An intelligent control method and system for AGV operation

Abstract

The present invention discloses an intelligent control method and system for AGV operation, relating to the field of automated control technology. The method comprises: analyzing the urgency of the current cargo handling task based on the cargo handling task requirements of a logistics warehousing and dispatching center to determine whether the current cargo handling task is in an immediate execution state and issuing an emergency execution warning instruction; selecting a preferentially matched AGV as the target AGV for the current cargo handling task; collecting the target AGV's operating speed and vehicle weight information while performing the current cargo handling task to predict the target AGV's remaining battery life (Csh) after performing the current cargo handling task; determining whether the target AGV performing the current cargo handling task is operating safely and efficiently, generating and executing corresponding level control instructions. The system includes a warning module, a matching module, a prediction module, and a control module.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to an AGV (Automated Guided Vehicle) vehicle operation intelligent control method and system. Background Art

[0002] With the rapid development of information technology and automated control technology, the application of automated control systems in industrial manufacturing, warehouse management and other fields has gradually matured, providing a solid foundation for improving production efficiency and optimizing resource allocation. In particular, in the field of intelligent logistics, the introduction of intelligent technology has significantly improved the efficiency of material handling and sorting, and promoted the transformation and upgrading of traditional logistics to intelligent logistics. In the field of intelligent logistics, AGV (automated guided vehicle) as an important material transportation equipment has gradually become a core component of modern warehousing and production systems with its high efficiency, low energy consumption and intelligent characteristics. However, in intelligent logistics scenarios, AGV operation control still faces many technical challenges, including task scheduling optimization, resource utilization improvement and operational safety assurance needs.

[0003] Although the existing AGV operation control methods have achieved some success in practical applications, there are still some shortcomings. First, the existing AGV operation control methods often rely on traditional task scheduling and path planning. These methods are usually based on fixed rules and are difficult to cope with complex and changeable logistics scenarios. Among them, the existing methods have obvious deficiencies in the dynamic adjustment of task priorities, the optimization of task and AGV matching efficiency, and the management of AGV battery status. As a result, AGVs may experience low scheduling efficiency, resource waste, and task execution failure in high-load task scenarios. In addition, for the management of battery status, traditional methods often lack the ability to be based on prediction and real-time analysis, making it difficult to reasonably evaluate power usage before task execution, which may lead to task interruption and excessive discharge of AGV batteries. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an AGV operation intelligent control method and system, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an AGV trolley operation intelligent control method, comprising the following steps;

[0006] S1. Analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction;

[0007] S2. After receiving the emergency execution warning instruction, select the AGV with priority matching as the target AGV to perform the current cargo handling task;

[0008] S3, collecting the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, to predict the remaining power value Csh of the target AGV after performing the current cargo handling task;

[0009] S4. Compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions.

[0010] Preferably, the specific steps of S1 include:

[0011] S11. According to the cargo handling task requirements of the logistics warehousing dispatch center, task information status of the cargo handling task is collected to obtain relevant task status data information, wherein the relevant task status data information includes the task deadline time point Tjz, the handling path length Lby, and the handled cargo weight Nby;

[0012] S12. Analyze the relevant task status data information and construct a task urgency coefficient Xjd after dimensionless processing. The task urgency coefficient Xjd is obtained by the following formula:

[0013] ;

[0014] Where Tjz is the task deadline, T is the current time, Lby is the length of the transport path, Nby is the weight of the transported goods, and Nby is the weight of the transported goods. max Indicates the maximum load of the AGV car, 、 and Both are expressed as weight values, and A is expressed as a correction constant.

[0015] Preferably, the specific step S1 further includes:

[0016] S13. Preset an urgency threshold J, compare and analyze the task urgency coefficient Xjd with the preset urgency threshold J to determine whether the current cargo handling task is in an immediate execution state, and issue an emergency execution warning instruction. The specific content is as follows:

[0017] If the task urgency coefficient Xjd ≥ the urgency threshold J, it means that the current cargo handling task is in an immediate execution state, and an emergency execution warning instruction is issued;

[0018] If the task urgency coefficient Xjd is less than the urgency threshold J, it means that the current cargo handling task is not in an immediate execution state, and no additional warning instructions are issued at this time.

[0019] Preferably, the specific steps of S2 include:

[0020] S21. After receiving the issued emergency execution warning instruction, collect the position status information of the starting point of the cargo handling task to obtain the task starting point location (X, Y). Then, with the task starting point location (X, Y) of the cargo handling task as the radiation center, collect the position status information of the AGVs within the radiation area to obtain the relevant AGV position status information. The relevant AGV position status information includes the standby point location (x, y) of each AGV.

[0021] S22, according to the task starting point position (X, Y) and the relevant AGV car position status information, and combined with the Euclidean geometry algorithm, obtain the task point distance value Lrw of several AGV cars, and use the task point distance value Lrw of the i-th AGV car i For example, it is obtained in the following way:

[0022] ;

[0023] Where X represents the X-axis coordinate value of the mission starting point. It is represented as the X-axis coordinate value of the spatial position of the i-th AGV car, and Y is represented as the Y-axis coordinate value of the task starting point. It is expressed as the Y-axis coordinate value of the spatial position of the i-th AGV.

[0024] Preferably, the specific step S2 further includes:

[0025] S23, based on the task point distance value Lrw of the i-th AGV car i In this way, the task point distance values ​​Lrw of several AGVs are obtained respectively, and the existing power status information of the AGVs within the radiation area is collected to obtain the existing power status information of the relevant AGVs, wherein the existing power status information of the relevant AGVs includes the existing power value Cxc of each AGV;

[0026] S24, the mission point distance values ​​Lrw of several AGVs are associated with the existing power status information of the relevant AGVs, and after dimensionless processing, the priority matching coefficients Xyx of several AGVs are constructed, and the priority matching coefficient Xyx of the i-th AGV is used. i For example, it is obtained in the following way:

[0027] ;

[0028] Where Lrw i Expressed as the mission point distance value of the i-th AGV car, Cxc i Represents the current power value of the i-th AGV car, where and are expressed as weight values.

[0029] Preferably, the specific step S2 further includes:

[0030] S25, based on obtaining the priority matching coefficient Xyx of the i-th AGV car i The priority matching coefficients Xyx of several AGVs are obtained respectively to construct a priority matching coefficient set, and the feature extraction of the constructed priority matching coefficient set is performed to obtain the maximum priority matching coefficient Xyx in the priority matching coefficient set. max , and prioritize the maximum matching coefficient Xyx max The corresponding AGV serves as the target AGV for executing the current cargo handling task.

[0031] Preferably, the specific steps of S3 include:

[0032] S31, based on the maximum value of the priority matching coefficient Xyx max The corresponding AGV serves as the target AGV for the current cargo handling task. The target AGV collects its running speed and weight information during the task, and obtains relevant running status data, including the running speed Vyx and weight Gzz of the target AGV.

[0033] S32. Analyze the relevant operating status data information of the target AGV and the relevant task status data information corresponding to the current cargo handling task being executed, and associate them with the existing power value Cxc of the target AGV. After dimensionless processing, obtain the remaining power prediction value Csh. The remaining power prediction value Csh is obtained using the following formula;

[0034] ;

[0035] In the formula, Gzz represents the vehicle weight, Nby represents the weight of the transported cargo, Lby represents the length of the transport path, and Vyx represents the running speed. It is represented as the friction coefficient of the road surface, K is represented as the energy consumption coefficient of the running speed, Expressed as the electric energy conversion coefficient, where It is expressed as the energy required by the target AGV to overcome friction when transporting goods. It is expressed as the energy consumed by the target AGV vehicle's handling speed.

[0036] Preferably, the specific steps of S4 include:

[0037] S41. Using the safe power value of the target AGV battery as the remaining power safety threshold C, the remaining power prediction value Csh is compared with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, and generates a corresponding level control instruction. The specific contents are as follows:

[0038] If the remaining power prediction value Csh is less than the remaining power safety threshold C, it means that the target AGV performing the current cargo handling task is not operating safely and effectively. This means that the current power value Cxc of the target AGV is insufficient to complete the current cargo handling task, and that performing the current cargo handling task will damage the target AGV battery, and a first-level control instruction is issued.

[0039] If the remaining power prediction value Csh ≥ the remaining power safety threshold C, it means that the target AGV car that is performing the current cargo handling task is operating safely and effectively, indicating that the existing power value Cxc of the target AGV car is sufficient to complete the current cargo handling task, and that performing the current cargo handling task will not cause damage to the target AGV car battery, and a secondary control instruction is issued.

[0040] Preferably, the specific step S4 further includes:

[0041] S42. Based on the received primary control instructions and secondary control instructions, corresponding control measures are executed. The specific contents are as follows:

[0042] When a first-level control instruction is received, the following is executed: by sending a task abort signal, the target AGV stops executing the current cargo handling task, and sends a signal to the warehouse dispatch center that the target AGV is low on battery, requesting immediate arrangement for emergency charging at the nearest charging station; based on the AGV corresponding to the priority matching coefficient Xyx, the AGV with sufficient existing battery value Cxc is selected to take over the current cargo handling task;

[0043] When a secondary control instruction is received, the execution content is as follows: by sending a task execution signal, the target AGV car is selected to perform the current cargo handling task, and the power changes of the target AGV car are monitored in real time; after the task is completed, according to the predicted value Csh of the remaining power of the target AGV car, the nearest charging station is selected for charging, and at the same time, the task completion status information and charging time of the target AGV car are fed back to the logistics warehousing dispatching center, and combined with the charging time of the AGV car, it is arranged to participate in subsequent cargo handling tasks.

[0044] An AGV trolley operation intelligent control system, including an early warning module, a matching module, a prediction module and a control module;

[0045] The warning module is used to analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction;

[0046] The matching module is used to screen out the preferentially matched AGV as the target AGV for executing the current cargo handling task after receiving the emergency execution warning instruction;

[0047] The prediction module is used to collect the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, so as to predict the remaining power value Csh of the target AGV after performing the current cargo handling task;

[0048] The control module is used to compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV vehicle performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions.

[0049] The present invention provides an AGV intelligent control method and system, which has the following beneficial effects:

[0050] (1) An intelligent control method and system for AGV operation optimizes the operation efficiency and reliability of AGV in intelligent logistics scenarios from the aspects of task scheduling, resource matching and operation safety, and has a significant overall effect; by introducing the task urgency coefficient Xjd, it can dynamically judge the urgency of the task according to the task deadline Tjz, the length of the transportation path Lby and the weight of the transported goods Nby, and accurately identify the tasks that need to be executed immediately in combination with the urgency threshold J; this function ensures that high-priority tasks can be responded to in a timely manner in complex multi-task scenarios, avoiding the task delay problem caused by fixed rules in traditional task scheduling. At the same time, the construction of the priority matching coefficient Xyx realizes the precise matching of tasks and AGVs. By comprehensively evaluating the task point distance value Lrw and the existing power value Cxc of each AGV, the target AGV suitable for executing the task can be quickly screened out, thereby improving the rationality of task allocation and scheduling efficiency. During operation, The power consumption of the target AGV is predicted, and combined with the relevant operating status data information collected in real time, the remaining power prediction value Csh after the task is completed is predicted; this function effectively avoids the risk of task interruption and over-discharge of the AGV battery due to the lack of power management in traditional methods; further, by comparing the remaining power prediction value Csh with the safe power threshold C, a control instruction of the corresponding level is generated. When the existing power value Cxc of the target AGV is insufficient to complete the current cargo handling task, emergency charging and task reallocation can be triggered in time; when the existing power value Cxc of the target AGV is sufficient, priority is given to ensuring the smooth completion of the task, and at the same time, the charging of the equipment and the scheduling of subsequent tasks are planned; overall, the present invention greatly optimizes resource utilization while improving task execution efficiency, and extends the battery life of the AGV through an intelligent power prediction and management mechanism, and also improves the completion efficiency of cargo handling tasks, providing reliable technical support for the efficient operation of intelligent logistics.

[0051] (2) By introducing the task urgency coefficient Xjd and the priority matching coefficient Xyx, the intelligent level of AGV operation is improved; the task urgency coefficient Xjd is calculated based on the task deadline time Tjz, the transportation path length Lby and the transportation cargo weight Nby, which can accurately assess the urgency of the task and dynamically issue emergency execution warning instructions in combination with the preset urgency threshold J, thereby ensuring that high-priority tasks can be processed in a timely manner; at the same time, the priority matching coefficient Xyx is constructed by combining the spatial position and power status of the AGV through the Euclidean geometry algorithm, realizing the intelligent matching of tasks and AGVs; by extracting the features of the priority matching coefficients Xyx of several AGVs, the appropriate AGV can be dynamically selected to perform the task, effectively avoiding the scheduling delay problem caused by equipment incompatibility in the traditional method, and improving the matching efficiency of tasks and AGVs.

[0052] (3) Based on the prediction and real-time analysis of the remaining power status of the target AGV, the power usage is reasonably evaluated before the task is executed, and the dual optimization of the target AGV's operating safety and power management is achieved; during the task execution, the relevant operating status data information of the target AGV can be collected in real time, including the target AGV's operating speed Vyx and body weight Gzz, and combined with the relevant task status data information and the existing power value Cxc corresponding to the current cargo handling task, the energy required for the target AGV to overcome friction and the energy consumed by the handling speed are analyzed to obtain the remaining power prediction value Csh; by comparing the remaining power prediction value Csh with the battery's safe power threshold C, it is possible to judge in advance whether the target AGV performing the current cargo handling task is operating safely and effectively, thereby avoiding the risk of task interruption due to insufficient power. ; When the existing power value Cxc of the target AGV car is insufficient to complete the current cargo handling task, and executing the current cargo handling task will cause damage to the battery of the target AGV car, the first-level control instruction is used to trigger an emergency charging request and reallocate the task to ensure that the high-priority task can be completed smoothly; when the existing power value Cxc of the target AGV car is sufficient, the second-level control instruction is used to select the target AGV car to execute the current cargo handling task, and after the task is completed, the nearest charging station is selected for charging. Combined with the charging time of the AGV car, it is arranged to participate in subsequent cargo handling tasks; compared with traditional power management methods that lack predictive capabilities, this method not only significantly reduces the risk of over-discharge of the battery of the target AGV car performing the task, but also extends the service life of the equipment through the intelligent power evaluation mechanism, effectively ensuring the operation continuity and stability of the entire logistics system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of an intelligent control method for AGV operation according to the present invention;

[0054] Figure 2 This is a block diagram of an intelligent control system for AGV operation according to the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 , the present invention provides an AGV trolley operation intelligent control method, comprising the following steps;

[0058] S1. Analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction;

[0059] S2. After receiving the emergency execution warning instruction, select the AGV with priority matching as the target AGV to perform the current cargo handling task;

[0060] S3, collecting the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, to predict the remaining power value Csh of the target AGV after performing the current cargo handling task;

[0061] S4. Compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions.

[0062] In this implementation, by constructing the task urgency coefficient Xjd, the priority matching coefficient Xyx and the remaining power prediction value Csh, the multiple technical deficiencies of the AGV operation control in the traditional intelligent logistics scenario are effectively solved, especially in terms of task scheduling optimization, resource utilization efficiency improvement and operation safety assurance. The dynamic construction and analysis of the task urgency coefficient Xjd can accurately judge the urgency of the task, and issue an emergency execution warning instruction based on the preset urgency threshold J, thereby ensuring that high-priority tasks can be responded to quickly, which effectively makes up for the problem of insufficient dynamic adjustment of task priority in the traditional scheduling method. In addition, by constructing the priority matching coefficient Xyx and extracting the maximum value, the precise matching between the task and the AGV is achieved, and the task is assigned to the target AGV with sufficient power and close distance, effectively solving the problem of insufficient dynamic adjustment of task priority in the traditional scheduling method. It solves the problem of inefficient matching of tasks and equipment resources in traditional logistics scenarios, and effectively avoids resource waste and task delays caused by improper equipment selection; more importantly, through the introduction and real-time analysis of the remaining power prediction value Csh, the power consumption of the AGV can be accurately predicted before the task is executed, and by comparing it with the remaining power safety threshold C, it can be judged whether the task can be completed safely and effectively. This function effectively solves the problem of task interruption and battery over-discharge caused by the lack of power prediction and real-time analysis capabilities in traditional methods; in summary, through intelligent task scheduling and resource matching optimization, not only the operating efficiency of AGV carts in complex logistics scenarios is improved, but also the safety assurance mechanism of power management significantly improves the reliability of the logistics system and the service life of AGV carts, providing support for the efficient and safe operation of modern intelligent logistics.

[0063] Example 2

[0064] Please refer to Figure 1 , specifically: S1 specific steps include:

[0065] S11. According to the cargo handling task requirements of the logistics warehousing dispatch center, task information status of the cargo handling task is collected to obtain relevant task status data information, wherein the relevant task status data information includes the task deadline time point Tjz, the handling path length Lby, and the handled cargo weight Nby;

[0066] It should be noted that the task deadline Tjz is generated by the logistics warehousing scheduling center based on the order delivery time and production plan. It indicates the time point when the task is completed and is mainly used to evaluate the time urgency of the task; the transportation path length Lby is calculated by the path planning system in the warehousing environment and generated in combination with the coordinates of the cargo departure point and destination to evaluate the time and energy consumption required for task execution; the weight of the transported cargo Nby is usually obtained by an automatic weighing device and is used to evaluate the impact of the task on the AGV load capacity and energy consumption; these parameters jointly determine the urgency of the task and the resource matching strategy, thereby providing an important basis for task priority sorting.

[0067] S12. Analyze the relevant task status data information and construct a task urgency coefficient Xjd after dimensionless processing. The task urgency coefficient Xjd is obtained by the following formula:

[0068] ;

[0069] Where Tjz is the task deadline, T is the current time, Lby is the length of the transport path, Nby is the weight of the transported goods, and Nby is the weight of the transported goods. max Indicates the maximum load of the AGV car, 、 and Both are expressed as weight values, and A is expressed as a correction constant.

[0070] It should be noted that the task urgency coefficient Xjd is an indicator that comprehensively evaluates the urgency of the cargo handling task. It is constructed based on the task deadline Tjz, the handling path length Lby, and the cargo weight Nby state parameters through dimensionless processing. Its function is to quantify the priority of the task and judge the urgency and execution order of the task. When the task urgency coefficient Xjd is high, it indicates that the task completion time is urgent, the path is long, and the cargo weight is heavy, and a suitable AGV needs to be scheduled to perform the task first. When the task urgency coefficient Xjd is low, the scheduling can be appropriately delayed. The task urgency coefficient Xjd is a key indicator for realizing dynamic adjustment of task priority, and plays an important role in ensuring the rational allocation of resources, improving scheduling efficiency and system responsiveness.

[0071] Specifically, the steps of S1 also include:

[0072] S13. Preset an urgency threshold J, compare and analyze the task urgency coefficient Xjd with the preset urgency threshold J to determine whether the current cargo handling task is in an immediate execution state, and issue an emergency execution warning instruction. The specific content is as follows:

[0073] If the task urgency coefficient Xjd ≥ the urgency threshold J, it means that the current cargo handling task is in an immediate execution state, and an emergency execution warning instruction is issued;

[0074] If the task urgency coefficient Xjd is less than the urgency threshold J, it means that the current cargo handling task is not in an immediate execution state, and no additional warning instructions are issued at this time.

[0075] In this implementation, by constructing the task urgency coefficient Xjd and the preset urgency threshold J, an accurate assessment of the urgency and priority of the cargo handling task is achieved, which effectively solves the problem of lack of dynamic task priority adjustment capability in traditional intelligent logistics systems; the construction of the task urgency coefficient Xjd comprehensively considers the multi-dimensional parameters of task deadline Tjz, handling path length Lby and handled cargo weight Nby, and further refines the analysis of task status through dimensionless processing and weight distribution to ensure the comprehensiveness and accuracy of urgency assessment; compared with traditional scheduling methods that rely on fixed rules, this method can dynamically adjust the priority according to task attributes in complex and changeable logistics scenarios, especially in the face of high-load, multi-task parallel operating environments, and can quickly identify critical tasks that need to be executed immediately to ensure timely delivery. efficient connection of logistics links; in addition, by comparing and analyzing with the preset urgency threshold J, the intelligent level of task scheduling is further enhanced; when the task urgency coefficient Xjd exceeds the urgency threshold J, an emergency execution warning instruction is issued in time, which buys valuable time for subsequent task scheduling and resource matching, and effectively avoids production and logistics interruptions caused by task delays; for non-urgent tasks, no additional warning instructions are issued, thereby reducing the load of the scheduling system and optimizing the utilization efficiency of scheduling resources; overall, the real-time, accurate and intelligent performance of task scheduling priority evaluation are outstanding, which can not only effectively improve resource utilization efficiency, but also ensure the operational stability and reliability of the logistics system in multi-task scenarios, and effectively solve the technical bottleneck of insufficient dynamic priority adjustment capabilities in existing methods.

[0076] Example 3

[0077] Please refer to Figure 1 , specifically: S2 specific steps include:

[0078] S21. After receiving the issued emergency execution warning instruction, collect the position status information of the starting point of the cargo handling task to obtain the task starting point location (X, Y). Then, with the task starting point location (X, Y) of the cargo handling task as the radiation center, collect the position status information of the AGVs within the radiation area to obtain the relevant AGV position status information. The relevant AGV position status information includes the standby point location (x, y) of each AGV.

[0079] It should be noted that the task starting point position (X, Y) and the standby point position (x, y) of each AGV are usually obtained through the positioning system (GPS positioning technology) in the logistics warehousing environment; the task starting point position (X, Y) refers to the starting position of the cargo handling task, which is determined by the scheduling system through the coordinate information when the task is assigned, and is used to plan the starting point of the cargo transportation route; the standby point position (x, y) of each AGV refers to the current position of the AGV in the standby state, which is uploaded to the scheduling system in real time by the built-in positioning module of the vehicle. By collecting this position information, the nearby AGV vehicles can be screened with the task starting point position (X, Y) as the center, and the distance between the task point and the vehicle can be calculated in combination with the Euclidean geometry algorithm. The role of these parameters is to evaluate the spatial relationship between each AGV vehicle and the task starting point, provide data support for constructing priority matching of tasks, and quickly determine the nearest and most suitable vehicle to perform the task, significantly improving the task response speed and resource matching efficiency.

[0080] S22, according to the task starting point position (X, Y) and the relevant AGV car position status information, and combined with the Euclidean geometry algorithm, obtain the task point distance value Lrw of several AGV cars, and use the task point distance value Lrw of the i-th AGV car i For example, it is obtained in the following way:

[0081] ;

[0082] Where X represents the X-axis coordinate value of the mission starting point. It is represented as the X-axis coordinate value of the spatial position of the i-th AGV car, and Y is represented as the Y-axis coordinate value of the task starting point. It is expressed as the Y-axis coordinate value of the spatial position of the i-th AGV.

[0083] Specifically, the steps of S2 also include:

[0084] S23, based on the task point distance value Lrw of the i-th AGV car i In this way, the task point distance values ​​Lrw of several AGVs are obtained respectively, and the existing power status information of the AGVs within the radiation area is collected to obtain the existing power status information of the relevant AGVs, wherein the existing power status information of the relevant AGVs includes the existing power value Cxc of each AGV;

[0085] It should be noted that the current power value Cxc of each AGV vehicle is usually obtained in real time through the battery management system (BMS) inside the AGV vehicle; the battery management system (BMS) collects battery status data through voltage, current and temperature sensor hardware components, and uploads real-time power information to the scheduling system through the built-in Wi-Fi communication module. The current power value Cxc refers to the current remaining available power of the AGV vehicle and is used to assess whether the vehicle has the ability to complete the task.

[0086] S24, the mission point distance values ​​Lrw of several AGVs are associated with the existing power status information of the relevant AGVs, and after dimensionless processing, the priority matching coefficients Xyx of several AGVs are constructed, and the priority matching coefficient Xyx of the i-th AGV is used. i For example, it is obtained in the following way:

[0087] ;

[0088] Where Lrw i Expressed as the mission point distance value of the i-th AGV car, Cxc i Represents the current power value of the i-th AGV car, where and are expressed as weight values.

[0089] It should be noted that the priority matching coefficient Xyx is a comprehensive indicator used to evaluate the degree of adaptability between each AGV and the cargo handling task. It is constructed by correlating the task point distance value Lrw and the vehicle's current power status Cxc and performing dimensionless processing. Specifically, the priority matching coefficient Xyx comprehensively considers the spatial relationship between the vehicle and the task starting point and whether its power status is sufficient, reflecting the priority of each vehicle in completing the task. The role of this priority matching coefficient Xyx is to provide a quantitative method that can quickly screen out target AGVs suitable for the current task.

[0090] Specifically, the steps of S2 also include:

[0091] S25, based on obtaining the priority matching coefficient Xyx of the i-th AGV car i The priority matching coefficients Xyx of several AGVs are obtained respectively to construct a priority matching coefficient set, and the feature extraction of the constructed priority matching coefficient set is performed to obtain the maximum priority matching coefficient Xyx in the priority matching coefficient set. max , and prioritize the maximum matching coefficient Xyx max The corresponding AGV serves as the target AGV for executing the current cargo handling task.

[0092] In this implementation, by constructing the priority matching coefficient Xyx and selecting the appropriate AGV as the target AGV, the accuracy and efficiency of task allocation are achieved, effectively solving the technical problem of low efficiency in matching tasks and equipment resources in traditional logistics systems; by collecting the task starting point position (X, Y) and the standby point position (x, y) of each AGV in the radiation area, and combining the Euclidean geometry algorithm to calculate the task point distance Lrw, this method can quickly evaluate the spatial relationship between the task position and the vehicle position, ensure that the task is preferentially assigned to the vehicle that is closer, thereby shortening the response time and improving In addition, by collecting the existing power value Cxc of the AGV and associating it with the distance Lrw of the task point, the construction of the priority matching coefficient Xyx comprehensively considers the multi-dimensional factors of the vehicle position and power status, dynamically balances the cost and feasibility of task execution, and significantly improves the rationality of task allocation; especially in complex multi-task scenarios, the dimensionless processing and feature extraction of the priority matching coefficient Xyx further enhance the level of intelligence, and through the feature extraction of the priority matching coefficient Xyx of several AGVs, the maximum priority matching coefficient Xyx in the priority matching coefficient set is obtained. max , and prioritize the maximum matching coefficient Xyx max The corresponding AGV serves as the target AGV to perform the current cargo handling task, ensuring that the task is always performed by a suitable target AGV, avoiding resource waste and scheduling inefficiency caused by matching errors; at the same time, for high-load task scenarios, by considering the battery status of the vehicle, the probability of a vehicle with insufficient power being misassigned is effectively reduced, thereby reducing the risk of task interruption and equipment loss; overall, significant improvements have been made in the matching efficiency between tasks and AGVs, resource utilization, and the safety and continuity of task execution, effectively solving the problem of insufficient task matching efficiency in traditional methods.

[0093] Example 4

[0094] Please refer to Figure 1 , specifically: S3 specific steps include:

[0095] S31, based on the maximum value of the priority matching coefficient Xyx max The corresponding AGV serves as the target AGV for the current cargo handling task. The target AGV collects its running speed and weight information during the task, and obtains relevant running status data, including the running speed Vyx and weight Gzz of the target AGV.

[0096] It should be noted that the running speed Vyx is measured by the speed sensor of the target AGV vehicle, which reflects the actual movement speed of the target AGV vehicle during the task execution; the body weight Gzz is the target AGV vehicle's own weight information, which is usually directly obtained through factory calibration and design parameters during equipment initialization, or it can be obtained by dynamically measuring the total weight with weighing equipment when loading and then subtracting the weight of the cargo. Its function is to provide key data support for energy consumption evaluation and operation status analysis during task execution; the running speed Vyx is the core indicator for calculating the task completion time and energy consumption during operation, while the body weight Gzz is an important factor in evaluating the vehicle's load capacity and energy consumption in the handling task; by collecting the running speed Vyx and body weight Gzz of the target AGV vehicle, the target vehicle's power consumption during task execution can be more accurately predicted, thereby judging whether the task can be completed within the power safety range, and ensuring the reliability and safety of task execution.

[0097] S32. Analyze the relevant operating status data information of the target AGV and the relevant task status data information corresponding to the current cargo handling task being executed, and associate them with the existing power value Cxc of the target AGV. After dimensionless processing, obtain the remaining power prediction value Csh. The remaining power prediction value Csh is obtained using the following formula;

[0098] ;

[0099] In the formula, Gzz represents the vehicle weight, Nby represents the weight of the transported cargo, Lby represents the length of the transport path, and Vyx represents the running speed. It is represented as the friction coefficient of the road surface, K is represented as the energy consumption coefficient of the running speed, Expressed as the electric energy conversion coefficient, where It is expressed as the energy required by the target AGV to overcome friction when transporting goods. It is expressed as the energy consumed by the target AGV vehicle's handling speed.

[0100] It should be noted that the remaining power prediction value Csh is the predicted value of the remaining power of the target AGV car after it has not performed the current cargo handling task. It is calculated by analyzing the relevant operating status data information and relevant task status data information of the car, and combining it with the existing power Cxc of the car. After dimensionless processing, the power consumption is quantified and compared on a unified scale. Its role is mainly reflected in the following two aspects: First, the remaining power prediction value Csh is used to determine whether the target AGV car has the ability to complete the task. If the predicted value is lower than the safe power threshold C, the scheduling strategy is adjusted in time before the task is executed to avoid task interruption or equipment damage caused by depletion of the car's power. Second, the remaining power prediction value Csh, as an important reference indicator for task scheduling and power management, can help the system plan the charging path and charging priority of the car after the task is completed, thereby improving the continuity of equipment operation and energy utilization efficiency. By predicting the remaining power prediction value Csh after the task, dynamic protection of operation safety can be achieved, energy consumption and failure rate can be reduced, and support for the efficient operation of the intelligent logistics system can be provided.

[0101] The operating speed energy consumption coefficient K is a parameter that reflects the change in energy consumption per unit time of the target AGV car at different speeds. Its acquisition method usually includes experimental measurement and simulation modeling. By recording the power consumption data of the car at different speeds during actual operation, a functional relationship between speed and energy consumption is established; the electric energy conversion coefficient It indicates the efficiency of converting the electrical energy stored in the battery into mechanical energy. It is usually determined by the manufacturer through experimental testing, and can also be obtained by monitoring the current, voltage and output power of the equipment during operation. The operating speed energy consumption coefficient K is used to evaluate the impact of the operating speed on the energy consumption of the trolley, so as to optimize the trolley's operating speed to reduce unnecessary energy waste; the electric energy conversion coefficient This directly affects the accuracy of the remaining power prediction value Csh.

[0102] In this implementation, through in-depth analysis of the relevant operating status data information and relevant task status data information of the target AGV vehicle, combined with the calculation of the remaining power prediction value Csh, the safety of the target AGV vehicle during operation and the reliability of task execution are significantly improved; in response to the problems of lack of power prediction ability and high task execution risk in traditional methods, starting from multiple key parameters, including the target AGV vehicle's operating speed Vyx, vehicle weight Gzz, transported cargo weight Nby, path length Lby, combined with considerations of path road friction and operating speed energy consumption, by calculating the energy required to overcome friction for transporting cargo and the energy consumed by the transport speed in sub-items, the remaining power prediction value Csh of the vehicle after the task is completed is accurately predicted. This power prediction method provides a clear energy consumption assessment before task execution, ensuring that the target AGV vehicle completes the task within a safe range, avoiding task interruption due to insufficient power and the risk of over-discharge of the AGV vehicle battery; especially through the power conversion coefficient By correcting the energy consumption, the prediction results are more in line with the actual operating conditions, providing reliable data support for subsequent task planning; compared with the traditional methods that rely on experience judgment or fixed rules, the real-time collection and analysis of operation data improves the precise control of task energy consumption, while extending the battery life of the target AGV car and improving the economic benefits of the equipment. Overall, this method not only effectively solves the problem of the lack of traditional power management, but also achieves a dual improvement in task execution efficiency and operation safety through an intelligent energy consumption prediction mechanism, providing more comprehensive and optimized technical support for the intelligent logistics system.

[0103] Example 5

[0104] Please refer to Figure 1 , specifically: S4 specific steps include:

[0105] S41. Using the safe power value of the target AGV battery as the remaining power safety threshold C, the remaining power prediction value Csh is compared with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, and generates a corresponding level control instruction. The specific contents are as follows:

[0106] It should be noted that the safe power value is the minimum power standard for judging whether the target AGV can safely complete the task. It is usually determined through experimental testing and battery characteristics analysis, with 20%-30% of the battery capacity as the critical value for safe operation. Its role is to provide an early warning mechanism for insufficient power.

[0107] If the remaining power prediction value Csh is less than the remaining power safety threshold C, it means that the target AGV performing the current cargo handling task is not operating safely and effectively. This means that the current power value Cxc of the target AGV is insufficient to complete the current cargo handling task, and that performing the current cargo handling task will damage the target AGV battery, and a first-level control instruction is issued.

[0108] If the remaining power prediction value Csh ≥ the remaining power safety threshold C, it means that the target AGV car that is performing the current cargo handling task is operating safely and effectively, indicating that the existing power value Cxc of the target AGV car is sufficient to complete the current cargo handling task, and that performing the current cargo handling task will not cause damage to the target AGV car battery, and a secondary control instruction is issued.

[0109] Specifically, the steps of S4 also include:

[0110] S42. Based on the received primary control instructions and secondary control instructions, corresponding control measures are executed. The specific contents are as follows:

[0111] When a first-level control instruction is received, the following is executed: by sending a task abort signal, the target AGV stops executing the current cargo handling task, and sends a signal to the warehouse dispatch center that the target AGV is low on battery, requesting immediate arrangement for emergency charging at the nearest charging station; based on the AGV corresponding to the priority matching coefficient Xyx, the AGV with sufficient existing battery value Cxc is selected to take over the current cargo handling task;

[0112] When a secondary control instruction is received, the execution content is as follows: by sending a task execution signal, the target AGV car is selected to perform the current cargo handling task, and the power changes of the target AGV car are monitored in real time; after the task is completed, according to the predicted value Csh of the remaining power of the target AGV car, the nearest charging station is selected for charging, and at the same time, the task completion status information and charging time of the target AGV car are fed back to the logistics warehousing dispatching center, and combined with the charging time of the AGV car, it is arranged to participate in subsequent cargo handling tasks.

[0113] In this implementation, by constructing a comparison mechanism between the remaining power prediction value Csh and the safe power threshold C, combined with the generated multi-level control instructions, the precise regulation of the target AGV vehicle's operating status and the dynamic guarantee of task safety are achieved; in response to the problems of the lack of power management prediction capability and insufficient task execution safety in traditional methods, this method takes power evaluation as the core, and before the task is executed, it analyzes the remaining power prediction value Csh of the target AGV vehicle in real time to determine whether the task can be completed within a safe range; when the remaining power prediction value Csh is lower than the safe power threshold C, the first-level control instruction is automatically triggered to stop the task execution, so as to avoid the target AGV vehicle's battery over-discharge and task interruption due to insufficient power, and send an emergency charging request to the dispatch center, and reallocate the task to other AGV vehicles with sufficient power; on the contrary, when the remaining power prediction value Csh is higher than the safe power threshold C, the second-level control instruction is generated to allow the task to be executed normally, and at the same time After the task is completed, the nearest charging path is used to ensure that the equipment can be quickly restored to a usable state; this method of dynamically generating control instructions effectively solves the problems of separation between task and power management and delayed operation decision-making in traditional systems, and provides safety guarantees for high-load task scenarios; in addition, by optimizing the execution strategies of different control instructions, the continuity of task execution and system resource utilization are further improved; the rapid response mechanism of the first-level control instructions can effectively avoid logistics chain interruptions caused by insufficient power, while the second-level control instructions ensure that the AGV cart maintains equipment health while efficiently performing tasks and extends its service life through real-time monitoring and charging path optimization; in general, the present invention not only effectively solves the problem of insufficient prediction and real-time analysis capabilities of traditional power management, but also significantly improves the system's operational safety, task completion rate and equipment reliability through intelligent control instruction generation and execution mechanism, providing support for the efficient operation of intelligent logistics systems.

[0114] Example 6

[0115] Please refer to Figure 1 and Figure 2 ,Specifically: An AGV trolley operation intelligent control system, including an early warning module, a matching module, a prediction module and a control module;

[0116] The warning module is used to analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction;

[0117] The matching module is used to screen out the preferentially matched AGV as the target AGV for executing the current cargo handling task after receiving the emergency execution warning instruction;

[0118] The prediction module is used to collect the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, so as to predict the remaining power value Csh of the target AGV after performing the current cargo handling task;

[0119] The control module is used to compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV vehicle performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions.

[0120] In this implementation, through the organic combination of the early warning module, matching module, prediction module and control module, the core problems of insufficient optimization of task scheduling, low resource utilization efficiency and lack of operational safety in intelligent logistics scenarios are effectively solved. The system is based on the dynamic construction of the task urgency coefficient Xjd, which can accurately identify high-priority tasks that need to be executed immediately, and combined with the comparative analysis of the urgency threshold J, it issues emergency execution early warning instructions through the early warning module to buy time for task scheduling, significantly improving the timeliness of task response; the matching module constructs the priority matching coefficient Xyx based on the Euclidean geometry algorithm and the power status of the AGV car, realizing the intelligent matching of tasks and AGV cars; through the feature extraction of the priority matching coefficient Xyx, the target AGV car suitable for the current task can be quickly screened out, effectively solving the low efficiency and resource waste of task and equipment matching in traditional methods. At the same time, the prediction module collects and analyzes the relevant operating status data information of the target AGV car, combines the existing power value Cxc, and calculates the remaining power prediction value Csh after the task is completed, providing a solid guarantee for the safety of task execution; the control module dynamically generates multi-level control instructions by comparing the remaining power prediction value Csh with the safety power threshold C, and avoids the risk of task interruption and over-discharge of the target AGV car battery through corresponding execution strategies, thereby effectively solving the problem of lack of predictive ability of power management in traditional logistics scenarios; in summary, the present invention has made significant progress in task priority identification, intelligent resource allocation, power management accuracy and operation safety, which not only improves the overall operation efficiency of the system, but also provides a highly intelligent and dynamic solution for AGV car operation scheduling in complex logistics scenarios.

[0121] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for AGV operation, characterized by: The following steps are included: S1. Analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction; S2. After receiving the emergency execution warning instruction, select the AGV with priority matching as the target AGV to perform the current cargo handling task; S3, collecting the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, to predict the remaining power value Csh of the target AGV after performing the current cargo handling task; S4. Compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions; The specific steps of S1 include: S11. According to the cargo handling task requirements of the logistics warehousing dispatch center, task information status of the cargo handling task is collected to obtain relevant task status data information, wherein the relevant task status data information includes the task deadline time point Tjz, the handling path length Lby, and the handled cargo weight Nby; S12. Analyze the relevant task status data information and construct a task urgency coefficient Xjd after dimensionless processing. The task urgency coefficient Xjd is obtained by the following formula: ； Where Tjz is the task deadline, T is the current time, Lby is the length of the transport path, Nby is the weight of the transported goods, and Nby is the weight of the transported goods. max Indicates the maximum load of the AGV car, 、 and All are expressed as weight values, and A is expressed as a correction constant; The specific steps of S3 include: S31, based on the maximum value of the priority matching coefficient Xyx max The corresponding AGV serves as the target AGV for the current cargo handling task. The target AGV collects its running speed and weight information during the task, and obtains relevant running status data, including the running speed Vyx and weight Gzz of the target AGV. S32. Analyze the relevant operating status data information of the target AGV and the relevant task status data information corresponding to the current cargo handling task being executed, and associate them with the existing power value Cxc of the target AGV. After dimensionless processing, obtain the remaining power prediction value Csh. The remaining power prediction value Csh is obtained using the following formula; ； In the formula, Gzz represents the vehicle weight, Nby represents the weight of the transported cargo, Lby represents the length of the transport path, and Vyx represents the running speed. It is represented as the friction coefficient of the road surface, K is represented as the energy consumption coefficient of the running speed, Expressed as the electric energy conversion coefficient, where It is the energy required by the target AGV to overcome friction when transporting goods. It is expressed as the energy consumed by the target AGV vehicle's handling speed.

2. The intelligent control method for AGV operation according to claim 1 is characterized in that: The specific steps of S1 also include: S13. Preset an urgency threshold J, compare and analyze the task urgency coefficient Xjd with the preset urgency threshold J to determine whether the current cargo handling task is in an immediate execution state, and issue an emergency execution warning instruction. The specific content is as follows: If the task urgency coefficient Xjd ≥ the urgency threshold J, it means that the current cargo handling task is in an immediate execution state, and an emergency execution warning instruction is issued; If the task urgency coefficient Xjd is less than the urgency threshold J, it means that the current cargo handling task is not in an immediate execution state, and no additional warning instructions are issued at this time.

3. The intelligent control method for AGV operation according to claim 2 is characterized in that: The specific steps of S2 include: S21. After receiving the issued emergency execution warning instruction, collect the position status information of the starting point of the cargo handling task to obtain the task starting point location (X, Y). Then, with the task starting point location (X, Y) of the cargo handling task as the radiation center, collect the position status information of the AGVs within the radiation area to obtain the relevant AGV position status information. The relevant AGV position status information includes the standby point location (x, y) of each AGV. S22, according to the task starting point position (X, Y) and the relevant AGV car position status information, and combined with the Euclidean geometry algorithm, obtain the task point distance value Lrw of several AGV cars, and use the task point distance value Lrw of the i-th AGV car i For example, it is obtained in the following way: ； Where X represents the X-axis coordinate value of the mission starting point. It is represented as the X-axis coordinate value of the spatial position of the i-th AGV car, and Y is represented as the Y-axis coordinate value of the task starting point. It is expressed as the Y-axis coordinate value of the spatial position of the i-th AGV.

4. The intelligent control method for AGV operation according to claim 3 is characterized in that: The specific steps of S2 also include: S23, based on the task point distance value Lrw of the i-th AGV car i In this way, the task point distance values ​​Lrw of several AGVs are obtained respectively, and the existing power status information of the AGVs within the radiation area is collected to obtain the existing power status information of the relevant AGVs, wherein the existing power status information of the relevant AGVs includes the existing power value Cxc of each AGV; S24, the mission point distance values ​​Lrw of several AGVs are associated with the existing power status information of the relevant AGVs, and after dimensionless processing, the priority matching coefficients Xyx of several AGVs are constructed, and the priority matching coefficient Xyx of the i-th AGV is used. i For example, it is obtained in the following way: ； Where Lrw i Expressed as the mission point distance value of the i-th AGV car, Cxc i Represents the current power value of the i-th AGV car, where and are expressed as weight values.

5. The intelligent control method for AGV operation according to claim 4 is characterized in that: The specific steps of S2 also include: S25, based on obtaining the priority matching coefficient Xyx of the i-th AGV car i The priority matching coefficients Xyx of several AGVs are obtained respectively to construct a priority matching coefficient set, and the feature extraction of the constructed priority matching coefficient set is performed to obtain the maximum priority matching coefficient Xyx in the priority matching coefficient set. max , and prioritize the maximum matching coefficient Xyx max The corresponding AGV serves as the target AGV for executing the current cargo handling task.

6. The intelligent control method for AGV operation according to claim 1 is characterized in that: The specific steps of S4 include: S41. Using the safe power value of the target AGV battery as the remaining power safety threshold C, the remaining power prediction value Csh is compared with the remaining power safety threshold C to determine whether the target AGV performing the current cargo handling task is operating safely and effectively, and generates a corresponding level control instruction. The specific contents are as follows: If the remaining power prediction value Csh is less than the remaining power safety threshold C, it means that the target AGV performing the current cargo handling task is not operating safely and effectively. This means that the current power value Cxc of the target AGV is insufficient to complete the current cargo handling task, and that performing the current cargo handling task will damage the target AGV battery, and a first-level control instruction is issued. If the remaining power prediction value Csh ≥ the remaining power safety threshold C, it means that the target AGV car that is performing the current cargo handling task is operating safely and effectively, indicating that the existing power value Cxc of the target AGV car is sufficient to complete the current cargo handling task, and that performing the current cargo handling task will not cause damage to the target AGV car battery, and a secondary control instruction is issued.

7. The intelligent control method for AGV operation according to claim 6 is characterized in that: The specific steps of S4 also include: S42. Based on the received primary control instructions and secondary control instructions, corresponding control measures are executed. The specific contents are as follows: When a first-level control instruction is received, the following is executed: by sending a task abort signal, the target AGV stops executing the current cargo handling task, and sends a signal to the warehouse dispatch center that the target AGV is low on battery, requesting immediate arrangement for emergency charging at the nearest charging station; based on the AGV corresponding to the priority matching coefficient Xyx, the AGV with sufficient existing battery value Cxc is selected to take over the current cargo handling task; When a secondary control instruction is received, the execution content is as follows: by sending a task execution signal, the target AGV car is selected to perform the current cargo handling task, and the power changes of the target AGV car are monitored in real time; after the task is completed, according to the predicted value Csh of the remaining power of the target AGV car, the nearest charging station is selected for charging, and at the same time, the task completion status information and charging time of the target AGV car are fed back to the logistics warehousing dispatching center, and combined with the charging time of the AGV car, it is arranged to participate in subsequent cargo handling tasks.

8. An AGV intelligent control system for implementing the AGV intelligent control method according to any one of claims 1 to 7, characterized in that: Including early warning module, matching module, prediction module and control module; The warning module is used to analyze the urgency of the current cargo handling task to determine whether the current cargo handling task is in an immediate execution state and issue an emergency execution warning instruction; The matching module is used to screen out the preferentially matched AGV as the target AGV for executing the current cargo handling task after receiving the emergency execution warning instruction; The prediction module is used to collect the running speed information and vehicle weight information of the target AGV when performing the current cargo handling task, so as to predict the remaining power value Csh of the target AGV after performing the current cargo handling task; The control module is used to compare the remaining power prediction value Csh with the remaining power safety threshold C to determine whether the target AGV vehicle performing the current cargo handling task is operating safely and effectively, generate and execute corresponding level control instructions.

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