Supply chain energy-saving scheduling method and system

Abstract

The invention relates to the technical field of supply chains, and provides a supply chain energy-saving scheduling method and system. The method comprises the following steps: receiving a real-time agricultural product demand of a demand node; interactively obtaining agricultural product storage information of distributed supply storage; performing demand coverage analysis according to the agricultural product storage information and the real-time agricultural product demand to obtain a storage supply topology; after real-time agricultural product requirements are combined by taking transportation full load as a constraint condition, carrying out a loading decision according to a combination result and a storage supply topology, and outputting a supply chain scheduling strategy; through multi-energy-saving target balance evaluation, screening from supply chain scheduling strategies to obtain a target scheduling strategy; and operating the target scheduling strategy to perform agricultural product demand supply of the demand node. The technical problems of energy waste and low transportation efficiency caused by no load or half load of the vehicle in the supply chain transportation process are solved, and the technical effects of improving the transportation efficiency, reducing the energy consumption and reducing the transportation cost by dynamically optimizing the transportation path and the vehicle load are achieved.

Description

Technical Field

[0001] This application relates to the technical field of supply chain, and particularly to a supply chain energy-saving scheduling method and system. Background Art

[0002] With the rapid development of the global economy and the diversification of consumer demands, supply chain management plays an increasingly important role in modern business activities. Especially in the field of agricultural product supply chain, due to the perishability and timeliness of agricultural products, efficient logistics scheduling has become the key to ensuring product quality and reducing losses. However, traditional supply chain scheduling methods face many challenges in the practical process. The phenomenon that transport vehicles operate in an empty or half-loaded state is widespread, resulting in low energy utilization efficiency, increased transportation costs and carbon emissions. At the same time, fixed transportation routes and scheduling strategies cannot adapt to real-time changing traffic conditions and demand fluctuations, causing extended transportation time and resource waste. In addition, due to long transportation time or poor transportation conditions, agricultural products are prone to deterioration or damage during transportation, further increasing the economic losses of the supply chain. Poor information transmission between various links of the supply chain also leads to inaccurate matching of warehousing, transportation and demand, affecting the overall efficiency.

[0003] Therefore, developing a supply chain energy-saving scheduling method and system that can combine multi-objective optimization and real-time data dynamic response has become the key to improving supply chain efficiency, reducing energy consumption and reducing losses of agricultural products. Summary of the Invention

[0004] This application provides a supply chain energy-saving scheduling method and system, aiming to solve the technical problems of energy waste and low transportation efficiency caused by empty or half-loaded vehicles during the supply chain transportation process.

[0005] In the first aspect disclosed in this application, a supply chain energy-saving scheduling method is provided. The method includes: receiving M real-time agricultural product demands of M demand nodes; interacting to obtain K agricultural product warehousing information of K distributed supply warehouses; performing demand coverage analysis based on the K agricultural product warehousing information and M real-time agricultural product demands to obtain M groups of warehousing supply topologies; under the constraint of full-load transportation, combining the M real-time agricultural product demands, and then making a loading decision based on the combination result and the M groups of warehousing supply topologies to output H supply chain scheduling strategies; screening the target scheduling strategy from the H supply chain scheduling strategies through multi-energy-saving target balance evaluation; and running the target scheduling strategy to supply the agricultural product demands of the M demand nodes.

[0006] Another aspect disclosed in this application provides a supply chain energy-saving scheduling system, which includes: an agricultural product demand receiving module: receiving M real-time agricultural product demands of M demand nodes; a warehousing information acquisition module: interactively obtaining K agricultural product warehousing information of K distributed supply warehouses; a demand coverage analysis module: performing demand coverage analysis based on the K agricultural product warehousing information and the M real-time agricultural product demands to obtain M sets of warehousing supply topologies; a loading decision-making module: under the constraint of full transportation load, after combining the M real-time agricultural product demands, making a loading decision according to the combination result and the M sets of warehousing supply topologies, and outputting H supply chain scheduling strategies; a strategy screening module: screening out a target scheduling strategy from the H supply chain scheduling strategies through multi-energy-saving target balance evaluation; a demand supply module: running the target scheduling strategy to supply the agricultural product demands of the M demand nodes.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned supply chain energy-saving scheduling method first receives M real-time agricultural product demands of M demand nodes. Subsequently, it interactively obtains K agricultural product warehousing information of K distributed supply warehouses. Then, it performs demand coverage analysis based on the K agricultural product warehousing information and the M real-time agricultural product demands to obtain M sets of warehousing supply topologies. Then, under the constraint of full transportation load, after combining the M real-time agricultural product demands, it makes a loading decision according to the combination result and the M sets of warehousing supply topologies, and outputs H supply chain scheduling strategies. Then, through multi-energy-saving target balance evaluation, it screens out a target scheduling strategy from the H supply chain scheduling strategies. Finally, it runs the target scheduling strategy to supply the agricultural product demands of the M demand nodes. It achieves the technical effects of improving transportation efficiency, reducing energy consumption, and reducing transportation costs by dynamically optimizing transportation routes and vehicle loads.

[0008] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0010] Figure 1 It is a schematic flowchart of a supply chain energy-saving scheduling method in an embodiment.

[0011] Figure 2 The present invention is a supply chain energy-saving scheduling system architecture diagram in one embodiment.

[0012] Explanation of the reference numerals: agricultural product demand receiving module 11, storage information acquiring module 12, demand coverage analyzing module 13, loading decision module 14, strategy screening module 15, demand supply module 16. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a supply chain energy-saving scheduling method and system to solve the technical problems of energy waste and low transportation efficiency caused by empty or half-loaded vehicles during supply chain transportation.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a supply chain energy-saving scheduling method, the method comprising: Receive M real-time agricultural product demands from M demand nodes.

[0017] In the embodiments of the present application, in the supply chain energy-saving scheduling method and system, first, it is necessary to receive M real-time agricultural product demands from M demand nodes. These demand nodes may be distributed in different geographical locations. For example, supermarkets, farmers' markets, catering enterprises, or consumer distribution points. Each demand node will submit in real time specific information such as the types, quantities, and expected delivery times of the agricultural products it needs. These real-time agricultural product demand data are transmitted to the scheduling system in real time through Internet of Things devices or information platforms to ensure the timeliness and accuracy of the information. For example, a certain supermarket may need 100 kilograms of fresh vegetables and requires delivery before 3 pm on the same day, while a certain catering enterprise may need 50 kilograms of fruits and requires delivery before 12 noon the next day. These demand data form the basis for the scheduling system to carry out subsequent optimization and decision-making. By collecting and integrating these real-time demand information, it is possible to more accurately analyze the demand distribution, predict the transportation volume, and formulate an efficient distribution plan, thereby reducing resource waste, lowering transportation costs, and ensuring that agricultural products can be delivered to each demand node in a timely and high-quality manner.

[0018] Interact to obtain the agricultural product storage information of K distributed supply warehouses.

[0019] In one embodiment, to ensure the timely supply and efficient distribution of agricultural products, it is necessary to interact with K distributed supply warehouses to obtain their detailed agricultural product storage information. These warehouses are distributed in different regions. For example, cold storages near farms, regional distribution centers, or large storage bases. Each warehouse will provide key information such as the types, quantities, storage conditions (such as temperature, humidity), available transportation capacity, and warehouse location of the agricultural products currently stored. For example, a certain warehouse may store 500 kilograms of apples with a storage temperature of 0°C to 4°C and currently has 3 transport vehicles available for distribution, and another warehouse may store 300 kilograms of tomatoes with a storage temperature of 10°C to 15°C and currently has 2 transport vehicles available. These warehouse information are transmitted to the scheduling system in real time through Internet of Things devices or information platforms to ensure the accuracy and timeliness of the data. By integrating these warehouse information, it is possible to comprehensively understand the resource distribution and availability of the supply chain, thereby optimizing the scheduling and distribution plan of agricultural products. This way of interacting and integrating warehouse information provides solid data support for the efficient scheduling and energy-saving operation of the supply chain.

[0020] Conduct demand coverage analysis based on the K agricultural product storage information and M real-time agricultural product demands to obtain M sets of warehouse supply topologies.

[0021] In one embodiment, based on K pieces of agricultural product storage information and M pieces of real-time agricultural product demands, demand coverage analysis is performed to generate M sets of storage supply topologies. The core of this process is to match the specific demands of each demand node with the supply capabilities of each storage facility, find the most suitable supply sources, and thus form a supply relationship network that covers all demands. Specifically, first, the demand information of each demand node is analyzed, including the types and quantities of agricultural products required, and then the supply capabilities of the K storage facilities are evaluated one by one, including the types and quantities of agricultural products stored. By comparing the demand and supply information, storage facilities that can meet the demands of each demand node are selected, and a set of storage supply topologies is generated. In this way, M sets of storage supply topologies are generated for the M demand nodes respectively, forming a supply network that covers all demands. These topologies consider the matching of the types and quantities of agricultural products, which can ensure the efficient operation and energy-saving scheduling of the supply chain, significantly improve the response speed and resource utilization rate of the supply chain, reduce transportation costs and energy consumption, and at the same time ensure that agricultural products can be delivered to each demand node in a timely and high-quality manner.

[0022] Further, the present application provides a method for performing demand coverage analysis based on the K pieces of agricultural product storage information and M pieces of real-time agricultural product demands to obtain M sets of storage supply topologies, and the method includes: Decompose the first real-time agricultural product demand based on the agricultural product type to obtain multiple real-time quantity demands for multiple sample agricultural products; traverse the K pieces of agricultural product storage information with the multiple sample agricultural products and multiple real-time quantity demands to perform demand coverage comparison, and obtain multiple agricultural product storage combinations that meet the multiple real-time quantity demands; through structured processing of the multiple agricultural product storage combinations, obtain the first set of storage supply topologies; and so on, perform demand coverage analysis based on the K pieces of agricultural product storage information and M pieces of real-time agricultural product demands to obtain the M sets of storage supply topologies.

[0023] Preferably, after obtaining K pieces of agricultural product storage information and M pieces of real-time agricultural product demands, first, decompose the real-time agricultural product demand of the first demand node among the M pieces of real-time agricultural product demands (the first real-time agricultural product demand), that is, assign values to the predefined agricultural product types according to the demand quantity of each agricultural product in the first real-time agricultural product demand, so as to split its demand into the specific quantities of different agricultural products. For example, a certain demand node may need 100 kg of apples, 50 kg of tomatoes, and 0 kg of bananas (if it does not need a certain agricultural product, its quantity is 0). These decomposed demands are called multiple real-time quantity demands of multiple sample agricultural products, where the real-time quantity demand of each sample agricultural product is at least 0. Subsequently, use these sample agricultural products and their corresponding real-time quantity demands to traverse the K pieces of agricultural product storage information for demand coverage comparison. In this process, each warehouse will be checked one by one to see if it stores the required agricultural products and if the storage quantity meets the demand quantity. If the storage quantity of a certain agricultural product in a certain warehouse is greater than or equal to the demand quantity of this agricultural product, it means that this warehouse can fully meet the demand for this agricultural product. At this time, the warehouse that meets the real-time quantity demand of this agricultural product will be used as a combination to form an agricultural product storage combination. For example, Warehouse A stores 200 kg of apples and 60 kg of tomatoes, Warehouse B stores 50 kg of apples and 100 kg of tomatoes, and the demand node needs 100 kg of apples and 70 kg of tomatoes. Then Warehouse A can be used as an agricultural product storage combination to meet the apple demand, and Warehouse B can be used as an agricultural product storage combination to meet the tomato demand. After obtaining multiple agricultural product storage combinations, perform structural processing on each agricultural product storage combination, that is, topologically connect the agricultural product storages in each agricultural product storage combination to form the storage supply topology of this agricultural product storage combination. In this way, multiple storage supply topologies can be obtained and stored in a set to form the first set of storage supply topologies. After that, repeat the above process, and sequentially decompose, perform demand coverage comparison, and enumerate permutations and combinations on the real-time agricultural product demands of the M demand nodes, and finally generate M sets of storage supply topologies. Each set of topologies ensures that its storage composition can fully cover the agricultural product demands of the corresponding demand node. Through this process, the demand and supply can be efficiently matched, the utilization of storage resources can be optimized, and it is ensured that agricultural products can be delivered to each demand node in a timely and high-quality manner.

[0024] Under the constraint of full-load transportation, combine the M pieces of real-time agricultural product demands, and make a loading decision according to the combination result and the M sets of storage supply topologies, and output H supply chain scheduling strategies.

[0025] In one embodiment, in order to further improve transportation efficiency and reduce energy consumption, M real-time agricultural product demands are combined and optimized with full transport as a constraint, and loading decisions are made in combination with M groups of storage supply topologies, and finally H supply chain scheduling strategies are output. The core of this process is to maximize the utilization rate of transport vehicles and reduce empty or low-load transportation by reasonably combining demands and optimizing loading plans, thereby achieving energy-saving goals. Specifically, M real-time agricultural product demands, including the weight, volume, and transportation conditions of agricultural products, are first obtained, and then multiple demands are combined with the full load capacity of the transport vehicle as a constraint to form several groups of demand sets. For example, if the full load capacity of a transport vehicle is 500 kg, and a certain demand node needs 100 kg of apples, and another demand node needs 150 kg of bananas, these two demands will be combined to make full use of the vehicle's transportation capacity. Subsequently, the most suitable storage and transportation path will be matched for each set of demand sets according to the M sets of storage supply topologies. For example, if a set of demand sets contains the demand for apples and bananas, the storage with sufficient storage of apples and bananas will be selected, and the storage closest to the demand node will be selected for delivery, ensuring that the vehicle can complete the delivery in the shortest path when fully loaded, thereby reducing transportation time and energy consumption. In this way, H supply chain scheduling strategies will be generated, each of which contains a set of demand sets, the corresponding storage supply topology, and the optimized transportation route. These strategies not only take into account the full load constraints of the transport vehicles, but also integrate multiple factors such as storage supply capacity, transportation distance, and the characteristics of the agricultural products themselves, thereby ensuring the efficient operation and energy-saving scheduling of the supply chain.

[0026] Further, the present application provides that after combining the M real-time agricultural product demands with full transport as a constraint condition, a loading decision is made according to the combination result and the M groups of storage supply topologies, and H supply chain scheduling strategies are outputted. The method includes: Interactively obtain multiple sample transportation characteristics of the multiple sample agricultural products, wherein the sample transportation characteristics include sample transportation weight, sample transportation space and sample transportation environment; use multiple sample transportation characteristics to traverse the M real-time agricultural product demands to obtain M real-time transportation demand characteristics; interactively obtain standard transportation characteristics of standard transportation vehicles; use the standard transportation characteristics as transportation condition constraints, and perform combined enumeration and screening on the M real-time agricultural product demands according to the M real-time transportation demand characteristics to obtain multiple demand supply allocations; make loading decisions based on the multiple demand supply allocations and M groups of warehousing supply topologies, and output the H supply chain scheduling strategies.

[0027] Optionally, when making a loading decision, first obtain the sample transportation characteristics of multiple sample agricultural products through an interactive method, including sample transportation weight, sample transportation space, and sample transportation environment. Among them, the sample transportation weight refers to the weight of each transportation unit (such as a bag or a box), including its own weight and the weight of the agricultural products it can carry; the sample transportation space refers to the volume and space occupancy of each transportation unit (such as a bag or a box), including its own volume and the volume of the agricultural products it can accommodate; the sample transportation environment refers to the environmental conditions required for agricultural products during transportation, such as temperature and humidity. In addition, obtain the standard transportation characteristics of standard transportation vehicles through an interactive method, including the maximum load capacity, maximum cargo volume, and transportation environmental conditions. Subsequently, traverse the M real-time agricultural product demands, match the traversed agricultural products with the multiple sample transportation characteristics of multiple sample agricultural products, determine the total real-time transportation mass, total real-time transportation volume, and real-time transportation condition distribution, and organize the characteristics belonging to each real-time agricultural product demand into a set to form M real-time transportation demand characteristics. After that, taking the standard transportation characteristics as constraint conditions, by enumerating all possible combinations of agricultural product demands, filter out the combinations that meet the constraints of the standard transportation characteristics, that is, compare the total weight of each enumerated combination to determine whether it exceeds the maximum load capacity, compare the total volume to determine whether it exceeds the maximum cargo volume. When the total weight and total volume of a certain combination both meet the standard transportation characteristics, this combination will be used as a demand supply allocation. In this way, multiple demand supply allocations can be obtained, and each demand supply allocation represents a feasible combination of agricultural product demands and can be completed in one transportation. Then, according to the multiple demand supply allocations, reorganize the M groups of warehousing supply topologies multiple times, and perform combinatorial enumeration on each reorganized warehousing supply topology with the condition that the demand nodes are not repeated to generate H supply chain scheduling strategies. Each strategy represents a complete supply chain scheduling plan, including loading information, transportation routes, etc. Through this process, multiple supply chain scheduling strategies can be efficiently generated to ensure that the agricultural product demands are met while optimizing transportation resources and costs.

[0028] Furthermore, the present application provides a method for combinatorially enumerating and filtering the M real-time agricultural product demands according to the M real-time transportation demand characteristics with the standard transportation characteristics as transportation condition constraints to obtain multiple demand supply allocations. The method includes: Decompose the standard transportation characteristics to obtain the standard transportation quality and the standard transportation space; extract M real-time total transportation masses from the M real-time transportation demand characteristics; with the standard transportation quality as a constraint, perform combinatorial enumeration of the M real-time agricultural product demands according to the M real-time total transportation masses to obtain multiple initial demand allocation combinations; extract M real-time total transportation volumes from the M real-time transportation demand characteristics; perform assembly simulation on the multiple initial demand allocation combinations according to the M real-time total transportation volumes, and screen the multiple initial demand allocation combinations according to the assembly simulation results and the standard transportation space to obtain the multiple demand-supply allocations.

[0029] Optionally, decompose the obtained standard transportation characteristics, extract the maximum load capacity and the maximum cargo volume in the standard transportation characteristics as the standard transportation quality and the standard transportation space, and then extract the real-time total transportation mass of each demand from the M real-time transportation demand characteristics to obtain M real-time total transportation mass data. Subsequently, with the standard transportation quality as a constraint, perform combinatorial enumeration on the M real-time agricultural product demands. In this process, all possible combinations of agricultural product demands will be enumerated while ensuring that the total weight does not exceed the standard transportation quality to obtain multiple initial demand allocation combinations. Then, extract the real-time total transportation volume of each demand from the M real-time transportation demand characteristics to obtain M real-time total transportation volume data, and perform assembly simulation on the multiple initial demand allocation combinations according to the M real-time total transportation volume data. Specifically, first calculate the total transportation volume of each initial demand allocation combination, which is obtained by adding up the real-time total transportation volume data of the demands involved in the initial demand allocation combination, and then, with the standard transportation space as a constraint, screen out the combinations whose total transportation volume does not exceed the standard transportation space, and use these combinations as the multiple demand-supply allocations to ensure that the total weight and total volume of the combinations meet the requirements. Through this process, multiple demand-supply allocations can be efficiently generated, providing a basis for subsequent supply chain scheduling strategies.

[0030] Furthermore, the method provided in this application further includes: Extract M real-time transportation condition distributions from the M real-time transportation demand characteristics; perform environmental adaptation fitness evaluation on the multiple demand-supply allocations according to the M real-time transportation condition distributions, and perform screening and updating on the multiple demand-supply allocations according to the evaluation results.

[0031] Optionally, the transportation condition distribution of each demand is extracted from the M real-time transportation demand characteristics as the M real-time transportation condition distributions. Subsequently, for each demand-supply allocation, an environmental adjustment fitness evaluation is performed according to the real-time transportation condition distribution of the agricultural product demand it contains. In this process, for each demand-supply allocation, the fitness score of its transportation conditions is calculated. If the transportation conditions of all demands are exactly the same, that is, the transportation conditions of all agricultural product demands in the combination are compatible (for example, the temperature requirement of one crop is 2°C to 8°C and the humidity requirement is 60% - 70%, and the temperature requirement of another crop is 4°C to 10°C and the humidity requirement is 65% - 75%. The temperature intersection of these two crops is 4°C - 8°C and the humidity intersection is 65% - 75%, so the transportation conditions of these two crops are compatible), the fitness score is 1; if the transportation conditions are partially compatible, the fitness score is the proportion of the compatible part, that is, the average of the temperature compatibility proportion and the humidity compatibility proportion; if the transportation conditions are completely incompatible, the fitness score is 0. Then, according to the results of the environmental adjustment fitness evaluation, multiple demand-supply allocations are screened and updated, and the demand-supply allocations with fitness scores higher than the preset fitness index are retained. Through this process, it is possible to screen out demand-supply allocations with similar transportation environmental conditions, ensure that agricultural products maintain suitable environmental conditions during transportation, and improve transportation efficiency and product quality.

[0032] Furthermore, the present application provides a method for making a loading decision based on the multiple demand-supply allocations and M sets of warehousing supply topologies with the constraint of full-load transportation, and outputting the H supply chain scheduling strategies. The method includes: According to the node composition of the first demand-supply allocation, the M sets of warehousing supply topologies are reorganized into N sets of warehousing supply topology groups, where N is a positive integer less than M; with the condition that demand nodes are not repeated, combination enumeration within the set is performed on the N sets of warehousing supply topology groups to obtain the first set of supply chain scheduling strategies, where each supply chain scheduling strategy includes the M warehousing supply topologies of the M demand nodes; and so on, making a loading decision according to the multiple demand-supply allocations and M sets of warehousing supply topologies to obtain multiple sets of supply chain scheduling strategies; removing the group division of the multiple sets of supply chain scheduling strategies to obtain the H supply chain scheduling strategies.

[0033] Preferably, for the first demand supply allocation, extract the demand nodes involved therein, and based on these demand nodes, screen out the warehousing supply topologies involved in each demand node from the M sets of warehousing supply topology reorganizations to form N sets of warehousing supply topology groups, with each set of warehousing supply topology groups corresponding to a demand node in the first demand supply allocation. Subsequently, for each demand node, extract all the optional warehousing topologies from the set of warehousing supply topology groups, and then, with the condition that the demand nodes are not repeated, combine all the optional warehousing topologies of all the demand nodes to ensure that each demand node is supplied by only one warehousing supply and the warehousing for different demand nodes is not repeated. In this way, the first set of supply chain scheduling strategies is obtained. This first set of supply chain scheduling strategies involves M demand nodes and their corresponding M warehousing supply topologies. After that, for each demand supply allocation, repeat the above steps to generate multiple sets of supply chain scheduling strategies, with each set of strategies corresponding to a demand supply allocation. Finally, remove the group classifications of the multiple sets of supply chain scheduling strategies, merge all the strategies, and obtain the final H supply chain scheduling strategies. Each strategy represents a complete supply chain scheduling plan, including loading information, warehousing information, etc. Through this process, multiple supply chain scheduling strategies can be efficiently generated to ensure that the agricultural product demand is met while optimizing transportation resources and costs.

[0034] Through multi-energy-saving target balance evaluation, the target scheduling strategy is screened out from the H supply chain scheduling strategies.

[0035] In one embodiment, in order to further optimize the scheduling strategy and achieve the balance of multiple energy-saving targets, a multi-objective evaluation is performed on the generated H supply chain scheduling strategies to screen out the optimal target scheduling strategy. The core of this process is to comprehensively consider multiple energy-saving related indicators. For example, vehicle utilization rate, energy consumption, transportation time, etc. By performing weighted calculations on these indicators, the supply chain scheduling strategy that performs optimally on multiple energy-saving targets is screened out as the target scheduling strategy. Finally, the target scheduling strategy will be output and implemented to guide the actual supply chain scheduling and distribution work. This multi-energy-saving target balance evaluation method can not only help supply chain managers achieve the goal of energy conservation and consumption reduction, but also find the best balance point among multiple key indicators to ensure the efficient and sustainable operation of the supply chain.

[0036] Furthermore, in this application, through multi-energy-saving target balance evaluation, the target scheduling strategy is screened out from the H supply chain scheduling strategies, and the method includes: According to the location characteristics of the M demand nodes and the K distributed supply warehouses, perform supply chain transportation fitting on the H supply chain scheduling strategies to obtain H supply chain transportation routes; perform supply chain loading and unloading fitting on the H supply chain scheduling strategies according to the H supply chain transportation routes to obtain H supply chain loading and unloading sequences; after aligning the H supply chain transportation routes and the H supply chain loading and unloading sequences, perform load calculation to obtain the average full load rate of H supply chains; perform energy consumption prediction on the H supply chain transportation routes according to the H supply chain loading and unloading sequences to obtain the total energy consumption of H supply chains; perform time-consuming prediction on the H supply chain transportation routes according to the H supply chain loading and unloading sequences to obtain the total time-consuming of H supply chains; perform summation calculation on the average full load rate of H supply chains, the total energy consumption of H supply chains and the total time-consuming of H supply chains based on a preset weighting rule to obtain the energy-saving coefficients of H supply chains; after serializing the energy-saving coefficients of H supply chains, select the target scheduling strategy from the H supply chain scheduling strategies according to the sorting result.

[0037] Optionally, after obtaining H supply chain scheduling strategies, it is necessary to conduct an energy-saving evaluation of these supply chain scheduling strategies to obtain the optimal supply chain scheduling strategy for subsequent agricultural product demand supply. First, obtain the location characteristics of M demand nodes and K distributed supply warehouses, then obtain the supply warehouses involved in each supply chain scheduling strategy from the H supply chain scheduling strategies, match the location characteristics of the supply warehouses involved in each supply chain scheduling strategy from the location characteristics of the K distributed supply warehouses, and then call the map service API to automatically fit an optimized route based on these location characteristics, so as to obtain H supply chain transportation routes. Subsequently, according to the H supply chain transportation routes, conduct loading and unloading sequence fitting. In this process, initialize the loading and unloading sequence as an empty list, then determine the starting point (warehouse), use the warehouse as the loading point, calculate the loading quantity, and add the loading operation to the loading and unloading sequence. Then traverse each demand node in the transportation route, use the demand node as the unloading point, calculate the unloading quantity, and add the unloading operation to the loading and unloading sequence. At the same time, update the current load of the vehicle and the supply quantity of the warehouse. For each supply chain transportation route, the above calculation process will be carried out according to the nodes passed in the route, so as to obtain H supply chain loading and unloading sequences. After that, align the H supply chain loading and unloading sequences with the H supply chain transportation routes, calculate the load rate (the ratio of the current load of the vehicle in the current interval to the maximum load) of each transportation interval (every two nodes), and then calculate the mean value of the obtained load rates to obtain the mean full-load rate of each supply chain transportation route; multiply the transportation distance of the supply chain transportation route by the basic energy consumption coefficient (determined according to the vehicle type and expert decision) to obtain the basic energy consumption, multiply the load energy consumption coefficient by the load of the vehicle in each interval and the distance of this interval, and then add up all the products to obtain the total energy consumption of the H supply chains; multiply the loading and unloading time coefficient by the loading and unloading quantity of each interval to obtain the loading and unloading time of each interval, and then add the predicted time when the map service API generates the supply chain transportation route to the loading and unloading time of each interval to obtain the total time of the H supply chains. Then, conduct a weighted calculation of the mean full-load rate, total energy consumption, and total time of the H supply chains to obtain the energy-saving coefficients of the H supply chains. Among them, when conducting the weighted calculation, the maximum-minimum method will be used to normalize the mean full-load rate, total energy consumption, and total time of the supply chains. After obtaining the energy-saving coefficients of the H supply chains, sort the energy-saving coefficients of the H supply chains in descending order, and extract the optimal supply chain scheduling strategy from the H supply chain scheduling strategies according to the sorting result as the target scheduling strategy. This process not only achieves the energy-saving goal but also ensures the efficient operation of the supply chain and the optimal utilization of resources.

[0038] Run the target scheduling strategy to supply the agricultural product demands of the M demand nodes.

[0039] In one embodiment, after the target scheduling strategy is determined, the supply of agricultural products to M demand nodes will be executed according to the detailed plan of the strategy. The core of this process is to convert the specific instructions in the scheduling strategy into actual operations to ensure that agricultural products can be delivered to each demand node efficiently and energy-savingly while meeting the requirements of time, quantity and quality. During the entire supply process, each link will be continuously monitored to ensure the efficient execution of the target scheduling strategy. This supply execution method based on the target scheduling strategy can not only improve the operational efficiency of the supply chain, but also significantly reduce energy consumption and environmental impact, providing supply chain managers with a scientific and reliable energy-saving scheduling solution.

[0040] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application receives M real-time agricultural product demands from M demand nodes; interactively obtains K agricultural product storage information from K distributed supply warehouses; performs demand coverage analysis based on the K agricultural product storage information and the M real-time agricultural product demands to obtain M groups of storage supply topologies; after combining the M real-time agricultural product demands with full transport load as a constraint, makes loading decisions based on the combination results and the M groups of storage supply topologies, and outputs H supply chain scheduling strategies; through multi-energy-saving target balance evaluation, selects the target scheduling strategy from the H supply chain scheduling strategies; runs the target scheduling strategy to supply the agricultural product demands of the M demand nodes. These technical effects jointly solve the technical problems of energy waste and low transportation efficiency caused by empty or half-loaded vehicles during supply chain transportation, and achieve the technical effects of improving transportation efficiency, reducing energy consumption, and reducing transportation costs by dynamically optimizing transportation routes and vehicle loads.

[0041] Embodiment 2 is based on the same inventive concept as a supply chain energy-saving scheduling method in the above embodiment. Figure 2 As shown, the present application provides a supply chain energy-saving scheduling system, the system comprising: The agricultural product demand receiving module 11 receives M real-time agricultural product demands from M demand nodes; the storage information acquisition module 12 interactively obtains K agricultural product storage information from K distributed supply storages; the demand coverage analysis module 13 performs demand coverage analysis based on the K agricultural product storage information and the M real-time agricultural product demands to obtain M groups of storage supply topologies; the loading decision module 14 performs loading decisions based on the combination results and the M groups of storage supply topologies after combining the M real-time agricultural product demands with full transport load as a constraint, and outputs H supply chain scheduling strategies; the strategy screening module 15 obtains a target scheduling strategy from the H supply chain scheduling strategies through a multi-energy-saving target balance evaluation; the demand supply module 16 runs the target scheduling strategy to supply the agricultural product demands of the M demand nodes.

[0042] Furthermore, the demand coverage analysis module 13 is further configured to execute the following method: Decompose the first real-time agricultural product demand based on the agricultural product type to obtain multiple real-time quantity demands for multiple sample agricultural products; traverse the K agricultural product storage information with the multiple sample agricultural products and multiple real-time quantity demands to perform demand coverage comparison, and obtain multiple agricultural product storage combinations that meet the multiple real-time quantity demands; through structuring the multiple agricultural product storage combinations, obtain the first group of storage supply topologies; and so on, perform demand coverage analysis based on the K agricultural product storage information and M real-time agricultural product demands to obtain the M groups of storage supply topologies.

[0043] Furthermore, the loading decision module 14 is further configured to execute the following method: Interactively obtain multiple sample transportation characteristics of the multiple sample agricultural products, where the sample transportation characteristics include sample transportation weight, sample transportation space, and sample transportation environment; traverse the M real-time agricultural product demands with the multiple sample transportation characteristics to obtain M real-time transportation demand characteristics; interactively obtain the standard transportation characteristics of standard transportation vehicles; with the standard transportation characteristics as transportation condition constraints, perform combined enumeration screening on the M real-time agricultural product demands according to the M real-time transportation demand characteristics to obtain multiple demand supply allocations; make a loading decision based on the multiple demand supply allocations and the M groups of storage supply topologies, and output the H supply chain scheduling strategies.

[0044] Furthermore, the loading decision module 14 is further configured to execute the following method: Decompose the standard transportation characteristics to obtain the standard transportation quality and standard transportation space; extract M real-time total transportation masses from the M real-time transportation demand characteristics; with the standard transportation quality as a constraint, perform combined enumeration of the M real-time agricultural product demands according to the M real-time total transportation masses to obtain multiple initial demand allocation combinations; extract M real-time total volumes from the M real-time transportation demand characteristics; perform assembly simulation on the multiple initial demand allocation combinations according to the M real-time total volumes, and screen and obtain the multiple demand supply allocations from the multiple initial demand allocation combinations according to the assembly simulation results and the standard transportation space.

[0045] Furthermore, the loading decision module 14 is further configured to execute the following method: Extract M real-time transportation condition distributions from the M real-time transportation demand characteristics; perform environmental adjustment fitness evaluation on the multiple demand supply allocations according to the M real-time transportation condition distributions, and perform screening and updating on the multiple demand supply allocations according to the evaluation results.

[0046] Further, the loading decision module 14 is further configured to execute the following method: According to the composition of the nodes allocated by the first demand supply, the M sets of warehousing supply topologies are reorganized into N warehousing supply topology sets, where N is a positive integer less than M; with the non-repetition of demand nodes as the combination condition, perform in-set combination enumeration on the N warehousing supply topology sets to obtain the first set of supply chain scheduling strategies, where each supply chain scheduling strategy includes the M warehousing supply topologies of the M demand nodes; and so on, perform loading decisions according to the multiple demand supply allocations and the M sets of warehousing supply topologies to obtain multiple sets of supply chain scheduling strategies; remove the group division of the multiple sets of supply chain scheduling strategies to obtain the H supply chain scheduling strategies.

[0047] Further, the policy screening module 15 is further configured to execute the following method: According to the location characteristics of the M demand nodes and the K distributed supply warehouses, perform supply chain transportation fitting on the H supply chain scheduling strategies to obtain H supply chain transportation routes; perform supply chain loading and unloading fitting on the H supply chain scheduling strategies according to the H supply chain transportation routes to obtain H supply chain loading and unloading sequences; after aligning the H supply chain transportation routes and the H supply chain loading and unloading sequences, perform load calculation to obtain the average full load rate of the H supply chains; perform energy consumption prediction on the H supply chain transportation routes according to the H supply chain loading and unloading sequences to obtain the total energy consumption of the H supply chains; perform time-consuming prediction on the H supply chain transportation routes according to the H supply chain loading and unloading sequences to obtain the total time-consuming of the H supply chains; perform summation calculation on the average full load rate of the H supply chains, the total energy consumption of the H supply chains, and the total time-consuming of the H supply chains based on a preset weighting rule to obtain the energy-saving coefficients of the H supply chains; after serializing the energy-saving coefficients of the H supply chains, screen the target scheduling strategy from the H supply chain scheduling strategies according to the sorting result.

[0048] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0050] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A supply chain energy-saving scheduling method, characterized in that: The method comprises: Receive M real-time agricultural product demands from M demand nodes; Interactively obtain K agricultural product storage information of K distributed supply warehouses; Perform demand coverage analysis based on the K agricultural product storage information and the M real-time agricultural product demands to obtain M groups of storage supply topologies; After combining the M real-time agricultural product demands with full transport load as a constraint, a loading decision is made according to the combination result and the M groups of storage supply topologies, and H supply chain scheduling strategies are output; Through a multi-energy-saving target balance evaluation, a target scheduling strategy is obtained from the H supply chain scheduling strategies; The target scheduling strategy is run to supply the agricultural products required by the M demand nodes.

2. A supply chain energy-saving scheduling method as claimed in claim 1, characterized in that: Demand coverage analysis is performed based on the K agricultural product storage information and the M real-time agricultural product demands to obtain M groups of storage supply topologies. The method includes: Decomposing the first real-time agricultural product demand based on the agricultural product type, and obtaining multiple real-time quantity demands of multiple sample agricultural products; Using the multiple sample agricultural products and multiple real-time quantity requirements to traverse the K agricultural product storage information to perform demand coverage comparison, and obtain multiple agricultural product storage combinations that meet the multiple real-time quantity requirements; By performing structural processing on the plurality of agricultural product storage combinations, a first set of storage supply topologies is obtained; By analogy, demand coverage analysis is performed based on the K agricultural product storage information and the M real-time agricultural product demands to obtain the M groups of storage supply topologies.

3. A supply chain energy-saving scheduling method as claimed in claim 2, characterized in that: After combining the M real-time agricultural product demands with full transport load as a constraint, a loading decision is made according to the combination result and the M groups of storage supply topologies, and H supply chain scheduling strategies are output. The method includes: Interactively obtaining a plurality of sample transportation characteristics of the plurality of sample agricultural products, wherein the sample transportation characteristics include sample transportation weight, sample transportation space and sample transportation environment; Using a variety of sample transportation characteristics to traverse the M real-time agricultural product demands to obtain M real-time transportation demand characteristics; Interactively obtain standard transport characteristics of standard transport vehicles; Taking the standard transportation characteristics as transportation condition constraints, the M real-time agricultural product demands are combined, enumerated and screened according to the M real-time transportation demand characteristics to obtain multiple demand supply allocations; A loading decision is made according to the multiple demand supply allocations and the M groups of warehousing supply topologies, and the H supply chain scheduling strategies are output.

4. A supply chain energy-saving scheduling method as claimed in claim 3, characterized in that: Taking full transport as a constraint condition and the standard transport characteristics as a transport condition constraint, the M real-time agricultural product demands are combined, enumerated and screened according to the M real-time transport demand characteristics to obtain multiple demand supply allocations, the method comprising: Decomposing the standard transport characteristics to obtain standard transport quality and standard transport space; Extracting M real-time transportation total masses from the M real-time transportation demand features; Taking the standard transport quality as a constraint, enumerating the combinations of the M real-time agricultural product demands according to the M real-time transport total masses, and obtaining a plurality of initial demand allocation combinations; Extracting M real-time transportation total volumes from the M real-time transportation demand features; An assembly simulation of the multiple initial demand allocation combinations is performed according to the M real-time total transport volumes, and the multiple demand supply allocations are obtained by screening the multiple initial demand allocation combinations according to the assembly simulation results and the standard transport space.

5. A supply chain energy-saving scheduling method as claimed in claim 4, characterized in that: The method further comprises: Extracting M real-time transportation condition distributions from the M real-time transportation demand features; The environmental adjustment adaptability of the multiple demand-supply allocations is evaluated according to the M real-time transportation condition distributions, and the multiple demand-supply allocations are screened and updated according to the evaluation results.

6. A supply chain energy-saving scheduling method as claimed in claim 4, characterized in that: Under the constraint condition of full transport load, loading decisions are made according to the multiple demand supply allocations and M groups of storage supply topologies, and the H supply chain scheduling strategies are outputted. The method includes: According to the node composition of the first demand supply allocation, the M groups of storage supply topologies are reorganized into N storage supply topology group sets, where N is a positive integer less than M; Taking non-repetition of demand nodes as a combination condition, enumerating the combinations within the N storage supply topology groups to obtain a first set of supply chain scheduling strategies, wherein each supply chain scheduling strategy includes the M storage supply topologies of the M demand nodes; By analogy, loading decisions are made according to the multiple demand supply allocations and the M groups of warehouse supply topologies to obtain multiple groups of supply chain scheduling strategies; The group divisions of the multiple groups of supply chain scheduling strategies are removed to obtain the H supply chain scheduling strategies.

7. A supply chain energy-saving scheduling method as claimed in claim 6, characterized in that: Through a multi-energy-saving target balance evaluation, a target scheduling strategy is obtained by screening the H supply chain scheduling strategies. The method includes: According to the location characteristics of the M demand nodes and the K distributed supply warehouses, supply chain transportation fitting is performed on the H supply chain scheduling strategies to obtain H supply chain transportation routes; Perform supply chain loading and unloading fitting on the H supply chain scheduling strategies according to the H supply chain transportation routes to obtain H supply chain loading and unloading sequences; After aligning the H supply chain transportation routes and the H supply chain loading and unloading sequences, load calculation is performed to obtain the average full load rate of the H supply chains; According to the H supply chain loading and unloading sequences, energy consumption forecasting of the H supply chain transportation routes is performed to obtain the total energy consumption of the H supply chains; According to the H supply chain loading and unloading sequences, the time consumption of the H supply chain transportation routes is predicted to obtain the total time consumption of the H supply chains; Based on a preset weighted rule, the average full load rate of the H supply chains, the total energy consumption of the H supply chains, and the total time consumption of the H supply chains are added and calculated to obtain energy saving coefficients of the H supply chains; After the H supply chain energy saving coefficients are serialized, the target scheduling strategy is obtained by screening from the H supply chain scheduling strategies according to the sorting result.

8. A supply chain energy-saving scheduling system, characterized in that: The system is used to execute a supply chain energy-saving scheduling method as described in any one of claims 1 to 7, comprising: Agricultural product demand receiving module: receives M real-time agricultural product demands from M demand nodes; Warehouse information acquisition module: interactively obtains K agricultural product storage information from K distributed supply warehouses; Demand coverage analysis module: performing demand coverage analysis based on the K agricultural product storage information and the M real-time agricultural product demands to obtain M groups of storage supply topologies; Loading decision module: after combining the M real-time agricultural product demands with full transport load as a constraint, a loading decision is made according to the combination result and the M groups of storage supply topologies, and H supply chain scheduling strategies are output; Strategy screening module: through multi-energy-saving target balance evaluation, the target scheduling strategy is screened from the H supply chain scheduling strategies; Demand and supply module: runs the target scheduling strategy to supply the agricultural products demand of the M demand nodes.

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