Electric power overhaul method and device based on hybrid dynamic motion primitives

Abstract

The invention discloses an electric power overhaul method and device based on hybrid dynamic motion primitives. The method comprises the following steps: acquiring a plurality of demonstration tracks of the equipotential robot; performing dynamic motion primitive modeling on each demonstration track to obtain a corresponding initial hybrid dynamic primitive model; evaluating the fluency of the demonstration trajectory according to the evaluation indexes to determine the model weight of each initial hybrid dynamic primitive model; determining a hybrid dynamic primitive model according to the initial hybrid dynamic primitive model and the model weight; the electric power maintenance task is written into the equipotential robot, so that the equipotential robot generates a maintenance track when receiving the electric power maintenance task; and controlling the equipotential robot to execute electric power overhaul operation based on the overhaul track. The technical problems that in the prior art, an equipotential robot can only learn a single track, and in the electric power overhaul process, due to the fact that too many inflection points exist in the generated track, a bucket arm vehicle violently shakes, the overhaul efficiency is low, and the working quality is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power maintenance, and in particular to an electric power maintenance method and device based on hybrid dynamic motion primitives. Background Art

[0002] In the prior art, equipotential operation robots for distribution networks are gradually used for power maintenance. The traditional trajectory programming method requires presetting a series of trajectory points, and then controlling the robot to pass through these points in sequence to achieve trajectory planning; this method has two problems in the actual operation of the equipotential robot. First, due to the long distance at high altitude, it is impossible to accurately teach, and the efficiency is low; second, the trajectory generated by this method has too many turning points, which will cause the boom truck to shake violently.

[0003] The existing robot trajectory learning method based on the probabilistic motion primitive model can only learn a single trajectory, which is random and not suitable for the complex and changeable power industry. In addition, the number of Gaussian basis functions is difficult to determine. Too many basis functions can easily lead to overfitting, which is not conducive to the generalization of the subsequent trajectory, while too few basis functions will not be able to fit the demonstration trajectory well.

[0004] In the above-mentioned related technologies, the medium-potential robot can only learn a single trajectory, and during the power maintenance process, the generated trajectory has too many turning points, causing the boom truck to shake violently, resulting in low maintenance efficiency and poor work quality. No effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a power maintenance method and device based on hybrid dynamic motion primitives, so as to at least solve the technical problems in the related art that a medium-potential robot can only learn a single trajectory, and during the power maintenance process, the generated trajectory has too many turning points, causing the boom truck to shake violently, resulting in low maintenance efficiency and poor work quality.

[0006] According to one aspect of an embodiment of the present invention, there is provided a power maintenance method based on hybrid dynamic motion primitives, comprising: obtaining multiple demonstration trajectories of an equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectories are motion trajectories of the equipotential robot during power maintenance; performing dynamic motion primitive modeling according to each of the demonstration trajectories to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories; evaluating the fluency of each of the demonstration trajectories according to an evaluation index to obtain an evaluation score; determining a model weight of the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories according to the evaluation score, wherein the model weight of the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories is determined according to the evaluation score. The model weight refers to the importance of each of the initial hybrid dynamic primitive models in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; the hybrid dynamic primitive model is determined according to the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectory and the model weight; the hybrid dynamic primitive model is written into the equipotential robot, so that when the equipotential robot receives the power maintenance task, it uses the hybrid dynamic primitive model to generate the maintenance trajectory according to the maintenance position indicated by the power maintenance task; the equipotential robot is controlled to perform the power maintenance operation based on the maintenance trajectory.

[0007] Optionally, multiple demonstration trajectories of the equipotential robot are obtained, including: obtaining multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determining the historical maintenance trajectories as the demonstration trajectories; and / or obtaining multiple demonstration trajectories by controlling the equipotential robot to simulate the power maintenance process.

[0008] Optionally, dynamic motion primitive modeling is performed according to each of the demonstration trajectories to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories, including: performing dynamic motion primitive modeling on each of the demonstration trajectories according to a proportional-differential controller and a trajectory shape learner to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories, wherein the initial hybrid dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to the time t, representing acceleration, α y represents the proportional parameter in the proportional-derivative controller, β yrepresents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to reach; the nonlinear term in the trajectory shape learner is adjusted to obtain the adjusted nonlinear term; on the basis of obtaining the initial hybrid dynamic primitive model, the convergence speed of the demonstration trajectory is adjusted based on the nonlinear term to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

[0009] Optionally, adjusting the nonlinear term in the trajectory shape learner to obtain the adjusted nonlinear term includes: determining a radial basis function using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the ith radial basis function, σ a represents the width of the a-th radial basis function, c a represents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term through linear superposition; according to the linear superposition of multiple radial basis functions, the adjusted nonlinear term is determined by using a second formula, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

[0010] Optionally, the evaluation index includes: a first evaluation index, a second evaluation index and a third evaluation index, and the fluency of each demonstration trajectory is evaluated according to the evaluation index to obtain an evaluation score, including at least one of the following: the fluency of the demonstration trajectory is evaluated using a third formula according to the first evaluation index to obtain a first evaluation score, wherein the first evaluation index is the first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the kth demonstration trajectory, m k represents the total number of trajectory points on the k-th demonstration trajectory, v i,krepresents the speed at the ith track point on the kth demonstration track, A(i) represents whether the speed at the ith track point on the kth demonstration track is zero, A(i)=1 represents that the speed at the ith track point on the kth demonstration track is zero, and A(i)=0 represents that the speed at the ith track point on the kth demonstration track is not zero; the fluency of the demonstration track is evaluated using the fourth formula according to the second evaluation index to obtain a second evaluation score, wherein the second evaluation index is the number of times the acceleration changes sign in each demonstration track, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th track point on the k-th demonstration track, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, and B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; the fluency of the demonstration trajectory is evaluated using the fifth formula according to the third evaluation index to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each demonstration trajectory, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents the state of whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0011] Optionally, determining the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score includes: using a sixth formula to fuse the first evaluation score, the second evaluation score and the third evaluation score to obtain the evaluation score, wherein the sixth formula is: L krepresents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score using the seventh formula, wherein the seventh formula is: K represents the total number of demonstration trajectories, δ k Represents the model weight of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0012] Optionally, determining the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory includes: determining the hybrid dynamic primitive model using an eighth formula according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k Represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0013] According to another aspect of an embodiment of the present invention, there is also provided an electric power maintenance device based on a hybrid dynamic motion primitive, comprising: a first acquisition unit, used to acquire a plurality of demonstration trajectories of an equipotential robot, wherein the equipotential robot is used for performing electric power maintenance, and the demonstration trajectory is a motion trajectory of the equipotential robot during the electric power maintenance process; a second acquisition unit, used to perform dynamic motion primitive modeling according to each of the demonstration trajectories, and obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories; a third acquisition unit, used to evaluate the fluency of each of the demonstration trajectories according to an evaluation index, and obtain an evaluation score; a first determination unit, used to determine the model of the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories according to the evaluation score. Weight, wherein the model weight refers to the importance of each of the initial hybrid dynamic primitive models in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; a second determination unit is used to determine the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories and the model weight; a generation unit is used to write the hybrid dynamic primitive model into the equipotential robot, so that the equipotential robot, when receiving the power maintenance task, uses the hybrid dynamic primitive model to generate the maintenance trajectory according to the maintenance position indicated by the power maintenance task; a control unit is used to control the equipotential robot to perform power maintenance operations based on the maintenance trajectory.

[0014] Optionally, the first acquisition unit includes: a first acquisition module, used to acquire multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determine the historical maintenance trajectory as the demonstration trajectory; and / or, a second acquisition module, used to acquire multiple demonstration trajectories by controlling the equipotential robot to simulate the power maintenance process.

[0015] Optionally, the second acquisition unit includes: a third acquisition module, which is used to perform dynamic motion primitive modeling on each of the demonstration trajectories according to the proportional-differential controller and the trajectory shape learner to obtain a starting mixed dynamic primitive model corresponding to each of the demonstration trajectories, wherein the starting mixed dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to the time t, representing acceleration, α y represents the proportional parameter in the proportional-derivative controller, β y represents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to reach; a fourth acquisition module is used to adjust the nonlinear term in the trajectory shape learner to obtain the adjusted nonlinear term; a fifth acquisition module is used to adjust the convergence speed of the demonstration trajectory based on the nonlinear term on the basis of obtaining the starting hybrid dynamic primitive model, so as to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

[0016] Optionally, the fourth acquisition module includes: a first determination submodule, configured to determine a radial basis function using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the ith radial basis function, σ a represents the width of the a-th radial basis function, c arepresents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term by linear superposition; a second determination submodule is used to determine the adjusted nonlinear term by using a second formula according to a plurality of the radial basis functions by linear superposition, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

[0017] Optionally, the evaluation index includes: a first evaluation index, a second evaluation index and a third evaluation index, and the third acquisition unit includes at least one of the following: a sixth acquisition module, used to evaluate the fluency of the demonstration trajectory using a third formula according to the first evaluation index to obtain a first evaluation score, wherein the first evaluation index is the first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the kth demonstration trajectory, m k represents the total number of trajectory points on the k-th demonstration trajectory, v i,k represents the speed at the ith track point on the kth demonstration track, A(i) represents whether the speed at the ith track point on the kth demonstration track is zero, A(i)=1 represents that the speed at the ith track point on the kth demonstration track is zero, and A(i)=0 represents that the speed at the ith track point on the kth demonstration track is not zero; a seventh acquisition module is used to evaluate the fluency of the demonstration track using the fourth formula according to the second evaluation index to obtain a second evaluation score, wherein the second evaluation index is the number of times the acceleration changes sign in each demonstration track, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th track point on the k-th demonstration track, a i+1,krepresents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are not the same, B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; an eighth acquisition module is used to evaluate the fluency of the demonstration trajectory using the fifth formula according to the third evaluation index to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each demonstration trajectory, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents the state of whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0018] Optionally, the first determination unit includes: a ninth acquisition module, configured to use a sixth formula to merge the first evaluation score, the second evaluation score, and the third evaluation score to obtain the evaluation score, wherein the sixth formula is: L k represents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; a first determination module is used to determine the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score using the seventh formula, wherein the seventh formula is: K represents the total number of demonstration trajectories, δ k Represents the model weight of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0019] Optionally, the second determination unit includes: a second determination module, configured to determine the hybrid dynamic primitive model using an eighth formula according to the initial hybrid dynamic primitive model corresponding to each demonstration trajectory and the model weight, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k Represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0020] According to another aspect of an embodiment of the present invention, there is further provided an electric power maintenance system based on a hybrid dynamic motion primitive, wherein the electric power maintenance system based on a hybrid dynamic motion primitive uses any one of the above-described electric power maintenance methods based on a hybrid dynamic motion primitive.

[0021] According to another aspect of an embodiment of the present invention, there is further provided a computer-readable storage medium, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned electric power maintenance methods based on hybrid dynamic motion primitives.

[0022] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein when the program is run, any one of the above-mentioned power maintenance methods based on hybrid dynamic motion primitives is executed.

[0023] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned power maintenance methods based on hybrid dynamic motion primitives is executed.

[0024] In an embodiment of the present invention, a plurality of demonstration trajectories of an equipotential robot are obtained, wherein the equipotential robot is used for power maintenance, and the demonstration trajectory is the motion trajectory of the equipotential robot during the power maintenance process; dynamic motion primitive modeling is performed according to each demonstration trajectory to obtain an initial hybrid dynamic primitive model corresponding to each demonstration trajectory; the fluency of each demonstration trajectory is evaluated according to an evaluation index to obtain an evaluation score; a model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score, wherein the model weight refers to the importance of each initial hybrid dynamic primitive model in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to a power maintenance task; the hybrid dynamic primitive model is determined according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory; the hybrid dynamic primitive model is written into the equipotential robot so that the equipotential robot generates a maintenance trajectory according to the maintenance position indicated by the power maintenance task using the hybrid dynamic primitive model when receiving the power maintenance task; and the equipotential robot is controlled to perform a power maintenance operation based on the maintenance trajectory. Through the above technical scheme, the model weights are obtained by dynamically modeling multiple demonstration trajectories and evaluating the smoothness of each demonstration trajectory, the models corresponding to the multiple demonstration trajectories are fused to obtain a hybrid dynamic motion primitive model, and a maintenance trajectory is generated according to the maintenance task based on the model to control the robot to perform maintenance according to the trajectory. The technical effect of integrating various data and factors to generate a maintenance trajectory for power maintenance using a hybrid dynamic primitive model to improve the smoothness of the trajectory is achieved, and the maintenance efficiency and maintenance quality of equipotential robots performing power maintenance tasks are improved, thereby solving the technical problems in related technologies that equipotential robots can only learn a single trajectory, and in the process of power maintenance, the generated trajectory has too many turning points, resulting in violent shaking of the boom truck, low maintenance efficiency and poor work quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a mobile terminal of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention;

[0027] Figure 2 is a flow chart of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention;

[0028] Figure 3 is a flow chart of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a demonstration trajectory according to an embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of the effect of nonlinear terms according to an embodiment of the present invention;

[0031] Figure 6 is a schematic diagram of radial basis function visualization according to an embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of an electric power maintenance device based on hybrid dynamic motion primitives according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] As introduced in the background technology, the medium-potential robot in the related art can only learn a single trajectory, and in the process of power maintenance, the generated trajectory has too many turning points, which causes the boom truck to shake violently, resulting in low maintenance efficiency and poor work quality. In view of the above defects, an electric power maintenance method and device based on hybrid dynamic motion primitives are provided in an embodiment of the present invention.

[0036] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0037] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0038] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the power maintenance method based on hybrid dynamic motion primitives in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks and combinations thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] According to an embodiment of the present invention, a method embodiment of an electric power maintenance method based on hybrid dynamic motion primitives is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] Figure 2 is a flow chart of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention, such as Figure 2 As shown, the method comprises the following steps:

[0041] Step S202, obtaining a plurality of demonstration trajectories of the equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectories are movement trajectories of the equipotential robot during the power maintenance process.

[0042] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 4 is a flow chart of a power maintenance method based on hybrid dynamic motion primitives according to an embodiment of the present invention.

[0043] like Figure 3 As shown, in this embodiment, multiple demonstration trajectories of equipotential robots can be obtained, and a hybrid dynamic primitive model can be established based on the fusion analysis of the multiple demonstration trajectories, and then a smoother and higher-quality maintenance trajectory can be generated according to the model, thereby improving the efficiency and quality of the equipotential robots in performing power maintenance operations.

[0044] It should be noted that Figure 3 The n shown in is only a serial number representing the number of demonstration trajectories, dynamic motion primitives (i.e., initial mixed dynamic primitive models) or weights (i.e., model weights). Of course, other letters may also be selected, such as k in the embodiment of the present invention, for representation, and no specific limitation is made here.

[0045] According to the above embodiment of the present invention, in the above step S202, multiple demonstration trajectories of the equipotential robot are obtained, including: obtaining multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determining the historical maintenance trajectories as demonstration trajectories; and / or, obtaining multiple demonstration trajectories by controlling the equipotential robot to simulate the power maintenance process.

[0046] Combine the following Figure 4 The above embodiments of the present invention are described in detail. Figure 4 is a schematic diagram of a demonstration trajectory according to an embodiment of the present invention.

[0047] Specifically, we can obtain Figure 4The multiple demonstration trajectories shown, assuming that the 5 collected demonstration trajectories are represented as follows: in, represents the i-th trajectory point on the k-th demonstration trajectory, m k Represents the total number of trajectory points on the kth demonstration trajectory. The demonstration trajectory here can be the demonstration trajectory recorded by the equipotential robot when performing power maintenance operations in the historical time period, or the equipotential robot can be controlled by kinesthetic teaching or other methods to simulate the power maintenance process to obtain multiple demonstration trajectories. During the kinesthetic teaching process, the operator can directly manually guide the robot end effector to make it experience a series of typical movements in the power maintenance operation process and record these movement trajectories. Of course, the equipotential robot can also be controlled by other methods to simulate the power maintenance process to obtain the demonstration trajectory, which will not be elaborated here.

[0048] Step S204, performing dynamic motion primitive modeling according to each demonstration trajectory to obtain an initial hybrid dynamic primitive model corresponding to each demonstration trajectory.

[0049] As above Figure 3 As shown, in this embodiment, dynamic motion primitive modeling can be performed based on each demonstration trajectory to obtain an initial mixed dynamic primitive model corresponding to each demonstration trajectory; the initial mixed dynamic primitive model here is a mixed dynamic primitive model obtained by performing dynamic primitive modeling on each demonstration trajectory. The mixed dynamic motion primitives (Mixed Dynamic Movement Primitives, Mixed DMPs) model is a customized extended model that combines dynamic motion primitives (DMP) theory and additional innovations (such as random forgetting factors and model weight calculation based on fluency evaluation). In the field of robotics and control, the DMP model has been widely studied and applied for learning and reproducing complex motion trajectories.

[0050] According to the above embodiment of the present invention, in the above step S204, dynamic motion primitive modeling is performed according to each demonstration trajectory to obtain an initial hybrid dynamic primitive model corresponding to each demonstration trajectory, including: performing dynamic motion primitive modeling on each demonstration trajectory according to a proportional-differential controller and a trajectory shape learner to obtain an initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the initial hybrid dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to time t, representing acceleration, α yrepresents the proportional parameter in the proportional-derivative controller, β y represents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to reach; the nonlinear term in the trajectory shape learner is adjusted to obtain the adjusted nonlinear term; on the basis of obtaining the initial hybrid dynamic primitive model, the convergence speed of the demonstration trajectory is adjusted based on the nonlinear term to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

[0051] Specifically, the dynamic motion primitive can be understood as a combination of a PD controller (i.e., a proportional-derivative controller) and a trajectory shape learner; usually, the most commonly used and simplest PD controller can be described by a spring-damper system: Where τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to time t, representing acceleration, α y represents the proportional parameter (P parameter) in the proportional-derivative controller, β y represents the differential parameter (D parameter) in the proportional-derivative controller; assuming that f represents the trajectory shape learner and is a nonlinear function, the dynamic motion primitive of a demonstration trajectory can be expressed as: (First expression); By modifying the target state g and the nonlinear term f in the first expression, the focus and shape of the demonstration trajectory can be adjusted. In order to obtain the expected trajectory shape, different nonlinear terms f need to be constructed. By adjusting different basis functions and their corresponding weights, a weighted complex trajectory can be obtained to obtain the desired trajectory shape. Generally, the complexity of the trajectory is proportional to the number of basis functions. If the convergence speed of the trajectory is to be adjusted, it is necessary to add a first-order derivative of the system state representing the speed. By adding a time factor τ to the original model, we can get the initial hybrid dynamic primitive model corresponding to each demonstration trajectory:

[0052]

[0053] In the above embodiment of the present invention, the nonlinear term in the trajectory shape learner is adjusted to obtain the adjusted nonlinear term, including: determining the radial basis function using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the i-th radial basis function, σ a represents the width of the a-th radial basis function, c a represents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term through linear superposition; according to the linear superposition of multiple radial basis functions, the adjusted nonlinear term is determined by the second formula, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

[0054] Combine the following Figure 5 and Figure 6 The above embodiments of the present invention are described in detail. Figure 5 is a schematic diagram of the effect of nonlinear terms according to an embodiment of the present invention, Figure 6 is a schematic diagram of radial basis function visualization according to an embodiment of the present invention.

[0055] In order to obtain the expected trajectory shape, different nonlinear terms f need to be constructed, which can be achieved by linearly superposing multiple normalized nonlinear basis functions and introducing a random forgetting factor γ a Acting on basis functions On the , set a probability value p, each time γ a The probability of p being equal to 1, and the probability of 1-p being equal to 0, similar to the dropout mechanism in convolutional neural networks, this function can be expressed as: The radial basis function can be expressed as: In the formula, a represents the number of radial basis function, σ a represents the width of the a-th radial basis function, c a represents the center position of the a-th radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, ω a represents the weight value of the a-th radial basis function, and s represents the phase variable of the first-order system, such as Figure 5As shown in the figure, in the DMP model, the weights of each radial basis function (RBF) determine the degree of influence of the RBF on the overall motion trajectory. Each RBF multiplies its output according to its weight value, and then all weighted RBF outputs are added together to obtain a weighted summation result. This result reflects the combined influence of multiple shape features and is a key part of generating the final motion trajectory. The activation of RBF refers to its ability to respond to a specific time point or motion stage. RBFs are usually activated in a specific time sequence throughout the movement process. This activation pattern is determined by the center position and width parameters of the RBF; the motion trajectory is shaped by adjusting the weights and activation order. The size of the weight value determines the strength of each RBF's contribution to the trajectory. The weighted summation combines the outputs of all RBFs to form a smooth nonlinear term, such as Figure 5 The effect of the nonlinear term shown in Figure 6 As shown in the figure, by adjusting the center position, width and weight value of the radial basis function, their activation mechanism can be controlled, thereby optimizing and adjusting the robot's motion trajectory to make it more consistent with the expected motion goals and environmental conditions. In the context of dynamic motion primitives (DMPs), Ps i usually refers to the phase variable or time variable in radial basis functions (RBFs), which controls the activation timing of RBFs. Ps i activation (phase variable activation or time variable activation) refers to the activation pattern of the radial basis function in time or phase as the motion proceeds in the DMP model.

[0056] Step S206, evaluating the fluency of each demonstration trajectory according to the evaluation index to obtain an evaluation score.

[0057] Optionally, the above evaluation indicators may include but are not limited to: a first evaluation indicator, a second evaluation indicator and a third evaluation indicator.

[0058] In this embodiment, the smoothness of each demonstration trajectory can be evaluated from multiple dimensions according to evaluation indicators to obtain an evaluation score. The evaluation of the smoothness of the demonstration trajectory here can be mainly based on the following three indicators: the duration of zero speed, the number of times the acceleration changes sign, and the duration of the absolute value of the acceleration exceeding the threshold. Of course, other evaluation indicators can also be selected according to actual conditions to evaluate the smoothness of the demonstration trajectory, and no specific restrictions are made here.

[0059] According to the above embodiment of the present invention, in the above step S206, the fluency of each demonstration trajectory is evaluated according to the evaluation index to obtain an evaluation score, including at least one of the following: the fluency of the demonstration trajectory is evaluated according to the first evaluation index using the third formula to obtain a first evaluation score, wherein the first evaluation index is the first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the k-th demonstration trajectory, m k represents the total number of trajectory points on the kth demonstration trajectory, v i,k represents the speed at the ith trajectory point on the kth demonstration trajectory, A(i) represents whether the speed at the ith trajectory point on the kth demonstration trajectory is zero, A(i)=1 represents that the speed at the ith trajectory point on the kth demonstration trajectory is zero, and A(i)=0 represents that the speed at the ith trajectory point on the kth demonstration trajectory is not zero; the fluency of the demonstration trajectory is evaluated using the fourth formula according to the second evaluation index to obtain a second evaluation score, wherein the second evaluation index is the number of times the acceleration changes its sign in each demonstration trajectory, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th trajectory point on the k-th demonstration trajectory, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, and B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; the fluency of the demonstration trajectory is evaluated using the fifth formula according to the third evaluation index to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each demonstration trajectory, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0060] Specifically, a velocity of zero usually indicates that the presenter may be paused due to its own posture or workspace transformation, which is not a feature of the trajectory itself, so it needs to be penalized; the following formula can be used: Calculate a first evaluation score for evaluating the fluency of the demonstration trajectory according to the first evaluation indicator, where i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the k-th demonstration trajectory, m k represents the total number of trajectory points on the kth demonstration trajectory, v i,k A(i) represents the speed at the i-th track point on the k-th demonstration track, A(i) represents whether the speed at the i-th track point on the k-th demonstration track is zero, A(i) = 1 represents that the speed at the i-th track point on the k-th demonstration track is zero, and A(i) = 0 represents that the speed at the i-th track point on the k-th demonstration track is not zero; in order to reduce the influence of burrs and noise in the speed curve, the number of acceleration sign changes is used as the screening criterion to select a smoother track, which can be used as follows: Calculate the second evaluation score for evaluating the fluency of the demonstration trajectory according to the second evaluation indicator, where: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th trajectory point on the k-th demonstration trajectory, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; some action jitters or action switching during the demonstration process may cause excessive instantaneous acceleration, which is an undesirable phenomenon during the demonstration process, and can be used using the formula: Calculate the third evaluation score for evaluating the fluency of the demonstration trajectory according to the third evaluation indicator, where: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0061] Step S208, determining the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score, wherein the model weight refers to the importance of each initial hybrid dynamic primitive model in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task.

[0062] As above Figure 3 As shown, in this embodiment, the evaluation scores obtained by evaluating the fluency of the demonstration trajectory from multiple evaluation indicators can be fused to obtain the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory.

[0063] According to the above embodiment of the present invention, in the above step S208, the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score, including: using the sixth formula to fuse the first evaluation score, the second evaluation score and the third evaluation score to obtain the evaluation score, wherein the sixth formula is: L k represents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score using the seventh formula, where the seventh formula is: K represents the total number of demonstration trajectories, δ k represents the model weights of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0064] Optionally, the first evaluation score, the second evaluation score and the third evaluation score are evaluation scores obtained by evaluating the fluency of each demonstration trajectory according to the first evaluation indicator, the second evaluation indicator and the third evaluation indicator respectively.

[0065] Specifically, the formula can be used: Calculate the comprehensive evaluation score of each demonstration trajectory in terms of fluency, where L k represents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; then the formula can be used: Calculate the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, where K represents the total number of demonstration trajectories, δ k represents the model weights of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0066] Step S210, determining a hybrid dynamic primitive model according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory.

[0067] As above Figure 3 As shown, in this embodiment, the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory obtained in the above steps can be fused to construct a hybrid dynamic primitive model.

[0068] According to the above embodiment of the present invention, in the above step S210, the hybrid dynamic primitive model is determined according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory, including: determining the hybrid dynamic primitive model using the eighth formula according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0069] Specifically, the formula can be used according to the initial hybrid dynamic primitive model and model weight corresponding to multiple demonstration trajectories: Fusion is performed to obtain a hybrid dynamic primitive model M DMP .

[0070] Step S212, writing the hybrid dynamic primitive model into the equipotential robot, so that when the equipotential robot receives an electric power maintenance task, it generates a maintenance trajectory according to the maintenance position indicated by the electric power maintenance task using the hybrid dynamic primitive model.

[0071] In this embodiment, the hybrid dynamic primitive model obtained in the above steps can be written into the equipotential robot so that when the equipotential robot receives a power maintenance task, it can use the model to automatically generate a maintenance trajectory for the power maintenance task according to the maintenance position indicated by the power maintenance task.

[0072] Step S214: Control the equipotential robot to perform power maintenance operations based on the maintenance trajectory.

[0073] In this embodiment, when performing power maintenance tasks, the equipotential robot can perform power maintenance operations based on the maintenance trajectory generated by the hybrid dynamic primitive model to complete the power maintenance tasks, thereby improving the maintenance efficiency and maintenance quality of the equipotential robot in performing power maintenance tasks.

[0074] It can be seen from the above that through the technical solution provided by the above embodiment of the present invention, multiple demonstration trajectories of an equipotential robot can be obtained, wherein the equipotential robot is used for power maintenance, and the demonstration trajectory is the motion trajectory of the equipotential robot during the power maintenance process; dynamic motion primitive modeling is performed according to each demonstration trajectory to obtain an initial mixed dynamic primitive model corresponding to each demonstration trajectory; the fluency of each demonstration trajectory is evaluated according to the evaluation index to obtain an evaluation score; the model weight of the initial mixed dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score, wherein the model weight refers to the importance of each initial mixed dynamic primitive model in the process of constructing the mixed dynamic primitive model, and the mixed dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; the mixed dynamic primitive model is determined according to the initial mixed dynamic primitive model corresponding to each demonstration trajectory and the model weight. Model; writing the hybrid dynamic primitive model into the equipotential robot so that when the equipotential robot receives the power maintenance task, it uses the hybrid dynamic primitive model to generate a maintenance trajectory according to the maintenance position indicated by the power maintenance task; controlling the equipotential robot to perform the power maintenance operation based on the maintenance trajectory, achieving the model weight obtained by respectively modeling multiple demonstration trajectories with dynamic motion primitives and evaluating the smoothness of each demonstration trajectory, fusing the models corresponding to the multiple demonstration trajectories to obtain the hybrid dynamic motion primitive model, and generating a maintenance trajectory according to the maintenance task based on the model, so as to control the robot to perform maintenance according to the trajectory, achieving the technical effect of integrating various data and factors to generate the maintenance trajectory of power maintenance using the hybrid dynamic primitive model to improve the trajectory smoothness, and improving the maintenance efficiency and maintenance quality of the equipotential robot performing power maintenance tasks.

[0075] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problems in the related art that the medium-potential robot can only learn a single trajectory, and during the power maintenance process, the generated trajectory has too many turning points, causing the boom truck to shake violently, resulting in low maintenance efficiency and poor work quality.

[0076] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0077] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0078] According to an embodiment of the present invention, there is also provided a power maintenance device based on hybrid dynamic motion primitives for implementing the above-mentioned power maintenance method based on hybrid dynamic motion primitives. Figure 7 is a schematic diagram of a power maintenance device based on a hybrid dynamic motion primitive according to an embodiment of the present invention, such as Figure 7 As shown, the device includes: a first acquisition unit 701, a second acquisition unit 703, a third acquisition unit 705, a first determination unit 707, a second determination unit 709, a generation unit 711 and a control unit 713. The power maintenance device based on hybrid dynamic motion primitives is described in detail below.

[0079] The first acquisition unit 701 is used to acquire multiple demonstration trajectories of the equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectories are movement trajectories of the equipotential robot during the power maintenance process.

[0080] The second acquisition unit 703 is used to perform dynamic motion primitive modeling according to each demonstration trajectory to obtain an initial mixed dynamic primitive model corresponding to each demonstration trajectory.

[0081] The third acquisition unit 705 is used to evaluate the fluency of each demonstration trajectory according to the evaluation index to obtain an evaluation score.

[0082] The first determination unit 707 is used to determine the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score, wherein the model weight refers to the importance of each initial hybrid dynamic primitive model in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task.

[0083] The second determining unit 709 is used to determine the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory.

[0084] The generating unit 711 is used to write the hybrid dynamic primitive model into the equipotential robot, so that when the equipotential robot receives an electric power maintenance task, it generates a maintenance trajectory according to the maintenance position indicated by the electric power maintenance task using the hybrid dynamic primitive model.

[0085] The control unit 713 is used to control the equipotential robot to perform power maintenance operations based on the maintenance trajectory.

[0086] It should be noted here that the above-mentioned first acquisition unit 701, second acquisition unit 703, third acquisition unit 705, first determination unit 707, second determination unit 709, generation unit 711 and control unit 713 correspond to steps S202 to S214 in the above-mentioned embodiments, and the seven units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0087] As can be seen from the above, in the scheme recorded in the above embodiment of the present invention, the first acquisition unit can be used to acquire multiple demonstration trajectories of the equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectory is the motion trajectory of the equipotential robot during the power maintenance process; then the second acquisition unit is used to perform dynamic motion primitive modeling according to each demonstration trajectory to obtain an initial mixed dynamic primitive model corresponding to each demonstration trajectory; then the third acquisition unit is used to evaluate the fluency of each demonstration trajectory according to the evaluation index to obtain an evaluation score; then the first determination unit is used to determine the model weight of the initial mixed dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score, wherein the model weight refers to the importance of each initial mixed dynamic primitive model in the process of constructing the mixed dynamic primitive model, and the mixed dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; then the second determination unit is used to determine the initial mixed dynamic primitive model corresponding to each demonstration trajectory and model weights to determine the hybrid dynamic primitive model; then, the generation unit is used to write the hybrid dynamic primitive model into the equipotential robot, so that when the equipotential robot receives the power maintenance task, it uses the hybrid dynamic primitive model to generate a maintenance trajectory according to the maintenance position indicated by the power maintenance task; finally, the control unit is used to control the equipotential robot to perform the power maintenance operation based on the maintenance trajectory, so as to achieve the model weight obtained by respectively modeling multiple demonstration trajectories with dynamic motion primitives and evaluating the smoothness of each demonstration trajectory, and to fuse the models corresponding to the multiple demonstration trajectories to obtain the hybrid dynamic motion primitive model, and to generate a maintenance trajectory according to the maintenance task based on the model, so as to control the robot to perform maintenance according to the trajectory, so as to achieve the technical effect of integrating various data and factors to generate the maintenance trajectory of power maintenance using the hybrid dynamic primitive model to improve the smoothness of the trajectory, and to improve the maintenance efficiency and maintenance quality of the equipotential robot in performing power maintenance tasks.

[0088] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problems in the related art that the medium-potential robot can only learn a single trajectory, and during the power maintenance process, the generated trajectory has too many turning points, causing the boom truck to shake violently, resulting in low maintenance efficiency and poor work quality.

[0089] Optionally, the first acquisition unit includes: a first acquisition module, used to acquire multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determine the historical maintenance trajectories as demonstration trajectories; and / or, a second acquisition module, used to acquire multiple demonstration trajectories by controlling the equipotential robot to simulate the power maintenance process.

[0090] Optionally, the second acquisition unit includes: a third acquisition module, which is used to perform dynamic motion primitive modeling on each demonstration trajectory according to the proportional-differential controller and the trajectory shape learner to obtain a starting mixed dynamic primitive model corresponding to each demonstration trajectory, wherein the starting mixed dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to time t, representing acceleration, α y represents the proportional parameter in the proportional-derivative controller, β y represents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to reach; the fourth acquisition module is used to adjust the nonlinear term in the trajectory shape learner to obtain the adjusted nonlinear term; the fifth acquisition module is used to adjust the convergence speed of the demonstration trajectory based on the nonlinear term on the basis of obtaining the initial hybrid dynamic primitive model, so as to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

[0091]

[0092] Optionally, the fourth acquisition module includes: a first determination submodule, configured to determine the radial basis function using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the i-th radial basis function, σ a represents the width of the a-th radial basis function, c arepresents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term through linear superposition; the second determination submodule is used to determine the adjusted nonlinear term by using the second formula through linear superposition according to multiple radial basis functions, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

[0093] Optionally, the evaluation index includes: a first evaluation index, a second evaluation index and a third evaluation index, and the third acquisition unit includes at least one of the following: a sixth acquisition module, which is used to evaluate the fluency of the demonstration trajectory using a third formula according to the first evaluation index to obtain a first evaluation score, wherein the first evaluation index is the first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the k-th demonstration trajectory, m k represents the total number of trajectory points on the kth demonstration trajectory, v i,k represents the speed at the ith trajectory point on the kth demonstration trajectory, A(i) represents whether the speed at the ith trajectory point on the kth demonstration trajectory is zero, A(i)=1 represents that the speed at the ith trajectory point on the kth demonstration trajectory is zero, and A(i)=0 represents that the speed at the ith trajectory point on the kth demonstration trajectory is not zero; a seventh acquisition module is used to evaluate the fluency of the demonstration trajectory using the fourth formula according to the second evaluation index to obtain a second evaluation score, wherein the second evaluation index is the number of times the acceleration changes its sign in each demonstration trajectory, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th trajectory point on the k-th demonstration trajectory, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, and B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; an eighth acquisition module is used to evaluate the fluency of the demonstration trajectory using the fifth formula according to the third evaluation index to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each demonstration trajectory, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0094] Optionally, the first determination unit includes: a ninth acquisition module, configured to use a sixth formula to merge the first evaluation score, the second evaluation score, and the third evaluation score to obtain an evaluation score, wherein the sixth formula is: L k represents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; a first determination module is used to determine the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score using the seventh formula, wherein the seventh formula is: K represents the total number of demonstration trajectories, δ k represents the model weights of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0095] Optionally, the second determination unit includes: a second determination module, configured to determine the hybrid dynamic primitive model using an eighth formula according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0096] According to another aspect of an embodiment of the present invention, there is further provided an electric power maintenance system based on a hybrid dynamic motion primitive, wherein the electric power maintenance system based on a hybrid dynamic motion primitive uses any of the above-mentioned electric power maintenance methods based on a hybrid dynamic motion primitive.

[0097] According to another aspect of an embodiment of the present invention, there is further provided a computer-readable storage medium, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned electric power maintenance methods based on hybrid dynamic motion primitives.

[0098] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0099] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining multiple demonstration trajectories of an equipotential robot, wherein the equipotential robot is used to perform power maintenance, and the demonstration trajectory is the motion trajectory of the equipotential robot during the power maintenance process; performing dynamic motion primitive modeling according to each demonstration trajectory to obtain an initial hybrid dynamic primitive model corresponding to each demonstration trajectory; evaluating the fluency of each demonstration trajectory according to an evaluation index to obtain an evaluation score; determining a model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score, wherein the model weight refers to the importance of each initial hybrid dynamic primitive model in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to a power maintenance task; determining a hybrid dynamic primitive model according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory; writing the hybrid dynamic primitive model into the equipotential robot so that the equipotential robot generates a maintenance trajectory according to the maintenance position indicated by the power maintenance task using the hybrid dynamic primitive model when receiving the power maintenance task; and controlling the equipotential robot to perform a power maintenance operation based on the maintenance trajectory.

[0100] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determining the historical maintenance trajectories as demonstration trajectories; and / or obtaining multiple demonstration trajectories by controlling the equipotential robot to simulate the power maintenance process.

[0101] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: performing dynamic motion primitive modeling on each demonstration trajectory according to the proportional-differential controller and the trajectory shape learner to obtain a starting hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the starting hybrid dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to time t, representing acceleration, α y represents the proportional parameter in the proportional-derivative controller, β yrepresents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to reach; the nonlinear term in the trajectory shape learner is adjusted to obtain the adjusted nonlinear term; on the basis of obtaining the initial hybrid dynamic primitive model, the convergence speed of the demonstration trajectory is adjusted based on the nonlinear term to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

[0102]

[0103] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining a radial basis function using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the i-th radial basis function, σ a represents the width of the a-th radial basis function, c a represents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term through linear superposition; according to the linear superposition of multiple radial basis functions, the adjusted nonlinear term is determined by the second formula, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

[0104] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: evaluating the fluency of the demonstration trajectory using a third formula according to the first evaluation index to obtain a first evaluation score, wherein the first evaluation index is a first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the k-th demonstration trajectory, m k represents the total number of trajectory points on the kth demonstration trajectory, v i,krepresents the speed at the ith trajectory point on the kth demonstration trajectory, A(i) represents whether the speed at the ith trajectory point on the kth demonstration trajectory is zero, A(i)=1 represents that the speed at the ith trajectory point on the kth demonstration trajectory is zero, and A(i)=0 represents that the speed at the ith trajectory point on the kth demonstration trajectory is not zero; the fluency of the demonstration trajectory is evaluated using the fourth formula according to the second evaluation index to obtain a second evaluation score, wherein the second evaluation index is the number of times the acceleration changes its sign in each demonstration trajectory, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th trajectory point on the k-th demonstration trajectory, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, and B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; the fluency of the demonstration trajectory is evaluated using the fifth formula according to the third evaluation index to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each demonstration trajectory, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

[0105] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: using a sixth formula to merge the first evaluation score, the second evaluation score, and the third evaluation score to obtain an evaluation score, wherein the sixth formula is: L k represents the evaluation score of the kth demonstration trajectory, n1 represents the first weight of the first evaluation score, n2 represents the second weight of the second evaluation score, and n3 represents the third weight of the third evaluation score; the model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score using the seventh formula, where the seventh formula is: K represents the total number of demonstration trajectories, δ k represents the model weights of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0106] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining the hybrid dynamic primitive model using the eighth formula according to the initial hybrid dynamic primitive model and the model weight corresponding to each demonstration trajectory, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

[0107] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned power maintenance methods based on hybrid dynamic motion primitives is executed.

[0108] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, execute any one of the above-mentioned power maintenance methods based on hybrid dynamic motion primitives.

[0109] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0110] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0115] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A power maintenance method based on hybrid dynamic motion primitives, characterized in that: include: Acquire multiple demonstration trajectories of an equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectories are motion trajectories of the equipotential robot during the power maintenance process; Performing dynamic motion primitive modeling according to each of the demonstration trajectories to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories; Evaluate the fluency of each demonstration trajectory according to the evaluation index to obtain an evaluation score; Determining a model weight of the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories according to the evaluation score, wherein the model weight refers to the importance of each of the initial hybrid dynamic primitive models in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; Determine the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model corresponding to each demonstration trajectory and the model weight; Writing the hybrid dynamic primitive model into the equipotential robot, so that the equipotential robot generates the maintenance trajectory according to the maintenance position indicated by the power maintenance task by using the hybrid dynamic primitive model when receiving the power maintenance task; The equipotential robot is controlled to perform an electric power maintenance operation based on the maintenance trajectory.

2. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 1 is characterized in that: Get multiple demonstration trajectories of the equipotential robot, including: Acquire multiple historical maintenance trajectories when the equipotential robot performs historical maintenance operations within a historical time period, and determine the historical maintenance trajectories as the demonstration trajectories; and / or, The equipotential robot is controlled to simulate the process of the power maintenance to obtain a plurality of demonstration trajectories.

3. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 1 is characterized in that: Performing dynamic motion primitive modeling according to each of the demonstration trajectories to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories includes: According to the proportional-differential controller and the trajectory shape learner, dynamic motion primitive modeling is performed on each of the demonstration trajectories to obtain a starting hybrid dynamic primitive model corresponding to each of the demonstration trajectories, wherein the starting hybrid dynamic primitive model N corresponding to the kth demonstration trajectory k The first expression of is: τ represents the time factor, y represents the current state of the proportional-derivative controller, is the first derivative of y with respect to time t, representing the velocity, is the second derivative of y with respect to the time t, representing acceleration, α y represents the proportional parameter in the proportional-derivative controller, β y represents the differential parameter in the proportional-differential controller, f represents the nonlinear term in the trajectory shape learner, g represents the target state of the proportional-differential controller, and the target state refers to the state that the proportional-differential controller needs to achieve; Adjusting the nonlinear term in the trajectory shape learner to obtain the adjusted nonlinear term; On the basis of obtaining the initial hybrid dynamic primitive model, the convergence speed of the demonstration trajectory is adjusted based on the nonlinear term to obtain the initial hybrid dynamic primitive model corresponding to each demonstration trajectory, wherein the convergence speed refers to the speed of the demonstration trajectory from the current state to the target state, and the initial hybrid dynamic primitive model M corresponding to the kth demonstration trajectory k The second expression is:

4. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 3 is characterized in that: Adjusting the nonlinear term in the trajectory shape learner to obtain the adjusted nonlinear term includes: The radial basis function is determined using a first formula, wherein the first formula is: a represents the number of the radial basis function, represents the i-th radial basis function, σ a represents the width of the a-th radial basis function, c a represents the center position of the ath radial basis function, x represents the coordinates of the trajectory point in the demonstration trajectory, and the radial basis function is used to obtain the nonlinear term through linear superposition; The adjusted nonlinear term is determined by linear superposition of the plurality of radial basis functions using a second formula, wherein the second formula is: ω a represents the weight value of the a-th radial basis function, s represents the phase variable of the first-order system, γ a represents the random forgetting factor of the a-th radial basis function.

5. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 1, characterized in that: The evaluation indicators include: a first evaluation indicator, a second evaluation indicator and a third evaluation indicator. The fluency of each demonstration trajectory is evaluated according to the evaluation indicators to obtain an evaluation score, including at least one of the following: The fluency of the demonstration trajectory is evaluated according to the first evaluation index using a third formula to obtain a first evaluation score, wherein the first evaluation index is the first duration of zero speed in each demonstration trajectory, and the third formula is: i represents the number of the trajectory point on the demonstration trajectory, represents the first evaluation score of the kth demonstration trajectory, m k represents the total number of trajectory points on the k-th demonstration trajectory, v i,k represents the speed at the ith track point on the kth demonstration track, A(i) represents whether the speed at the ith track point on the kth demonstration track is zero, A(i)=1 represents that the speed at the ith track point on the kth demonstration track is zero, and A(i)=0 represents that the speed at the ith track point on the kth demonstration track is not zero; The fluency of the demonstration trajectory is evaluated according to the second evaluation index using a fourth formula to obtain a second evaluation score, wherein the second evaluation index is the number of acceleration sign changes in each demonstration trajectory, and the fourth formula is: represents the second evaluation score of the k-th demonstration trajectory, a i,k represents the acceleration at the i-th track point on the k-th demonstration track, a i+1,k represents the acceleration at the i+1th trajectory point on the kth demonstration trajectory, B(i) represents whether the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same, B(i)=1 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are different, B(i)=0 represents that the signs of the velocity at the i-th trajectory point on the kth demonstration trajectory and the acceleration at the i+1th trajectory point are the same; The fluency of the demonstration trajectory is evaluated according to the third evaluation index using the fifth formula to obtain a third evaluation score, wherein the third evaluation index is the second duration of the absolute acceleration exceeding the acceleration threshold in each of the demonstration trajectories, the absolute acceleration refers to the absolute value of the acceleration, and the fifth formula is: represents the third evaluation score of the k-th demonstration trajectory, |a i,k |The absolute acceleration at the ith trajectory point on the kth demonstration trajectory, a0 represents the acceleration threshold, C(i) represents the state of whether the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=1 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory exceeds the acceleration threshold, C(i)=0 represents that the absolute acceleration at the ith trajectory point on the kth demonstration trajectory does not exceed the acceleration threshold.

6. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 1, characterized in that: Determining a model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory according to the evaluation score includes: The first evaluation score, the second evaluation score and the third evaluation score are integrated using the sixth formula to obtain the evaluation score, wherein the sixth formula is: L k represents the evaluation score of the k-th demonstration trajectory, n1 represents a first weight of the first evaluation score, n2 represents a second weight of the second evaluation score, and n3 represents a third weight of the third evaluation score; The model weight of the initial hybrid dynamic primitive model corresponding to each demonstration trajectory is determined according to the evaluation score using the seventh formula, wherein the seventh formula is: K represents the total number of demonstration trajectories, δ k Represents the model weight of the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

7. The electric power maintenance method based on hybrid dynamic motion primitives according to claim 1, characterized in that: Determining the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model corresponding to each demonstration trajectory and the model weight includes: The hybrid dynamic primitive model is determined using the eighth formula according to the initial hybrid dynamic primitive model corresponding to each demonstration trajectory and the model weight, wherein the eighth formula is: M DMP represents the hybrid dynamic primitive model, M k Represents the initial hybrid dynamic primitive model corresponding to the k-th demonstration trajectory.

8. An electric power maintenance device based on hybrid dynamic motion primitives, characterized in that: include: A first acquisition unit is used to acquire a plurality of demonstration trajectories of an equipotential robot, wherein the equipotential robot is used for power maintenance, and the demonstration trajectories are motion trajectories of the equipotential robot during the power maintenance process; A second acquisition unit is used to perform dynamic motion primitive modeling according to each of the demonstration trajectories to obtain an initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories; A third acquisition unit is used to evaluate the fluency of each demonstration trajectory according to the evaluation index to obtain an evaluation score; A first determining unit is used to determine the model weight of the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories according to the evaluation score, wherein the model weight refers to the importance of each of the initial hybrid dynamic primitive models in the process of constructing the hybrid dynamic primitive model, and the hybrid dynamic primitive model is used to generate a maintenance trajectory according to the power maintenance task; A second determining unit, configured to determine the hybrid dynamic primitive model according to the initial hybrid dynamic primitive model corresponding to each of the demonstration trajectories and the model weight; A generating unit, used for writing the hybrid dynamic primitive model into the equipotential robot, so that the equipotential robot generates the maintenance trajectory according to the maintenance position indicated by the power maintenance task by using the hybrid dynamic primitive model when receiving the power maintenance task; A control unit is used to control the equipotential robot to perform power maintenance operations based on the maintenance trajectory.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the electric power maintenance method based on hybrid dynamic motion primitives as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the power maintenance method based on hybrid dynamic motion primitives described in any one of claims 1 to 7 is performed.

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