-factor logistics price generation method, characterized in that: include: Collect historical logistics data and use the analytic hierarchy process to extract the core factors affecting logistics costs and the influencing factors of the core factors from the historical logistics data; Use clustering algorithms to process historical logistics data to obtain basic information; Construct an objective function to minimize costs based on basic information and the impact factors of core influencing factors; The objective function is used as the fitness function of the particle swarm algorithm, the particle swarm algorithm is used to solve the optimal transportation plan, and a transportation price is generated for the optimal transportation plan.
2. The method according to claim 1, characterized in that: Collect historical logistics data and use the analytic hierarchy process to extract the core factors affecting logistics costs and the influencing factors of the core factors, including: Collect historical logistics data, including the type of transportation vehicle, the type of goods carried by the transportation vehicle, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; Establishing a hierarchical model, wherein the hierarchical model includes a lowest level, a middle level, and a highest level; Constructing a judgment matrix, and comparing the influencing factors pairwise by using the judgment matrix; The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is normalized and recorded as W. The elements of W are the ranking weights of the relative importance of the factors at the same level to the factors at the previous level. Perform consistency check on sorting weights; Calculate the relative importance of all factors at each level to the highest level from the highest level to the lowest level; Multiple influencing factors whose weights reach a set threshold are screened out as core influencing factors, and the weights of the core influencing factors are extracted as influencing factors.
3. The method according to claim 2, characterized in that The historical logistics data is processed using clustering algorithms to obtain basic information, including: Group the transportation types of historical logistics data to obtain sample groups corresponding to each transportation type; The data in the sample group are clustered according to the transportation cost to obtain data classes with multiple cost ranges; Count the starting and ending points involved in each data class, and establish the corresponding relationship between cost range, transportation type and path starting and ending points; Extract the average path distance based on the paths between the starting and ending points of the paths that match the transportation type; Calculate the unit distance cost range based on the average path distance and the corresponding cost range, and establish the corresponding relationship between the type of transportation and the unit distance cost range; The unit weight cost and unit volume cost are calculated based on the rated cargo weight and rated cargo volume corresponding to the vehicle type, and the correspondence between the vehicle type and the unit distance cost range.
4. The method according to claim 3, characterized in that The objective function of minimizing cost is constructed based on the basic information and the influencing factors of the core influencing factors, including: ； Among them, T() is the time cost, O() is the transportation cost, X i A transportation plan, which includes transfer nodes between the starting point and the end point, and the types of transportation used for the sections between the transfer nodes; and is the set weight factor; ； in, is the distance of the jth road section in the i-th transportation plan; is the influencing factor for selecting the kth means of transportation; is the transportation cost per kilometer per unit weight of the k-th means of transport, and x is the weight of the cargo; is the transportation cost per kilometer per unit volume of the k-th means of transportation, and y is the volume of the goods; is the influencing factor of cargo weight; is the influencing factor of cargo volume; The insurance amount for the k-th mode of transport when the cargo type is g.
5. The system according to claim 6, characterized in that The objective function is used as the fitness function of the particle swarm algorithm, the particle swarm algorithm is used to solve the optimal transportation plan, and a transportation price is generated for the optimal transportation plan, including: Use particle swarm algorithm to solve the best transportation plan and calculate the time cost and transportation cost of the best transportation plan; Preset the expected profit per unit time, calculate the product of the expected profit per unit time and the time cost, and get the expected profit; Calculate the expected profit and the transportation cost to get the transportation price using the best transportation plan.
6. A multi-factor logistics price generation system, characterized in that: include: AHP module is used to collect historical logistics data and extract the core influencing factors and influencing factors of the core influencing factors from the historical logistics data using AHP; The data processing module is used to process historical logistics data using clustering algorithms to obtain basic information; Function building module, used to build the objective function of minimizing cost based on basic information and the impact factors of core influencing factors; The scheme optimization module is used to use the objective function as the fitness function of the particle swarm algorithm, use the particle swarm algorithm to solve the best transportation scheme, and generate a transportation price for the best transportation scheme.
7. The system according to claim 6, characterized in that The hierarchical analysis module includes: A data collection unit, used to collect historical logistics data, wherein the historical logistics data includes the type of transportation vehicle, the type of goods carried by the transportation vehicle, the number of goods, the weight and volume of each goods, the starting point and the end point, the transportation distance, the transportation cost, and the total transaction amount; A hierarchical construction unit, used to establish a hierarchical structure model, wherein the hierarchical structure model includes a lowest layer, a middle layer and a highest layer; A matrix construction unit, used to construct a judgment matrix, and compare the influencing factors pairwise through the judgment matrix; The intra-layer sorting unit is used to correspond to the eigenvector of the maximum eigenroot of the judgment matrix, which is recorded as W after normalization. The elements of W are the sorting weights of the relative importance of the factors at the same level to the factors at the previous level. A consistency check unit, used to check the consistency of the sorting weights; The inter-layer sorting unit is used to calculate the relative importance of all factors in each layer to the highest layer from the highest layer to the lowest layer; The factor screening unit is used to screen out multiple influencing factors whose weights reach a set threshold as core influencing factors, and extract the weights of the core influencing factors as influencing factors.
8. The system according to claim 7, characterized in that The data processing module comprises: The first processing unit is used to group the transportation tool types of the historical logistics data to obtain a sample group corresponding to each transportation tool type; The second processing unit is used to perform clustering processing on the data in the sample group according to the transportation cost to obtain data classes with multiple cost ranges; The third processing unit is used to count the starting point and end point involved in each data class, and establish the corresponding relationship between the cost range, the type of transportation tool and the starting point and end point of the path; a fourth processing unit, configured to extract an average path distance based on the path between the starting point and the end point of the path that matches the type of transportation; a fifth processing unit, configured to calculate a unit distance cost range based on the average path distance and the corresponding cost range, and establish a corresponding relationship between the type of transportation and the unit distance cost range; The sixth processing unit is used to calculate the unit weight cost and the unit volume cost based on the rated cargo weight and the rated cargo volume corresponding to the vehicle type, and the corresponding relationship between the vehicle type and the unit distance cost range.
9. A terminal, characterized in that: include: A memory, used for storing a multi-factor logistics price generation program; A processor is used to implement the steps of the multi-factor logistics price generation method as described in any one of claims 1 to 5 when executing the multi-factor logistics price generation program.
10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a multi-factor logistics price generation program, which, when executed by a processor, implements the steps of the multi-factor logistics price generation method as described in any one of claims 1 to 5.