AGV deployment quantity evaluation method, system, computer and storage medium

Abstract

The present invention discloses an AGV deployment quantity evaluation method, system, computer and storage medium, belonging to the field of data prediction, the method comprising the following steps: obtaining information elements in a demand scenario; obtaining theoretical timeliness related to the number of AGVs and key factors affecting the theoretical timeliness based on the information elements; constructing a time efficiency model based on the theoretical timeliness and the key factors; and obtaining the AGV deployment threshold based on the time efficiency model. The present invention constructs a time efficiency model based on theoretical timeliness and key factors, which can comprehensively consider the positive and negative effects of changes in the number of AGVs m on time efficiency, and then clarify the AGV deployment threshold, balance the relationship between capacity and cost, and adapt to the AGV deployment quantity evaluation work in different scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of data prediction, and in particular relates to an AGV deployment quantity evaluation method, system, computer and storage medium. Background Art

[0002] The development and progress of science and technology has led to the continuous improvement of the degree of automation in all walks of life. In the field of automated warehousing and logistics, automated control technology has been paid more attention and applied, and the Automatic Guided Vehicle (AGV) came into being. AGV is an important technology for automated handling and loading and unloading, and its application scope and technical level have developed rapidly.

[0003] AGV can travel along a specified guide path, using rechargeable batteries as its power source. Its route and behavior are generally controlled by a computer; or an electromagnetic track is used to set up its route, and the AGV moves and acts according to the information provided by the electromagnetic track.

[0004] The operating capacity of a single AGV is limited. With the increasing scale of automation, a single AGV can no longer meet the requirements of improving production efficiency. Therefore, in current use, multiple AGVs are deployed to cooperate with each other to adapt to production needs. However, due to the high cost of AGV deployment, depending on the demand scenario, too many AGVs will lead to higher production costs, while too few AGVs will lead to insufficient capacity and fail to meet production needs. It is difficult to accurately weigh the relationship between capacity and cost, that is, it is difficult to accurately determine the number of AGVs that need to be deployed in different scenarios. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an AGV deployment quantity evaluation method, system, computer and storage medium to solve the technical problems in the prior art that it is difficult to determine the number of AGVs to be deployed in different scenarios due to different demand scenarios, and it is difficult to balance capacity and cost.

[0006] To achieve the above object, the present invention provides an AGV deployment quantity evaluation method, comprising:

[0007] Obtain information elements within the demand scenario;

[0008] Based on the information elements, obtain the theoretical timeliness related to the number of AGVs and the key factors affecting the theoretical timeliness;

[0009] Constructing a time efficiency model based on the theoretical time efficiency and the key factors;

[0010] The AGV deployment threshold is obtained based on the time efficiency model.

[0011] Optionally, the information elements include a single trip, the number of orders, the distance between two order endpoints and the number of channels.

[0012] Optionally, the process of obtaining the theoretical timeliness based on the information element includes:

[0013] Obtaining a distance indicator based on the single trip and the number of orders, and obtaining a time indicator for a single AGV to complete the distance indicator;

[0014] The theoretical time efficiency is obtained based on the time index and the number of AGVs.

[0015] Optionally, the calculation formula of the theoretical aging is:

[0016]

[0017] In the formula, A represents the theoretical timeliness, m represents the number of AGVs, and t0 represents the time index.

[0018] Optionally, the process of obtaining the key factors includes:

[0019] Analyze the influence of the number of channels on the theoretical time efficiency to obtain a first influence factor;

[0020] Based on the first influencing factor, the distance between the two order endpoints and the impact of the number of orders on the theoretical time limit are analyzed to obtain a second influencing factor.

[0021] Optionally, the expression of the first impact factor is:

[0022]

[0023] The expression of the second impact factor is:

[0024]

[0025] In the formula, B1 represents the first influencing factor, t0 represents the time index, m represents the number of AGVs, k represents the number of channels, B2 represents the second influencing factor, and s i Indicates the distance between two order endpoints, s max Indicates the maximum distance between two order endpoints, s min It represents the minimum distance between two order endpoints, s represents the total journey, and n represents the number of orders.

[0026] Optionally, the expression of the constructed time efficiency model is:

[0027]

[0028] In the formula, ρ tIndicates time efficiency, A represents theoretical time efficiency, B1 represents the first influencing factor, B2 represents the second influencing factor, and n represents the number of orders.

[0029] To achieve the above-mentioned purpose, the present invention also provides an AGV deployment quantity evaluation system, which is applied to the AGV deployment quantity evaluation method, and the system includes: an extraction module, an analysis module and a calculation module;

[0030] The extraction module is used to extract information elements in the demand scenario and obtain theoretical timeliness based on the information elements;

[0031] The analysis module is used to obtain key factors affecting the theoretical timeliness based on the information elements;

[0032] The calculation module is used to construct a time efficiency model based on the theoretical time efficiency and the key factors, and obtain an AGV deployment threshold based on the time efficiency model.

[0033] To achieve the above objectives, the present invention also provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the AGV deployment quantity evaluation method when executing the computer program.

[0034] To achieve the above-mentioned purpose, the present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, each step of the AGV deployment quantity evaluation method is implemented.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] According to different demand scenarios, the theoretical time efficiency related to the number of AGVs m is obtained, and the theoretical capacity change caused by the change of the number of AGVs m in a certain scenario can be clarified. By obtaining the key factors, the impact of different single trips, the number of orders, the distance between the two order endpoints and the number of channels on the theoretical time efficiency can be analyzed. The time efficiency model is constructed based on the theoretical time efficiency and the key factors, and the positive and negative impacts of the change of the number of AGVs m on the time efficiency can be comprehensively considered. Then, the AGV deployment threshold is clarified by finding the extreme value in the time efficiency. On the premise of meeting production needs, the optimal number of AGVs deployed is clarified, the relationship between capacity and cost is balanced, and different demand scenarios can be adapted to complete the evaluation of the number of AGVs deployed in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0038] Figure 1 It is a flow chart of the AGV deployment quantity evaluation method in the first embodiment of the present invention;

[0039] Figure 2 It is a flow chart of the AGV deployment quantity evaluation method in the second embodiment of the present invention;

[0040] Figure 3 It is a structural block diagram of the AGV deployment quantity evaluation system in the third embodiment of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Embodiment 1

[0044] like Figure 1 As shown, this embodiment provides an AGV deployment quantity evaluation method, which specifically includes the following steps:

[0045] Step S10: extracting information elements in the demand scenario, wherein the information elements include a single trip, the number of orders, the distance between two order endpoints, and the number of channels, and obtaining a theoretical time efficiency related to the number m of AGVs based on the information elements;

[0046] In the scenario where AGV is required for transportation, the information elements that affect the AGV's capacity are the key points to clarify the number of AGVs deployed. The single trip refers to the trip of a single AGV from the loading and unloading point to the target point. It can be understood that if the loading and unloading position is not the same as the initial position of the AGV, then the single trip refers to the trip from the initial position to the loading and unloading point to the target point. The distance between the two order endpoints refers to the distance between the target points when two or more AGVs execute an order. The theoretical time limit refers to the relationship between the number of AGVs m and the completion time without considering the influence of any other factors and on the premise that the number of orders and the single trip are clear. According to different demand scenarios, the theoretical time limit related to the number of AGVs m is obtained, and the theoretical capacity changes caused by changes in the number of AGVs m in a certain scenario can be clarified.

[0047] Step S20: Acquire key factors affecting the theoretical timeliness based on the information elements;

[0048] Under the premise that the number of orders and the number of channels are clear, adding AGVs will lead to conflicts in the AGV's travel paths, which will lead to deadlock. In order to prevent deadlock, only measures such as avoidance, waiting, and traffic control can be taken. The shorter the distance between the two order endpoints, the higher the probability of deadlock. In order to prevent deadlock, only measures such as avoidance, waiting, and traffic control can be taken. Taking corresponding measures will also cause the AGV to not run according to the optimal travel path, thereby reducing efficiency, that is, having a negative impact on the theoretical time efficiency. By obtaining the key factors, the impact on the theoretical time efficiency under different conditions of the single trip, the distance between the two order endpoints, the number of orders, and the number of channels can be analyzed.

[0049] Step S30: constructing a time efficiency model through the theoretical time efficiency and the key factors to determine the AGV deployment threshold.

[0050] By constructing the time efficiency model based on the theoretical time efficiency and the key factors, the positive and negative effects of the change in the number of AGVs m on the time efficiency can be comprehensively considered, and then the AGV deployment threshold can be clarified by finding the extreme value in the time efficiency. On the premise of meeting production needs, the optimal number of AGVs deployed is determined, the relationship between capacity and cost is balanced, and different demand scenarios can be adapted to complete the AGV deployment quantity evaluation work under different scenarios.

[0051] Embodiment 2

[0052] like Figure 2 As shown, this embodiment provides a method for evaluating the number of AGV deployments, and the method includes the following steps:

[0053] Step S100: obtaining a distance index through the single trip and the number of orders, and obtaining a time index for a single AGV to complete the distance index;

[0054] Step S101: Based on the time index, determine the correlation between the change in the number m of AGVs and the theoretical time efficiency;

[0055] The calculation formula of the distance index is: s0=s1+s2+...+s n ,

[0056] Among them, s0 represents the distance index, s1, s2, ..., s n They represent single trips of different orders, and n represents the number of orders. The specific destination point to be delivered for each order varies depending on the situation. By counting the destination point of each order, the distance indicator can be clarified.

[0057] The calculation formula of the time index is:

[0058]

[0059] Among them, t0 represents the time index, and v represents the average operating speed of a single AGV;

[0060] The calculation formula of the theoretical aging is:

[0061]

[0062] Among them, A represents the theoretical time, and m represents the number of AGVs.

[0063] On the premise that a single AGV completes all orders, increasing the number of AGVs in use can increase the theoretical time efficiency. That is, by deploying more AGVs, without considering the influence of other factors, the theoretical time efficiency will increase infinitely.

[0064] Step S102: analyzing the influence of the number of channels on the theoretical time efficiency to obtain a first influence factor;

[0065] The calculation formula of the first impact factor is:

[0066]

[0067] Among them, B1 represents the first influencing factor, t0 represents the time index, m represents the number of AGVs, and k represents the number of channels. It can be understood that, under the premise that the number of orders and the interval distance between the two order endpoints are clear, if the number of channels is more, the more paths can be selected, and the probability of loss is lower. On the contrary, if the number of channels is less, the number of paths can be selected, and the probability of loss is higher. On this basis, increasing the number of AGVs put into use will make the deadlock in the channel more serious, and the efficiency loss will also show a geometric growth. The first influencing factor represents the efficiency loss caused by traffic control, priority, and selection of non-optimal paths in order to produce collisions with the target.

[0068] Step S103: Based on the first influencing factor, analyzing the influence of the interval distance between the two order endpoints and the number of orders on the theoretical time efficiency to obtain a second influencing factor;

[0069] The calculation formula of the second impact factor is:

[0070]

[0071] Among them, B2 represents the second impact factor, s i Indicates the distance between two order endpoints, s max Indicates the maximum distance between two order endpoints, s min Indicates the minimum interval between two order endpoints, s indicates the total trip, and n indicates the number of orders. Under the premise that the number of channels is fixed, the efficiency loss is also related to the number of orders and the interval between the two order endpoints. If the order endpoints are close, the probability of AGV path conflict caused by the shorter single trip is greater due to the fixed round-trip speed of the AGV. Similarly, the more orders there are, the greater the probability of AGV path conflict. At the same time, this situation is also affected by the first influencing factor. Therefore, by obtaining the second influencing factor, the negative impact of the information elements on the theoretical timeliness in different scenarios can be further clarified.

[0072] In order to prevent the accidental occurrence of maximum and minimum values ​​from affecting subsequent judgments, the maximum and minimum values ​​of the interval distance between the two order endpoints are excluded.

[0073] Step S104: The theoretical time efficiency and the key factors are used to construct a time efficiency model through the following formula:

[0074]

[0075] Among them, ρ tIndicates time efficiency, A represents theoretical time efficiency, B1 represents the first influencing factor, B2 represents the second influencing factor, and n represents the number of orders.

[0076] In combination with steps S100 to S104, the time efficiency model can be converted into:

[0077]

[0078] Step S105: using the time efficiency model, obtaining the first-order partial derivative of the AGV quantity m to determine the AGV placement threshold;

[0079] Specifically, under the time efficiency model, the calculation formula of the AGV number m is:

[0080]

[0081] Through the time efficiency model and the calculation formula of the AGV number m, when the information elements of a certain scene are clear, the AGV number m in the time efficiency has an extreme value. Within this extreme value range, as the AGV number m increases, the time efficiency increases accordingly. After exceeding the extreme value, the increase in the AGV number m will lead to a decrease in the time efficiency, that is, the extreme value of the AGV number m is the AGV deployment threshold. After clarifying the AGV deployment threshold, the relationship between the AGV capacity and cost in the scene can be balanced, and the AGV deployment quantity evaluation work in different scenes can be completed. That is, in a certain space, when the number of orders is in a saturated state, the deployment threshold is used to determine the AGV number m with the highest time efficiency for processing orders, so as to achieve a balance between capacity and cost.

[0082] Step S106: constructing a distance efficiency model related to the number m of AGVs based on the information elements, so as to determine the minimum value of AGV deployment through the distance efficiency model;

[0083] After obtaining the placement threshold, the extreme value of the specific number of AGVs placed under full load can be determined to ensure the balance between capacity and cost. However, in some cases, there is also a low load state, that is, the number of orders is small. In this case, by determining the placement floor value, the balance between capacity and cost can be further optimized, and the corresponding orders can be completed at a lower cost, providing users with more choices. Under low load conditions, the distance efficiency is positively correlated with the energy consumption of the AGV. The optimal distance efficiency means the optimal energy consumption of the AGV, which means the optimal cost.

[0084] Specifically, the theoretical road efficiency related to the number m of AGVs is obtained through the information elements;

[0085] The calculation formula of the theoretical road efficiency is:

[0086]

[0087] Among them, C represents the theoretical road efficiency, m represents the number of AGVs, and n represents the number of orders. It represents the difference between the travel of a single AGV executing two orders that are closest to each other and the travel of multiple AGVs executing two orders that are closest to each other.

[0088] When the number of orders is determined, increasing the number of AGVs m will shorten the overall distance. Since when multiple AGVs execute orders, there is a situation where one AGV is closer to the next order after completing an order, so this AGV can be given priority to handle it nearby. Compared with one AGV, multiple AGVs are equivalent to multiple points on the map, which can effectively save distance.

[0089] However, as the number m of AGVs increases, there is also an increase due to the use of traffic control, priority, selection of non-optimal paths, etc. to prevent deadlock, just like the time efficiency. Therefore, by obtaining the obstacle factors that affect the theoretical road efficiency and constructing a route efficiency model through the theoretical road efficiency and the obstacle factors, the bottom value of AGV deployment is determined.

[0090] The calculation formula of the hindering factor is:

[0091]

[0092] Wherein, D represents the hindering factor.

[0093] The travel efficiency model is:

[0094]

[0095] Among them, ρ s Represents the route efficiency model.

[0096] The first-order partial derivative of the AGV quantity m is calculated for the distance efficiency model to determine the bottom value of AGV deployment.

[0097] Specifically, under the distance efficiency model, the calculation formula of the AGV number m is:

[0098]

[0099] That is, when the information elements are fixed, the number of AGVs also has an extreme value in the route efficiency model. Within this extreme value range, as the number of AGVs m increases, the route efficiency will increase. After exceeding the extreme value, the route efficiency will decrease. This extreme value is the AGV deployment bottom value. The deployment bottom value means that in a certain space, if the order is in a low-load state, the lowest number of AGVs m that can be determined when only the cost needs to be considered. When the number of orders is small and there is no construction period limit, by determining the AGV deployment bottom value, the AGV deployment bottom value and the AGV deployment threshold are used as endpoints, respectively, to form an AGV deployment range, providing customers with an optional AGV deployment range, which can further balance the relationship between capacity and cost, and provide a clearer guidance for the deployment of AGVs.

[0100] Embodiment 3

[0101] like Figure 3 As shown, this embodiment provides an AGV deployment quantity evaluation system, which is applied to the AGV deployment quantity evaluation method in the above embodiment, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0102] The system comprises:

[0103] An extraction module 10 is used to extract information elements in the demand scenario, wherein the information elements include a single trip, the number of orders, the distance between two order endpoints, and the number of channels, and obtain a theoretical time efficiency related to the number m of AGVs based on the information elements;

[0104] The extraction module 10 comprises:

[0105] The first unit is used to obtain a distance index through the single trip and the number of orders, and obtain a time index for a single AGV to complete the distance index;

[0106] The second unit is used to determine the correlation between the change of the AGV quantity m and the theoretical time efficiency based on the time index.

[0107] An analysis module 20, for obtaining key factors affecting the theoretical timeliness based on the information elements;

[0108] The analysis module 20 comprises:

[0109] The third unit is used to analyze the influence of the number of channels on the theoretical time efficiency to obtain a first influence factor;

[0110] The fourth unit is used to analyze the distance between the two order endpoints and the impact of the number of orders on the theoretical time efficiency based on the first impact factor to obtain a second impact factor

[0111] The calculation module 30 is used to construct a time efficiency model through the theoretical time efficiency and the key factors to determine the AGV deployment threshold.

[0112] The calculation module 30 includes:

[0113] The fifth unit is used to construct a time efficiency model by using the theoretical time efficiency and the key factors through the following formula:

[0114]

[0115] Among them, ρ t Indicates time efficiency, A represents theoretical time efficiency, B1 represents the first influencing factor, B2 represents the second influencing factor, and n represents the number of orders;

[0116] The sixth unit is used to obtain the first-order partial derivative of the AGV quantity m through the time efficiency model to determine the AGV deployment threshold.

[0117] The system further includes a restriction module 40, which is used to construct a distance efficiency model related to the number m of AGVs based on the information elements, so as to determine the minimum value of AGV deployment through the distance efficiency model.

[0118] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the AGV deployment quantity evaluation method as described in the above technical solution is implemented.

[0119] The present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the AGV deployment quantity evaluation method as described in the above technical solution is implemented.

[0120] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the number of AGV deployments, characterized in that: The following steps are involved: Obtaining information elements in the demand scenario; the information elements include a single trip, the number of orders, the distance between two order endpoints, and the number of channels; Based on the information elements, obtain the theoretical timeliness related to the number of AGVs and the key factors affecting the theoretical timeliness; The process of obtaining theoretical timeliness based on the information elements includes: Obtaining a distance indicator based on the single trip and the number of orders, and obtaining a time indicator for a single AGV to complete the distance indicator; The theoretical time efficiency is obtained based on the time index and the number of AGVs; The calculation formula of the theoretical aging is: In the formula, A represents the theoretical timeliness, m represents the number of AGVs, and t0 represents the time index; The process of obtaining key factors includes: Analyze the influence of the number of channels on the theoretical time efficiency to obtain a first influence factor; Based on the first influencing factor, analyzing the influence of the interval distance between the two order endpoints and the number of orders on the theoretical time efficiency to obtain a second influencing factor; Constructing a time efficiency model based on the theoretical time efficiency and the key factors; The expression of the constructed time efficiency model is: In the formula, ρ t Indicates time efficiency, A represents theoretical time efficiency, B1 represents the first influencing factor, B2 represents the second influencing factor, and n represents the number of orders; The expression of the first impact factor is: The expression of the second impact factor is: In the formula, B1 represents the first influencing factor, t0 represents the time index, m represents the number of AGVs, k represents the number of channels, B2 represents the second influencing factor, and s i Indicates the distance between two order endpoints, s max Indicates the maximum distance between two order endpoints, s min It represents the minimum distance between two order endpoints, s represents the total distance, and n represents the number of orders; The AGV deployment threshold is obtained based on the time efficiency model.

2. An AGV deployment quantity evaluation system, applied to the AGV deployment quantity evaluation method according to claim 1, characterized in that: The system comprises: an extraction module, an analysis module and a calculation module; The extraction module is used to extract information elements in the demand scenario and obtain theoretical timeliness based on the information elements; The analysis module is used to obtain key factors affecting the theoretical timeliness based on the information elements; The calculation module is used to construct a time efficiency model based on the theoretical time efficiency and the key factors, and obtain an AGV deployment threshold based on the time efficiency model.

3. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the AGV deployment quantity evaluation method as described in claim 1 is implemented.

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