Logistics plant AGV task endurance distribution method based on residual electric quantity prediction

Abstract

The invention relates to the technical field of logistics task distribution analysis, in particular to a logistics factory AGV task endurance distribution method based on residual electric quantity prediction, which comprises the following steps: scanning a cargo handover point in a logistics task path, detecting whether the handover point is provided with a wireless charging module or not, acquiring a charging power value of the wireless charging module, and generating a chargeable handover point set; according to the residence time of the chargeable handover point and the charging power value, calculating an expected charging amount for executing charging at the chargeable handover point, and generating a multi-task efficiency gain value in combination with the residual electric quantity of the AGV; and selecting the AGV of which the multi-task efficiency gain value exceeds a preset gain threshold value to allocate a logistics task, and inserting the chargeable junction point into a task sequence. According to the invention, low-income charging tasks are prevented from being allocated to the high-electric-quantity AGV, and the economical efficiency of task allocation and the energy utilization rate are improved; and under the states of task peak or battery aging and the like, through a dynamic gain threshold adjustment mechanism, the stability of the candidate set is ensured, and the risk of AGV task interruption is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics task allocation analysis, and particularly to a method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction. Background Art

[0002] With the development of intelligent manufacturing and industrial automation, automated guided vehicles (AGVs) have been widely used in logistics plant areas to achieve efficient transfer of goods between different workstations. In the current AGV scheduling system, during the task allocation process, scheduling decisions are mainly based on task priority, path length, or the current idle state, and less consideration is given to the impact of the power status of AGVs on the ability to continuously execute tasks, resulting in the following problems easily occurring under complex working conditions:

[0003] Traditional systems usually adopt a periodic centralized charging strategy or set a fixed threshold for scheduling charging, and cannot flexibly cope with factors such as dynamic task flows, non-uniform charging point layouts, and battery aging, resulting in AGVs frequently interrupting tasks or being forced to return to the charging area, affecting the overall logistics efficiency.

[0004] Most existing solutions rely on a static database to mark the positions of fixed charging piles, lack the ability to dynamically perceive "potential charging opportunity points" in the path, and ignore rechargeable resources such as wireless charging modules deployed at handover points, resulting in low utilization rate of system resources.

[0005] The scheduling model is fragmented, and it is difficult to coordinate tasks and charging behaviors. Traditional scheduling algorithms separate the task path planning and charging operations, and fail to evaluate the impact of charging behaviors on the task path cost, resulting in inefficient phenomena such as "high detour and low recharge" and "high power insertion for charging", and even causing scheduling conflicts and path blockages.

[0006] There is a lack of a power value evaluation mechanism, and it is difficult to quantify the charging benefits: Existing methods only use the absolute recharge amount as the evaluation basis, ignoring the relative value differences of AGVs' recharge behaviors at different remaining power stages, and unable to achieve refined and economical task-charging collaborative optimization. Summary of the Invention

[0007] The present invention provides a method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction, a new AGV task allocation method that integrates power status prediction, path structure perception, charging behavior scheduling, and environmental constraint modeling, improves the task endurance ability and charging efficiency, and realizes intelligent energy scheduling for complex plant area logistics systems.

[0008] A method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction includes the following steps:

[0009] S1, Charging Junction Point Identification: Scan the goods transfer points in the logistics task path, detect whether a wireless charging module is deployed at the transfer point and obtain its charging power value, and generate a set of rechargeable transfer points;

[0010] S2, Efficiency Gain Calculation: Calculate the expected supplementary power for charging at the rechargeable transfer point based on the residence time and charging power value at the rechargeable transfer point, and generate a multi-task efficiency gain value in combination with the remaining power of the AGV;

[0011] S3, Gain Priority Allocation: Select the AGVs with multi-task efficiency gain values exceeding the preset gain threshold to allocate logistics tasks, and insert the rechargeable transfer points into the task sequence.

[0012] Optionally, the S1 specifically includes:

[0013] S11, Analyze the key node coordinates of the logistics task path and extract the location identifiers of all goods transfer points;

[0014] S12, Send a wireless detection signal to the physical node corresponding to the location identifier. When a response signal including a charging signature is received, it is determined that a wireless charging module is deployed;

[0015] S13, Decode the charging power value and available time period from the response signal;

[0016] S14, Aggregate the information of all transfer points determined to have a wireless charging module deployed to generate a set of rechargeable transfer points.

[0017] Optionally, the S12 specifically includes:

[0018] Take each goods transfer point as a physical node and send a wireless detection signal to the physical node in turn. The detection signal carries identity and request information, including the AGV identity number initiating the detection, the current detection timestamp, and a detection instruction code for triggering a response;

[0019] Send the detection signal. After the physical node receives the detection signal, if a wireless charging module is deployed at this physical node, it returns a structured response signal. If the response signal meets the determination criteria, this physical node is determined to be a rechargeable transfer point with a deployed wireless charging module, and its power is decoded and verified.

[0020] Optionally, the element structure of the set of rechargeable transfer points is (location identifier, charging power value, available time period).

[0021] Optionally, the S2 specifically includes:

[0022] S21. Calculate the expected supplementary power: Obtain the residence time and charging power value of each rechargeable transfer point in the set of rechargeable transfer points, and calculate the expected supplementary power according to the transmission efficiency coefficient;

[0023] S22. Determine the gain reference value: Combine the expected supplementary power and the current remaining power of the AGV to generate a charging gain coefficient;

[0024] S23. Calculate the path optimization amount: Obtain the original task path length without embedding charging, subtract the new path length after embedding the rechargeable transfer point, and generate a path shortening rate;

[0025] S24. Synthesize the gain value: When the charging gain coefficient is greater than the gain critical value, superimpose the path shortening rate on the charging gain coefficient according to the dynamic weight factor, and output the multi-task efficiency gain value.

[0026] Optionally, the expected supplementary power is calculated as:

[0027] ;

[0028] Where represents the supplementary power of the AGV at the rechargeable transfer point , represents the expected residence time at the transfer point, is the effective charging power of this rechargeable transfer point, is the transmission efficiency coefficient.

[0029] Optionally, the path shortening rate is calculated as:

[0030] ; Where represents the path shortening rate, indicating the relative saving percentage brought by path optimization, represents the total length of the task path without embedding the charging point, represents the adjusted path length after embedding the charging point.

[0031] Optionally, S2 further includes a transmission efficiency coefficient compensation rule:

[0032] Specifically, if there are interference sources or different ground materials around the rechargeable transfer point, the transmission efficiency coefficient is dynamically adjusted according to the interference source or different ground materials .

[0033] Optionally, S3 specifically includes:

[0034] S31. Determine the basic gain threshold according to the task backlog rate in the factory area and the battery type, combine the historical task interruption rate of the AGV to calculate the floating compensation value, and generate a preset gain threshold;

[0035] S32, Candidate AGV Screening: Select AGVs with multitask efficiency gain values greater than a preset gain threshold to form a high-gain candidate set;

[0036] S33, Spatiotemporal Conflict Resolution: Detect the expected time window for each AGV in the high-gain candidate set to reach the rechargeable handover point. When the time window has a high overlap with the available time period of the rechargeable handover point, generate the coordinates of the valid insertion point; specifically, for each candidate AGV, obtain the time interval when it is expected to reach the handover point , and the available charging period of the handover point for comparison, and calculate the time window overlap:

[0037] ;

[0038] If the overlap meets: ;

[0039] Then this handover point is marked as a valid insertion point, and its coordinates are recorded and enter the set of feasible insertion points .

[0040] In the above: represents the time when the AGV is expected to reach the rechargeable handover point, represents the time when the AGV is expected to leave the rechargeable handover point, represents the available start time of the charging module at this handover point, represents the available end time of the charging module at this handover point, represents the overlapping amount of the actual available charging period of the AGV with the time window of the available period of the handover point, represents the two-dimensional coordinates of the rechargeable handover point j

[0041] S34, Task Sequence Reconstruction: Embed the coordinates of the valid insertion point into the original task path according to the charging period requirements, generate a new task sequence including the charging behavior, and allocate it to the corresponding AGV.

[0042] Optionally, the preset gain threshold :

[0043] ;

[0044] Among them, is the preset gain threshold, is the basic gain threshold, set according to the battery type, represents the backlog rate coefficient, is the interruption rate coefficient, represents the current task backlog rate, represents the total number of currently unassigned tasks, represents the current number of available AGVs, Indicates the task interruption rate in the past 7 days, Indicates the total number of task interruptions that occurred to the AGV within the past 7 days, Indicates the total number of tasks executed by the AGV within the past 7 days.

[0045] Advantages of the present invention:

[0046] In the present invention, by introducing a wireless detection response + measured load verification mechanism into the AGV task path, the handover points for deploying wireless charging modules are dynamically identified and their true power outputs are evaluated. Accurate modeling of the charging capacity is achieved in complex factory environments such as metal shelves and electromagnetic interference. Compared with traditional solutions that rely on static databases, faulty charging points are automatically excluded, and the accuracy of resource availability identification is improved. The dynamic transmission efficiency compensation mechanism can adjust the efficiency coefficient in real time according to the shelf occlusion rate and ground material, effectively avoiding charging estimation deviations; a triple set including location, power, and available time is constructed to provide multi-dimensional charging capacity constraint support for task scheduling.

[0047] In the present invention, by performing a ratio operation on the expected charge replenishment amount and the remaining battery power of the current AGV, a "charging gain coefficient" is generated, and a dynamic activation threshold is set. Only when the gain is significant, are path optimization factors considered to synthesize the final efficiency gain value. This avoids high-battery AGVs being assigned low-benefit charging tasks, improving the economy and energy utilization rate of task allocation; in states such as peak task periods or battery aging, through the dynamic gain threshold adjustment mechanism, the stability of the candidate set is ensured and the risk of AGV task interruption is significantly reduced.

[0048] In the present invention, based on the overlap calculation of the predicted arrival time of the AGV and the available charging time period at the handover point, and setting a matching condition of ≥80% available window to generate valid insertion points, ensuring that the charging behavior does not interfere with the execution of the main task. The task path reconstruction logic embeds the charging behavior according to the insertable time period, solving the task and charging conflict problem caused by the decoupling of traditional charging scheduling and logistics paths; avoiding insertion failures and task delays in scenarios with low overlap or high latency, and improving the task on-time completion rate. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 Schematic diagram for verifying the charging power value of the embodiment of the present invention;

[0051] Figure 2Schematic diagram of transmission efficiency coefficient compensation according to an embodiment of the present invention;

[0052] Figure 3 Schematic diagram of the method flow according to an embodiment of the present invention. Detailed implementation manners

[0053] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art can also implement it using other alternative methods for some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0054] As Figures 1 - 3 shown, a method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction includes the following steps:

[0055] S1, Identification of charging handover points: Scan the goods handover points in the logistics task path, detect whether a wireless charging module is deployed at the handover point and obtain its charging power value, and generate a set of rechargeable handover points;

[0056] S2, Calculation of efficiency gain: Calculate the expected supplementary power for charging at the rechargeable handover point according to the residence time and charging power value of the rechargeable handover point, and generate a multi-task efficiency gain value in combination with the remaining power of the AGV;

[0057] S3, Gain-priority allocation: Select the AGVs with multi-task efficiency gain values exceeding the preset gain threshold to allocate logistics tasks, and insert the rechargeable handover points into the task sequence.

[0058] S1 specifically includes:

[0059] S11, Path node parsing and handover point extraction: Given the task path topology , where each path node contains the following fields: ; where is the two-dimensional coordinate, indicates whether it is a goods handover point.

[0060] Extract all handover points: ; represents the set of handover point positions (only includes the nodes marked as "handover" in the task path), is the position identifier of the th handover point, represents the handover point 's two-dimensional space coordinate, represents the node 's type field, with a value of handover (indicating a goods handover point), represents the path set The th path node, is the set of all nodes in the AGV task path.

[0061] S12, Wireless Detection Signal Sending and Response Verification:

[0062] S121, Each handover point is regarded as a physical node , and wireless detection signals are sent to these physical nodes in sequence ;

[0063] S122, Composition of the Wireless Detection Signal: The detection signal carries identity and request information, and its structure consists of three parts, totaling 16 bytes:

[0064] AGV ID: The identity number of the AGV initiating the detection;

[0065] Time Stamp: The current detection timestamp, used for synchronization and anti-replay;

[0066] Probe Request_Code: A specific detection instruction code, used to trigger a response.

[0067] The signal is sent through a communication frequency of 125 kHz to improve anti-interference ability and adapt to the complex electromagnetic environment in industrial plants.

[0068] S123, Format and Content of the Response Signal: After receiving the detection signal, if the physical node is equipped with a wireless charging module, it will return a structured response signal , which contains three parts of information:

[0069] Charge Code: Identifies whether the physical node has a wireless charging function;

[0070] Power Code: Represents the charging power encoding of the current module;

[0071] Time Slot: Information about the available time period of the charging module.

[0072] S124, Judgment Criterion: If the value of the Charge Code field in the response signal is 0xA5, the system determines that the node is a rechargeable handover point equipped with a wireless charging module, and performs power decoding and verification on it subsequently.

[0073] S13, Charging Power and Time Period Decoding and Dynamic Verification:

[0074] Nominal Power Decoding: , Indicates the handover point The nominal charging power value decoded from the response signal Represents a decoding function for extracting a value from an encoded field Indicates the field encoding representing the power level in the response signal

[0075] Dynamic power verification: Apply a standard simulated load to the rechargeable handover point (Known resistance value), measure the actual measured voltage , calculate the true output power : ;

[0076] Power deviation judgment: , is the power deviation percentage of the handover point , representing the degree of difference between the actual measured power and the nominal value; if , then accept the nominal value; otherwise, replace it with the actual measured average value:

[0077] , is the deviation tolerance threshold, set to 5%;

[0078] Available time period parsing: .

[0079] S14, rechargeable handover point set Construction: For each goods handover point that meets the charging conditions , generate a triple structure: ; Aggregate to generate a rechargeable handover point set:

[0080] .

[0081] S2 specifically includes:

[0082] S21, expected charge calculation: For each rechargeable handover point , according to the residence time , charging power and transmission efficiency coefficient calculate the expected charge of this point :

[0083] ;

[0084] Among them, represents the charge of the AGV at the rechargeable handover point , represents the expected residence time at the handover point, is the effective charging power of this rechargeable handover point, is the wireless energy transfer efficiency coefficient, ranging from 0.82 to 0.90, depending on the on-site environment.

[0085] S22, calculation of the charging gain coefficient: Divide the above-mentioned supplementary power by the current remaining power of the AGV to generate the charging gain coefficient : ;

[0086] where, represents the relative power gain of the charging behavior to the current AGV, represents the supplementary power brought by a rechargeable transfer point, represents the current remaining power of the AGV.

[0087] S23, calculation of the path shortening rate: Compare the original task path length with the path length after embedding the rechargeable transfer point to obtain the path optimization value (i.e., the path shortening rate):

[0088] ; where, represents the path shortening rate, indicating the relative saving percentage brought by path optimization, represents the total length of the task path without embedding the charging point, represents the adjusted path length after embedding the charging point.

[0089] S24, synthesis of the multi-task efficiency gain value: When (set gain activation threshold), weight and superimpose the path shortening rate according to the congestion status dynamic weight to generate the final multi-task efficiency gain value :

[0090] ;

[0091] where, represents the final multi-task efficiency gain value, used as the basis for task priority allocation, represents the path shortening weight factor, adjusted according to the congestion situation in the factory area (value range 0.2 - 0.8), represents the gain activation threshold, with a default value of 0.3 (i.e., the supplementary power needs to reach 30% of the remaining power).

[0092] It also includes the wireless energy transfer efficiency compensation rule: If there are interference sources such as metal shelves or different floor materials around the transfer point, it can be set according to the following rules :

[0093] If the metal coverage rate of the shelf > 50%, then ;

[0094] Otherwise, if the ground is concrete, then ;

[0095] Otherwise, by default .

[0096] S3 specifically includes:

[0097] S31, preset gain threshold: Determine the basic gain threshold according to the plant task backlog rate and the AGV battery type , and generate a floating compensation amount in combination with the AGV historical task interruption rate, so as to form a preset gain threshold :

[0098] ;

[0099] Among them, is the preset gain threshold (used to screen high-performance AGVs), is the basic gain threshold, set according to the battery type, 0.45 for NMC type and 0.40 for LFP type, represents the backlog rate coefficient, and the recommended value is 0.15, is the interruption rate coefficient, and the recommended value is 0.08, represents the current task backlog rate, represents the total number of tasks to be allocated currently, represents the current number of available AGVs, represents the task interruption rate in the past 7 days, represents the total number of task interruptions that the AGV has had in the past 7 days, represents the total number of tasks executed by the AGV in the past 7 days.

[0100] S32, candidate AGV screening: Screen all multi-task efficiency gain values greater than the dynamic threshold and the current remaining power greater than the power safety lower limit of the AGV to form a high-gain candidate set :

[0101] ;

[0102] Among them, is the high-gain candidate AGV set, represents the th multi-task efficiency gain value of the AGV (from S2), represents the th remaining power of the AGV, represents the power safety bottom line, set to 20% of the rated capacity.

[0103] S33, Space-time Conflict Resolution and Insertion Point Identification: For each candidate AGV, obtain the time interval when it is expected to reach the handover point , and compare it with the available charging time period of the handover point to calculate the overlap degree of the time window:

[0104] ;

[0105] If the overlap degree meets: ;

[0106] Then this handover point is marked as a valid insertion point, and its coordinates are recorded and enter the set of feasible insertion points .

[0107] In the above: represents the time when the AGV is expected to reach the rechargeable handover point, represents the time when the AGV is expected to leave the rechargeable handover point, represents the available start time of the charging module at this handover point, represents the available end time of the charging module at this handover point, represents the overlap amount of the actually available charging time period of the AGV and the available time period of the handover point, represents the two-dimensional coordinates of the rechargeable handover point j.

[0108] S34, Task Sequence Reconstruction: Embed the valid insertion points into the original task path to construct a new task sequence including charging behavior , and the embedding rules are as follows:

[0109] Charging start time: , represents the start time of the finally arranged charging behavior;

[0110] Charging duration: , represents the total time that the AGV is planned to stay at this handover point, represents the duration of the actually executable charging behavior;

[0111] Schematic diagram of path structure modification:

[0112] Original path: New path: (A is the previous task, B is the charging insertion point, C is the subsequent task);

[0113] New task sequence is assigned to the corresponding AGV and written into the task scheduling table.

[0114] The present invention encompasses any alternatives, modifications, equivalent methods, and solutions made to the essence and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without such descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0115] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction, characterized in that, It includes the following steps: S1, Chargeable handover point identification: Scan the goods handover points in the logistics task path, detect whether a wireless charging module is deployed at the handover point and obtain its charging power value, and generate a set of chargeable handover points; S2, Efficiency gain calculation: According to the residence time and charging power value of the chargeable handover points, calculate the expected supplementary power for charging at the chargeable handover points, and generate a multi-task efficiency gain value in combination with the remaining power of the AGV; S3, Gain priority allocation: Select the AGVs with multi-task efficiency gain values exceeding the preset gain threshold to allocate logistics tasks, and insert the chargeable handover points into the task sequence.

2. The method for allocating the mission endurance of an AGV in a logistics plant area based on residual power prediction according to claim 1, wherein The specific content of S1 includes: S11, Analyze the key node coordinates of the logistics task path, and extract the location identifiers of all goods handover points; S12, Send a wireless detection signal to the physical node corresponding to the location identifier. When a response signal including a charging signature is received, it is determined that a wireless charging module is deployed; S13, Decode the charging power value and available time period from the response signal; S14, Aggregate the information of all handover points determined to be deployed with wireless charging modules to generate a set of chargeable handover points.

3. A method for allocating the mission endurance of an AGV in a logistics plant area based on residual power prediction according to claim 2, wherein The specific content of S12 includes: Take each goods handover point as a physical node, and sequentially send wireless detection signals to the physical nodes. The detection signals carry identity and request information, including the AGV identity number initiating the detection, the current detection timestamp, and a detection instruction code for triggering a response; Send the detection signal. After the physical node receives the detection signal, if the physical node is deployed with a wireless charging module, it returns a structured response signal. If the response signal meets the determination criteria, the physical node is determined to be a chargeable handover point deployed with a wireless charging module, and its power is decoded and verified.

4. A method for allocating the mission endurance of an AGV in a logistics plant area based on residual power prediction according to claim 2, characterized in that The element structure of the set of chargeable handover points is (location identifier, charging power value, available time period).

5. A method for allocating the mission endurance of AGVs in a logistics plant area based on residual power prediction according to claim 1, wherein, The specific content of S2 includes: S21, Calculate the expected supplementary power: Obtain the residence time and charging power value of each chargeable handover point in the set of chargeable handover points, and calculate the expected supplementary power according to the transmission efficiency coefficient; S22, Determine the gain reference value: Generate a charging gain coefficient in combination with the expected supplementary power and the current remaining power of the AGV; S23, Calculate the path optimization amount: Obtain the length of the original task path without embedding charging, subtract the length of the new path after embedding the chargeable handover points, and generate a path shortening rate; S24, Synthesize the gain value: When the charging gain coefficient is greater than the gain critical value, superimpose the path shortening rate on the charging gain coefficient according to the dynamic weight factor, and output the multi-task efficiency gain value.

6. The method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction according to claim 5, wherein, The calculation of the expected supplementary power is: ； Among them, represents the supplementary power of the AGV at the rechargeable handover point and represents the expected residence time at the handover point, is the effective charging power of this rechargeable handover point, and is the transmission efficiency coefficient.

7. A method for allocating the mission endurance of an AGV in a logistics plant area based on residual power prediction according to claim 5, characterized in that The calculation of the path shortening rate is: ; wherein, represents the path shortening rate, which represents the relative saving percentage brought by path optimization, represents the total length of the task path without embedding charging points, represents the adjusted path length after embedding charging points.

8. A method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction according to claim 5, characterized in that S2 also includes a transmission efficiency coefficient compensation rule: Specifically, if there are interference sources or different ground materials around the rechargeable connection point, the transmission efficiency coefficient is dynamically adjusted according to the interference sources or different ground materials .

9. A method for allocating the endurance of AGV tasks in a logistics plant area based on residual power prediction according to claim 1, characterized in that, The specific content of S3 includes: S31, Determine the basic gain threshold according to the task backlog rate in the factory area and the battery type, calculate the floating compensation value in combination with the historical task interruption rate of the AGV, and generate the preset gain threshold; S32, Candidate AGV screening: Select the AGVs with multi-task efficiency gain values greater than the preset gain threshold to form a high-gain candidate set; S33, Space-time conflict resolution: Detect the predicted time window for each AGV in the high-gain candidate set to reach the rechargeable handover point. When the time window has a high overlap with the available time period of the rechargeable handover point, generate the coordinates of the effective insertion point; S34, Task sequence reconstruction: Embed the coordinates of the effective insertion point into the original task path according to the charging period requirements, generate a new task sequence including the charging behavior, and allocate it to the corresponding AGV.

10. A method for allocating the mission endurance of an AGV in a logistics plant area based on residual power prediction according to claim 9, characterized in that The preset gain threshold : ； Among them, is the preset gain threshold, is the basic gain threshold, which is set according to the battery type, represents the backlog rate coefficient, is the interruption rate coefficient, represents the current task backlog rate, represents the total number of tasks to be allocated currently, represents the current number of available AGVs, represents the task interruption rate in the past 7 days, represents the total number of task interruptions that occurred to the AGV in the past 7 days, represents the total number of tasks executed by the AGV in the past 7 days.

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