Electric power material warehouse-in and warehouse-out intelligent management and control method and device based on intelligent carrying

Abstract

The invention provides an electric power material warehouse-in and warehouse-out intelligent management and control method and device based on intelligent carrying, and relates to the field of electric power material management.The method comprises the steps that real-time data in a warehouse are collected through Internet of Things equipment and a sensor, and the real-time data are collected according to the collected real-time data; performing real-time calculation on an advancing path of the intelligent carrying equipment in the warehouse by utilizing dynamic path optimization, and adjusting the advancing path of the equipment according to the real-time environment data, the material storage position data and the carrying task demand data; the intelligent carrying equipment carries out advancing path planning when carrying tasks are carried out in the warehouse, and according to an error correction mechanism, errors in the material warehouse-in and warehouse-out process are corrected; the material demand is dynamically predicted according to the historical inventory data and the external demand data by using the established global task scheduling prediction model, and the priority ranking of the tasks in the warehouse and the dynamic scheduling of the inventory are performed, so that the automatic and intelligent management of the warehouse-in and warehouse-out of the materials is realized, and the material management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of power material management, and in particular to a method and device for intelligent management and control of power material in and out of a warehouse based on intelligent transportation. Background Art

[0002] With the development of the power industry, the types and quantities of power materials are increasing. The traditional management methods of power material in and out of the warehouse have gradually failed to meet the requirements of modern power companies for the efficiency, accuracy and safety of material management. The existing management methods of material in and out of the warehouse mainly rely on manual operations, which usually require manual work such as material handling, sorting, recording and storage. Although some warehouses have introduced automated equipment, most power material management systems are still in a relatively traditional state and have many problems:

[0003] 1. Inefficiency: The process of manual handling, recording and inspection is cumbersome and often time-consuming, resulting in low warehouse management efficiency.

[0004] 2. Error-prone: Manual operations are prone to record errors, misplacement or omission of materials, etc., especially when the amount of materials is large, the error rate increases, affecting the overall operation.

[0005] 3. Information asymmetry: Traditional material management relies on paper records or simple spreadsheets, lacking real-time data updates and dynamic tracking, which can easily lead to inaccurate or delayed inventory information.

[0006] 4. Poor safety: Manual operation provides poor security for materials and equipment in the warehouse, and is prone to accidents such as misoperation and collision.

[0007] 5. Inflexible response: When it is necessary to adjust the warehouse layout or respond to urgent material needs, traditional management methods are slow to respond and have poor flexibility.

[0008] Therefore, how to realize the intelligent and automated handling and control of power materials has become a research hotspot in the current field of power material management. Traditional manual management methods are gradually being replaced by intelligent equipment, which promotes the management of materials in and out of the warehouse to be more efficient, accurate and safe. In order to achieve this goal, it has become a trend in the industry to integrate multiple advanced technologies such as automated equipment, information recognition technology, and real-time monitoring systems to improve the intelligent level of power material management. Summary of the invention

[0009] In view of this, in order to solve the problems of low efficiency, high error rate and information lag in the existing management of power material entry and exit, the purpose of the present invention is to propose an intelligent management and control method and device for power material entry and exit based on intelligent transportation. The method utilizes intelligent transportation equipment, Internet of Things technology, information identification technology and real-time positioning system to realize the automated and intelligent management of material entry and exit, thereby improving the efficiency of material management, ensuring the accuracy and timeliness of inventory information, reducing the risk of manual intervention, and improving the safety and flexibility of warehouse operations.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] Based on the above objectives, in a first aspect, the present invention provides an intelligent management and control method for power material in and out of the warehouse based on intelligent transportation, comprising the following steps:

[0012] Collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as handling task demand data of intelligent handling equipment imported by IoT devices;

[0013] Based on the collected real-time data, dynamic path optimization is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time, and the equipment travel path is adjusted according to the real-time environmental data, material storage location data and handling task demand data;

[0014] Intelligent handling equipment plans the path when performing handling tasks in the warehouse, and corrects errors in the process of materials entering and leaving the warehouse through real-time data monitoring, error prediction and automatic correction based on the error correction mechanism;

[0015] The established global task scheduling prediction model is used to dynamically predict material demand based on historical inventory data and external demand data, and to prioritize tasks within the warehouse and dynamically schedule inventory.

[0016] As a further solution of the present invention, real-time environmental data, equipment status data and material storage location data collected in the warehouse are acquired through sensors, RFID tags and cameras installed in the warehouse.

[0017] As a further solution of the present invention, the real-time environmental data includes the temperature, humidity, light intensity and air flow velocity data in the warehouse, the temperature and humidity in the air are measured by the temperature and humidity probes of the temperature and humidity sensor, the light intensity in the warehouse is collected by the light sensor, and the data is transmitted to the central control system through the wireless module;

[0018] The equipment status data includes the operating status, power, speed and position data of the intelligent handling equipment, which are collected through embedded power monitoring sensors, position and speed sensors and inertial sensors. The power monitoring sensor is used to monitor the remaining power of the intelligent handling battery, and the power data is transmitted to the central control system through the battery management system (BMS); the position and speed sensor includes the GPS (global positioning system) and speed sensor configured on the intelligent handling equipment, which tracks the current position of the handling equipment through real-time positioning; the inertial sensor includes an accelerometer and a gyroscope installed on the intelligent handling equipment, which are used to measure the direction, speed and acceleration of the intelligent handling equipment;

[0019] The material storage location data is collected through RFID tags, and the tag information is read in real time by an RFID reader installed in the warehouse to obtain the storage location and quantity of the materials.

[0020] As a further solution of the present invention, in the handling task requirement data of the intelligent handling equipment imported by the Internet of Things device, the data structure of each handling task requirement is:

[0021] T task ={T type , P start , P end , P priority , T deadline}

[0022] Among them, T type is the task type (inbound, outbound, transfer); start is the starting position of the task; P end is the task target position; P priority is the task priority; T deadline The deadline for the task.

[0023] As a further solution of the present invention, when the travel path of the intelligent handling equipment in the warehouse is calculated in real time by using dynamic path optimization, heuristic search (A* algorithm) path planning is used to search for the optimal path from the starting point to the target point in the warehouse, including the following steps:

[0024] Set the starting point P start and the end point P goal , initialize the open list (Open List) and the closed list (ClosedList);

[0025] Calculate the path cost g(n) from the starting point to the current node, that is, the known path length;

[0026] Estimate the distance h(n) from the current node to the end point, and calculate the total cost f(n) based on the path cost of the current node;

[0027] The next node is selected according to the minimum total cost f(n) value until the target node is reached.

[0028] As a further solution of the present invention, the calculation formula of the total cost f(n) is:

[0029] f(n)=g(n)+h(n)

[0030] In the formula, f(n) is the total cost function, which represents the total cost of the current node and is used to select the path; g(n) is the actual cost, that is, the actual distance from the starting point to the current node n; h(n) is the heuristic estimated distance from the current node n to the target node;

[0031] Among them, the calculation formula of the actual distance g(n) from the starting point to the current node is:

[0032]

[0033] Where, d i From the starting point to node n i distance;

[0034] The calculation formula for the heuristic estimated distance h(n) is:

[0035]

[0036] In the formula, x goal ,y goal Represents the horizontal and vertical coordinates of the target position, P goal =(x goal ,y goal ); x n ,y n is the horizontal and vertical coordinates of the current node n.

[0037] As a further solution of the present invention, according to the error correction mechanism, the errors in the process of materials entering and leaving the warehouse are corrected through real-time data monitoring, error prediction and automatic correction, including the following steps:

[0038] The path error is predicted through the Kalman filter, and the path is corrected according to the prediction results;

[0039] Determine whether the device path is consistent with the target path through real-time sensor monitoring, error prediction and automatic correction mechanism;

[0040] If they are consistent, the motion state of the intelligent handling equipment is corrected in a closed loop through PID control.

[0041] As a further solution of the present invention, when predicting the path error through the Kalman filter, the state vector of the intelligent transport device at the kth moment is set to x k; The state transfer matrix is ​​A, which represents the transfer relationship from the previous state to the current state; the control input matrix is ​​B, which represents the impact of external factors on demand changes; the acceleration of the control push device is u k ; process noise is w k , the observed value is g k ; The observation matrix is ​​H; The observation noise is v k , then, the Kalman filter prediction model is expressed as:

[0042] x k =Ax k-1 +Bu k +w k

[0043] y k =Hx k +v k

[0044] Among them, y k It is the observation value at the kth moment. When the Kalman filter prediction model performs error prediction, it predicts the path error by analyzing the historical path, motion trajectory and external environment changes of the equipment.

[0045] As a further solution of the present invention, when closed-loop PID control is used to perform closed-loop correction on the motion state of the intelligent transporting device, the motion speed and direction of the intelligent transporting device are adjusted in real time.

[0046] As a further solution of the present invention, priority sorting of tasks in the warehouse and dynamic scheduling of inventory are performed, and the priority of tasks is set as follows during priority sorting:

[0047] P task =α·Demand forecast error+β·Order urgency+γ·Inventory level

[0048] Where P task is the priority of the task, which is used to characterize the urgency of the task in the entire scheduling. The higher the priority of the task, the earlier it will be executed; α is the weight coefficient of the demand forecast error, which characterizes the influence of the demand forecast error on the task priority. The larger the demand forecast error, the higher the task priority; the demand forecast error is the difference between the material demand predicted by the Kalman filter prediction model and the actual demand; β is the weight coefficient of the order urgency, which characterizes the influence of the order urgency on the task priority. If the order is very urgent, the task priority should be higher; γ is the weight coefficient of the inventory level, which indicates the influence of the inventory level on the task priority.

[0049] As a further solution of the present invention, during the dynamic scheduling of inventory, the inventory allocation is optimized according to the scheduling results, and the inventory optimization target is determined, wherein:

[0050]

[0051] In the formula, k represents the kth time period, is the demand forecast value at the kth moment, the actual inventory is the actual quantity of materials stored in the warehouse at the kth moment, and λ is the penalty coefficient for excess inventory.

[0052] In a second aspect, the present invention provides an intelligent management and control device for electric power materials in and out of the warehouse based on intelligent transportation, comprising:

[0053] The data collection module is used to collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as the handling task demand data of intelligent handling equipment imported by IoT devices;

[0054] The dynamic path optimization module is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time based on the collected real-time data and adjust the equipment travel path according to the real-time environmental data, material storage location data and handling task demand data;

[0055] The error correction module is used for path planning when intelligent handling equipment performs handling tasks in the warehouse. According to the error correction mechanism, the errors in the process of materials entering and leaving the warehouse are corrected through real-time data monitoring, error prediction and automatic correction;

[0056] The global task scheduling module is used to use the established global task scheduling prediction model to dynamically predict material demand based on historical inventory data and external demand data, and to prioritize tasks within the warehouse and dynamically schedule inventory.

[0057] As a further solution of the present invention, the data acquisition module includes:

[0058] Environmental data sensors: monitor environmental factors such as temperature, humidity, and light in the warehouse;

[0059] Equipment status sensor: monitors the operating status of intelligent handling equipment, including battery power, equipment failure, operating speed and other parameters;

[0060] Material storage location sensor: detects real-time information about material storage locations to ensure accurate positioning of materials;

[0061] Intelligent handling task data collection sensor: obtains the handling task requirements of intelligent handling equipment in real time, including task type, material type, handling volume and other information.

[0062] Compared with the prior art, the intelligent control method and device for power material in-and-out storage based on intelligent transportation proposed in the present invention has the following beneficial effects:

[0063] 1. The present invention automatically performs material handling tasks through intelligent handling equipment, reduces manual intervention, and significantly improves warehouse work efficiency. The equipment can dynamically adjust the handling path according to real-time task requirements, avoiding the tediousness and inefficiency of manual path planning, and achieving rapid response and execution of tasks.

[0064] 2. Through real-time monitoring of material storage locations and environmental data through IoT devices and sensors, intelligent handling equipment can efficiently locate and transport materials. The material storage location and channel planning in the warehouse are optimized, avoiding unnecessary space waste and improving the storage density of the warehouse.

[0065] 3. The error correction mechanism of the present invention can monitor the status of the intelligent handling equipment in real time when performing tasks, and correct the deviation caused by errors by dynamically adjusting the path. This real-time error prediction and automatic correction function effectively reduces the loss, damage or misalignment of materials during the handling process, ensuring the accuracy and safety of materials.

[0066] 4. Use historical inventory data and external demand data to predict material demand, and combine with the global task scheduling model to dynamically adjust task priorities and inventory allocation according to real-time demand. This system can avoid excess or shortage of inventory, ensure that materials are put in and out of the warehouse on demand, further improve the intelligence and accuracy of inventory management, and reduce inventory management costs.

[0067] 5. Through dynamic path optimization technology, intelligent handling equipment can select the optimal path in the warehouse according to real-time environmental data and task requirements, reducing equipment travel time and energy consumption. At the same time, the adaptability of the path optimization algorithm enables the system to adjust the equipment path in time according to obstacles in the warehouse, environmental changes and other conditions, avoiding traffic congestion or route blockage, and ensuring efficient completion of handling tasks.

[0068] 6. The present invention can effectively reduce potential safety hazards in the handling process, such as equipment collision, material loss, and mis-access, through intelligent monitoring, path optimization, and error correction. At the same time, the equipment status monitoring module tracks the health status of the equipment in real time, can detect potential faults in advance and issue warnings, and improve the reliability of the equipment and the safety of the warehouse.

[0069] In summary, the intelligent management and control method and device for the entry and exit of electric power materials based on intelligent handling of the present invention not only improves the automation and intelligence level of the warehouse, but also significantly improves the efficiency and accuracy of material handling, storage, and scheduling. Through the comprehensive application of the Internet of Things, dynamic path optimization, error correction mechanism, and global task scheduling prediction model, the present invention realizes the intelligence, accuracy, and efficiency of warehouse management, and provides a revolutionary technical solution for the entry and exit management of electric power materials.

[0070] These and other aspects of the present application will be more concise and understandable in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the exemplary embodiments or related technical descriptions. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0072] Figure 1 The present invention is a flowchart of an intelligent management and control method for the entry and exit of electric power materials based on intelligent transportation according to an embodiment of the present invention.

[0073] Figure 2 The present invention is a flowchart of an embodiment of the present invention which uses heuristic search path planning in an intelligent management and control method for electric power materials in and out of the warehouse based on intelligent transportation.

[0074] Figure 3 The present invention provides a flowchart corrected according to an error correction mechanism in an intelligent management and control method for the entry and exit of electric power materials based on intelligent transportation in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are 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.

[0076] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0077] Aiming at the problems of low efficiency, high error rate and information lag in the existing management of power material in and out of the warehouse, the present invention proposes an intelligent management and control method and device for power material in and out of the warehouse based on intelligent handling. The method utilizes intelligent handling equipment, Internet of Things technology, information identification technology and real-time positioning system to realize the automated and intelligent management of material in and out of the warehouse, thereby improving the efficiency of material management, ensuring the accuracy and timeliness of inventory information, reducing the risk of manual intervention, and improving the safety and flexibility of warehouse operations.

[0078] See also Figure 1 As shown, an embodiment of the present invention provides an intelligent management and control method for power material in and out of the warehouse based on intelligent transportation, and the method includes the following steps:

[0079] Step S10: collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as handling task demand data of intelligent handling equipment imported by IoT devices.

[0080] In this step, real-time environmental data, equipment status data, and material storage location data collected in the warehouse are obtained through sensors, RFID tags, and cameras installed in the warehouse.

[0081] The real-time environmental data includes the temperature, humidity, light intensity and air velocity data in the warehouse. The temperature and humidity in the air are measured by the temperature and humidity probes of the temperature and humidity sensor, the light intensity in the warehouse is collected by the light sensor, and the data is transmitted to the central control system through the wireless module;

[0082] The equipment status data includes the operating status, power, speed and position data of the intelligent handling equipment, which are collected through embedded power monitoring sensors, position and speed sensors and inertial sensors. The power monitoring sensor is used to monitor the remaining power of the intelligent handling battery, and the power data is transmitted to the central control system through the battery management system (BMS); the position and speed sensor includes the GPS (global positioning system) and speed sensor configured on the intelligent handling equipment, which tracks the current position of the handling equipment through real-time positioning; the inertial sensor includes an accelerometer and a gyroscope installed on the intelligent handling equipment, which are used to measure the direction, speed and acceleration of the intelligent handling equipment;

[0083] The material storage location data is collected through RFID tags, and the tag information is read in real time by an RFID reader installed in the warehouse to obtain the storage location and quantity of the materials.

[0084] In this embodiment, in the handling task requirement data of the intelligent handling equipment imported by the Internet of Things device, the data structure of each handling task requirement is:

[0085] T task ={T type , P start , P end , P priority , T deadline}

[0086] Among them, T type is the task type (inbound, outbound, transfer); start is the starting position of the task; P end is the task target position; P priority is the task priority; T deadline The deadline for the task.

[0087] Step S20: Based on the collected real-time data, the travel path of the intelligent handling equipment in the warehouse is calculated in real time using dynamic path optimization, and the equipment travel path is adjusted according to the real-time environmental data, material storage location data and handling task requirement data.

[0088] In this step, when the dynamic path optimization is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time, the heuristic search (A* algorithm) path planning is used to search for the optimal path from the starting point to the target point in the warehouse, including the following steps:

[0089] Step S201: Set the starting point P start and the end point P goal , initialize the open list (Open List) and the closed list (Closed List);

[0090] Step S202, calculate the path cost g(n) from the starting point to the current node, that is, the known path length;

[0091] Step S203, estimating the distance h(n) from the current node to the end point, and calculating the total cost f(n) according to the path cost of the current node;

[0092] Step S204: Select the next node according to the minimum total cost f(n) value until the target node is reached.

[0093] In this embodiment, the calculation formula of the total cost f(n) is:

[0094] f(n)=g(n)+h(n)

[0095] In the formula, f(n) is the total cost function, which represents the total cost of the current node and is used to select the path; g(n) is the actual cost, that is, the actual distance from the starting point to the current node n; h(n) is the heuristic estimated distance from the current node n to the target node;

[0096] Among them, the calculation formula of the actual distance g(n) from the starting point to the current node is:

[0097]

[0098] Where, d i From the starting point to node n i distance;

[0099] The calculation formula for the heuristic estimated distance h(n) is:

[0100]

[0101] In the formula, x goal ,y goal Represents the horizontal and vertical coordinates of the target position, P goal =(x goal ,y goal ); x n ,y n is the horizontal and vertical coordinates of the current node n.

[0102] Step S30: The intelligent handling equipment plans the travel path when performing the handling task in the warehouse, and corrects the errors in the process of materials entering and leaving the warehouse through real-time data monitoring, error prediction and automatic correction according to the error correction mechanism.

[0103] In this step, see Figure 3 As shown, according to the error correction mechanism, the errors in the material storage and warehousing process are corrected through real-time data monitoring, error prediction and automatic correction, including the following steps:

[0104] Step S301: predict the path error through the Kalman filter, and correct the path according to the prediction result;

[0105] Step S302: Determine whether the device path is consistent with the target path through real-time sensor monitoring, error prediction and automatic correction mechanism;

[0106] Step S303: If they are consistent, the motion state of the intelligent handling device is corrected in a closed loop through PID control.

[0107] In this embodiment, when predicting the path error through the Kalman filter, the state vector of the intelligent transport device at the kth moment is set to x k ; The state transfer matrix is ​​A, which represents the transfer relationship from the previous state to the current state; the control input matrix is ​​B, which represents the impact of external factors on demand changes; the acceleration of the control push device is u k ; process noise is w k , the observed value is g k ; The observation matrix is ​​H; The observation noise is v k, then, the Kalman filter prediction model is expressed as:

[0108] x k =Ax k-1 +Bu k +w k

[0109] y k =Hx k +v k

[0110] Among them, y k It is the observation value at the kth moment. When the Kalman filter prediction model performs error prediction, it predicts the path error by analyzing the historical path, motion trajectory and external environment changes of the equipment.

[0111] In this embodiment, when the closed-loop PID control is used to perform closed-loop correction on the motion state of the intelligent transport device, the motion speed and direction of the intelligent transport device are adjusted in real time.

[0112] For example, if the following tasks need to be performed in the warehouse:

[0113] 1. Task 1: Transfer a batch of materials from point A to point B (priority: high, deadline: within 10 minutes).

[0114] 2. Task 2: Move a batch of materials from point C to point D (priority: medium, deadline: within 30 minutes).

[0115] 3. Task 3: Ship a batch of materials from point E to point F (priority: low, deadline: within 60 minutes).

[0116] The scheduling process is:

[0117] 1. Based on the task priority, the system first arranges the intelligent handling equipment to perform task 1.

[0118] 2. When Task 1 is executed, the system dynamically calculates the optimal path through the A* algorithm to ensure that the task can be completed efficiently.

[0119] 3. After completing Task 1, the system automatically arranges the device to perform Task 2 and avoids other devices that are performing tasks during path planning.

[0120] 4. Since Task 3 has a lower priority, the system schedules it for execution last based on the status of the idle devices.

[0121] After a task is completed, the system automatically generates a task report, recording the execution time, equipment status and task completion status for the administrator to conduct subsequent analysis and optimization. The intelligent management and control method of power material in and out of the warehouse based on intelligent handling of the present invention realizes the intelligent management of power material warehouses by integrating Internet of Things devices, sensors, dynamic path optimization algorithms and task scheduling mechanisms. By collecting environmental data, equipment status and material storage information in real time, the system can dynamically adjust the equipment path, optimize the execution of handling tasks, and efficiently utilize warehouse resources, improve material storage and access efficiency, reduce labor costs, and enhance the flexibility and accuracy of warehouse management.

[0122] Step S40: Use the established global task scheduling prediction model to dynamically predict material demand based on historical inventory data and external demand data, and perform priority sorting of tasks in the warehouse and dynamic scheduling of inventory.

[0123] In this step, the priority of tasks in the warehouse and the dynamic scheduling of inventory are performed. The priority of tasks is set as follows during the priority sorting:

[0124] P task =α·Demand forecast error+β·Order urgency+γ·Inventory level

[0125] Where P task is the priority of the task, which is used to characterize the urgency of the task in the entire scheduling. The higher the priority of the task, the earlier it will be executed; α is the weight coefficient of the demand forecast error, which characterizes the influence of the demand forecast error on the task priority. The larger the demand forecast error, the higher the task priority; the demand forecast error is the difference between the material demand predicted by the Kalman filter prediction model and the actual demand; β is the weight coefficient of the order urgency, which characterizes the influence of the order urgency on the task priority. If the order is very urgent, the task priority should be higher; γ is the weight coefficient of the inventory level, which indicates the influence of the inventory level on the task priority.

[0126] In this embodiment, during the dynamic scheduling of inventory, the inventory allocation is optimized according to the scheduling results, and the inventory optimization target is determined, wherein:

[0127]

[0128] In the formula, k represents the kth time period, is the demand forecast value at the kth moment, the actual inventory is the actual quantity of materials stored in the warehouse at the kth moment, and λ is the penalty coefficient for excess inventory.

[0129] The intelligent management and control method for the in-and-out storage of electric power materials based on intelligent handling of the present invention automatically performs the material handling tasks through intelligent handling equipment, reduces manual intervention, and significantly improves the work efficiency of the warehouse. The equipment can dynamically adjust the handling path according to real-time task requirements, avoiding the tediousness and inefficiency of manual path planning, and realizing rapid response and execution of tasks. Through real-time monitoring of material storage locations and environmental data through Internet of Things devices and sensors, intelligent handling equipment can efficiently locate and transport materials. The material storage location and channel planning in the warehouse are optimized, avoiding unnecessary waste of space and improving the storage density of the warehouse.

[0130] The error correction mechanism of the present invention can monitor the status of intelligent handling equipment in real time when performing tasks, and correct the deviation caused by errors by dynamically adjusting the path. This real-time error prediction and automatic correction function effectively reduces the loss, damage or dislocation of materials during transportation, ensuring the accuracy and safety of materials. Material demand forecasting is carried out using historical inventory data and external demand data, combined with a global task scheduling model, and task priority and inventory allocation are dynamically adjusted according to real-time demand. This system can avoid excess or shortage of inventory, ensure that materials are stored and shipped out on demand, further enhance the intelligence and accuracy of inventory management, and reduce inventory management costs.

[0131] Through dynamic path optimization technology, the intelligent handling equipment can select the optimal path in the warehouse according to real-time environmental data and task requirements, reducing equipment travel time and energy consumption. At the same time, the adaptability of the path optimization algorithm enables the system to adjust the equipment path in time according to obstacles in the warehouse, environmental changes and other conditions, avoiding traffic congestion or route blockage, and ensuring efficient completion of the handling task.

[0132] The present invention can effectively reduce safety hazards in the handling process, such as equipment collision, material loss, and mis-access, through intelligent monitoring, path optimization, and error correction. At the same time, the equipment status monitoring module tracks the health status of the equipment in real time, can detect potential faults in advance and issue warnings, and improve the reliability of the equipment and the safety of the warehouse.

[0133] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0134] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0135] In a second aspect of the embodiments of the present invention, the present invention further provides an intelligent control device for power material in and out of the warehouse based on intelligent transportation, comprising:

[0136] The data collection module is used to collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as the handling task demand data of intelligent handling equipment imported by IoT devices;

[0137] The dynamic path optimization module is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time based on the collected real-time data and adjust the equipment travel path according to the real-time environmental data, material storage location data and handling task demand data;

[0138] The error correction module is used for path planning when intelligent handling equipment performs handling tasks in the warehouse. According to the error correction mechanism, the errors in the process of materials entering and leaving the warehouse are corrected through real-time data monitoring, error prediction and automatic correction;

[0139] The global task scheduling module is used to use the established global task scheduling prediction model to dynamically predict material demand based on historical inventory data and external demand data, and to prioritize tasks within the warehouse and dynamically schedule inventory.

[0140] In this embodiment, the data acquisition module includes:

[0141] Environmental data sensors: monitor environmental factors such as temperature, humidity, and light in the warehouse;

[0142] Equipment status sensor: monitors the operating status of intelligent handling equipment, including battery power, equipment failure, operating speed and other parameters;

[0143] Material storage location sensor: detects real-time information about material storage locations to ensure accurate positioning of materials;

[0144] Intelligent handling task data collection sensor: obtains the handling task requirements of intelligent handling equipment in real time, including task type, material type, handling volume and other information.

[0145] Through the above detailed steps, the intelligent control device for electric power materials in and out of the warehouse based on intelligent transportation of the present invention is used to execute the steps of the intelligent control method for electric power materials in and out of the warehouse based on intelligent transportation in the above embodiment, which will not be repeated here.

[0146] The intelligent control device for power material in and out of the warehouse based on intelligent handling of the present invention not only improves the automation and intelligence level of the warehouse, but also significantly improves the efficiency and accuracy of material handling, storage, and scheduling. Through the comprehensive application of the Internet of Things, dynamic path optimization, error correction mechanism, and global task scheduling prediction model, the present invention realizes the intelligence, accuracy, and efficiency of warehouse management, and provides a revolutionary technical solution for the management of power material in and out of the warehouse.

[0147] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope disclosed in the embodiments of the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0148] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0149] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. An intelligent management and control method for power material in and out of storage based on intelligent transportation, characterized in that: The method comprises the following steps: Collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as handling task demand data of intelligent handling equipment imported by IoT devices; Based on the collected real-time data, dynamic path optimization is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time, and the equipment travel path is adjusted according to the real-time environmental data, material storage location data and handling task demand data; Intelligent handling equipment plans the path when performing handling tasks in the warehouse, and corrects errors in the process of materials entering and leaving the warehouse through real-time data monitoring, error prediction and automatic correction based on the error correction mechanism; The established global task scheduling prediction model is used to dynamically predict material demand based on historical inventory data and external demand data, and to prioritize tasks within the warehouse and dynamically schedule inventory.

2. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 1 is characterized in that: Real-time environmental data, equipment status data, and material storage location data collected in the warehouse are obtained through sensors, RFID tags, and cameras installed in the warehouse.

3. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 2 is characterized in that: The real-time environmental data includes the temperature, humidity, light intensity and air velocity data in the warehouse. The temperature and humidity in the air are measured by the temperature and humidity probes of the temperature and humidity sensor, the light intensity in the warehouse is collected by the light sensor, and the data is transmitted to the central control system through the wireless module; The equipment status data includes the operating status, power, speed and position data of the intelligent handling equipment, which are collected through embedded power monitoring sensors, position and speed sensors and inertial sensors. The power monitoring sensor is used to monitor the remaining power of the intelligent handling battery, and the power data is transmitted to the central control system through the battery management system; the position and speed sensor includes the GPS and speed sensor configured on the intelligent handling equipment, which tracks the current position of the handling equipment through real-time positioning; the inertial sensor includes an accelerometer and a gyroscope installed on the intelligent handling equipment, which are used to measure the direction, speed and acceleration of the intelligent handling equipment; The material storage location data is collected through RFID tags, and the tag information is read in real time by an RFID reader installed in the warehouse to obtain the storage location and quantity of the materials.

4. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 1 is characterized in that: In the handling task requirement data of the intelligent handling equipment imported by the IoT device, the data structure of each handling task requirement is: T task ={T type ,P start ,P end ,P priority ,T deadline } Among them, T type is the task type; P start is the starting position of the task; P end is the task target position; P priority is the task priority; T deadline The deadline for the task.

5. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 4 is characterized in that: When using dynamic path optimization to calculate the travel path of intelligent handling equipment in the warehouse in real time, heuristic search path planning is used to search for the optimal path from the starting point to the target point in the warehouse, including the following steps: Set the starting point P start and the end point P goal , initialize the open list and the closed list; Calculate the path cost g(n) from the starting point to the current node, that is, the known path length; Estimate the distance h(n) from the current node to the end point, and calculate the total cost f(n) based on the path cost of the current node; The next node is selected according to the minimum total cost f(n) value until the target node is reached.

6. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 5 is characterized in that: The calculation formula of the total cost f(n) is: f(n)=g(n)+h(n) In the formula, f(n) is the total cost function, which represents the total cost of the current node and is used to select the path. g(n) is the actual cost, that is, the actual distance from the starting point to the current node n. h(n) is the heuristic estimated distance from the current node n to the target node; Among them, the calculation formula of the actual distance g(n) from the starting point to the current node is: Where, d i From the starting point to node n i distance; The calculation formula for the heuristic estimated distance h(n) is: In the formula, x goal ,y goal Represents the horizontal and vertical coordinates of the target position, P goal =(x goal ,y goal ); x n ,y n is the horizontal and vertical coordinates of the current node n.

7. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 1 is characterized in that: According to the error correction mechanism, the errors in the material storage and outbound process are corrected through real-time data monitoring, error prediction and automatic correction, including the following steps: The path error is predicted through the Kalman filter, and the path is corrected according to the prediction results; Determine whether the device path is consistent with the target path through real-time sensor monitoring, error prediction and automatic correction mechanism; If they are consistent, the motion state of the intelligent handling equipment is corrected in a closed loop through PID control.

8. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 7, characterized in that: When predicting the path error through the Kalman filter, the state vector of the intelligent handling device at the kth moment is set to x k ; The state transfer matrix is ​​A, which represents the transfer relationship from the previous state to the current state; the control input matrix is ​​B, which represents the impact of external factors on demand changes; the acceleration of the control push device is u k ; process noise is w k , the observed value is g k ; The observation matrix is ​​H; The observation noise is v k , then, the Kalman filter prediction model is expressed as: x k =Ax k-1 +Bu k +w k y k =Hx k +v k Among them, y k It is the observation value at the kth moment. When the Kalman filter prediction model performs error prediction, it predicts the path error by analyzing the historical path, motion trajectory and external environment changes of the equipment.

9. The intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to claim 8, characterized in that: When closed-loop PID control is used to perform closed-loop correction on the motion state of the intelligent handling equipment, the motion speed and direction of the intelligent handling equipment are adjusted in real time.

10. An intelligent control device for power material in and out of the warehouse based on intelligent transportation, characterized in that: The system is used to execute the intelligent management and control method for power material in and out of the warehouse based on intelligent transportation according to any one of claims 1 to 9, and comprises: The data collection module is used to collect real-time data in the warehouse through IoT devices and sensors, including real-time environmental data, equipment status data, and material storage location data collected by sensors, as well as the handling task demand data of intelligent handling equipment imported by IoT devices; The dynamic path optimization module is used to calculate the travel path of the intelligent handling equipment in the warehouse in real time based on the collected real-time data and adjust the equipment travel path according to the real-time environmental data, material storage location data and handling task demand data; The error correction module is used for path planning when intelligent handling equipment performs handling tasks in the warehouse. According to the error correction mechanism, the errors in the material entry and exit process are corrected through real-time data monitoring, error prediction and automatic correction; The global task scheduling module is used to use the established global task scheduling prediction model to dynamically predict material demand based on historical inventory data and external demand data, and to prioritize tasks within the warehouse and dynamically schedule inventory.

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