Transmission scheduling method and system based on intelligent storage

Abstract

The invention is suitable for the technical field of intelligent warehouse management, and provides a transmission scheduling method and system based on intelligent warehousing, and the method comprises the steps: determining a scheduling device for carrying out the transmission scheduling of a target object, obtaining an initial task priority index generated for the target object according to a task requirement, and obtaining a historical operation record of the scheduling equipment. According to the method, the specified object type similar to the target object in physical property is selected as the reference standard, so that the analysis of the current fluctuation characteristic data is more contrasted. According to the technical scheme, the selection of the specified loading type, especially the consistency in weight or volume, ensures that the equipment state can be maintained in a relatively stable standard state when the dispatching equipment processes similar cargoes, thereby improving the accuracy and reliability of the dispatching system for evaluating the equipment state.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent warehousing management, and in particular relates to a transmission scheduling method and system based on intelligent warehousing. Background Art

[0002] Existing intelligent warehouse management technologies have made significant progress in improving warehouse system efficiency and reducing manual intervention. Traditional warehouse management methods rely on scheduling systems to automatically arrange the transfer and storage of goods, utilizing technologies such as barcodes and RFID to track and manage goods. However, existing scheduling methods often prioritize tasks based solely on their order of priority, lacking comprehensive consideration of multi-dimensional information such as equipment status, environmental factors, and cargo characteristics. This is particularly true when assessing real-time status during equipment load changes and item handling, resulting in limited adaptability and intelligence.

[0003] Current scheduling systems often fail to fully consider the impact of cargo's physical characteristics on equipment operation. For example, in actual warehousing operations, the type of cargo being transported can significantly impact the load, energy consumption, and stability of dispatching equipment. However, existing systems often rely solely on task priority as the sole decision-making basis and fail to effectively incorporate cargo's physical characteristics (such as weight and volume) into scheduling decisions. This results in inadequately optimizing equipment status when handling specialized cargo, leading to frequent equipment overloads and unstable operation, impacting the efficiency and safety of the entire warehousing system.

[0004] In addition, existing technologies lack dynamic optimization mechanisms when it comes to monitoring and analyzing current fluctuations during equipment operation. Traditional systems mostly monitor data such as current fluctuations and load changes in equipment at a static monitoring level, and fail to make real-time adjustments through in-depth analysis of historical operating data. This results in the equipment being unable to make timely adaptive adjustments in certain situations, thereby affecting the priority of task execution and the timeliness of task completion. This not only affects the operating efficiency of the equipment, but also brings potential safety hazards to a certain extent. Existing technologies lack a dynamic adjustment mechanism based on the correlation between current fluctuation characteristic data and task priority, and are unable to effectively deal with complex situations such as uneven equipment load or changes in operating load. Summary of the Invention

[0005] The purpose of the present invention is to provide a transmission scheduling method and system based on intelligent warehousing, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented as follows: a transmission scheduling method based on intelligent warehousing, the method comprising: Determine the scheduling device for transmission scheduling of the target payload, obtain the initial task priority index generated for the target payload according to the task requirements, and obtain the historical operation records of the scheduling device; Intelligently analyze historical job records to select several first-category historical tasks that match the target payload type, and several second-category historical tasks that match the specified payload type, and ensure that the occurrence time of the selected first-category and second-category historical tasks meets the preset time window requirements; Extracting first-type current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the target load type, and second-type current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the designated load type, from the first-type historical tasks and the second-type historical tasks; Based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data, respectively draw a current fluctuation curve that changes with time and a standard control current fluctuation curve; The current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The initial task priority index is optimized and adjusted through the adjustment factor.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the designated cargo type specifically refers to a cargo type that is consistent with the target cargo in physical properties and has special safety requirements.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the step of extracting, from the first and second historical tasks, first-type current fluctuation characteristic data of the scheduling device when transporting cargo that is consistent with the target payload type, and second-type current fluctuation characteristic data of the scheduling device when transporting cargo that is consistent with the specified payload type includes: Intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling equipment during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the first-category current fluctuation characteristic data of the first-category historical task; Each second-category historical task is intelligently interpreted, all current fluctuation characteristic data of the scheduling equipment during the execution of the second-category historical task are extracted, and the mean of these current fluctuation characteristic data is calculated, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of respectively drawing a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data include: Determine the timestamp of each piece of first-category current fluctuation characteristic data and second-category current fluctuation characteristic data based on historical operation records; With time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis, based on several first-type current fluctuation characteristic data and second-type current fluctuation characteristic data, a current current fluctuation curve and a standard control current fluctuation curve reflecting their changes over time are drawn.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The step of optimizing and adjusting the initial task priority index by the adjustment factor includes: Compare the current fluctuation curve with the standard control current fluctuation curve point by point, and calculate the average slope of the two respectively; Based on the difference in average slope between the current fluctuation curve and the standard control current fluctuation curve, a slope deviation is calculated, and the slope deviation is used as an adjustment factor; Retrieve the preset priority index optimization formula and modify the initial task priority index in combination with the adjustment factor.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

[0012] A transmission scheduling system based on intelligent warehousing, comprising: a data acquisition module, a historical task screening module, a current fluctuation characteristic data extraction module, a curve drawing module, and a priority index optimization module, wherein: A data acquisition module is used to determine the scheduling device for the target payload's transmission scheduling, obtain the initial task priority index generated for the target payload according to the task requirements, and obtain the historical operation records of the scheduling device; The historical task screening module is used to intelligently analyze historical operation records, filter out several first-category historical tasks that are consistent with the target payload type, and several second-category historical tasks that are consistent with the specified payload type, and ensure that the occurrence time of the filtered first-category and second-category historical tasks meets the preset time window requirements; The designated cargo type specifically refers to cargo that is consistent with the target cargo in physical properties and has special safety requirements; a current fluctuation characteristic data extraction module, configured to extract, from the first and second historical tasks, first-category current fluctuation characteristic data of the dispatching device when transporting cargo of the same type as the target load, and second-category current fluctuation characteristic data of the dispatching device when transporting cargo of the same type as the designated load; A curve drawing module, for drawing a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data; The priority index optimization module is used to compare the current current fluctuation curve with the standard control current fluctuation curve, and calculate the adjustment factor based on the degree of deviation between the two, and optimize the initial task priority index through the adjustment factor.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the current fluctuation characteristic data extraction module specifically includes: A first historical task interpretation unit is used to intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data to define it as the first-category current fluctuation characteristic data of the first-category historical task; The second historical task interpretation unit is used to intelligently interpret each second-category historical task, extract all current fluctuation characteristic data of the scheduling equipment during the execution of the second-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the curve drawing module specifically includes: A timestamp recording unit, configured to determine a timestamp of each piece of first-category current fluctuation characteristic data and second-category current fluctuation characteristic data based on historical operation records; The curve drawing unit is used to use time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis, and to draw a current current fluctuation curve and a standard control current fluctuation curve reflecting the change over time based on a number of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the priority index optimization module specifically includes: An average slope calculation unit is used to compare the current current fluctuation curve with the standard control current fluctuation curve point by point and calculate the average slope of the two respectively; an adjustment factor determination unit, configured to calculate a slope deviation based on an average slope difference between a current current fluctuation curve and a standard control current fluctuation curve, and use the slope deviation as an adjustment factor; An optimization formula application unit is used to call a preset priority index optimization formula and modify the initial task priority index in combination with an adjustment factor; The priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

[0016] Compared with the prior art, the present invention has the following beneficial effects: First, by selecting a specific load type with similar physical properties to the target load as a reference standard, the present invention makes the analysis of current fluctuation characteristic data more comparative. The selection of the specified load type, particularly with regard to consistency in weight or volume, ensures that the dispatching equipment maintains a relatively stable standard state when handling similar cargo, thereby improving the accuracy and reliability of the dispatching system's equipment status assessment. This approach effectively avoids the scheduling deviations caused by the lack of a stable reference state in traditional dispatching methods, ensuring the efficient operation of the equipment during task execution.

[0017] Secondly, by intelligently parsing historical operation records and extracting current fluctuation characteristic data, the present invention can accurately identify and analyze the equipment's operating status when transporting cargo of the same type as the target load, and use this information to optimize task priorities. This dynamic optimization process allows for timely adjustment of task scheduling, improving both the timeliness of task completion and the efficiency of equipment utilization.

[0018] Finally, the present invention can dynamically adjust the initial task priority index based on the slope deviation of the current fluctuation curve, ensuring that when the equipment operation is unstable or there is a potential safety risk, the system can automatically increase the priority of the corresponding task, thereby effectively avoiding equipment failures and safety accidents, and improving the safety and stability of the warehousing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a method provided by an embodiment of the present invention; Figure 2 A flow chart of obtaining the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data in the method provided in an embodiment of the present invention; Figure 3 A flow chart of drawing a current current fluctuation curve and a standard control current fluctuation curve in the method provided in an embodiment of the present invention; Figure 4 A flowchart of modifying the initial task priority index in the method provided in an embodiment of the present invention; Figure 5 An application architecture diagram of the system provided by an embodiment of the present invention; Figure 6 A structural block diagram of a current fluctuation characteristic data extraction module in a system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a curve drawing module in a system provided by an embodiment of the present invention; Figure 8 This is a structural block diagram of the priority index optimization module in the system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0022] Specifically, a transmission scheduling method based on intelligent warehousing includes the following steps: Step S100 : determining a scheduling device for scheduling transmission of a target load, obtaining an initial task priority index generated for the target load according to task requirements, and obtaining historical operation records of the scheduling device.

[0023] In the embodiments of this invention, target cargo refers to specific items that need to be transported, stored, or processed within the intelligent warehousing system. The type and physical characteristics (such as weight and volume) of the target cargo are key factors in the system's selection based on scheduling requirements. In an intelligent warehousing system, target cargo is an object that needs to be dispatched within the storage space according to specific transport rules or requirements. These rules are typically based on a comprehensive consideration of factors such as item type, storage location, and safety requirements.

[0024] Dispatching equipment refers to the various devices that carry out the handling, transfer, or distribution of items within intelligent warehousing systems. These typically include automated handling robots, automated stackers, automated forklifts, and conveyor belts. These devices perform operations based on task requirements and the specific characteristics of the target load (such as size and weight). Dispatching equipment not only performs physical handling tasks but also receives and executes instructions through an integrated control system, optimizing task execution in real time and ensuring efficient warehousing system operation.

[0025] The initial task priority index is calculated based on multiple factors such as the type of target load, the urgency of the task, and transportation requirements, and is usually generated dynamically through the system's scheduling algorithm. This priority index can be obtained through rule configuration in the existing scheduling system, combining real-time data from warehouse management with task instructions. Specifically, the initial task priority index assigns a priority value to each task based on the timeliness of the task, the specificity of the item, and existing data in the warehouse environment. In the prior art, similar task priority calculation methods have been applied in the field of automated warehousing, but the specific algorithm used in the present invention is optimized and customized according to different types of load tasks.

[0026] Historical operation records refer to the various data generated by dispatching equipment when executing tasks in the past. These records contain basic task information and the status of the dispatching equipment, including task type, execution time, task completion status, and equipment performance. These records are typically generated automatically by the equipment itself through its built-in monitoring system or recorded by the warehouse system's control module. By analyzing historical operation records, the system can understand the efficiency of different tasks and dispatching equipment, identify potential anomalies, and optimize the scheduling of future tasks. The data from historical operation records is typically stored in the warehouse system's database and can be used as a reference for subsequent scheduling decisions, helping dispatching equipment to implement more precise task optimization.

[0027] Furthermore, the transmission scheduling method based on intelligent warehousing further includes the following steps: Step S200: Intelligently analyze historical job records to select several first-category historical tasks that are consistent with the target payload type, and several second-category historical tasks that are consistent with the specified payload type, and ensure that the occurrence time of the selected first-category historical tasks and second-category historical tasks meets the preset time window requirements.

[0028] The designated cargo type specifically refers to a cargo type that is identical or highly similar to the target cargo in physical properties and has special safety requirements.

[0029] In this embodiment of the present invention, "identical or highly similar physical properties" refers to items that are similar in weight or volume, preferably weight. In other words, the weight of the cargo in the second category of historical tasks is very close to the target load. This ensures that the scheduling system can effectively reference equipment performance, carrying capacity, and other factors when scheduling these historical tasks.

[0030] Selecting cargo types with special safety requirements as the second category of historical tasks means that these cargo types generally require higher safety measures during transportation, such as preventing vibration and impact. Such cargo often places high demands on equipment stability. Therefore, during the dispatch process, the dispatch equipment must remain stable to ensure transportation safety.

[0031] The significance of selecting this type of cargo as the second category of historical tasks lies in the fact that, due to the unique nature of transporting these cargoes, dispatching equipment must maintain a relatively stable operating state during these tasks. By analyzing historical tasks associated with these special cargoes, the dispatching system can better identify the stable state of equipment under these special conditions, providing a reference for scheduling the target cargo. This approach effectively provides a "standard" state for the transport of the target cargo, ensuring that equipment always maintains a safe and efficient operating state during the mission.

[0032] Furthermore, the transmission scheduling method based on intelligent warehousing further includes the following steps: Step S300: extracting the first type of current fluctuation characteristic data of the scheduling device when transmitting cargo consistent with the target load type and the second type of current fluctuation characteristic data when transmitting cargo consistent with the specified load type from the first type of historical tasks and the second type of historical tasks.

[0033] Specifically, Figure 2 A flow chart for obtaining first-type current fluctuation characteristic data and second-type current fluctuation characteristic data is shown.

[0034] Extracting the first type of current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the target load type, and the second type of current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the specified load type from the first type of historical tasks and the second type of historical tasks specifically includes the following steps: Step S301: Intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the first-category current fluctuation characteristic data of the first-category historical task; Step S302 , intelligently interpret each second-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the second-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

[0035] In embodiments of the present invention, the current fluctuation characteristic data of a dispatching device refers to the fluctuation data generated by the device's current consumption during task execution. Current fluctuations can reflect information such as load changes and state fluctuations during device operation. This data can help determine the device's operating status and efficiency during task execution. Current fluctuation characteristic data is typically collected in real time by current sensors or other monitoring devices on the dispatching device and stored in a data recording system. This data is typically stored in historical operation records as a detailed record of device performance and task execution status.

[0036] The first-category current fluctuation characteristic data for a dispatched device during its execution of the first-category historical task is defined as the mean of all current fluctuation characteristic data for the first-category historical task. This means that the mean provides an overall, stable level of current fluctuation, reduces the interference of occasional fluctuations, and reflects the overall energy efficiency and stability of the dispatched device when executing a specific type of task. This mean represents the "standard" current fluctuation characteristic of the device for that type of task and provides a comparison benchmark for subsequent task scheduling.

[0037] Similarly, the current fluctuation characteristic data for the second category of historical missions is averaged in the same manner. This allows us to extract the current fluctuation characteristic data when the equipment is carrying cargo consistent with the specified payload type. Because the second category of cargo often has unique safety requirements and transportation complexities, the current fluctuation characteristic data generated by the equipment during these missions can be used as a proxy for the equipment's performance in a "stable operating state." This data helps us understand the equipment's operating status under specific mission conditions, providing a valuable reference for subsequent mission scheduling.

[0038] The significance of this implementation lies in that, by extracting and comparing current fluctuation characteristics from different types of tasks, the dispatch system can accurately understand the stability, efficiency, and potential performance variations of equipment when handling different tasks. The average of historical tasks of the first and second categories provides a comparative benchmark, helping the system identify performance fluctuations of equipment under different transport tasks, thereby optimizing dispatch tasks and ensuring that equipment always maintains optimal working conditions during transport tasks.

[0039] This invention introduces the direction of current fluctuation characteristic data in detail. In fact, during the research process, it also involves exploration of other related fields, such as equipment load fluctuation analysis, vibration data monitoring and the impact of ambient temperature changes on equipment current consumption. These factors may also have an important impact on the working status of the scheduling equipment and provide valuable reference for further optimization of the scheduling algorithm.

[0040] Furthermore, the transmission scheduling method based on intelligent warehousing further includes the following steps: In step S400 , a current current fluctuation curve and a standard control current fluctuation curve that change with time are respectively drawn based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data.

[0041] Specifically, Figure 3 A flow chart for drawing a current fluctuation curve and a standard control current fluctuation curve is shown.

[0042] The method of drawing a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data specifically includes the following steps: Step S401, determining the timestamp of each piece of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data according to historical operation records; Step S402 , with time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis, a current fluctuation curve and a standard control current fluctuation curve reflecting their changes over time are drawn based on a plurality of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data.

[0043] In this embodiment of the present invention, the generation of the current fluctuation curve and the standard control current fluctuation curve relies on detailed analysis and processing of the first and second types of current fluctuation characteristic data. First, step S401 extracts the corresponding timestamps from each piece of current fluctuation characteristic data in the historical operation log. These timestamps ensure the temporal sequence of the data, enabling the subsequent drawing of the current fluctuation curve over time.

[0044] In step S402, the current fluctuation curve is plotted point by point based on each piece of historical data, using time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis. To improve the smoothness and accuracy of the curve, the raw current fluctuation characteristic data is typically smoothed and de-noised using techniques such as sliding average, exponential smoothing, or Kalman filtering. These techniques help eliminate random noise in the data and highlight the current fluctuation trend of the device under stable operating conditions, thereby generating a more reliable curve.

[0045] The generation of a standard reference current fluctuation curve typically relies on a standardized model or historical empirical data. This data can be generated by analyzing the performance of previous equipment under similar workloads or operating conditions, serving as a reference for normal equipment operation. By comparing the current current fluctuation curve with the standard reference current fluctuation curve, the difference between the current equipment state and the standard state can be identified, providing a basis for subsequent scheduling optimization, priority adjustment, and other decision-making.

[0046] The significance of generating these two curves lies in providing an intuitive time-series view of the operating status of dispatched equipment. By comparing the curve's shape, fluctuation, and deviation, it is possible to analyze whether the equipment is experiencing abnormal fluctuations, overload, or other potential failure risks. These analysis results directly influence the optimization and adjustment of subsequent dispatch tasks, improving the scientific and accurate nature of equipment scheduling and ensuring the stability and safety of equipment during task execution.

[0047] Furthermore, the transmission scheduling method based on intelligent warehousing further includes the following steps: In step S500 , the current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated according to the degree of deviation between the two. The initial task priority index is optimized and adjusted by the adjustment factor.

[0048] Specifically, Figure 4 A flow chart for modifying the initial task priority index is shown.

[0049] The current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The optimization adjustment of the initial task priority index by the adjustment factor specifically includes the following steps: Step S501, comparing the current current fluctuation curve with the standard reference current fluctuation curve point by point, and calculating the average slope of the two respectively; Step S502 , calculating a slope deviation based on the average slope difference between the current fluctuation curve and the standard control current fluctuation curve, and using the slope deviation as an adjustment factor; Step S503: retrieve a preset priority index optimization formula, and modify the initial task priority index in combination with the adjustment factor.

[0050] The priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

[0051] In embodiments of the present invention, the average slope can be calculated using conventional numerical differentiation methods. Typically, this involves performing a point-by-point calculation on each segment of the curve to determine the rate of change between each segment, thereby obtaining the slope of each segment. By averaging the slopes of all segments, the average slope of the entire current fluctuation curve can be calculated. To avoid errors caused by excessive data fluctuations, the raw data is typically smoothed before calculation to ensure a more stable and reliable calculated slope.

[0052] Slope deviation is used as an adjustment factor because the slope itself can reflect the dynamic response and stability of the device during operation. If the slope of the current fluctuation curve deviates significantly from the slope of the standard control current fluctuation curve, this means that the device's operating status has become abnormal, possibly due to excessive load, unstable operation, or other potential faults. By using slope deviation as an adjustment factor, the device status can be dynamically adjusted during task scheduling to optimize the device's operating efficiency and safety. Using slope deviation as an adjustment factor not only accurately reflects changes in device status, but also optimizes task priorities, allowing the scheduling system to make more scientific decisions based on the actual status of the device, thereby reducing the risk of equipment overload and improving overall work efficiency.

[0053] The benefit of this entire technical solution lies in the fact that by incorporating current fluctuation characteristic data and combining it with slope deviation adjustments, it is possible to more accurately assess and optimize the device status during task execution. This data-driven dynamic adjustment method can identify abnormal device fluctuations in real time and implement timely optimization, avoiding task delays or equipment failures caused by device anomalies. This optimization method not only improves task execution efficiency but also effectively reduces safety risks during device operation, enhancing system stability and reliability, and resolving the problem of traditional scheduling methods that prevent accurate and real-time assessment of device status.

[0054] For example, assume the system's initial task priority index is 100, representing the priority of the current task. Based on historical job records and analysis, the system calculates the slopes of the current current fluctuation curve and the standard reference current fluctuation curve to be 0.3 and 0.2, respectively. At this point, the slope deviation is 0.1, meaning the slope of the current current fluctuation curve is 0.1 higher than that of the standard curve. This slope deviation serves as an adjustment factor and is input into the preset priority index optimization formula. The formula then adjusts the initial task priority index based on this deviation.

[0055] For example, assuming the adjustment coefficient is 0.5, when the slope deviation is 0.1, the calculated adjustment factor is 0.1 × 0.5 = 0.05, which means the priority index is adjusted by 5%. Then, the initial task priority index of 100 is increased by 5% based on this adjustment factor, and the final optimized priority index is 105.

[0056] Further, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0057] In another preferred embodiment of the present invention, a transmission scheduling system based on intelligent warehousing includes: The data acquisition module 100 is used to determine the scheduling device for scheduling the transmission of the target load, obtain the initial task priority index generated for the target load according to the task requirements, and obtain the historical operation records of the scheduling device.

[0058] In the embodiments of this invention, target cargo refers to specific items that need to be transported, stored, or processed within the intelligent warehousing system. The type and physical characteristics (such as weight and volume) of the target cargo are key factors in the system's selection based on scheduling requirements. In an intelligent warehousing system, target cargo is an object that needs to be dispatched within the storage space according to specific transport rules or requirements. These rules are typically based on a comprehensive consideration of factors such as item type, storage location, and safety requirements.

[0059] Dispatching equipment refers to the various devices that carry out the handling, transfer, or distribution of items within intelligent warehousing systems. These typically include automated handling robots, automated stackers, automated forklifts, and conveyor belts. These devices perform operations based on task requirements and the specific characteristics of the target load (such as size and weight). Dispatching equipment not only performs physical handling tasks but also receives and executes instructions through an integrated control system, optimizing task execution in real time and ensuring efficient warehousing system operation.

[0060] The initial task priority index is calculated based on multiple factors such as the type of target load, the urgency of the task, and transportation requirements, and is usually generated dynamically through the system's scheduling algorithm. This priority index can be obtained through rule configuration in the existing scheduling system, combining real-time data from warehouse management with task instructions. Specifically, the initial task priority index assigns a priority value to each task based on the timeliness of the task, the specificity of the item, and existing data in the warehouse environment. In the prior art, similar task priority calculation methods have been applied in the field of automated warehousing, but the specific algorithm used in the present invention is optimized and customized according to different types of load tasks.

[0061] Historical operation records refer to the various data generated by dispatching equipment when executing tasks in the past. These records contain basic task information and the status of the dispatching equipment, including task type, execution time, task completion status, and equipment performance. These records are typically generated automatically by the equipment itself through its built-in monitoring system or recorded by the warehouse system's control module. By analyzing historical operation records, the system can understand the efficiency of different tasks and dispatching equipment, identify potential anomalies, and optimize the scheduling of future tasks. The data from historical operation records is typically stored in the warehouse system's database and can be used as a reference for subsequent scheduling decisions, helping dispatching equipment to implement more precise task optimization.

[0062] Furthermore, the intelligent warehousing-based transmission scheduling system also includes: The historical task screening module 200 is used to intelligently analyze historical operation records, screen out a number of first-category historical tasks that are consistent with the target payload type, and a number of second-category historical tasks that are consistent with the specified payload type, and ensure that the occurrence time of the screened first-category historical tasks and second-category historical tasks meets the preset time window requirements.

[0063] The designated cargo type specifically refers to a cargo type that is consistent with the target cargo in physical properties and has special safety requirements.

[0064] In this embodiment of the present invention, "identical or highly similar physical properties" refers to items that are similar in weight or volume, preferably weight. In other words, the weight of the cargo in the second category of historical tasks is very close to the target load. This ensures that the scheduling system can effectively reference equipment performance, carrying capacity, and other factors when scheduling these historical tasks.

[0065] Selecting cargo types with special safety requirements as the second category of historical tasks means that these cargo types generally require higher safety measures during transportation, such as preventing vibration and impact. Such cargo often places high demands on equipment stability. Therefore, during the dispatch process, the dispatch equipment must remain stable to ensure transportation safety.

[0066] The significance of selecting this type of cargo as the second category of historical tasks lies in the fact that, due to the unique nature of transporting these cargoes, dispatching equipment must maintain a relatively stable operating state during these tasks. By analyzing historical tasks associated with these special cargoes, the dispatching system can better identify the stable state of equipment under these special conditions, providing a reference for scheduling the target cargo. This approach effectively provides a "standard" state for the transport of the target cargo, ensuring that equipment always maintains a safe and efficient operating state during the mission.

[0067] Furthermore, the intelligent warehousing-based transmission scheduling system also includes: The current fluctuation characteristic data extraction module 300 is used to extract the first type of current fluctuation characteristic data of the scheduling equipment when transmitting goods consistent with the target load type, and the second type of current fluctuation characteristic data when transmitting goods consistent with the specified load type from the first type of historical tasks and the second type of historical tasks.

[0068] Specifically, Figure 6 FIG. 3 is a structural block diagram of a current fluctuation characteristic data extraction module 300 in a system provided by an embodiment of the present invention.

[0069] In a preferred embodiment of the present invention, the current fluctuation characteristic data extraction module 300 specifically includes: The first historical task interpretation unit 301 is configured to intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data to define them as the first-category current fluctuation characteristic data of the first-category historical task; The second historical task interpretation unit 302 is used to intelligently interpret each second-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the second-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

[0070] In embodiments of the present invention, the current fluctuation characteristic data of a dispatching device refers to the fluctuation data generated by the device's current consumption during task execution. Current fluctuations can reflect information such as load changes and state fluctuations during device operation. This data can help determine the device's operating status and efficiency during task execution. Current fluctuation characteristic data is typically collected in real time by current sensors or other monitoring devices on the dispatching device and stored in a data recording system. This data is typically stored in historical operation records as a detailed record of device performance and task execution status.

[0071] The first-category current fluctuation characteristic data for a dispatched device during its execution of the first-category historical task is defined as the mean of all current fluctuation characteristic data for the first-category historical task. This means that the mean provides an overall, stable level of current fluctuation, reduces the interference of occasional fluctuations, and reflects the overall energy efficiency and stability of the dispatched device when executing a specific type of task. This mean represents the "standard" current fluctuation characteristic of the device for that type of task and provides a comparison benchmark for subsequent task scheduling.

[0072] Similarly, the current fluctuation characteristic data for the second category of historical missions is averaged in the same manner. This allows us to extract the current fluctuation characteristic data when the equipment is carrying cargo consistent with the specified payload type. Because the second category of cargo often has unique safety requirements and transportation complexities, the current fluctuation characteristic data generated by the equipment during these missions can be used as a proxy for the equipment's performance in a "stable operating state." This data helps us understand the equipment's operating status under specific mission conditions, providing a valuable reference for subsequent mission scheduling.

[0073] The significance of this implementation lies in that, by extracting and comparing current fluctuation characteristics from different types of tasks, the dispatch system can accurately understand the stability, efficiency, and potential performance variations of equipment when handling different tasks. The average of historical tasks of the first and second categories provides a comparative benchmark, helping the system identify performance fluctuations of equipment under different transport tasks, thereby optimizing dispatch tasks and ensuring that equipment always maintains optimal working conditions during transport tasks.

[0074] This invention introduces the direction of current fluctuation characteristic data in detail. In fact, during the research process, it also involves exploration of other related fields, such as equipment load fluctuation analysis, vibration data monitoring and the impact of ambient temperature changes on equipment current consumption. These factors may also have an important impact on the working status of the scheduling equipment and provide valuable reference for further optimization of the scheduling algorithm.

[0075] Furthermore, the intelligent warehousing-based transmission scheduling system also includes: The curve drawing module 400 is used to draw a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data.

[0076] Specifically, Figure 7 FIG. 4 is a structural block diagram of a curve drawing module 400 in a system provided by an embodiment of the present invention.

[0077] In a preferred embodiment of the present invention, the curve drawing module 400 specifically includes: A timestamp recording unit 401 is used to determine the timestamp of each piece of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data according to historical operation records; The curve drawing unit 402 is used to draw a current current fluctuation curve and a standard control current fluctuation curve reflecting their changes over time based on a number of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data, using time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis.

[0078] In this embodiment of the present invention, the generation of the current fluctuation curve and the standard reference current fluctuation curve relies on detailed analysis and processing of the first and second types of current fluctuation characteristic data. First, the timestamp recording unit 401 extracts the corresponding timestamp from each piece of current fluctuation characteristic data in the historical operation log. These timestamps ensure the temporal sequence of the data, enabling the subsequent drawing of the current fluctuation curve over time.

[0079] In curve plotting unit 402, the current fluctuation curve is plotted point by point based on each piece of historical data, using time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis. To improve the smoothness and accuracy of the curve, the raw current fluctuation characteristic data is typically smoothed and de-noised using techniques such as sliding average, exponential smoothing, or Kalman filtering. These techniques help eliminate random noise in the data and highlight the current fluctuation trend of the device under stable operating conditions, thereby generating a more reliable curve.

[0080] The generation of a standard reference current fluctuation curve typically relies on a standardized model or historical empirical data. This data can be generated by analyzing the performance of previous equipment under similar workloads or operating conditions, serving as a reference for normal equipment operation. By comparing the current current fluctuation curve with the standard reference current fluctuation curve, the difference between the current equipment state and the standard state can be identified, providing a basis for subsequent scheduling optimization, priority adjustment, and other decision-making.

[0081] The significance of generating these two curves lies in providing an intuitive time-series view of the operating status of dispatched equipment. By comparing the curve's shape, fluctuation, and deviation, it is possible to analyze whether the equipment is experiencing abnormal fluctuations, overload, or other potential failure risks. These analysis results directly influence the optimization and adjustment of subsequent dispatch tasks, improving the scientific and accurate nature of equipment scheduling and ensuring the stability and safety of equipment during task execution.

[0082] Furthermore, the intelligent warehousing-based transmission scheduling system also includes: The priority index optimization module 500 is used to compare the current fluctuation curve with the standard control current fluctuation curve, and calculate an adjustment factor according to the degree of deviation between the two, and optimize and adjust the initial task priority index through the adjustment factor.

[0083] Specifically, Figure 8 It shows a structural block diagram of the priority index optimization module 500 in the system provided by an embodiment of the present invention.

[0084] In a preferred embodiment of the present invention, the priority index optimization module 500 specifically includes: The average slope calculation unit 501 is used to compare the current current fluctuation curve with the standard reference current fluctuation curve point by point and calculate the average slope of the two respectively; An adjustment factor determination unit 502 is configured to calculate a slope deviation based on an average slope difference between a current current fluctuation curve and a standard control current fluctuation curve, and use the slope deviation as an adjustment factor; The optimization formula application unit 503 is used to call a preset priority index optimization formula and modify the initial task priority index in combination with the adjustment factor; The priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

[0085] In embodiments of the present invention, the average slope can be calculated using conventional numerical differentiation methods. Typically, this involves performing a point-by-point calculation on each segment of the curve to determine the rate of change between each segment, thereby obtaining the slope of each segment. By averaging the slopes of all segments, the average slope of the entire current fluctuation curve can be calculated. To avoid errors caused by excessive data fluctuations, the raw data is typically smoothed before calculation to ensure a more stable and reliable calculated slope.

[0086] Slope deviation is used as an adjustment factor because the slope itself can reflect the dynamic response and stability of the device during operation. If the slope of the current fluctuation curve deviates significantly from the slope of the standard control current fluctuation curve, this means that the device's operating status has become abnormal, possibly due to excessive load, unstable operation, or other potential faults. By using slope deviation as an adjustment factor, the device status can be dynamically adjusted during task scheduling to optimize the device's operating efficiency and safety. Using slope deviation as an adjustment factor not only accurately reflects changes in device status, but also optimizes task priorities, allowing the scheduling system to make more scientific decisions based on the actual status of the device, thereby reducing the risk of equipment overload and improving overall work efficiency.

[0087] The benefit of this entire technical solution lies in the fact that by incorporating current fluctuation characteristic data and combining it with slope deviation adjustments, it is possible to more accurately assess and optimize the device status during task execution. This data-driven dynamic adjustment method can identify abnormal device fluctuations in real time and implement timely optimization, avoiding task delays or equipment failures caused by device anomalies. This optimization method not only improves task execution efficiency but also effectively reduces safety risks during device operation, enhancing system stability and reliability, and resolving the problem of traditional scheduling methods that prevent accurate and real-time assessment of device status.

[0088] For example, assume the system's initial task priority index is 100, representing the priority of the current task. Based on historical job records and analysis, the system calculates the slopes of the current current fluctuation curve and the standard reference current fluctuation curve to be 0.3 and 0.2, respectively. At this point, the slope deviation is 0.1, meaning the slope of the current current fluctuation curve is 0.1 higher than that of the standard curve. This slope deviation serves as an adjustment factor and is input into the preset priority index optimization formula. The formula then adjusts the initial task priority index based on this deviation.

[0089] For example, assuming the adjustment coefficient is 0.5, when the slope deviation is 0.1, the calculated adjustment factor is 0.1 × 0.5 = 0.05, which means the priority index is adjusted by 5%. Then, the initial task priority index of 100 is increased by 5% based on this adjustment factor, and the final optimized priority index is 105.

[0090] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0091] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0092] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A transmission scheduling method based on intelligent warehousing, characterized in that: The method comprises: Determine the scheduling device for transmission scheduling of the target payload, obtain the initial task priority index generated for the target payload according to the task requirements, and obtain the historical operation records of the scheduling device; Intelligently analyze historical job records to select several first-category historical tasks that match the target payload type, and several second-category historical tasks that match the specified payload type, and ensure that the occurrence time of the selected first-category and second-category historical tasks meets the preset time window requirements; Extracting first-type current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the target load type, and second-type current fluctuation characteristic data of the dispatching device when transporting cargo that is consistent with the designated load type, from the first-type historical tasks and the second-type historical tasks; Based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data, respectively draw a current fluctuation curve that changes with time and a standard control current fluctuation curve; The current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The initial task priority index is optimized and adjusted through the adjustment factor.

2. The transmission scheduling method based on intelligent warehousing according to claim 1 is characterized in that: The designated cargo type specifically refers to a cargo type that is consistent with the target cargo in physical properties and has special safety requirements.

3. The transmission scheduling method based on intelligent warehousing according to claim 2 is characterized in that: The steps of extracting first-type current fluctuation characteristic data of the scheduling device when transporting cargo that is consistent with the target load type and second-type current fluctuation characteristic data of the scheduling device when transporting cargo that is consistent with the specified load type from the first-type historical tasks and the second-type historical tasks include: Intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling equipment during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the first-category current fluctuation characteristic data of the first-category historical task; Each second-category historical task is intelligently interpreted, all current fluctuation characteristic data of the scheduling equipment during the execution of the second-category historical task are extracted, and the mean of these current fluctuation characteristic data is calculated, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

4. The transmission scheduling method based on intelligent warehousing according to claim 3 is characterized in that: The steps of respectively drawing a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data include: Determine the timestamp of each piece of first-category current fluctuation characteristic data and second-category current fluctuation characteristic data based on historical operation records; With time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis, based on several first-type current fluctuation characteristic data and second-type current fluctuation characteristic data, a current current fluctuation curve and a standard control current fluctuation curve reflecting their changes over time are drawn.

5. The transmission scheduling method based on intelligent warehousing according to claim 1 is characterized in that: The current current fluctuation curve is compared with the standard control current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The steps of optimizing the initial task priority index using the adjustment factor include: Compare the current fluctuation curve with the standard control current fluctuation curve point by point, and calculate the average slope of the two respectively; Based on the difference in average slope between the current fluctuation curve and the standard control current fluctuation curve, a slope deviation is calculated, and the slope deviation is used as an adjustment factor; Retrieve the preset priority index optimization formula and modify the initial task priority index in combination with the adjustment factor.

6. The transmission scheduling method based on intelligent warehousing according to claim 5 is characterized in that: The priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

7. A transmission scheduling system based on intelligent warehousing, characterized in that: The system includes: a data acquisition module, a historical task screening module, a current fluctuation characteristic data extraction module, a curve drawing module and a priority index optimization module, wherein: A data acquisition module is used to determine the scheduling device for the target payload's transmission scheduling, obtain the initial task priority index generated for the target payload according to the task requirements, and obtain the historical operation records of the scheduling device; The historical task screening module is used to intelligently analyze historical operation records, filter out several first-category historical tasks that are consistent with the target payload type, and several second-category historical tasks that are consistent with the specified payload type, and ensure that the occurrence time of the filtered first-category and second-category historical tasks meets the preset time window requirements; The designated cargo type specifically refers to a cargo type that is consistent with the target cargo in physical properties and has special safety requirements; a current fluctuation characteristic data extraction module, configured to extract, from the first and second historical tasks, first-category current fluctuation characteristic data of the dispatching device when transporting cargo of the same type as the target load, and second-category current fluctuation characteristic data of the dispatching device when transporting cargo of the same type as the designated load; A curve drawing module, for drawing a current current fluctuation curve and a standard control current fluctuation curve that change with time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data; The priority index optimization module is used to compare the current current fluctuation curve with the standard control current fluctuation curve, and calculate the adjustment factor based on the degree of deviation between the two, and optimize the initial task priority index through the adjustment factor.

8. The intelligent warehousing-based transmission scheduling system according to claim 7 is characterized in that: The current fluctuation feature data extraction module specifically includes: A first historical task interpretation unit is used to intelligently interpret each first-category historical task, extract all current fluctuation characteristic data of the scheduling device during the execution of the first-category historical task, and calculate the mean of these current fluctuation characteristic data to define it as the first-category current fluctuation characteristic data of the first-category historical task; The second historical task interpretation unit is used to intelligently interpret each second-category historical task, extract all current fluctuation characteristic data of the scheduling equipment during the execution of the second-category historical task, and calculate the mean of these current fluctuation characteristic data, which is defined as the second-category current fluctuation characteristic data of the second-category historical task.

9. The intelligent warehousing-based transmission scheduling system according to claim 8, characterized in that: The curve drawing module specifically includes: A timestamp recording unit, configured to determine the timestamp of each piece of first-category current fluctuation characteristic data and second-category current fluctuation characteristic data based on historical operation records; The curve drawing unit is used to use time as the horizontal axis and the current fluctuation characteristic data value as the vertical axis, and to draw a current current fluctuation curve and a standard control current fluctuation curve reflecting the change over time based on a number of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data.

10. The intelligent warehousing-based transmission scheduling system according to claim 9, characterized in that: The priority index optimization module specifically includes: An average slope calculation unit is used to compare the current current fluctuation curve with the standard control current fluctuation curve point by point and calculate the average slope of the two respectively; an adjustment factor determination unit, configured to calculate a slope deviation based on an average slope difference between a current current fluctuation curve and a standard control current fluctuation curve, and use the slope deviation as an adjustment factor; The optimization formula application unit is used to call the preset priority index optimization formula and modify the initial task priority index in combination with the adjustment factor; The priority index optimization formula is: , where PI final Refers to the revised task priority index, PI initial Refers to the initial task priority index, S current Refers to the average slope of the current fluctuation curve, S norms Refers to the average slope of the standard control current fluctuation curve, Refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

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