Industrial internet-of-things automatic storage intelligent scheduling system and method

Abstract

The invention relates to the technical field of industrial Internet of Things, and discloses an industrial Internet of Things automatic storage intelligent scheduling system and method, and the method comprises the steps: determining a parameter range of storage equipment in a target scheduling scene, setting different scheduling parameters, and enabling the equipment to schedule a storage task according to the parameters; then acquiring position information of articles in the storage tasks under different parameters, generating a storage space state distribution diagram, acquiring equipment operation data, determining a response delay rate, and fitting to obtain a scheduling efficiency coefficient; and finally, determining the task matching degree based on the efficiency coefficient and the state distribution diagram, and further obtaining the optimal scheduling time. Through data-driven intelligent scheduling optimization, efficient utilization of the storage space and improvement of the equipment operation efficiency are realized, the optimal scheduling time can be accurately determined, the scheduling efficiency and the intelligent level of automatic storage in the industrial Internet of Things environment are improved, and the industrial application value is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things, and specifically to an industrial Internet of Things automated warehousing intelligent scheduling system and method. Background Art

[0002] With the rapid development of industrial IoT technology, automated warehousing systems are increasingly being used in modern industrial production. Traditional industrial warehousing scheduling systems often suffer from low scheduling efficiency when faced with complex warehousing tasks. On the one hand, it is difficult to dynamically adjust scheduling parameters based on changes in actual warehousing scenarios, resulting in a mismatch between equipment operating status and task requirements. This prevents warehousing equipment from fully utilizing its performance advantages when handling different types of warehousing tasks. On the other hand, traditional systems lack scientific planning and analysis of storage space utilization, making it impossible to monitor the distribution of items within the storage space in real time. This can easily lead to wasted storage space or chaotic layout, which in turn affects the efficiency of warehousing tasks.

[0003] Existing technologies have significant shortcomings in data collection and processing. They are unable to comprehensively and accurately collect operational data from storage equipment under different scheduling parameters, making it difficult to accurately assess equipment response delays, resulting in an inability to promptly identify bottlenecks in equipment operation. Furthermore, traditional scheduling methods lack a comprehensive analysis of storage tasks and storage space status, and are unable to determine optimal scheduling times based on efficient algorithmic models. This makes the completion process of storage tasks unscientific and unreasonably complex, increasing the cost and time consumption of warehouse management.

[0004] In practice, traditional Industrial Internet of Things (IIoT) automated warehouse scheduling systems also face the problem of irrational parameter settings. Due to a lack of systematic research into the parameter ranges of target scheduling scenarios, scheduling parameter settings often rely on empirical judgment and are unable to adapt to the diverse demands of warehousing tasks. This often leads to wasted equipment resources and backlogs during the scheduling process. Furthermore, existing systems have a relatively simple approach to integrating warehousing tasks, failing to fully leverage IIoT communication protocols to integrate data such as inventory records, order information, and equipment parameters. This makes it difficult to form a comprehensive and accurate basic scheduling task, hindering the accuracy and effectiveness of scheduling decisions. Summary of the Invention

[0005] The purpose of the present invention is to provide an industrial Internet of Things automated warehousing intelligent scheduling system and method to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an industrial Internet of Things automated warehousing intelligent scheduling method, the method comprising:

[0007] Determine the parameter range of the storage equipment in the target scheduling scenario, set different scheduling parameters within the parameter range, and the target scheduling equipment schedules the storage tasks according to the set different scheduling parameters;

[0008] Data collection is performed on storage tasks that have undergone different scheduling parameters in the target scheduling equipment to obtain the location information of items in the storage tasks under different scheduling parameters. Then, based on all the location information, the state distribution diagram of the storage space under different scheduling parameters is determined;

[0009] Collect the operating data of storage equipment under different scheduling parameters, determine the response delay rate of the storage equipment under different scheduling parameters based on all the operating data, and perform efficiency coefficient fitting on all the response delay rates to obtain the scheduling efficiency coefficient of the storage equipment;

[0010] The matching degree of the warehousing tasks under different scheduling parameters is determined based on the scheduling efficiency coefficient and all state distribution diagrams, and the optimal scheduling time when the warehousing tasks are scheduled is determined through all the matching degrees.

[0011] Preferably, data collection is performed on storage tasks that have undergone different scheduling parameters in the target scheduling device to obtain location information of items in the storage tasks under different scheduling parameters, specifically including:

[0012] For each warehousing task under each scheduling parameter, obtain positioning data consisting of position signals transmitted by IoT sensors in the warehousing environment;

[0013] The location information of the items in the storage task under each scheduling parameter is determined according to the positioning data.

[0014] Preferably, determining the state distribution diagram of the storage space under different scheduling parameters based on all the location information specifically includes:

[0015] For each storage task under each scheduling parameter, use the spatial modeling tool to analyze the operation trajectory of the storage task and obtain a dynamic layout diagram of the storage space;

[0016] A state distribution diagram of the storage space under each scheduling parameter is determined according to the location information of the items in the storage task and the dynamic layout diagram.

[0017] Preferably, determining the response delay rate of the storage equipment under different scheduling parameters based on all operating data specifically includes:

[0018] For storage equipment under different scheduling parameters, the completion time of each task node is determined based on the operation data of the storage equipment;

[0019] Determine the standard cycle for storage equipment to process tasks;

[0020] Determine the delay value of each task node based on all completion times and the standard period;

[0021] The response delay rate of the storage equipment is determined by all delay values, and then the response delay rate of the storage equipment under different scheduling parameters is obtained.

[0022] Preferably, determining the matching degree of warehousing tasks under different scheduling parameters based on the scheduling efficiency coefficient and all state distribution graphs specifically includes:

[0023] Obtain the ideal state distribution diagram of the space when warehouse task scheduling is smooth;

[0024] For storage tasks under different scheduling parameters, determining the distribution difference between the item distribution in the storage task and the ideal distribution according to the state distribution diagram of the storage space and the ideal state distribution diagram;

[0025] The matching degree of the warehousing task is determined based on the scheduling efficiency coefficient and the distribution difference, and then the matching degree of the warehousing task under different scheduling parameters is obtained.

[0026] Preferably, the warehousing task is a basic scheduling task obtained by integrating data based on warehousing records, order information and equipment parameters using the industrial Internet of Things communication protocol.

[0027] Preferably, the equipment parameters are execution parameters corresponding to the transport instructions or sorting instructions.

[0028] Preferably, determining the distribution difference between the distribution of items in the storage task and the ideal distribution according to the state distribution diagram of the storage space and the ideal state distribution diagram specifically includes:

[0029] For each storage task under each scheduling parameter, extract the actual coordinate set of the items in the state distribution map and the target coordinate set of the items in the ideal state distribution map;

[0030] The coordinate deviation values of the corresponding positions in the actual coordinate set and the target coordinate set are counted, and the distribution difference is determined by the average value of all coordinate deviation values.

[0031] Preferably, determining the standard cycle of storage equipment processing tasks specifically includes:

[0032] Obtain the time records of storage equipment completing similar tasks during historical scheduling;

[0033] The median of all time records is calculated, and the median is used as the standard cycle time for the storage equipment to process tasks.

[0034] Preferably, the present invention further includes an industrial Internet of Things automated warehousing intelligent scheduling system, wherein the system includes a warehousing task scheduling time determination device, and the warehousing task scheduling time determination device includes:

[0035] A parameter setting module is used to determine the parameter range of the storage equipment in the target scheduling scenario, set different scheduling parameters within the parameter range, and instruct the target scheduling equipment to schedule the storage tasks according to the set different scheduling parameters;

[0036] The data processing module is used to control the data collection of storage tasks that have undergone different scheduling parameters in the target scheduling equipment, obtain the location information of the items in the storage tasks under different scheduling parameters, and then determine the state distribution map of the storage space under different scheduling parameters based on all the location information;

[0037] The data processing module is further configured to collect operating data of storage equipment under different scheduling parameters, determine the response delay rate of the storage equipment under different scheduling parameters based on all the operating data, perform efficiency coefficient fitting on all the response delay rates, and obtain the scheduling efficiency coefficient of the storage equipment;

[0038] The strategy generation module is used to determine the matching degree of the warehousing tasks under different scheduling parameters based on the scheduling efficiency coefficient and all state distribution diagrams, and determine the optimal scheduling time when the warehousing tasks are completed through all matching degrees.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The Industrial Internet of Things (IIoT) automated warehousing intelligent scheduling system and method provided by this invention can improve the efficiency and intelligence of warehousing scheduling. By determining the parameter range of warehousing equipment in target scheduling scenarios and setting different scheduling parameters, the system can flexibly adjust scheduling strategies based on actual needs and adapt to diverse warehousing task scenarios. This avoids the limitations of traditional methods that rely on experience for parameter setting and improves the matching of scheduling parameters with task requirements.

[0041] Data is collected from storage tasks that experience different scheduling parameters in the target scheduling equipment, and the location information of the items is determined by combining the location signals transmitted by the IoT sensors. The operation trajectory is then analyzed through spatial modeling tools to obtain a state distribution map of the storage space. This process enables real-time and accurate grasp of the storage space status, helps optimize the storage space layout, improve space utilization, and avoid waste of storage space and layout chaos.

[0042] By collecting the operating data of storage equipment under different scheduling parameters, determining the response delay rate by calculating the delay value between the task node completion time and the standard cycle, and performing efficiency coefficient fitting, it is possible to accurately evaluate the operating efficiency of the equipment, promptly discover bottleneck problems in equipment operation, and provide a scientific basis for the optimal scheduling of equipment, effectively reducing equipment response delays and improving equipment operating efficiency.

[0043] Based on the scheduling efficiency coefficient and the state distribution diagram, the matching degree of the warehousing task is determined, and then the optimal scheduling time is determined, which realizes scientific decision-making on the scheduling of warehousing tasks, avoids the blindness of scheduling time determination in traditional methods, enables warehousing tasks to be completed within the optimal time, improves the execution efficiency of warehousing tasks, and reduces the time and cost consumption of warehousing management.

[0044] This method uses warehouse records, order information, and equipment parameters as its basis, integrating data using the Industrial Internet of Things (IIoT) communication protocol to generate basic scheduling tasks. This ensures the comprehensiveness and accuracy of scheduling tasks and provides reliable data support for subsequent scheduling optimization. The system's parameter setting module, data processing module, and strategy generation module collaborate to automate the entire process, from parameter setting and data collection and analysis to strategy generation. This improves the automation and intelligence level of warehouse scheduling, enabling the system to handle warehouse scheduling tasks more efficiently and accurately, adapting to the complex warehouse management needs of the IIoT environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a working principle diagram of the industrial Internet of Things automated warehousing intelligent scheduling method according to the present invention;

[0046] Figure 2 Flowchart generated for the storage space status distribution map;

[0047] Figure 3 Flowchart for response delay rate calculation;

[0048] Figure 4 Flowchart for determining the matching degree of warehousing tasks. DETAILED DESCRIPTION

[0049] 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.

[0050] See also Figures 1-4 The present invention relates to an industrial Internet of Things automated warehousing intelligent scheduling method, and the specific implementation steps are as follows:

[0051] Determine the parameter range for storage equipment in the target scheduling scenario, set different scheduling parameters within this parameter range, and the target scheduling equipment will schedule storage tasks based on the set scheduling parameters. Storage tasks are based on incoming records, order information, and equipment parameters, and are derived from data integration using the Industrial Internet of Things communication protocol. Equipment parameters are the execution parameters corresponding to handling instructions or sorting instructions.

[0052] Data is collected from storage tasks within the target scheduling device that undergo different scheduling parameters. The location information of items in these storage tasks under different scheduling parameters is obtained, and then the state distribution diagram of the storage space under these scheduling parameters is determined based on this location information. For each storage task under each scheduling parameter, positioning data consisting of position signals transmitted by IoT sensors in the storage environment is obtained. Based on this positioning data, the location information of items in each storage task under each scheduling parameter is determined. Furthermore, for each storage task under each scheduling parameter, a spatial modeling tool is used to analyze the operation trajectory of the storage task, generating a dynamic layout diagram of the storage space. Based on the location information of items in the storage task and the dynamic layout diagram, the state distribution diagram of the storage space under each scheduling parameter is determined.

[0053] The operating data of storage equipment under different scheduling parameters is collected. Based on all the operating data, the response delay rate of the storage equipment under different scheduling parameters is determined. The efficiency coefficient is fitted for all response delay rates to obtain the scheduling efficiency coefficient of the storage equipment. For storage equipment under different scheduling parameters, the completion time of each task node is determined based on the storage equipment's operating data. The time records of the storage equipment completing similar tasks in the historical scheduling process are obtained, and the median of all time records is calculated. The median is used as the standard period for the storage equipment to process tasks. The delay value of each task node is determined based on all completion times and standard periods. The response delay rate of the storage equipment is determined based on all delay values, and the response delay rate of the storage equipment under different scheduling parameters is obtained.

[0054] Based on the scheduling efficiency coefficient and all state distribution graphs, the matching degree of storage tasks under different scheduling parameters is determined. The optimal scheduling time when the storage tasks are scheduled is determined through all matching degrees. The ideal state distribution graph of the space when the storage tasks are smoothly scheduled is obtained. For storage tasks under different scheduling parameters, the actual coordinate set of the items in the state distribution graph and the target coordinate set of the items in the ideal state distribution graph are extracted. The coordinate deviation values of the corresponding positions in the actual coordinate set and the target coordinate set are calculated. The distribution difference is determined by the average value of all coordinate deviation values. The matching degree of the storage tasks is determined based on the scheduling efficiency coefficient and the distribution difference. Then, the matching degree of the storage tasks under different scheduling parameters is obtained, and the optimal scheduling time is determined through all matching degrees.

[0055] Example 1:

[0056] When collecting data on storage tasks that have undergone different scheduling parameters in the target scheduling device to obtain the location information of items in the storage tasks under different scheduling parameters, the specific implementation method is as follows:

[0057] For each warehousing task under each scheduling parameter, IoT sensors deployed in the warehouse environment are needed to obtain location signals. These IoT sensors can be of various types, such as RFID sensors, which use radio frequency identification technology to locate and track tagged items. When an item moves within the warehouse space, an RFID reader can read the signal emitted by the tag, thereby obtaining information about the item's location. Infrared sensors, on the other hand, determine an object's location by detecting whether the object blocks or reflects infrared light. When an item passes through the infrared sensor's detection area, the sensor generates a corresponding signal change, which is used to determine the item's location.

[0058] Acquiring location signals requires real-time reception of signals transmitted by these sensors. A warehouse environment may have multiple sensors deployed at various locations to provide comprehensive coverage of the entire storage space. Upon detecting an item, each sensor transmits a signal containing the item's location information to the data acquisition system. These location signals, which may take the form of electrical or digital signals, contain parameters related to the item's location within the storage space, such as its orientation and distance relative to the sensor.

[0059] These location signals need to be integrated and processed to form positioning data. Because signals from different sensors may have different formats and characteristics, they require unified processing and conversion. For example, signals from RFID sensors need to be parsed to extract the tag ID and corresponding reader location information. Infrared sensor signals require determining the specific location of an item based on signal variations from multiple sensors. Through processing steps such as signal collection, filtering, and calibration, these dispersed location signals are integrated into positioning data that reflects the item's location within the warehouse space.

[0060] After obtaining positioning data, it needs to be analyzed and calculated to determine the location information of the items in the storage task under each scheduling parameter. This requires the use of specific positioning algorithms to achieve precise positioning. For example, the triangulation positioning algorithm uses multiple reference points with known locations (such as sensor locations) to measure the distance or angle between the target item and these reference points, and then uses geometric triangulation to calculate the coordinates of the target item. Specifically, when an item is in the storage space, multiple RFID readers or infrared sensors can detect the item, measure the distance or angle to the item, and then use triangulation to determine the item's specific location.

[0061] Another commonly used positioning algorithm is the fingerprint positioning algorithm, which requires building a location fingerprint database for the warehouse space. During the offline phase, signal characteristics (such as RFID signal strength and infrared signal pattern) are collected at each location point to form a location fingerprint. During the online positioning phase, the signal characteristics of the current item are collected and matched with the location fingerprints in the database. The location corresponding to the most similar fingerprint is found, thereby determining the item's location.

[0062] When using positioning algorithms for calculations, it's necessary to consider various interference factors in the warehouse environment, such as signal obstruction and reflection from shelves and equipment. These factors can lead to positioning errors. Therefore, during the calculation process, positioning data must be corrected and optimized. For example, random errors can be reduced by collecting signals multiple times and taking the average. Alternatively, algorithms such as Kalman filtering can be used to smooth the positioning results and improve positioning accuracy.

[0063] Through the above steps, the specific location information of the items in the storage space for each storage task under each scheduling parameter can be determined. This location information includes detailed coordinate information such as the shelf number, layer number, and column number where the item is located, accurately describing the item's location in the storage space. For example, the location information for an item might be expressed as being on shelf number 3, layer 2, column 5.

[0064] Throughout the entire data collection and location determination process, ensuring real-time and accurate data is crucial. Real-time data requires timely detection of item location changes to facilitate timely adjustment of scheduling parameters; accuracy ensures a reliable foundation for subsequent analysis of storage space status distribution and assessment of scheduling efficiency. Therefore, IoT sensors require regular performance inspection and maintenance to ensure proper operation. Simultaneously, the software and hardware of the data collection and processing systems must be optimized to improve data processing speed and accuracy.

[0065] Depending on the scheduling parameters, the frequency and method of data collection may need to be adjusted. For example, when scheduling parameters vary significantly, the frequency of data collection may need to be increased to more thoroughly record the changes in item location under different scheduling parameters. By collecting and analyzing item location information under multiple scheduling parameters, comprehensive data support can be provided for the subsequent determination of the storage space status distribution map, thereby better understanding the impact of different scheduling parameters on the location distribution of items in storage tasks.

[0066] Example 2:

[0067] When determining the state distribution diagram of the storage space under different scheduling parameters based on all the location information, the specific implementation method is as follows:

[0068] For each storage task under each scheduling parameter, spatial modeling tools are needed to analyze its trajectory. Spatial modeling tools can be professional 3D modeling software or software specifically designed for warehouse simulation. These tools digitally simulate the movement of items in a storage task, enabling trajectory analysis.

[0069] When analyzing the movement trajectory, it's necessary to obtain the location information of the items in the storage task under each scheduling parameter. This location information, obtained through the previous data collection step, contains the specific coordinates of the items at different points in time. This location information is input into the spatial modeling tool, which connects the item locations in chronological order to form the item's movement path within the storage space. For example, if an item moves from shelf A to shelf B and then to the sorting station, its movement path will be intuitively presented in the modeling tool.

[0070] In addition to analyzing the movement paths of items, spatial modeling tools also analyze the dwell time of items at various locations. For example, how long does an item stay at shelf A for pickup, or at a sorting station for sorting? This dwell time analysis is crucial for understanding warehouse operational efficiency and space utilization.

[0071] Spatial modeling tools also consider the operational status of warehouse equipment. For example, the movement paths of equipment like forklifts and stackers as they move items, as well as the interactions between these devices. When multiple devices operate simultaneously within a warehouse space, their paths may intersect or overlap. Spatial modeling tools analyze these situations to determine the impact of equipment operation on the state of the warehouse space.

[0072] By comprehensively analyzing the movement trajectory, spatial modeling tools can construct a dynamic layout diagram of the warehouse space. This dynamic layout diagram is not static, but rather reflects the distribution of items and equipment within the warehouse space at different points in time. For example, at a given point in time, the dynamic layout diagram will show which shelves have items, which items are being moved, and the location of equipment.

[0073] Dynamic layout diagrams can also demonstrate warehouse space utilization. By analyzing the distribution of items and equipment within a space, you can calculate the space utilization of different areas, understanding which areas are fully utilized and which areas are wasted. This provides a valuable reference for optimizing warehouse space layout and scheduling parameters.

[0074] The storage space's status distribution map is determined based on the item location information and the dynamic layout diagram. First, the item location information is marked on the dynamic layout diagram. Each item's specific location, such as shelf number, layer number, and column number, must be accurately marked on the diagram. This allows for a clear view of the distribution of all items on the dynamic layout diagram.

[0075] The annotated dynamic layout diagram is further processed and analyzed. The distances and relative positions between items need to be considered. For example, whether certain items are placed on adjacent shelves may affect subsequent handling efficiency. Furthermore, the type and attributes of the items, such as weight, size, and storage requirements, also need to be considered; these factors can also affect the state distribution of the storage space.

[0076] When determining the state distribution diagram, it's also important to consider the warehouse's structure and facility layout. For example, the location of aisles, entrances and exits, and sorting stations can restrict the movement of items and equipment, thus affecting the state distribution of the warehouse. Therefore, the location and layout of these facilities must be accurately reflected in the state distribution diagram.

[0077] The status distribution diagram needs to intuitively present the status of the storage space. Different colors or symbols can be used to represent different types of items or different storage states. For example, red could represent items waiting to be shipped out, blue could represent items in stock, and green could represent items being transported. This way, the status distribution diagram can quickly understand the distribution and status of items in the storage space.

[0078] The state distribution diagram will vary depending on the scheduling parameters. For example, when the handling speed in the scheduling parameter changes, the movement trajectory and residence time of the items will also change accordingly, resulting in a change in the state distribution diagram. Therefore, it is necessary to generate a state distribution diagram for each storage task under each scheduling parameter for comparison and analysis.

[0079] When generating a status distribution map, it's crucial to ensure data accuracy and completeness. Errors or missing location information can lead to inaccurate status distribution maps, impacting subsequent assessments of scheduling efficiency and the determination of optimal scheduling times. Therefore, rigorous data verification and validation are essential when annotating item locations and constructing dynamic layouts.

[0080] The status distribution diagram also needs to reflect the dynamic changes in the storage space. This can be achieved by generating status distribution diagrams at different time points or creating animations of the status distribution to illustrate how the storage space changes during the scheduling process. This is very helpful for analyzing the impact of different scheduling parameters on the storage space status.

[0081] Through the above steps, we can ultimately determine the state distribution diagrams of the storage space under each scheduling parameter. These state distribution diagrams can comprehensively and accurately reflect information such as the distribution of items in the storage space, the operating status of equipment, and space utilization under different scheduling parameters. This provides an important basis for subsequently determining the matching degree of storage tasks based on the scheduling efficiency coefficient and the state distribution diagrams. By analyzing the state distribution diagrams under multiple scheduling parameters, we can find the optimal scheduling parameters, thereby improving the efficiency and accuracy of warehouse scheduling.

[0082] Example 3:

[0083] When determining the response delay rate of storage equipment under different scheduling parameters based on all operating data, the specific implementation method is as follows:

[0084] For warehouse equipment under different scheduling parameters, operational data must be collected. This data is collected through the equipment's built-in sensors, control system, and warehouse management system. Built-in sensors, such as encoders and current sensors, monitor the equipment's motion status and motor current in real time. The control system records the equipment's operating instructions, task start and end times, and other information. The warehouse management system integrates the equipment's task execution data throughout the entire scheduling process.

[0085] After acquiring operational data, the completion time of each task node needs to be determined from this data. Task nodes include key time points such as task receipt, execution start, intermediate processing steps, and task completion. For example, for a moving task, the completion time of a task node may include: the time when the warehouse management system sends the moving instruction to the equipment, the time when the equipment receives the instruction and begins moving, the time when the equipment reaches the target shelf, the time when the item is picked up, the time when it moves to the designated location, and the time when the item is placed down and completion is confirmed. These time points must be accurately extracted from the operational data to ensure the accuracy of time records.

[0086] The next step is to determine the standard processing cycle for warehouse equipment tasks. This requires obtaining historical records of the time it took the equipment to complete similar tasks during scheduling. Similar tasks refer to tasks that are similar in terms of task type (such as inbound, outbound, and sorting), cargo attributes (such as weight and size), and operational processes (such as single-threaded handling or multi-process collaboration). Historical time records can be extracted from equipment operation logs or the warehouse management system database. These records contain the specific time required to complete each task, such as the execution time data for a certain type of outbound task over the past 30 days.

[0087] After obtaining the time records, the data needs to be processed to calculate the median. First, all time records are sorted from least to most. If the number of time records is odd, the median is the value in the middle. If the number is even, the median is the average of the two middle values. For example, if there are seven time records sorted as 120s, 125s, 130s, 135s, 140s, 145s, and 150s, the fourth number in the middle, 135s, is the median. If there are eight records, such as 120s, 125s, 130s, 135s, 140s, 145s, 150s, and 155s, the median is calculated as the average of the two middle values, 135s and 140s, or 137.5s. This median is determined as the standard processing period for the equipment, reflecting the typical time it takes to complete similar tasks under normal operating conditions.

[0088] After obtaining the standard cycle, it is necessary to compare the completion time of each task node with the standard cycle to determine the delay value of each task node. Specifically, for each task node's actual completion time, calculate the difference between it and the time of the corresponding node in the standard cycle. For example, if the expected completion time of a task node in the standard cycle is 30s, and the actual completion time is 35s, then the delay value of this node is 5s. If the actual completion time is earlier than the expected time, the delay value is negative, but when calculating the response delay rate, usually only the impact of positive delays on efficiency is considered.

[0089] After the delay values of all task nodes are determined, these values need to be used to calculate the response delay rate of the storage equipment. The calculation of the response delay rate needs to comprehensively consider the delay of all nodes. For example, the average value of all delay values can be calculated, or the delay of different nodes can be weighted (different weights are assigned according to the importance of the node). For example, for a task with 5 task nodes, the delay values of each node are 2s, 0s, 3s, 1s, and 4s respectively. The average delay value is , the average value can be used as a parameter representing the response delay rate.

[0090] During the calculation process, attention should be paid to data validity and handling of outliers. If the completion time of a task node in a particular run data item deviates significantly from the normal range (e.g., due to an equipment failure), the data should be screened and removed to prevent outliers from interfering with the response delay rate calculation results. For example, if a time record takes three times longer than normal due to a sudden equipment failure, such data should be excluded from the statistical sample.

[0091] For different scheduling parameters, the corresponding response delay rate needs to be calculated separately. For example, when the device operating speed in the scheduling parameters is set to high speed, medium speed, and low speed, operating data under these three parameters is collected and the response delay rate for each is calculated according to the above steps. This can determine the device's response delay performance under different scheduling parameters, providing data support for subsequent efficiency coefficient fitting.

[0092] Throughout the process of determining the response delay rate, it's crucial to ensure both the real-time and integrity of operational data. Real-time performance requires the data acquisition system to promptly record the completion time of each task node, avoiding timestamp lags or loss. Integrity ensures coverage of all critical task nodes, ensuring no important processing steps are missed. Therefore, regular maintenance and calibration of the data acquisition system are essential to ensure proper functioning of sensors and control systems, while optimizing data storage and transmission processes to prevent data loss or errors.

[0093] Furthermore, for complex warehousing tasks, it may be necessary to break the task down into multiple subtasks and calculate the response delay rate for each subtask separately. For example, for a complex task involving transport and sorting, the transport and sorting stages can be treated as independent subtasks, with their respective standard cycle times and delay values determined, allowing for a more detailed analysis of efficiency issues in each stage.

[0094] Through the above steps, the response delay rate of storage equipment under different scheduling parameters is ultimately obtained. This delay rate data can intuitively reflect the impact of scheduling parameters on equipment response efficiency. For example, under high-speed scheduling parameters, the equipment may have an increased response delay rate due to excessive load; while under low-speed parameters, the response delay rate may decrease, but overall operating efficiency will also decrease. This data provides a basis for subsequent efficiency coefficient fitting of the response delay rate, and then derives the scheduling efficiency coefficient of the storage equipment. This allows the advantages and disadvantages of different scheduling parameters to be evaluated from an efficiency perspective, providing a basis for determining the optimal scheduling time.

[0095] Example 4:

[0096] When determining the matching degree of warehousing tasks under different scheduling parameters based on the scheduling efficiency coefficient and all state distribution graphs, the specific implementation method is as follows:

[0097] To obtain an ideal spatial distribution map for smooth warehouse task scheduling, this map must be developed based on the design specifications of the warehouse space, basic rules for item storage, and empirical data from past efficient scheduling cases. For example, in an automated warehouse, an ideal distribution map would specify layout principles such as heavy goods should be stored on the bottom shelves, frequently shipped items should be located near entrances and exits, and similar items should be stored together. The map would also digitally represent the standard coordinate positions of items on the shelves.

[0098] For warehousing tasks under different scheduling parameters, the actual coordinate sets of the items must be extracted from the state distribution map. For example, for a warehousing task under certain scheduling parameters, assume the warehousing system is currently processing the warehousing of 100 electronic products. The state distribution map, using location data collected by IoT sensors, records the specific location of each product on the shelf. For example, Product A is located on Shelf 5, Layer 3, Column 8, while Product B is located on Shelf 7, Layer 2, Column 4. This specific location information constitutes the actual coordinate set. Simultaneously, the corresponding target coordinate set is extracted from the ideal state distribution map. Based on the aforementioned storage rules, this target coordinate set specifies that Product A should be located on Shelf 5, Layer 3, Column 5 (close to the aisle for easy outbound delivery) and Product B should be located on Shelf 7, Layer 2, Column 2 (collectively placed with similar products).

[0099] Count the coordinate deviations between the corresponding locations in the actual coordinate set and the target coordinate set. For example, for three-dimensional coordinates (x, y, z), if product A's actual coordinates are (5, 3, 8) and its target coordinates are (5, 3, 5), the deviation along the z-axis is 3. For product B's actual coordinates are (7, 2, 4) and its target coordinates are (7, 2, 2), the deviation along the z-axis is 2. If the batch of warehousing tasks contains 100 products, calculate the coordinate deviations along the x, y, and z dimensions for each product individually and then aggregate all the deviations.

[0100] The distribution difference is determined by the average of all coordinate deviation values. Using the above example, after calculating the coordinate deviation values of 100 products, the total deviation on the x-axis is 20, the total deviation on the y-axis is 15, and the total deviation on the z-axis is 30. Taking the weighted average of the deviation values of the three dimensions (assuming the weights of the x, y, and z axes are all 1 / 3), the average deviation value is: (This is just a calculation logic example and does not involve the specific validity of the value.) This value reflects the degree of difference between the actual distribution of items and the ideal distribution under the current scheduling parameters. The larger the value, the more obvious the distribution difference, and the scheduling process may have problems such as low space utilization and chaotic item placement.

[0101] After obtaining the distribution variance, the previously calculated scheduling efficiency coefficient is combined to determine the matching degree of the warehousing task. The scheduling efficiency coefficient is a comprehensive indicator obtained by fitting the device response delay rate. For example, if the average device response delay rate under certain scheduling parameters is 15%, it can be converted into an efficiency coefficient of 0.85 using a specific fitting model (such as linear or nonlinear fitting). (The value is for illustration only and does not represent actual results.) The matching degree calculation requires a comprehensive consideration of the efficiency coefficient and the distribution variance, for example, using a weighted multiplication method: Matching degree = Efficiency coefficient × (1 - Distribution variance). Assuming an efficiency coefficient of 0.85 and a distribution variance of 0.217, the matching degree is 0.85 × (1 - 0.217) = 0.665 (this is for demonstration purposes only, not actual data).

[0102] For different scheduling parameters, the above steps need to be repeated to calculate the respective matching degrees. For example, when the handling speed in the scheduling parameters is adjusted from 0.5m / s to 0.8m / s, the state distribution diagram under this parameter is re-collected, the actual coordinate set is extracted, and the distribution difference is calculated after comparison with the ideal coordinate set. The matching degree is then calculated based on the new efficiency coefficient. Assuming that the distribution difference becomes 0.18 after adjustment, and the efficiency coefficient increases to 0.9 due to the reduction in the equipment response delay rate, the matching degree is 0.9×(1-0.18)=0.738, which is an increase from the 0.665 before the adjustment, indicating that the scheduling parameter adjustment has a positive impact on the matching degree of the warehousing task.

[0103] In practice, the ideal distribution map is not static and can be dynamically adjusted based on changes in warehousing operations. For example, when a warehouse introduces a new product line or adjusts the delivery frequency, the target coordinate set in the ideal distribution map must be replanned to adapt to the new business needs. Furthermore, the collection of the actual coordinate set requires high-precision IoT positioning technologies, such as UWB positioning or visual positioning systems, to ensure the accuracy of location data and avoid distortion in the distribution difference calculation caused by positioning errors.

[0104] For complex warehousing tasks (such as sorting high-variety, low-volume orders), the ideal distribution map must consider order combination optimization strategies. For example, items within the same order should be stored together as much as possible to reduce sorting paths. In this case, the comparison between the actual coordinate set and the target coordinate set must not only consider the positional deviation of individual items but also assess the degree of clustering of order items. By calculating the deviation between the actual cluster center of the order items and the ideal cluster center, a more comprehensive reflection of distribution variability can be achieved.

[0105] During the matching calculation process, the weighting of the efficiency coefficient and distribution diversity should be determined based on the actual needs of warehouse management. If a warehouse prioritizes equipment efficiency, a higher weight can be assigned to the efficiency coefficient; if a warehouse focuses more on space utilization and proper item placement, a higher weight can be assigned to distribution diversity. This flexible weighting allows the matching indicator to adapt to different warehouse management objectives.

[0106] By comparing and analyzing the matching degrees under multiple scheduling parameters, the optimal scheduling time for completing warehouse tasks can be determined. For example, when the matching degree of scheduling parameter A is 0.75, parameter B is 0.82, and parameter C is 0.78, the scheduling parameter combination corresponding to parameter B is considered optimal. At this time, the optimal scheduling time for completing this batch of warehouse tasks can be determined based on the task execution time data under parameter B. This entire process provides data support for warehouse scheduling decisions by quantitatively analyzing the matching degree between scheduling efficiency and spatial distribution, avoiding the blindness of traditional empirical scheduling.

[0107] Example 5:

[0108] When determining the standard cycle for storage equipment processing tasks, the specific implementation methods are as follows:

[0109] The warehouse management system or equipment monitoring system needs to collect time records for similar tasks completed by warehouse equipment during historical scheduling. For example, a stacker crane in an automated warehouse is responsible for moving goods from the entrance to a designated shelf. This type of task can be defined as a single-transfer weight of 50kg to 80kg, a distance of 10m to 15m, and a target shelf with 3 to 5 floors. The system extracts time records for 200 such tasks completed by the stacker crane from the operation logs of the past two months. These records include the specific time taken for each task from receiving the instruction to completing the placement of the goods, such as 45 seconds for the first task, 52 seconds for the second task, and 48 seconds for the third task.

[0110] After collecting the time records, they need to be sorted and organized. Arrange the 200 time records from least to most to form an ordered sequence. For example, the first 10 records after sorting are 42 seconds, 43 seconds, 45 seconds, 45 seconds, 46 seconds, 47 seconds, 48 seconds, 48 seconds, 49 seconds, and 50 seconds, and the last 10 records are 78 seconds, 79 seconds, 80 seconds, 81 seconds, 82 seconds, 83 seconds, 84 seconds, 85 seconds, 86 seconds, and 87 seconds. The purpose of sorting is to accurately find the middle value later to ensure the accuracy of the median calculation.

[0111] Calculate the median of all time records. Since there are 200 time records, an even number, we need to find the two middle values and calculate their average. The middle value is calculated as: 200 ÷ 2 = 100, meaning the 100th and 101st data points are the two middle values. Assuming that in the sorted sequence, the 100th data point is 60 seconds and the 101st data point is 62 seconds, the median is (60 + 62) ÷ 2 = 61 seconds. This 61-second period is determined to be the standard cycle time for this type of warehousing task, reflecting the typical time required for the stacker crane to complete this type of task under normal operating conditions.

[0112] When determining the standard cycle, it's important to consider the criteria for defining similar tasks. This definition requires comprehensive consideration of multiple dimensions, such as cargo weight, dimensions, transport distance, target location altitude, and task type (inbound, outbound, or sorting). For example, for sorting tasks, similar tasks may require the same cargo type, similar sorting routes, and consistent operational procedures. If different types of tasks are combined to calculate the median, the standard cycle becomes unrepresentative and fails to accurately reflect the time characteristics of the equipment processing specific tasks.

[0113] The scope of time record collection also needs to be appropriately determined. If the time span is too short, it may not capture the full range of normal equipment operating conditions, such as the difference between peak and off-peak periods, or the impact of minor equipment wear on efficiency. If the time span is too long, the equipment may have undergone upgrades or maintenance, resulting in a mismatch between earlier time records and current equipment performance. Therefore, based on the equipment's maintenance cycle and the stability of the business model, a sample of recent time records (such as one month, three months, or six months) is typically selected to ensure data timeliness and applicability.

[0114] When collecting time records, the data must be screened to eliminate outliers. Outliers can be caused by equipment failure, system errors, human error, and other factors. For example, a task record may take 200 seconds due to a stacker crane sensor malfunction, far exceeding the normal range. If these outliers are not eliminated, they will seriously affect the median calculation and cause the standard period to deviate from the actual situation. Methods for filtering outliers include setting reasonable thresholds, such as considering data exceeding ±3 standard deviations from the mean as outliers, or manually reviewing and eliminating obviously unreasonable records.

[0115] For complex warehousing equipment, it may be necessary to determine standard cycles for different task scenarios. For example, the time taken by an AGV in an automated warehouse to travel empty and fully loaded varies significantly. Therefore, it is necessary to collect time records for both empty and fully loaded handling tasks and calculate the median of each as the standard cycle. Similarly, for tasks in different shelving areas, such as handling tasks between high- and low-level shelves, standard cycles also need to be determined separately because the equipment may operate at different speeds.

[0116] In practice, the standard cycle is not fixed and requires regular updates based on the equipment's operating status and evolving business needs. For example, after a major overhaul or upgrade, the equipment's operating efficiency may significantly change. In this case, it's necessary to recollect time records, calculate the median, and update the standard cycle. Similarly, if a warehouse adjusts its cargo storage strategy, resulting in changes in handling distances or task processes, the standard cycle determination method and data collection scope must also be adjusted accordingly.

[0117] Once the standard cycle is determined, it can be used to evaluate the equipment's operating efficiency under the current scheduling parameters. For example, if the actual completion time of a stacker crane for similar tasks under certain scheduling parameters is generally higher than the standard cycle, this may indicate that the equipment is overloaded or the scheduling parameters are improperly set, requiring further analysis and adjustment. Conversely, if the actual completion time is generally lower than the standard cycle, this may indicate room for efficiency improvement or that the calculated sample for the standard cycle contains a large number of outliers, necessitating a data review.

[0118] By determining a reasonable standard cycle, we provide an important reference for the subsequent calculation of delay values for task nodes. For example, in a certain transport task, the standard cycle specifies a 5-second timeframe from receiving the instruction to starting the move, but the actual operation data shows a completion time of 8 seconds for this node, resulting in a 3-second delay. By analyzing the delay values of all task nodes, we can fully understand the efficiency bottlenecks of equipment during the scheduling process, providing data support for optimizing scheduling parameters and equipment operation strategies.

[0119] Determining the standard cycle for warehouse equipment processing tasks is a process based on historical data, task characteristics, and data processing and screening. Accurately calculating the median yields a typical time indicator reflecting the equipment's normal operating efficiency. This provides crucial foundational data for evaluating and optimizing warehouse scheduling efficiency, ensuring that the scheduling system can rationally allocate tasks and adjust parameters based on the equipment's actual performance.

[0120] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[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. The intelligent scheduling method for automated warehousing in the industrial Internet of Things is characterized by: The steps include: Determine the parameter range of the storage equipment in the target scheduling scenario, set different scheduling parameters within the parameter range, and the target scheduling equipment schedules the storage tasks according to the set different scheduling parameters; Data collection is performed on storage tasks that have undergone different scheduling parameters in the target scheduling equipment to obtain the location information of items in the storage tasks under different scheduling parameters. Then, based on all the location information, the state distribution diagram of the storage space under different scheduling parameters is determined; Collect the operating data of storage equipment under different scheduling parameters, determine the response delay rate of the storage equipment under different scheduling parameters based on all the operating data, and perform efficiency coefficient fitting on all the response delay rates to obtain the scheduling efficiency coefficient of the storage equipment; The matching degree of the warehousing task under different scheduling parameters is determined based on the scheduling efficiency coefficient and all state distribution diagrams, and the optimal scheduling time when the warehousing task completes the scheduling is determined through all the matching degrees.

2. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 1, wherein: Data collection is performed on storage tasks that have undergone different scheduling parameters in the target scheduling device, and the location information of items in the storage tasks under different scheduling parameters is obtained, including: For each warehousing task under each scheduling parameter, obtain positioning data consisting of position signals transmitted by IoT sensors in the warehousing environment; The location information of the items in the storage task under each scheduling parameter is determined according to the positioning data.

3. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 1, characterized in that: The state distribution diagram of the storage space under different scheduling parameters is determined based on all the location information, including: For each storage task under each scheduling parameter, use the spatial modeling tool to analyze the operation trajectory of the storage task and obtain a dynamic layout diagram of the storage space; A state distribution diagram of the storage space under each scheduling parameter is determined according to the location information of the items in the storage task and the dynamic layout diagram.

4. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 1, wherein: Determine the response delay rate of storage equipment under different scheduling parameters based on all operating data, including: For storage equipment under different scheduling parameters, the completion time of each task node is determined based on the operation data of the storage equipment; Determine the standard cycle for storage equipment to process tasks; Determine the delay value of each task node based on all completion times and the standard period; The response delay rate of the storage equipment is determined by all delay values, and then the response delay rate of the storage equipment under different scheduling parameters is obtained.

5. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 1, wherein: Determining the matching degree of warehousing tasks under different scheduling parameters based on the scheduling efficiency coefficient and all state distribution graphs specifically includes: Obtain the ideal state distribution diagram of the space when warehouse task scheduling is smooth; For storage tasks under different scheduling parameters, determining the distribution difference between the item distribution in the storage task and the ideal distribution according to the state distribution diagram of the storage space and the ideal state distribution diagram; The matching degree of the warehousing task is determined based on the scheduling efficiency coefficient and the distribution difference, and then the matching degree of the warehousing task under different scheduling parameters is obtained.

6. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 1, characterized in that: The warehousing task is a basic scheduling task obtained by integrating data based on warehousing records, order information and equipment parameters using the Industrial Internet of Things communication protocol.

7. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 6, characterized in that: The equipment parameters are execution parameters corresponding to the transport instruction or the sorting instruction.

8. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 5, characterized in that: Determining the distribution difference between the item distribution in the storage task and the ideal distribution according to the state distribution diagram of the storage space and the ideal state distribution diagram specifically includes: For each storage task under each scheduling parameter, extract the actual coordinate set of the items in the state distribution map and the target coordinate set of the items in the ideal state distribution map; The coordinate deviation values of the corresponding positions in the actual coordinate set and the target coordinate set are counted, and the distribution difference is determined by the average value of all coordinate deviation values.

9. The method for intelligent scheduling of automated warehousing in the industrial Internet of Things according to claim 4, characterized in that: Determine the standard cycle of storage equipment processing tasks, including: Obtain the time records of storage equipment completing similar tasks during historical scheduling; The median of all time records is calculated, and the median is used as the standard cycle time for the storage equipment to process tasks.

10. An industrial Internet of Things automated warehousing intelligent scheduling system, including a warehousing task scheduling time determination device, characterized in that: The storage task scheduling time determination device includes: A parameter setting module is used to determine the parameter range of the storage equipment in the target scheduling scenario, set different scheduling parameters within the parameter range, and instruct the target scheduling equipment to schedule the storage tasks according to the set different scheduling parameters; The data processing module is used to control the data collection of storage tasks that have undergone different scheduling parameters in the target scheduling equipment, obtain the location information of the items in the storage tasks under different scheduling parameters, and then determine the state distribution map of the storage space under different scheduling parameters based on all the location information; The data processing module is further configured to collect operating data of storage equipment under different scheduling parameters, determine the response delay rate of the storage equipment under different scheduling parameters based on all the operating data, perform efficiency coefficient fitting on all the response delay rates, and obtain the scheduling efficiency coefficient of the storage equipment; The strategy generation module is used to determine the matching degree of the warehousing tasks under different scheduling parameters based on the scheduling efficiency coefficient and all state distribution diagrams, and determine the optimal scheduling time when the warehousing tasks are completed through all matching degrees.

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