Method and apparatus for dispensing material

Abstract

This application provides a material delivery method and apparatus, belonging to the field of automation technology. The material delivery method includes: acquiring material demand information from several production lines; generating material delivery tasks for several production lines based on the material demand information, and driving several material delivery trolleys to execute the material delivery tasks for the several production lines respectively; acquiring the delivery status of the several material delivery trolleys; and, when a material delivery trolley is unable to execute the material delivery task, executing an emergency scheduling algorithm to schedule a portion of the goods from the material delivery trolleys currently executing tasks to a target production line; wherein, the target production line refers to the production line to which the material delivery trolley unable to execute its material delivery task is headed. This method enables emergency scheduling in case of trolley malfunction.

Description

Technical Field

[0001] This application relates to the field of automation technology, and in particular to a method and apparatus for distributing materials. Background Technology

[0002] In industrial production, warehousing and logistics, and power plant operations, material distribution is a crucial link in ensuring production / operation continuity. Especially in a power plant material transportation scenario, auxiliary materials such as desulfurizers and denitrifiers required for boiler combustion, and lubricating oil required for turbine lubrication, must be delivered accurately and on time to the corresponding equipment (production line). Any supply interruption will directly affect the stability of unit operation and may even lead to shutdown accidents. Traditional material distribution methods mostly rely on manual vehicle driving or manual handling, which suffers from low efficiency, high labor costs, and large delivery errors. Furthermore, manual distribution cannot achieve 24-hour uninterrupted operation and cannot meet the high requirements of power plants for timely and stable material supply.

[0003] With the development of unmanned technology, unmanned vehicles are gradually being applied to the field of material delivery. However, most existing unmanned delivery systems are simple delivery modes with a single unmanned vehicle corresponding to a fixed material-consuming equipment, lacking the ability to coordinate and schedule multiple vehicles and multiple equipment. In a material transportation scenario at a power plant, this deficiency is even more prominent: the material-consuming equipment (production lines) within the power plant are scattered (such as boiler areas, turbine areas, denitrification unit areas, etc.), and the material consumption patterns of different production lines vary greatly, with different levels of importance. In addition, some transportation routes need to avoid high-pressure equipment and high-temperature areas. Existing systems do not fully consider multiple dimensions such as the importance of production lines, the degree of material demand, and detour costs, and rely only on a single dimension for scheduling, making it difficult to achieve accurate on-demand delivery of materials, which can easily lead to material backlog or supply shortages. More importantly, when one or more unmanned vehicles malfunction, the existing system is unable to select the optimal single vehicle or combination of multiple vehicles from a massive number of unmanned vehicles, taking into account factors such as the importance of the production line, the urgency of the demand, and the cost of detours, for detour delivery. The efficiency of reallocating transportation tasks is low and the cost is high, which can easily lead to the shutdown of high-importance production lines due to material shortages, resulting in significant economic losses.

[0004] Therefore, developing a fully automated, unmanned material delivery system that can combine multiple input factors such as production line importance, material demand, and detour costs, with the lowest cost as the optimization condition, to select the optimal single or multi-vehicle combination from a massive number of unmanned vehicles for detour delivery, and to achieve precise docking and emergency dispatch of multiple unmanned vehicles with multiple material-using equipment, has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, this application provides a material distribution method and apparatus, which aims to realize emergency material dispatch in emergency situations.

[0006] In a first aspect, this application provides a method for distributing materials, comprising: Obtain material requirements information for several production lines; Based on the material demand information of several production lines, material delivery tasks for several production lines are generated, and several material delivery vehicles are driven to perform the material delivery tasks for the several production lines respectively. The system acquires the delivery status of several material delivery carts. When a material delivery cart is unable to perform its delivery task, an emergency scheduling algorithm is executed to allocate a portion of the goods from the currently executing material delivery carts to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

[0007] Optionally, the steps of generating material delivery tasks for several production lines based on their material demand information include: Based on the material requirements information of several production lines and the importance of several production lines, determine the overall priority of several production lines; Obtain the delivery time and cost when the delivery vehicle performs material delivery tasks on the production line; Material delivery tasks are assigned to material delivery vehicles based on the overall priority of the production line and the delivery time and cost when the delivery vehicle performs the material delivery task.

[0008] Optionally, the step of executing an emergency scheduling algorithm to dispatch a portion of goods from the material delivery carts currently performing the task to the target production line when a material delivery cart is unable to perform the material delivery task includes: Get the material delivery tasks in progress ;in, Indicates the first A material delivery cart In progress: material delivery tasks; Obtain the production line corresponding to the material delivery task And the material demand time window corresponding to the production line. The importance of production lines Material delivery volume ;in, Indicates the first The first material delivery task corresponding to the individual production lines The deadline for material requirements; Indicates the first The first material delivery task corresponding to the individual production lines The importance of; Indicates the first The first material delivery task corresponding to the individual production lines The amount of materials delivered; Obtain the detour distance of several material delivery vehicles to the target production line. Comprehensive cost of detour for material delivery carts The remaining load capacity of the material delivery cart Feasibility of detouring for material delivery carts ;in, Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. The extra delivery time incurred at each production line; Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. Additional delivery costs incurred at each production line; Indicates the first The amount of materials stored in the material delivery cart excluding the first After calculating the necessary material quantity for each production line, the remaining material quantity; Indicates the first Feasibility of having a material delivery vehicle bypass the target production line; Obtain the material requirements of the target production line Based on the material requirements of the target production line The remaining load capacity of the material delivery cart Material demand window for production lines Detour time Comprehensive cost of detour From several material delivery carts Several candidate cars were obtained through screening. ; Based on detour distance Comprehensive costs Detour time ,importance From several candidate cars The detour car was determined in the middle. and the amount of materials delivered by the bypass vehicle .

[0009] Optionally, based on the detour distance Comprehensive costs Detour time ,importance From several candidate cars The detour car was determined in the middle. and the amount of materials delivered by the bypass vehicle The steps include: Based on several candidate cars The detour cost, the importance of the production line to be visited, and the detour time were used to score several candidate cars. Determine whether the remaining load capacity of the candidate vehicle with the highest score meets the material requirements of the target production line. If the conditions are met, the candidate car with the highest score will be selected as the detour car.

[0010] Optionally, the optimization method further includes: If the remaining load capacity of the candidate vehicle with the highest score does not meet the material requirements of the target production line. Then, an optimization method is adopted, which constructs a fitness function based on the total cost of detours and the delivery time of detours, and selects from several candidate vehicles. The optimal set of bypass vehicles and the optimal temporary material allocation scheme for the bypass vehicle set are obtained through screening.

[0011] Optionally, the optimization method includes any one of the following: genetic algorithm, particle swarm optimization, annealing algorithm, and ant colony optimization.

[0012] Optionally, the comprehensive detour cost is calculated based on the detour distance of the material vehicles and the distance between the production line and the material warehouse to which the detour vehicles travel.

[0013] Optionally, the scoring formula for the candidate material delivery cart is as follows:

[0014] In the formula, Indicates the first The rating of the candidate material delivery carts , Indicates cost weight. Indicates the importance weight of the production line. Indicates the weight of the detour distance. This indicates the weight of the detour time.

[0015] Optionally, the fitness function constructed using the total detour cost and detour delivery time is as follows:

[0016] In the formula, This represents the fitness value of the temporary material allocation scheme for the group of bypass vehicles. , , These are the weighting coefficients. This represents the total cost of the detour for the set of detour vehicles. This indicates the time required for detour delivery.

[0017] Secondly, this application provides a material delivery device, comprising: The acquisition module is used to acquire material requirement information for several production lines. The conventional scheduling module is used to generate material delivery tasks for several production lines based on the material demand information of several production lines, and drive several material delivery vehicles to execute the material delivery tasks of the several production lines respectively. The emergency scheduling module is used to obtain the delivery status of several material delivery carts. When a material delivery cart is unable to perform its delivery task, the emergency scheduling algorithm is executed to dispatch a portion of the goods from the material delivery carts that are currently performing their tasks to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

[0018] The beneficial effects of the technical solution provided in this application include: (1) Enables unmanned delivery of multiple unmanned vehicles and multiple production lines throughout the entire process, eliminating the need for manual participation in material transportation and loading / unloading, thereby significantly improving delivery efficiency and reducing labor costs.

[0019] (2) Innovatively introduce multi-dimensional input factors (production line importance, material demand, detour distance, comprehensive cost, etc.) to construct a scientific comprehensive scoring model and optimization target system, avoid scheduling imbalance caused by a single factor, and ensure priority guarantee for high-importance production lines.

[0020] (3) The fault emergency dispatching link has creative screening logic. It selects the best single vehicle through comprehensive scoring or selects the best combination of multiple vehicles through improved genetic algorithm. It achieves the lowest comprehensive cost while ensuring delivery timeliness and effectively responds to fault scenarios of single or multiple unmanned vehicles.

[0021] (4) The system is highly adaptable and can dynamically adjust parameters according to the production line priority and cost structure of different industrial scenarios. It is suitable for material distribution scenarios with multiple equipment and multiple vehicles, such as power plants and warehousing logistics, and has a wide range of application prospects. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a material delivery method according to an embodiment of this application; Figure 2 A flowchart illustrating step S102 provided in an embodiment of this application; Figure 3 A flowchart illustrating step S103 provided in an embodiment of this application; Figure 4 A structural block diagram of a material delivery device provided in an embodiment of this application; Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0024] The attached figures are labeled as follows: 11: Acquisition module; 12: Regular scheduling module; 13: Emergency scheduling module; 21: Processor; 22: Memory. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] Figure 1 A flowchart illustrating a material delivery method according to an embodiment of this application. See also... Figure 1 ,include: S101. Obtain material requirements information for several production lines.

[0027] Among them, the material demand information is the remaining usage time of the materials calculated by the production line based on the actual remaining material quantity and the production line's material consumption rate. Once the remaining usage time of the materials is received from the production line, a corresponding material delivery task can be generated.

[0028] S102. Based on the material demand information of several production lines, generate material delivery tasks for several production lines, and drive several material delivery vehicles to execute the material delivery tasks for the several production lines respectively.

[0029] See Figure 2 In some examples, step S102 includes: S1021. Based on the material requirements information of several production lines and the importance of several production lines, determine the overall priority of several production lines.

[0030] The formula for calculating the overall priority is as follows:

[0031] In the formula, Indicates the first individual production lines Overall priority Indicates the first individual production lines The urgency of material demand Indicates the first individual production lines The importance of the production line.

[0032] S1022. Obtain the delivery time and delivery cost when the delivery vehicle performs material delivery tasks on the production line.

[0033] S1023. Based on the overall priority of the production line and the delivery time and cost when the delivery cart performs the material delivery task, assign material delivery tasks to the material delivery cart.

[0034] In some examples, step S1023 includes: An improved greedy algorithm or an improved genetic algorithm is used to allocate tasks, ensuring that high-priority production lines receive delivery resources first, and the total delivery cost is optimal.

[0035] The core formula is as follows: Improved Greedy Algorithm: Adopting a strategy of "optimal matching of comprehensive priority and transportation suitability," the transportation suitability... ( Total transportation time (For transportation costs).

[0036] Improved Genetic Algorithm: Objective Function The constraints include , , Priority matching principle.

[0037] S103. Obtain the delivery status of several material delivery carts. When a material delivery cart is unable to perform the material delivery task, execute an emergency scheduling algorithm to dispatch a portion of the goods from the material delivery carts currently performing the task to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

[0038] See Figure 3 In some examples, step S103 includes: S1031. Obtain the material delivery task currently in progress. ;in, Indicates the first A material delivery cart Material delivery task in progress.

[0039] S1032, Obtain material delivery task Corresponding production line and the amount of materials delivered to the production line Material demand time window ,importance ;in, Indicates the first individual production lines The amount of materials delivered; Indicates the first individual production lines The deadline for material requirements; Indicates the first individual production lines The importance of.

[0040] In some examples, the material delivery volume of the production line This refers to the material delivery carts operating according to material delivery tasks. The amount of materials transported to the production line.

[0041] In some examples, material demand time windows This refers to the downtime of the production line. If the production line does not receive material replenishment within the material demand window, it will be shut down due to material shortage.

[0042] In some examples, importance This refers to the degree of importance of the production tasks being performed by each production line. Based on the degree of importance of the production tasks, the importance of the production line is determined, and materials are then allocated to the production lines with higher importance first.

[0043] S1033, Obtain the detour distance of several material delivery carts to the target production line. The overall cost of material delivery carts The remaining load capacity of the material delivery cart Feasibility of detouring for material delivery carts ;in, Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. Detour distance when at each production line; Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. Additional delivery costs incurred at each production line; Indicates the first The amount of materials stored in the material delivery cart excluding the first The amount of material remaining after the necessary material quantity for each production line; Indicates the first The feasibility of having a material delivery vehicle detour to the target production line.

[0044] In some examples, the overall cost is calculated based on the detour distance of the material delivery vehicle and the distance between the production line the detour vehicle is heading to and the material warehouse. The overall cost needs to consider both the extra distance the material delivery vehicle travels during the detour and the fact that, due to the material delivery vehicle allocating materials to the target production line, the production line originally intended for the material delivery vehicle will receive less replenishment, thus requiring the next material replenishment more sooner. If the detour by the material delivery vehicle causes the production line originally intended for the material delivery vehicle to require additional material replenishment, the cost of the additional material replenishment needs to be considered.

[0045] The specific calculation process is as follows: Detour energy consumption cost ,in For the first Energy consumption per unit distance for unmanned vehicles in Taiwan For detour distance, Price per unit of energy consumption.

[0046] Detour time cost ,in For detour time, It is the opportunity cost coefficient per unit time (preset by the system and linked to production line downtime losses).

[0047] Supplemental material costs ,in The total cost (including energy consumption and depreciation) of dispatching new vehicles to replenish materials to the original target production line. This is the cost-sharing coefficient (the proportion borne by detour vehicles, default 0.3-0.5).

[0048] Total cost: .

[0049] S1034. Obtain the material requirements of the target production line. Based on the material requirements of the target production line Remaining load Material demand time window Detour time Comprehensive cost of detour From several material delivery carts Several candidate cars were obtained through screening. .

[0050] In some examples, the filtering criteria used in step S1034 are as follows: (1) Remaining load ( For the first The task share undertaken by the material delivery carts, and the time per cart When multiple vehicles are combined ).

[0051] (2) Total time after detour ( This represents the remaining time of the original task. For the first The detour time of each material delivery vehicle The remaining available time of materials for the production line to which the material delivery cart is headed.

[0052] (3) Feasibility of detour: .

[0053] (4) Overall cost: ( (Preset a cost threshold for the system).

[0054] S1035, Based on detour distance Comprehensive costs Detour time ,importance From several candidate cars Determine the detour car and the amount of materials delivered by the bypass vehicle .

[0055] In some examples, step S1035 includes: Step 1: Based on several candidate cars The detour cost, the importance of the production line to be visited, and the detour time are factors that influence several candidate vehicles. Scoring will be conducted.

[0056] In some examples, candidate cars The scoring formula is as follows:

[0057] In the formula, Indicates the first The ratings of the candidate cars, , Indicates cost weight. Indicates the importance weight of the production line. Indicates the weight of the detour distance. This indicates the weight of the detour time.

[0058] Step 2: Determine whether the remaining load of the candidate vehicle with the highest score meets the material requirements of the target production line. If the conditions are met, the candidate car with the highest score is selected as the bypass car; if not, the bypass car combination and the material delivery quantity of each bypass car in the bypass car combination are determined from several candidate cars.

[0059] In some examples, the steps of determining the detour cart combination and the material delivery quantity of each detour cart in the detour cart combination from a plurality of candidate carts include: If the remaining load capacity of the candidate vehicle with the highest score does not meet the material requirements of the target production line. Then, an optimization method is adopted, which constructs a fitness function based on the total detour cost and detour delivery time, and selects from the candidate car set. The optimal set of bypass vehicles and the optimal temporary material allocation scheme for the bypass vehicle set are obtained by screening. Any one of the following algorithms can be used: swarm optimization, annealing, or ant colony optimization.

[0060] In some examples, the fitness function, constructed using the total cost of the detour and the delivery time of the detour, is as follows:

[0061] In the formula, This represents the fitness value of the temporary material allocation scheme for the group of bypass vehicles. , , These are the weighting coefficients. This indicates the score for the combination of cars that navigate around obstacles. This represents the total cost of the detour by the combination of detour vehicles. This indicates the detour delivery time for the detour vehicle combination.

[0062] In some examples, , , .

[0063] Genetic algorithm parameters: population size 80-120, number of iterations 50-80, crossover probability 0.7-0.9, mutation probability 0.02-0.05.

[0064] Task splitting rules: Based on the vehicle's remaining load capacity and overall score, tasks are split proportionally. ( (For the combined vehicle set), ensure that high-scoring vehicles take on more share; after iterative solution, output the multi-vehicle combination and task splitting scheme with the highest fitness value, ensuring that the total comprehensive cost of the combination is the lowest, the demand of high-importance production lines is prioritized, and all tasks are completed within the demand time window.

[0065] The implementation process of this system will be explained in detail below using a specific scenario of a power plant: The power plant has 10 core production lines (equipment 1-10), of which equipment 1-3 (boiler desulfurization / denitrification device) is a core-level production line (weight 1.5), equipment 4-6 (steam turbine lubrication equipment) is an important-level production line (weight 1.2), and equipment 7-10 is a general-level production line (weight 1.0); it is equipped with 50 explosion-proof unmanned vehicles (vehicles 1-50), and the material storage point is located in the auxiliary workshop of the power plant.

[0066] (1) Module configuration and data acquisition Material information acquisition module: Install corresponding types of material sensors and time counters on each production line. The fixed material consumption time of device 1 is 4 hours, the current material storage can support 30 minutes, and the urgency of material demand is... Each batch requires 200 kg of material.

[0067] Unmanned vehicle status acquisition module: Vehicle 1 is responsible for delivering desulfurizing agent to equipment 1-3. When a fault occurs, it is heading to equipment 1 and its current location is about 5km away from equipment 1. The status information (location, remaining load, energy consumption, etc.) of vehicles 2-50 is uploaded in real time.

[0068] Production line priority configuration module: preset core-level production line weight 1.5, important-level 1.2, and ordinary-level 1.0.

[0069] Cost accounting module: Vehicle 3's detour distance is 8km, energy consumption per unit distance is 0.2kWh / km, energy price per unit is 1.2 yuan / kWh, detour time is 20 minutes, k=5 yuan / minute, replenishment material cost is 80 yuan, cost allocation coefficient is 0.4. Overall cost: Yuan.

[0070] (2) Emergency dispatch process for equipment failure (equipment 1 corresponds to vehicle 1 failure, requiring 200kg of desulfurizing agent) Step 1: Integrating Tasks and Input Factors Material replenishment task: Target production line equipment 1 (core level, ), , minute.

[0071] Overall Priority .

[0072] The multi-dimensional input factors for normal autonomous vehicles, including the detour distance, overall cost, and remaining load of candidate vehicles such as vehicles 3, 8, and 15, have been integrated.

[0073] Step 2: Pre-screening of candidate vehicles Vehicles with a remaining load of less than 200 kg that cannot be delivered to the original target production line on time after detouring are excluded, resulting in the candidate set. : Vehicle 3: Remaining load 300kg, detour distance 8km, total cost 133.92 yuan, overall rating: =0.4×(1 / 133.92)+0.3×1.5+0.15×(1 / 8)+0.15×(1 / 20) ≈0.003+0.45+0.01875+0.0075≈0.47925; Vehicle 8: Remaining load 250kg, detour distance 10km, total cost 168 yuan, overall rating: ≈0.4×(1 / 168)+0.3×1.5+0.15×(1 / 10)+0.15×(1 / 25) ≈0.0024+0.45+0.015+0.006≈0.4734.

[0074] Vehicle 15: Remaining load 350kg, detour distance 12km, total cost 195 yuan, overall rating: ≈0.4×(1 / 195)+0.3×1.5+0.15×(1 / 12)+0.15×(1 / 30) ≈0.0021+0.45+0.0125+0.005≈0.4696.

[0075] Step 3: Optimal selection of a single car The candidate vehicles are ranked according to their overall scores: Vehicle 3 > Vehicle 8 > Vehicle 15; Verification of vehicle 3: Remaining load 300kg≥200kg, detour time 20 minutes≤30 minutes, meets the requirements, vehicle 3 is determined to be the optimal single detour vehicle; After receiving the instruction, vehicle 3 detours to equipment 1 to deliver 200kg of desulfurizing agent, and then continues to perform its original task. The central dispatch module simultaneously dispatches new vehicles to replenish materials for vehicle 3's original target production line.

[0076] Example of a multi-vehicle combination scenario (Equipment 1 requires 500kg of material, and a single vehicle cannot carry the load). Candidate car set: Among them, the remaining load capacity of vehicle 3 is 300kg, the remaining load capacity of vehicle 8 is 250kg, and the remaining load capacity of vehicle 15 is 350kg.

[0077] After iteratively solving the problem using a genetic algorithm (when encoding chromosomes, the first half of the chromosome is encoded using vehicle combinations, and the second half of the chromosome is encoded using the temporary delivery volume of the vehicle (i.e., the temporary material quantity delivered to the target production line)), the optimal combination is vehicle 3 (capable of carrying 300kg, score 0.47925) + vehicle 8 (capable of carrying 200kg, score 0.4734), with a total comprehensive cost of 133.92 + 134.4 = 268.32 yuan, and the combination has the highest fitness value.

[0078] Vehicles 3 and 8 completed the delivery according to their respective shares, and both delivered to Equipment 1 within 30 minutes, ensuring the continuous operation of the core production line.

[0079] Example of a multi-vehicle failure + multi-vehicle combination scenario (vehicle 1 and vehicle 2 fail simultaneously). Vehicle 1 is responsible for delivering desulfurizing agent to equipment 1-3 (core level). In the event of a malfunction, it failed to complete the delivery tasks for equipment 1 (requires 200kg, remaining usable time 30 minutes) and equipment 2 (requires 150kg, remaining usable time 90 minutes). Vehicle 2 is responsible for delivering lubricating oil to equipment 4-6 (important level). In the event of a malfunction, it failed to complete the delivery task for equipment 4 (requires 150kg, remaining usable time 60 minutes). Candidate car set: The overall scores and loads of each vehicle are as follows: Vehicle 3: Rating 0.47925, Load 300kg; Vehicle 8: Rating 0.4734, Load 250kg; Vehicle 15: Rating 0.4696, Load 350kg; Vehicle 20: Rating 0.4512, Load 200kg; The improved genetic algorithm iteratively solves the problem according to the principle of "prioritizing core production lines and minimizing overall cost," yielding the optimal combination scheme: Vehicle 3: Receive 200kg of desulfurizing agent from Equipment 1 (core grade preferred), completion time 20 minutes; Vehicle 8: Receive 150kg of lubricating oil (important grade) from Equipment 4 + 50kg of desulfurizing agent from Equipment 2, with a completion time of 45 minutes; Vehicle 15: Receives 100kg of desulfurizing agent remaining from Equipment 2, completion time 35 minutes; The total combined cost was 133.92 + 168 + 195 = 506.92 yuan. All tasks were completed within the required time window, and there was no shortage of materials for core and important production lines.

[0080] Through the above embodiments, this system has achieved optimal detour decision-making driven by multiple factors in the power plant scenario, which not only ensures priority supply of materials to the core production line, but also achieves the lowest overall cost and effectively copes with the complex situation of single or multiple unmanned vehicles malfunctioning.

[0081] Figure 4 This is a structural block diagram of a material delivery device provided in one embodiment of this application. See also... Figure 4 ,include: Module 11 is used to acquire material requirement information for several production lines; The conventional scheduling module 12 is used to generate material delivery tasks for several production lines based on the material demand information of several production lines, and drive several material delivery vehicles to execute the material delivery tasks of the several production lines respectively. Emergency dispatch module 13 is used to obtain the delivery status of several material delivery carts. When a material delivery cart is unable to perform the material delivery task, an emergency dispatch algorithm is executed to dispatch a portion of the goods from the material delivery carts that are currently performing the task to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

[0082] Figure 5 This is a structural block diagram of an electronic device provided according to an embodiment of this application. See also... Figure 5 Electronic devices may include Figure 4 The aforementioned material delivery method apparatus typically includes a processor 21 and a memory 22. The processor 21 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 21 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. The memory 22 may include one or more computer-readable storage media, which may be non-transitory. The memory 22 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage medium in memory 22 is used to store at least one instruction for execution by processor 21 to implement the material delivery method performed by an electronic device provided in the method embodiments of this application.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for distributing materials, characterized in that, include: Obtain material requirements information for several production lines; Based on the material demand information of several production lines, material delivery tasks for several production lines are generated, and several material delivery vehicles are driven to perform the material delivery tasks for the several production lines respectively. The system acquires the delivery status of several material delivery carts. When a material delivery cart is unable to perform its delivery task, an emergency scheduling algorithm is executed to allocate a portion of the goods from the currently executing material delivery carts to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

2. The material distribution method according to claim 1, characterized in that, The steps for generating material delivery tasks for several production lines based on their material requirements information include: Based on the material requirements information of several production lines and the importance of several production lines, determine the overall priority of several production lines; Obtain the delivery time and cost when the delivery vehicle performs material delivery tasks on the production line; Material delivery tasks are assigned to material delivery vehicles based on the overall priority of the production line and the delivery time and cost when the delivery vehicle performs the material delivery task.

3. The material distribution method according to claim 1, characterized in that, The step of executing an emergency scheduling algorithm to dispatch a portion of goods from the currently executing material delivery carts to the target production line when a material delivery cart is unable to perform the material delivery task includes: Get the material delivery tasks in progress ;in, Indicates the first A material delivery cart In progress: material delivery tasks; Obtain the production line corresponding to the material delivery task And the material demand time window corresponding to the production line. The importance of production lines Material delivery volume ;in, Indicates the first The first material delivery task corresponding to the individual production lines The deadline for material requirements; Indicates the first The first material delivery task corresponding to the individual production lines The importance of; Indicates the first The first material delivery task corresponding to the individual production lines The amount of materials delivered; Obtain the detour distance of several material delivery vehicles to the target production line. Comprehensive cost of detour for material delivery carts The remaining load capacity of the material delivery cart Feasibility of detouring for material delivery carts ;in, Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. The extra delivery time incurred at each production line; Indicates the first After the material delivery cart travels from its current location to the target production line, it then proceeds to the next production line. Additional delivery costs incurred at each production line; Indicates the first The amount of materials stored in the material delivery cart excluding the first After calculating the necessary material quantity for each production line, the remaining material quantity; Indicates the first Feasibility of having a material delivery vehicle bypass the target production line; Obtain the material requirements of the target production line Based on the material requirements of the target production line The remaining load capacity of the material delivery cart Material demand window for production lines Detour time Comprehensive cost of detour From several material delivery carts Several candidate cars were obtained through screening. ; Based on detour distance Comprehensive costs Detour time ,importance From several candidate cars The detour car was determined in the middle. and the amount of materials delivered by the bypass vehicle .

4. The material distribution method according to claim 3, characterized in that, Based on detour distance Comprehensive costs Detour time ,importance From several candidate cars The detour car was determined in the middle. and the amount of materials delivered by the bypass vehicle The steps include: Based on several candidate cars The detour cost, the importance of the production line to be visited, and the detour time were used to score several candidate cars. Determine whether the remaining load capacity of the candidate vehicle with the highest score meets the material requirements of the target production line. If the conditions are met, the candidate car with the highest score will be selected as the detour car.

5. The material distribution method according to claim 4, characterized in that, The optimization method further includes: If the remaining load capacity of the candidate vehicle with the highest score does not meet the material requirements of the target production line. Then, an optimization method is adopted, which constructs a fitness function based on the total cost of detours and the delivery time of detours, and selects from several candidate vehicles. The optimal set of bypass vehicles and the optimal temporary material allocation scheme for the bypass vehicle set are obtained through screening.

6. The material distribution method according to claim 5, characterized in that, The optimization method includes any one of the following: genetic algorithm, particle swarm optimization, annealing algorithm, and ant colony optimization.

7. The material distribution method according to claim 4, characterized in that, The comprehensive detour cost is calculated based on the detour distance of the material vehicles and the distance between the production line and the material warehouse to which the vehicles travel.

8. The material distribution method according to claim 4, characterized in that, The scoring formula for the candidate material delivery cart is as follows: In the formula, Indicates the first The rating of the candidate material delivery carts , Indicates cost weight. Indicates the importance weight of the production line. Indicates the weight of the detour distance. This indicates the weight of the detour time.

9. The material distribution method according to claim 4, characterized in that, The fitness function, constructed using the total cost of detours and the delivery time via detours, is as follows: In the formula, This represents the fitness value of the temporary material allocation scheme for the group of bypass vehicles. , , These are the weighting coefficients. This represents the total cost of the detour for the set of detour vehicles. This indicates the time required for detour delivery.

10. A material delivery device, characterized in that, include: The acquisition module is used to acquire material requirement information for several production lines. The conventional scheduling module is used to generate material delivery tasks for several production lines based on the material demand information of several production lines, and drive several material delivery vehicles to execute the material delivery tasks of the several production lines respectively. The emergency scheduling module is used to obtain the delivery status of several material delivery carts. When a material delivery cart is unable to perform its delivery task, the emergency scheduling algorithm is executed to dispatch a portion of the goods from the material delivery carts that are currently performing their tasks to the target production line. Location; wherein, the target production line This refers to a material delivery trolley that is unable to perform material delivery tasks. The production line we're heading to.

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