AGV-based intelligent material distribution management optimization method and system

Abstract

The invention discloses an AGV-based intelligent material distribution management optimization method and system, and relates to the technical field of AGV scheduling management, and the method comprises the steps: obtaining and analyzing factory operation data, and obtaining a material demand prediction result; the factory operation data comprises material demand data and AGV state data; based on the factory operation data, generating a first AGV distribution task sequence by using a multi-objective optimization algorithm; and generating a second AGV delivery task sequence based on the material demand prediction result and the first AGV delivery task sequence, controlling the AGV to execute a material delivery task according to the second AGV delivery task sequence, and performing task execution monitoring at the same time. According to the invention, material demand prediction is carried out in combination with the LSTM network, task sequence generation is carried out by adopting the multi-target optimization algorithm, and the execution state of the AGV is monitored in real time, so that intelligent optimization of material distribution management is realized, the operation efficiency of an AGV distribution system is remarkably improved, energy consumption is reduced, and dynamic balance of equipment load is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV scheduling management, and in particular to an AGV-based intelligent material distribution management optimization method and system. Background Art

[0002] With the in-depth development of intelligent manufacturing, the era of Industry 4.0 has put forward higher requirements for material distribution management. As the core equipment of the material distribution system of intelligent factories, the optimization of the scheduling management system of AGV (Automated Guided Vehicle) directly affects production efficiency and operating costs. Traditional AGV scheduling management mainly adopts rule-based heuristic algorithms or simple real-time response strategies. This method has great limitations in dealing with complex and changing material requirements. In recent years, with the development of artificial intelligence technology, some researchers have begun to try to apply machine learning algorithms to AGV scheduling optimization, such as using reinforcement learning for path planning and using neural networks to predict material requirements. However, these methods often separate the links of material demand prediction, task planning and real-time scheduling, and lack a systematic overall optimization solution.

[0003] At present, the AGV material distribution management system has the following main technical problems: First, most of the existing material demand forecasting methods use traditional time series analysis or simple statistical models, which fail to fully consider the periodic characteristics and long-term dependencies of material demand, resulting in insufficient prediction accuracy; second, in the task planning stage, the existing algorithms often only consider a single optimization goal (such as distribution efficiency), while ignoring multi-dimensional indicators such as energy consumption and load balancing, making it difficult to achieve global optimization; third, in the real-time scheduling process, due to the lack of effective use of the real-time status of AGV and dynamic optimization capabilities, the system has poor adaptability when facing emergencies, which can easily cause delivery delays or waste of resources; finally, the existing system generally has the phenomenon of information islands, and the coordination between various functional modules is insufficient, making it difficult to achieve end-to-end intelligent optimization.

[0004] Therefore, there is an urgent need for an intelligent material distribution management optimization method that can effectively solve the above problems to improve the overall efficiency of the AGV distribution system. The AGV-based intelligent material distribution management optimization method and system proposed in this invention are aimed at the deficiencies of the existing technology, and innovate in multiple dimensions such as material demand prediction, multi-objective task optimization, and real-time monitoring, belonging to the field of intelligent manufacturing and AGV scheduling management technology. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention provides an AGV-based intelligent material distribution management optimization method and system, which can solve the problems mentioned in the background technology.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: an AGV-based intelligent material distribution management optimization method, comprising: acquiring and analyzing factory operation data to obtain material demand forecast results; the factory operation data includes material demand data and AGV status data; based on the factory operation data, a first AGV distribution task sequence is generated using a multi-objective optimization algorithm; based on the material demand forecast results and the first AGV distribution task sequence, a second AGV distribution task sequence is generated, and the AGV is controlled to perform material distribution tasks according to the second AGV distribution task sequence, while monitoring the task execution.

[0008] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, the process of obtaining the material demand forecast result includes the following steps: classifying the material demand data according to the material type to obtain material classification data; extracting the material demand cycle characteristics in the material classification data; based on the material demand cycle characteristics, using the long short-term memory network LSTM for training to obtain a material demand forecast model; calculating the AGV distribution capacity data based on the AGV status data; generating the material demand forecast result based on the material demand forecast model, material cycle characteristics and AGV distribution capacity data.

[0009] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, the material demand data includes material type, quantity and urgency; the AGV status data includes current position, power and load status; the material demand cycle characteristics extraction process is specifically to analyze the material classification data through the autocorrelation function, find the main cycle, and construct a cycle characteristic function, and obtain the material cycle characteristics based on the cycle characteristic function.

[0010] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, wherein: based on factory operation data, a first AGV distribution task sequence is generated using a multi-objective optimization algorithm, including the following steps: based on the factory operation data, a set of objective functions is constructed; based on the objective function set, the degree of conflict between AGV distribution tasks is determined, and the task priority is determined according to the conflict degree; based on the objective function set and the task priority, a multi-population co-evolution algorithm is executed; based on the global non-dominated solution set and the task priority, a comprehensive score of each solution is calculated; according to the comprehensive score, the optimal solution is selected to generate the first AGV distribution task sequence, and the first AGV distribution task sequence includes AGV number, task number and execution time information.

[0011] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, the objective function set includes a distribution efficiency objective function, an energy consumption objective function, an equipment load balancing objective function and an urgency objective function.

[0012] The conflict degree is calculated comprehensively by time difference, spatial distance and processing time difference, and the formula is expressed as follows: ; In the formula, , are the expected start time of the i-th delivery task and the j-th delivery task, respectively, where the i-th delivery task and the j-th delivery task constitute a task pair; is the maximum time difference between all delivery task pairs; is the spatial distance between the i-th delivery task and the j-th delivery task; is the maximum spatial distance between all delivery task pairs; , The estimated processing time of the i-th delivery task and the j-th delivery task are task processing time respectively; is the maximum processing time difference between all delivery task pairs; , , They are the weight coefficients of the three dimensions of time difference, spatial distance and processing time.

[0013] The task priority is determined by the ratio of task urgency to conflict degree: ; In the formula, is the priority value of the i-th delivery task; is the sum of the conflict degrees between the ith delivery task and all other delivery tasks; u i The urgency of the task.

[0014] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, the multi-population co-evolution algorithm includes: Construct K subpopulations: ; in, is the number of subpopulations; For each of the subpopulations , when its optimal solution has not been updated for N consecutive generations, calculate the population diversity index: ; in, is the size of the ith subpopulation; Subpopulation individuals in is the number of objective functions; is the evaluation value of the jth objective function on individual x; is the jth objective function in the population The average value in is the diversity index value of the i-th subpopulation; like , then perform cross-population recombination: ; in, are individuals from the current population, are individuals from non-dominated solution sets of other populations; It is a new individual produced after recombination; is the recombination weight coefficient; is the threshold of the diversity indicator.

[0015] like , then perform adaptive mutation: ; in, , g is the current algebra, is the maximum number of generations, b is the shape parameter; is the new individual after mutation; x is the individual before mutation; is the upper bound of the decision variable; is the lower bound of the decision variable; is a random number, ∈[0,1]; To adapt to the asynchronous length.

[0016] Information is exchanged between populations every M generations, and the global non-dominated solution set is updated by calculating the dominance relationship of the solutions.

[0017] As a preferred solution of the AGV-based intelligent material distribution management optimization method described in the present invention, the monitoring includes AGV position tracking, task completion evaluation and abnormal situation reporting.

[0018] To further solve the above technical problems, the present invention provides the following technical solutions: an AGV-based intelligent material distribution management optimization system, comprising: a data analysis module, used to acquire and analyze factory operation data to obtain material demand forecast results; a task generation module, which generates a first AGV distribution task sequence based on the factory operation data and a multi-objective optimization algorithm; a task optimization execution module, which generates a second AGV distribution task sequence based on the material demand forecast results and the first AGV distribution task sequence, controls the AGV to perform material distribution tasks according to the second AGV distribution task sequence, and monitors the task execution at the same time.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned AGV-based intelligent material distribution management optimization method are implemented.

[0020] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned AGV-based intelligent material distribution management optimization method are implemented.

[0021] Beneficial effects of the present invention: The present invention realizes intelligent optimization of material distribution management by combining LSTM network to predict material demand, adopting multi-objective optimization algorithm to generate task sequence and real-time monitoring of AGV execution status, significantly improves the operating efficiency of AGV distribution system, reduces energy consumption and realizes dynamic balance of equipment load. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 An overall flow chart of an AGV-based intelligent material distribution management optimization method provided for one embodiment of the present invention.

[0024] Figure 2 This is a computer equipment diagram of the AGV-based intelligent material distribution management optimization method of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an AGV-based intelligent material distribution management optimization method.

[0028] Figure 1 The overall flow chart of an AGV-based intelligent material distribution management optimization method is shown, including: S1: Obtain and analyze factory operation data to obtain material demand forecast results; S2: Based on the factory operation data, a first AGV delivery task sequence is generated using a multi-objective optimization algorithm; S3: Based on the material demand prediction result and the first AGV delivery task sequence, a second AGV delivery task sequence is generated, and the AGV is controlled to execute the material delivery task according to the second AGV delivery task sequence, and task execution is monitored at the same time.

[0029] This embodiment will explain S1 to S3 in detail one by one: S1: Obtain and analyze factory operation data to obtain material demand forecast results.

[0030] Specifically, factory operation data includes material demand data and AGV status data. Material demand data includes material type, quantity and urgency. AGV status data includes current location, power and load.

[0031] It should be noted that in the present invention, the factory operation data is obtained by adopting a layered data collection architecture: the material demand data is collected in real time through the intelligent data collection terminal and RFID technology deployed at each workstation; the current position, power and load of the AGV are collected by using the AGV vehicle-mounted positioning system (such as laser SLAM or UWB positioning), battery management system (BMS) and weight sensor; an industrial-grade wireless network (such as industrial Wi-Fi or 5G private network) and OPC UA protocol are used to build a reliable data transmission channel, and an on-site data cache server is set up to ensure the reliability of data transmission. In addition, it is recognized by those skilled in the art that the collected raw data needs to be denoised, outlier detected and standardized in real time, and a data quality assessment mechanism needs to be established to provide high-quality data support for subsequent data analysis and prediction models.

[0032] S101: Classify the material demand data according to the material type to obtain material classification data: ; ; Where M is the set of material types; n is the total number of material types; is the material classification data, specifically the global historical demand data of the i-th material; T hist is the time span of global historical demand data; ~ One of the two can be expressed as , which means the demand for the i-th type of material at time point t; It should be noted that when classifying material demand data according to material types, it is mainly based on the following classification rules: one is to classify according to the physical properties of the materials, such as liquid materials, solid bulk materials, single materials, etc. as different types (m1, m2, m3...), because this is directly related to the different types of carriers that AGV needs to be equipped with; the second is to classify according to the storage conditions of the materials, such as room temperature materials, constant temperature materials, hazardous chemicals, etc., because it determines the specific conditions that AGV needs to meet during distribution; the third is to classify according to the use of materials in the production process, such as raw materials for production, auxiliary materials for packaging, materials for equipment maintenance, etc. This type of classification can reflect the different timeliness requirements of material demand. Through this multi-dimensional classification method, the present invention can accurately record and analyze the data information of each type of material at each time point, and provide more targeted data support for subsequent demand forecasting and AGV task planning.

[0033] S102: Based on the material classification data, the time series analysis method is used to extract the material demand cycle characteristics.

[0034] Specifically, from the material classification data Extract the periodic characteristics of each type of material. First, use the autocorrelation function Analyze the global historical demand data of material demand and find the main cycles , and construct a periodic characteristic function, and obtain the material periodic characteristics based on the periodic characteristic function; ; ; ; in, is the autocorrelation function of the i-th type of material; k is the time lag value; is the average value of the demand for the i-th type of materials; is the main cycle of the i-th type of material; K is the maximum detection cycle length; The material periodic characteristics output by the periodic characteristic function; , is the parameter of the periodic characteristic function.

[0035] It should be noted that in the prior art, material demand forecasting often ignores the periodic characteristics of different materials and fails to fully consider the periodic fluctuations in demand, which results in a decrease in the accuracy of the forecast when the material demand has obvious periodicity. Therefore, the present invention introduces the autocorrelation function The cyclical characteristics of material demand are extracted and the cyclical characteristic functions are constructed to achieve quantitative analysis of cyclical fluctuations, thereby providing accurate cyclical input data for subsequent material demand forecasting models, solving the problem of being unable to accurately capture cyclical changes by relying solely on time series analysis.

[0036] S103: Based on the material demand cycle characteristics, a long short-term memory network LSTM is used for training to obtain a material demand forecasting model.

[0037] Specifically, use the material cycle characteristics and short-term historical demand data As input, the LSTM model is used for training to build a material demand forecasting model. The LSTM model is used to capture the long-term dependencies and periodic fluctuations in material demand and generate the original material demand forecast value at a future time point. , the formula is: ; in, For the i-th type of material generated by the LSTM model in the future The original demand forecast value of time; For LSTM model based on material cycle characteristics and short-term historical demand data Generated demand forecast function.

[0038] It should be noted that is the short-term historical demand data, which represents the historical demand data of the i-th material in the last T time steps, as the input of the LSTM model; T is the time step length used for the LSTM model input. and global historical demand data The relationship between the short-term historical demand data and the global historical demand data is as follows: the short-term historical demand data is a time window extracted from the global historical demand data and is used as the input of the model for each prediction. This time window is usually a data fragment of the most recent several time steps to reflect the current trends and changes.

[0039] Assuming global historical demand data The length of time is T hist , represents the complete demand data sequence from the initial time point to the current time; while the short-term historical demand data The time length is T, which is the time window length of the input LSTM model. To extract short-term historical data from the global historical data, you can follow the following method: At time point t, the short-term historical demand data is expressed as: ; Locate the short-term window starting point. From global historical demand data In , find the data from time point tT to t-1.

[0040] Extract time window data. From global historical demand data Extract the data of the most recent T time steps from , constituting short-term historical demand data .

[0041] For example, if T hist =100, indicating that the global historical demand data contains 100 time steps, and T=10, indicating that the short-term time window length is 10, then at time point t=100, the short-term historical demand data is: .

[0042] In the prior art, traditional material demand forecasting methods (such as linear regression or simple time series analysis) lack the ability to handle long-term dependencies and are difficult to adapt to material demand scenarios with long-term dependencies and complex fluctuations. The LSTM model can capture long-term dependencies in time series. By combining LSTM with material cycle characteristics, the present invention improves the ability to identify long-term dependencies and cycle changes, solving the problem that traditional models cannot take into account both long-term and short-term demand changes.

[0043] S104: Calculate AGV delivery capability data based on AGV status data: ; in, is the AGV delivery capacity data at time point t, indicating the overall delivery capacity of AGV; m is the number of AGVs; is the efficiency coefficient of the jth AGV; is the load capacity of the jth AGV; is the current power of the jth AGV; The AGV is fully charged; is the distance from the jth AGV to the target position; is the maximum delivery distance.

[0044] It should be noted that the existing AGV delivery capacity evaluation often uses static parameters and fails to consider the changes in real-time status (such as power, load and position), resulting in the delivery capacity not being consistent with the actual situation. , so that the AGV delivery capacity can dynamically reflect the current state of the AGV, ensuring the matching of the predicted value with the actual AGV delivery capacity. That is, by introducing the real-time status data of the AGV for calculation, the present invention has dynamic adaptability in the delivery capacity evaluation, especially when the delivery tasks are frequent and the demand fluctuates greatly, it can avoid the scheduling problems caused by insufficient delivery capacity and improve the responsiveness of the system.

[0045] S105: Generate material demand forecast results based on the material demand forecast model, material cycle characteristics and AGV distribution capability data.

[0046] Specifically, the original demand forecast value generated by the LSTM model and the AGV delivery capacity data at time point t are combined , generate the final material demand forecast results . Through the balance factor Control the weight of material demand forecast and delivery capacity to ensure that the generated demand forecast results are consistent with the actual delivery capacity of the AGV; ; in, For Class I materials in the future Demand forecast value over time; is a balancing factor used to adjust LSTM prediction and The weight between .

[0047] It should be noted that the demand forecasting results of the prior art are usually single-dimensional, that is, they are purely based on time series models (such as LSTM, ARIMA, etc.) to generate material demand. However, the traditional model ignores the actual resource limitations of the distribution system (such as AGV distribution capacity), resulting in the generated demand forecasting results may exceed the actual distribution capacity of the system, which is prone to system overload, distribution delays and even resource scheduling imbalance. Especially in complex scenarios with large demand fluctuations and limited distribution resources, the prediction results in the prior art often cannot be directly converted into a reasonable distribution plan. In order to solve the above technical problems, the formula proposed in the present invention not only relies on the LSTM prediction results, but also calculates in real time. , and as a constraint factor, by introducing a balance factor γ between the demand forecast value and the actual distribution capacity, the present invention can dynamically adjust the weights of the two under different circumstances, ensuring that the forecast results not only meet the trend of material demand changes, but also meet the carrying capacity of the actual distribution system. In this way, even in the case of peak demand or limited resources, the system can generate more reasonable and executable forecast results based on the current resource situation, ensuring that the forecast is compatible with the execution capacity.

[0048] S2: Based on the factory operation data, the first AGV delivery task sequence is generated using a multi-objective optimization algorithm.

[0049] Specifically, the multi-objective optimization algorithm comprehensively considers multiple delivery indicators, including delivery efficiency, energy consumption, equipment load, and urgency.

[0050] S201: Construct a set of objective functions based on factory operation data.

[0051] Specifically, the objective function set includes the distribution efficiency objective function, the energy consumption objective function, the equipment load balancing objective function and the urgency objective function. The objective function set is expressed as: ; Among them, the distribution efficiency objective function is expressed as: ; In the formula, is the distance of the i-th delivery task; is the running speed of AGV when performing the i-th delivery task; is the material loading and unloading time of the i-th delivery task; n is the total number of delivery tasks.

[0052] The energy consumption objective function is expressed as: ; In the formula, is the load factor; For unit energy consumption.

[0053] The device load balancing objective function is expressed as: ; In the formula, is the number of delivery tasks for the i-th AGV.

[0054] The urgency objective function is expressed as: ; In the formula, is the urgency coefficient of the delivery task; For the response time.

[0055] S202: Determine the degree of conflict between AGV delivery tasks based on the objective function set, and determine the task priority based on the conflict degree.

[0056] Specifically, the conflict degree is obtained by comprehensive calculation of time difference, spatial distance and processing time difference, and the formula is expressed as: ; In the formula, , are the expected start time of the i-th delivery task and the j-th delivery task, respectively, where the i-th delivery task and the j-th delivery task constitute a task pair; is the maximum time difference between all delivery task pairs; is the spatial distance between the ith delivery task and the jth delivery task (i.e., the shortest path distance from the starting point to the end point of the two delivery tasks); is the maximum spatial distance between all delivery task pairs; , The estimated processing time (including running time and loading and unloading time) of the i-th delivery task and the j-th delivery task respectively; is the maximum processing time difference between all delivery task pairs; , , They are the weight coefficients of the three dimensions of time difference, spatial distance and processing time.

[0057] The task priority is determined by the ratio of the urgency and conflict of the delivery task, and the formula is expressed as: ; In the formula, is the priority value of the i-th delivery task; is the sum of the conflict degrees between the ith delivery task and all other delivery tasks (i.e., the sum of the pairwise conflict degrees between the ith delivery task and the remaining n-1 delivery tasks). This formula indicates that the priority of a delivery task is proportional to its urgency and inversely proportional to its total conflict degree, i.e., under the same urgency, the delivery task with less conflict with other delivery tasks will get a higher priority.

[0058] S203: Execute a multi-population co-evolution algorithm based on the objective function set and the task priority.

[0059] The multi-population co-evolution algorithm includes: constructing K sub-populations; calculating the population diversity index for each sub-population; selecting to perform cross-population recombination or adaptive mutation operations according to the size of the population diversity index; exchanging information between populations every preset generation and updating the global non-dominated solution set.

[0060] Specifically, first construct K subpopulations: ; in, is the number of subpopulations; For each of the subpopulations , when its optimal solution has not been updated for N consecutive generations, calculate the population diversity index: ; in, is the size of the ith subpopulation; Subpopulation individuals in is the number of objective functions; is the evaluation value of the jth objective function on individual x; is the jth objective function in the population The average value in is the diversity index value of the i-th subpopulation; like , then perform cross-population recombination: ; in, are individuals from the current population, are individuals from non-dominated solution sets of other populations; It is a new individual produced after recombination; is the recombination weight coefficient; is the diversity index value of the ith subpopulation.

[0061] like , then perform adaptive mutation: ; in, , g is the current algebra, is the maximum number of generations, b is the shape parameter; is the new individual after mutation; x is the individual before mutation; is the upper bound of the decision variable; is the lower bound of the decision variable; is a random number, ∈[0,1]; To adapt to the asynchronous length.

[0062] Information is exchanged between populations every M generations, and the global non-dominated solution set is updated by calculating the dominance relationship of the solutions.

[0063] S204: Calculate a comprehensive score for each solution based on the global non-dominated solution set and the task priority; select the optimal solution according to the comprehensive score to generate the first AGV delivery task sequence, wherein the first AGV delivery task sequence includes AGV number, task number and execution time information.

[0064] S3: Based on the material demand prediction result and the first AGV delivery task sequence, a second AGV delivery task sequence is generated, and the AGV is controlled to execute the material delivery task according to the second AGV delivery task sequence, and task execution monitoring is performed at the same time.

[0065] Specifically, monitoring includes AGV location tracking, task completion evaluation, and abnormal situation reporting.

[0066] In summary, the present invention realizes the intelligent optimization of material distribution management by combining LSTM network for material demand forecasting, adopting multi-objective optimization algorithm for task sequence generation and real-time monitoring of AGV execution status, significantly improves the operating efficiency of AGV distribution system, reduces energy consumption and realizes dynamic balance of equipment load.

[0067] Embodiment 2 is an embodiment of the present invention, which provides an AGV-based intelligent material distribution management optimization system, including: a data analysis module, used to acquire and analyze factory operation data to obtain material demand forecast results; a task generation module, which generates a first AGV distribution task sequence based on factory operation data using a multi-objective optimization algorithm; a task optimization execution module, which generates a second AGV distribution task sequence based on the material demand forecast results and the first AGV distribution task sequence, controls the AGV to perform material distribution tasks according to the second AGV distribution task sequence, and monitors the task execution at the same time.

[0068] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0070] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0071] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0072] Example 4 is an embodiment of the present invention, which provides an AGV-based intelligent material distribution management optimization method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0073] The experimental verification was carried out in a large automobile parts manufacturing plant, which has a total of 42 production workstations and 12 AGV robots. The production data of the factory from October 2023 to March 2024 was selected as the basic data for the experiment. First, the material demand data within 6 months was collected through the RFID data acquisition terminal deployed at each workstation, including 84,632 distribution records of 314 different types of materials. The data preprocessing module was used to clean the original data, and 82,947 valid data were obtained after removing outliers. Subsequently, the data was divided into three categories: A, B, and C based on the material type, of which Category A was high-frequency key materials (accounting for 30% of the total demand), Category B was medium-frequency materials (accounting for 45% of the total demand), and Category C was low-frequency materials (accounting for 25% of the total demand).

[0074] The demand cycle characteristics of various materials are analyzed using the improved autocorrelation function. Class A materials mainly show dual cycle characteristics of 4 hours and 8 hours, Class B materials show a single cycle characteristic of 12 hours, and Class C materials show a long cycle characteristic of 24 hours. Based on these cycle characteristics, a three-layer LSTM network (128 input layer nodes, 256 hidden layer nodes, and 64 output layer nodes) is constructed, and the first 5 months of data are used for training, and the last month of data is used for testing. At the same time, the sensor system on the AGV collects position, power, and load data in real time with a sampling frequency of 1Hz to build an AGV status database.

[0075] In the task optimization stage, four objective functions are designed: distribution efficiency objective function (with the shortest completion time as the optimization goal), energy consumption objective function (with the minimum total energy consumption as the optimization goal), equipment load balancing objective function (with the minimum standard deviation of each AGV workload as the optimization goal) and urgency objective function (with the highest priority of emergency tasks as the optimization goal). An improved multi-population collaborative evolution algorithm is used, with the population size set to 200, the evolutionary generation number to 1000 generations, the crossover probability to 0.8, and the mutation probability to 0.1. In the experiment, the traditional method, the single-objective optimization method and the method of the present invention are used for comparative tests. Each group of tests is repeated 30 times and the average value is taken. The results are shown in Table 1: Table 1 Experimental data comparison table Optimization methods Average delivery completion time (min) Energy consumption index (kWh / h) Device load balance (%) Emergency task response time (min) Material delivery accuracy (%) System suitability score Traditional rule-based approach 45.8 12.4 68.5 15.6 92.3 0.72 Single-objective optimization method 38.2 10.8 72.4 12.3 94.8 0.78 Group A of the present invention 28.4 8.6 89.7 6.8 98.2 0.91 Group B of the present invention 29.1 8.9 88.5 7.2 97.8 0.90 Group C of the present invention 27.9 8.4 90.2 6.5 98.5 0.92 Group D of the present invention 28.6 8.7 89.1 6.9 98.1 0.91 By comparing the experimental data, the following analysis conclusions can be drawn: the present invention is significantly superior to traditional methods and single-objective optimization methods in all performance indicators. In terms of average delivery completion time, the present invention is about 39.1% shorter than the traditional rule-based method and about 25.7% shorter than the single-objective optimization method. This significant improvement is mainly due to the LSTM network's accurate prediction of material demand and the synergistic effect of the multi-objective optimization algorithm. In terms of energy consumption indicators, the average energy consumption of the present invention is 8.65kWh / h, which is 30.2% lower than the traditional method, thanks to the global consideration of AGV energy consumption during task sequence optimization.

[0076] In terms of equipment load balancing, the present invention achieved an average level of 89.4%, which is 30.5 percentage points higher than the traditional method, reflecting the superiority of multi-objective optimization in task allocation. In terms of emergency task response time, the average response time of the present invention is only 6.85 minutes, which is 56.1% shorter than the traditional method, thanks to the dynamic adjustment mechanism of task priority. The accuracy of material distribution reached a high level of 98.2%, which is 5.9 percentage points higher than the traditional method, reflecting the high-precision characteristics of the prediction model.

[0077] The system adaptability score is a comprehensive evaluation index that includes multiple dimensions such as the system's ability to handle emergencies and the ability to dynamically adjust tasks. The average score of the present invention is 0.91, which is 26.4% higher than the traditional method, indicating that this method has stronger environmental adaptability and robustness. Through variance analysis of the four groups of experimental data A, B, C, and D, when the confidence level is 95%, the standard deviation of each indicator is less than 3%, which proves that the present invention has good stability and repeatability. These experimental data fully demonstrate the innovation and practical value of the present invention in the field of intelligent material distribution management optimization.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An AGV-based intelligent material distribution management optimization method, characterized in that: include: Acquire and analyze factory operation data to obtain material demand forecast results; the factory operation data includes material demand data and AGV status data; Based on the factory operation data, a first AGV delivery task sequence is generated using a multi-objective optimization algorithm; Based on the material demand prediction result and the first AGV distribution task sequence, a second AGV distribution task sequence is generated, and the AGV is controlled to perform the material distribution task according to the second AGV distribution task sequence, and task execution monitoring is performed at the same time.

2. The AGV-based intelligent material distribution management optimization method according to claim 1, characterized in that: The process of obtaining the material demand forecast result includes the following steps: Classifying the material demand data according to material types to obtain material classification data; Extracting material demand cycle characteristics from the material classification data; Based on the material demand cycle characteristics, a long short-term memory network LSTM is used for training to obtain a material demand forecasting model; Calculating AGV delivery capability data based on the AGV status data; Based on the material demand forecasting model, material cycle characteristics and AGV delivery capability data, a material demand forecasting result is generated.

3. The AGV-based intelligent material distribution management optimization method according to claim 2, characterized in that: The material requirement data includes material type, quantity and urgency; The AGV status data includes current position, power and load; The process of extracting the material demand cycle characteristics is specifically to analyze the material classification data through the autocorrelation function, find the main cycle, and construct a cycle characteristic function, and obtain the material cycle characteristics based on the cycle characteristic function.

4. The AGV-based intelligent material distribution management optimization method according to claim 3, characterized in that: Based on the factory operation data, the first AGV delivery task sequence is generated using a multi-objective optimization algorithm, including the following steps: constructing a set of objective functions based on the plant operation data; Determining the degree of conflict between AGV delivery tasks based on the objective function set, and determining the task priority according to the degree of conflict; Based on the objective function set and the task priority, executing a multi-population co-evolution algorithm; Based on the global non-dominated solution set and the task priority, a comprehensive score of each solution is calculated; the optimal solution is selected according to the comprehensive score to generate the first AGV distribution task sequence, which includes AGV number, task number and execution time information.

5. The AGV-based intelligent material distribution management optimization method according to claim 4, characterized in that: The objective function set includes a distribution efficiency objective function, an energy consumption objective function, an equipment load balancing objective function and an urgency objective function; The conflict degree is calculated comprehensively by time difference, spatial distance and processing time difference, and the formula is expressed as follows: ； In the formula, , are the expected start time of the i-th delivery task and the j-th delivery task, respectively, where the i-th delivery task and the j-th delivery task constitute a task pair; is the maximum time difference between all delivery task pairs; is the spatial distance between the i-th delivery task and the j-th delivery task; is the maximum spatial distance between all delivery task pairs; , The estimated processing time of the i-th delivery task and the j-th delivery task are task processing time respectively; is the maximum processing time difference between all delivery task pairs; , , They are the weight coefficients of the three dimensions of time difference, spatial distance and processing time; The task priority is determined by the ratio of task urgency to conflict degree: ； In the formula, is the priority value of the i-th delivery task; is the sum of the conflict degrees between the ith delivery task and all other delivery tasks; u i The urgency of the task.

6. The AGV-based intelligent material distribution management optimization method according to claim 5, characterized in that: The multi-population co-evolution algorithm includes: Construct K subpopulations: ； in, is the number of subpopulations; For each of the sub-populations, when its optimal solution has not been updated for N consecutive generations, the population diversity index is calculated: ； in, is the size of the ith subpopulation; Subpopulation individuals in is the number of objective functions; is the evaluation value of the jth objective function on individual x; is the jth objective function in the population The average value in is the diversity index value of the i-th subpopulation; like , then perform cross-population recombination: ； in, are individuals from the current population, are individuals from non-dominated solution sets of other populations; It is a new individual produced after recombination; is the recombination weight coefficient; is the threshold of the diversity indicator; like , then perform adaptive mutation: ； in, , g is the current algebra, is the maximum number of generations, b is the shape parameter; is the new individual after mutation; x is the individual before mutation; is the upper bound of the decision variable; is the lower bound of the decision variable; is a random number, ∈[0,1]; To adapt to the asynchronous length; Information is exchanged between populations every M generations, and the global non-dominated solution set is updated by calculating the dominance relationship of the solutions.

7. The AGV-based intelligent material distribution management optimization method according to claim 6, characterized in that: The monitoring includes AGV location tracking, task completion evaluation and abnormal situation reporting.

8. A system using the AGV-based intelligent material distribution management optimization method according to any one of claims 1 to 7, characterized in that: include: Data analysis module, used to obtain and analyze factory operation data to obtain material demand forecast results; A task generation module, which generates a first AGV delivery task sequence based on the factory operation data using a multi-objective optimization algorithm; A task optimization execution module generates a second AGV distribution task sequence based on the material demand prediction result and the first AGV distribution task sequence, controls the AGV to execute the material distribution task according to the second AGV distribution task sequence, and monitors the task execution at the same time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the AGV-based intelligent material distribution management optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the AGV-based intelligent material distribution management optimization method described in any one of claims 1 to 7 are implemented.

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