A transmission scheduling method and system based on intelligent warehousing

Abstract

The application is suitable for the technical field of intelligent warehouse management, and provides a transmission scheduling method and system based on intelligent warehouse, the method comprises the following steps: determining a scheduling device for transmission scheduling of a target carrier, obtaining an initial task priority index generated for the target carrier according to task requirements, and obtaining a historical operation record of the scheduling device. The application selects a specified carrier type similar to the target carrier in physical characteristics as a control standard, so that the analysis of current fluctuation characteristic data is more controllable. The selection of the specified carrier type, especially the consistency in weight or volume, ensures that the device state can be maintained at a relatively stable standard state when the scheduling device processes similar goods, thereby improving the accuracy and reliability of the device state evaluation of the scheduling system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent warehouse management, and particularly relates to a transmission scheduling method and system based on intelligent warehouse. BACKGROUND

[0002] The existing intelligent warehouse management technology has made significant progress in improving the efficiency of the warehouse system and reducing manual intervention. The traditional warehouse management method automatically arranges the transmission and access of goods through a scheduling system, and uses barcodes, RFID and other technologies to realize goods tracking and management. However, the scheduling method in the prior art often only arranges the scheduling order according to the priority of the task, lacks comprehensive consideration of multi-dimensional information such as device state, environmental factors and goods characteristics, and especially in real-time state evaluation when the device load changes and the goods are transported, the adaptability and intelligence of the system are limited.

[0003] The current scheduling system usually fails to fully consider the influence of the physical characteristics of the goods on the operation of the device. For example, in actual warehouse operations, the type of goods transported may have a great influence on the load, energy consumption, stability, etc. of the scheduling device, but the existing system mostly takes the task priority as the only decision basis, and fails to effectively incorporate the physical characteristics (such as weight, volume, etc.) of the goods into the scheduling decision. This leads to the fact that when handling special goods, the device state cannot be fully optimized, and the device overloads or runs unstably, which affects the running efficiency and safety of the entire warehouse system.

[0004] In addition, the existing technology lacks a dynamic optimization mechanism in the monitoring and analysis of current fluctuations, especially in the monitoring and analysis of current fluctuations. The traditional system stays in the static monitoring level of the current fluctuations, load changes and other data of the device, and fails to make real-time adjustments through deep analysis of historical operation data, which leads to the fact that the device cannot make adaptive adjustments in time under certain conditions, thereby affecting the priority of task execution and the timeliness of task completion. This not only affects the running efficiency of the device, but also to a certain extent, brings potential safety hazards. The existing technology lacks a dynamic adjustment mechanism based on the correlation between current fluctuation characteristic data and task priority, and cannot effectively deal with complex situations such as uneven device load or operation load change. SUMMARY

[0005] The present application aims to provide a transmission scheduling method and system based on intelligent warehouse, which aims to solve the problems raised in the background art.

[0006] The present application is implemented as follows: a transmission scheduling method based on intelligent warehouse, the method comprising:

[0007] The scheduling device for determining the target load for transmission scheduling acquires an initial task priority index generated according to the task requirements of the target load, and acquires the historical operation records of the scheduling device;

[0008] Intelligently analyzing the historical operation records, filtering out a plurality of first type historical tasks consistent with the type of the target load and a plurality of second type historical tasks consistent with the specified load type, and ensuring that the occurrence time of the filtered first type historical tasks and second type historical tasks meets the preset time window requirement;

[0009] Extracting first type current fluctuation characteristic data of the scheduling device when transmitting goods consistent with the type of the target load and second type current fluctuation characteristic data when transmitting goods consistent with the specified load type from the first type historical tasks and the second type historical tasks;

[0010] According to the first type current fluctuation characteristic data and the second type current fluctuation characteristic data, a current fluctuation curve changing with time and a standard comparison current fluctuation curve are respectively drawn;

[0011] Comparing the current fluctuation curve with the standard comparison current fluctuation curve, and calculating an adjustment factor according to the deviation degree between the two, and optimizing and adjusting the initial task priority index through the adjustment factor.

[0012] As a further limitation of the technical scheme of the embodiment of the application, the specified load type specifically refers to the type of goods consistent with the target load in physical properties and belonging to the type of goods with special safety requirements.

[0013] As a further limitation of the technical scheme of the embodiment of the application, the step of extracting the first type current fluctuation characteristic data of the scheduling device when transmitting goods consistent with the type of the target load and the second type current fluctuation characteristic data when transmitting goods consistent with the specified load type from the first type historical tasks and the second type historical tasks includes:

[0014] Intelligently interpreting each first type historical task, extracting all current fluctuation characteristic data of the scheduling device during the execution of the first type historical task, and calculating the mean value of the current fluctuation characteristic data, which is defined as the first type current fluctuation characteristic data of the first type historical task;

[0015] Intelligently interpreting each second type historical task, extracting all current fluctuation characteristic data of the scheduling device during the execution of the second type historical task, and calculating the mean value of the current fluctuation characteristic data, which is defined as the second type current fluctuation characteristic data of the second type historical task.

[0016] As a further limitation of the technical scheme of the embodiment of the application, the step of drawing the current current fluctuation curve and the standard control current fluctuation curve changing with time according to the first type current fluctuation characteristic data and the second type current fluctuation characteristic data respectively comprises:

[0017] According to the historical operation record, the time stamp of each piece of first type current fluctuation characteristic data and second type current fluctuation characteristic data is determined;

[0018] Taking time as the horizontal coordinate and current fluctuation characteristic data value as the vertical coordinate, the current current fluctuation curve and the standard control current fluctuation curve reflecting the change with time are drawn based on the first type current fluctuation characteristic data and the second type current fluctuation characteristic data respectively.

[0019] As a further limitation of the technical scheme of the embodiment of the application, the step of comparing the current current fluctuation curve with the standard control current fluctuation curve and calculating the adjustment factor according to the deviation degree between the two comprises:

[0020] The current current fluctuation curve and the standard control current fluctuation curve are compared point by point, and the average slopes of the two are calculated respectively;

[0021] Based on the average slope difference of the current current fluctuation curve and the standard control current fluctuation curve, the slope deviation is calculated, and the slope deviation is taken as the adjustment factor;

[0022] The preset priority index optimization formula is called, and the initial task priority index is corrected combined with the adjustment factor.

[0023] As a further limitation of the technical scheme of the embodiment of the application, the priority index optimization formula is: , wherein PI final refers to the corrected task priority index, PI initial refers to the initial task priority index, S current refers to the average slope of the current 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.

[0024] A transmission scheduling system based on intelligent storage, the system comprises: 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:

[0025] The data acquisition module is configured to determine a scheduling device for scheduling transmission of the target carrier, acquire an initial task priority index generated according to a task requirement for the target carrier, and acquire historical operation records of the scheduling device;

[0026] The historical task screening module is configured to intelligently analyze the historical operation records, screen a plurality of first-type historical tasks consistent with the type of the target carrier and a plurality of second-type historical tasks consistent with a specified carrier type from the historical operation records, and ensure that occurrence times of the screened first-type historical tasks and the second-type historical tasks meet preset time window requirements.

[0027] The specified carrier type specifically refers to a type of goods consistent with the target carrier in physical properties and belonging to a type of goods with special safety requirements.

[0028] The current fluctuation feature data extraction module is configured to extract, from the first-type historical tasks and the second-type historical tasks, first-type current fluctuation feature data of the scheduling device when transmitting goods consistent with the type of the target carrier and second-type current fluctuation feature data of the scheduling device when transmitting goods consistent with the specified carrier type.

[0029] The curve drawing module is configured to draw a current fluctuation curve varying with time and a standard control current fluctuation curve according to the first-type current fluctuation feature data and the second-type current fluctuation feature data, respectively.

[0030] The priority index optimization module is configured to compare the current fluctuation curve with the standard control current fluctuation curve, and calculate an adjustment factor according to a deviation degree between the current fluctuation curve and the standard control current fluctuation curve, and optimize and adjust the initial task priority index by using the adjustment factor.

[0031] As a further limitation of the technical scheme of the embodiment of the application, the current fluctuation feature data extraction module specifically includes:

[0032] The first historical task interpretation unit is configured to intelligently interpret each first-type historical task, extract all current fluctuation feature data of the scheduling device during execution of the first-type historical task, and calculate a mean value of the current fluctuation feature data, which is defined as the first-type current fluctuation feature data of the first-type historical task.

[0033] The second historical task interpretation unit is configured to intelligently interpret each second-type historical task, extract all current fluctuation feature data of the scheduling device during execution of the second-type historical task, and calculate a mean value of the current fluctuation feature data, which is defined as the second-type current fluctuation feature data of the second-type historical task.

[0034] As a further limitation of the technical scheme of the embodiment of the application, the curve drawing module specifically includes:

[0035] a timestamp recording unit configured to determine the timestamp of each piece of the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data according to the historical operation records;

[0036] a curve plotting unit configured to plot the current fluctuation curve and the standard control current fluctuation curve reflecting the change of the current fluctuation characteristic data over time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data respectively.

[0037] As a further limitation of the technical solutions of the embodiments of the present application, the priority index optimization module specifically comprises:

[0038] an average slope calculation unit configured to compare the current fluctuation curve and the standard control current fluctuation curve point by point and calculate the average slope of both curves respectively;

[0039] an adjustment factor determination unit configured to calculate the slope deviation based on the difference between the average slopes of the current fluctuation curve and the standard control current fluctuation curve and take the slope deviation as the adjustment factor;

[0040] an optimization formula application unit configured to retrieve the preset priority index optimization formula and correct the initial task priority index in combination with the adjustment factor;

[0041] The priority index optimization formula is: wherein PI final refers to the corrected 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.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] Firstly, the present application selects a designated load type similar to the target load in physical properties as the control standard, making the analysis of current fluctuation characteristic data more controllable. The selection of the designated load type, especially the consistency in weight or volume, ensures that the device state can be maintained at a relatively stable standard state when handling similar goods, thereby improving the accuracy and reliability of the scheduling system in evaluating the device state. This approach effectively avoids the scheduling deviation caused by the lack of stable reference state in the traditional scheduling method, ensuring efficient operation of the device during task execution.

[0044] Secondly, by intelligently analyzing historical operation records and extracting current fluctuation characteristic data, the application can accurately identify and analyze the running state of the device when transporting goods of the same type as the target load, and optimize the task priority based on this. This dynamic optimization process can timely adjust the scheduling order of the task, improve the timeliness of task completion and the use efficiency of the device.

[0045] Finally, the application can dynamically adjust the initial task priority index based on the slope deviation of the current fluctuation curve, ensure that the system can automatically improve the priority of the corresponding task when the device is unstable or there is a potential safety risk, thereby effectively avoiding the occurrence of device failure and safety accidents, and improving the safety and stability of the warehouse system. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The flowchart of the method provided for the embodiment of the application;

[0047] Figure 2 The flowchart of acquiring the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data in the method provided for the embodiment of the application;

[0048] Figure 3 The flowchart of drawing the current fluctuation curve and the standard comparison current fluctuation curve in the method provided for the embodiment of the application;

[0049] Figure 4 The flowchart of correcting the initial task priority index in the method provided for the embodiment of the application;

[0050] Figure 5 The application architecture diagram of the system provided for the embodiment of the application;

[0051] Figure 6 The structural block diagram of the current fluctuation characteristic data extraction module in the system provided for the embodiment of the application;

[0052] Figure 7 The structural block diagram of the curve drawing module in the system provided for the embodiment of the application;

[0053] Figure 8 The structural block diagram of the priority index optimization module in the system provided for the embodiment of the application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0055] Figure 1A flow chart of the method provided by the embodiment of the application is shown.

[0056] Specifically, a transmission scheduling method based on intelligent warehousing, the method specifically comprises the following steps:

[0057] In step S100, a scheduling device for transmission scheduling of a target carrier is determined, an initial task priority index generated according to a task requirement for the target carrier is obtained, and a historical operation record of the scheduling device is obtained.

[0058] In the embodiment of the application, the target carrier refers to a specific item that needs to be transported, stored or processed in the intelligent warehousing system. The type and physical characteristics (such as weight, volume, etc.) of the target carrier are key factors for the system to select according to scheduling requirements. In the intelligent warehousing system, the target carrier is an object that needs to be scheduled in the warehouse space according to specific transmission rules or requirements. These rules are usually based on the type of the item, the storage location, the safety requirements and other factors.

[0059] The scheduling device refers to various devices in the intelligent warehousing system that perform item handling, transportation or distribution tasks, usually including automated handling robots, automatic stacking machines, automatic forklifts, conveyors, etc. These devices perform corresponding operations according to task requirements and specific characteristics (such as size, weight, etc.) of the target carrier. The scheduling device not only performs physical handling tasks, but also can receive and execute instructions through an integrated control system, optimize the task execution process in real time, and ensure the efficient operation of the warehouse system.

[0060] The initial task priority index is calculated according to the type of the target carrier, the urgency of the task, the transportation requirements and other factors, and is usually dynamically generated by the scheduling algorithm of the system. This priority index can be obtained through rule configuration in the existing scheduling system, combined with real-time data and task instructions of warehouse management. Specifically, the initial task priority index is based on the timeliness of the task, the particularity of the item and the existing data in the warehouse environment to assign a priority value to each task. In the prior art, similar task priority calculation methods have been applied in automated warehousing, but the specific algorithm used in the present application is optimized and customized according to different types of carrier tasks.

[0061] Historical operation records refer to various data generated by the scheduling device when it performs tasks in the past. These records contain basic information about the tasks and the state of the scheduling device, including task type, execution time, task completion status, device performance, etc. The source of these records is usually generated automatically by the device itself through the built-in monitoring system, or recorded by the control module of the warehouse system. By analyzing historical operation records, the system can understand the work efficiency of different tasks and scheduling devices, identify potential abnormal situations, and thus optimize the scheduling of future tasks. Historical operation records are usually stored in the database of the warehouse system and can be used as a reference for subsequent scheduling decisions to help scheduling devices perform more accurate task optimization.

[0062] Further, the intelligent warehouse-based transmission scheduling method further comprises the following steps:

[0063] Step S200, intelligently analyze historical operation records, filter out a number of first type historical tasks consistent with the target load type and a number of second type historical tasks consistent with the specified load type, and ensure that the occurrence time of the filtered first type historical tasks and second type historical tasks meets the preset time window requirement.

[0064] The specified load type specifically refers to a type of goods that is consistent or highly similar in physical properties to the target load and belongs to a type of goods with special safety requirements.

[0065] In the embodiments of the present application, "consistent or highly similar in physical properties" refers to items similar in weight or volume, preferably weight. That is, the goods in the second type of historical tasks are very similar in weight to the target load, which ensures that the scheduling system can effectively reference device performance, carrying capacity, etc. when scheduling these historical tasks.

[0066] Selecting a type of goods with special safety requirements as the second type of historical task means that these goods usually require high safety measures during transportation, such as preventing vibration and avoiding impact. Such goods often have high requirements for the stability of the device, so the state of the scheduling device must be maintained in a stable state during scheduling to ensure the safety of the transportation process.

[0067] The significance of selecting this type of goods as the second type of historical task is that due to the particularity of transporting these goods, the scheduling device must maintain a relatively stable operating state when performing tasks. By analyzing historical tasks related to these special goods, the scheduling system can better identify the stable state of the device under such special conditions, and thus provide a reference for the scheduling of the target load. This method is equivalent to providing a "standard" state for the transportation of the target load, so as to ensure that the device always maintains a safe and efficient working state when performing tasks.

[0068] Furthermore, the intelligent warehousing-based transmission scheduling method also includes the following steps:

[0069] Step S300: Extract the first type of current fluctuation characteristic data of the scheduling device when transmitting goods consistent with the target cargo type and the second type of current fluctuation characteristic data when transmitting goods consistent with the specified cargo type from the first type of historical task and the second type of historical task.

[0070] Specifically, Figure 2 The flowchart for obtaining the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data is shown.

[0071] The extraction of first-type current fluctuation characteristic data and second-type current fluctuation characteristic data from the first and second types of historical tasks, when the scheduling equipment transmits goods consistent with the target cargo type, specifically includes the following steps:

[0072] Step S301: Intelligent interpretation of each first type of historical task, extract all current fluctuation feature data of the scheduling device during the execution of the first type of historical task, calculate the mean of these current fluctuation feature data, and define it as the first type of current fluctuation feature data of the first type of historical task.

[0073] Step S302: Intelligent interpretation is performed on each second type of historical task. All current fluctuation characteristic data of the scheduling device during the execution of the second type of historical task are extracted, and the mean of these current fluctuation characteristic data is calculated and defined as the second type of current fluctuation characteristic data of the second type of historical task.

[0074] In this embodiment of the invention, the current fluctuation characteristic data of the scheduling device refers to the fluctuation data generated by the current consumption of the device during task execution. Current fluctuations can reflect information such as load changes and status fluctuations during device operation, which helps determine the device's working status and efficiency during task execution. Current fluctuation characteristic data is typically collected in real time by the scheduling device's current sensors or other monitoring devices and stored through a data recording system. This data is usually stored in historical operation records as a detailed record of device performance and task execution status.

[0075] The mean of all current fluctuation characteristic data of the scheduling device during the execution of the first type of historical task is defined as the first type of current fluctuation characteristic data of the first type of historical task. The reason is that the mean can provide an overall and stable current fluctuation level, reduce the interference of accidental fluctuations, and reflect the overall energy efficiency and stability of the scheduling device when performing a specific type of task. This mean represents the "standard" current fluctuation characteristic of the device under this type of task, which can provide a comparison benchmark for subsequent task scheduling.

[0076] Similarly, the current fluctuation characteristic data of the second type of historical task is also calculated by the same method to extract the current fluctuation characteristic data of the device when performing tasks consistent with the specified load type. Since the second type of goods usually has special safety requirements and transportation complexity, the current fluctuation characteristic data generated by the device when performing these tasks can be used as the performance of the device in a "stable operating state". These data help to understand the working state of the device under special task conditions, thereby providing an important reference for subsequent task scheduling.

[0077] The significance of this implementation process is that by extracting and comparing current fluctuation characteristic data in different types of tasks, the scheduling system can accurately understand the stability, efficiency and potential performance changes of the device when handling different tasks. The mean of the first and second types of historical tasks provides a comparison benchmark that can help the system identify performance fluctuations of the device under different transportation tasks, thereby optimizing scheduling tasks and ensuring that the device remains in a suitable working state during transportation tasks.

[0078] The invention details the current fluctuation characteristic data in this direction. In fact, during the research process, other related fields are also explored, such as device load fluctuation analysis, vibration data monitoring, and the impact of environmental temperature changes on device current consumption. These factors can also have a significant impact on the working state of the scheduling device and provide valuable references for further optimizing the scheduling algorithm.

[0079] Further, the transmission scheduling method based on intelligent storage further includes the following steps:

[0080] Step S400, according to the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data, respectively draw the current fluctuation curve and the standard comparison current fluctuation curve changing with time.

[0081] Specifically, Figure 3 A flowchart for drawing the current fluctuation curve and the standard comparison current fluctuation curve is shown.

[0082] The current current fluctuation curve and the standard comparison current fluctuation curve changing with time are drawn according to the first type current fluctuation characteristic data and the second type current fluctuation characteristic data, and specifically include the following steps:

[0083] In step S401, the time stamp of each piece of first type current fluctuation characteristic data and second type current fluctuation characteristic data is determined according to historical operation records.

[0084] In step S402, the current current fluctuation curve and the standard comparison current fluctuation curve reflecting the change with time are drawn based on the first type current fluctuation characteristic data and the second type current fluctuation characteristic data, respectively, with time as the horizontal coordinate and the current fluctuation characteristic data value as the vertical coordinate.

[0085] In the embodiment of the present application, the generation of the current current fluctuation curve and the standard comparison current fluctuation curve depends on the detailed analysis and processing of the first type current fluctuation characteristic data and the second type current fluctuation characteristic data. First, in step S401, the corresponding time stamp is extracted from each piece of current fluctuation characteristic data in the historical operation records. These time stamps ensure the time sequence of the data, so that the current fluctuation curve changing with time can be drawn in the subsequent steps.

[0086] In step S402, the current current fluctuation curve is drawn point by point according to each piece of historical data, with time as the horizontal coordinate and the current fluctuation characteristic data value as the vertical coordinate. In order to improve the smoothness and accuracy of the curve, it is usually necessary to perform smoothing and denoising processing on the original current fluctuation characteristic data, using techniques such as moving average, exponential smoothing method or Kalman filter, etc. These techniques help to eliminate random noise in the data and highlight the current fluctuation trend of the device in the stable working state, so as to generate a more reliable curve.

[0087] The generation method of the standard comparison current fluctuation curve usually depends on the standard model or historical experience data, which can be generated by the performance of the previous device under similar work load or operating state, as a reference benchmark for the normal working state of the device. By comparing the current current fluctuation curve with the standard comparison current fluctuation curve, the difference between the current state and the standard state of the device can be identified, thereby providing a basis for decision-making for subsequent scheduling optimization, priority adjustment, etc.

[0088] The significance of generating these two curves lies in that they provide an intuitive time sequence view for judging the working state of the scheduled device. By comparing the shape, fluctuation degree and deviation of the curves, it can be analyzed whether the device has abnormal fluctuation, excessive load or other potential fault risks. These analysis results will directly affect the optimization adjustment of subsequent scheduling tasks, improve the scientificity and accuracy of device scheduling, and ensure the stability and safety of the device in the task execution process.

[0089] Further, the transmission scheduling method based on intelligent storage further includes the following steps:

[0090] In step S500, the current current fluctuation curve is compared with the standard comparison current fluctuation curve, and an adjustment factor is calculated according to the deviation between the two, and the initial task priority index is optimized and adjusted by the adjustment factor.

[0091] Specifically, Figure 4 A flowchart for correcting the initial task priority index is shown.

[0092] The comparison between the current current fluctuation curve and the standard comparison current fluctuation curve, and the calculation of the adjustment factor according to the deviation between the two, and the optimization and adjustment of the initial task priority index by the adjustment factor specifically includes the following steps:

[0093] In step S501, the current current fluctuation curve is compared with the standard comparison current fluctuation curve point by point, and the average slopes of the two are calculated respectively.

[0094] In step S502, the slope deviation is calculated based on the average slope difference between the current current fluctuation curve and the standard comparison current fluctuation curve, and the slope deviation is taken as the adjustment factor.

[0095] In step S503, the preset priority index optimization formula is called, and the initial task priority index is corrected in combination with the adjustment factor.

[0096] The priority index optimization formula is: where PI final refers to the corrected task priority index, PI initial refers to the initial task priority index, S current refers to the average slope of the current current fluctuation curve, S norms refers to the average slope of the standard comparison current fluctuation curve, refers to the adjustment factor, and K refers to the adjustment factor adjustment coefficient.

[0097] In the embodiment of the application, the average slope can be calculated by the numerical differentiation method in the prior art, which usually calculates the change rate between each small segment of the curve by point by point, so as to obtain the slope of each segment. By calculating the average value of the slopes of all small segments, the average slope of the entire current fluctuation curve can be obtained. In order to avoid errors caused by too large data fluctuation, the original data is usually smoothed before calculation to ensure that the calculated slope is more stable and reliable.

[0098] The slope deviation is used as the adjustment factor because the slope itself can reflect the dynamic response and stability of the device during operation. If there is a large deviation between the slope of the current fluctuation curve and the slope of the standard control current fluctuation curve, it means that the working state of the device is abnormal, which may be due to excessive load of the device, unstable operation or other potential faults. By using the slope deviation as the adjustment factor, the device state can be dynamically adjusted during task scheduling, optimizing the operation efficiency and safety of the device. As an adjustment factor, the slope deviation not only accurately reflects the changes in the state of the device, but also optimizes the priority of the task, so that the scheduling system can make more scientific decisions based on the actual state of the device, thereby reducing the risk of overloading the device and improving overall work efficiency.

[0099] The whole technical solution has the benefit that by introducing current fluctuation characteristic data and combining slope deviation for adjustment, the state of the device during task execution can be more accurately evaluated and optimized. This data-driven dynamic adjustment method can identify abnormal fluctuations in the device in real time and optimize them in a timely manner, avoiding task delays or device failures caused by device abnormalities. This optimization method not only improves the efficiency of task execution, but also effectively reduces the safety risks in device operation, improves the stability and reliability of the system, and solves the problem of not being able to accurately evaluate the state of the device in real time in traditional scheduling methods.

[0100] For example, assume that the initial task priority index of the system is 100, indicating the priority of the current task. Through historical job records and analysis, the system calculates the slopes of the current current fluctuation curve and the standard control current fluctuation curve as 0.3 and 0.2, respectively. At this time, the slope deviation is 0.1, i.e. the slope of the current current fluctuation curve is 0.1 higher than that of the standard curve. This slope deviation is used as an adjustment factor and is input into the preset priority index optimization formula, which modifies the initial task priority index according to this deviation.

[0101] For example, assume that the adjustment coefficient is 0.5, so when the slope deviation is 0.1, the calculated adjustment factor is 0.1 x 0.5 = 0.05, i.e. the adjustment amplitude of the priority index is 5%. Then, the initial task priority index 100 is increased by 5% according to this adjustment factor, and the final optimized priority index is 105.

[0102] Further, Figure 5 The application architecture diagram of the system provided by the embodiment of the application is shown.

[0103] In another preferred embodiment provided by the application, a transmission scheduling system based on intelligent warehousing comprises:

[0104] The data acquisition module 100 is configured to determine a scheduling device for transmitting the target carrier, acquire an initial task priority index generated according to a task requirement for the target carrier, and acquire a historical operation record of the scheduling device.

[0105] In the embodiments of the present application, the target carrier refers to a specific item that needs to be transported, stored or processed in the intelligent warehouse system. The type and physical characteristics (such as weight, volume, etc.) of the target carrier are key factors for the system to select according to scheduling requirements. In the intelligent warehouse system, the target carrier is an object that needs to be scheduled in the warehouse space according to specific transportation rules or requirements. These rules are usually based on the type of the item, the storage location, the safety requirements and other factors.

[0106] The scheduling device refers to various devices that perform item handling, transportation or distribution tasks in the intelligent warehouse system, usually including automated handling robots, automatic stacking machines, automatic forklifts, conveyor belts, etc. These devices perform corresponding operations according to task requirements and specific characteristics (such as size, weight, etc.) of the target carrier. The scheduling device not only performs physical handling tasks, but also can receive and execute instructions through an integrated control system, optimize the task execution process in real time, and ensure the efficient operation of the warehouse system.

[0107] The initial task priority index is calculated based on the type of the target carrier, the urgency of the task, the transportation requirements and other factors, and is usually dynamically generated by the scheduling algorithm of the system. This priority index can be obtained through rule configuration in the existing scheduling system, combined with real-time data and task instructions of warehouse management. Specifically, the initial task priority index is based on the timeliness of the task, the particularity of the item and the existing data in the warehouse environment to assign a priority value to each task. In the prior art, similar task priority calculation methods have been applied in automated warehouses, but the specific algorithm used in the present application is optimized and customized according to different types of carrier tasks.

[0108] The historical operation record refers to various data generated by the scheduling device when performing tasks in the past. These records contain basic information of the task and the state of the scheduling device, including task type, execution time, task completion status, device performance, etc. The source of these records is usually automatically generated by the device itself through the built-in monitoring system, or recorded by the control module of the warehouse system. By analyzing the historical operation record, the system can understand the work efficiency of different tasks and scheduling devices, identify potential abnormal situations, and thus optimize the scheduling of future tasks. The data of the historical operation record is usually stored in the database of the warehouse system and can be used as a reference for subsequent scheduling decisions to help the scheduling device perform more accurate task optimization.

[0109] Further, the intelligent warehouse-based transportation scheduling system further comprises:

[0110] The historical task screening module 200 is configured to intelligently analyze historical task records, screen out a plurality of first type historical tasks consistent with the target cargo type and a plurality of second type historical tasks consistent with the specified cargo type, and ensure that the occurrence time of the screened first type historical tasks and the second type historical tasks meets the preset time window requirement.

[0111] The specified cargo type specifically refers to a cargo type that is consistent with the target cargo in physical properties and belongs to a special security requirement cargo type.

[0112] In the embodiment of the present application, "consistent or highly close in physical properties" refers to similar items in weight or volume, preferably weight. That is, the goods in the second type historical tasks are very close to the target cargo in weight, which can ensure that the scheduling system can effectively refer to the device performance, carrying capacity, etc. when scheduling these historical tasks.

[0113] Selecting the special security requirement cargo type as the second type historical task means that these goods usually require high security measures during transportation, such as preventing vibration and avoiding impact. Such goods often have high requirements for the stability of the device, so the state of the scheduling device must be kept in a stable state during scheduling to ensure the safety of the transportation process.

[0114] The significance of selecting this type of goods as the second type historical task is that, due to the particularity of transporting these goods, the scheduling device must maintain a relatively stable operating state when performing tasks. By analyzing the historical tasks related to these special goods, the scheduling system can better identify the stable state of the device under such special conditions, and thus provide a reference for the scheduling of the target cargo. This method is equivalent to providing a "standard" state for the transportation of the target cargo, so as to ensure that the device always maintains a safe and efficient working state when performing tasks.

[0115] Further, the intelligent warehouse-based transportation scheduling system further comprises:

[0116] The current fluctuation feature data extraction module 300 is configured to extract first type current fluctuation feature data of the scheduling device when transporting goods consistent with the target cargo type and second type current fluctuation feature data of the scheduling device when transporting goods consistent with the specified cargo type from the first type historical tasks and the second type historical tasks.

[0117] Specifically, Figure 6 The structure block diagram of the current fluctuation feature data extraction module 300 in the system provided by the embodiment of the present application is shown.

[0118] In the preferred embodiments provided by the present application, the current fluctuation feature data extraction module 300 specifically comprises:

[0119] The first historical task interpretation unit 301 is configured to intelligently interpret each first-type historical task, extract all current fluctuation feature data of the scheduling device during the execution of the first-type historical task, and calculate the mean value of the current fluctuation feature data, which is defined as the first-type current fluctuation feature data of the first-type historical task.

[0120] The second historical task interpretation unit 302 is configured to intelligently interpret each second-type historical task, extract all current fluctuation feature data of the scheduling device during the execution of the second-type historical task, and calculate the mean value of the current fluctuation feature data, which is defined as the second-type current fluctuation feature data of the second-type historical task.

[0121] In the embodiments of the present application, the current fluctuation feature data of the scheduling device refers to the fluctuation data generated by the current consumption of the device during the execution of the task. The current fluctuation can reflect the load change, state fluctuation and other information of the device during the operation, which can help to judge the working state and efficiency of the device during the execution of the task. The current fluctuation feature data is usually collected by the current sensor or other monitoring devices of the scheduling device in real time and stored through the data recording system. These data are usually stored in the historical job record as a detailed record of the performance of the device and the execution state of the task.

[0122] The reason for defining the mean value of all current fluctuation feature data of the scheduling device during the execution of the first-type historical task as the first-type current fluctuation feature data of the first-type historical task is that the mean value can provide an overall and stable current fluctuation level, reduce the interference of accidental fluctuations, and reflect the overall energy efficiency and stability of the scheduling device during the execution of a specific type of task. This mean value represents the "standard" current fluctuation feature of the device under this type of task, which can provide a comparison benchmark for subsequent task scheduling.

[0123] Similarly, the current fluctuation feature data of the second-type historical task is also calculated by the same method to extract the current fluctuation feature data of the device when executing the goods consistent with the specified load type. Since the second-type goods usually have special safety requirements and transportation complexity, the current fluctuation feature data generated by the device during the execution of these tasks can be used as the performance of the device under "stable running state". These data are helpful to understand the working state of the device under special task conditions, thereby providing an important reference for subsequent task scheduling.

[0124] The significance of this implementation process is that by extracting and comparing the current fluctuation characteristic data in different types of tasks, the scheduling system can accurately understand the stability, efficiency and potential performance changes of the equipment when processing different tasks. The mean of the first and second types of historical tasks provides a comparison benchmark that can help the system identify performance fluctuations of the equipment under different transportation tasks, thereby optimizing the scheduling tasks and ensuring that the equipment remains in a suitable working state during transportation tasks.

[0125] The present application details the current fluctuation characteristic data in this direction. In fact, during the research process, other related fields were also explored, such as equipment load fluctuation analysis, vibration data monitoring, and the impact of environmental temperature changes on equipment current consumption. These factors can also have a significant impact on the working state of the scheduled equipment and provide valuable references for further optimizing the scheduling algorithm.

[0126] Further, the transmission scheduling system based on intelligent storage further includes:

[0127] The curve drawing module 400 is configured to draw the current fluctuation curve and the standard control current fluctuation curve changing over time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data.

[0128] Specifically, Figure 7 The structure block diagram of the curve drawing module 400 in the system provided by the embodiment of the present application is shown.

[0129] In the preferred embodiment provided by the present application, the curve drawing module 400 specifically includes:

[0130] The timestamp recording unit 401 is configured to determine the timestamp of each piece of first type of current fluctuation characteristic data and second type of current fluctuation characteristic data according to the historical operation record;

[0131] The curve drawing unit 402 is configured to draw the current fluctuation curve and the standard control current fluctuation curve reflecting the change over time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data as the horizontal coordinate of time and the vertical coordinate of the current fluctuation characteristic data value.

[0132] In the embodiment of the present application, the generation of the current fluctuation curve and the standard control current fluctuation curve depends on the detailed analysis and processing of the first type of current fluctuation characteristic data and the second type 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 record. These timestamps ensure the time sequence of the data, so that the current fluctuation curve changing over time can be drawn in the subsequent steps.

[0133] In the curve drawing unit 402, the current fluctuation characteristic data value is taken as the ordinate, and the current fluctuation curve is drawn point by point according to each piece of historical data. In order to improve the smoothness and accuracy of the curve, it is usually necessary to smooth and denoise the original current fluctuation characteristic data, and techniques such as moving average, exponential smoothing method or Kalman filtering are adopted. These techniques help to eliminate random noise in the data and highlight the current fluctuation trend of the device in the stable working state, thereby generating a more reliable curve.

[0134] The generation method of the standard comparison current fluctuation curve usually depends on standard models or historical experience data, which can be generated by the performance of previous devices under similar workloads or operating states, as a reference benchmark for the normal working state of the device. By comparing the current fluctuation curve with the standard comparison current fluctuation curve, the difference between the current state and the standard state of the device can be identified, thereby providing a basis for decision-making for subsequent scheduling optimization, priority adjustment, etc.

[0135] The significance of generating the two curves lies in that they provide an intuitive time sequence view for judging the working state of the scheduled device. By comparing the shape, fluctuation degree and deviation of the curves, it can be analyzed whether the device has abnormal fluctuation, excessive load or other potential fault risks. These analysis results will directly affect the optimization adjustment of subsequent scheduling tasks, improve the scientificity and accuracy of device scheduling, and ensure the stability and safety of the device in the task execution process.

[0136] Further, the transmission scheduling system based on intelligent storage further comprises:

[0137] The priority index optimization module 500 is used for comparing the current current fluctuation curve with the standard comparison current fluctuation curve, and calculating an adjustment factor according to the deviation degree between the two curves, and optimizing and adjusting the initial task priority index through the adjustment factor.

[0138] Specifically, Figure 8 The structure block diagram of the priority index optimization module 500 in the system provided by the embodiment of the application is shown.

[0139] In the preferred embodiment provided by the application, the priority index optimization module 500 specifically comprises:

[0140] The average slope calculation unit 501 is used for comparing the current current fluctuation curve with the standard comparison current fluctuation curve point by point, and calculating the average slope of the two curves respectively;

[0141] The adjustment factor determination unit 502 is used for calculating the slope deviation based on the average slope difference between the current current fluctuation curve and the standard comparison current fluctuation curve, and taking the slope deviation as the adjustment factor.

[0142] The optimization formula application unit 503 is used to call a preset priority index optimization formula and correct the initial task priority index in combination with an adjustment factor;

[0143] The priority index optimization formula is: where PI final refers to the corrected task priority index, PI initial refers to the initial task priority index, S current refers to the average slope of the current 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.

[0144] In the embodiment of the application, the average slope can be calculated by using the numerical differentiation method in the prior art. Generally, the change rate between each small segment of the curve is obtained by point-by-point calculation for each small segment, so as to obtain the slope of each segment. The average slope of the entire current fluctuation curve can be obtained by calculating the average value of the slopes of all small segments. In order to avoid errors caused by excessive data fluctuation, the original data is usually smoothed before calculation to ensure that the calculated slope is more stable and reliable.

[0145] The slope deviation is used as the adjustment factor because the slope itself can reflect the dynamic response and stability of the device during operation. If there is a large deviation between the slope of the current current fluctuation curve and the slope of the standard control current fluctuation curve, it means that the working state of the device is abnormal, which may be due to excessive load of the device, unstable operation or other potential faults. By using the slope deviation as the adjustment factor, the device state can be dynamically adjusted during task scheduling, and the operation efficiency and safety of the device can be optimized. The slope deviation as the adjustment factor can not only accurately reflect the change of the device state, but also optimize the task priority, so that the scheduling system can make more scientific decisions according to the actual state of the device, thereby reducing the risk of overloading of the device and improving the overall work efficiency.

[0146] The whole technical solution has the benefit that by introducing current fluctuation characteristic data and combining the slope deviation for adjustment, the state of the device during task execution can be more accurately evaluated and optimized. This data-driven dynamic adjustment method can identify abnormal fluctuations of the device in real time and optimize them in time, avoiding task delays or device failures caused by device abnormalities. This optimization method not only improves the efficiency of task execution, but also effectively reduces the safety risk in device operation, improves the stability and reliability of the system, and solves the problem of being unable to accurately evaluate the state of the device in real time in the traditional scheduling method.

[0147] For example, assume that the initial task priority index is 100, which represents the priority of the current task. Through historical job records and analysis, the system calculates that the slopes of the current current fluctuation curve and the standard current fluctuation curve are 0.3 and 0.2, respectively. At this time, the slope deviation is 0.1, i.e., the slope of the current current fluctuation curve is 0.1 higher than that of the standard curve. This slope deviation is used as an adjustment factor, which is input into the preset priority index optimization formula. The formula corrects the initial task priority index according to the deviation.

[0148] For example, assume that the adjustment coefficient is 0.5. When the slope deviation is 0.1, the calculated adjustment factor is 0.1 x 0.5 = 0.05, i.e., the adjustment range of the priority index is 5%. Then, the initial task priority index 100 is increased by 5% according to the adjustment factor, and the final optimized priority index is 105.

[0149] It should be understood that although each step in the flowchart of each embodiment of the present application is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in each embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0151] Any combination of the technical features of the above-mentioned embodiments can be combined. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0152] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

[0153] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A transmission scheduling method based on intelligent warehousing, characterized in that, The method includes: The scheduling device for the target payload is identified, the initial task priority index generated for the target payload according to the task requirements is obtained, and the historical operation records of the scheduling device are obtained. The system intelligently analyzes historical task records, filters out several first-class historical tasks that match the target cargo type, and several second-class historical tasks that match the specified cargo type, and ensures that the occurrence time of the filtered first-class and second-class historical tasks meets the preset time window requirements. Extract the first type of current fluctuation characteristic data of the scheduling equipment when transmitting goods that are consistent with the target cargo type from the first type of historical task and the second type of historical task; and extract the second type of current fluctuation characteristic data when transmitting goods that are consistent with the specified cargo type. Based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data, the current fluctuation curve and the standard reference current fluctuation curve are plotted as they change over time. The current current fluctuation curve is compared with the standard reference current fluctuation curve, and an adjustment factor is calculated based on the degree of deviation between the two. The initial task priority index is then optimized and adjusted using the adjustment factor.

2. The transmission scheduling method based on intelligent warehousing according to claim 1, characterized in that, The specified cargo type specifically refers to a cargo type that is consistent with the target cargo in terms of physical characteristics and belongs to a cargo type with special safety requirements.

3. The transmission scheduling method based on intelligent warehousing according to claim 2, characterized in that, The steps for extracting Type I current fluctuation characteristic data of the scheduling equipment when transmitting goods consistent with the target cargo type and Type II current fluctuation characteristic data when transmitting goods consistent with the specified cargo type from Type I historical tasks and Type II historical tasks include: For each first-class historical task, intelligent interpretation is performed, all current fluctuation characteristic data of the scheduling equipment during the execution of the first-class historical task are extracted, and the mean of these current fluctuation characteristic data is calculated and defined as the first-class current fluctuation characteristic data of the first-class historical task. For each second type of historical task, intelligent interpretation is performed to extract all current fluctuation characteristic data of the scheduling equipment during the execution of the second type of historical task, and the mean of these current fluctuation characteristic data is calculated and defined as the second type of current fluctuation characteristic data of the second type of historical task.

4. The transmission scheduling method based on intelligent warehousing according to claim 3, characterized in that, The steps for plotting the current fluctuation curve and the standard reference current fluctuation curve over time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data include: Based on historical operation records, determine the timestamps for each type I current fluctuation characteristic data and type II current fluctuation characteristic data. With time as the horizontal axis and current fluctuation characteristic data as the vertical axis, based on several first-type and second-type current fluctuation characteristic data, the current fluctuation curve reflecting its change over time and the standard reference current fluctuation curve are plotted.

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

6. The transmission scheduling method based on intelligent warehousing according to claim 5, characterized in that, The priority index optimization formula is as follows: , where PI final This refers to the revised task priority index, PI. initial This refers to the initial task priority index, S. current This refers to the average slope of the current fluctuation curve, S. norms This refers to the average slope of the standard reference current fluctuation curve. K 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 filtering module, a current fluctuation feature data extraction module, a curve plotting module, and a priority index optimization module, wherein: The data acquisition module is used to determine the scheduling device for transmission scheduling of the target payload, acquire the initial task priority index generated for the target payload according to the task requirements, and acquire the historical operation records of the scheduling device. The historical task filtering module is used to intelligently analyze historical operation records, filter out a number of first-class historical tasks that are consistent with the target cargo type, and a number of second-class historical tasks that are consistent with the specified cargo type, and ensure that the occurrence time of the filtered first-class historical tasks and second-class historical tasks meets the preset time window requirements. The specified cargo type specifically refers to cargo types that are consistent with the target cargo in terms of physical characteristics and belong to cargo types with special safety requirements; The current fluctuation feature data extraction module is used to extract the first type of current fluctuation feature data of the scheduling equipment when transmitting goods consistent with the target cargo type, and the second type of current fluctuation feature data when transmitting goods consistent with the specified cargo type from the first type of historical tasks and the second type of historical tasks. The curve plotting module is used to plot the current current fluctuation curve and the standard reference current fluctuation curve over time based on the first type of current fluctuation characteristic data and the second type of current fluctuation characteristic data, respectively. The priority index optimization module is used to compare the current current fluctuation curve with the standard reference current fluctuation curve, and calculate the adjustment factor based on the degree of deviation between the two. The initial task priority index is then optimized and adjusted using the adjustment factor.

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

9. The intelligent warehousing-based transmission scheduling system according to claim 8, characterized in that, The curve drawing module specifically includes: The timestamp recording unit is used to determine the timestamp of each type I current fluctuation characteristic data and type II current fluctuation characteristic data based on historical operation records. The curve plotting unit is used to plot the current current fluctuation curve and the standard reference current fluctuation curve, with time as the horizontal axis and current fluctuation characteristic data as the vertical axis, based on several first-type and second-type current fluctuation characteristic data respectively.

10. The intelligent warehousing-based transmission scheduling system according to claim 9, characterized in that, The priority index optimization module specifically includes: The average slope calculation unit 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. The adjustment factor determination unit is used to calculate the slope deviation based on the average slope difference between the current current fluctuation curve and the standard control current fluctuation curve, and to use the slope deviation as the adjustment factor. The optimization formula application unit is used to retrieve 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 as follows: , where PI final This refers to the revised task priority index, PI. initial This refers to the initial task priority index, S. current This refers to the average slope of the current fluctuation curve, S. norms This refers to the average slope of the standard reference current fluctuation curve. K refers to the adjustment factor, and K refers to the adjustment coefficient of the adjustment factor.

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