Steel industry chain logistics cost analysis system based on blockchain technology

Abstract

The application provides a steel industry chain logistics cost analysis system based on a blockchain technology, and the system comprises the following steps: obtaining a logistics cost model of a target transportation task constructed in advance; taking the minimum transportation cost and the shortest transportation time as optimization targets, optimizing the logistics cost model according to a first multi-objective optimization algorithm to determine a plurality of first optimal transportation schemes; determining a simplified logistics cost model according to the plurality of first optimal transportation schemes; and taking the minimum transportation cost and the shortest transportation time as optimization targets, optimizing the simplified logistics cost model according to a second multi-objective optimization algorithm to determine a second optimal transportation scheme. By using the algorithm with fast convergence speed to optimize and simplify the logistics cost model first, and then using the model with better convergence effect to optimize, the most reasonable transportation scheme can be quickly and accurately found out, and waste of transportation capacity is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of logistics management, and particularly relates to a steel industry chain logistics cost analysis system based on a blockchain technology. BACKGROUND

[0002] With the rapid development of modern society, the demand for logistics freight is rapidly increasing, and the role of the logistics management system is increasingly significant.

[0003] The existing logistics management platform cannot plan logistics scheduling instructions in advance, cannot manage logistics from a macro perspective, and is difficult to plan the most reasonable transportation mode and path in a short time, so the transportation effect is poor and the transportation capacity is easily wasted. SUMMARY

[0004] Therefore, the application provides a steel industry chain logistics cost analysis system based on a blockchain technology, which aims to solve the problem that the existing logistics management platform easily causes waste of transportation capacity.

[0005] A first aspect of the embodiment of the application provides a steel industry chain logistics cost analysis system based on a blockchain technology, characterized in that the system comprises:

[0006] a model acquisition module configured to acquire a logistics cost model of a target transportation operation that is pre-constructed; the target transportation operation corresponds to multiple transportation schemes; and the logistics cost model is used to calculate transportation cost and transportation time under each transportation scheme;

[0007] a first optimization module configured to optimize the logistics cost model according to a first multi-objective optimization algorithm, so as to determine multiple first optimal transportation schemes, with the minimum transportation cost and the shortest transportation time as optimization objectives;

[0008] a model simplification module configured to determine a simplified logistics cost model according to the multiple first optimal transportation schemes;

[0009] a second optimization module configured to optimize the simplified logistics cost model according to a second multi-objective optimization algorithm, so as to determine a second optimal transportation scheme, with the minimum transportation cost and the shortest transportation time as optimization objectives;

[0010] wherein the convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm; and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm.

[0011] A second aspect of the embodiment of the application provides a steel industry chain logistics cost analysis method based on a blockchain technology, comprising:

[0012] obtain a logistics cost model of a target transportation task, the target transportation task corresponding to multiple transportation schemes, the logistics cost model being used to calculate transportation cost and transportation time under each transportation scheme;

[0013] optimize the logistics cost model according to a first multi-objective optimization algorithm, with the optimization objective being minimum transportation cost and shortest transportation time, to determine multiple first optimal transportation schemes;

[0014] determine a simplified logistics cost model according to the multiple first optimal transportation schemes;

[0015] optimize the simplified logistics cost model according to a second multi-objective optimization algorithm, with the optimization objective being minimum transportation cost and shortest transportation time, to determine a second optimal transportation scheme;

[0016] wherein the convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm, and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm.

[0017] A third aspect of the embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the steel industry chain logistics cost analysis system based on the blockchain technology according to the first aspect.

[0018] A fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executable on a processor to implement the steps of the steel industry chain logistics cost analysis system based on the blockchain technology according to the first aspect.

[0019] The steel industry chain logistics cost analysis system based on the blockchain technology provided by the embodiment of the present application includes: obtaining a logistics cost model of a target transportation task, the target transportation task corresponding to multiple transportation schemes, the logistics cost model being used to calculate transportation cost and transportation time under each transportation scheme; optimizing the logistics cost model according to a first multi-objective optimization algorithm, with the optimization objective being minimum transportation cost and shortest transportation time, to determine multiple first optimal transportation schemes; determining a simplified logistics cost model according to the multiple first optimal transportation schemes; optimizing the simplified logistics cost model according to a second multi-objective optimization algorithm, with the optimization objective being minimum transportation cost and shortest transportation time, to determine a second optimal transportation scheme; wherein the convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm, and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm. By using the algorithm with fast convergence speed to optimize and simplify the logistics cost model first, and then using the model with better convergence effect to optimize, the most reasonable transportation scheme can be quickly and accurately found out, and waste of transportation capacity is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor under the premise of the drawings.

[0021] Figure 1 is an application scenario diagram of a steel industry chain logistics cost analysis system based on a blockchain technology provided by an embodiment of the present application;

[0022] Figure 2 is a structural schematic diagram of a steel industry chain logistics cost analysis system based on a blockchain technology provided by an embodiment of the present application;

[0023] Figure 3 is a realization flowchart of a steel industry chain logistics cost analysis method based on a blockchain technology provided by an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons of ordinary skill in the art will readily recognize that embodiments of the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0026] Figure 1 is an application scenario diagram of a steel industry chain logistics cost analysis system based on a blockchain technology provided by an embodiment of the present application. The steel industry chain logistics cost analysis system based on a blockchain technology of the embodiment of the present application can include but is not limited to being applied in the scenario. The scenario includes: an electronic device 11, a logistics node terminal 12.

[0027] The electronic device 11 can be connected with a plurality of logistics node terminals 12. A plurality of transportation tasks and a plurality of transportation schemes corresponding to each transportation task are pre-stored in the electronic device 11. The electronic device 11 can be a separate device or a device in a logistics management system, which is not limited herein. After receiving an analysis instruction, the electronic device 11 analyzes various information of a steel industry chain customer, market, sales and the entire enterprise internal through a data mining technology, establishes a logistics cost model, optimizes the logistics cost model, obtains an optimal transportation scheme, and sends the optimal transportation scheme to the corresponding logistics node terminal 12.

[0028] The electronic device 11 can be a terminal or a server, and the terminal can be a mobile phone, a notebook computer, a computer and the like, and the server can be a separate physical server, a server cluster composed of a plurality of servers, a cloud server or the like, which is not limited herein. The logistics node terminal 12 can be a mobile phone, a notebook computer, a vehicle-mounted terminal or the like, which is not limited herein.

[0029] Figure 2 is a structural schematic diagram of a steel industry chain logistics cost analysis system based on a blockchain technology provided by an embodiment of the present application. As shown in Figure 2 , in some embodiments, the steel industry chain logistics cost analysis system based on the blockchain technology is applied to the electronic device 11 shown in Figure 1 , and the system comprises:

[0030] The model obtaining module 210 is configured to obtain a logistics cost model of a target transportation task pre-constructed; the target transportation task corresponds to a plurality of transportation schemes; and the logistics cost model is used to calculate transportation cost and transportation time under each transportation scheme.

[0031] In this embodiment, the steel industry chain logistics information data is analyzed by summarizing, and information such as the type, quantity, transportation cost, inventory data, raw material price and sales price of goods per day is summarized. The steel industry chain logistics information data is statistically analyzed and researched, and information data statistics such as the flow direction of goods, the distribution of transportation volume, the number of vehicles and the flow direction of vehicles are tracked in real time. According to the statistical information, a logistics cost model corresponding to a target transportation task can be constructed. The target transportation task refers to the process of transporting a preset quantity of goods from a departure place to a target place. For example, the departure place is Shijiazhuang, the target place is Handan, and 100 tons of steel is transported. At this time, the transportation route can be Shijiazhuang-Handan, or Shijiazhuang-Xingtai-Handan; the transportation mode can be railway transportation or truck transportation; and a plurality of transportation schemes of the target transportation task of Shijiazhuang-Handan can be composed of the transportation route, the transportation mode and other transportation influencing factors (such as transportation speed, number of transportation vehicles, loading capacity of each vehicle and the like) not mentioned above.

[0032] The first optimization module 220 is configured to optimize the logistics cost model according to a first multi-objective optimization algorithm, so as to determine a plurality of first optimal transportation schemes, with the minimum transportation cost and the shortest transportation time as the optimization objectives.

[0033] In this embodiment, the first multi-objective optimization algorithm can be a multi-objective intelligent water drop algorithm, a multi-objective particle swarm algorithm, a multi-objective ant colony algorithm, or an improved algorithm of the above algorithms, such as a multi-objective multi-phase particle swarm optimization algorithm, a multi-objective quantum behavior particle swarm optimization algorithm, or an intelligent water drop algorithm combined with a random multi-neighborhood path reconnection algorithm. The convergence speed of the first multi-objective optimization algorithm should be fast, that is, the first optimal transportation scheme can be quickly calculated.

[0034] The model simplification module 230 is configured to determine a simplified logistics cost model according to the plurality of first optimal transportation schemes.

[0035] In this embodiment, only the part corresponding to the first optimal transportation scheme in the logistics cost model is retained, and the other parts are deleted, so as to obtain the simplified logistics cost model.

[0036] The second optimization module 240 is configured to optimize the simplified logistics cost model according to a second multi-objective optimization algorithm, so as to determine a second optimal transportation scheme, with the minimum transportation cost and the shortest transportation time as the optimization objectives.

[0037] In this embodiment, the convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm, and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm.

[0038] In this embodiment, when the final optimization scheme is calculated by the optimization algorithm after simplification, the first optimization algorithm with fast calculation speed is used to preliminarily screen the transportation schemes, and the second multi-objective optimization algorithm does not need to try all the schemes during optimization, so that the time for finding the optimal solution by the second multi-objective optimization algorithm can be effectively reduced. The second multi-objective optimization algorithm should be an algorithm with higher optimization precision. The second multi-objective optimization algorithm can be a multi-objective random frog leap algorithm or a multi-objective random frog leap algorithm combined with a random multi-neighborhood path reconnection algorithm. When the second multi-objective optimization algorithm reaches the maximum iteration number and still cannot find the second optimal transportation scheme, iteration can be continued until the optimal solution is found.

[0039] In this embodiment, a logistics cost model of a target transportation task is obtained in advance; a first multi-objective optimization algorithm is used to optimize the logistics cost model to determine a plurality of first optimal transportation schemes, with the minimum transportation cost and the shortest transportation time as the optimization objectives; a simplified logistics cost model is determined according to the plurality of first optimal transportation schemes; a second multi-objective optimization algorithm is used to optimize the simplified logistics cost model to determine a second optimal transportation scheme, with the minimum transportation cost and the shortest transportation time as the optimization objectives; the convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm; and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm. The algorithm with the faster convergence speed is used to optimize the logistics cost model first, and then the algorithm with the better convergence effect is used to optimize the logistics cost model, so that the most reasonable transportation scheme can be quickly and accurately found, and waste of transportation capacity can be avoided.

[0040] In some embodiments, the first multi-objective optimization algorithm is an improved multi-objective intelligent water drop algorithm.

[0041] The first optimization module 220 is specifically configured to optimize the logistics cost model according to the multi-objective intelligent water drop algorithm, and determine the transportation cost threshold and the transportation time threshold.

[0042] The transportation scheme with the transportation cost within the transportation cost threshold and the transportation time within the transportation time threshold is selected from various transportation schemes as the first optimal transportation scheme.

[0043] In some embodiments, the system further includes an algorithm disturbance module 250.

[0044] The algorithm disturbance module 250 is configured to randomly disturb the position of the water drop by using a crossover operator and / or a non-uniform random mutation operator when the multi-objective intelligent water drop algorithm optimizes the logistics cost model, so as to prevent falling into a local optimal solution.

[0045] In this embodiment, the multi-objective intelligent water drop algorithm is improved by using a crossover operator and / or a non-uniform random mutation operator to prevent falling into a local optimal solution.

[0046] The crossover operator simulates the process of recombination of parent genes. It mainly includes uniform crossover, simulated binary crossover, partial mapping crossover, order crossover, unit placement order crossover, linear order crossover, priority preservation crossover, position-based crossover, and cyclic crossover. The mutation operator simulates the chromosome mutation in the biological evolution process, and mainly includes uniform mutation, non-uniform mutation, and polynomial mutation. The improved scheme of the present application uses a crossover operator and a non-uniform mutation operator to randomly disturb the position of each generation of water drops.

[0047] In this embodiment, the specific way of random disturbance is as follows: k pairs of water droplets are randomly selected for each dimension of the water droplet position matrix in turn to perform cross operation, and the elements of the corresponding dimension of the water droplets are exchanged in each cross operation and the fitness value is recalculated. When the fitness value of the new water droplet is better than that of the original water droplet, the original water droplet is replaced, so that it has a certain probability of jumping out of the local optimal solution. And / or using a non-uniform mutation operator, k dimensions are randomly selected for each water droplet in turn to perform disturbance, and when the fitness value of the new water droplet is better than that of the original water droplet, the original water droplet is replaced, so that it has a certain probability of jumping out of the local optimal solution.

[0048] In some embodiments, the second multi-objective optimization algorithm is a multi-objective random frog leap algorithm.

[0049] The second optimization module 240 is specifically configured to optimize the simplified logistics cost model according to the multi-objective random frog leap algorithm, and determine the optimal transportation cost and the optimal transportation time.

[0050] The second optimal transportation scheme is selected from various transportation schemes according to the optimal transportation cost and the optimal transportation time.

[0051] In this embodiment, if there is a transportation scheme corresponding to the optimal transportation cost and the optimal transportation time obtained by optimization, the transportation scheme is taken as the second optimal transportation scheme, otherwise, the transportation scheme closest to the optimal solution obtained by optimization in terms of transportation cost and transportation time is taken as the second optimal transportation scheme. In finding the closest transportation scheme, the transportation cost can be given priority, or the transportation time can be given priority, or the transportation cost and the transportation time can be weighted for comprehensive consideration, which is not limited here.

[0052] In some embodiments, the system further comprises a re-optimization module 260.

[0053] The re-optimization module 260 is configured to re-optimize the logistics cost model after the model parameters of the logistics cost model of the target transportation task pre-constructed are changed.

[0054] In this embodiment, when the model parameters of the logistics cost model are changed, for example, the increase and decrease of logistics transfer nodes, the change of logistics tools, the change of logistics time periods, etc., the calculation method of logistics cost (transportation cost and transportation time) will change greatly, and the original optimal transportation scheme may no longer be the optimal scheme, so it is necessary to re-optimize the logistics cost model after the parameters are changed.

[0055] In some embodiments, after S204, the system further comprises a first determination module 270.

[0056] The first determining module 270 is specifically configured to determine the coverage rate between the first multi-objective optimization algorithm and the second multi-objective optimization algorithm according to a preset coverage rate formula.

[0057] According to the coverage rate, it is determined whether the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm need to be replaced when re-optimization is performed.

[0058] The preset coverage rate formula is as follows:

[0059]

[0060] ISC(A, B) = SC(A, B) - SC(B, A) 2)

[0062] ISC(A, B) is the coverage rate, card(I) is the number of elements in the set I, A is the first multi-objective optimization algorithm, B is the second multi-objective optimization algorithm, e is the solution calculated by the algorithm B, a is the solution calculated by the algorithm A. i

[0063] In this embodiment, the non-dominated solution set of the first multi-objective optimization algorithm and the second multi-objective optimization algorithm can be obtained after each optimization is completed. Through the above formula, the coverage rate between the non-dominated solution sets obtained by the two algorithms can be calculated. When ISC(A, B) > 0, it indicates that the performance of the algorithm A is better than that of the algorithm B. When ISC(A, B) = 0, it indicates that the performance of the algorithm A is not much different from that of the algorithm B. At this time, the algorithm needs to be replaced so that the two algorithms can complete different optimization work.

[0064] In some embodiments, the system further includes a second determining module 280.

[0065] The second determining module 280 is specifically configured to determine the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm respectively according to the fast non-dominated sorting population entropy algorithm.

[0066] According to the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm, it is determined whether the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm need to be replaced when re-optimization is performed.

[0067] In this embodiment, the formula of the fast non-dominated sorting population entropy algorithm is as follows:

[0068]

[0069] PE is the population entropy, N is the number of individuals in the population, p i is the number of individuals in the i-th species, and S is the number of species in the population.​

[0070] When PE=0, it means that there is only one species in the population, that is, all individuals in the population are on the same non-dominated front, at this time the population diversity is the smallest, which is not conducive to population evolution and communication between individuals to produce high-quality individuals. When PE=1, it means that the number of species in the population is equal to the number of individuals in the population, that is, there is only one individual for each species, and the individuals in the population are distributed on N non-dominated fronts. At this time, the diversity of the population is the largest, which is most conducive to population evolution and communication between individuals to produce high-quality individuals.

[0071] In some embodiments, each target transportation task includes a sending link, a running link, a transfer link, an arrival link and a two-end service link. The system further comprises a model establishing module 290;

[0072] The model establishing module 290 is specifically configured to obtain a task cost calculation formula and a task time calculation formula corresponding to each link;

[0073] A logistics cost model of the target transportation task is constructed according to the task cost calculation formula and the task time calculation formula corresponding to each link.

[0074] In this embodiment, the task cost calculation formula and the task time calculation formula of each transportation link can be determined by using regression fitting, neural network and the like.

[0075] In this embodiment, when constructing the logistics cost model of the target transportation task, the mutual influence between the links is also considered, and the task cost calculation formula and the task time calculation formula corresponding to each link are corrected to make them more consistent with the actual logistics situation.

[0076] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0077] Figure 3 is the implementation flowchart of the steel industry chain logistics cost analysis method based on the blockchain technology provided by an embodiment of the present application. As shown in Figure 3 The steel industry chain logistics cost analysis method based on the blockchain technology comprises:

[0078] S201, obtaining a logistics cost model of a target transportation task constructed in advance; the target transportation task corresponds to multiple transportation schemes; the logistics cost model is used to calculate the transportation cost and the transportation time under each transportation scheme;

[0079] S202, optimizing the logistics cost model according to a first multi-objective optimization algorithm, to determine a plurality of first optimal transportation schemes, with the optimization objectives of minimum transportation cost and shortest transportation time;

[0080] S203, determining a simplified logistics cost model according to the plurality of first optimal transportation schemes;

[0081] S204, optimizing the simplified logistics cost model according to a second multi-objective optimization algorithm, to determine a second optimal transportation scheme, with the optimization objectives of minimum transportation cost and shortest transportation time;

[0082] The convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm, and the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm.

[0083] Optionally, the first multi-objective optimization algorithm is an improved multi-objective intelligent water droplet algorithm.

[0084] S202 can include: optimizing the logistics cost model according to the multi-objective intelligent water droplet algorithm, to determine a transportation cost threshold and a transportation time threshold;

[0085] From various transportation schemes, a transportation scheme with transportation cost within the transportation cost threshold and transportation time within the transportation time threshold is selected as the first optimal transportation scheme.

[0086] Optionally, the method further includes:

[0087] When the multi-objective intelligent water droplet algorithm optimizes the logistics cost model, a crossover operator and / or a non-uniform random mutation operator are used to randomly disturb the position of the water droplet, to prevent falling into a local optimal solution.

[0088] Optionally, the second multi-objective optimization algorithm is a multi-objective random frog leap algorithm.

[0089] S204 can include: optimizing the simplified logistics cost model according to the multi-objective random frog leap algorithm, to determine an optimal transportation cost and an optimal transportation time;

[0090] According to the optimal transportation cost and the optimal transportation time, a second optimal transportation scheme is selected from various transportation schemes.

[0091] Optionally, the method further includes:

[0092] When the model parameters of the logistics cost model of the target transportation operation pre-constructed are changed, the logistics cost model after the parameters are changed is re-optimized.

[0093] Optionally, the method further includes:

[0094] After the second optimal transportation scheme is determined, a coverage rate between the first multi-objective optimization algorithm and the second multi-objective optimization algorithm is determined according to a preset coverage rate formula;

[0095] According to the coverage rate, it is determined whether the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm need to be replaced when re-optimization is performed;

[0096] The preset coverage rate formula is:

[0097]

[0098] ISC(A,B)=SC(A,B)-SC(B,A)

[0099] Wherein, ISC(A,B) is the coverage rate, card(I) is the number of elements in the set I, A is the first multi-objective optimization algorithm, B is the second multi-objective optimization algorithm, e is the solution calculated by the algorithm B, a i is the solution calculated by the algorithm A.

[0100] Optionally, the method further comprises:

[0101] According to the fast non-dominated sorting population entropy algorithm, the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm are determined respectively;

[0102] According to the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm, it is determined whether the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm need to be replaced when re-optimization is performed.

[0103] Optionally, each target transportation operation comprises a sending link, a running link, a transfer link, an arrival link and a two-end service link. The method further comprises:

[0104] Obtaining the operation cost calculation formula and the operation time calculation formula corresponding to each link;

[0105] According to the operation cost calculation formula and the operation time calculation formula corresponding to each link, a logistics cost model of the target transportation operation is constructed.

[0106] The logistics cost analysis device provided in the embodiment can be used to execute the method embodiments, and has similar implementation principles and technical effects, which will not be described here.

[0107] Figure 4 is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in the figure, Figure 4As shown, one embodiment of the electronic device 4 provided by the present application includes a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. The processor 40 implements the steps in each of the above-described embodiments of the method for analyzing logistics costs of a steel industry chain based on a blockchain technology when executing the computer program 42, such as Figure 3 As shown, the processor 40 implements the functions of each of the above-described modules / units in each of the above-described embodiments of the system when executing the computer program 42, such as Figure 2 As shown, the processor 40 implements the functions of each of the above-described modules / units in each of the above-described embodiments of the system when executing the computer program 42, such as

[0108] For example, the computer program 42 can be divided into one or more modules / units, one or more of which are stored in the memory 41 and executed by the processor 40 to complete the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 42 in the electronic device 4.

[0109] The electronic device 4 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal can include, but is not limited to, the processor 40 and the memory 41. Those skilled in the art can understand that Figure 4 The electronic device 4 is only an example and does not constitute a limitation on the electronic device 4, and can include more or fewer components than shown, or combine certain components, or different components, for example, the terminal can also include an input / output device, a network access device, a bus, and the like.

[0110] The processor 40 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0111] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 can also include both the internal storage unit and the external storage device of the electronic device 4. The memory 41 is used to store computer programs and other programs and data required by the terminal. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0112] The embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in the steel industry chain logistics cost analysis system based on the blockchain technology.

[0113] The computer readable storage medium stores a computer program 42, the computer program 42 includes program instructions, the program instructions are executed by the processor 40 to realize all or part of the processes in the embodiment method, and the related hardware can also be completed by the computer program 42. The computer program 42 can be stored in a computer readable storage medium, and the computer program 42 can realize the steps of each method embodiment when being executed by the processor 40. The computer program 42 includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0114] The computer readable storage medium can be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The computer readable storage medium can also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, or the like. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the terminal. The computer readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0115] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the unit and module in the system can be referred to the corresponding process in the foregoing method embodiments, which will not be described here.

[0117] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0118] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0119] In the embodiments provided herein, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other division methods may be used, such as multiple units or components being combined or integrated into another system, or some features being ignored or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface, or the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0120] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0121] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0122] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A steel industry chain logistics cost analysis system based on blockchain technology, characterized by: include: A model acquisition module is used to obtain a pre-built logistics cost model of a target transportation operation; The target transportation operation corresponds to multiple transportation plans; The logistics cost model is used to calculate the transportation cost and transportation time under each transportation plan; a first optimization module, configured to optimize the logistics cost model according to a first multi-objective optimization algorithm with minimizing transportation cost and minimizing transportation time as optimization objectives, so as to determine a plurality of first optimal transportation solutions; a model simplification module, configured to determine a simplified logistics cost model based on the plurality of first optimal transportation solutions; A second optimization module is configured to optimize the simplified logistics cost model according to a second multi-objective optimization algorithm with the minimum transportation cost and the shortest transportation time as optimization objectives, so as to determine a second optimal transportation plan; The convergence speed of the first multi-objective optimization algorithm is greater than that of the second multi-objective optimization algorithm; the convergence effect of the second multi-objective optimization algorithm is greater than that of the first multi-objective optimization algorithm; The first multi-objective optimization algorithm is an improved multi-objective intelligent water drop algorithm; The first optimization module is specifically configured to optimize the logistics cost model according to a multi-objective intelligent water drop algorithm to determine a transportation cost threshold and a transportation time threshold; Selecting, from various transportation plans, a transportation plan whose transportation cost is within a transportation cost threshold and whose transportation time is within a transportation time threshold as the first optimal transportation plan; The system further comprises: an algorithmic perturbation module; The algorithm perturbation module is used to randomly perturb the water drop position using a crossover operator and / or a non-uniform random mutation operator when the multi-objective intelligent water drop algorithm optimizes the logistics cost model, so as to prevent falling into a local optimal solution; The second multi-objective optimization algorithm is a multi-objective random frog leaping algorithm; The second optimization module is specifically configured to optimize the simplified logistics cost model according to a multi-objective random frog leaping algorithm to determine the optimal transportation cost and the optimal transportation time; Selecting the second optimal transportation plan from various transportation plans based on the optimal transportation cost and the optimal transportation time; After determining the second optimal transportation solution, the system further includes: a first determination module; The first determining module is specifically configured to determine a coverage ratio between the first multi-objective optimization algorithm and the second multi-objective optimization algorithm according to a preset coverage ratio formula; determining, based on the coverage rate, whether it is necessary to replace the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm when re-optimizing; The preset coverage formula is: Among them, ISC( A , B ) is the coverage rate, card ( I ) is a set I The number of elements in A is the first multi-objective optimization algorithm, B For the second multi-objective optimization algorithm, e For the algorithm B The calculated solution is, a i For the algorithm A The calculated solution.

2. The steel industry chain logistics cost analysis system based on blockchain technology according to claim 1 is characterized in that: The algorithm disturbance module is specifically used to: For each dimension of the droplet position matrix, k pairs of droplets are randomly selected and crossover operations are performed. Each crossover operation swaps the elements of the corresponding dimension of the droplet and recalculates the fitness value. When the fitness value of the new droplet is better than the original droplet, it replaces the original droplet, so that it has a certain probability of escaping the local optimal solution. And / or, use a non-uniform mutation operator to randomly select k dimensions for each water droplet and perform perturbations in turn. When the fitness value of the new water droplet is better than the original water droplet, it replaces the original water droplet, so that it has a certain probability of escaping the local optimal solution.

3. The steel industry chain logistics cost analysis system based on blockchain technology according to claim 1 is characterized in that: The system further comprises: a re-optimization module; The re-optimization module is used to re-optimize the logistics cost model after the parameters of the pre-built target transportation operation are changed when the model parameters of the logistics cost model are changed.

4. The steel industry chain logistics cost analysis system based on blockchain technology according to claim 1 is characterized in that: The system further includes: a second determination module; The second determination module is specifically configured to determine the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm respectively according to a population entropy algorithm of a fast non-dominated sorting; According to the population diversity of the first multi-objective optimization algorithm and the population diversity of the second multi-objective optimization algorithm, it is determined whether the first multi-objective optimization algorithm and / or the second multi-objective optimization algorithm needs to be replaced when re-optimization is performed.

5. The steel industry chain logistics cost analysis system based on blockchain technology according to claim 1 is characterized in that: The formula of the population entropy algorithm for fast non-dominated sorting is as follows: in, PE is the population entropy, N is the number of individuals in the population, p i For the i The number of individuals in a species, S is the number of species in the population.

6. The steel industry chain logistics cost analysis system based on blockchain technology according to any one of claims 1 to 5, characterized in that: Each target transportation operation includes a sending link, an operating link, a transfer link, an arrival link, and a service link at both ends; the system also includes: a model building module; The model building module is specifically used to obtain the operation cost calculation formula and operation time calculation formula corresponding to each link; The logistics cost model of the target transportation operation is constructed based on the operation cost calculation formula and operation time calculation formula corresponding to each link.

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