Predictive control method, device, equipment and storage medium for remote control model

Abstract

The present application provides a predictive control method, device, equipment and storage medium for a remote control model. The method includes: monitoring whether the remote control model receives channel values ​​at continuous time points; in response to monitoring that the remote control model does not receive the channel value at the first time point, inputting the target curve segment in the first channel value curve into the predictive control algorithm to obtain the first channel value output by the predictive control algorithm; wherein the first channel value curve is constructed based on the channel value received by the remote control model before the first time point, the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, and the first channel value represents the channel value that is not received by the remote control model at the first time point; and the model is controlled according to the first channel value. The method provided in the present application can avoid sudden stops or abnormal behaviors of the remote control model in the event of signal loss or delay, thereby improving the stability of the system and user experience.

Description

Technical Field

[0001] The present application relates to the field of machine control, and in particular, to a predictive control method, device, equipment and storage medium for a remote control model. Background Art

[0002] At present, in the operation process of remote control models, the operator first receives the images and sounds generated by the model through sensory organs such as human eyes and ears. This information is transmitted to the brain for analysis and processing, and then the operator's brain controls the body to make corresponding actions to operate the controls of the remote control. The electronic part of the remote control samples the actions of these controls and converts them into digital signals. The software part then calculates the channel values ​​according to the user's settings and the sampled digital signals. Then, the radio frequency module of the remote control modulates these channel values ​​into wireless signals and sends them out. After receiving the wireless signal, the receiver demodulates it into digital signal channel values ​​and transmits the control information to the controlled device through wired signals or wired protocols. The controlled device performs corresponding actions according to the received control information, and the linkage of multiple controlled devices finally achieves the control effect expected by the user, that is, generates new model images and sounds and other information. In the entire control system, the receiver and the controlled device together constitute the receiving end of the control signal, and the operator inputs the operation instructions through the controls of the remote control, and the remote control sends the corresponding control signal, thereby completing the entire control process.

[0003] However, for model remote control scenarios, wireless transmission may experience packet loss during practical use, such as due to environmental wireless interference, hardware errors, receiver CRC check failure, etc. For such scenarios, once packet loss occurs when the receiver receives a data packet, the channel value of the previous cycle is used for this cycle. If packet loss continues for multiple cycles, the channel value received by the receiver last time is used for multiple cycles. If it continues for too many cycles, it is judged to be out of control, causing the model to have problems such as incoherent control and loss of some data.

[0004] Therefore, the present application provides a predictive control method, device, equipment and storage medium for a remote control model to solve one of the above technical problems. Summary of the invention

[0005] The purpose of this application is to provide a predictive control method, device, equipment and storage medium for a remote control model, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0006] According to the specific implementation of the present application, in a first aspect, the present application provides a predictive control method for a remote control model, comprising: monitoring whether the remote control model receives channel values ​​at continuous time points; wherein the channel value is value data sent by a remote control for model control of the remote control model; in response to monitoring that the remote control model does not receive the channel value at a first time point, inputting a target curve segment in a first channel value curve into a predictive control algorithm to obtain a first channel value output by the predictive control algorithm; wherein the first channel value curve is constructed based on the channel values ​​received by the remote control model before the first time point, and the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, the specified number being less than or equal to the total number of channel values ​​used to construct the first channel value curve, and the first channel value represents a prediction of the channel value that the remote control model has not received at the first time point; and performing model control according to the first channel value.

[0007] In one implementation, the predictive control algorithm is trained based on pre-collected remote control model receiving channel values ​​and remote control sending channel values.

[0008] In one embodiment, the method further includes: in response to monitoring that the remote control model receives a channel value, updating the first channel value curve based on the received channel value.

[0009] In one embodiment, the input of the predictive control algorithm also includes adjustable parameters based on operator input, and the adjustable parameters are used to replace existing parameters of the predictive control algorithm, or to change the operating mode of the predictive control algorithm, so as to increase or decrease the delay predicted by the predictive control algorithm, and / or make the channel value predicted by the predictive control algorithm fit or move away from a second channel value curve composed of the channel values ​​sent by the remote control; the method also includes: inputting the target curve segment and the adjustable parameters into the predictive control algorithm to obtain a second channel value output by the predictive control algorithm; wherein the second channel value represents the channel value predicted that the remote control model has not received at the first time point, and the second channel value is the same as or different from the first channel value; and performing model control according to the second channel value.

[0010] In one embodiment, the predictive control algorithm includes at least one of the following: derivative, anti-derivative algorithm; proportional-integral-differential algorithm; proportional-integral-differential deformation algorithm; big data model algorithm.

[0011] According to a specific embodiment of the present application, in a second aspect, the present application provides a predictive control device for a remote control model, comprising:

[0012] A monitoring unit, monitoring whether the remote control model receives channel values ​​at continuous time points; wherein the channel values ​​are value data sent by the remote control for model control of the remote control model; an input-output unit, in response to monitoring that the remote control model does not receive the channel value at a first time point, inputs a target curve segment in a first channel value curve into a predictive control algorithm to obtain a first channel value output by the predictive control algorithm; wherein the first channel value curve is constructed based on the channel values ​​received by the remote control model before the first time point, and the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, the specified number is less than or equal to the total number of channel values ​​used to construct the first channel value curve, and the first channel value represents a prediction of the channel value that the remote control model has not received at the first time point; a control unit, used to perform model control according to the first channel value.

[0013] In one implementation, the predictive control algorithm is trained based on pre-collected remote control model receiving channel values ​​and remote control sending channel values.

[0014] In one implementation, the monitoring unit is further configured to: in response to monitoring that the remote control model receives a channel value, update the first channel value curve based on the received channel value.

[0015] According to a specific implementation of the present application, in a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement any one of the methods described in the first aspect.

[0016] According to the specific implementation of the present application, in a fourth aspect, the present application provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the method described in any one of the first aspects.

[0017] Compared with the prior art, the above solution of the embodiment of the present application has at least the following beneficial effects:

[0018] The present application provides a predictive control method for a remote control model, which realizes smooth control of the remote control model by monitoring whether the remote control model receives channel values ​​at continuous time points and generating channel values ​​using a predictive control algorithm when the channel values ​​are not received. This method can avoid sudden stops or abnormal behaviors of the remote control model in the event of signal loss or delay, thereby improving the stability of the system and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart showing a predictive control method for a remote control model is shown;

[0020] Figure 2 A schematic diagram of the process of applying a predictive control algorithm to assist model control is shown;

[0021] Figure 3 A schematic diagram of the process of training a predictive control algorithm is shown;

[0022] Figure 4 A flow chart showing another predictive control method for a remote control model is shown;

[0023] Figure 5 A flow chart of a predictive control method for a remote control model according to an embodiment of the present application is shown;

[0024] Figure 6 A schematic diagram showing the effect of channel value processing in the related art is shown;

[0025] Figure 7 A schematic diagram showing the channel value processing effect of the present application is shown;

[0026] Figure 8 A unit block diagram of a predictive control device for a remote control model according to an embodiment of the present application is shown;

[0027] Fig. 9 The invention is a block diagram of an electronic device for predictive control of a remote control model according to an exemplary embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0029] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0030] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0031] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0032] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0033] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0034] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0035] At present, in the operation process of remote control models, the operator first receives the images and sounds generated by the model through sensory organs such as human eyes and ears. This information is transmitted to the brain for analysis and processing, and then the operator's brain controls the body to make corresponding actions to operate the controls of the remote control. The electronic part of the remote control samples the actions of these controls and converts them into digital signals. The software part then calculates the channel values ​​according to the user's settings and the sampled digital signals. Then, the radio frequency module of the remote control modulates these channel values ​​into wireless signals and sends them out. After receiving the wireless signal, the receiver demodulates it into digital signal channel values ​​and transmits the control information to the controlled device through wired signals or wired protocols. The controlled device performs corresponding actions according to the received control information, and the linkage of multiple controlled devices finally achieves the control effect expected by the user, that is, generates new model images and sounds and other information. In the entire control system, the receiver and the controlled device together constitute the receiving end of the control signal, and the operator inputs the operation instructions through the controls of the remote control, and the remote control sends the corresponding control signal, thereby completing the entire control process.

[0036] However, for model remote control scenarios, wireless transmission may experience packet loss during practical use, such as due to environmental wireless interference, hardware errors, receiver CRC check failure, etc. For such scenarios, once packet loss occurs when the receiver receives a data packet, the channel value of the previous cycle is used for this cycle. If packet loss continues for multiple cycles, the channel value received by the receiver last time is used for multiple cycles. If it continues for too many cycles, it is judged to be out of control, causing the model to have problems such as incoherent control and loss of some data.

[0037] In view of this, the present application provides a predictive control method for a remote control model to solve the above problems.

[0038] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0039] The embodiment provided in this application is an embodiment of a predictive control method for a remote control model.

[0040] Combine the following Figure 1 The embodiments of the present application are described in detail.

[0041] Figure 1 A flow chart of a predictive control method for a remote control model is shown, Figure 1 As shown, the method is applied to a remote control model, and includes the following steps S101 to S103.

[0042] Step S101, monitoring whether the remote control model receives channel values ​​at consecutive time points.

[0043] The channel value is value data sent by the remote controller for controlling the remote control model.

[0044] Step S102, in response to monitoring that the remote control model does not receive the channel value at the first time point, inputting the first channel value curve into the predictive control algorithm to obtain the first channel value output by the predictive control algorithm.

[0045] The first channel value curve is constructed based on the channel value received by the remote control model, and the first channel value represents the channel value that is not received by the predicted remote control model at the first time point.

[0046] Step S103: performing model control according to the first channel value.

[0047] In the embodiment of the present application, by monitoring whether the remote control model receives channel values ​​at consecutive time points, and generating channel values ​​using a predictive control algorithm when no channel values ​​are received, smooth control of the remote control model is achieved. This method can prevent the remote control model from suddenly stopping or behaving abnormally in the event of signal loss or delay, thereby improving the stability of the system and user experience.

[0048] For example, suppose that the remote control model encounters signal interference during flight, resulting in the failure to receive the channel value sent by the remote controller at a certain time point. At this time, the system can use the first channel value curve constructed by the received channel values, use the predictive control algorithm to predict the channel value at that time point, and continue to control the flight of the model accordingly. For example, if the channel values ​​received by the remote control model at the first few time points are 100, 102, and 105, respectively, then when the channel value is not received at the fourth time point, the predictive control algorithm can predict a reasonable channel value (such as 107) based on these historical data, so that the remote control model can continue to maintain stable flight.

[0049] Figure 2 A schematic diagram of the process of applying predictive control algorithm to assist model control is shown.

[0050] For example, Figure 2 As shown, the remote control model receives the channel value sent by the remote control at continuous time points through the receiver. When the remote control model receives the channel value at the first time point without packet loss, the channel value directly output is used for model control. Correspondingly, when the remote control model does not receive the channel value at the first time point, the first channel value curve received previously is used as input data, and the first channel value is predicted and output through the predictive control algorithm for model control.

[0051] In the embodiment of the present application, the predictive control algorithm is trained based on pre-collected remote control model receiving channel values ​​and remote control sending channel values.

[0052] In some embodiments, the remote control transmission channel value is obtained based on the control value of the remote control. In addition, in other embodiments, the remote control transmission channel value is obtained based on the measurement of the information sent by the remote control.

[0053] Figure 3 A schematic diagram of the process of training the predictive control algorithm is shown.

[0054] For example, Figure 3 As shown, firstly, a certain number of channel values ​​previously received by the remote control model are collected and a first channel value curve is constructed as input to the predictive control algorithm. When signal loss occurs, the predictive control algorithm predicts and outputs a predicted channel value based on the received first channel value curve. The system compares the predicted channel value with the channel value sent by the remote control, and evaluates the difference between the predicted value and the actual value through the comparison result. Then, according to the difference, the algorithm parameters or structure are adjusted to optimize the predictive control algorithm to ensure that the predicted channel value more accurately reflects the operator's intention, thereby improving the system's responsiveness and user experience.

[0055] Figure 4 A flowchart of another predictive control method for a remote control model is shown. Figure 4As shown, the process includes the following steps S401 to S404.

[0056] Step S401, monitoring whether the remote control model receives channel values ​​at consecutive time points.

[0057] The channel value is value data sent by the remote controller for controlling the remote control model.

[0058] Step S402, in response to monitoring that the remote control model does not receive the channel value at the first time point, inputting the first channel value curve into the predictive control algorithm to obtain the first channel value output by the predictive control algorithm.

[0059] The first channel value curve is constructed based on the channel value received by the remote control model, and the first channel value represents the channel value that is not received by the predicted remote control model at the first time point.

[0060] Step S403: performing model control according to the first channel value.

[0061] Step S404: in response to monitoring that the remote control model receives a channel value, updating the first channel value curve based on the received channel value.

[0062] In the embodiment of the present application, a dynamic update mechanism for the first channel value curve is added to ensure that the predictive control algorithm can reflect the latest operation data in a timely manner. This dynamic update mechanism improves the real-time performance and accuracy of the predictive control algorithm, and enables the remote control model to better adapt to the real-time operation of the operator.

[0063] In some embodiments, the channel value received by the receiver can be added to and updated in the case where the receiver has not lost packets. In other embodiments, the predicted first channel value can also be used to add and update the first channel value curve. In addition, when a new channel value is received again after the receiver loses packets, the channel value predicted and output by the prediction control algorithm when packet loss occurs is compared with the newly received channel value, and then one of the two is added to and updated in the first channel value curve, or the two values ​​are compromised by a specific algorithm and added to and updated in the channel value algorithm.

[0064] For example, during the operation of the remote control model, whenever a new channel value is received, the system will immediately update these new values ​​to the first channel value curve. For example, if the remote control model receives a new channel value of 110 at a certain point in time, the system will immediately add this value to the first channel value curve so that the predictive control algorithm can use this latest data in subsequent predictions. In this way, even if the operator suddenly changes the operating method, the predictive control algorithm can quickly adapt to ensure that the control of the remote control model is smoother and more accurate.

[0065] In an embodiment of the present application, the input of the predictive control algorithm also includes adjustable parameters based on operator input, and the adjustable parameters are used to replace existing parameters of the predictive control algorithm, or to change the operating mode of the predictive control algorithm, so as to increase or decrease the delay predicted by the predictive control algorithm, and / or make the channel value predicted by the predictive control algorithm fit into or move away from the second channel value curve formed by the channel value sent by the remote control.

[0066] Figure 5 A flow chart of a predictive control method for a remote control model according to an embodiment of the present application is shown, as shown in FIG. Figure 5 As shown, the process includes the following steps S501 to S503.

[0067] Step S501, monitoring whether the remote control model receives channel values ​​at consecutive time points.

[0068] The channel value is value data sent by the remote controller for controlling the remote control model.

[0069] Step S502, in response to monitoring that the remote control model does not receive the channel value at the first time point, input the target curve segment and the adjustable parameter into the predictive control algorithm to obtain a second channel value output by the predictive control algorithm.

[0070] The second channel value represents a channel value that is not received by the predicted remote control model at the first time point, and the second channel value is the same as or different from the first channel value.

[0071] Step S503: performing model control according to the second channel value.

[0072] The embodiment of the present application introduces adjustable parameters, allowing the operator to adjust the output of the predictive control algorithm according to his or her own operating habits. This method improves the flexibility of the system and enables the predictive control algorithm to better match the needs of different operators, thereby improving the user experience.

[0073] For example, suppose an operator finds that the output of the predictive control algorithm lags behind when using the remote control model. He can reduce the delay of the predictive control algorithm by adjusting the adjustable parameters. For example, the operator can set the adjustable parameter to a smaller value so that the channel value output by the predictive control algorithm is closer to the actual operation value. On the contrary, if the operator feels that the output of the predictive control algorithm is too aggressive, he can set the adjustable parameter to a larger value so that the channel value output by the predictive control algorithm is smoother. In this way, the operator can adjust the output of the predictive control algorithm according to his own operating habits and preferences, so as to obtain a better operating experience.

[0074] In the embodiments of the present application, when the adjustable parameters are used to replace the existing parameters of the predictive control algorithm, it is equivalent to adjusting the existing operation structure in the predictive control algorithm. In addition, the predictive control algorithm can also be configured with at least two operation modes. When the adjustable parameters are used to change the operation mode of the predictive control algorithm, it indicates that a switch has been made among several operation modes that have been configured for the predictive control algorithm. In addition to the above embodiments, the present application can also use other methods to modify the predictive control algorithm, which are not described in detail in this application.

[0075] In some embodiments, the aforementioned data source for updating the first channel value curve is also applicable to adjusting the adjustable parameters.

[0076] Figure 6 A schematic diagram of the channel value processing effect in the related art is shown.

[0077] Figure 7 A schematic diagram of the channel value processing effect of the present application is shown.

[0078] In related technologies, such as Figure 6 As shown, when packet loss occurs, the related art adopts the method of directly outputting the channel value received by the receiver, and there is a large difference between the channel value actually received by the receiver and the channel value that the receiver should receive. Figure 7 As shown, when packet loss occurs, the predictive control algorithm predicts and outputs the channel value that the receiver should receive based on the first channel value curve. The difference between the channel value and the actual channel value received by the receiver is small, and the performance is better in actual model control. Furthermore, the present application also introduces adjustable parameters to adjust the output of the predictive control algorithm. For example, Figure 7 The output data of prediction value 1 and prediction value 2 are channel values ​​processed by the predictive control algorithm, which aims to eliminate the delay from the operator's generation of operation ideas to the remote control model completing the corresponding actions. The difference lies in the different adjustable parameters. Prediction value 2 is more in line with the curve, meeting user needs, and can improve the real-time operation and user experience.

[0079] Illustratively, the predictive control algorithm in the present application includes at least one of the following.

[0080] ① Derivative and anti-derivative algorithms.

[0081] ②Proportional-integral-derivative (PID) algorithm.

[0082] ③Proportional-integral-differential deformation algorithm.

[0083] ④Big data model algorithm.

[0084] The above optional algorithms have their own characteristics and applicable scenarios, and this application can select one or several of them for use. Among them, for the scenario of selecting several of them as predictive control algorithms, this application can exemplarily use different algorithms to obtain prediction results respectively, and then give corresponding weights by verifying the authenticity of the results, so as to obtain the final predicted channel value by weighting the results. Of course, this is only an exemplary description, and the actual application method is not unique.

[0085] In addition, for the scenario where one of the two algorithms is selected as the predictive control algorithm, the present application provides implementation details of two of the algorithms. Figure 5 An exemplary description is given.

[0086] As one feasible embodiment, the present application may use derivative and anti-derivative methods to implement a predictive control algorithm.

[0087] First, prepare for algorithm debugging. Use an oscilloscope to measure the remote control control value (under normal operator control model conditions), use the remote control channel operation to convert it into a channel value, read the channel value received by the receiver through the program, and adjust the algorithm to make the algorithm output value closer to the algorithm target value. Further, the algorithm core solution is used to obtain the operation curve. Assume that the number of channel values ​​used in the algorithm operation is 3, for example, Figure 7 For example, select 3 points t 0 ,t 1 ,t 2 , corresponding to the three points x 0 、x 1 、x 2 As the channel values ​​used to form the target curve segment. Assuming that the delay time is known to be m, the time and value correspondence of "remote control value corresponding to channel value" is: t 0 -m corresponds to value x 0 ,t 1 -m corresponds to value x 1 ,t 2 -m corresponds to value x 2 , this correspondence can be used to find the channel value sent by the corresponding transmitter when training the algorithm. Calculate the derivative:

[0088] Actual output value calculation:

[0089] The calculation of k value can also be done by other methods, as long as the ultimate goal is to make the output curve closer to the target curve. After the budget is completed, all the calculation results are calculated to form a line segment. Furthermore, the output data curve obtained by the current algorithm and the input data curve operated by the operator are checked to adjust the k value calculation formula by the algorithm, such as using multi-order derivative calculation methods. Adjust the final data formula:

[0090] a and b are constant values ​​added. Adjust the number of "channel values ​​received by the receiver" used in the calculation, such as adjusting it to 4, 10, or 100. Add safety mechanisms in the calculation. If there is a large deviation between the predicted value and the actual value received later, correct it immediately and restart the calculation. Let the operator perform multiple operations, use the algorithm to simulate the output, and verify the accuracy of the algorithm operation. Finally, the operator adjusts the parameters. The operator can adjust the predicted value (increase or decrease the delay, predict the channel to deviate from or get closer to the target value) through the parameters provided by the algorithm to make it more in line with the operator's feel, improve the operator's operating effect and performance, and open parameter items. Switch the calculation formula of the k value and the final data formula: The a and b in the calculation use the "channel value received by the receiver" quantity.

[0091] As another feasible embodiment, the present application may adopt a big data model algorithm to implement a predictive control algorithm.

[0092] First, data collection is performed. The operator is asked to operate the remote control for a long time and many times, and the remote control control value is read through the program. The channel operation in the remote control is used to convert it into the channel value, and the channel value received by the receiver is read through the program. Secondly, model training is performed, and the collected data is put into the big data model, so that the big data model can continue to learn and simulate to find a suitable algorithm, such as setting an accuracy of 99% within the error range of 0.5% of the data. Finally, the operator adjusts the parameters. The calculation results are provided to the operator for adjustment to meet his or her operating feel adaptation.

[0093] The present application also provides an apparatus embodiment that is consistent with the above embodiment, which is used to implement the method steps of the above embodiment. The explanation based on the same name meaning is the same as the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0094] like Figure 8 As shown, the present application provides a predictive control device 800 for a remote control model, comprising:

[0095] The monitoring unit 801 monitors whether the remote control model receives channel values ​​at continuous time points. The channel value is value data sent by the remote control for model control of the remote control model. The input-output unit 802, in response to monitoring that the remote control model does not receive the channel value at the first time point, inputs the target curve segment in the first channel value curve into the predictive control algorithm to obtain the first channel value output by the predictive control algorithm. The first channel value curve is constructed based on the channel values ​​received by the remote control model before the first time point, and the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, the specified number is less than or equal to the total number of channel values ​​used to construct the first channel value curve, and the first channel value represents the channel value that the remote control model does not receive at the first time point. The control unit 803 is used to perform model control according to the first channel value.

[0096] In one implementation, the predictive control algorithm is trained based on pre-collected remote control model receiving channel values ​​and remote control sending channel values.

[0097] In one implementation, the monitoring unit 803 is further configured to: in response to monitoring that the remote control model receives a channel value, update the first channel value curve based on the received channel value.

[0098] Fig. 9 It is a block diagram of an electronic device 900 for predictive control of a remote control model according to an exemplary embodiment.

[0099] like Fig. 9 As shown, one embodiment of the present application provides an electronic device 900. The electronic device 900 includes a memory 901, a processor 902, and an input / output (I / O) interface 903. The memory 901 is used to store instructions. The processor 902 is used to call the instructions stored in the memory 901 to execute the method for predictive control of a remote control model in an embodiment of the present application. The processor 902 is connected to the memory 901 and the I / O interface 903, respectively, for example, through a bus system and / or other forms of connection mechanisms (not shown). The memory 901 can be used to store programs and data, including the program of the method for predictive control of a remote control model involved in the embodiment of the present application, and the processor 902 executes various functional applications and data processing of the electronic device 900 by running the program stored in the memory 901.

[0100] In the embodiment of the present application, the processor 902 can be implemented in at least one hardware form of a digital signal processor (Digital Signal Processing, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). The processor 902 can be a central processing unit (Central Processing Unit, CPU) or one or a combination of other forms of processing units with data processing capabilities and / or instruction execution capabilities.

[0101] The memory 901 in the embodiment of the present application may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD), etc.

[0102] In the embodiment of the present application, the I / O interface 903 may be used to receive input instructions (such as digital or character information, and key signal input related to user settings and function control of the electronic device 900), and may also output various information to the outside (such as images or sounds). In the embodiment of the present application, the I / O interface 903 may include one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a mouse, a joystick, a trackball, a microphone, a speaker, and a touch panel.

[0103] In some embodiments, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, any of the methods described above is performed.

[0104] In some embodiments, the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, any of the methods described above is performed.

[0105] Although operations are described in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in serial order, or that all shown operations be performed to achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0106] The methods, devices, equipment and storage media of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement various method steps. It should also be noted that the words "device" and "module" used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0107] Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations or procedures.

[0108] The foregoing description of the implementation of the present application has been given for the purpose of illustration and description. The foregoing description is not exhaustive nor is it intended to limit the present application to the exact form disclosed, and various variations and modifications may exist according to the above teachings or may be obtained from the practice of the present application. These embodiments are selected and described in order to illustrate the principles of the present application and its practical application, so that those skilled in the art can utilize the present application in various embodiments and various modifications suitable for the specific purpose contemplated.

[0109] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0110] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0111] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present application, it should not be understood as requiring the operations to be performed in the specific order or serial order shown, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0112] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to encompass any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the field of the present application that are not disclosed in the present application. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following scope of rights.

[0113] It should be understood that the present application is not limited to the precise structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the scope of the appended claims.

[0114] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A predictive control method for a remote control model, characterized in that: Applied to remote control models, including the following steps: Monitoring whether the remote control model receives a channel value at consecutive time points; wherein the channel value is value data sent by the remote controller for model control of the remote control model; In response to monitoring that the remote control model does not receive a channel value at a first time point, inputting a target curve segment in a first channel value curve into a predictive control algorithm to obtain a first channel value output by the predictive control algorithm; The first channel value curve is constructed based on the channel values ​​received by the remote control model before the first time point, the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, the specified number is less than or equal to the total number of channel values ​​used to construct the first channel value curve, and the first channel value represents a predicted channel value that the remote control model has not received at the first time point; Performing model control according to the first channel value; The predictive control algorithm is trained based on the pre-collected remote control model receiving channel value and the remote control sending channel value; The method further comprises: In response to monitoring that the remote control model receives a channel value, updating the first channel value curve based on the received channel value; The input of the predictive control algorithm also includes an adjustable parameter based on an operator input, wherein the adjustable parameter is used to replace an existing parameter of the predictive control algorithm, or to change an operation mode of the predictive control algorithm, so as to increase or decrease the delay predicted by the predictive control algorithm, and / or to make the channel value predicted by the predictive control algorithm fit in with or move away from a second channel value curve formed by the channel values ​​sent by the remote controller; The method further comprises: Inputting the target curve segment and the adjustable parameter into the predictive control algorithm to obtain a second channel value output by the predictive control algorithm; wherein the second channel value represents a channel value that is predicted not to be received by the remote control model at a first time point, and the second channel value is the same as or different from the first channel value; Model control is performed according to the second channel value.

2. The predictive control method according to claim 1, characterized in that: The predictive control algorithm includes at least one of the following: Derivative and antiderivative algorithms; Proportional-integral-derivative algorithm; Proportional-integral-derivative deformation algorithm; Big data model algorithms.

3. A predictive control device based on the predictive control method for a remote control model according to claim 1, characterized in that: include: A monitoring unit, used to monitor whether the remote control model receives a channel value at consecutive time points; wherein the channel value is value data sent by the remote controller for model control of the remote control model; an input-output unit, in response to monitoring that the remote control model does not receive a channel value at a first time point, inputting a target curve segment in a first channel value curve into a predictive control algorithm to obtain a first channel value output by the predictive control algorithm; wherein the first channel value curve is constructed based on the channel values ​​received by the remote control model before the first time point, the target curve segment is composed of a specified number of channel values ​​last received by the remote control model, the specified number is less than or equal to the total number of channel values ​​used to construct the first channel value curve, and the first channel value represents a channel value predicted not to be received by the remote control model at the first time point; A control unit is used to perform model control according to the first channel value.

4. The predictive control device according to claim 3, characterized in that: The predictive control algorithm is trained based on pre-collected remote control model receiving channel values ​​and remote control sending channel values.

5. The predictive control device according to claim 3, characterized in that: The monitoring unit is also used for: In response to monitoring that the remote control model receives a channel value, the first channel value curve is updated based on the received channel value.

6. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 2.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 2 is implemented.

Images