Workshop intelligent warehouse logistics system

Abstract

The invention discloses a workshop intelligent warehouse logistics system, relates to the technical field of warehouse logistics, and solves the technical problems that an existing AGV runs according to a set running route, but not necessarily the optimal running route, the transportation efficiency is low, and the AGV cannot be supplied to a production line for production in time. According to the invention, based on the production information, the demand quantity of the AGV trolley is analyzed; obtaining the current position of the AGV, and planning a plurality of transportation paths of the AGV from the current position to the target process position based on the constructed three-dimensional model of the workshop; analyzing the transportation duration of each transportation path of the AGV, and obtaining an optimal transportation path; by analyzing the demand quantity of the AGVs and planning the optimal transportation path of the AGVs, the operation time of the AGVs is shortened, the logistics transportation efficiency is improved, it is ensured that the materials are accurately delivered to the destination in time, and the production efficiency of a production line is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of warehousing and logistics, and specifically relates to an intelligent warehousing and logistics system for a workshop. Background Art

[0002] AGV is an automated logistics equipment that can not only drive autonomously in some industrial application sites, but also switch between different terrains, work areas and transportation tasks, and thus autonomously complete various work processes such as material handling and material sorting.

[0003] AGV carts can receive materials at the warehouse entrance and move them to a designated storage location, or take materials out of the storage location and deliver them to the outbound area; as well as transport raw materials or semi-finished products from the warehouse to different workstations on the production line as needed, etc., reducing reliance on manual porters. However, existing AGV carts travel along a predetermined route, which is not necessarily the optimal route. As a result, there is a problem of low transportation efficiency and inability to supply production lines in a timely manner.

[0004] Therefore, the present invention proposes an intelligent warehousing and logistics system for a workshop. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a workshop intelligent warehousing and logistics system, which is used to solve the technical problems that the existing AGV carts travel along a predetermined route, but not necessarily the optimal route, have low transportation efficiency, and cannot supply production lines in a timely manner.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a workshop intelligent warehousing logistics system, including an information processing module, and an information collection module and a material transportation module connected thereto;

[0007] Information collection module: used to collect workshop information and AGV information; workshop information includes the workshop's three-dimensional map information and the material consumption rate of the production line; AGV information includes the AGV's load-bearing capacity, size and current position;

[0008] Information processing module: used to build a 3D model of the workshop based on the 3D map of the workshop; and to analyze the demand for AGVs based on production information;

[0009] Material transportation module: used to obtain the current position of the AGV, plan several transportation paths for the AGV from the current position to the target process position; analyze the transportation time of each transportation path of the AGV, and obtain the optimal transportation path.

[0010] Preferably, the three-dimensional model of the construction workshop includes:

[0011] The workshop is scanned by a laser scanner to collect point cloud data and images from various angles of the workshop; the collected point cloud data and images are imported into the GIS system to establish a three-dimensional model of the workshop; among them, the point cloud data and images are the three-dimensional map information of the workshop.

[0012] The laser scanner of the present invention can capture the shape and size of objects with millimeter-level accuracy and generate detailed point cloud data. This high accuracy is essential for manufacturing, maintenance and renovation work; scanning is not limited to visible surfaces, but can also capture information of hidden or difficult-to-reach areas to ensure the integrity and accuracy of the model. Laser scanning and image acquisition technology greatly shortens the time of on-site measurement and data collection and improves work efficiency; after importing point cloud data and images into the GIS system, a three-dimensional model of the workshop can be generated, which is convenient for intuitive understanding and analysis of spatial layout and structure.

[0013] Preferably, the analyzing the demand for AGV vehicles based on production information includes:

[0014] Determine whether the current material transportation efficiency is less than the material consumption rate of the current production line; if yes, increase the number of AGVs; if no, do not increase the number of AGVs.

[0015] Preferably, if the current material conveying efficiency is less than the material consumption rate of the current production line, the increased number of AGV carts k is calculated by the formula k = (CV-AV) / W-k0; wherein CV is the material consumption rate of the production line; AV is the conveying rate of the current k0 AGV carts; W is the rated load-bearing capacity of the AGV cart, and k0 is the number of AGV carts initially allocated for factory production.

[0016] The present invention dynamically adjusts the number of AGVs according to the actual needs of the production line, adapts to changes in different production rhythms, and improves the adaptability and flexibility of the system; through precise calculation, it avoids excessive deployment of AGV carts, reduces unnecessary capital investment and operating costs, and also prevents excess and waste of materials; ensures the timely supply of materials, reduces the downtime of the production line, and thus improves the overall production efficiency and throughput.

[0017] Preferably, the material consumption rate is the amount of material consumed by factory production per unit time; the material conveying rate is the amount of material conveyed by k0 AGVs per unit time.

[0018] Preferably, the planning of several transport paths of the AGV from the current position to the target position includes:

[0019] In the three-dimensional model of the workshop, several transportation paths for the AGV from the current position to the target position are planned, and the corners of each path and the angle of each corner are marked; among them, the transportation path is the operation path of the AGV from the current position to the material collection point and then to the target position; the target position is the production line position of the workshop.

[0020] Preferably, analyzing the transportation time of each transportation path of the AGV to obtain the optimal transportation path includes:

[0021] According to the amount of materials carried by the AGV, analyze the running speed of the AGV when it is moving straight, marked as V0;

[0022] According to the amount of materials carried by the AGV and the angle of the corner in the transportation path, the turning time of the AGV is analyzed and marked as TGij; where i is the number of the corner and j is the number of the transportation path; i and j are positive integers;

[0023] The transportation time TX of the AGV in the jth transportation path is calculated by the formula TXj=Sj / V0+ΣTGij; the transportation time TX of each transportation path is compared, and the one with the smallest transportation time TX is the optimal transportation path; where Sj is the length of the jth transportation path; Σ is the sum of i.

[0024] The present invention can achieve refined management of the logistics process and improve the controllability and predictability of workshop logistics through accurate analysis of the straight-line running speed and turning time of the AGV. By calculating the transportation time of each path and selecting the path with the shortest total time, the transportation efficiency of the AGV can be maximized, the invalid waiting time can be reduced, and the overall production efficiency can be improved. At the same time, selecting the path with the shortest transportation time usually also means the lowest energy consumption, because this reduces unnecessary acceleration, deceleration and turning, thereby saving electricity, which has a positive impact on environmental protection and cost control.

[0025] Preferably, analyzing the running speed of the AGV when it is moving straight forward according to the amount of material carried by the AGV includes:

[0026] The amount of materials carried by the AGV, the running speed during air transport, and the resistance encountered are input into the speed attenuation coefficient evaluation model to obtain the speed attenuation coefficient; wherein the speed attenuation coefficient evaluation model is constructed by the FNN neural network model;

[0027] The running speed of the AGV when it is moving straight is V0=VY×α, where α is the speed attenuation coefficient; VY is the running speed of the AGV when it is transported by air.

[0028] The FNN neural network model of the present invention can handle the complex nonlinear relationship between input variables and output variables, which means that even if the relationship between the amount of material carried, the running speed and the resistance is not a simple linear proportion, the model can still accurately capture and express it; the neural network can self-adjust weights and biases through training, which means that it can learn from historical data and gradually optimize the ability to predict the speed attenuation coefficient. As the data accumulates, the prediction accuracy will continue to improve; and by continuously collecting and analyzing the operating data of the AGV, the model can be continuously iterated and optimized to further improve the accuracy of speed prediction and system performance.

[0029] Preferably, the resistance of the AGV is the product of the total gravity of the AGV and the sliding friction coefficient; wherein the sliding friction coefficient is obtained from a sliding friction coefficient lookup table.

[0030] Preferably, analyzing the turning time of the AGV according to the amount of material carried by the AGV and the angle of the corner in the transportation path includes:

[0031] Build a digital twin model of the workshop based on the workshop's 3D model and AGV information;

[0032] In the digital twin model of the workshop, the AGV performs transportation simulation in sequence according to the planned transportation path, and sets two monitoring nodes at the corners of the transportation path, one at each end of the corner. When the AGV passes through a monitoring node, the timestamp of the AGV passing through the monitoring node is recorded. The difference between the timestamps of the two monitoring nodes is the time the AGV spends at the corner; the difference in timestamps is an absolute value; and the running speed is the current straight-ahead running speed.

[0033] The present invention simulates the driving conditions of the AGV car on the transport path through the digital twin model, monitors the time taken by the AGV car at the corner, and can accurately analyze the transportation time at the corner by recording the timestamp difference of the AGV car at the corner. It can more accurately evaluate the total transportation time required for each transport path, and then compare different transport paths, select the path with the shortest total time, and optimize logistics efficiency.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. The present invention increases the number of AGVs by analyzing whether the material transportation efficiency meets the material consumption; by analyzing the transportation time of each transportation path of the AGV, wherein the transportation time of the transportation path takes into account the running speed of the AGV when carrying materials of different weights and the turning time at the corner of the transportation path, the transportation time of the AGV on different transportation paths is more accurately evaluated, and then the shortest transportation path, that is, the most efficient transportation path, is selected; by increasing the number of AGVs and selecting the optimal transportation path, the material transportation efficiency of the AGV is improved, the timely supply of materials on the production line is guaranteed, and continuous normal production is ensured, thereby improving the overall production efficiency;

[0036] 2. The present invention collects workshop information and AGV information; constructs a three-dimensional model of the workshop; and, based on production information, analyzes the demand for AGVs; can predict and reasonably allocate AGV resources in advance, avoid resource surplus or shortage, and optimize cost and efficiency; obtains the current position of the AGV, and plans several transportation paths for the AGV from the current position to the target process position; analyzes the transportation time of each transportation path of the AGV, and obtains the optimal transportation path; by planning the optimal transportation path, reduces the operating time of the AGV, reduces energy consumption, improves logistics efficiency, and ensures that materials are delivered to the destination in a timely and accurate manner; optimizes the logistics management of the workshop, and ensures the production efficiency of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a schematic diagram of the system structure flow of the present invention;

[0039] Figure 2 It is a schematic diagram of the process of the present invention;

[0040] Figure 3 Schematic diagram of the optimal path analysis method of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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.

[0042] See also Figure 1-Figure 2 , the first aspect of the present invention provides a workshop intelligent warehousing logistics system, including an information processing module, and an information collection module and a material transportation module connected thereto;

[0043] The information collection module collects workshop information and AGV information; workshop information includes the three-dimensional map information of the workshop and the material consumption rate of the production line; AGV information includes the load-bearing capacity, size and current position of the AGV;

[0044] The information processing module constructs a three-dimensional model of the workshop based on the three-dimensional map of the workshop;

[0045] Specifically, the workshop is scanned by a laser scanner to collect point cloud data and images from various angles of the workshop; the collected point cloud data and images are imported into the GIS system to establish a three-dimensional model of the workshop; wherein the point cloud data and images are the three-dimensional map information of the workshop.

[0046] It should be noted that the laser scanner emits laser pulses, measures the time difference from the laser being emitted to the laser being reflected back to the receiver, and calculates the distance to the target point through the speed of light and the time difference. In this way, the scanner can record the position coordinates of each reflection point in space, forming a large amount of point data, namely "point cloud"; the point cloud data contains the three-dimensional geometric information of the internal structure of the workshop.

[0047] And, based on production information, analyze the demand for AGVs;

[0048] Specifically, determine whether the current material transportation efficiency is less than the material consumption rate of the current production line; if yes, increase the number of AGVs; if no, do not increase the number of AGVs; the number of AGVs initially allocated for factory production is k0.

[0049] Among them, the material consumption rate is the amount of material consumed by the factory production per unit time; the material transportation rate is the amount of material transported by k0 AGVs per unit time; the unit time can be 30 minutes or 1 hour, etc.;

[0050] It should be noted that the material consumption rate varies with the order quantity and delivery time. That is, the more orders there are and the shorter the delivery time is, the more output needs to be produced per unit time, which means that more materials are consumed.

[0051] If the current material transportation efficiency is less than the material consumption rate of the current production line, the additional number of AGV carts k is calculated by the formula k = (CV-AV) / W-k0; where CV is the material consumption rate of the production line; AV is the transportation rate of the current k0 AGV carts; W is the rated load-bearing capacity of the AGV cart, and k0 is the number of AGV carts initially allocated for factory production.

[0052] The present invention analyzes the number of AGV carts that need to be additionally added through the difference between the material consumption rate and the material transportation efficiency, so as to timely meet the production demand of the supply production line; when the calculated number of AGV carts required to be added is a decimal, the result is rounded up to ensure that sufficient materials are transported to the production line.

[0053] See also Figure 3 ,The material transportation module obtains the current position of the AGV and plans several transportation paths for the AGV from the current position to the target position;

[0054] Specifically, several transport paths for the AGV from the current position to the target position are planned in the three-dimensional model of the workshop, and the number of corners on each path and the angle of each corner are counted; the transport path is the operating path of the AGV from the current position to the material collection point and then to the target position; the target position is the production line position of the workshop.

[0055] According to the planned transportation route, analyze the transportation time of each transportation route of the AGV vehicle to obtain the optimal transportation route;

[0056] Specifically, according to the amount of materials carried by the AGV, the running speed of the AGV when it is moving straight is analyzed, which is marked as V0;

[0057] The amount of material carried by the AGV and the resistance it encounters are input into the speed attenuation coefficient evaluation model to obtain the speed attenuation coefficient;

[0058] Among them, the resistance of the AGV is the product of the total gravity of the AGV and the sliding friction coefficient; the sliding friction coefficient is obtained from the sliding friction coefficient lookup table;

[0059] Through simulation software, such as SolidWorks Simulation, several experimental groups are set up to test the pulling force F required to pull the AGV trolley to run under different gravity on the horizontal plane, and the sliding friction coefficient α of the AGV trolley under gravity G is calculated by the formula α=F / G; wherein, a test is performed every time the gravity carried by the AGV trolley increases by M; M is a positive integer.

[0060] According to the sliding friction coefficient calculated by the test, the sliding friction coefficients with a difference less than a threshold are grouped together and averaged to obtain the sliding friction coefficient corresponding to the load-bearing gravity range of the group of AGVs; after the data obtained from the experiment are sorted out according to this operation, a sliding friction coefficient lookup table is obtained.

[0061] It should be noted that, in the sliding friction coefficient lookup table, the sliding friction coefficient corresponding to the gravity range closest to the gravity carried by the AGV is selected as the sliding friction coefficient of the AGV;

[0062] If the gravity carried by the AGV is between two certain gravity ranges and the degree of proximity is the same, then the average of the sliding friction coefficients corresponding to the two gravity ranges is the sliding friction coefficient of the AGV.

[0063] Among them, the speed attenuation coefficient evaluation model is constructed by the FNN neural network model;

[0064] The construction process of the velocity attenuation coefficient evaluation model is as follows:

[0065] Extract several different amounts of materials carried by the AGV, as well as the resistance and speed attenuation coefficients it encounters, from historical data; the amount of materials carried and the resistance it encounters are used as input data of the FNN neural network model; the speed attenuation coefficient is used as output data of the FNN neural network model; the FNN neural network model is trained through the input data and output data to obtain a speed attenuation coefficient evaluation model;

[0066] The current amount of material carried by the AGV and the resistance it encounters are input into the speed attenuation coefficient evaluation model, that is, the speed attenuation coefficient of the current AGV operation is output.

[0067] Based on the speed attenuation coefficient, the running speed of the AGV when it is moving straight is calculated as V0=VY×α; where VY is the rated running speed of the AGV.

[0068] And, according to the amount of materials carried by the AGV and the angle of the corner in the transportation path, the turning time of the AGV is analyzed, which is marked as TGij; where i is the number of the corner, j is the number of the transportation path; i and j are positive integers;

[0069] Build a digital twin model of the workshop based on the workshop's 3D model and AGV information;

[0070] In the digital twin model of the workshop, the AGV performs transportation simulation in sequence according to the planned transportation route, and sets two monitoring nodes at the corners of the transportation route, one at each end of the corner. When the AGV passes through a monitoring node, the timestamp of the AGV passing through the monitoring node is recorded. The difference between the timestamps of the two monitoring nodes is the time the AGV spends at the corner. The difference in timestamps is an absolute value. The running speed is the current straight running speed. The AGV carries the currently required amount of materials for transportation simulation.

[0071] Based on the running speed when going straight and the time spent on turning, the transportation time TX of the AGV car on the jth transportation path is calculated by the formula TXj=Sj / V0+ΣTGij; the transportation time TX of each transportation path is compared, and the one with the smallest transportation time TX is the optimal transportation path; among them, Sj is the length of the jth transportation path; Σ is the sum of i.

[0072] The present invention evaluates the quality of each transport path by transport time; the quality of the transport path is mainly closely related to the length of the transport path, the straight-line running speed of the AGV, and the time taken by the AGV to turn at a corner. If the transport path is shorter, the straight-line running speed is faster, and the time taken by the AGV to turn is shorter, it indicates that the transport path is better and the transport time is shorter.

[0073] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0074] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A workshop intelligent warehousing and logistics system, characterized in that: It includes an information processing module, and an information collection module and a material transportation module connected thereto; Information collection module: used to collect workshop information and AGV information; workshop information includes the workshop's three-dimensional map information and the material consumption rate of the production line; AGV information includes the AGV's load-bearing capacity, size and current position; Information processing module: used to build a 3D model of the workshop based on the 3D map of the workshop; and to analyze the demand for AGVs based on production information; Material transportation module: used to obtain the current position of the AGV, plan several transportation paths for the AGV from the current position to the target process position; analyze the transportation time of each transportation path of the AGV, and obtain the optimal transportation path.

2. The workshop intelligent warehousing and logistics system according to claim 1 is characterized in that: The three-dimensional model of the workshop is constructed, including: The workshop is scanned by a laser scanner to collect point cloud data and images from various angles of the workshop; the collected point cloud data and images are imported into the GIS system to establish a three-dimensional model of the workshop; among them, the point cloud data and images are the three-dimensional map information of the workshop.

3. The workshop intelligent warehousing and logistics system according to claim 1 is characterized in that: The analysis of the demand for AGV vehicles based on production information includes: Determine whether the current material transportation efficiency is less than the material consumption rate of the current production line; if yes, increase the number of AGVs; if no, do not increase the number of AGVs.

4. The workshop intelligent warehousing and logistics system according to claim 3 is characterized in that: If the current material transportation efficiency is less than the material consumption rate of the current production line, the additional number of AGV carts k is calculated by the formula k = (CV-AV) / W-k0; where CV is the material consumption rate of the production line; AV is the transportation rate of the current k0 AGV carts; W is the rated load-bearing capacity of the AGV cart, and k0 is the number of AGV carts initially allocated for factory production.

5. The workshop intelligent warehousing and logistics system according to claim 4 is characterized in that: The material consumption rate is the amount of material consumed by factory production per unit time; the material transportation rate is the amount of material transported by k0 AGV vehicles per unit time.

6. The workshop intelligent warehousing and logistics system according to claim 1 is characterized in that: The planning of several transport paths of the AGV from the current position to the target position includes: In the three-dimensional model of the workshop, several transportation paths for the AGV from the current position to the target position are planned, and the corners of each path and the angle of each corner are marked; among them, the transportation path is the operation path of the AGV from the current position to the material collection point and then to the target position; the target position is the production line position of the workshop.

7. The workshop intelligent warehousing and logistics system according to claim 6 is characterized in that: The analysis of the transport time of each transport path of the AGV to obtain the optimal transport path includes: According to the amount of materials carried by the AGV, analyze the running speed of the AGV when it is moving straight, marked as V0; According to the amount of materials carried by the AGV and the angle of the corner in the transportation path, the turning time of the AGV is analyzed and marked as TGij; where i is the number of the corner and j is the number of the transportation path; i and j are positive integers; The transportation time TX of the AGV in the jth transportation path is calculated by the formula TXj=Sj / V0+ΣTGij; the transportation time TX of each transportation path is compared, and the one with the smallest transportation time TX is the optimal transportation path; where Sj is the length of the jth transportation path; Σ is the sum of i.

8. The workshop intelligent warehousing and logistics system according to claim 7 is characterized in that: The analysis of the running speed of the AGV when it is moving straight forward based on the amount of material carried by the AGV includes: The amount of materials carried by the AGV, the running speed during air transport, and the resistance encountered are input into the speed attenuation coefficient evaluation model to obtain the speed attenuation coefficient; wherein the speed attenuation coefficient evaluation model is constructed by the FNN neural network model; The running speed of the AGV when it is moving straight is V0=VY×α, where α is the speed attenuation coefficient; VY is the running speed of the AGV when it is transported by air.

9. The workshop intelligent warehousing and logistics system according to claim 8, characterized in that: The resistance of the AGV is the product of the total gravity of the AGV and the sliding friction coefficient; wherein the sliding friction coefficient is obtained from the sliding friction coefficient lookup table.

10. The workshop intelligent warehousing and logistics system according to claim 7, characterized in that: The method of analyzing the turning time of the AGV according to the amount of material carried by the AGV and the angle of the corner in the transportation path includes: Build a digital twin model of the workshop based on the workshop's 3D model and AGV information; In the digital twin model of the workshop, the AGV performs transportation simulation in sequence according to the planned transportation path, and sets two monitoring nodes at the corners of the transportation path, one at each end of the corner. When the AGV passes through a monitoring node, the timestamp of the AGV passing through the monitoring node is recorded. The difference between the timestamps of the two monitoring nodes is the time the AGV spends at the corner; the difference in timestamps is an absolute value; and the running speed is the current straight-ahead running speed.

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