Intelligent warehouse logistics scheduling method and system, electronic equipment and storage medium

Abstract

The invention discloses an intelligent warehouse logistics scheduling method and system, electronic equipment and a storage medium, relates to the technical field of logistics scheduling, and solves the problem that the transportation state of an intelligent transport vehicle cannot be analyzed in a prospective manner, and the method comprises the steps: obtaining to-be-processed logistics orders in an intelligent warehouse, distributing the intelligent transport vehicles for different to-be-processed logistics orders, and enabling the to-be-processed logistics orders to be stored in the intelligent warehouse; the method comprises the following steps: analyzing a delivery path of a preferred transport vehicle, analyzing to obtain a blockage risk path point of the delivery path and a barrier-free driving distance of the preferred transport vehicle, analyzing a blockage condition of the blockage risk path point in the delivery path of the preferred transport vehicle, and when the preferred transport vehicle begins to drive, analyzing a real-time state of the preferred transport vehicle to obtain a barrier-free driving distance of the preferred transport vehicle; and the standby transport vehicles are scheduled according to the parking positions of the preferred transport vehicles. According to the invention, prospective analysis of the transport states of the intelligent transport vehicles is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of logistics scheduling, and specifically relates to an intelligent warehouse logistics scheduling method and system, an electronic device, and a storage medium. BACKGROUND

[0002] With the acceleration of the process of global economic integration, the vigorous development of e-commerce and manufacturing, and the increasing importance of warehouse logistics as a key link in the supply chain, a large number of intelligent transport vehicles are used in modern intelligent warehouses to automatically transport logistics orders in the intelligent warehouse, greatly improving the efficiency of logistics handling. In the prior art, the logistics scheduling method for intelligent warehouses usually schedules logistics orders in terms of path and time based on order density or order timeliness. However, in intelligent warehouse logistics scheduling, the transportation state of the intelligent transport vehicle also greatly affects the efficiency of logistics scheduling. Since different intelligent transport vehicles exist in the intelligent warehouse, the intelligent transport vehicle may be blocked or continuously work, causing the battery temperature to be too high, and thus the actual endurance mileage and the theoretical endurance mileage differ greatly, reducing the transportation efficiency. However, the prior art usually has a lag in analyzing the blocking situation and the endurance situation of the intelligent transport vehicle during operation, and cannot prospectively analyze the blocking risk and the endurance situation of the intelligent transport vehicle at the initial stage of path planning. Therefore, the application provides an intelligent warehouse logistics scheduling method, device, and medium. SUMMARY

[0003] The application aims to provide an intelligent warehouse logistics scheduling method, device, and medium to solve the problem of being unable to prospectively analyze the transportation state of the intelligent transport vehicle as described in the background.

[0004] The application can be achieved by the following technical solutions. In a first aspect, an intelligent warehouse logistics scheduling method includes the following steps. Step S1: obtaining logistics orders to be processed in the intelligent warehouse and assigning intelligent transport vehicles to different logistics orders to be processed. Step S2: analyzing the delivery path of the preferred transport vehicle to obtain the blocking risk path point of the delivery path and the obstacle-free driving distance of the preferred transport vehicle. Step S3: analyzing the blocking situation of the blocking risk path point in the delivery path of the preferred transport vehicle. Step S4: analyzing the real-time state of the preferred transport vehicle when the preferred transport vehicle starts driving. Step S5: scheduling a backup transport vehicle according to the parking position of the preferred transport vehicle.

[0005] Further, the allocation process in step S1 includes the following sub-steps: Step S11, a plane rectangular coordinate system is constructed with length as horizontal and vertical axes, and the intelligent warehouse is placed in the plane rectangular coordinate system; Step S12, the storage location and the target location of the to-be-processed logistics order are obtained, the Euclidean distance between the storage location and the target location of the to-be-processed order is calculated and recorded as the target distance of the to-be-processed logistics order; Similarly, the initial positions of all intelligent transport vehicles in the intelligent warehouse are obtained, the Euclidean distance between the initial position of the intelligent transport vehicle and the storage location of the to-be-processed order is calculated and recorded as the pickup distance of the intelligent transport vehicle; Step S13, the pickup distance of the intelligent transport vehicle and the target distance of the to-be-processed logistics order are added to obtain the complete driving distance of the intelligent transport vehicle; Step S14, the minimum value of the pickup distance is obtained by traversing and comparing the pickup distances of all candidate transport vehicles, and the candidate transport vehicle corresponding to the minimum value of the pickup distance is recorded as the preferred transport vehicle, and the remaining candidate transport vehicles are recorded as backup transport vehicles; Step S15, the preferred transport vehicle will reach the storage location of the to-be-processed logistics order from the initial position according to the preset path, and transport the to-be-processed logistics order to the target location.

[0006] Further, the analysis process in step S2 includes the following sub-steps: Step S21, the path of the preferred transport vehicle from the storage location of the to-be-processed logistics order to the target location is recorded as the delivery path of the preferred transport vehicle; Step S22, the delivery paths of different preferred transport vehicles at the current time are obtained, and the driving speed of the preferred transport vehicle on the delivery path is recorded as the delivery speed; Step S23, the historical delivery speed of the preferred transport vehicle is obtained, the maximum value and the minimum value of the historical delivery speed are obtained by traversing and comparing the historical delivery speed, and the historical delivery speed set is obtained after excluding the maximum value and the minimum value of the historical delivery speed; Step S24, the historical delivery speed in the historical delivery speed set is added to obtain the average value, and the standard deviation of the historical delivery speed set is calculated by the standard deviation formula; Step S25, the historical average delivery speed and the standard deviation are added as the right end point of the driving speed, and the historical average delivery speed minus the standard deviation is recorded as the left end point of the driving speed to construct the driving speed interval of the preferred transport vehicle; Step S26, the delivery path of the preferred transport vehicle is discretized into delivery path points, and the Euclidean distance between the delivery path points of different preferred transport vehicles is calculated and recorded as the path distance; Step S27, the vehicle length of the preferred transport vehicle is obtained, and the delivery path points with a path distance less than or equal to the vehicle length are recorded as jam risk path points, the Euclidean distance between the storage location and the first jam risk path point is calculated and recorded as the jam-free driving distance of the preferred transport vehicle.

[0007] Further, the analysis process in step S3 includes the following sub-steps: Step S31, the jam-free driving distance of the preferred transport vehicle is divided by the left endpoint of the driving speed interval to obtain the longest driving duration of the preferred transport vehicle to reach the jam risk path point; Similarly, the jam-free distance of the preferred transport vehicle is divided by the right endpoint of the driving speed interval to obtain the shortest driving duration of the preferred transport vehicle to reach the jam risk path point; Step S32, the current time is obtained and a time window for the preferred transport vehicle to reach the jam risk path point is constructed, and the construction process of the time window is as follows: The current time is added to the shortest driving duration to obtain the left endpoint of the time window for the preferred transport vehicle to reach the jam risk path point; Similarly, the current time is added to the longest driving duration to obtain the right endpoint of the time window for the preferred transport vehicle to reach the jam risk path point; Step S33, the time windows for different preferred transport vehicles to reach the jam risk path point are compared; If there is any overlapping interval in the time windows for the preferred transport vehicles to reach the jam risk path point, the corresponding time window is recorded as a jam time window, and the preferred transport vehicle is dispatched; If there is no overlapping interval in the time windows for the preferred transport vehicles to reach the jam risk path point, the complete driving distance of the preferred transport vehicle is obtained, and the complete driving distance is divided by the left endpoint value of the driving speed interval to obtain the longest driving duration of the preferred transport vehicle; The dispatching process in step S33 includes the following sub-steps: Step S331, the left endpoints of the jam time windows of different preferred transport vehicles are obtained, the preferred transport vehicle corresponding to the smaller one of the left endpoints of the jam time windows is recorded as a priority transport vehicle, and the preferred transport vehicle corresponding to the larger one of the left endpoints of the jam time windows is recorded as a waiting transport vehicle; Step S332, the right endpoint of the jam time window of the priority transport vehicle is subtracted from the left endpoint of the jam time window of the waiting transport vehicle to obtain the waiting duration of the waiting transport vehicle; Step S333, the waiting transport vehicle starts driving after the priority transport vehicle starts driving and the waiting duration elapses.

[0008] Further, the analysis process in step S4 includes the following sub-steps: Step S41, collect the real-time battery temperature of the preferred transport vehicle and the real-time environment temperature, and set the prediction function of the battery temperature corresponding to the preferred transport vehicle; Step S42, obtain the battery working temperature interval of the preferred transport vehicle, and compare the real-time battery temperature of the preferred transport vehicle with the battery working temperature interval; If the real-time battery temperature of the preferred transport vehicle does not belong to the battery working temperature interval, the preferred transport vehicle is viewed; If the real-time battery temperature of the preferred transport vehicle belongs to the battery working temperature interval, step S43 is entered; Step S43, set the initial value of the working duration of the preferred transport vehicle, substitute the real-time environment temperature and the initial value of the working duration into the prediction function of the battery temperature, iteratively solve the working duration until the predicted battery temperature is greater than or equal to the right endpoint value of the battery working temperature interval, and record the corresponding working duration as the limit temperature rising duration of the preferred transport vehicle; Step S44, obtain the real-time working duration of the preferred transport vehicle, and obtain the remaining temperature rising duration of the preferred transport vehicle by subtracting the real-time working duration from the limit temperature rising duration; Step S45, obtain the longest driving duration of the preferred transport vehicle, and obtain the remaining driving duration of the preferred transport vehicle by subtracting the real-time working duration from the longest driving duration; Compare the remaining driving duration with the remaining temperature rising duration; If the remaining temperature rising duration is greater than or equal to the remaining driving duration, the preferred transport vehicle is continuously monitored; If the remaining temperature rising duration is less than the remaining driving duration, the remaining temperature rising duration is multiplied by the left endpoint value of the driving speed interval to obtain the allowed driving distance of the preferred transport vehicle, and the remaining driving distance of the preferred transport vehicle is obtained by subtracting the allowed driving distance from the complete driving distance; Step S46, analyze whether the preferred transport vehicle can complete the remaining driving distance in the temperature saturation state.

[0009] Further, the analysis process in step S46 includes the following sub-steps: Step S461, when the real-time battery temperature of the preferred transport vehicle reaches the right endpoint of the battery working temperature interval, collect the real-time battery power of the preferred transport vehicle at different time nodes; Step S462, subtract the real-time battery power corresponding to the current node from the real-time battery power corresponding to the last time node to obtain the real-time power decay of the preferred transport vehicle corresponding to the current time node; Step S463, divide the real-time power decay by the fixed time interval to obtain the real-time power decay rate of the preferred transport vehicle corresponding to the current time node; Similarly, the real-time power decay rates of the preferred transport vehicle corresponding to different time nodes are calculated; Step S464, adding and averaging the real-time power attenuation rates of the preferred transport vehicle at different time nodes to obtain an average power attenuation rate of the preferred transport vehicle in a temperature saturation state; Step S465, dividing the real-time battery power of the preferred transport vehicle at the current time node by the average power attenuation rate to obtain a driving duration of the preferred transport vehicle; Step S466, multiplying the driving duration by the left end point of the driving speed interval to obtain a limit driving distance of the preferred transport vehicle; Step S467, comparing the limit driving distance of the preferred transport vehicle with the remaining driving distance; If the limit driving distance of the preferred transport vehicle is greater than or equal to the remaining driving distance, no operation is performed; If the limit driving distance of the preferred transport vehicle is less than the remaining driving distance, the transport of the preferred transport vehicle is stopped, and the position of the preferred transport vehicle at the corresponding time is recorded as a parking position.

[0010] Further, the scheduling process in the step S5 includes the following sub-steps: Step S51, calculating the Euclidean distance between the standby transport vehicle and the parking position and adding and summing the remaining driving distance to obtain a required distance of the standby transport vehicle; Step S52, repeating the steps S41-S45 to calculate an allowed driving distance of the standby transport vehicle, and scheduling the standby transport vehicle to the parking position if the allowed driving distance is greater than or equal to the required distance.

[0011] In a second aspect, an intelligent warehouse logistics scheduling system includes: A data acquisition module is configured to acquire position information of a to-be-processed logistics order and temperature data and power data of a preferred transport vehicle in an intelligent warehouse; A vehicle allocation module is configured to select a preferred transport vehicle for the to-be-processed logistics order according to a pickup distance of a candidate transport vehicle; A path analysis module is configured to analyze a delivery path of the preferred transport vehicle, and analyze a congestion risk path point of the delivery path and an unobstructed driving distance of the preferred transport vehicle and send them to a congestion analysis module; The congestion analysis module is configured to analyze a congestion situation of the congestion risk path point in the delivery path of the preferred transport vehicle, and analyze a longest driving duration or generate an intelligent scheduling signal, and send the longest driving duration to a state analysis module if the longest driving duration is obtained, or send the intelligent scheduling signal to an intelligent scheduling module if the intelligent scheduling signal is generated; The state analysis module is configured to analyze a real-time state of the preferred transport vehicle, and analyze a parking position of the preferred transport vehicle and send it to the intelligent scheduling module; The intelligent scheduling module is configured to schedule a standby transport vehicle according to the parking position of the preferred transport vehicle or the intelligent scheduling signal.

[0012] In a third aspect, an electronic device includes: a memory storing a computer program; a processor in communication with the memory, when the computer program is executed by the processor, implementing the intelligent warehouse logistics scheduling method.

[0013] In a fourth aspect, a computer readable storage medium stores a computer program, when the computer program is executed by a processor, implementing the intelligent warehouse logistics scheduling method.

[0014] In summary, due to the adoption of the above technical solutions, the present application has the following advantages: 1. The present application first acquires the logistics orders to be processed in the intelligent warehouse, and allocates intelligent transport vehicles for different logistics orders to be processed, then analyzes the delivery path of the preferred transport vehicle, analyzes the blocked risk path points of the delivery path and the obstacle-free driving distance of the preferred transport vehicle, and at the same time, analyzes the congestion situation of the blocked risk path points in the delivery path of the preferred transport vehicle, and analyzes whether the preferred transport vehicle is affected by congestion, so as to determine whether the preferred transport vehicle needs to be preliminarily scheduled, and the present application realizes the prospective analysis of the delivery path of the preferred transport vehicle. 2. The present application also analyzes the real-time state of the preferred transport vehicle when the preferred transport vehicle starts driving, analyzes the influence of the battery temperature and battery capacity of the preferred transport vehicle on the transportation process, and then schedules the standby transport vehicle according to the parking position of the preferred transport vehicle, and the present application realizes the prospective analysis of the transportation state of the intelligent transport vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings.

[0016] Figure 1 The method flowchart of the present application; Figure 2 The schematic diagram of different positions in the warehouse in the present application; Figure 3 The schematic diagram of the blocked risk path points in the present application; Figure 4 The overall system block diagram of the present application; Figure 5 The structural schematic diagram of the electronic device in the present application. DETAILED DESCRIPTION

[0017] The technical solutions of the present application will be described clearly and completely below in connection with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0018] Embodiment one: please refer to Figures 1-3 The technical solution provided by the present application is as follows: Step S1, obtaining the logistics orders to be processed in the intelligent warehouse, and allocating intelligent transport vehicles for different logistics orders to be processed; In this embodiment, the allocation process in step S1 includes the following sub-steps: Step S11, constructing a plane rectangular coordinate system with length as the horizontal and vertical axes, and placing the intelligent warehouse in the plane rectangular coordinate system; Step S12, obtaining the storage location and target location of the logistics order to be processed, calculating the Euclidean distance between the storage location and the target location of the order to be processed and recording it as the target distance of the logistics order to be processed; It needs to be explained that please refer to Figure 2 As shown in the figure, the storage location is the location of the logistics order to be processed in the warehouse, for example, a, b, c, d area in the warehouse, etc., and the target location is the loading area that the logistics order to be processed needs to reach; Similarly, obtain the initial position of all intelligent transport vehicles in the intelligent warehouse, calculate the Euclidean distance between the initial position of the intelligent transport vehicle and the storage location of the order to be processed, and record it as the pickup distance of the intelligent transport vehicle; Step S13, adding the pickup distance of the intelligent transport vehicle and the target distance of the logistics order to be processed to obtain the complete driving distance of the intelligent transport vehicle; It needs to be explained that the complete driving distance refers to the distance required by the intelligent transport vehicle to complete the transportation, including reaching the storage location and the target location; Step S14, traversing and comparing the pickup distances of all candidate transport vehicles to obtain the minimum value of the pickup distance, and recording the candidate transport vehicle corresponding to the minimum value of the pickup distance as the preferred transport vehicle, and recording the remaining candidate transport vehicles as backup transport vehicles; In the specific implementation process, when there is a logistics order to be processed, the preferred transport vehicle will be used to transport the logistics order to be processed; Step S15, the preferred transport vehicle will reach the storage location of the logistics order to be processed from the initial position according to the preset path, and transport the logistics order to be processed to the target location; It needs to be explained that the path between each storage location and the target location is preset, and the path between the initial location and the storage location is the connection between the two location points.

[0019] In step S2, the delivery path of the preferred transport vehicle is analyzed to obtain the congestion risk path point of the delivery path and the unobstructed driving distance of the preferred transport vehicle. In this embodiment, the analysis process in step S2 includes the following sub-steps: In step S21, the path of the preferred transport vehicle from the storage location of the to-be-processed logistics order to the target location is recorded as the delivery path of the preferred transport vehicle. In step S22, the delivery paths of different preferred transport vehicles at the current time are obtained, and the driving speed of the preferred transport vehicle on the delivery path is recorded as the delivery speed. In step S23, the historical delivery speed of the preferred transport vehicle is obtained, the maximum and minimum values of the historical delivery speed are obtained by traversing and comparing the historical delivery speed, and the historical delivery speed set is obtained after eliminating the maximum and minimum values of the historical delivery speed. In step S24, the historical average delivery speed is obtained by adding and averaging the historical delivery speeds in the historical delivery speed set, and the standard deviation of the historical delivery speed set is calculated by the standard deviation formula. In step S25, the historical average delivery speed and the standard deviation are added as the right end point of the driving speed, and the historical average delivery speed minus the standard deviation is taken as the left end point of the driving speed to construct the driving speed interval of the preferred transport vehicle. In step S26, referring to Figure 3 The delivery path of the preferred transport vehicle is discretized into delivery path points, and the Euclidean distance between different delivery path points of the preferred transport vehicle is calculated and recorded as the path distance. In the specific implementation process, the delivery path of the preferred transport vehicle is discretized into different delivery path points at a fixed interval, and the fixed interval refers to the interval between adjacent delivery path points. In this embodiment, the fixed interval can be the vehicle length of the preferred transport vehicle. In step S27, the vehicle length of the preferred transport vehicle is obtained, and the delivery path points with a path distance less than or equal to the vehicle length are recorded as the congestion risk path points, and the Euclidean distance between the storage location and the first congestion risk path point is calculated and recorded as the unobstructed driving distance of the preferred transport vehicle.

[0020] In step S3, the congestion of the congestion risk path point in the delivery path of the preferred transport vehicle is analyzed. In this embodiment, the analysis process in step S3 includes the following sub-steps: Step S31, divide the barrier-free running distance of the preferred transport vehicle by the left endpoint of the running speed interval to obtain the longest running duration of the preferred transport vehicle to reach the congestion risk path point; Similarly, divide the barrier-free distance of the preferred transport vehicle by the right endpoint of the running speed interval to obtain the shortest running duration of the preferred transport vehicle to reach the congestion risk path point; Step S32, obtain the current time and construct the time window of the preferred transport vehicle to reach the congestion risk path point, and the construction process of the time window is specifically as follows: Add the current time and the shortest running duration to obtain the left endpoint of the time window of the preferred transport vehicle to reach the congestion risk path point; Similarly, add the current time and the longest running duration to obtain the right endpoint of the time window of the preferred transport vehicle to reach the congestion risk path point; Step S33, compare the time windows of the different preferred transport vehicles to reach the congestion risk path point; If there is any overlapping interval in the time window of the preferred transport vehicle to reach the congestion risk path point, the corresponding time window is recorded as the congestion time window, and the preferred transport vehicle is dispatched; If there is no overlapping interval in the time window of the preferred transport vehicle to reach the congestion risk path point, the complete running distance of the preferred transport vehicle is obtained, and the complete running distance is divided by the left endpoint value of the running speed interval to obtain the longest running duration of the preferred transport vehicle; It needs to be illustrated that if the time window of the preferred transport vehicle A to reach the congestion risk path point is [14:20:01, 14:20:07], and the time window of the preferred transport vehicle B to reach the congestion risk path point is [14:20:05, 14:20:09], there is an overlapping interval [14:20:05, 14:20:07] in the time window of the preferred transport vehicles A and B to reach the congestion risk path point, and [14:20:01, 14:20:07] and [14:20:05, 14:20:09] are recorded as the congestion time window; In the specific implementation process, the dispatching process in step S33 includes the following sub-steps: Step S331, obtain the left endpoints of the congestion time windows of different preferred transport vehicles, record the preferred transport vehicle corresponding to the smaller one of the left endpoints of the congestion time windows as the priority passing transport vehicle, and record the preferred transport vehicle corresponding to the larger one of the left endpoints of the congestion time windows as the waiting passing transport vehicle; Step S332, subtract the left endpoint of the congestion time window of the waiting passing transport vehicle from the right endpoint of the congestion time window of the priority passing transport vehicle to obtain the waiting duration of the waiting passing transport vehicle; Step S333, the waiting passing transport vehicle starts running after the priority passing transport vehicle starts running and passes the waiting duration.

[0021] Step S4, when the preferred transport vehicle starts to travel, analyzing the real-time status of the preferred transport vehicle; In this embodiment, the analysis process in step S4 includes the following sub-steps: Step S41 , collecting the real-time battery temperature and real-time ambient temperature TSS of the preferred transport vehicle, assuming that the prediction function of the battery temperature corresponding to the preferred transport vehicle is TDC=F(TSS,TGZ); Where TDC is the predicted battery temperature, F(TSS, TGZ) is an arbitrary function, and TGZ is the working time of the preferred transport vehicle. It should be explained that the real-time ambient temperature can be obtained through the temperature sensor. In the specific implementation process, the battery temperature prediction function can be obtained by fitting the historical battery temperature, historical ambient temperature and historical working hours of the preferred transport vehicle; Step S42, obtaining the battery operating temperature range of the preferred transport vehicle, and comparing the real-time battery temperature of the preferred transport vehicle with the battery operating temperature range; If the real-time battery temperature of the preferred transport vehicle does not fall within the battery operating temperature range, the preferred transport vehicle is checked; If the real-time battery temperature of the preferred transport vehicle is within the battery operating temperature range, proceed to step S43; Step S43: Set an initial value for the operating time of the preferred transport vehicle, substitute the real-time ambient temperature and the initial operating time value into the battery temperature prediction function, iteratively solve the operating time until the predicted battery temperature is greater than or equal to the right endpoint of the battery operating temperature range, and record the corresponding operating time as the maximum heating time of the preferred transport vehicle; Step S44, obtaining the real-time working time of the preferred transport vehicle, and subtracting the real-time working time from the maximum heating time to obtain the remaining heating time of the preferred transport vehicle; It should be explained that the maximum heating time refers to the time required for the real-time battery temperature of the preferred transport vehicle to rise from the initial temperature to the right endpoint value of the battery operating temperature range, the real-time working time refers to the working time of the preferred transport vehicle, and the remaining heating time refers to the time required for the preferred transport vehicle to rise from the current temperature to the right endpoint value of the battery operating temperature range. In this embodiment, when the real-time working time is greater than or equal to the maximum heating time, the corresponding preferred transport vehicle will stop working. Therefore, in step S44, it is directly assumed that the maximum heating time is greater than the real-time working time. Step S45, obtaining the longest driving time of the preferred transport vehicle, and subtracting the real-time working time from the longest driving time to obtain the remaining driving time of the preferred transport vehicle; Compare the remaining driving time with the remaining heating time; If the remaining temperature duration is greater than or equal to the remaining driving duration, the preferred transport vehicle is continuously monitored; If the remaining temperature duration is less than the remaining driving duration, the remaining temperature duration is multiplied by the left end point value of the driving speed interval to obtain the allowed driving distance of the preferred transport vehicle, and the complete driving distance is subtracted by the allowed driving distance to obtain the remaining driving distance of the preferred transport vehicle; It should be explained that the allowed driving distance refers to the minimum value of the distance driven by the preferred transport vehicle at the lowest speed within the remaining temperature duration, so as to calculate the maximum value of the remaining driving distance, wherein the allowed driving distance is a part of the driving distance, and therefore the complete driving distance is greater than the allowed driving distance; In step S46, whether the preferred transport vehicle can complete the remaining driving distance in the temperature saturation state is analyzed; It should be explained that the temperature saturation state refers to that the real-time battery temperature of the preferred transport vehicle reaches the right end point value of the battery working temperature interval; Specifically, the analysis process in step S46 includes the following sub-steps: In step S461, when the real-time battery temperature of the preferred transport vehicle reaches the right end point of the battery working temperature interval, the real-time battery power of the preferred transport vehicle at different time nodes is collected; In step S462, the real-time battery power at the previous time node is subtracted by the real-time battery power at the current time node to obtain the real-time power attenuation of the preferred transport vehicle at the current time node; In step S463, the real-time power attenuation is divided by the fixed time interval to obtain the real-time power attenuation rate of the preferred transport vehicle at the current time node; Similarly, the real-time power attenuation rate of the preferred transport vehicle at different time nodes is calculated; Specifically, the fixed time interval is the fixed time interval between adjacent time nodes; In step S464, the real-time power attenuation rates of the preferred transport vehicle at different time nodes are added and averaged to obtain the average power attenuation rate of the preferred transport vehicle in the temperature saturation state; In step S465, the real-time battery power of the preferred transport vehicle at the current time node is divided by the average power attenuation rate to obtain the endurance duration of the preferred transport vehicle; In step S466, the endurance duration is multiplied by the left end point of the driving speed interval to obtain the limit endurance mileage of the preferred transport vehicle; It should be explained that the endurance duration is multiplied by the left end point of the driving speed interval to obtain the minimum value of the limit endurance mileage, and if the endurance duration is multiplied by the right end point of the driving speed interval, the maximum value of the limit endurance mileage will be obtained, which will result in that the calculated limit endurance mileage is larger than the actual limit endurance mileage; Step S467, comparing the limit cruising range of the preferred transport vehicle with the remaining driving distance; If the limit cruising range of the preferred transport vehicle is greater than or equal to the remaining driving distance, no operation is performed. If the limit cruising range of the preferred transport vehicle is less than the remaining driving distance, the transport of the preferred transport vehicle is stopped, and the position of the preferred transport vehicle at the corresponding time is recorded as the parking position.

[0022] Step S5, dispatching the standby transport vehicle according to the parking position of the preferred transport vehicle; In the embodiment, the dispatching process in step S5 includes the following sub-steps: Step S51, calculating the Euclidean distance between the standby transport vehicle and the parking position and adding the remaining driving distance to obtain the required mileage of the standby transport vehicle; Step S52, repeating steps S41-S45 to calculate the allowed driving distance of the standby transport vehicle, and dispatching the standby transport vehicle with the allowed driving distance greater than or equal to the required mileage to the parking position.

[0023] In the present application, if the corresponding calculation formula appears, the above calculation formula is calculated by de-dimensioning the numerical value, and the weight coefficient, the proportion coefficient and other coefficients existing in the formula are set to obtain a result value of quantizing each parameter. The size of the weight coefficient and the proportion coefficient only needs to not affect the proportional relationship between the parameters and the result value.

[0024] Embodiment two: please refer to Figure 4 As shown in the figure, based on another concept of the same invention, an intelligent warehouse logistics scheduling system is proposed, which includes a data acquisition module, a vehicle allocation module, a path analysis module, a congestion analysis module, a state analysis module and an intelligent scheduling module. The data collection module is used for collecting position information of the to-be-processed logistics order in the intelligent warehouse and temperature data and power data of the preferred transport vehicle; the vehicle allocation module is used for selecting the preferred transport vehicle for the to-be-processed logistics order according to a pickup distance of the to-be-selected transport vehicle; the path analysis module is used for analyzing a delivery path of the preferred transport vehicle, and obtaining a jam risk path point of the delivery path and an unobstructed driving distance of the preferred transport vehicle and sending them to the jam analysis module; the jam analysis module is used for analyzing a jam situation of the jam risk path point in the delivery path of the preferred transport vehicle, and obtaining a longest driving duration or generating an intelligent scheduling signal, and if the longest driving duration is obtained, sending it to the state analysis module, and if the intelligent scheduling signal is generated, sending it to the intelligent scheduling module; the state analysis module is used for analyzing a real-time state of the preferred transport vehicle, and obtaining a parking position of the preferred transport vehicle and sending it to the intelligent scheduling module; and the intelligent scheduling module is used for scheduling a backup transport vehicle according to the parking position of the preferred transport vehicle or the intelligent scheduling signal.

[0025] Embodiment three Figure 5 It is a structural schematic diagram of an electronic device, which can include a processor, a communications interface, a memory and a communications bus, wherein the processor, the communications interface and the memory complete communications with each other through the communications bus. The processor can call logical instructions in the memory to execute an intelligent warehouse logistics scheduling method, which includes obtaining to-be-processed logistics orders in an intelligent warehouse and allocating intelligent transport vehicles for different to-be-processed logistics orders, analyzing a delivery path of a preferred transport vehicle, obtaining a jam risk path point of the delivery path and an unobstructed driving distance of the preferred transport vehicle, analyzing a jam situation of the jam risk path point in the delivery path of the preferred transport vehicle, analyzing a real-time state of the preferred transport vehicle when the preferred transport vehicle starts driving, and scheduling a backup transport vehicle according to a parking position of the preferred transport vehicle.

[0026] Further, the logic instructions in the memory described above can be implemented in the form of software functional units and sold or used as standalone products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or partly or parts of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0027] In another aspect, the present application also provides a computer program product, which includes a computer program stored on a computer readable storage medium, and the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute an intelligent warehouse logistics scheduling method provided by the above-mentioned methods, and the method includes: obtaining a to-be-processed logistics order in an intelligent warehouse, and allocating an intelligent transport vehicle to different to-be-processed logistics orders, analyzing a delivery path of a preferred transport vehicle, analyzing a jam risk path point of the delivery path and an unobstructed driving distance of the preferred transport vehicle, analyzing a jam situation of the jam risk path point in the delivery path of the preferred transport vehicle, analyzing a real-time state of the preferred transport vehicle when the preferred transport vehicle starts to drive, and scheduling a backup transport vehicle according to a parking position of the preferred transport vehicle.

[0028] In another aspect, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement an intelligent warehouse logistics scheduling method provided by the above-mentioned methods, and the method includes: obtaining a to-be-processed logistics order in an intelligent warehouse, and allocating an intelligent transport vehicle to different to-be-processed logistics orders, analyzing a delivery path of a preferred transport vehicle, analyzing a jam risk path point of the delivery path and an unobstructed driving distance of the preferred transport vehicle, analyzing a jam situation of the jam risk path point in the delivery path of the preferred transport vehicle, analyzing a real-time state of the preferred transport vehicle when the preferred transport vehicle starts to drive, and scheduling a backup transport vehicle according to a parking position of the preferred transport vehicle.

[0029] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0030] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0031] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent warehousing logistics scheduling method, characterized in that: Methods include: Step S1: Obtain pending logistics orders in the intelligent warehouse and allocate intelligent transport vehicles to different pending logistics orders; Step S2: Analyze the delivery route of the preferred transport vehicle to obtain the congestion risk points on the delivery route and the barrier-free driving distance of the preferred transport vehicle; Step S3, analyzing the congestion situation of the congestion risk path points in the delivery path of the preferred transport vehicle; Step S4, when the preferred transport vehicle starts to travel, analyzing the real-time status of the preferred transport vehicle; Step S5: dispatching the backup transport vehicles according to the parking position of the preferred transport vehicle.

2. The intelligent warehousing logistics scheduling method according to claim 1, characterized in that: The allocation process in step S1 includes the following sub-steps: Step S11, constructing a plane rectangular coordinate system with length as the horizontal and vertical axes, and placing the intelligent warehouse in the plane rectangular coordinate system; Step S12, obtaining the storage location and target location of the pending logistics order, calculating the Euclidean distance between the storage location and the target location of the pending logistics order and recording it as the target distance of the pending logistics order; Similarly, obtain the initial positions of all smart transport vehicles in the smart warehouse, calculate the Euclidean distance between the initial position of the smart transport vehicle and the warehouse location of the pending order and record it as the pickup distance of the smart transport vehicle; Step S13, adding the pickup distance of the intelligent transport vehicle to the target distance of the logistics order to be processed to obtain the complete driving distance of the intelligent transport vehicle; Step S14: traverse and compare the pickup distances of all candidate transport vehicles to obtain the minimum pickup distance, and record the candidate transport vehicle corresponding to the minimum pickup distance as the preferred transport vehicle, and record the remaining candidate transport vehicles as backup transport vehicles; In step S15 , the preferred transport vehicle will follow the preset path from the initial location to the storage location of the pending logistics order, and transport the pending logistics order to the target location.

3. The intelligent warehousing logistics scheduling method according to claim 2, characterized in that: The analysis process in step S2 includes the following sub-steps: Step S21, recording the route of the preferred transport vehicle from the storage location of the pending logistics order to the target location as the delivery route of the preferred transport vehicle; Step S22, obtaining the delivery routes of different preferred transport vehicles at the current moment, and recording the driving speed of the preferred transport vehicle on the delivery route as the delivery speed; Step S23, obtaining the historical delivery speeds of the preferred transport vehicle, traversing and comparing the historical delivery speeds to obtain the maximum and minimum values ​​of the historical delivery speeds, and eliminating the maximum and minimum values ​​of the historical delivery speeds to obtain a set of historical delivery speeds; Step S24, summing up the historical delivery speeds in the historical delivery speed set and taking the average value to obtain the historical average delivery speed, and calculating the standard deviation of the historical delivery speed set using the standard deviation formula; Step S25: The historical average delivery speed plus the standard deviation is used as the right endpoint of the driving speed, and the historical average delivery speed minus the standard deviation is used as the left endpoint of the driving speed to construct the driving speed range of the preferred transport vehicle; Step S26, discretizing the delivery path of the preferred transport vehicle into delivery path points, and calculating the Euclidean distance between the delivery path points of different preferred transport vehicles and recording it as the path distance; Step S27: Obtain the vehicle length of the preferred transport vehicle, and record the delivery path points with a path distance less than or equal to the vehicle length as congestion risk path points. Calculate the Euclidean distance between the storage location and the first congestion risk path point and record it as the barrier-free driving distance of the preferred transport vehicle.

4. The intelligent warehousing logistics scheduling method according to claim 3, characterized in that: The analysis process in step S3 includes the following sub-steps: Step S31: Divide the obstacle-free driving distance of the preferred transport vehicle by the left endpoint of the driving speed range to obtain the longest driving time of the preferred transport vehicle to reach the congestion risk path point; Similarly, the barrier-free distance of the preferred transport vehicle is divided by the right endpoint of the driving speed range to obtain the shortest driving time for the preferred transport vehicle to reach the congestion risk path point; Step S32: Get the current time and construct a time window for the preferred transport vehicle to arrive at the congestion risk path point. The construction process of the time window is as follows: The sum of the current time and the shortest travel time is used as the left endpoint of the time window for the preferred transport vehicle to arrive at the congestion risk path point; Similarly, the current time is added to the longest travel time and the sum is used as the right endpoint of the time window for the preferred transport vehicle to arrive at the congestion risk path point; Step S33, comparing the time windows of different preferred transport vehicles arriving at the congestion risk path points; If there is any overlapping interval between the time windows of the preferred transport vehicle arriving at the congestion risk path point, the corresponding time window will be recorded as the congestion time window, and the preferred transport vehicle will be dispatched; If the time windows of all preferred transport vehicles arriving at the congestion risk path point do not overlap, the complete driving distance of the preferred transport vehicle is obtained, and the complete driving distance is divided by the left endpoint value of the driving speed interval to obtain the longest driving time of the preferred transport vehicle; The scheduling process in step S33 includes the following sub-steps: Step S331: Obtain the left endpoints of the congestion time windows of different preferred transport vehicles, record the preferred transport vehicle corresponding to the smaller left endpoint of the congestion time window as the priority transport vehicle, and record the preferred transport vehicle corresponding to the larger left endpoint of the congestion time window as the waiting transport vehicle; Step S332: Subtract the left endpoint of the congestion window of the waiting vehicle from the right endpoint of the congestion window of the priority vehicle and take the absolute value to obtain the waiting time of the waiting vehicle. Step S333, the waiting vehicle starts to move after the priority vehicle starts to move and the waiting time has passed.

5. The intelligent warehousing logistics scheduling method according to claim 4, characterized in that: The analysis process in step S4 includes the following sub-steps: Step S41, collecting the real-time battery temperature and real-time ambient temperature of the preferred transport vehicle, and setting a prediction function for the battery temperature corresponding to the preferred transport vehicle; Step S42, obtaining the battery operating temperature range of the preferred transport vehicle, and comparing the real-time battery temperature of the preferred transport vehicle with the battery operating temperature range; If the real-time battery temperature of the preferred transport vehicle does not fall within the battery operating temperature range, the preferred transport vehicle is checked; If the real-time battery temperature of the preferred transport vehicle is within the battery operating temperature range, proceed to step S43; Step S43: Set an initial value for the operating time of the preferred transport vehicle, substitute the real-time ambient temperature and the initial operating time value into the battery temperature prediction function, iteratively solve the operating time until the predicted battery temperature is greater than or equal to the right endpoint of the battery operating temperature range, and record the corresponding operating time as the maximum heating time of the preferred transport vehicle; Step S44, obtaining the real-time working time of the preferred transport vehicle, and subtracting the real-time working time from the maximum heating time to obtain the remaining heating time of the preferred transport vehicle; Step S45, obtaining the longest driving time of the preferred transport vehicle, and subtracting the real-time working time from the longest driving time to obtain the remaining driving time of the preferred transport vehicle; Compare the remaining driving time with the remaining heating time; If the remaining warming time is greater than or equal to the remaining driving time, the preferred transport vehicle will be continuously monitored; If the remaining warm-up time is less than the remaining driving time, the remaining warm-up time is multiplied by the left endpoint of the driving speed range to obtain the allowed driving distance of the preferred transport vehicle. The allowed driving distance is subtracted from the complete driving distance to obtain the remaining driving distance of the preferred transport vehicle. Step S46 , analyzing whether the preferred transport vehicle can complete the remaining driving distance when it is in a temperature saturation state.

6. The intelligent warehousing logistics scheduling method according to claim 5, characterized in that: The analysis process in step S46 includes the following sub-steps: Step S461: When the real-time battery temperature of the preferred transport vehicle reaches the right end point of the battery operating temperature range, the real-time battery power of the preferred transport vehicle at different time points is collected; Step S462: Subtract the real-time battery power corresponding to the current node from the real-time battery power corresponding to the previous time node to obtain the real-time battery power attenuation of the preferred transport vehicle corresponding to the current time node; Step S463: Divide the real-time power attenuation by the fixed time interval to obtain the real-time power attenuation rate of the preferred transport vehicle corresponding to the current time node; Similarly, the real-time power attenuation rate of the preferred transport vehicle corresponding to different time nodes is calculated; Step S464, adding up the real-time power attenuation rates of the preferred transport vehicle at different time points and taking the average value to obtain the average power attenuation rate of the preferred transport vehicle when the preferred transport vehicle is in a temperature saturation state; Step S465: Divide the real-time battery power of the preferred transport vehicle at the current time point by the average battery decay rate to obtain the endurance of the preferred transport vehicle; Step S466: Multiply the cruising time by the left endpoint of the driving speed range to obtain the maximum cruising range of the preferred transport vehicle; Step S467, comparing the maximum cruising range of the preferred transport vehicle with the remaining driving distance; If the maximum cruising range of the preferred transport vehicle is greater than or equal to the remaining driving distance, no action will be taken; If the maximum cruising range of the preferred transport vehicle is less than the remaining driving distance, the transportation of the preferred transport vehicle is stopped, and the position of the preferred transport vehicle at the corresponding moment is recorded as the parking position.

7. The intelligent warehousing logistics scheduling method according to claim 6, characterized in that: The scheduling process in step S5 includes the following sub-steps: Step S51, calculating the Euclidean distance between the standby transport vehicle and the parking location and adding it to the remaining driving distance to obtain the required mileage of the standby transport vehicle; Step S52 , repeating steps S41 - S45 to calculate the allowed driving distance of the standby transport vehicle, and dispatching the standby transport vehicle with the allowed driving distance greater than or equal to the required mileage to the parking position.

8. An intelligent warehousing logistics scheduling system, characterized in that: In combination with an intelligent warehousing logistics scheduling method according to any one of claims 1 to 7, comprising: The data collection module is used to collect the location information of pending logistics orders in the smart warehouse and the temperature and power data of the preferred transport vehicle; The vehicle allocation module is used to select the preferred transport vehicle for the pending logistics order based on the pickup distance of the selected transport vehicle; The path analysis module is used to analyze the delivery path of the preferred transport vehicle, obtain the congestion risk path points of the delivery path and the barrier-free driving distance of the preferred transport vehicle, and send them to the congestion risk module; The congestion analysis module is used to analyze the congestion situation of the congestion risk points in the delivery route of the preferred transport vehicle, and the analysis determines the maximum driving time or generates an intelligent dispatch signal. If the maximum driving time is obtained, it is sent to the status analysis module; if an intelligent dispatch signal is generated, it is sent to the intelligent dispatch module; The status analysis module is used to analyze the real-time status of the preferred transport vehicle, obtain the parking location of the preferred transport vehicle through analysis, and send it to the intelligent scheduling module; The intelligent dispatching module is used to dispatch the backup transport vehicles according to the parking position of the preferred transport vehicle or the intelligent dispatching signal.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing a computer program; A processor is communicatively connected to the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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