Material conveying equipment management and control method and system for workpiece logistics conveying

Abstract

The invention discloses a material conveying equipment management and control method and system for workpiece logistics conveying, and belongs to the field of equipment management and control. The material conveying equipment management and control method comprises the following steps: collecting data of material attributes and environmental parameters; calculating an optimal material conveying path by using a hybrid optimization algorithm in combination with a production environment and material characteristics; using a material demand prediction model to predict the material demand and time in the future preset time based on the optimal material conveying path; in the material conveying process, the quality of the materials is monitored in real time; the predictive maintenance model is used for analyzing the speed and the frequency of the material conveying equipment, and fault occurrence is predicted through real-time monitoring and analysis of equipment operation data. Through real-time monitoring and analysis of equipment operation data, the predictive maintenance model can recognize possible faults in advance, so that a maintenance team can take preventive measures before the faults occur.

Description

Technical Field

[0001] The present invention relates to the field of equipment control, and in particular, to a material conveying equipment control method and system for workpiece logistics transportation. Background Art

[0002] Workpiece logistics refers to the entire transportation process of transferring workpieces (i.e., in-process or finished items) from one production site to another production site or storage area during the production process. This process involves the handling, sorting, temporary storage and final delivery of materials. It is an important part of the manufacturing and processing industries to ensure the smooth progress of the production process.

[0003] Material conveying equipment refers to the machinery and equipment used to realize the logistics transportation of workpieces, including but not limited to conveyor belts, automated robots, conveyor rollers, sorting machines, etc. The function of the equipment is to automatically or semi-automatically convey, distribute and process workpieces to support the efficient operation of the production line; through effective equipment management, the efficient operation of the material conveying system can be ensured, downtime can be reduced, production speed can be accelerated, thereby improving overall production efficiency.

[0004] Without accurate material demand forecasts, companies often find it difficult to optimize inventory management and replenishment strategies, which may lead to excessive inventory or out-of-stock situations of materials, thereby affecting production efficiency and increasing costs. Traditional material conveying equipment operation strategies lack the ability to adjust according to actual production needs, often leading to unnecessary increases in energy consumption and equipment wear, lack of efficiency and cost-effectiveness. Due to the lack of real-time monitoring of material supply and demand, traditional production systems often face problems such as production interruptions and downtime, which directly affect production efficiency and corporate profitability.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to overcome the above problems, the present invention aims to propose a material conveying equipment control method and system for workpiece logistics transportation, with the aim of solving the problems of production interruption and shutdown often faced by traditional production systems due to the lack of real-time monitoring of material supply and demand, which directly affect production efficiency and profitability of the enterprise.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, a material conveying equipment control method for workpiece logistics transportation is provided, and the material conveying equipment control method comprises the following steps: S1. Collect data on material attributes and environmental parameters; S2. Based on the collected data, combined with the production environment and material characteristics, the hybrid optimization algorithm is used to calculate the optimal material transportation path; S3. Use the material demand forecasting model to forecast the material demand and time within a preset time in the future based on the optimal material transportation path, and adjust the speed and frequency of the material transportation equipment according to the forecast results and preset production needs; S4. During the material transportation process, the quality of the materials is monitored in real time. If defects are found in the materials, they are corrected through the feedback mechanism; S5. Use the predictive maintenance model to analyze the speed and frequency of conveying equipment, predict the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implement maintenance measures based on the prediction results; Among them, based on the collected data, combined with the production environment and material characteristics, the hybrid optimization algorithm is used to calculate the optimal material transportation path, including the following steps: S21. Based on the collected data, build an environmental model and use the Bellman-Ford algorithm to determine the shortest path from the starting point to all vertices; S22, based on the shortest path obtained by the Bellman-Ford algorithm, generating an initial population of the genetic algorithm, wherein the individuals in the initial population represent the path, and setting a fitness function; S23, based on the initial population of the genetic algorithm and the set fitness function, the population is optimized through elite crossover and mutation mechanisms, and combined with the shortest path of the Bellman-Ford algorithm to find a path solution with better fitness; S24, repeating steps S22-S24 until a preset condition is met, such as reaching a preset number of iterations or finding a path that meets the requirements, and selecting the path with the highest fitness as the optimal solution.

[0008] Optionally, constructing an environment model based on the collected data and using the Bellman-Ford algorithm to determine the shortest path from the starting point to all vertices includes the following steps: S211. Obtain data on material attributes and environmental parameters, and build an environmental model; S212, setting an initial value for each vertex in the environment model and setting the weight of the edge, while traversing each edge in the environment model; S213, repeat the relaxation operation for each edge, and check whether the known shortest path length from the starting point to the preset vertex is shortened by the current edge; If a shorter path is obtained through the current edge, update the shortest path length and predecessor node of the vertex; S214. Repeat steps S222-S223 until a preset number of iterations is reached, which includes checking all edges so that no shorter path updates are generated in a complete iteration, finding the shortest paths of all vertices, and recording the shortest paths from the starting point to all vertices.

[0009] Optionally, based on the initial population of the genetic algorithm and the set fitness function, the population is optimized through elite crossover and mutation mechanisms, and combined with the shortest path of the Bellman-Ford algorithm, finding a path solution with better fitness includes the following steps: S231, using the shortest path and the generated initial population as inputs of the genetic algorithm; S232. In the genetic algorithm, individuals with high fitness are selected as parents for crossover and random mutation to generate and retain new offspring with the highest fitness and optimize the path population; S233. Perform fitness evaluation on each newly generated path. The fitness function should be designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted and updated. S234, check whether the preset number of iterations is reached, if so, execute the final path selection; and check whether there is already a path that meets the preset performance requirements; S235. Select the path with the highest fitness from the current population as the final optimal solution.

[0010] Optionally, in a genetic algorithm, individuals with high fitness are selected as parents for crossover and random mutation to generate and retain new offspring with the highest fitness, and the path population is optimized, including the following steps: S2321. In the genetic algorithm, select a fitness individual that meets the preset requirements from the current path as a parent; S2322, perform a crossover operation with a set probability to generate new offspring individuals; S2323, perform random mutation operations on individuals in the population and introduce new gene combinations; S2324. After the crossover and mutation operations, the individual with the highest fitness in the path is retained.

[0011] Optionally, a fitness evaluation is performed on each newly generated path. The fitness function should be designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted. Updating the population includes the following steps: S2331. Set standards for suitability assessment based on specific production environments and material characteristics; S2332, quantify the performance of each path using the fitness calculation formula; S2333. For each path in the population, calculate the fitness value of each path according to the fitness function; S2334. Sort the paths according to the calculated fitness values; S2335. According to the sorting result, delete the paths that do not meet the preset requirements and update the population; The fitness calculation formula is: ; In the formula, M represents the fitness value of the path; L represents the total length of the path; T stands for time; S represents the safety score; The weight coefficient representing the total length of the path; represents the weight coefficient of time; The weight coefficient representing the safety score.

[0012] Optionally, using a material demand forecasting model, based on an optimal material conveying path, forecasting the material demand and time within a preset future time, and adjusting the speed and frequency of the conveying equipment according to the forecast results and preset production requirements includes the following steps: S31, inputting current production data and historical material utilization into a pre-configured material demand forecasting model; S32, setting a predicted time range, and analyzing and predicting material demand and time points within the time range based on the optimal material transportation path; S33. Adjust the speed and delivery frequency of the conveying equipment according to the material demand forecast results and the preset production demand; S34. Monitor the material supply situation and the real-time demand of the production line in real time, and optimize the speed and frequency of the conveying equipment based on real-time data feedback.

[0013] Optionally, setting a predicted time range, analyzing and predicting material demand and time points within the time range based on the optimal material delivery path includes the following steps: S321, setting the time range of prediction; S322, inputting the optimal material transportation path information into the material demand forecasting model, including factors such as the length of the path, the estimated transportation time, possible delays or material losses, etc., which have an important impact on the forecast of material demand; S323. Analyze the material utilization trend and production rhythm using the material demand forecasting model based on the set time range and integrated data, including current production data, historical material usage, and transportation path characteristics; Predict the material demand at a set time in the future based on the analysis results (the forecast results include the material demand at each time point, taking into account the production plan and possible market changes); S324. List the expected material demand and associated time points at each important time point within the predetermined time range.

[0014] Optionally, using a predictive maintenance model to analyze the speed and frequency of the conveying equipment, predicting the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implementing maintenance measures based on the prediction results include the following steps: S51, collecting operation data of conveying equipment; S52, extracting characteristic data of the operation data, and selecting a predictive maintenance model according to the characteristic data; S53, training initial parameters of the predictive maintenance model, and improving prediction accuracy by optimizing the parameters; S54, using the trained predictive maintenance model to predict the operating status of the conveying equipment within a preset time in the future; S55. Analyze the prediction results and identify the failure risk of the conveying equipment; S56. Adjust maintenance measures based on the prediction results and take corresponding maintenance measures to prevent failures; S57. Continuously monitor equipment status and update predictive maintenance models.

[0015] Optionally, analyzing the prediction results and identifying the failure risk of the conveying equipment includes the following steps: S551. Review the operation status data within a preset future time predicted by the predictive maintenance model; S552, setting warning thresholds based on operating standards and historical data, and marking predicted performance that exceeds the warning threshold as a potential risk; S553. Use the seasonal component in the predictive maintenance model to detect abnormal behaviors that do not conform to normal seasonal patterns and identify potential failures; S554. Classify potential faults according to the risk size and nature in the test results, and analyze the causes of predicted abnormalities; S555. For different risk categories identified, formulate response measures including adjusting operating parameters, planning advance maintenance or temporary shutdown inspection, and create a report containing prediction results, risk points and preventive measures.

[0016] According to another aspect of the present invention, there is also provided a material conveying equipment control system for workpiece logistics transportation, the system comprising: a data collection module, a path optimization module, a material demand prediction module, a material quality monitoring module and a predictive maintenance module; The data collection module, the route optimization module, the material demand prediction module, the material quality monitoring module and the predictive maintenance module are connected in sequence; Data collection module, used to collect data on material attributes and environmental parameters; The path optimization module is used to calculate the optimal material transportation path based on the collected data, combined with the production environment and material characteristics, using a hybrid optimization algorithm; The material demand forecasting module is used to use the material demand forecasting model to forecast the material demand and time within a preset time in the future based on the optimal material transportation path, and adjust the speed and frequency of the material conveying equipment according to the forecast results and preset production needs; The material quality monitoring module is used to monitor the quality of materials in real time during the material transportation process. If defects are found in the materials, they will be corrected through the feedback mechanism; The predictive maintenance module is used to analyze the speed and frequency of conveying equipment using a predictive maintenance model, predict the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implement maintenance measures based on the prediction results.

[0017] Compared with the prior art, this application has the following beneficial effects: 1. The environmental model constructed by the Bellman-Ford algorithm in the present invention can flexibly adapt to different material characteristics and production environments, can process graphs with negative weights, and provide practical and feasible path solutions for material transportation; the genetic algorithm is used to optimize path selection, and can further explore possible path combinations based on the shortest path determined by the Bellman-Ford algorithm to find lower-cost or more efficient paths; by accurately adjusting the fitness function and considering multiple factors such as cost, time and safety, the algorithm can comprehensively evaluate and select the optimal logistics path; as environmental conditions change, the genetic algorithm can quickly adapt to new transportation needs and constraints through iterative evolution to ensure the continuous and stable operation of logistics equipment.

[0018] 2. By accurately predicting the material demand and time, the present invention can help enterprises optimize material inventory management and replenishment strategies to avoid material waste and out-of-stock situations. By adjusting the operating speed and frequency of the conveying equipment according to the predicted data, it can more flexibly respond to changes in production demand. Timely adjustment of the speed and frequency of the conveying equipment can not only save energy consumption according to actual demand, but also reduce excessive use and wear of the equipment and extend the service life of the equipment. By real-time monitoring of the material supply situation and the needs of the production line and timely adjusting the parameters of the conveying equipment, it can ensure that the materials are in place in time, reduce production interruptions and downtime, and thus improve overall production efficiency.

[0019] 3. Through real-time monitoring and analysis of equipment operation data, the present invention's predictive maintenance model can identify possible failures in advance, allowing the maintenance team to take preventive measures before the failure occurs. Regular and targeted maintenance can effectively extend the service life of the equipment. Predictive maintenance allows enterprises to formulate maintenance plans based on the actual operating conditions of the equipment instead of relying on fixed maintenance schedules. By avoiding unnecessary maintenance and reducing the need for emergency repairs, predictive maintenance helps to significantly reduce overall maintenance costs. The stability and reliability of the equipment's operating status directly affects production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown: Figure 1 is a flow chart of a material conveying equipment control method for workpiece logistics transportation according to an embodiment of the present invention; Figure 2 It is a principle block diagram of a material conveying equipment control system for workpiece logistics transportation according to an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0021] In the figure: 1. Data collection module; 2. Path optimization module; 3. Material demand forecasting module; 4. Material quality monitoring module; 5. Predictive maintenance module. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] According to an embodiment of the present invention, a method and system for controlling material conveying equipment for workpiece logistics transportation are provided.

[0024] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a material conveying equipment control method for workpiece logistics transportation is provided, and the material conveying equipment control method includes the following steps: S1. Collect data on material properties and environmental parameters.

[0025] It should be explained that material attributes include weight, size, shape, material and surface characteristics; Among them, weight includes the mass of the workpiece, which affects the load and power requirements of the conveying system.

[0026] The dimensions include length, width and height, which determine the space occupied by the workpiece on the conveyor line and the possible arrangement.

[0027] Shape includes the external appearance of the workpiece, which affects how it can be stably conveyed and possible storage methods.

[0028] Material includes the type of material the workpiece is made of (e.g., metal, plastic, wood, etc.), which affects sensitivity to environmental factors and handling requirements.

[0029] Surface properties, such as smoothness, hardness, and whether or not a coating is applied, affect friction and wear during conveying.

[0030] Environmental parameters include temperature, humidity, airflow, dust, and vibration.

[0031] Temperature includes the temperature conditions within the factory. Certain materials may need to be stored or handled at specific temperatures to avoid deformation or damage.

[0032] Humidity includes the ambient humidity and is critical for certain materials such as wood or paper, which absorb moisture easily.

[0033] Airflow includes the airflow conditions within the plant, which have an impact on the conveying of bulk powder or granular materials.

[0034] Dust includes the dust content in the environment and is particularly important for materials that require a high cleanliness environment.

[0035] Vibration includes vibration from other machinery or equipment, which may affect delicate parts or equipment.

[0036] When implementing control methods, this data is collected by installing various sensors; for example, weight information is collected using load sensors, size and shape can be measured through vision systems such as cameras and laser scanning, material and surface characteristics may be obtained through specific sensing technologies or manual data entry, and environmental parameters are monitored in real time through devices such as temperature and humidity sensors, airflow and dust detectors.

[0037] S2. Based on the collected data, combined with the production environment and material characteristics, the hybrid optimization algorithm is used to calculate the optimal material transportation path.

[0038] Based on the collected data, combined with the production environment and material characteristics, the hybrid optimization algorithm is used to calculate the optimal material transportation path, which includes the following steps: S21. Based on the collected data, build an environmental model and use the Bellman-Ford algorithm to determine the shortest path from the starting point to all vertices.

[0039] Preferably, constructing an environment model based on the collected data and using the Bellman-Ford algorithm to determine the shortest path from the starting point to all vertices includes the following steps: S211. Obtain data on material attributes and environmental parameters, and build an environmental model; S212, setting an initial value for each vertex in the environment model and setting the weight of the edge, while traversing each edge in the environment model; S213, repeat the relaxation operation for each edge, and check whether the known shortest path length from the starting point to the preset vertex is shortened by the current edge; If a shorter path is obtained through the current edge, update the shortest path length and predecessor node of the vertex; S214. Repeat steps S222-S223 until a preset number of iterations is reached, which includes checking all edges so that no shorter path updates are generated in a complete iteration, finding the shortest paths of all vertices, and recording the shortest paths from the starting point to all vertices.

[0040] It should be explained that sensors and data acquisition systems are used to collect detailed data about materials and the environment, such as the weight, size, shape, etc. of the aforementioned materials, as well as the temperature, humidity, etc. of the environment; based on the collected data, a software tool (such as MATLAB or Python) is used to build a graphical representation of the environment model, in which vertices represent locations (such as warehouses, processing areas, etc.), edges represent paths or material flow directions, and edge weights can be distances, costs, or time; the shortest path estimate for each vertex is set to infinity (except for the starting point, which is set to 0) to ensure that the algorithm can iterate from the starting point, and weights are set for each edge based on the actual impact of the materials and the environment. For example, heavier materials or unfavorable environmental conditions may result in higher transportation costs or times. Each edge in the graph is traversed in preparation for relaxation operations.

[0041] Traverse each edge (u, v). If the path to v through u is shorter than the currently known path, update the shortest path length and predecessor node to v. If a shorter path is found, update the shortest path estimate and predecessor node information of the vertex. The number of iterations is usually set to the number of vertices minus 1, because in a graph without negative-weight cycles, the shortest path contains at most all vertices minus one edge. If all relaxation operations in a complete iteration do not update any shortest path, the algorithm terminates and all shortest paths are considered to have been found. The shortest paths from the starting point to all vertices and their lengths are recorded.

[0042] The Bellman-Ford algorithm is a classic dynamic programming algorithm for finding single-source shortest paths in graphs where weights may be negative. It progressively reduces the estimated shortest paths to each vertex by repeatedly relaxing edges until these estimates no longer change.

[0043] S22. Based on the shortest path obtained by the Bellman-Ford algorithm, an initial population of the genetic algorithm is generated, wherein the individuals in the initial population represent the path, and a fitness function is set.

[0044] It should be explained that the shortest paths of all vertices are obtained from the Bellman-Ford algorithm as candidate paths, and these paths are converted into individuals in the genetic algorithm. The node sequence of each path is regarded as a chromosome, where the nodes represent genes. A certain number of these paths are selected to form an initial population, including directly using all the shortest paths, or randomly selecting a part of the paths to ensure the diversity of the population. The fitness function is used to evaluate the quality of the path individuals, and is usually defined based on the cost, length or degree of satisfaction of specific constraints of the path.

[0045] Implementation steps include cost assessment, constraint considerations, and environmental factors; Cost evaluation: Define a fitness function to minimize the path cost. For example, the total cost of a path can be calculated based on the travel time, energy consumption, or other operation costs of each segment in the path.

[0046] Constraint consideration: If there are specific transportation constraints (such as time windows, load limits, etc.), the fitness function also needs to take these factors into account. Paths that do not meet the constraints will receive a lower fitness score.

[0047] Environmental factors: Depending on changes in environmental parameters, the fitness function may need to be adjusted to reflect the impact of environmental changes on path selection.

[0048] S23. Based on the initial population of the genetic algorithm and the set fitness function, the population is optimized through elite crossover and mutation mechanisms, and combined with the shortest path of the Bellman-Ford algorithm to find a path solution with better fitness.

[0049] Preferably, based on the initial population of the genetic algorithm and the set fitness function, the population is optimized through elite crossover and mutation mechanisms, and combined with the shortest path of the Bellman-Ford algorithm, finding a path solution with better fitness includes the following steps: S231, using the shortest path and the generated initial population as inputs of the genetic algorithm; S232. In the genetic algorithm, individuals with high fitness are selected as parents for crossover and random mutation to generate and retain new offspring with the highest fitness and optimize the path population; S233. Perform fitness evaluation on each newly generated path. The fitness function should be designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted and updated. S234, check whether the preset number of iterations is reached, if so, execute the final path selection; and check whether there is already a path that meets the preset performance requirements; S235. Select the path with the highest fitness from the current population as the final optimal solution.

[0050] It should be explained that the shortest path result obtained by the Bellman-Ford algorithm and the initial population generated by the genetic algorithm are used as input data. These data are loaded into the algorithm in the genetic algorithm software or programming environment (such as Python or MATLAB), and fitness proportional selection (such as roulette wheel selection method) is used to select parents. Individuals with high fitness have a higher chance of being selected to participate in the generation of the next generation. Single-point or multi-point crossover methods are applied to select one or more points on the chromosomes of parent individuals for cutting and recombination, which helps to combine the good qualities of the two parents. Genes are randomly selected in the newly generated offspring for random modifications with a small probability. Mutations increase the genetic diversity of the population and help avoid local optimality.

[0051] The fitness of each path is calculated based on the total cost, time or degree of satisfying specific transportation constraints of the path. The fitness function may need to be adjusted to adapt to changing environments or production needs. All individuals in the population are sorted according to the fitness value, the individuals with the highest fitness are retained, and the individuals with lower fitness are eliminated. A preset number of iterations is set as one of the termination conditions of the algorithm. The number of iterations is tracked to determine whether it has reached the set value. After each iteration, check whether there are any paths that have met the preset performance requirements, such as cost, time, etc. After the number of iterations is reached or the existing paths meet the performance requirements, select the path with the highest fitness from the population. This path is used as the optimal solution to the problem for actual logistics transportation.

[0052] How Genetic Algorithms Work: By simulating the process of natural selection, such as survival of the fittest, the solution is continuously optimized. This algorithm is particularly effective in path optimization because it can explore a large number of possible path combinations and find the solution with the best cost or efficiency.

[0053] Preferably, in the genetic algorithm, the steps of selecting individuals with high fitness as parents for crossover and random mutation to generate and retain new offspring with the highest fitness and optimizing the path population include the following steps: S2321. In the genetic algorithm, select a fitness individual that meets the preset requirements from the current path as a parent; S2322, perform a crossover operation with a set probability to generate new offspring individuals; S2323, perform random mutation operations on individuals in the population and introduce new gene combinations; S2324. After the crossover and mutation operations, the individual with the highest fitness in the path is retained.

[0054] It needs to be explained that, first, the fitness of each path individual in the current population is evaluated, and it is calculated according to its cost, time, and the degree to which it meets specific constraints. A selection strategy (such as roulette, tournament selection, etc.) is used to select individuals with high fitness from the current population as parents. Such a selection increases the chance of passing on excellent genes to the next generation. The selected parents are paired and prepared for a crossover operation. One or more crossover points are randomly selected on the paired parent genomes, and then the gene segments between these points are swapped to produce new offspring. The crossover probability controls the frequency of this operation. According to the set mutation probability, individuals in the population are randomly selected for mutation, gene points are randomly selected on the chromosomes of the selected individuals, and these gene points are randomly modified (such as flipping and replacing) to introduce new gene combinations, increase the diversity of the population, and prevent the algorithm from premature convergence. The fitness of all offspring after crossover and mutation is evaluated, and the individuals with the highest fitness are selected from all current individuals and newly generated offspring to form a new population, including retaining a certain proportion of optimal solutions (elite retention strategy) to ensure the inheritance of excellent genes.

[0055] Selection, crossover and mutation: These three operations are the core mechanisms of genetic algorithms, simulating the survival competition, inheritance and mutation in natural selection, which helps the algorithm explore and find the optimal solution in the global search space.

[0056] Fitness evaluation: The design of the fitness function is crucial, as it directly affects the selection pressure of the genetic algorithm and the quality of the offspring. Fitness usually reflects the quality of the solution, such as the cost, speed or other relevant indicators of the path.

[0057] Preferably, fitness evaluation is performed on each newly generated path. The fitness function should be designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted. Updating the population includes the following steps: S2331. Set standards for suitability assessment based on specific production environments and material characteristics; S2332, quantify the performance of each path using the fitness calculation formula; S2333. For each path in the population, calculate the fitness value of each path according to the fitness function; S2334. Sort the paths according to the calculated fitness values; S2335. According to the sorting result, delete the paths that do not meet the preset requirements and update the population; The fitness calculation formula is: ; In the formula, M represents the fitness value of the path; L represents the total length of the path; T stands for time; S represents the safety score; The weight coefficient representing the total length of the path; represents the weight coefficient of time; The weight coefficient representing the safety score.

[0058] S24, repeating steps S22-S24 until a preset condition is met, such as reaching a preset number of iterations or finding a path that meets the requirements, and selecting the path with the highest fitness as the optimal solution.

[0059] It should be explained that the production environment (such as temperature, humidity, vibration) and material characteristics (such as weight, shape, material) are taken into consideration, and the standards for fitness evaluation are set. According to the goals of logistics transportation, key evaluation indicators are selected, such as transportation cost, time, and path safety. According to the set evaluation standards, a formula for calculating fitness is designed. Common formulas can be based on a combination of factors such as the transportation cost, time, and load limit of the path. Ensure that the formula reflects the relative weights of all important factors, and adjust the parameters through testing to adapt them to actual applications. Traverse each path in the current population and calculate the fitness formula according to the fitness formula. The fitness value of each path is calculated by the formula, and the fitness value is associated with the corresponding path individual and recorded for sorting and updating. The paths in the population are sorted according to the fitness value, and the paths with high fitness values ​​are ranked first. The sorting results are checked to ensure that the individuals (paths) with the highest fitness are retained first. According to the fitness value and the requirements of the production environment, the standards for retaining or deleting paths are set, and the paths that do not meet the preset requirements in the sorting results are deleted. The paths with low fitness are eliminated from the population. The population is updated by retaining excellent genes and introducing new paths to form a new path combination for the next round of optimization.

[0060] Fitness function design: The design of the fitness function should comprehensively consider the goals and actual environment of logistics transportation to ensure that it can reflect the comprehensive performance of the path. The weight and parameter settings of the formula are particularly important.

[0061] Fitness sorting and screening: Fitness sorting and screening can ensure that the algorithm gradually converges to the optimal path. Eliminating individuals with low fitness is an important part of the genetic algorithm, which helps prevent the algorithm from falling into the local optimum.

[0062] S3. Use the material demand forecasting model to predict the material demand and time within a preset time in the future based on the optimal material transportation path, and adjust the speed and frequency of the material conveying equipment according to the forecast results and preset production needs.

[0063] Preferably, using the material demand forecasting model, based on the optimal material conveying path, forecasting the material demand and time within a preset time in the future, and adjusting the speed and frequency of the conveying equipment according to the forecast results and preset production requirements includes the following steps: S31, inputting current production data and historical material utilization into a pre-configured material demand forecasting model; S32, setting a predicted time range, and analyzing and predicting material demand and time points within the time range based on the optimal material transportation path; Preferably, setting a predicted time range, analyzing and predicting the material demand and time point in the time range based on the optimal material delivery path includes the following steps: S321, setting the time range of prediction; S322, inputting the optimal material transportation path information into the material demand forecasting model, including factors such as the length of the path, the estimated transportation time, possible delays or material losses, etc., which have an important impact on the forecast of material demand; S323. Analyze the material utilization trend and production rhythm using the material demand forecasting model based on the set time range and integrated data, including current production data, historical material usage, and transportation path characteristics; Predict material demand at a set time in the future based on the analysis results. The forecast results include material demand at each time point, taking into account production plans and possible market changes; S324. List the expected material demand and associated time points at each important time point within the predetermined time range.

[0064] It needs to be explained that the operating data of the current production line is collected, such as production speed, production batch, product type, etc., and historical data is collected, including past material usage records, consumption rates, inventory levels, etc. The collected data is input into the material demand forecasting model, and the data is imported using a database or data interface tool. The start and end time of the forecast is determined, which may be a time range of one month, one quarter, or one year in the future. The defined time range parameters are entered into the forecasting model, and the time window of the model is adjusted. The distance from the supply source to the destination is measured, and the transportation time is estimated based on the path length and traffic conditions, taking into account possible transportation delays and logistics. Material loss ratio, input these path-related information into the prediction model as factors affecting material demand, integrate current production data, historical material usage and optimal path characteristics, use the model to analyze material utilization trends, including consumption rate, replenishment cycle, etc., consider changes in production plans and market demand, analyze the impact of production rhythm on material demand, and generate material demand forecasts for set time points in the future based on the model analysis results. List the expected material demand at each important time point (such as the end of the month, the end of the quarter, holidays, etc.), combine production plans and possible market changes, adjust the forecast results, and ensure the accuracy and operability of the forecast.

[0065] Material demand forecasting models: Use time series analysis, regression analysis, or machine learning models to predict future material demand based on input production and material data.

[0066] Integrate data impact: By taking into account the characteristics of the transportation route, it is possible to more accurately predict the arrival time and quantity of materials, thereby optimizing inventory and replenishment strategies.

[0067] Trend and rhythm analysis: By analyzing production and market trends, forecasting models can provide more flexible and accurate demand forecasts, helping companies prepare materials in advance and avoid out-of-stock or excess inventory.

[0068] S33. Adjust the speed and delivery frequency of the conveying equipment according to the material demand forecast results and the preset production demand; S34. Monitor the material supply situation and the real-time demand of the production line in real time, and optimize the speed and frequency of the conveying equipment based on real-time data feedback.

[0069] What needs to be explained is that the predicted material demand and production plan should be analyzed and predicted, the demand intensity of material supply should be determined, and the speed and delivery frequency of the conveying equipment should be adjusted according to the demand intensity to quickly respond to production peaks and avoid production line shutdowns waiting for materials. When demand is low, the speed and frequency should be reduced to reduce energy consumption and wear. Sensors, data acquisition systems and monitoring software should be used to monitor the material supply and production line demand in real time, and a feedback mechanism should be established. When material supply or production demand changes, real-time data can be quickly fed back to the control end. The control end dynamically adjusts the speed and frequency of the conveying equipment according to the real-time data, increases the speed and frequency when materials are in short supply to ensure that production needs are met, and reduces the speed and frequency when materials are in surplus to avoid inventory backlogs.

[0070] Dynamic adjustment strategy: By real-time monitoring and dynamic adjustment of the speed and frequency of conveying equipment, enterprises can more effectively respond to changes in production demand, reduce fluctuations in material supply, and improve production efficiency and material utilization.

[0071] Feedback control end Real-time monitoring and feedback control systems are important components of automated and intelligent production. They can improve the flexibility and responsiveness of the production process. By reducing the delivery speed and frequency when demand is low, it can not only save energy, but also reduce equipment wear and extend the service life of the equipment.

[0072] S4. During the material transportation process, the quality of the materials is monitored in real time. If defects are found in the materials, they are corrected through the feedback mechanism.

[0073] It needs to be explained that high-precision sensors and monitoring cameras are configured to capture the quality status of materials during the transportation process (such as size, color, surface integrity, etc.) in real time, automatically identify material defects and abnormalities, conduct real-time analysis of the collected data, and evaluate the material quality according to preset quality standards. If any material that deviates from the normal standard is detected, an alarm will be immediately issued, and an automatic feedback mechanism will be established. Once a quality problem is found, the relevant production and quality control departments will be notified immediately to adjust the relevant production parameters or stop the production process to prevent more defective products from being generated. Based on the feedback information, specific corrective measures will be taken, such as adjusting machine settings, replacing raw materials, or readjusting the production process, to improve specific links that affect quality and ensure that the problem does not occur again.

[0074] The self-feedback mechanism continuously monitors the output of a process or system and compares it with established standards or targets. Once a deviation is detected, corrective measures are taken automatically without human intervention.

[0075] Feedback from the feedback mechanism includes monitoring and data collection, data analysis and evaluation, deviation detection and alarm, feedback execution and corrective measures; the specific steps are as follows: Use high-precision sensors and cameras to collect various data of materials during the conveying process, such as size, color, surface integrity, etc. Compare and analyze the collected data with the preset quality standards in real time to evaluate the quality of the materials; When it is detected that the material quality does not meet the standard, an alarm will be automatically issued; Automatically execute feedback measures according to preset processes, such as notifying relevant personnel or directly adjusting machine parameters; Relevant personnel take specific measures based on the feedback information, such as adjusting machine settings, replacing raw materials, etc.

[0076] The following are some examples of the application of the feedback mechanism: Suppose that on a production line, there is a machine responsible for packaging products. In order to ensure the quality of packaging, the machine is equipped with a self-feedback mechanism.

[0077] Sensors on the machine monitor the sealing condition of the packaging box in real time; If the sensor detects that a box is not sealed tightly, it will immediately identify this as a quality deviation, automatically pause the production of the machine, and feedback the problem to the operator; After receiving the feedback, the operator checked and adjusted the machine’s seals; After the adjustment is completed, the machine automatically resumes production while continuing to monitor the sealing quality; Record the deviation and measures taken for future prevention and optimization.

[0078] S5. Use the predictive maintenance model to analyze the speed and frequency of the conveying equipment. Through real-time monitoring and analysis of the equipment operation data, predict the occurrence of failures and implement maintenance measures based on the prediction results.

[0079] Preferably, using a predictive maintenance model to analyze the speed and frequency of the conveying equipment, predicting the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implementing maintenance measures based on the prediction results include the following steps: S51, collecting operation data of conveying equipment; S52, extracting characteristic data of the operation data, and selecting a predictive maintenance model according to the characteristic data; S53, training initial parameters of the predictive maintenance model, and improving prediction accuracy by optimizing the parameters; S54, using the trained predictive maintenance model to predict the operating status of the conveying equipment within a preset time in the future; S55. Analyze the prediction results and identify the failure risks of the conveying equipment.

[0080] It needs to be explained that, through sensors and data acquisition systems, the operating data of the conveying equipment, such as temperature, vibration, current, pressure, etc., are collected in real time, and the collected data are stored in a database or cloud platform to ensure the integrity and accessibility of the data; key feature data, such as peak value, mean value, standard deviation, etc., are extracted from the original data, and a suitable predictive maintenance model is selected according to the characteristics of the extracted feature data; the initial parameters of the model are trained using historical data and the extracted feature data; the model parameters are optimized through cross-validation, grid search and other methods to improve the accuracy of the prediction; the trained and optimized model is used to predict the operating status and potential failures of the conveying equipment within a preset time in the future; the prediction results are fed back to the relevant maintenance team and control; based on the prediction results, possible failure risks and failure types are identified; and according to the risk assessment results, corresponding maintenance or repair plans are formulated.

[0081] Among them, using the trained predictive maintenance model to predict the operating status of the conveying equipment within a preset time in the future includes the following steps: Collect historical operating data of conveying equipment, including temperature, vibration, pressure, speed and other parameters; Use historical data to train predictive maintenance models; Deploy trained and validated predictive maintenance models into production environments; Collect the current operation data of the equipment in real time and input the real-time data into the prediction model; The model predicts the operating status of the equipment within a preset time in the future based on real-time data; Evaluate the prediction results output by the model to determine their credibility; Based on the prediction results and preset thresholds, a decision is made whether to perform maintenance.

[0082] The model uses the data currently being generated by the device to predict how the device will operate in the next period of time. In simple terms, it analyzes the current situation to guess whether the device will work normally in the future or what problems may arise.

[0083] Preferably, analyzing the prediction results and identifying the failure risk of the conveying equipment includes the following steps: S551. Review the operation status data within a preset future time predicted by the predictive maintenance model; S552, setting warning thresholds based on operating standards and historical data, and marking predicted performance that exceeds the warning threshold as a potential risk; S553. Use the seasonal component in the predictive maintenance model to detect abnormal behaviors that do not conform to normal seasonal patterns and identify potential failures; S554. Classify potential faults according to the risk size and nature in the test results, and analyze the causes of predicted abnormalities; S555. For different risk categories identified, formulate response measures including adjusting operating parameters, planning advance maintenance or temporary shutdown inspection, and create a report containing prediction results, risk points and preventive measures.

[0084] It should be explained that the predictive data generated by the predictive maintenance model should be reviewed regularly, including the equipment operating status within a preset time in the future. The trends and patterns of the equipment operating status in the predictive data should be evaluated to facilitate the discovery of potential anomalies. Reasonable warning thresholds should be set based on operating standards and historical data to determine whether the predicted performance exceeds the normal range. When the predicted performance exceeds the threshold, it should be marked as a potential risk for further analysis. The seasonal components in the model should be used to analyze whether the predicted data conforms to the normal seasonal pattern. Abnormal behaviors that do not conform to the normal seasonal pattern should be identified. These anomalies may be signs of potential failures. Potential failures should be classified according to the size and nature of the risk to facilitate different response measures. The causes of the predicted anomalies, such as equipment wear, improper operation or changes in the external environment, should be analyzed. Corresponding response measures should be formulated for different risk categories, such as adjusting operating parameters, planning advance maintenance or temporary shutdown inspections, and creating detailed reports containing prediction results, risk points and preventive measures to facilitate relevant teams to take action.

[0085] S56. Adjust maintenance measures based on the prediction results and take corresponding maintenance measures to prevent failures; S57. Continuously monitor equipment status and update predictive maintenance models.

[0086] It should be explained that the frequency and type of maintenance plan should be adjusted according to the prediction results. For example, if a certain equipment is predicted to have a high risk of failure, key inspections or replacement of wearing parts can be arranged in advance, and targeted maintenance measures such as lubrication, cleaning, adjustment and parts replacement can be implemented to prevent failures from occurring. According to the actual condition of the equipment and the prediction results, maintenance resources should be reasonably allocated, maintenance costs and efficiency should be optimized, and sensors and monitoring systems should be continuously used to track the operating status of the equipment in real time, such as temperature, vibration, current and other data. The results of maintenance execution and the performance data of the equipment after maintenance should be fed back to the prediction model to help the model learn and adapt to changes.

[0087] The corresponding maintenance measures include the following: 1) Preventive maintenance: Based on the predicted failure time, maintenance work can be scheduled in advance to avoid equipment downtime at critical moments.

[0088] Replace wearing parts even if they have not yet completely failed.

[0089] 2) Adjust the maintenance plan: Modify maintenance schedules to schedule maintenance work before predicted failures occur.

[0090] Adjust maintenance priorities based on the severity of predicted failures.

[0091] 3) Resource allocation: Ensure necessary spare parts and tools are available to enable quick maintenance when required.

[0092] Assign specialized technicians and ensure they are available during forecasted maintenance windows.

[0093] 4) Operational changes: If it is predicted that equipment is more likely to fail under certain conditions, change operating procedures to avoid those conditions.

[0094] Reduce equipment load or run time that could cause failure.

[0095] 5) Monitoring and detection: Increase the frequency of monitoring of critical components to more accurately track their degradation.

[0096] Use more advanced detection techniques to identify potential signs of failure. Continuously monitoring equipment status and updating predictive maintenance models involves the following steps: Evaluate the performance of the current forecasting model to determine if it needs to be updated; Use methods such as cross-validation to check the accuracy and generalization ability of the model; Retrain the model using the newly integrated dataset and adjust the model structure and parameters based on the results of the model evaluation; Use an independent test set to validate the updated model performance and ensure the model’s accuracy and reliability when predicting new data.

[0097] Deploy updated, validated models into production, replacing older models and ensuring real-time monitoring uses the latest predictive technology.

[0098] A specific example is as follows: the factory has an automated conveying equipment for transporting important automotive parts, such as engine covers, doors and chassis. The weight, size and material of the parts vary, and the requirements for the conveying equipment are also different.

[0099] The factory has installed a variety of sensors, such as load cells, vision systems, and temperature and humidity sensors to collect data on material attributes (weight, size, shape, material, surface characteristics) and environmental parameters (temperature, humidity, airflow, dust, vibration).

[0100] Using the collected data and the current production environment, a hybrid optimization algorithm (combining Bellman-Ford algorithm and genetic algorithm) is used to calculate the optimal material transportation path. This ensures cost-effective and timely material transportation.

[0101] Using predictive maintenance models, equipment operating data, including temperature, vibration, and current, is regularly reviewed to identify potential failure risks.

[0102] When the model predicts that a piece of equipment may fail, the maintenance plan can be adjusted in advance, such as increasing lubrication or replacing worn parts, to prevent equipment failure and production stagnation.

[0103] Through the above examples, applying advanced data analysis and predictive maintenance models to conveying equipment can not only improve operational efficiency but also significantly reduce operational risks.

[0104] According to another embodiment of the present invention, Figure 2 As shown, a material conveying equipment control system for workpiece logistics transportation is also provided, the system comprising: a data collection module 1, a path optimization module 2, a material demand prediction module 3, a material quality monitoring module 4 and a predictive maintenance module 5; The data collection module 1, the route optimization module 2, the material demand prediction module 3, the material quality monitoring module 4 and the predictive maintenance module 5 are connected in sequence; Data collection module 1, used to collect data on material attributes and environmental parameters; Path optimization module 2, used to calculate the optimal material transportation path based on the collected data, combined with the production environment and material characteristics, using a hybrid optimization algorithm; The material demand forecasting module 3 is used to use the material demand forecasting model to forecast the material demand and time within a preset time in the future based on the optimal material conveying path, and adjust the speed and frequency of the conveying equipment according to the forecast results and preset production requirements; The material quality monitoring module 4 is used to monitor the quality of the material in real time during the material transportation process. If any defects are found in the material, corrections are made through the feedback mechanism; The predictive maintenance module 5 is used to analyze the speed and frequency of the conveying equipment using the predictive maintenance model, predict the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implement maintenance measures based on the prediction results.

[0105] Figure 3An embodiment of a computer device of the present invention is shown. The computer device can be a server. The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the processor of the computer device is used to provide computing and control capabilities, the memory of the computer device includes a non-volatile storage medium and an internal memory, the non-volatile storage medium stores an operating system, a computer program and a database, the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium, the database of the computer device is used to store static information and dynamic information data, the network interface of the computer device is used to communicate with an external terminal through a network connection, and the computer program is executed by the processor to implement the steps in the above method embodiment.

[0106] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0108] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0109] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0110] In summary, with the help of the above-mentioned technical scheme of the present invention, the environmental model constructed by the Bellman-Ford algorithm can flexibly adapt to different material characteristics and production environments, can process graphs with negative weights, and provide practical and feasible path solutions for material transportation; the genetic algorithm is used to optimize path selection, and can further explore possible path combinations based on the shortest path determined by the Bellman-Ford algorithm to find lower-cost or more efficient paths; by accurately adjusting the fitness function and considering multiple factors such as cost, time and safety, the algorithm can comprehensively evaluate and select the optimal logistics path; as environmental conditions change, the genetic algorithm can quickly adapt to new transportation needs and constraints through iterative evolution to ensure the continuous and stable operation of logistics equipment; by accurately predicting material demand and time, the present invention can optimize material inventory management and replenishment strategies, avoid material waste and out-of-stock situations, and adjust the operating speed and Frequency, can more flexibly respond to changes in production demand, timely adjustment of the speed and frequency of conveying equipment can not only save energy consumption according to actual demand, but also reduce excessive use and wear of equipment, extend the service life of equipment, by real-time monitoring of material supply and production line needs, timely adjustment of conveying equipment parameters, can ensure that materials are in place in time, reduce production interruptions and downtime, thereby improving overall production efficiency; the present invention through real-time monitoring and analysis of equipment operation data, predictive maintenance model can identify possible failures in advance, so that the maintenance team can take preventive measures before the failure occurs, regular and targeted maintenance can effectively extend the service life of equipment, predictive maintenance allows enterprises to formulate maintenance plans according to the actual operating conditions of the equipment, rather than relying on fixed maintenance schedules, by avoiding unnecessary maintenance and reducing the need for emergency repairs, predictive maintenance helps to significantly reduce overall maintenance costs, the stability and reliability of equipment operation status directly affects production efficiency.

[0111] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A material conveying equipment control method for workpiece logistics transportation, characterized in that: The material conveying equipment control method comprises the following steps: S1. Collect data on material attributes and environmental parameters; S2. Based on the collected data, combined with the production environment and material characteristics, the hybrid optimization algorithm is used to calculate the optimal material transportation path; S3. Use the material demand forecasting model to forecast the material demand and time within a preset time in the future based on the optimal material transportation path, and adjust the speed and frequency of the material conveying equipment according to the forecast results and preset production needs; S4. During the material transportation process, the quality of the materials is monitored in real time. If defects are found in the materials, they are corrected through the feedback mechanism; S5. Use the predictive maintenance model to analyze the speed and frequency of conveying equipment, predict the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implement maintenance measures based on the prediction results; The method of calculating the optimal material transportation path based on the collected data, combined with the production environment and material characteristics, using a hybrid optimization algorithm includes the following steps: S21. Based on the collected data, build an environmental model and use the Bellman-Ford algorithm to determine the shortest path from the starting point to all vertices; S22, based on the shortest path obtained by the Bellman-Ford algorithm, generating an initial population of the genetic algorithm, wherein the individuals in the initial population represent the path, and setting a fitness function; S23, based on the initial population of the genetic algorithm and the set fitness function, the population is optimized through elite crossover and mutation mechanisms, and combined with the shortest path of the Bellman-Ford algorithm to find a path solution with better fitness; S24. Repeat steps S22-S24 until the preset conditions are met, and select the path with the highest fitness as the optimal solution.

2. A material conveying equipment control method for workpiece logistics transportation according to claim 1, characterized in that: The process of constructing an environment model based on the collected data and determining the shortest path from the starting point to all vertices using the Bellman-Ford algorithm includes the following steps: S211. Obtain data on material attributes and environmental parameters, and build an environmental model; S212, setting an initial value for each vertex in the environment model and setting the weight of the edge, while traversing each edge in the environment model; S213, repeating the relaxation operation for each edge, and checking whether the known shortest path length from the starting point to the preset vertex is shortened by the current edge; If a shorter path is obtained through the current edge, update the shortest path length and predecessor node of the vertex; S214, repeating steps S222-S223 until a preset number of iterations is reached, finding the shortest paths to all vertices, and recording the shortest paths from the starting point to all vertices.

3. A material conveying equipment control method for workpiece logistics transportation according to claim 2, characterized in that: The initial population based on the genetic algorithm and the set fitness function, optimizing the population through elite crossover and mutation mechanism, and combining the shortest path of the Bellman-Ford algorithm to find a path solution with better fitness includes the following steps: S231, using the shortest path and the generated initial population as inputs of the genetic algorithm; S232. In the genetic algorithm, individuals with high fitness are selected as parents for crossover and random mutation to generate and retain new offspring with the highest fitness and optimize the path population; S233. Perform fitness evaluation on each newly generated path. The fitness function should be designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted and updated. S234, check whether the preset number of iterations is reached, if so, execute the final path selection; and check whether there is already a path that meets the preset performance requirements; S235. Select the path with the highest fitness from the current population as the final optimal solution.

4. A material conveying equipment control method for workpiece logistics transportation according to claim 3, characterized in that: In the genetic algorithm, individuals with high fitness are selected as parents for crossover and random mutation to generate and retain new offspring with the highest fitness, and the path population is optimized, including the following steps: S2321. In the genetic algorithm, select a fitness individual that meets the preset requirements from the current path as a parent; S2322, perform a crossover operation with a set probability to generate new offspring individuals; S2323, perform random mutation operations on individuals in the population and introduce new gene combinations; S2324. After the crossover and mutation operations, the individual with the highest fitness in the path is retained.

5. A material conveying equipment control method for workpiece logistics transportation according to claim 4, characterized in that: The fitness function is designed according to the requirements of the specific production environment and material characteristics. The fitness value of each individual is calculated according to the fitness function, and the population is sorted. The population update includes the following steps: S2331. Set standards for suitability assessment based on specific production environments and material characteristics; S2332, quantify the performance of each path using the fitness calculation formula; S2333. For each path in the population, calculate the fitness value of each path according to the fitness function; S2334. Sort the paths according to the calculated fitness values; S2335. According to the sorting result, delete the paths that do not meet the preset requirements and update the population; Wherein, the fitness calculation formula is: ； In the formula, M represents the fitness value of the path; L represents the total length of the path; T stands for time; S represents the safety score; The weight coefficient representing the total length of the path; represents the weight coefficient of time; The weight coefficient representing the safety score.

6. A material conveying equipment control method for workpiece logistics transportation according to claim 1, characterized in that: The method of using the material demand prediction model to predict the material demand and time within a preset time in the future based on the optimal material transportation path, and adjusting the speed and frequency of the material transportation equipment according to the prediction results and the preset production demand includes the following steps: S31, inputting current production data and historical material utilization into a pre-configured material demand forecasting model; S32, setting a predicted time range, and analyzing and predicting material demand and time points within the time range based on the optimal material transportation path; S33. Adjust the speed and delivery frequency of the conveying equipment according to the material demand forecast results and the preset production demand; S34. Monitor the material supply situation and the real-time demand of the production line in real time, and optimize the speed and frequency of the conveying equipment based on real-time data feedback.

7. A material conveying equipment control method for workpiece logistics transportation according to claim 6, characterized in that: The setting of the predicted time range, based on the optimal material transportation path, analyzing and predicting the material demand and time point in the time range includes the following steps: S321, setting the time range of prediction; S322, inputting the optimal material transportation path information into the material demand prediction model; S323. Analyze the material utilization trend and production rhythm based on the set time range and integrated data using the material demand forecasting model; Predict material demand at a set time point in the future based on analysis results; S324. List the expected material demand and associated time points at each important time point within the predetermined time range.

8. A material conveying equipment control method for workpiece logistics transportation according to claim 1, characterized in that: The method of using a predictive maintenance model to analyze the speed and frequency of conveying equipment, predicting the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implementing maintenance measures based on the prediction results includes the following steps: S51, collecting operation data of conveying equipment; S52, extracting characteristic data of the operation data, and selecting a predictive maintenance model according to the characteristic data; S53, training initial parameters of a predictive maintenance model; S54, using the trained predictive maintenance model to predict the operating status of the conveying equipment within a preset time in the future; S55. Analyze the prediction results and identify the failure risk of the conveying equipment; S56. Adjust maintenance measures based on the prediction results and take corresponding maintenance measures; S57. Continuously monitor equipment status and update predictive maintenance models.

9. A material conveying equipment control method for workpiece logistics transportation according to claim 8, characterized in that: The analysis and prediction results to identify the failure risk of the conveying equipment include the following steps: S551. Review the operation status data within a preset future time predicted by the predictive maintenance model; S552, setting warning thresholds based on operating standards and historical data, and marking predicted performance that exceeds the warning threshold as a potential risk; S553. Use the seasonal component in the predictive maintenance model to detect abnormal behaviors that do not conform to normal seasonal patterns and identify potential failures; S554. Classify potential faults according to the risk size and nature in the test results, and analyze the causes of predicted abnormalities; S555. For different risk categories identified, formulate response measures including adjusting operating parameters, planning advance maintenance or temporary shutdown inspection, and create a report containing prediction results, risk points and preventive measures.

10. A material conveying equipment control system for workpiece logistics transportation, used to implement the material conveying equipment control method for workpiece logistics transportation according to any one of claims 1 to 9, characterized in that: The system includes: data collection module, route optimization module, material demand forecasting module, material quality monitoring module and predictive maintenance module; The data collection module, the route optimization module, the material demand prediction module, the material quality monitoring module and the predictive maintenance module are connected in sequence; Data collection module, used to collect data on material attributes and environmental parameters; The path optimization module is used to calculate the optimal material transportation path based on the collected data, combined with the production environment and material characteristics, using a hybrid optimization algorithm; The material demand forecasting module is used to use the material demand forecasting model to forecast the material demand and time within a preset time in the future based on the optimal material transportation path, and adjust the speed and frequency of the material conveying equipment according to the forecast results and preset production needs; The material quality monitoring module is used to monitor the quality of materials in real time during the material transportation process. If defects are found in the materials, they will be corrected through the feedback mechanism; The predictive maintenance module is used to analyze the speed and frequency of conveying equipment using a predictive maintenance model, predict the occurrence of failures through real-time monitoring and analysis of equipment operation data, and implement maintenance measures based on the prediction results.

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