Performance analysis method and analysis device for high-density suspended robotic storage system

Abstract

The present invention proposes a performance analysis method and an analysis device for a high-density suspended robot storage system, and the performance analysis method specifically includes the following steps: S1: constructing a model of the high-density suspended robot storage system based on a semi-open queuing network; S2: based on the constructed semi-open queuing network model, using a matrix geometry method to analyze the "track-changing robot-picking robot" pair twice to obtain the expected number of track-changing robots in the node containing the track-changing operation; S3: based on the expected number of track-changing robots in the node containing the track-changing operation, using a matrix geometry method to analyze the "picking robot-order" pair to obtain an analytical solution of the indicator. The analysis method proposed in the present invention can evaluate a specific storage system and perform layout optimization and resource allocation on this basis, thereby improving work efficiency and reducing operating costs.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse system modeling optimization, and in particular to a performance analysis method and an analysis device for a high-density suspended robot warehouse system. Background Art

[0002] At present, the developed economy has spawned strong and diversified logistics demand, and the demand drives the improvement and upgrading of logistics service supply. As one of the national pillar industries, the logistics industry is constantly maintaining integrity and innovation in the context of new technology and economic development in the new era. The prosperous demand for traditional logistics and the emergence of new logistics formats have led to a continuous increase in our demand for logistics infrastructure. As we all know, land resources are scarce, and the land supply for warehousing and logistics facilities is even tighter. According to incomplete statistics, the per capita storage area in my country is 0.52 square meters, which is 1 / 14 of the level in the United States, reflecting the dilemma of severe shortage of logistics infrastructure supply. In addition, labor costs remain high, and the traditional manual logistics service model is also unsustainable. How to meet the growing demand for logistics facilities under the dual constraints of limited land supply space and expensive labor costs has become a practical problem that needs to be solved in my country.

[0003] There are two ways to solve this problem of logistics facilities: First, adopt the "high-density + robot" logistics facility solution. To increase space, replace manpower with automated robots, and realize flexible storage and retrieval of goods while storing goods at high density; second, to conduct high-quality modeling and analysis of the warehousing and logistics system, improve the planning and operation level of warehousing and logistics facilities, and tap the service potential of new / renovated warehousing and logistics systems. The high-density suspended robot warehousing system is a new type of goods-to-person picking system. At present, it has been initially applied in the domestic footwear, clothing, toys and other industries, significantly increasing the storage capacity and reducing the system operating costs. However, the academic community lacks systematic research on the modeling, simulation and optimization technology of high-density suspended robot warehousing systems. How to layout the spatial form of high-density suspended robot warehousing systems, configure multiple types of resources, and optimize the allocation of cargo spaces have not yet been involved.

[0004] The prior art lacks a performance analysis method for a high-density suspended robotic warehousing system.

[0005] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0006] In order to solve the technical problem that the prior art cannot effectively analyze and evaluate a high-density suspended robotic storage system, the present invention proposes a performance analysis method and an analysis device for a high-density suspended robotic storage system.

[0007] To this end, the performance analysis method of the high-density suspended robotic storage system proposed in the present invention specifically includes the following steps:

[0008] S1: Modeling of high-density suspended robotic warehousing system based on semi-open queuing network;

[0009] S2: Based on the constructed semi-open queueing network model, the “track-changing robot-picking robot” pair is analyzed twice using matrix geometry methods to obtain the expected number of track-changing robots in the node containing the track-changing operation;

[0010] S3: Based on the expected number of track-changing robots in the nodes that contain track-changing operations, the matrix geometry method is used to analyze the “picking robot-order” pair to obtain the analytical solution of the indicator.

[0011] Furthermore, the step S1 specifically includes dividing the system operation process into several service nodes according to the characteristics of the system, and determining the number of service desks and the passing probability of each service node, calculating the first-order moment and second-order moment of the service time of each service node according to the actual system layout and size, drawing the queuing network model of the high-density suspended robot warehousing system based on the semi-open queuing network, and marking and explaining each service node.

[0012] Furthermore, when calculating the first-order moment and second-order moment of the service time of each service node, the required data include warehouse layout data, cargo box dimensions, picking robot data, track-changing robot data, and workstation data.

[0013] Furthermore, in step S2, the first matrix geometry method analysis of the "track-changing robot-picking robot" pair includes:

[0014] S21: Analyze system characteristics and define appropriate state variables;

[0015] S22: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable.

[0016] Furthermore, the step S3 specifically includes analyzing system characteristics, defining appropriate state variables, solving the semi-open queuing network model based on matrix geometry methods, obtaining steady-state probability values ​​corresponding to each state variable, and solving the analytical solution of the indicator based on the steady-state probability values ​​obtained by the matrix geometry method.

[0017] Furthermore, the method of analyzing the "picking robot-order" pair using the matrix geometry method comprises the following steps:

[0018] S31: Analyze system characteristics and define appropriate state variables;

[0019] S32: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable;

[0020] S33: Based on the steady-state probability value obtained by matrix geometry method, the analytical solution of the indicator is obtained.

[0021] Furthermore, it is characterized in that the indicators include system throughput, resource utilization and waiting time.

[0022] The present invention proposes an analysis device, which specifically includes a processor and a storage medium for storing a computer program. The processor can implement the performance analysis method of the above-mentioned high-density suspended robotic warehousing system by executing the computer program.

[0023] The present invention proposes a processor, which can implement the performance analysis method of the above-mentioned high-density suspended robotic warehousing system by executing a computer program.

[0024] The present invention proposes a computer-readable storage medium for storing a computer program, wherein the computer program can be executed by a processor, and the computer program can implement the performance analysis method of the above-mentioned high-density suspended robotic warehousing system during the process of being executed by the processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The analysis method proposed in the present invention can evaluate a specific high-density suspended robot storage system and perform layout optimization and resource allocation on this basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of an analysis method according to an embodiment of the present invention;

[0028] Figure 2 is a front view of a high-density suspended robotic storage system according to an embodiment of the present invention;

[0029] Figure 3 is a top view of a high-density suspended robotic storage system according to an embodiment of the present invention;

[0030] Figure 4 is a side view of a high-density suspended robotic storage system according to an embodiment of the present invention;

[0031] Figure 5Schematic diagram of a semi-open queuing network model of a high-density suspended robotic warehousing system according to an embodiment of the present invention;

[0032] Figure 6 It is a schematic diagram of the relative error of the analytical model relative to the simulation model in the scene (height 10, width 100, length 80) of the embodiment of the present invention;

[0033] Figure 7 It is a schematic diagram of the relative error of the analytical model relative to the simulation model in the scene (height 10, width 110, length 73) of the embodiment of the present invention;

[0034] Figure 8 It is a schematic diagram of the relative error of the analytical model relative to the simulation model in the scene (height 12, width 91, length 73) of the embodiment of the present invention;

[0035] Fig. 9 It is a schematic diagram of the relative error of the analytical model relative to the simulation model in the scene (height 12, width 100, length 67) of the embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described with reference to the accompanying drawings.

[0037] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for both fixing and circuit connection.

[0038] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0039] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0040] like Figure 1 As shown, the performance analysis method of the high-density suspended robotic storage system proposed in the embodiment of the present invention specifically includes the following steps:

[0041] S1: Modeling of high-density suspended robotic warehousing system based on semi-open queuing network.

[0042] Figure 2-Figure 4 In the figure, 1 represents the picking robot, 2 represents the track-changing robot, 3 represents the dense storage area, 4 represents the cargo box, 5 represents the picking robot running track, 6 represents the workstation, and 7 represents the track-changing robot running track. Figure 2-Figure 4 As shown in the figure, in a high-density suspended robotic warehousing system, inventory items are stored in boxes, which are stacked on top of each other. There are two types of robots in the system, one is a picking robot that moves in two directions, and the other is a track-changing robot used to switch the running track of the picking robot. The picking robot moves along the track installed on the ceiling and transports the target box between the storage / picking location and the workstation located at the bottom of the system. The track-changing robot moves along the lateral track and transports the picking robot between the tracks. The workstation is used to extract the target goods from the picked box according to the customer order. There is a buffer for the picking robot to queue up. After the picking robot places the picked box at the workstation, it is assumed that the picking robot quickly grabs a box that needs to be stored. Each picking task is followed by a storage task (double command task). According to the characteristics of the system, the system's operating process is appropriately divided into several service nodes, and the number of service desks and the passing probability of each service node are determined. According to the actual system layout and size, the first-order moment and second-order moment of the service time of each service node are calculated. Based on the semi-open queuing network, the queuing network model of the high-density suspended robot warehousing system is drawn, and each service node is marked and explained.

[0043] When calculating the first-order and second-order moments of the service time of each service node, the required data mainly include: warehouse layout data (length L, width W, height H), container size (length l, width w, height h b ), data of picking robots (number N BP , moving speed v BP , Grab and lift speed v L ), data of track-changing robots (number N TC , moving speed v TC , Grasp and lift time t lu ), data of workstations (number n w , Service time T p ). In the simulation verification, four warehouse layouts were considered: height 10, width 100, length 80; height 10, width 110, length 73; height 12, width 91, length 73; height 12, width 100, length 67.

[0044] <![CDATA[h b ]]> l w <![CDATA[N BP ]]> <![CDATA[N TC ]]> <![CDATA[n w ]]> <![CDATA[v BP ]]> <![CDATA[v TC ]]> <![CDATA[v L ]]> <![CDATA[t lu ]]> <![CDATA[T p ]]> 0.33 0.6 0.8 25 10 4 3 3 1.6 1.2 U[5，15]

[0045] Table 1 Basic information of warehouse

[0046] Figure 3 The semi-open queuing network model of a high-density suspended robotic warehousing system under a dedicated storage strategy is presented, and the relevant nodes involved are introduced in detail in Table 2.

[0047]

[0048] Table 2 Detailed description of service nodes

[0049] S2: Based on the constructed semi-open queuing network model, the “track-changing robot-picking robot” pair is analyzed twice using matrix geometry methods to obtain the expected number of track-changing robots in the nodes that include track-changing operations.

[0050] According to the track-changing situation of the system, the nodes in the system that include track-changing operations (node ​​2, node 7, node 12) are determined; first, node 2 is taken as the first integrated node, and node 7 and node 12 are taken as the second integrated nodes, and the matrix geometry method is applied to calculate the expected number of track-changing robots in the two integrated nodes; then node 7 and node 12 are analyzed, and the two track-changing nodes are taken as an integrated node respectively, and the matrix geometry method is applied again to calculate the expected number of track-changing robots in the two nodes.

[0051] The first matrix geometry analysis of the pair “track-changing robot-picking robot” includes:

[0052] S21: Analyze system characteristics and define appropriate state variables.

[0053]

[0054]

[0055]

[0056] Among them, define the state variable k1 is the index of the state variable, Indicates the number of picking robots waiting in the queue, represents the number of track-changing robots in node 2, represents the number of track-changing robots in nodes 7 and 12, N TC is the total number of track-changing robots in the system, express The steady-state probability, steady-state probability vector Represents the steady-state probability set of all state variables when the number of waiting picking robots is i1.

[0057] S22: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable.

[0058] The specific form of the matrix is ​​as follows:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] represents the order arrival rate of the track change node, where λ is the order arrival rate of the system, W is the number of lanes in the warehouse, represents the service rate when there are i track-changing robots in the first integrated node obtained by the approximate mean analysis method, represents the service rate when there are i track-changing robots in the second integrated node, and the matrix Represents the steady-state probability vector arrive The transfer matrix, Indicates from To the steady-state probability vector The transfer matrix, Indicates from arrive The transfer matrix, C 1 is from To the steady-state probability vector The transfer matrix, B 1 is the steady-state probability vector arrive The transfer matrix, A 1 is from arrive The transfer matrix, is a dimension of ((N TC +1)*(N TC +2) / 2)×((N TC +1)*(N TC +2) / 2) square matrix, and The dimensions are ((N TC +1)*(N TC+2) / 2)×(N TC +1) and (N TC +1)×((N TC +1)(N TC +2) / 2), B 1 , C 1 and A 1 All dimensions are (N TC +1)×(N TC +1) square.

[0066]

[0067] represents the R obtained in the i-th iteration 1 , when the condition is met When , the iteration ends, ε is the given tolerance. Through the above iterative algorithm, the transfer rate matrix R can be obtained 1 .

[0068] By solving the following system of equations, we can obtain and in, and The dimensions are ((N TC +1)(N TC +2) / 2)×1 and (N TC +1)×1 unit vector.

[0069]

[0070]

[0071] The other steady-state probability vectors can be calculated as follows.

[0072]

[0073] Based on the steady-state probability, the expected number of track-changing robots in node 2 can be obtained: and the expected number of track-changing robots in nodes 7 and 12 Among them, π 1 (k, i, j) represents the state variable represented by (k, i, j) The steady-state probability.

[0074]

[0075]

[0076] In order to obtain the number of service stations for node 2 and the number of servers in nodes 7 and 12 (st c), expanding the two expected quantities by times.

[0077]

[0078]

[0079] The second matrix geometry analysis of the “track-changing robot-picking robot” pair includes:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Among them, define the state variable k2 is the index of the state variable, Indicates the number of picking robots waiting in the queue, represents the number of track-changing robots in node 7, represents the number of track-changing robots in node 12, st c is the total number of track-changing robots in nodes 7 and 12, express The steady-state probability, π 2 (k, i, j) represents the state variable represented by (k, i, j) The steady-state probability, steady-state probability vector represents the steady-state probability set of all state variables when the number of waiting picking robots is i2. By using matrix geometry methods, the steady-state probability vector can be obtained: Based on the steady-state probability, the expected values ​​of the track-changing robots in nodes 7 and 12 can be obtained respectively. Furthermore, in order to obtain the number of servers in nodes 7 and 12 Expand the two expected quantities times.

[0088] S3: Based on the expected number of track-changing robots in the nodes that include track-changing operations, the matrix geometry method is used to analyze the “picking robot-order” pair to obtain analytical solutions for indicators such as system throughput, resource utilization, and waiting time.

[0089] Analyze the system characteristics, define appropriate state variables, and solve the semi-open queuing network model based on matrix geometry methods to obtain the steady-state probability values ​​corresponding to each state variable. Based on the steady-state probability values ​​obtained by matrix geometry methods, the analytical solutions of system throughput, resource utilization, waiting time and other indicators are solved.

[0090] The method of analyzing the pair "picking robot-order" using matrix geometry method includes the following steps:

[0091] S31: Analyze system characteristics and define appropriate state variables.

[0092]

[0093]

[0094]

[0095] Among them, define the state variable k s is the index of the state variable, Indicates the number of orders waiting in the queue, Indicates the number of picking robots in comprehensive node 1 (nodes 10, 11, 12, 13, 14, 15), Represents the number of picking robots in comprehensive node 2 (nodes 1, 2, 3, 4, 5, 6, 7, 8, 9), N BP is the total number of picking robots in the system, express The steady-state probability, steady-state probability vector Indicates the number of waiting orders is i s The steady-state probability set of all state variables.

[0096] S32: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable.

[0097] The specific form of the matrix is ​​as follows:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] represents the service rate when there are i picking robots in the first integrated node, represents the service rate when there are i picking robots in the second integrated node, and the matrix Represents the steady-state probability vector arrive The transfer matrix, Indicates from To the steady-state probability vector The transfer matrix, Indicates from arrive The transfer matrix, C s is from To the steady-state probability vector The transfer matrix, B s is the steady-state probability vector arrive The transfer matrix, A s is from arrive The transfer matrix, is a dimension of ((N BP +1)*(N BP +2) / 2)×((N BP +1)*(N BP +2) / 2) square matrix, and The dimensions are ((N BP +1)*(N BP +2) / 2)×(N BP +1) and (N BP +1)×((N BP +1)(N BP +2) / 2), B s , C s and A s All dimensions are (N BP +1)×(N BP +1) square.

[0105]

[0106] represents the R obtained in the i-th iteration s , when the condition is met When , the iteration ends, ε is the given tolerance. Through the above iterative algorithm, the transfer rate matrix R can be obtained s .

[0107] By solving the following system of equations, we can obtain and in, and The dimensions are ((N BP +1)(N BP +2) / 2)×1 and (N BP +1)×1 unit vector.

[0108]

[0109]

[0110] Other steady-state probability vectors can also be calculated as follows.

[0111]

[0112] S33: Solved by matrix geometry method Obtain analytical solutions for metrics such as system throughput, resource utilization, and waiting time.

[0113] express The sum of all elements in

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122] Among them, the random variable L oq Indicates the number of orders waiting in the queue, L dc represents the number of busy robots, L W represents the number of picking robots waiting in the buffer of a single workstation, THT represents the system throughput, and W BP represents the time the order waits for the picking robot, W W represents the waiting time of the picking robot in the workstation buffer, U BP , U TC and U Wrepresent the utilization rates of the picking robot, track-changing robot and workstation, respectively; E(·) represents the expected value of each random variable; π s (k, i, j) represents the state variable represented by (k, i, j) The steady-state probability, N w is the number of workstations, λ is the order arrival rate, EL W (i) indicates a comprehensive node When there are i picking robots in a single workstation buffer, the number of picking robots waiting in the buffer is Q W (i) indicates a comprehensive node The probability that a single workstation is busy when there are i picking robots in .

[0123] A simulation model of a high-density suspended robotic warehousing system was constructed in the simulation software Arena (version 14.0). 32 scenarios were generated by changing the warehouse layout (4 layouts), order arrival rate (2 arrival rates), and storage strategy (4 strategies). In each scenario, the total number of storage locations was 80,000, and the running warm-up time of each simulation scenario was 100 hours and the running time was 1,000 hours. The relative error δ (δ = |AS| / S) was used to evaluate the accuracy of the analysis model, where S and A correspond to the simulation results and analytical results, respectively. Figure 6-Figure 9 The six indicators (THT, U TC , U BP , U W , W BP , W W ), Figure 6-Figure 9 The square in the middle represents THT and the circle represents U TC , the upper triangle represents U BP , the lower triangle represents U W , the diamond represents W w , the left triangle represents W BP Through simulation, the accuracy of the performance analysis method of the high-density suspended robot storage system of the present invention can be verified.

[0124] An embodiment of the present invention further provides a processor, which can implement the performance analysis method of the above-mentioned high-density suspended robotic warehousing system by executing a computer program.

[0125] An embodiment of the present invention also provides a computer-readable storage medium for storing a computer program. The computer program can be executed by a processor, and the computer program can implement the performance analysis method of the above-mentioned high-density suspended robot warehousing system during the execution of the processor.

[0126] An embodiment of the present invention further provides an analysis device, including a processor and a storage medium for storing a computer program. The processor can implement the above-mentioned performance analysis method of the high-density suspended robotic warehousing system by executing the computer program.

[0127] The analysis method proposed in the present invention is used to perform performance analysis and evaluation on a high-density suspended robotic storage system. It can evaluate a specific high-density suspended robotic storage system and perform layout optimization and resource allocation on this basis. It can also design strategies for the efficient operation of resources, thereby improving the efficiency of the high-density suspended robotic storage system and reducing its cost.

[0128] The analysis method proposed in the present invention can not only provide an evaluation standard for evaluating system simulation results, but also explore spatial morphological layout and resource allocation solutions that match business needs through multi-scenario analysis.

[0129] The above disclosure is only the preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the present invention. It should be pointed out that for those skilled in the art, any equivalent changes made to the scheme of the present invention without departing from the design structure and principle of the present invention are considered to be within the protection scope of the present invention.

Claims

1. A performance analysis method for a high-density suspended robotic storage system, characterized in that , specifically including the following steps: S1: Modeling of high-density suspended robotic warehousing system based on semi-open queuing network; S2: Based on the constructed semi-open queuing network model, the "track-changing robot-picking robot" pair is analyzed twice using matrix geometry methods to obtain the expected number of track-changing robots in the node containing the track-changing operation; S3: Based on the expected number of track-changing robots in the nodes that contain track-changing operations, the matrix geometry method is used to analyze the "picking robot-order" pair to obtain the analytical solution of the indicator; The step S1 specifically includes dividing the system operation process into several service nodes according to the characteristics of the system, determining the number of service desks and the passing probability of each service node, calculating the first-order moment and second-order moment of the service time of each service node according to the actual system layout and size, drawing the queuing network model of the high-density suspended robot warehousing system based on the semi-open queuing network, and marking and explaining each service node.

2. The performance analysis method of the high-density suspended robotic storage system according to claim 1 is characterized in that: When calculating the first-order moment and second-order moment of the service time of each service node, the required data include warehouse layout data, cargo box dimensions, picking robot data, track-changing robot data, and workstation data.

3. The performance analysis method of the high-density suspended robotic storage system according to claim 1 is characterized in that: In step S2, the first matrix geometry method analysis of the "track changing robot-picking robot" pair includes: S21: Analyze system characteristics and define appropriate state variables; S22: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable.

4. The performance analysis method of the high-density suspended robotic storage system according to claim 1 is characterized in that: The step S3 specifically includes analyzing system characteristics, defining appropriate state variables, solving the semi-open queuing network model based on matrix geometry methods, obtaining steady-state probability values ​​corresponding to each state variable, and solving the analytical solution of the indicator based on the steady-state probability values ​​obtained by the matrix geometry method.

5. The performance analysis method of the high-density suspended robotic storage system according to claim 4 is characterized in that: The method for analyzing the "picking robot-order" pair using matrix geometry method comprises the following steps: S31: Analyze system characteristics and define appropriate state variables; S32: Based on the matrix geometry method, the semi-open queueing network model is solved to obtain the steady-state probability value corresponding to each state variable; S33: Based on the steady-state probability value obtained by matrix geometry method, the analytical solution of the indicator is obtained.

6. The performance analysis method of a high-density suspended robotic storage system according to any one of claims 4-5, characterized in that: The metrics include system throughput, resource utilization, and latency.

7. An analysis device, characterized in that , specifically includes a processor and a storage medium for storing a computer program. The processor can implement the performance analysis method of the high-density suspended robot warehousing system described in any one of claims 1-6 by executing the computer program.

8. A processor, characterized in that The processor can implement the performance analysis method of the high-density suspended robot storage system described in any one of claims 1-6 by executing a computer program.

9. A computer-readable storage medium for storing a computer program, characterized in that , the computer program can be executed by a processor, and the computer program can implement the performance analysis method of the high-density suspended robot storage system described in any one of claims 1-6 during the execution of the processor.

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