Control method, device and equipment of material transport vehicle, medium and product

Abstract

The invention discloses a control method, device and equipment of a material transport vehicle, a medium and a product. The method comprises the steps that the resonant frequency and a to-be-processed data set of the material transport vehicle are acquired; based on the resonant frequency of the material transport vehicle, performing mean value processing on the to-be-processed data in the to-be-processed data set to obtain current expected torques at a plurality of time points; a weight sequence is determined, values in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is gradually increased from small to large and then gradually decreased from large to small; determining a target torque according to the weight sequence and the current expected torque at each time point; and the target torque is sent to a driver, so that the driver controls a motor to provide power output based on the target torque, and through the technical scheme, the influence of the resonance phenomenon on the material transport vehicle can be reduced.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of vehicle technology, and in particular, to a control method, device, equipment, medium and product for a material transport vehicle. Background Art

[0002] Resonance may occur when the drive correction frequency of the material transporter is close to the resonant frequency of the material transporter. Resonance is a physical phenomenon that significantly increases the vibration amplitude of the drive output torque when the drive correction frequency of the material transporter is close to the resonant frequency of the material transporter. This is because the energy of the system is concentrated at a specific frequency, resulting in an amplified amplitude.

[0003] If the amplitude of the resonance is too large, it may cause damage to the material transport vehicle. Therefore, how to reduce the impact of the resonance phenomenon on the material transport vehicle has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] Embodiments of the present invention provide a control method, device, equipment, medium and product for a material transport vehicle to reduce the influence of resonance on the material transport vehicle.

[0005] According to one aspect of the present invention, a control method for a material transport vehicle is provided, comprising:

[0006] Obtain the resonant frequency of the material transport vehicle and the data set to be processed;

[0007] Based on the resonance frequency of the material transport vehicle, performing mean processing on the data to be processed in the data set to be processed to obtain current expected torques at multiple time points;

[0008] Determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small;

[0009] Determining a target torque according to the weight sequence and the current expected torque at each time point;

[0010] The target torque is sent to the driver, so that the driver controls the motor to provide power output based on the target torque.

[0011] According to another aspect of the present invention, a control device for a material transport vehicle is provided, the control device for the material transport vehicle comprising:

[0012] An acquisition module, used for acquiring the resonance frequency of the material transport vehicle and a set of data to be processed;

[0013] A processing module, configured to perform mean processing on the data to be processed in the data set to be processed based on the resonance frequency of the material transport vehicle, so as to obtain current expected torques at multiple time points;

[0014] A weight sequence determination module is used to determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small;

[0015] A target torque determination module, used to determine the target torque according to the weight sequence and the current expected torque at each time point;

[0016] The control module is used to send a target torque to the driver, so that the driver controls the motor to provide power output based on the target torque.

[0017] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method of the material transport vehicle described in any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the control method of a material transport vehicle according to any embodiment of the present invention when executed.

[0022] According to another aspect of the present invention, a computer program product is provided. When the computer program is executed by a processor, the computer program implements the control method of the material transport vehicle as described in any one of the embodiments of the present invention.

[0023] The embodiment of the present invention obtains the resonance frequency of the material transport vehicle and a set of data to be processed; based on the resonance frequency of the material transport vehicle, performs mean processing on the data to be processed in the set of data to be processed to obtain the current expected torque at multiple time points; determines a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small; determines the target torque according to the weight sequence and the current expected torque at each time point; sends the target torque to the driver, so that the driver controls the motor to provide power output based on the target torque, thereby reducing the impact of the resonance phenomenon on the material transport vehicle.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 is a flow chart of a control method of a material transport vehicle in an embodiment of the present invention;

[0027] Figure 2 is a schematic structural diagram of a control device for a material transport vehicle in an embodiment of the present invention;

[0028] Figure 3 It is a structural schematic diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a control method for a material transport vehicle provided in an embodiment of the present invention. This embodiment can be applied to the case of controlling a material transport vehicle. The method can be executed by a control device for a material transport vehicle in an embodiment of the present invention. The device can be implemented in software and / or hardware, such as Figure 1 As shown, the method specifically comprises the following steps:

[0034] S110, obtaining the resonance frequency of the material transport vehicle and a set of data to be processed.

[0035] In this embodiment, the set of data to be processed includes: historical data to be processed and predicted data to be processed at multiple time points. It should be noted that the number of time points is related to the resonance frequency. For example, if the resonance frequency is 100ms and the predicted frequency is 10ms, the number of time points is 10. The set of data to be processed includes: predicted data at 10 time points, predicted data at 9 time points, predicted data at 8 time points, predicted data at 7 time points, predicted data at 6 time points, predicted data at 5 time points, predicted data at 4 time points, predicted data at 3 time points, predicted data at 2 time points, predicted data at 1 time point, historical data at 0 time point, historical data at -1 time point, historical data at -2 time point, historical data at -3 time point, historical data at -4 time point, historical data at -5 time point, historical data at -6 time point, historical data at -7 time point, and historical data at -8 time point.

[0036] In this embodiment, the resonance frequency of the material transport vehicle may be obtained by obtaining a driver correction frequency of the material transport vehicle, and if resonance occurs, determining the driver correction frequency of the material transport vehicle as the resonance frequency of the material transport vehicle.

[0037] In this embodiment, the method for obtaining the set of data to be processed can be: obtaining relevant data of the material transport vehicle, determining the historical data to be processed and the predicted data to be processed at multiple time points based on the relevant data of the material transport vehicle, and generating a set of data to be processed based on the historical data to be processed and the predicted data to be processed at multiple time points.

[0038] Optionally, obtain the resonant frequency of the material transport vehicle, including:

[0039] Obtain the drive output torque variation curve of the material transport vehicle;

[0040] Determining a driver correction frequency of the material transport vehicle according to a driver output torque variation curve of the material transport vehicle;

[0041] If it is determined according to the driver output torque change curve of the material transport vehicle that the change in the driver output torque meets a preset condition, the driver correction frequency of the material transport vehicle is determined as the resonance frequency of the material transport vehicle.

[0042] In this embodiment, the horizontal coordinate of the driver output torque variation curve of the material transport vehicle is time, and the vertical coordinate is the driver output torque. According to the driver output torque variation curve of the material transport vehicle, the driver correction frequency of the material transport vehicle can be determined by: determining the driver correction frequency of the material transport vehicle according to the time point at which the driver output torque variation amplitude in the driver output torque variation curve of the material transport vehicle tends to be stable.

[0043] In this embodiment, the preset condition may be a divergent oscillation. If it is determined according to the driver output torque change curve of the material transport vehicle that the change in the driver output torque satisfies the divergent oscillation, it is determined that a resonance phenomenon occurs. Since a resonance phenomenon occurs when the driver correction frequency is close to the resonance frequency, the driver correction frequency of the material transport vehicle may be determined as the resonance frequency of the material transport vehicle when a resonance phenomenon occurs.

[0044] Optionally, the set of data to be processed includes: multiple groups of historical data to be processed and predicted data to be processed at multiple time points;

[0045] Accordingly, obtaining the data set to be processed includes:

[0046] Obtain historical operating and environmental data of material transport vehicles;

[0047] The historical operation data and environmental data of the material transport vehicle are processed based on the algorithm to obtain multiple groups of historical data to be processed and predicted data to be processed at multiple time points, wherein the data to be processed include: the acceleration of the material transport vehicle and the speed of the material transport vehicle.

[0048] In this embodiment, the historical operating data and environmental data of the material transport vehicle are processed based on the algorithm to obtain multiple sets of historical data to be processed and predicted data to be processed at multiple time points. The method can be: determine the number of time points according to the resonant frequency of the material transport vehicle and the predicted frequency of the algorithm, and process the historical operating data and environmental data of the material transport vehicle based on the algorithm to obtain multiple sets of historical data to be processed and predicted data to be processed at multiple time points corresponding to the number of time points. For example, if the resonance frequency of the material transport vehicle is 100ms and the prediction frequency of the algorithm is 10ms, then the number of time points is determined to be the ratio of the resonance frequency of the material transport vehicle to the prediction frequency of the algorithm, that is, 10. Based on the algorithm, the historical operation data and environmental data of the material transport vehicle are processed to obtain prediction data at time point 1, prediction data at time point 2, prediction data at time point 3, prediction data at time point 4, prediction data at time point 5, prediction data at time point 6, prediction data at time point 7, prediction data at time point 8, prediction data at time point 9, prediction data at time point 10, and multiple sets of historical data to be processed. The multiple sets of historical data to be processed include: historical data at time point 0, historical data at time point -1, historical data at time point -2, historical data at time point -3, historical data at time point -4, historical data at time point -5, historical data at time point -6, historical data at time point -7, and historical data at time point -8.

[0049] S120, based on the resonance frequency of the material transport vehicle, performing mean processing on the data to be processed in the data set to be processed to obtain current expected torques at multiple time points.

[0050] In this embodiment, based on the resonance frequency of the material transport vehicle, the method of performing mean processing on the data to be processed in the data set to be processed to obtain the current expected torque at multiple time points can be: performing mean processing on the data to be processed at the Ith time point in the data set to be processed to obtain the current expected torque at the Ith time point, wherein the data to be processed at the Ith time point includes: the predicted data at the Ith time point and the first number of data to be processed before the Ith time point. I is a positive integer, I is greater than 0, and less than a first value, wherein the first value is equal to the ratio of the resonance frequency to the algorithm prediction frequency.

[0051] In this embodiment, based on the resonant frequency of the material transport vehicle, the data to be processed in the data set to be processed is averaged to obtain the current expected torque at multiple time points in the following manner: based on the resonant frequency of the material transport vehicle, the data to be processed in the data set to be processed is averaged to obtain the target acceleration and target speed at multiple time points, and the current expected torque at multiple time points is determined according to the target acceleration and target speed at multiple time points. Based on the resonant frequency of the material transport vehicle, the data to be processed in the data set to be processed is averaged to obtain the current expected torque at multiple time points in the following manner: the data to be processed in the data set to be processed includes: the acceleration to be processed and the speed to be processed, and the torque to be processed is determined according to the acceleration to be processed and the speed to be processed in the data set to be processed. Based on the resonant frequency of the material transport vehicle, the torque to be processed in the data set to be processed is averaged to obtain the current expected torque at multiple time points.

[0052] In a specific example, mean processing is performed on the data to be processed at the first time point in the data set to be processed to obtain the current expected torque at the first time point. The data to be processed at the first time point include: historical data at time point 0, historical data at time point -1, historical data at time point -2, historical data at time point -3, historical data at time point -4, historical data at time point -5, historical data at time point -6, historical data at time point -7, historical data at time point -8 and predicted data at the first time point. The mean of the historical data at time point 0, historical data at time point -1, historical data at time point -2, historical data at time point -3, historical data at time point -4, historical data at time point -5, historical data at time point -6, historical data at time point -7, historical data at time point -8 and predicted data at the first time point is determined as the current expected torque at the first time point. The data to be processed at the second time point in the set of data to be processed are processed by averaging to obtain the current expected torque at the second time point. The data to be processed at the second time point include: historical data at time point 0, historical data at time point -1, historical data at time point -2, historical data at time point -3, historical data at time point -4, historical data at time point -5, historical data at time point -6, historical data at time point -7, predicted data at the first time point, and predicted data at the second time point. The average of the historical data at time point 0, historical data at time point -1, historical data at time point -2, historical data at time point -3, historical data at time point -4, historical data at time point -5, historical data at time point -6, historical data at time point -7, predicted data at the first time point, and predicted data at the second time point is determined as the current expected torque at the second time point. By analogy, the current expected torque at multiple time points is obtained.

[0053] Optionally, based on the driver correction frequency of the material transport vehicle, the data to be processed in the data set to be processed are averaged to obtain current expected torques at multiple time points, including:

[0054] Determine multiple historical torques at each time point according to multiple groups of historical data to be processed at each time point;

[0055] Determine the predicted torque at each time point according to the predicted data to be processed at each time point;

[0056] Based on the driver correction frequency of the material transport vehicle, a plurality of historical torques and predicted torques at each time point in the to-be-processed data set are averaged to obtain a current expected torque at each time point.

[0057] In this embodiment, the data to be processed include: acceleration and speed; according to the multiple groups of historical data to be processed at each time point, the method of determining the multiple historical torques at each time point can be: according to the multiple groups of historical accelerations and historical speeds at each time point, the multiple historical torques at each time point are determined. For example, according to the multiple groups of historical accelerations, historical speeds and the first formula at each time point, the multiple historical torques at each time point are determined, wherein the first formula is:

[0058] F = m*a+k*V;

[0059] Among them, F is the predicted torque, m is the weight of the material transport vehicle, a is the predicted acceleration of the material transport vehicle, V is the predicted speed of the material transport vehicle, and k is the drag coefficient.

[0060] In this embodiment, the method of determining the predicted torque at each time point according to the predicted data to be processed at each time point may be: determining the predicted torque at each time point according to the predicted acceleration and predicted speed at each time point.

[0061] In this embodiment, based on the driver correction frequency of the material transport vehicle, the multiple historical torques and predicted torques at each time point in the data set to be processed are averaged to obtain the current expected torque at each time point. The method may be: the number of time points is determined based on the driver correction frequency of the material transport vehicle, and the multiple historical torques and predicted torques at each time point corresponding to the number of time points in the data set to be processed are averaged to obtain the current expected torque at each time point. For example, the historical torque at time point 0, the historical torque at time point -1, the historical torque at time point -2, the historical torque at time point -3, the historical torque at time point -4, the historical torque at time point -5, the historical torque at time point -6, the historical torque at time point -7, the historical torque at time point -8 and the predicted torque at time point 1 are averaged to determine the current expected torque at time point 1.

[0062] Optionally, determining the predicted torque at each time point according to the predicted data to be processed at each time point includes:

[0063] Determining the predicted torque at each time point based on the predicted acceleration of the material transport vehicle at each time point, the predicted speed of the material transport vehicle at each time point, and the first formula;

[0064] Among them, the first formula is:

[0065] F = m*a+k*V;

[0066] Among them, F is the predicted torque, m is the weight of the material transport vehicle, a is the predicted acceleration of the material transport vehicle, V is the predicted speed of the material transport vehicle, and k is the drag coefficient.

[0067] In this embodiment, the deadweight of the material transport vehicle and the resistance coefficient are both pre-acquired parameters.

[0068] S130, determining a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small.

[0069] In this embodiment, the weight sequence can be determined by: determining the weight sequence based on an S-shaped curve, and the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small. For example, the weight sequence can be: 9, 36, 82, 146, 227, 227, 146, 82, 36, 9.

[0070] In this embodiment, each value in the weight sequence can customize the S-curve amplitude according to needs.

[0071] S140: Determine a target torque according to the weight sequence and the current expected torque at each time point.

[0072] In this embodiment, based on the weight sequence and the current expected torque at each time point, the target torque can be determined by taking the ratio of the sum of the products of each numerical value in the weight sequence and the current expected torque at the corresponding time point to the sum of each numerical value in the weight sequence as the target torque.

[0073] In this embodiment, since the average value is taken within a specific time (resonance frequency), some surges and oscillations are removed. In the early stage of the S curve, it increases slowly, in the middle stage, it increases fixedly to improve the response phenomenon, and in the later stage, it converges slowly. Compared with the existing filtering method, it reduces the complex formula calculation and maintains the desired filtering effect.

[0074] Optionally, determining the target torque according to the weight sequence and the current expected torque at each time point includes:

[0075] Determine the weight corresponding to each time point according to the weight sequence;

[0076] The target torque is determined as the ratio of the sum of the products of the current expected torque at each time point and the weight corresponding to each time point to the sum of the values ​​in the weight sequence.

[0077] In this embodiment, the method of determining the weight corresponding to each time point according to the weight sequence can be: determine each value in the weight sequence as the weight corresponding to each time point in turn. For example, if the weight sequence is: 9, 36, 82, 146, 227, 227, 146, 82, 36, 9. Then 9 is determined as the weight corresponding to the first time point, 36 is determined as the weight corresponding to the second time point, 82 is determined as the weight corresponding to the third time point, 146 is determined as the weight corresponding to the fourth time point, 227 is determined as the weight corresponding to the fifth time point, 227 is determined as the weight corresponding to the sixth time point, 146 is determined as the weight corresponding to the seventh time point, 82 is determined as the weight corresponding to the eighth time point, 36 is determined as the weight corresponding to the ninth time point, and 9 is determined as the weight corresponding to the tenth time point.

[0078] In a specific example, the weight sequence is first determined. The values ​​in the weight sequence first increase from small to large, and then decrease from large to small. The total value in the weight sequence is 1000, and the weight sequence is 9, 36, 82, 146, 227, 227, 146, 82, 36, 9. At t = 10, the corresponding relationship between the weight and the current expected torque at each time point is shown in Table 2:

[0079] Table 2

[0080]

[0081] The current expected torque at the first time point is 0, the current expected torque at the second time point is 5, the current expected torque at the third time point is 5, the current expected torque at the fourth time point is 6, the current expected torque at the fifth time point is 7, the current expected torque at the sixth time point is 7, the current expected torque at the seventh time point is 6, the current expected torque at the eighth time point is 5, the current expected torque at the ninth time point is 4, and the current expected torque at the tenth time point is 3.

[0082] t=10, target torque=(9×0+36×5+82×5+146×6+227×7+227×7+146×6+82×5+36×4+9×3) / 1000.

[0083] t=11, the corresponding relationship between the weight and the current expected torque at each time point is shown in Table 3:

[0084] Table 3

[0085]

[0086] t=11, target torque=(9×5+36×5+82×6+146×7+227×7+227×6+146×5+82×4+36×3+9×1) / 1000.

[0087] By analogy, this embodiment of the present invention is not limited to this.

[0088] S150, sending a target torque to the driver, so that the driver controls the motor to provide power output based on the target torque.

[0089] The technical solution of the embodiment of the present invention first performs mean processing on the data to be processed in the data set to be processed based on the resonance frequency, and then cooperates with the weighted average filtering of the S-curve to calculate the target torque, and sends the target torque to the driver so that the driver controls the motor to provide power output based on the target torque, thereby reducing the impact of the resonance phenomenon on the material transport vehicle.

[0090] The technical solution of this embodiment is to obtain the resonance frequency of the material transport vehicle and a set of data to be processed; based on the resonance frequency of the material transport vehicle, perform mean processing on the data to be processed in the set of data to be processed to obtain the current expected torque at multiple time points; determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small; determine the target torque based on the weight sequence and the current expected torque at each time point; send the target torque to the driver, so that the driver controls the motor to provide power output based on the target torque, which can reduce the impact of the resonance phenomenon on the material transport vehicle.

[0091] Embodiment 2

[0092] Figure 2 This is a schematic diagram of the structure of a control device for a material transport vehicle provided by an embodiment of the present invention. This embodiment can be applied to the control of a material transport vehicle. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides a control function for a material transport vehicle, such as Figure 2 As shown, the control device of the material transport vehicle specifically includes: an acquisition module 210 , a processing module 220 , a weight sequence determination module 230 , a target torque determination module 240 and a control module 250 .

[0093] Among them, the acquisition module is used to obtain the resonance frequency of the material transport vehicle and the data set to be processed;

[0094] A processing module, configured to perform mean processing on the data to be processed in the data set to be processed based on the resonance frequency of the material transport vehicle, so as to obtain current expected torques at multiple time points;

[0095] A weight sequence determination module is used to determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small;

[0096] A target torque determination module, used to determine the target torque according to the weight sequence and the current expected torque at each time point;

[0097] The control module is used to send a target torque to the driver, so that the driver controls the motor to provide power output based on the target torque.

[0098] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0099] Embodiment 3

[0100] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0101] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a control method for a material transport vehicle.

[0104] In some embodiments, the control method of the material transport vehicle may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the control method of the material transport vehicle described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the control method of the material transport vehicle in any other appropriate manner (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0110] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0112] An embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the control method of the material transport vehicle according to any embodiment of the present invention is implemented.

[0113] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A control method for a material transport vehicle, characterized in that: include: Obtain the resonant frequency of the material transport vehicle and the data set to be processed; Based on the resonance frequency of the material transport vehicle, performing mean processing on the data to be processed in the data set to be processed to obtain current expected torques at multiple time points; Determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small; Determining a target torque according to the weight sequence and the current expected torque at each time point; The target torque is sent to the driver, so that the driver controls the motor to provide power output based on the target torque.

2. The method according to claim 1, characterized in that Determining the target torque according to the weight sequence and the current expected torque at each time point includes: Determine the weight corresponding to each time point according to the weight sequence; The target torque is determined as the ratio of the sum of the products of the current expected torque at each time point and the weight corresponding to each time point to the sum of the values ​​in the weight sequence.

3. The method according to claim 1, characterized in that Get the resonant frequency of the material transport vehicle, including: Obtain the drive output torque variation curve of the material transport vehicle; Determining a driver correction frequency of the material transport vehicle according to a driver output torque variation curve of the material transport vehicle; If it is determined according to the driver output torque change curve of the material transport vehicle that the change in the driver output torque meets a preset condition, the driver correction frequency of the material transport vehicle is determined as the resonance frequency of the material transport vehicle.

4. The method according to claim 1, characterized in that: The set of data to be processed includes: multiple groups of historical data to be processed and predicted data to be processed at multiple time points; Accordingly, obtaining the data set to be processed includes: Obtain historical operating and environmental data of material transport vehicles; The historical operation data and environmental data of the material transport vehicle are processed based on the algorithm to obtain multiple groups of historical data to be processed and predicted data to be processed at multiple time points, wherein the data to be processed include: the acceleration of the material transport vehicle and the speed of the material transport vehicle.

5. The method according to claim 4, characterized in that Based on the driver correction frequency of the material transport vehicle, the data to be processed in the data set to be processed are averaged to obtain the current expected torque at multiple time points, including: Determine multiple historical torques at each time point according to multiple groups of historical data to be processed at each time point; Determine the predicted torque at each time point according to the predicted data to be processed at each time point; Based on the driver correction frequency of the material transport vehicle, a plurality of historical torques and predicted torques at each time point in the to-be-processed data set are averaged to obtain a current expected torque at each time point.

6. The method according to claim 5, characterized in that According to the predicted data to be processed at each time point, the predicted torque at each time point is determined, including: Determining the predicted torque at each time point based on the predicted acceleration of the material transport vehicle at each time point, the predicted speed of the material transport vehicle at each time point, and the first formula; Among them, the first formula is: F = m*a + k*V; Among them, F is the predicted torque, m is the weight of the material transport vehicle, a is the predicted acceleration of the material transport vehicle, V is the predicted speed of the material transport vehicle, and k is the drag coefficient.

7. A control device for a material transport vehicle, characterized in that: include: An acquisition module, used for acquiring the resonance frequency of the material transport vehicle and a set of data to be processed; A processing module, configured to perform mean processing on the data to be processed in the data set to be processed based on the resonance frequency of the material transport vehicle, so as to obtain current expected torques at multiple time points; A weight sequence determination module is used to determine a weight sequence, wherein the numerical values ​​in the weight sequence are fitted to obtain an S-shaped curve, and the change trend of the S-shaped curve is: first increasing from small to large, and then decreasing from large to small; A target torque determination module, used to determine the target torque according to the weight sequence and the current expected torque at each time point; The control module is used to send a target torque to the driver, so that the driver controls the motor to provide power output based on the target torque.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method of the material transport vehicle according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the control method of the material transport vehicle according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the control method of the material transport vehicle according to any one of claims 1-6.

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