Automated warehouse stacker operation control method and system

Abstract

The present invention relates to the field of automatic control technology, and in particular to an operation control method and system for a stacker crane in an automated three-dimensional warehouse. The present invention first analyzes the change in the mass of cargo carried by the stacker crane at each path monitoring point to obtain a stacking offset sub-parameter; further analyzes the curvature curve of the transport path to obtain the transport complexity of the stacker crane at each path monitoring point; and then combines the changes in the transport speed at the path monitoring points of all historical paths to analyze and obtain the speed offset index of the stacker crane at the current path monitoring point; and then obtains the speed adjustment parameters of the stacker crane at the current path monitoring point; and controls the transport speed of the stacker crane according to the speed adjustment parameters. The present invention comprehensively evaluates the operational stability of the stacker crane during the current transport process by analyzing the stacking stability, path complexity, and transport speed changes in the historical paths of the stacker crane, and further adjusts and controls the transport speed of the stacker crane, thereby improving the transport stability of the stacker crane.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an operation control method and system for a stacker crane in an automated stereoscopic warehouse. Background Art

[0002] Automated warehouses (AWGs) are a vital component of modern logistics systems. Through efficient storage and retrieval mechanisms, they improve warehouse space utilization and operational efficiency. Stacker cranes are the core of the conveying system within automated warehouses. They receive dispatch instructions and automatically move goods in and out of the warehouse, making their operational control crucial. During this process, stacker cranes must adjust their transport speed based on the specific transport task, ensuring smooth operation while maintaining high speeds.

[0003] The traditional method of adjusting the transport speed is mainly a multi-stage speed control method, that is, the stacker crane has different set transport speeds in different sections, and is appropriately accelerated under simple road conditions to improve the transport efficiency; and appropriately decelerated under complex road conditions such as curves to ensure the smoothness of the transport process; but this method may cause the stacker crane to switch between different speed sections in actual application. The acceleration will suddenly change, which will cause a large impact on the stacker crane and the goods it carries, affecting the stability and life of the equipment, and even causing the goods to fall, resulting in poor operational stability of the stacker crane. Summary of the Invention

[0004] In order to solve the technical problem that the existing technology has poor control over the transport speed of stacker cranes in automated high-bay warehouses, which in turn leads to poor operational stability of the stacker cranes, the present invention aims to provide an operation control method and system for stacker cranes in automated high-bay warehouses. The technical solutions adopted are as follows:

[0005] The present invention provides an operation control method for a stacker crane in an automated warehouse, the method comprising:

[0006] In the current transport task of the stacker crane in the automated warehouse, the curvature curve of the stacker crane's transport path is obtained, and the load mass and transport speed of the stacker crane when it passes through each path monitoring point on the transport path are obtained;

[0007] Based on the change in the load mass at each path monitoring point, a stacking offset sub-parameter of the stacker at each path monitoring point is obtained; based on the position of each path monitoring point in the transport path and the change in the curvature curve, the transport complexity of the stacker at each path monitoring point is obtained; at the current path monitoring point, the change in the transport speed at all path monitoring points in the past is combined with the corresponding stacking offset sub-parameter and the transport complexity to obtain the speed offset index of the stacker at the current path monitoring point;

[0008] At the current path monitoring point, the stacking offset sub-parameters at all historical path monitoring points are integrated, combined with the stacker load information and the speed offset index, to obtain the speed adjustment parameters of the stacker; and the transport speed of the stacker is controlled according to the speed adjustment parameters.

[0009] Furthermore, the method for obtaining the stacking offset sub-parameter includes:

[0010] At each path monitoring point, a stacking instability parameter at each path monitoring point is obtained based on a mass deviation of the load mass relative to a preset standard mass; the mass deviation is positively correlated with the stacking instability parameter;

[0011] Obtaining a stacking instability weight at each path monitoring point according to a mass change rate of the load mass at each path monitoring point; the mass change rate is negatively correlated with the stacking instability weight;

[0012] The stack instability parameter is weighted by using the stack instability weight, and the weighted result is used as the stack offset sub-parameter at the corresponding path monitoring point.

[0013] Furthermore, the method for obtaining the transportation complexity includes:

[0014] According to the monotonic change of the curvature curve, the curvature curve is segmented to obtain all curvature segments;

[0015] Obtaining a complexity parameter based on the concentrated features of the derivatives of the curvature in the curvature segment to which each path monitoring point belongs and the concentrated features of the curvature in the next adjacent curvature segment;

[0016] Perform a negative correlation mapping between the position of each path monitoring point and the spatial distance between the position of the path monitoring point corresponding to the end point of the corresponding curvature segment, and use the negative correlation mapping result as the complexity weight;

[0017] The complexity parameter is weighted using the complexity weight, and the weighted result is used as the transportation complexity at the corresponding path monitoring point.

[0018] Furthermore, the method for obtaining the curvature segmentation includes:

[0019] The curvature curve is differentiated to obtain a curvature derivative curve; in the curvature derivative curve, the points where the curvature derivative is 0 and the curvature derivatives of adjacent curvature derivatives have different signs are corresponding to the curvature values are used as segmentation points of the curvature curve; all curvature segments of the curvature curve are obtained using all segmentation points.

[0020] Furthermore, the method for obtaining the complexity parameter includes:

[0021] In the curvature segment to which each path monitoring point belongs, the mean of the curvature derivatives of all curvatures is normalized and used as the first complex parameter; in the curvature segment to which each path monitoring point belongs, the mean of all curvatures is used as the second complex parameter; and the product of the first complex parameter and the second complex parameter is used as the complexity parameter.

[0022] Furthermore, the method for obtaining the speed deviation index includes:

[0023] The product of the speed change rate of the transport speed at the path monitoring point of each historical path, the stacking offset sub-parameter, and the transport complexity is used as the offset sub-parameter at the path monitoring point of each historical path; the offset sub-parameters at the path monitoring points of all historical paths are combined to obtain a first offset parameter;

[0024] Using the variance of the transport speed at the path monitoring points of all historical routes as a second offset parameter;

[0025] A speed offset index is obtained according to the first offset parameter and the second offset parameter, and both the first offset parameter and the second offset parameter are positively correlated with the speed offset index.

[0026] Furthermore, the method for obtaining the speed adjustment parameter includes:

[0027] The product of the total number of loads and the total mass of the loads in the current transport task of the stacker is used as the inertial instability factor of the stacker; at the current path monitoring point, the product of the variance of the stacking offset sub-parameter at all path monitoring points in the historical path and the inertial instability factor is used as the stacking offset index;

[0028] After normalizing the product of the stacking offset index and the speed offset index, the normalized result is used as the speed adjustment parameter.

[0029] Furthermore, the method for controlling the transport speed of the stacker according to the speed adjustment parameter includes:

[0030] The initial P parameter of the PID controller is obtained using the Ziegler–Nichols method; the speed adjustment parameter is subtracted from a constant 1 as a parameter adjustment weight; the initial P parameter is weighted using the parameter adjustment weight, and the weighted result is used as a modified P parameter; and the transport speed of the stacker is adjusted in real time based on the modified P parameter and the PID algorithm.

[0031] Furthermore, the method for obtaining the curvature curve includes:

[0032] Obtain the curvature value at each monitoring point on the transport path; construct a two-dimensional coordinate system with the serial number of the monitoring point as the horizontal axis parameter and the curvature value as the vertical axis parameter; map the curvature values corresponding to all monitoring points on the transport path to the two-dimensional coordinate system to fit the curvature curve.

[0033] The present invention also proposes an automated warehouse stacker operation control system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the automated warehouse stacker operation control method are implemented.

[0034] The present invention has the following beneficial effects:

[0035] The present invention obtains the curvature curve of the stacker's transport path in the current transport task of the automated warehouse stacker, and obtains the load mass and transport speed of the stacker when it passes each path monitoring point on the transport path, providing data preparation for subsequent analysis; according to the change of the load mass at each path monitoring point, the stacking offset sub-parameter of the stacker at each path monitoring point is obtained, and the stacking offset sub-parameter indirectly reflects the transport stability of the stacker when it passes each path monitoring point; according to the position of each path monitoring point in the transport path, combined with the change of the curvature curve, the transport complexity of the stacker at each path monitoring point is obtained, and the transport complexity indirectly evaluates the operating stability of the stacker from the perspective of path change. ; At the current path monitoring point, the changes in the transport speed at the path monitoring points of all historical paths are integrated, combined with the corresponding stacking offset sub-parameters and the transport complexity, to obtain the speed offset index of the stacker at the current path monitoring point. The speed offset index quantifies the operational stability of the stacker at the current transport speed at the current path monitoring point; At the current path monitoring point, the stacking offset sub-parameters at the path monitoring points of all historical paths are integrated, combined with the stacker load information and the speed offset index, to obtain the speed adjustment parameter of the stacker. The speed adjustment parameter is used to control the smoothness of the subsequent transport speed changes, so as to reduce the poor transport stability caused by unreasonable transport speed; Finally, the transport speed of the stacker is controlled according to the speed adjustment parameter. The present invention comprehensively evaluates the operational stability of the stacker during the current transport process by analyzing the stacking stability, path complexity and transport speed changes in the historical paths of the stacker, and further adjusts and controls the transport speed of the stacker, thereby improving the transport stability of the stacker. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 any creative work.

[0037] Figure 1 A flowchart of an automated warehouse stacker operation control method provided by one embodiment of the present invention;

[0038] Figure 2 A flow chart of a method for obtaining transportation complexity provided by one embodiment of the present invention;

[0039] Figure 3 A flow chart of a method for obtaining a velocity offset index provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an automated warehouse stacker crane operation control method and system proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0042] The specific scheme of the operation control method and system of the automated warehouse stacker provided by the present invention is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of an automated warehouse stacker operation control method provided by one embodiment of the present invention, specifically including:

[0044] Step S1 : in the current transport task of the stacker crane in the automated warehouse, obtain the curvature curve of the stacker crane's transport path, and obtain the load mass and transport speed of the stacker crane when it passes through each path monitoring point on the transport path.

[0045] In one embodiment of the present invention, all transport paths of each stacker are obtained through the control system of the automated warehouse, and then the curvature curve of each transport path is obtained; wherein addressing pieces are set at equal intervals on each transport path, such as one every 1.5m, and each addressing piece can be regarded as a path monitoring point in the transport path, and an encoder is installed on the stacker to measure the position of the stacker in real time; when the stacker passes through the addressing piece, the system automatically calculates and records the current position coordinates of the stacker, and then obtains the transport speed of the stacker when passing through each path monitoring point through the motor shaft speed, and at the same time, the weight sensor on the stacker loading platform will automatically record the load mass on the loading platform; by analyzing the operation status of the stacker, the transport speed of the stacker can be adjusted in real time, thereby improving the operation stability of the stacker.

[0046] It should be noted that the current transportation task of the stacker refers to a complete transportation process corresponding to the stacker's outbound or inbound transportation after loading the goods. The specific transportation time, transportation speed and transportation route are all planned and scheduled by the control system of the automated warehouse. It is already an existing technology, as is obtaining position coordinates through encoders and obtaining transportation speed through motor shaft speed, and will not be repeated here.

[0047] It should be noted that the transportation path of the current transportation task may also include the transportation of the stacker close to the ground and the lifting and lowering transportation to the corresponding warehouse position. The embodiment of the present invention takes into account that the stacker generally performs uniform linear motion when lifting and falling, and the operation is relatively stable, so only the complex road conditions involved in the ground transportation of the stacker are analyzed and adjusted.

[0048] Preferably, in one embodiment of the present invention, considering that the transport path may have complex conditions such as bends, the curvature information at each monitoring point on the path can be calculated to obtain a curvature curve of the transport path. Different curvatures correspond to different path complexities, so that the transport complexity of the stacker can be subsequently obtained by combining the curvature. Therefore, the method for obtaining the curvature curve includes:

[0049] Obtain the curvature value at each monitoring point on the transport path; construct a two-dimensional coordinate system with the serial number of the monitoring point as the horizontal axis parameter and the curvature value as the vertical axis parameter; map the curvature values corresponding to all monitoring points on the transport path to the two-dimensional coordinate system to fit the curvature curve.

[0050] It should be noted that obtaining curvature and fitting curvature curves are both existing technologies well known to those skilled in the art and will not be elaborated herein.

[0051] Step S2: Based on the change in the load mass at each path monitoring point, the stacking offset sub-parameter of the stacker at each path monitoring point is obtained; based on the position of each path monitoring point in the transportation path and the change in the curvature curve, the transportation complexity of the stacker at each path monitoring point is obtained; at the current path monitoring point, the change in the transportation speed at the path monitoring points of all historical routes is comprehensively considered, combined with the corresponding stacking offset sub-parameter and the transportation complexity, to obtain the speed offset index of the stacker at the current path monitoring point.

[0052] Considering that when the stacker's cargo platform is stacked more, higher, or irregularly, its stability during transportation is worse, and the center of gravity of the cargo is more likely to shift due to acceleration and deceleration, inertia, and complex road conditions, which in turn causes the mass of the cargo on the cargo platform to change. The more drastic the change, the greater the possibility of stacking offset of the stacker, which indirectly indicates that the current transportation stability of the stacker is worse. Therefore, the embodiment of the present invention first obtains the stacking offset sub-parameter of the stacker at each path monitoring point based on the change in the mass of the cargo at each path monitoring point; the stacking offset sub-parameter indirectly reflects the transportation stability of the stacker when passing through each path monitoring point. The larger the stacking offset sub-parameter, the worse the transportation stability.

[0053] Preferably, in one embodiment of the present invention, considering that the mass of the cargo loaded by the stacker at the beginning of the current transport task is usually based on a standard fixed value, the greater the difference between the load mass collected at each monitoring point along the path during the transport process and the standard fixed value, the worse the stacking stability; and considering that the smaller the mass change rate of the load mass, the greater the possibility that the current load mass change is caused by the offset of the stacking center of gravity, and the higher the confidence level in evaluating the stacking stability; therefore, the method for obtaining the stacking offset sub-parameter includes:

[0054] At each path monitoring point, the stacking instability parameter at each path monitoring point is obtained based on the mass deviation of the load relative to the preset standard mass; the mass deviation is positively correlated with the stacking instability parameter;

[0055] According to the mass change rate of the load at each path monitoring point, the stacking instability weight at each path monitoring point is obtained; the mass change rate is negatively correlated with the stacking instability weight;

[0056] The stack instability parameter is weighted by the stack instability weight, and the weighted result is used as the stack offset sub-parameter at the corresponding path monitoring point.

[0057] As an example, the calculation formula of the stacking offset sub-parameter is: ;in, For stacker crane The stacking offset sub-parameters at each path monitoring point; For stacker crane The load mass collected at each monitoring point on the route; To preset standard quality; For stacker crane The mass change rate of the load at each monitoring point on the path; is a preset positive constant; For stacker crane Stack instability parameters at each path monitoring point; For stacker crane The stack instability weight at each path monitoring point.

[0058] Among them, the method for obtaining the mass change rate is: using the serial number of each path monitoring point as the horizontal axis parameter and the corresponding load mass as the vertical axis parameter, constructing data points and mapping them to a two-dimensional coordinate system, fitting the load mass change curve corresponding to the load mass collected at all path monitoring points based on the least squares method, and taking the derivative of the load mass change curve at each path monitoring point as the corresponding mass change rate; in other examples, the mass change rate can also be directly calculated using the calculation formula of the data change rate. It and the derivative method are both well-known technologies in the field and will not be repeated here.

[0059] It should be noted that in the calculation formula of the stacking offset sub-parameter, the preset standard mass is the total mass of the goods loaded by the stacker at the transportation starting point in the current transportation task, which is a standard value; the load mass is collected by the weight sensor and may change; the preset normal number is specifically 0.01, which avoids the denominator being 0 without affecting the calculation result and ensures that the fraction is meaningful. The implementer can also set it by himself or use other negative correlation mapping methods, such as taking the mass change rate as an exponential function with the natural constant e as the base. The x in is not described here.

[0060] Considering that in the current transportation task, the stacker crane travels between different shelves in the stereoscopic warehouse and may pass through multiple bends and corners. When passing through these areas, it is necessary to change speed appropriately to ensure the stability of the goods on the stacker crane's loading platform; considering that when the stacker crane is traveling on a curve, when it is about to enter the curve, the curvature of the path usually increases first and then decreases. At this time, the transportation complexity is high. At the same time, in order to avoid the centripetal force causing the stacker crane to lose control, it is usually decelerated appropriately; and then when it is about to leave the curve, the path curvature usually decreases gradually. At this time, the transportation complexity is low, and it will be accelerated appropriately to improve transportation efficiency. Therefore, the transportation complexity of the stacker crane is not consistent in different curve conditions. Therefore, the embodiment of the present invention obtains the transportation complexity of the stacker crane at each path monitoring point according to the position of each path monitoring point in the transportation path and the change of the curvature curve; the transportation complexity evaluates the operational stability of the stacker crane from the perspective of path change.

[0061] It should be noted that the corner area of the curve targeted in the embodiment of the present invention is a U-shaped running path.

[0062] Preferably, in one embodiment of the present invention, the method for obtaining transportation complexity includes:

[0063] See also Figure 2 , which shows a flow chart of a method for obtaining transportation complexity provided by an embodiment of the present invention, specifically comprising:

[0064] Step S201 : According to the monotonic change of the curvature curve, the curvature curve is segmented to obtain all curvature segments.

[0065] Considering that the curvature at different locations in the transportation path is not consistent, the complexity of the path is also not consistent. Therefore, one embodiment of the present invention first divides the curvature curve into segments according to the monotonic change of the curvature curve to obtain all curvature segments. The monotonic change of the curvature curve can be roughly divided into three cases, including monotonic increase, monotonic decrease, and monotonic invariance. Considering that the derivative can reflect the monotonic change of the curvature, in a preferred embodiment of the present invention, the method for obtaining the curvature segment includes:

[0066] The curvature curve is differentiated to obtain a curvature derivative curve; in the curvature derivative curve, the points where the curvature derivative is 0 and the curvature derivatives of the adjacent preceding and following curvature derivatives have different signs are used as segmentation points of the curvature curve; all curvature segments of the curvature curve are obtained using all segmentation points.

[0067] When the curvature derivative is 0, and the adjacent curvature derivatives are one positive and one negative, it means that the changing trend of the curvature has changed; the curvature segments can be roughly divided into three categories through the above segmentation method. One is the curvature segment corresponding to the straight section when entering or exiting the curve, which is also the curvature segment with monotonically unchanged curvature; one is the curvature segment corresponding to the first half of the curve, which is also the curvature segment with monotonically increasing curvature when initially entering the curve; one is the curvature segment corresponding to the second half of the curve, which is also the curvature segment with monotonically decreasing curvature when about to leave the curve; among them, according to common sense analysis, the path complexity of the first half of the curve is the largest, the path complexity of the second half of the curve is second, and the path complexity of the straight section is the smallest, and the corresponding transportation complexity is the same.

[0068] Step S202 : Obtaining a complexity parameter based on the concentrated features of the derivatives of the curvature in the curvature segment to which each path monitoring point belongs and the concentrated features of the curvature in the next adjacent curvature segment.

[0069] Considering that the larger the curvature derivative in the curvature segment to which the path monitoring point belongs, the greater the possibility that the curvature is monotonically increasing, the more likely the path monitoring point is located in the first half of the curve, and the greater the transportation complexity; considering that the curvature in the adjacent next curvature segment is also larger, it further shows that the more likely the path monitoring point is located in the first half of the curve, and the greater the transportation complexity.

[0070] In a preferred embodiment of the present invention, the method for obtaining the complexity parameter includes:

[0071] In the curvature segment to which each path monitoring point belongs, the mean of the curvature derivatives of all curvatures is normalized and used as the first complex parameter; in the curvature segment to which each path monitoring point belongs, the mean of all curvatures is used as the second complex parameter; and the product of the first complex parameter and the second complex parameter is used as the complexity parameter.

[0072] The calculation formula of the complexity parameter is: ;in, For stacker crane The complexity parameters at each path monitoring point; is the S-shaped function; For stacker crane The mean value of the curvature derivative in the curvature segment to which each path monitoring point belongs; For stacker crane The mean curvature of the next adjacent curvature segment to which the path monitoring point belongs.

[0073] In the calculation formula of complexity parameter, the specific use is The function normalizes the mean value of the curvature derivative in the curvature segment to which the path monitoring point belongs to obtain a first complexity parameter; the larger the mean value of the curvature derivative is positive, the larger the normalized value is, the greater the possibility of being in the first half of the curve, and the larger the complexity parameter is; on the contrary, the smaller the mean value of the curvature derivative is negative, the smaller the normalized value is, the greater the possibility of being in the second half of the curve, and the smaller the complexity parameter is; at the same time, the larger the curvature value in the adjacent next curvature segment, the larger the second complexity parameter is, further indicating that the path monitoring point is more likely to be in the first half of the curve or about to enter the first half of the curve, and the larger the complexity parameter is; then the first complexity parameter and the second complexity parameter are multiplied and combined to obtain the complexity parameter; in other examples, basic mathematical operations such as addition or weighted summation or positive correlation mapping methods can be used to combine the two, which will not be repeated here.

[0074] In step S203 , a negative correlation mapping is performed between the position of each path monitoring point and the spatial distance between the position of the path monitoring point corresponding to the end point of the corresponding curvature segment, and the negative correlation mapping result is used as the complexity weight.

[0075] Considering that no matter whether the path monitoring point is located on a curve or a straight line, the stacker crane needs to change speed appropriately when it is about to enter the next road section, such as the straight section entering the first half of the curve, the front half of the curve entering the second half of the curve, and the second half of the curve entering the straight section, the closer to the intersection between sections under different conditions, the greater the transportation complexity.

[0076] Therefore, as an example, the spatial distance between the position of each path monitoring point and the position of the path monitoring point corresponding to the end point of the curvature segment to which it belongs is added to the preset non-zero positive constant 1, and then the inverse operation is performed to obtain the complexity weight; the complexity weight evaluates the confidence level of transportation complexity from the perspective of the location of each path monitoring point.

[0077] In other examples, implementers can also use the difference in sequence numbers between path monitoring points to approximate the spatial distance, or use the spatial distance as an exponential function with the natural constant e as the base. In order to realize negative correlation mapping, other negative correlation mapping methods can also be used, which will not be described here.

[0078] Step S204: weighting the complexity parameter using the complexity weight, and using the weighted result as the transportation complexity at the corresponding path monitoring point.

[0079] As an example, the complexity weight is multiplied and combined with the complexity parameter, and the weighted result is the transportation complexity at the corresponding path monitoring point.

[0080] At this point, the transportation complexity of each monitoring point along the stacker crane's route is obtained.

[0081] Taking into account that the goods on the stacker's loading platform are usually affected by inertia, that is, the faster the speed changes, the more likely the goods are to tilt and fall due to inertia; and considering that when the stacking offset of the goods on the stacker's loading platform is more serious and the current transportation complexity is greater, if the transportation speed changes still greatly, the transportation speed at the corresponding path monitoring point will cause the stacker's operating stability to be lower; therefore, the embodiment of the present invention comprehensively considers the changes in transportation speed at the path monitoring points of all historical paths at the current path monitoring point, combined with the corresponding stacking offset sub-parameters and transportation complexity, to obtain the speed offset index of the stacker at the current path monitoring point; the speed offset index combines the stacking offset and path complexity at the path monitoring points of all historical paths from the perspective of transportation speed, and analyzes and quantifies the operating stability of the stacker at the current path monitoring point at the current transportation speed.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the speed offset index includes:

[0083] See also Figure 3, which shows a flow chart of a method for obtaining a velocity offset index provided by an embodiment of the present invention, specifically comprising:

[0084] Step S301: The product of the speed change rate of the transport speed at the path monitoring point of each historical route, the stacking offset sub-parameter and the transport complexity is used as the offset sub-parameter at the path monitoring point of each historical route; the offset sub-parameters at the path monitoring points of all historical routes are combined to obtain the first offset parameter.

[0085] Taking into account that at the current path monitoring point, the larger the stacking offset sub-parameter at the path monitoring points of all historical paths and the higher the transportation complexity, the worse the cargo stability at the path monitoring points of the historical path. If the speed change rate of the transportation speed at the path monitoring points of the historical path is still greater at this time, it means that the setting of the transportation speed at the path monitoring points of the historical path is unreasonable; and considering that the transportation speed at the path monitoring points of all historical paths is set unreasonably, it means that under the long-term cumulative influence, the operation stability of the current path monitoring point is lower, and the possibility of the current transportation speed setting being unreasonable is also greater.

[0086] The first offset parameter comprehensively evaluates the transportation conditions of all historical path monitoring points and the irrationality of the transportation speed at the current path monitoring point. The larger the first offset parameter is, the greater the transportation irrationality of the current transportation speed is, and the worse the operating stability of the stacker crane is.

[0087] As an example, the calculation formula of the first offset parameter is: ;in, is the first offset parameter; is the serial number of the monitoring point along the route; is the total number of monitoring points along the route, and is also the sequence number of the current monitoring point along the route; For stacker crane The rate of change of transport speed at each monitoring point on the route; For stacker crane The transportation complexity of the transportation speed at each path monitoring point; For stacker crane The stacking offset sub-parameters at each path monitoring point; For stacker crane The offset sub-parameter at each path monitoring point.

[0088] In other examples, implementers may also combine the speed change rate of the transport speed at the path monitoring point of each historical route, the stacking offset sub-parameter, and the transport complexity through basic mathematical operations such as addition or weighted summation, or positive correlation mapping, which will not be elaborated here.

[0089] Step S302: The variance of the transport speed at the monitoring points of all historical routes is used as a second offset parameter.

[0090] Considering that the more dramatic the changes in transport speed at all historical route monitoring points, the lower the stacker's operational stability, and that variance can reflect the severity of data changes, one embodiment of the present invention uses the variance of transport speed at all historical route monitoring points as a second offset parameter. The second offset parameter comprehensively considers the changes in transport speed at all route monitoring points and assesses the irrationality of the transport speed setting as of the current route monitoring point. The larger the second offset parameter, the greater the irrationality of the current transport speed, and the worse the stacker's operational stability.

[0091] It should be noted that, in other examples, implementers may also use other discrete parameter measurement methods such as standard deviation to measure the severity of changes in transportation speed, which will not be elaborated here.

[0092] Step S303 : obtaining a speed offset index according to the first offset parameter and the second offset parameter, wherein both the first offset parameter and the second offset parameter are positively correlated with the speed offset index.

[0093] As an example, the product of the first and second offset parameters is used as the speed offset index; the larger the speed offset index, the more unreasonable the transport speed setting at the current route monitoring point. In other examples, implementers can also combine the two using basic mathematical operations such as addition or weighted summation, or by using positive correlation mapping, which will not be detailed here.

[0094] Step S3: At the current path monitoring point, the stacking offset sub-parameters at all historical path monitoring points are integrated with the stacker load information and the speed offset index to obtain the speed adjustment parameter of the stacker; and the transport speed of the stacker is controlled according to the speed adjustment parameter.

[0095] Considering that the greater the difference in the stacking offset sub-parameters at path monitoring points on different historical routes, the more likely the stacker is to experience stacking offsets under the current stacking method and transportation mode as of the current path monitoring point, and the worse the stacker's operational stability. At the same time, if the speed offset index is also larger, the worse the current stacker's operational stability is. Therefore, at the current path monitoring point, the embodiment of the present invention combines the stacking offset sub-parameters at path monitoring points on all historical routes, the stacker's load information, and the speed offset index to obtain the stacker's speed adjustment parameter. The speed adjustment parameter reflects the stacker's operational stability at the current path monitoring point. The larger the speed adjustment parameter, the worse the operational stability. Subsequent adjustments to the transport speed should be careful to avoid large changes to ensure operational stability.

[0096] Preferably, in one embodiment of the present invention, the method for obtaining the speed adjustment parameter includes:

[0097] The product of the total number of loads and the total mass of the loads in the current transport task of the stacker is taken as the inertial instability factor of the stacker. At the current path monitoring point, the variance of the stacking offset sub-parameters at all historical path monitoring points and the product of the inertial instability factor are taken as the stacking offset index. After the product of the stacking offset index and the speed offset index are normalized, the normalized result is used as the speed adjustment parameter.

[0098] The calculation formula of speed adjustment parameter is: ;in, The speed adjustment parameter of the stacker at the current path monitoring point; is the total mass of the cargo carried by the stacker crane in the current transportation task; The total amount of cargo carried by the stacker in the current transport task; is the inertial instability factor of the stacker crane in the current transportation task; is the variance of the stacking offset sub-parameter at all historical path monitoring points at the current path monitoring point; is the speed deviation index of the stacker at the current path monitoring point; is the standard normalization function.

[0099] In the calculation formula of the speed adjustment parameter, linear normalization is specifically used for normalization. Other normalization methods can also be used, which will not be repeated here. The greater the total number and total mass of the load, the greater the inertial instability factor of the stacker. When the speed changes, the stack is more likely to be unstable and fall due to inertia, and the operational stability of the stacker is worse. The greater the variance of the stacking offset sub-parameter at the path monitoring points of all historical routes, the more likely the stacking offset is to occur at the current path monitoring point, and the worse the operational stability of the stacker. The larger the speed offset index, the unreasonable the current transport speed of the stacker, and the worse the operational stability of the stacker.

[0100] After obtaining the speed adjustment parameters of the stacker at the current path monitoring point, the transport speed of the stacker can be controlled according to the speed adjustment parameters.

[0101] Preferably, in one embodiment of the present invention, considering that the PID control method is a commonly used control method and is overly stable, the transport speed can be further controlled in real time based on the PID control algorithm; wherein the method of controlling the transport speed of the stacker crane according to the speed adjustment parameter includes:

[0102] The initial P parameters of the PID controller are obtained using the Ziegler–Nichols method. The speed adjustment parameter is subtracted from the constant 1 and used as the parameter adjustment weight. The initial P parameters are weighted using the parameter adjustment weight, and the weighted result is used as the modified P parameter. The stacker crane's transport speed is adjusted in real time based on the modified P parameter and the PID algorithm.

[0103] It should be noted that the initial P parameter refers to the proportional gain parameter in the PID control algorithm. The difference between the constant 1 and the speed adjustment parameter is used as the parameter adjustment weight of the proportional gain parameter. When the speed adjustment parameter is larger, the operational stability of the stacker at the current path monitoring point is worse. The subsequent transportation speed change should be appropriately smoothed. By adjusting the initial P parameter, that is, the proportional gain parameter, the adjustment process is made smoother and more stable. At the same time, the Ziegler–Nichols method is used to obtain the integral gain parameter I and differential gain parameter D of the PID controller, and then the P parameter, integral gain parameter I and differential gain parameter D are comprehensively corrected to adjust the transportation speed of the stacker in real time, so as to improve the transportation efficiency and operational stability at the same time.

[0104] It should be noted that the Ziegler–Nichols method and the PID algorithm are both existing technologies well known to those skilled in the art and will not be described in detail here.

[0105] The present invention also proposes an operation control system for an automated high-bay warehouse stacker crane. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps S1 to S3 of an automated high-bay warehouse stacker crane operation control method are implemented.

[0106] In summary, the present invention obtains the curvature curve of the stacker's transport path in the current transport task of the automated warehouse stacker, and obtains the load mass and transport speed of the stacker when it passes through each path monitoring point on the transport path; then analyzes and obtains the stacking offset sub-parameters of the stacker at each path monitoring point; further analyzes and obtains the transport complexity of the stacker at each path monitoring point; and then analyzes and obtains the speed offset index of the stacker at the current path monitoring point in combination with the changes in the transport speed at the path monitoring points of all historical paths; and then obtains the speed adjustment parameters of the stacker at the current path monitoring point; and controls the transport speed of the stacker according to the speed adjustment parameters. The present invention comprehensively evaluates the operational stability of the stacker during the current transport process by analyzing the stacking stability, path complexity, and transport speed changes in the stacker's historical paths, and further adjusts and controls the transport speed of the stacker, thereby improving the transport stability of the stacker.

[0107] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for controlling the operation of a stacker crane in an automated warehouse, characterized in that: The method comprises: In the current transport task of the stacker crane in the automated warehouse, the curvature curve of the stacker crane's transport path is obtained, and the load mass and transport speed of the stacker crane when it passes through each path monitoring point on the transport path are obtained; Based on the change in the load mass at each path monitoring point, a stacking offset sub-parameter of the stacker at each path monitoring point is obtained; based on the position of each path monitoring point in the transport path and the change in the curvature curve, the transport complexity of the stacker at each path monitoring point is obtained; at the current path monitoring point, the change in the transport speed at all path monitoring points in the past is combined with the corresponding stacking offset sub-parameter and the transport complexity to obtain the speed offset index of the stacker at the current path monitoring point; At the current path monitoring point, the stacking offset sub-parameters at all historical path monitoring points are integrated, combined with the stacker load information and the speed offset index, to obtain a speed adjustment parameter for the stacker; and the transport speed of the stacker is controlled according to the speed adjustment parameter. The method for obtaining the speed deviation index includes: The product of the speed change rate of the transport speed at the path monitoring point of each historical path, the stacking offset sub-parameter, and the transport complexity is used as the offset sub-parameter at the path monitoring point of each historical path; the offset sub-parameters at the path monitoring points of all historical paths are combined to obtain a first offset parameter; Using the variance of the transport speed at the path monitoring points of all historical routes as a second offset parameter; Obtaining a speed offset index according to the first offset parameter and the second offset parameter, wherein both the first offset parameter and the second offset parameter are positively correlated with the speed offset index; The method for obtaining the speed adjustment parameter includes: The product of the total number of loads and the total mass of the loads in the current transport task of the stacker is used as the inertial instability factor of the stacker; at the current path monitoring point, the product of the variance of the stacking offset sub-parameter at all path monitoring points in the historical path and the inertial instability factor is used as the stacking offset index; After normalizing the product of the stacking offset index and the speed offset index, the normalized result is used as the speed adjustment parameter.

2. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 1, wherein: The method for obtaining the stacking offset sub-parameter includes: At each path monitoring point, a stacking instability parameter at each path monitoring point is obtained based on a mass deviation of the load mass relative to a preset standard mass; the mass deviation is positively correlated with the stacking instability parameter; Obtaining a stacking instability weight at each path monitoring point according to a mass change rate of the load mass at each path monitoring point; the mass change rate is negatively correlated with the stacking instability weight; The stack instability parameter is weighted by using the stack instability weight, and the weighted result is used as the stack offset sub-parameter at the corresponding path monitoring point.

3. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 1, wherein: The method for obtaining the transportation complexity includes: According to the monotonic change of the curvature curve, the curvature curve is segmented to obtain all curvature segments; Obtaining a complexity parameter based on the concentrated features of the derivatives of the curvature in the curvature segment to which each path monitoring point belongs and the concentrated features of the curvature in the next adjacent curvature segment; Perform a negative correlation mapping between the position of each path monitoring point and the spatial distance between the position of the path monitoring point corresponding to the end point of the corresponding curvature segment, and use the negative correlation mapping result as the complexity weight; The complexity parameter is weighted using the complexity weight, and the weighted result is used as the transportation complexity at the corresponding path monitoring point.

4. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 3, wherein: The method for obtaining the curvature segmentation includes: The curvature curve is differentiated to obtain a curvature derivative curve; in the curvature derivative curve, the points where the curvature derivative is 0 and the curvature derivatives of adjacent curvature derivatives have different signs are corresponding to the curvature values are used as segmentation points of the curvature curve; all curvature segments of the curvature curve are obtained using all segmentation points.

5. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 3, wherein: The method for obtaining the complexity parameter includes: In the curvature segment to which each path monitoring point belongs, the mean of the curvature derivatives of all curvatures is normalized and used as the first complex parameter; in the curvature segment to which each path monitoring point belongs, the mean of all curvatures is used as the second complex parameter; and the product of the first complex parameter and the second complex parameter is used as the complexity parameter.

6. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 1, wherein: The method for controlling the transport speed of the stacker according to the speed adjustment parameter includes: The initial P parameter of the PID controller is obtained using the Ziegler–Nichols method; the speed adjustment parameter is subtracted from a constant 1 as a parameter adjustment weight; the initial P parameter is weighted using the parameter adjustment weight, and the weighted result is used as a modified P parameter; and the transport speed of the stacker is adjusted in real time based on the modified P parameter and the PID algorithm.

7. The method for controlling the operation of a stacker crane in an automated warehouse according to claim 1, wherein: The method for obtaining the curvature curve includes: Obtain the curvature value at each monitoring point on the transport path; construct a two-dimensional coordinate system with the serial number of the monitoring point as the horizontal axis parameter and the curvature value as the vertical axis parameter; map the curvature values corresponding to all monitoring points on the transport path to the two-dimensional coordinate system to fit the curvature curve.

8. An automated warehouse stacker operation control system, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for controlling the operation of a stacker crane in an automated warehouse are implemented as described in any one of claims 1 to 7.

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