Three-dimensional warehouse intelligent scheduling method and system for obtaining congestion coefficient based on data analysis

Abstract

The invention provides a three-dimensional warehouse intelligent scheduling method for obtaining congestion coefficients based on data analysis, and relates to the technical field of data analysis and algorithms. The method comprises the following steps: S1, establishing a basic access model; s2, acquiring a congestion coefficient threshold value and a historical access proportion and a historical congestion coefficient corresponding to the current moment, and adjusting the state of the entrance and exit of the three-dimensional warehouse; s3, obtaining all access tasks, and judging whether to start a simulated annealing algorithm to obtain a task permutation combination with the shortest time; and S4, if the simulated annealing algorithm is started to obtain the task permutation and combination with the shortest required time, outputting the task permutation and combination and executing all the access tasks in sequence, otherwise, directly executing all the access tasks according to the sequence of obtaining the access tasks. According to the method, the congestion coefficient is introduced into the three-dimensional warehouse scheduling system, the congestion problem at the entrance and exit of the three-dimensional warehouse can be effectively solved, the task permutation and combination with the shortest time are obtained by combining the simulated annealing algorithm, and the waiting time of a user during storage and taking is shortened.

Description

Technical Field

[0001] The present invention relates to the field of data analysis and algorithm technology, and in particular to a method and system for intelligent dispatching of a warehouse based on data analysis to obtain a congestion coefficient. Background Art

[0002] Due to its space-intensive characteristics, vertical parking garages play an important role in alleviating the problem of parking difficulties in cities. Generally speaking, due to the constraints of land and site conditions, for large-scale vertical parking garages, only multiple completely independent vertical parking garages can be built, and each garage has relatively few entrances and exits. The entrances and exits of the vertical parking garages will be congested, causing users to wait too long and reducing the operational efficiency of the vertical parking garages.

[0003] In response to the above problems, the application number is: 202011195662.0, which discloses a data processing method and a cloud server based on artificial intelligence and a smart parking lot. The method obtains a parking lot operation log set and obtains a congested area data set based on the parking lot operation log set, obtains a vehicle parking trajectory set based on the parking lot operation log set, and obtains a vehicle parking reservation request set based on the congested area data set, and then obtains parking space management data based on the vehicle parking trajectory set and the vehicle parking reservation request set. Finally, the parking space dynamic adjustment data of the parking lot operation log set is determined according to the parking space management data, and the number of open parking spaces in the smart parking lot at different time periods is adjusted based on the parking space adjustment data and a preset artificial intelligence model. This can avoid congestion in the smart parking lot caused by a large number of vehicles pouring into the smart parking lot in a short period of time, but it still has the following disadvantages: by judging that there may be congestion, the number of open parking spaces is adjusted, so that some parking spaces in an unused state cannot work, which reduces the utilization rate of the parking spaces in the vertical parking lot to a certain extent, and it still cannot realize the function of shortening the waiting time of users as much as possible when different users need to store and pick up cars at the same time. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to solve the congestion problem existing at the entrance and exit of the vertical warehouse and reduce the waiting time of users when storing and retrieving items.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent dispatching of a warehouse based on data analysis to obtain a congestion coefficient, comprising the following steps:

[0006] S1: Establish basic access model;

[0007] S2: Obtain the congestion coefficient threshold and the historical access ratio and historical congestion coefficient corresponding to the current moment, and adjust the state of the entrance and exit of the vertical warehouse;

[0008] S3: Obtain all access tasks and determine whether to start the simulated annealing algorithm to obtain the task combination with the shortest required time. The specific process of the simulated annealing algorithm is as follows:

[0009] S301: Obtain the access task set A to be assigned = {T c1 ...T cm , T q1 ...T qn}, where T c1 ...T cm To store tasks, T q1 ...T qn According to the different order of the access tasks in the task set, a total of i task combinations are obtained: P1(A)...P i (A), SumT1...SumT i The time required for the corresponding task permutation and combination, given the initial temperature w0, w0 = min (2000, i), set the internal energy acceptable temperature eps = 1;

[0010] S302: Generate an initial solution SumT x , SumT x The time required to execute the task directly in the order in which all access tasks are obtained;

[0011] S303: Randomly swap the order of the two tasks to generate a different solution SumT x ';

[0012] S304: Determine whether to accept the new solution SumT x ';

[0013] S305: Press w b+1 =w b *ΔT updates the temperature, ΔT is the temperature drop coefficient, and b is a natural number;

[0014] S306: Repeat steps S303-S305, when the temperature w b+1 When eps is reached, the simulated annealing algorithm ends, and the current task arrangement order is the order of the task arrangement combination with the shortest required time;

[0015] S307: Output the task arrangement and combination with the shortest required time;

[0016] S4: If the simulated annealing algorithm is started to obtain the task combination with the shortest required time, the task combination is output and all access tasks are executed in sequence; otherwise, all access tasks are directly executed in the order in which the access tasks are obtained.

[0017] Preferably, in step S3, the specific process of determining whether to start the simulated annealing algorithm is: if the historical congestion coefficient reaches the congestion coefficient threshold, the simulated annealing algorithm is started, otherwise it is not started.

[0018] Preferably, in step S3, if the simulated annealing algorithm is started and the calculation time exceeds 5 seconds, the simulated annealing algorithm ends immediately and outputs the task arrangement and combination with the shortest current required time.

[0019] Preferably, the step S304 determines whether to accept the new solution SumT x 'The specific process is: if SumT x ' <SumT x , accept the new solution and update the current solution to SumT x '; If SumT x '>=SumT x , then with probability Accept the new solution, where k is the Boltzmann constant and w is the current temperature.

[0020] Preferably, in step S1, the basic access model includes an elevator, a traverse trolley, a carrier and a turntable; the movement time between adjacent layers of the elevator is t d The movement time of adjacent rows of the traverse trolley is t h The time for the transporter to pick up and place goods is t b , the rotating disk rotation time is t z .

[0021] Preferably, the mathematical model of the time required for each access task in the basic access model is:

[0022] When the elevator is at the starting position, O(i0, j0) is the entrance and exit currently operated by the user, Q1(i1, j1) is the coordinate of the target storage location,

[0023] The time required for a single inventory task is t c =2t b +|i1-i0|*t d +|j1-j0|*t h ;

[0024] The time required for a single pickup is t q =2t b +max(|i1-i0|*t d ,t z )+2|j1-j0|*t h ;

[0025] When the elevator is not at the starting position but at Q2 (i2, j2), O (i0, j0) is the entrance and exit currently operated by the user, and Q1 (i1, j1) is the coordinate of the target storage location.

[0026] The time required for a single inventory is t c ′=|i2-i0|*t d +|j2-j0|*t h +2t b +|i1-i0|*t d +|j1-j0|*t h ;

[0027] The time required for a single pickup is t q ′=|i2-i1|*t d +2|j2-j1|*t h +2t b +max(|i1-i0|*t d ,t z )+|j2-j0|*t h .

[0028] Preferably, in step S2, the specific process of adjusting the entrance and exit status of the vertical warehouse is: judging whether the historical congestion coefficient reaches the threshold; if the congestion coefficient reaches the threshold, setting the entrance and exit corresponding to the access ratio as the storage entrance or the pickup entrance according to the historical access ratio at the corresponding time; if the congestion coefficient does not reach the threshold, setting all entrances and exits to a state that allows both storage and pickup.

[0029] Preferably, the calculation formula of the congestion coefficient D is: Among them, B is the maximum number of goods passing through the entrance and exit per unit time, a is the actual number of goods passing through per unit time at the current moment, c is the capacity of the warehouse, Δt is the difference between the current average access time and the historical average access time, and r and l are dynamic adjustment coefficients.

[0030] Preferably, the period for obtaining the historical access ratio and the historical congestion coefficient is from Monday to Sunday, with 15 minutes as the minimum acquisition time period.

[0031] Corresponding to the above method, the present invention also provides a vertical warehouse intelligent dispatching system for obtaining congestion coefficient based on data analysis, including the following modules:

[0032] Modeling module: used to establish basic access model;

[0033] Module for adjusting entrance and exit status: used to obtain the congestion coefficient threshold and historical data corresponding to the current moment, including the access ratio and congestion coefficient, and adjust the status of the entrance and exit of the vertical warehouse;

[0034] Obtaining the optimal task arrangement and combination module: used to obtain all access tasks and determine whether to start the simulated annealing algorithm to obtain the task arrangement and combination with the shortest required time;

[0035] Task execution module: used to output the task arrangement and combination and execute all access tasks in sequence after the optimal task arrangement and combination module is completed; when the optimal task arrangement and combination module is not started, all access tasks are directly executed in the order of obtaining the access tasks.

[0036] The advantages of the present invention are:

[0037] (1) The present invention predicts the access demand and congestion situation in different time periods based on historical data, including historical access ratios and historical congestion coefficients, and dynamically adjusts the state of the vertical warehouse entrances and exits, which can effectively solve the problem of congestion at the entrances and exits of the vertical warehouse during peak hours;

[0038] (2) According to the different historical congestion coefficients, the simulated annealing algorithm is judged whether to start. If it is judged that congestion may occur, the simulated annealing algorithm is started, which can shorten the time required to complete all access tasks while solving the congestion, thereby reducing the waiting time of users. If it is judged that there is no congestion, the simulated annealing algorithm is not started, which can avoid the waste of resources.

[0039] (3) The present invention can also flexibly adjust the solution range and convergence speed of the simulated annealing algorithm to avoid the simulated annealing algorithm calculating time being too long, further ensuring that the user's waiting time can be reduced and the user experience can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flowchart of the overall embodiment 1 of the present invention;

[0041] Figure 2 The congestion prediction map of Embodiment 1 of the present invention;

[0042] Figure 3 This is a flow chart of adjusting the entry and exit status of Example 1 of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 1As shown, it is a flowchart of the whole embodiment 1 of the present invention. The present invention provides a method for intelligent dispatching of a warehouse based on data analysis to obtain a congestion coefficient, including the following steps:

[0046] S1: Establish a basic access model; the specific modeling process is:

[0047] S101: Establish a corresponding relationship between each storage location through a coordinate matrix. For example, (2,3) represents storage location No. 3 on the 2nd floor. Suppose the movement time between adjacent floors of the elevator is t d , the adjacent movement time of the transverse trolley is t h The time for the transporter to pick up and put down the goods is t b , the turntable rotation time is t z , because the lifting and lowering of the elevator and the rotation of the turntable are currently carried out synchronously, so the time of the two takes the maximum value;

[0048] S102: Determine the initial position of the elevator;

[0049] S103: If the elevator is initially located at the starting position, that is, the elevator is initially located at the entrance and exit, let O(i0, j0) be the entrance and exit currently operated by the user, Q1(i1, j1) be the target storage location coordinates,

[0050] t c is the time required for a single inventory. The specific inventory process is: the carrier transports the goods to the elevator, the elevator rises to the floor where Q1(i1, j1) is located, and after reaching the target floor, the transverse trolley moves horizontally to the target storage location, and the carrier transports the goods to the target storage location. The mathematical model of this inventory process is: t c =2t b +|i1-i0|*t d +|j1-j0|*t h ,

[0051] t q is the time required for a single pickup. The specific pickup process is as follows: before the trolley moves horizontally to the target storage location, the carrier moves the goods to the elevator, and the elevator begins to descend. During the descent, the turntable rotates at the same time. After the elevator descends to the entrance and exit floor, the trolley moves horizontally to the entrance and exit position, and the carrier moves the goods to the entrance and exit storage location. The mathematical model of the pickup process is: q =2t b +max(|i1-i0|*t d ,t z )+2|j1-j0|*t h ;

[0052] If the elevator is not initially located at the starting position but at Q2(i2, j2), let O(i0, j0) be the entrance and exit currently operated by the user, and Q1(i1, j1) be the coordinates of the target storage location.

[0053] t c ' is the time required for a single inventory. The specific inventory process is as follows: the elevator descends to the entrance and exit level, the trolley moves horizontally to the entrance and exit position, the carrier moves the goods to the elevator, the elevator rises to the floor where Q1(i1, j1) is located, and after reaching the target floor, the trolley moves horizontally to the target storage location, and the carrier moves the goods to the target storage location. The mathematical model of this inventory process is: t c ′=|i2-i0|*t d +|j2-j0|*t h +2t b +|i1-i0|*t d +|j1-j0|*t h ,

[0054] t q ' is the time required for a single pickup. The specific pickup process is as follows: the elevator rises to the target cargo layer, the trolley moves horizontally to the target storage location, the carrier moves the cargo to the elevator, the elevator starts to descend, and the turntable rotates during the descent. After the elevator reaches the entrance and exit floor, the trolley moves horizontally to the entrance and exit position, and the carrier moves the cargo to the entrance and exit storage location. The mathematical model of this pickup process is: t q ′=|i2-i1|*t d +2|j2-j1|*t h +2t b +max(|i1-i0|*t d ,t z )+|j2-j0|*t h .

[0055] S2: Take Monday to Sunday as a statistical cycle and 15 minutes as the minimum statistical time period, that is, there are 96 time periods in a day. Count the access ratio and congestion coefficient of the corresponding time period as historical data, and calculate the congestion coefficient threshold D. Among them, B is the maximum throughput capacity of the entrance and exit, that is, the number of goods that can pass through the entrance and exit per unit time, a is the actual number of goods that pass through per unit time at the current moment, c is the capacity of the warehouse, that is, the total number of goods that the warehouse can accommodate, Δt is the difference between the current average access time and the historical average access time, and r and l are dynamic adjustment coefficients.

[0056] like Figure 2As shown in the figure, by collecting historical and real-time data of the vertical warehouse operation, cleaning and preparing the data, processing missing values ​​and abnormal values, etc., a congestion prediction map is obtained after analysis and sorting. Combined with the prediction map, the congestion coefficient and access situation of each time period of the day can be predicted, and special needs during peak hours can be responded to in a timely manner;

[0057] like Figure 3 As shown, after obtaining the congestion coefficient threshold and the historical access ratio and historical congestion coefficient corresponding to the current moment, the state of the entrance and exit of the vertical warehouse is adjusted. The specific process of adjusting the state of the entrance and exit of the vertical warehouse is: first obtain the historical access ratio and historical congestion coefficient corresponding to the current moment, and then determine whether the historical congestion coefficient reaches the threshold; if the congestion coefficient reaches the threshold, according to the historical access ratio at the corresponding moment, set the entrance and exit corresponding to the access ratio as the storage entrance or the pickup entrance; if the congestion coefficient does not reach the threshold, set all entrances and exits to a state that allows both storage and retrieval.

[0058] S3: After the entrance and exit of the warehouse are adjusted to the corresponding state, all access tasks are obtained. If the congestion coefficient reaches the threshold, the SA algorithm is started, specifically the simulated annealing algorithm, and the basic access model is called to obtain the task arrangement and combination with the shortest required time. Otherwise, it will not be started. The specific process of using the simulated annealing algorithm to obtain the task arrangement and combination with the shortest required time is as follows:

[0059] S301: Obtain the access task set A to be assigned = {T c1 ...T cm , T q1 ...T qn}, where T c1 ...T cm is the inventory task, t c1 ...t cm is the time required for each inventory task, T q1 ...T qn For the pickup task, t q1 ...t qn is the time required for each pickup task; according to the different order of each storage and pickup task in the task set, a total of i task combinations are obtained: P1(A)...P i (A), T1...T i In order to complete the time required for all access tasks in the corresponding task arrangement and combination in sequence, when calculating the time required for each access task, the access time mathematical model in the basic access model is called to calculate the time required for each task, and the initial temperature w0 is given, w0=min(2000,i), and the internal energy acceptable temperature eps=1 is set;

[0060] S302: Generate an initial solution SumT x , SumTx The time required to execute the task directly in the order in which all access tasks are obtained;

[0061] S303: Randomly swap the order of the two tasks to generate a different solution SumT x ', at this time, different task orders will result in different solutions SumT x The reason is:

[0062] The elevator stops at the target location after the inventory task is completed, and stops at the entrance and exit level after the pickup task is completed, that is, the movement trajectory of the subsequent tasks is determined;

[0063] Assume that there is only one entrance and exit working, and there are three tasks {T c1 , T c2 , T q1} Queue execution, call the mathematical model of the time required for access in the basic access model to calculate t c1 =110s,t c2 =150s, t q1 =90s;

[0064] Since tasks are executed in sequence, each task needs to wait for the previous task to be completed before it can be executed. Therefore, the task order is different, the user's waiting time is also different, and the sum of the time from the creation to the end of the execution of each task is also different, specifically:

[0065] When the order of executing tasks is T c1 , T c2 , T q1 When , the sum of the execution time of each task is: SumT x =t c1 +(t c1 +t c2 )+(t c1 +t c2 +t q1 )=3t c1 +2t c2 +t q1 =720s;

[0066] Change the order of executing tasks to T q1 , T c1 , T c2 When , the sum of the execution time of each task is: SumT x '=t q1 +(t q1 +t c1 )+(t q1 +t c1 +t c2 )=3t q1 +2t c1 +tc2 =640s;

[0067] That is, by optimizing the order of task execution and calculating the total time spent by each user, the user group as a whole spends less time;

[0068] The two tasks may also be executed at the same time at different entrances and exits. Assuming there are still three tasks {T c1 , T c2 , T q1} queued for execution, but T c2 Relative T c1 , T q1 For tasks that are executed concurrently with independent entrances and exits, that is, c2 With T c1 Execute at the same time, then execute T c2 The waiting time is 0;

[0069] When the order of executing tasks is T c1 , T c2 , T q1 When T c1 After the execution is completed, T q1 Execute immediately without waiting for T c2 The execution is completed, and the sum of the execution time of each task is: SumT x =t c1 +t c2 +(t c1 +t q1 )=2t c1 +t c2 +t q1 =460s

[0070] Change the order of executing tasks to T q1 , T c1 , T c2 When T q1 and T c2 At the same time, T q1 After the execution is completed, T c2 Execute immediately without waiting for T c1 The execution is completed, and the sum of the execution time of each task is: SumT x '=t q1 +t c1 +(t q1 +t c2 )=2t q1 +t c1 +t c2 =440s;

[0071] It can be seen that changing the order of tasks will result in a different sum of all task execution times.

[0072] S304: Determine whether to accept the new solution SumT x ', if SumT x ' <SumT x , accept the new solution and update the current solution to SumT x '; If SumT x '>=SumT x , then with probability Accept the new solution, where k is the Boltzmann constant and w is the current temperature;

[0073] S305: Press w b+1 =w b *ΔT updates the temperature, ΔT is the temperature drop coefficient, and b is a natural number;

[0074] S306: Repeat steps S303-S305, when the temperature w b+1 When eps is reached, the simulated annealing algorithm ends, and the current task arrangement order is the order of the task arrangement combination with the shortest required time;

[0075] S307: Output the task arrangement and combination with the shortest required time;

[0076] This embodiment also combines a dynamic convergence method when starting the simulated annealing algorithm, that is, when the calculation time of the simulated annealing algorithm exceeds 5 seconds, the algorithm ends immediately and outputs the task arrangement combination with the shortest current required time.

[0077] S4: If the simulated annealing algorithm is started to obtain the task combination with the shortest required time, the task combination is output and all access tasks are executed in sequence. If the simulated annealing algorithm is not started, all access tasks are directly executed in the order in which the access tasks are obtained.

[0078] This embodiment counts and analyzes the access data from two dimensions: access ratio and congestion coefficient, dynamically adjusts the state of the entrance and exit of the vertical warehouse, realizes more scientific utilization of storage resources, improves the operation efficiency and resource utilization rate of the vertical warehouse, can effectively solve the problem of congestion at the entrance and exit of the vertical warehouse during peak hours, reduces the waiting time of users, and provides users with better access services;

[0079] It also predicts the situation in different time periods based on different congestion coefficients, and then determines whether to start the simulated annealing algorithm. The core idea is to accept a worse solution with a certain probability to avoid falling into the local optimal solution too early, and to have a greater probability of finding the global optimal solution. It can also complete all access tasks in the shortest possible time while avoiding congestion. It also combines the dynamic convergence method to flexibly adjust the algorithm's solution range and convergence speed to further ensure that the user's waiting time can be reduced. If it is determined that there will be no congestion, the simulated annealing algorithm will not be started to avoid unnecessary waste of resources.

[0080] Example 2

[0081] Corresponding to the first embodiment of the present invention, the second embodiment of the present invention further provides a vertical warehouse intelligent dispatching system for obtaining a congestion coefficient based on data analysis, including the following modules:

[0082] Modeling module: used to establish the basic access model, including the following units:

[0083] Establishing coordinate system unit: It is used to establish corresponding relationship between each storage location through coordinate matrix. For example, (2,3) represents storage location No. 3 on the 2nd floor. Suppose the movement time between adjacent floors of the elevator is t d , the adjacent movement time of the transverse trolley is t h The time for the transporter to pick up and put down the goods is t b , the turntable rotation time is t z , because the lifting and lowering of the elevator and the rotation of the turntable are currently carried out synchronously, so the time of the two takes the maximum value;

[0084] Elevator position determination unit: used to determine the initial position of the elevator;

[0085] Establish a mathematical model unit: used to calculate the time required for storage and retrieval. The specific calculation method is as follows:

[0086] If the elevator is initially located at the starting position, that is, the elevator is initially located at the entrance and exit, let O(i0, j0) be the entrance and exit currently operated by the user, and Q1(i1, j1) be the target storage location coordinates.

[0087] t c is the time required for a single inventory. The specific inventory process is: the carrier transports the goods to the elevator, the elevator rises to the floor where Q1(i1, j1) is located, and after reaching the target floor, the transverse trolley moves horizontally to the target storage location, and the carrier transports the goods to the target storage location. The mathematical model of this inventory process is: t c =2t b +|i1-i0|*t d +|j1-j0|*t h ,

[0088] t q is the time required for a single pickup. The specific pickup process is as follows: before the trolley moves horizontally to the target storage location, the carrier puts the goods on the elevator, and the elevator starts to descend. During the descent, the turntable rotates at the same time. After the elevator descends to the floor where the entrance and exit are located, the trolley moves horizontally to the entrance and exit position, and the carrier moves the goods to the entrance and exit storage location. The mathematical model of the pickup process is: q =2t b +max(|i1-i0|*t d ,t z)+2|j1-j0|*t h ;

[0089] If the elevator is not initially located at the starting position but at Q2(i2, j2), let O(i0, j0) be the entrance and exit currently operated by the user, and Q1(i1, j1) be the coordinates of the target storage location.

[0090] t c ' is the time required for a single inventory. The specific inventory process is as follows: the elevator descends to the entrance and exit level, the trolley moves horizontally to the entrance and exit position, the carrier moves the goods to the elevator, the elevator rises to the floor where Q1(i1, j1) is located, and after reaching the target floor, the trolley moves horizontally to the target storage location, and the carrier moves the goods to the target storage location. The mathematical model of this inventory process is: t c ′=|i2-i0|*t d +|j2-j0|*t h +2t b +|i1-i0|*t d +|j1-j0|*t h ,

[0091] t q ' is the time required for a single pickup. The specific pickup process is as follows: the elevator rises to the target cargo layer, the trolley moves horizontally to the target storage location, the carrier moves the cargo to the elevator, the elevator starts to descend, and the turntable rotates during the descent. After the elevator reaches the entrance and exit floor, the trolley moves horizontally to the entrance and exit position, and the carrier moves the cargo to the entrance and exit storage location. The mathematical model of this pickup process is: t q ′=|i2-i1|*t d +2|j2-j1|*t h +2t b +max(|i1-i0|*t d ,t z )+|j2-j0|*t h .

[0092] Module for adjusting entrance and exit status: used to obtain the congestion coefficient threshold, historical data corresponding to the current moment, including the access ratio and congestion coefficient, and adjust the status of the entrance and exit of the vertical warehouse. The specific adjustment process is: first obtain the historical access ratio and historical congestion coefficient corresponding to the current moment, and then determine whether the historical congestion coefficient reaches the threshold; if the congestion coefficient reaches the threshold, according to the historical access ratio at the corresponding moment, set the entrance and exit corresponding to the access ratio as the storage entrance or the pickup entrance; if the congestion coefficient does not reach the threshold, set all entrances and exits to a state that allows both storage and retrieval;

[0093] The module for obtaining the optimal task arrangement and combination is used to start the simulated annealing algorithm unit to obtain the task arrangement and combination with the shortest required time when the congestion coefficient reaches the congestion coefficient threshold; the simulated annealing algorithm unit is used to perform the following steps: first, obtain the access task set A to be allocated = {T c1 ...T cm , T q1 ...T qn}, where T c1 ...T cm is the inventory task, T q1 ...T qn is a pickup task; according to the different order of the storage and retrieval tasks in the task set, a total of i task combinations are obtained:

[0094] P1(A)...P i (A), T1...T i The time required for the corresponding task combination is given, the initial temperature w0 is given, w0 = min (2000, i), and the internal energy acceptable temperature eps = 1; an initial solution SumT is generated x , SumT x is the time required to execute the tasks directly in the order of obtaining all access tasks; randomly swapping the order between two tasks generates a different solution SumT x ', then determine whether to accept the new solution SumT x ', if SumT x ' <SumT x , accept the new solution and update the current solution to SumT x '; If SumT x '>=SumT x , then with probability Accept the new solution, where k is the Boltzmann constant and w is the current temperature; press w b+1 =w b *ΔT updates the temperature, ΔT is the cooling coefficient, b is a natural number; repeat the random order of the two tasks and determine whether to accept the new solution. When the temperature w b+1 When eps is reached, the simulated annealing algorithm ends, and the current task arrangement order is the order of the task arrangement combination with the shortest required time; finally, the task arrangement combination with the shortest required time is output.

[0095] Task execution module: used to output the task arrangement and combination and execute all access tasks in sequence after the optimal task arrangement and combination module is completed; when the optimal task arrangement and combination module is not started, all access tasks are directly executed in the order of obtaining the access tasks.

[0096] The mathematical model building unit in the modeling module of this embodiment can calculate the required time of each access task at any time when the optimal task arrangement and combination module is working, that is, when the congestion coefficient reaches the congestion coefficient threshold and it is judged that congestion may occur, so as to cooperate with the optimal task arrangement and combination module to obtain the task arrangement and combination with the shortest required time, and then cooperate with the entrance and exit status adjustment module and the task execution module, so as to effectively solve the problem of congestion during the peak period of the vertical warehouse, improve the operation efficiency and resource utilization of the vertical warehouse, reduce the waiting time of users, and enhance the user experience; if the congestion coefficient does not reach the threshold, that is, when it is judged that congestion will not occur, the optimal task arrangement and combination module will not work, so as to avoid waste of resources.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent dispatching of warehouses based on data analysis to obtain congestion coefficients, characterized in that: The following steps are involved: S1: Establish basic access model; S2: Obtain the congestion coefficient threshold and the historical access ratio and historical congestion coefficient corresponding to the current moment, and adjust the state of the entrance and exit of the vertical warehouse; S3: Obtain all access tasks and determine whether to start the simulated annealing algorithm to obtain the task combination with the shortest required time. The specific process of the simulated annealing algorithm is as follows: S301: Obtain the access task set A to be assigned = {T c1 ...T cm , T q1 ...T qn }, where T c1 ...T cm is the inventory task, T q1 ...T qn is a pickup task; according to the different order of the storage and retrieval tasks in the task set, a total of i task combinations are obtained: P1(A)...P i (A), SumT1...SumT i The time required for the corresponding task permutation and combination, given the initial temperature w0, w0 = min (2000, i), set the internal energy acceptable temperature eps = 1; S302: Generate an initial solution SumT x , SumT x The time required to execute the task directly in the order in which all access tasks are obtained; S303: Randomly swap the order of the two tasks to generate a different solution SumT x '; S304: Determine whether to accept the new solution SumT x '; S305: Press w b+1 =w b *ΔT updates the temperature, ΔT is the temperature drop coefficient, and b is a natural number; S306: Repeat steps S303-S305, when the temperature w b+1 When eps is reached, the simulated annealing algorithm ends, and the current task arrangement order is the order of the task arrangement combination with the shortest required time; S307: Output the task arrangement and combination with the shortest required time; S4: If the simulated annealing algorithm is started to obtain the task combination with the shortest required time, the task combination is output and all access tasks are executed in sequence; otherwise, all access tasks are directly executed in the order in which the access tasks are obtained.

2. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 1 is characterized in that: In step S3, the specific process of determining whether to start the simulated annealing algorithm is as follows: if the historical congestion coefficient reaches the congestion coefficient threshold, the simulated annealing algorithm is started, otherwise it is not started.

3. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 2 is characterized in that: In step S3, if the simulated annealing algorithm is started and the calculation time exceeds 5 seconds, the simulated annealing algorithm is terminated immediately and the task arrangement combination with the shortest current required time is output.

4. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 1 is characterized in that: The step S304 determines whether to accept the new solution SumT x 'The specific process is: if SumT x ' <SumT x , accept the new solution and update the current solution to SumT x '; If SumT x '>=SumT x , then with probability Accept the new solution, where k is the Boltzmann constant and w is the current temperature.

5. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 1 is characterized in that: In step S1, the basic access model includes an elevator, a traverse trolley, a carrier and a turntable; the movement time between adjacent layers of the elevator is t d The movement time of adjacent rows of the traverse trolley is t h The time for the transporter to pick up and place goods is t b , the rotating disk rotation time is t z .

6. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 5 is characterized in that: The mathematical model of the time required for each access task in the basic access model is: When the elevator is at the starting position, O(i0, j0) is the entrance and exit currently operated by the user, Q1(i1, j1) is the coordinate of the target storage location, The time required for a single inventory is t c =2t b +|i1-i0|*t d +|j1-j0|*t h ; The time required for a single pickup is t q =2t b +max(|i1-i0|*t d ,t z )+2|j1-j0|*t h ; When the elevator is not at the starting position but at Q2 (i2, j2), O (i0, j0) is the entrance and exit currently operated by the user, and Q1 (i1, j1) is the coordinate of the target storage location. The time required for a single inventory is t c ′=|i2-i0|*t d +|j2-j0|*t h +2t b +|i1-i0|*t d +|j1-j0|*t h ; The time required for a single pickup is t q ′=|i2-i1|*t d +2|j2-j1|*t h +2t b +max(|i1-i0|*t d ,t z )+|j2-j0|*t h .

7. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 1 is characterized in that: In step S2, the specific process of adjusting the state of the entrances and exits of the vertical warehouse is as follows: determine whether the historical congestion coefficient reaches the threshold; if the congestion coefficient reaches the threshold, set the entrances and exits corresponding to the access ratio as the storage entrance or the pickup entrance according to the historical access ratio at the corresponding time; if the congestion coefficient does not reach the threshold, set all entrances and exits to a state that allows both storage and retrieval.

8. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 7 is characterized in that: The calculation formula of the congestion coefficient D is: Among them, B is the maximum number of goods passing through the entrance and exit per unit time, a is the actual number of goods passing through per unit time at the current moment, c is the capacity of the warehouse, Δt is the difference between the current average access time and the historical average access time, and r and l are dynamic adjustment coefficients.

9. The method for intelligent dispatching of a warehouse based on data analysis to obtain congestion coefficient according to claim 7 is characterized in that: The period for obtaining historical access ratios and historical congestion coefficients is from Monday to Sunday, with 15 minutes as the minimum acquisition period.

10. A smart dispatching system for warehouses based on data analysis to obtain congestion coefficients, characterized in that: Includes the following modules: Modeling module: used to establish basic access model; Module for adjusting entrance and exit status: used to obtain the congestion coefficient threshold and historical data corresponding to the current moment, including the access ratio and congestion coefficient, and adjust the status of the entrance and exit of the vertical warehouse; Obtaining the optimal task arrangement and combination module: used to obtain all access tasks and determine whether to start the simulated annealing algorithm to obtain the task arrangement and combination with the shortest required time; Task execution module: used to output the task arrangement and combination and execute all access tasks in sequence after the optimal task arrangement and combination module is completed; when the optimal task arrangement and combination module is not started, all access tasks are directly executed in the order of obtaining the access tasks.

Images