Intelligent control method and system for lower limb hip and knee rehabilitation training equipment

Abstract

The invention relates to the technical field of medical rehabilitation instruments, in particular to an intelligent control method and system for lower limb hip and knee rehabilitation training equipment. The method comprises the following steps: identifying and acquiring fatigue key points of a target training object for executing a current walking situation, constructing a multi-dimensional myoelectric fatigue thermal model based on myoelectric fatigue sensing data of lower limb hip and knee rehabilitation training equipment, and performing thermal analysis on the fatigue key points by using the multi-dimensional myoelectric fatigue thermal model, the speed and the frequency of the lower limb hip and knee rehabilitation training equipment are controlled; and calculating a lower limb hip-knee time sequence state variable of the lower limb hip-knee rehabilitation training device transferred from the adjacent training electromyogram to the current training electromyogram through reflection contraction replacement of the worst solution pure vertex, and controlling the current rehabilitation training amplitude of the lower limb hip-knee rehabilitation training device according to the lower limb hip-knee time sequence state variable. The hip and knee joint training effect of the lower limb hip and knee rehabilitation training equipment on the user can be intelligently controlled, and the recovery rate of hip and knee joint injuries is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of medical rehabilitation equipment, and in particular to an intelligent control method and system for lower limb hip and knee rehabilitation training equipment. Background Art

[0002] Lower limb hip and knee rehabilitation training equipment is mainly used to help patients with rehabilitation training in the hip and knee areas, especially for patients after hip replacement or knee surgery, or patients with lower limb dysfunction due to sports injuries, aging, etc. Most existing rehabilitation equipment assists movement through manual adjustment or simple electronic control systems. Although it can meet certain treatment needs, it still has major defects in precise control, personalized training, and intelligent feedback. With the continuous development of intelligent technology, more and more researchers are committed to improving the efficiency and effectiveness of rehabilitation training by introducing intelligent control systems.

[0003] At present, most lower limb hip and knee rehabilitation training equipment still relies on traditional mechanical design and manual adjustment, lacking real-time matching with the patient's physiological state and motor ability. The control systems of these devices are usually relatively simple, and it is difficult to automatically control reasonable training parameters according to the patient's rehabilitation progress and muscle fatigue. On the other hand, although some devices have introduced simple electronic control systems, they often lack intelligence, data feedback and personalized adjustment functions, and cannot provide customized rehabilitation control solutions according to the needs of different patients. In addition, the existing technology lacks the function of evaluating and analyzing the patient's hip and knee joint injuries, which makes it difficult to quantify the effect of rehabilitation training. Patients can often only rely on subjective feelings for adjustment, making it difficult to achieve accurate and effective rehabilitation training. Summary of the invention

[0004] The present invention overcomes the deficiencies of the prior art and provides an intelligent control method and system for lower limb hip and knee rehabilitation training equipment.

[0005] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides an intelligent control method for a lower limb hip and knee rehabilitation training device, comprising the following steps: S102: Obtain static data of the lower limb hip and knee joints and force distribution data of the thigh and calf dynamic training of the target training object, and use the static data of the lower limb hip and knee joints and the force distribution data to perform Gaussian distribution calculation on the gait walking of the target training object to obtain the current walking posture of the target training object; S104: Based on the lower limb hip and knee injury rating radar chart of the target training object, the preset rehabilitation training strategy of the lower limb hip and knee rehabilitation training device is subjected to recognition variational inference learning from the activity interval to the rehabilitation training amplitude, and a rehabilitation training amplitude pattern recognition model is generated. The current rehabilitation training amplitude of the current walking posture is obtained through the rehabilitation training amplitude pattern recognition model to control the lower limb hip and knee rehabilitation training device; S106: Identify and obtain the fatigue-prone key points of the target training object when performing the current walking posture, build a multi-dimensional electromyographic fatigue thermal model based on the electromyographic fatigue sensor data of the lower limb hip and knee rehabilitation training equipment, and use the multi-dimensional electromyographic fatigue thermal model to perform thermal analysis on the fatigue-prone key points to control the rate and frequency of the lower limb hip and knee rehabilitation training equipment; S108: Calculate the lower limb hip and knee timing state variables of the lower limb hip and knee rehabilitation training equipment transferred from the adjacent training electromyogram to the current training electromyogram through the reflex contraction replacement of the worst solution simple vertex, and control the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training equipment according to the change in the lower limb hip and knee timing state.

[0006] More specifically, the step S102 includes the following steps: Monitoring the static data of the lower limb hip and knee joints of the target training subject through an inertial measurement unit sensor on a lower limb hip and knee rehabilitation training device, and defining the initial static gait of the target training subject according to the static data of the lower limb hip and knee joints; Acquire force sensor monitoring data of a lower limb hip and knee rehabilitation training device, extract force distribution data of a target training subject's thigh and calf during dynamic training on the lower limb hip and knee rehabilitation training device through the force sensor monitoring data, and preset a gait covariance function of the target training subject's dynamic training starting from an initial static gait based on the force distribution data; Acquire a human motion knowledge graph based on a big data network, identify the force distribution data through the human motion knowledge graph, and output the gait force characteristics of the target training object and the standard force movement law of dynamic walking starting from the initial static gait; Constructing a Gaussian process regression model for gait transfer, performing covariance calculation on a gait covariance function in the Gaussian process regression model based on the gait force characteristics, generating a covariance matrix, and determining a Gaussian gait prior distribution describing the target training subject's walking training from an initial stationary gait according to the covariance matrix; Based on the standard force movement law, a probability likelihood function for dynamic training starting from an initial static gait is constructed, the logarithm of the probability likelihood function is calculated and the partial derivative is solved to obtain the gradient of the probability likelihood function, the gradient is used to infer the Gaussian gait prior distribution to obtain the Gaussian gait posterior distribution, and the current walking posture of the target training object is determined according to the Gaussian gait posterior distribution description.

[0007] More specifically, the step S104 includes the following steps: Obtaining a preset rehabilitation training strategy for a lower limb hip and knee rehabilitation training device, obtaining preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings through the preset rehabilitation training strategy, and obtaining the rehabilitation training amplitude corresponding to each preset activity interval; Obtain the lower limb hip and knee case reports of the target training object and the injury rating criteria for the lower limb hip and knee, and add descriptions to the rating indicators of each injury description information in the lower limb hip and knee case reports through the description conditions of the injury rating criteria, so as to generate a radar chart of the lower limb hip and knee injury rating of the target training object; A variational autoencoder is constructed based on the preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings, and a variational autodecoder is constructed based on the rehabilitation training amplitude corresponding to each preset activity interval; Constructing a latent recognition space of lower limb hip and knee walking posture, inputting the lower limb hip and knee injury rating radar chart into a variational autoencoder and mapping it to the latent recognition space to obtain an implicit activity interval coding sequence, and performing variational inference learning on the latent variable distribution of the implicit activity interval coding sequence in a variational autodecoder; Through variational inference learning, the reconstruction loss difference of the rehabilitation training range planning under the constraint of the lower limb hip and knee injury rating radar chart is obtained, and the allowable reconstruction loss threshold is preset based on the current walking posture of the target training object; If the reconstruction loss difference is greater than the allowable reconstruction loss threshold, the reconstruction loss difference is minimized to train the rehabilitation training amplitude corresponding to the activity interval until the reconstruction loss difference is less than the allowable reconstruction loss threshold, thereby obtaining a rehabilitation training amplitude pattern recognition model of the lower limb hip and knee rehabilitation training equipment under the constraints of the lower limb hip and knee injury rating radar chart; The current walking posture is identified through a rehabilitation training amplitude pattern recognition model to obtain a current rehabilitation training amplitude suitable for the target training object, and the power joints of the lower limb hip and knee rehabilitation training equipment are intelligently controlled according to the current rehabilitation training amplitude.

[0008] More specifically, the step of obtaining a lower limb hip and knee case report of a target training subject and a criterion for rating the lower limb hip and knee injuries, overwriting and adding descriptions of rating indicators to each injury description information in the lower limb hip and knee case report through the description conditions of the injury rating criterion, and generating a radar chart for rating the lower limb hip and knee injuries of the target training subject specifically includes the following steps: Obtaining a lower limb hip and knee case report of a target training subject, and extracting a plurality of injury description information about the lower limb hip and knee of the target training subject through the lower limb hip and knee case report; Based on the big data network, the injury rating criteria for the lower limb hip and knee are obtained, and a descriptive condition set for each different injury rating is constructed according to the injury rating criteria, and each descriptive condition is defined as a conditional predicate; A gain trade-off algorithm is introduced to calculate the information gain between each injury description information and each condition predicate, and the coverage of the injury rating to the injury description information is determined according to the information gain, and the rating index of each injury rating in the injury rating criteria is obtained, and the radar feature matrix of the lower limb hip and knee injury description is established based on the rating index of each injury rating; If the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is not added and ignored; if the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is added to describe the rating feature of the injury description information, and the described rating feature is fitted into the radar feature matrix; Repeat the above steps of adding the injury ratings corresponding to the conditional predicates to the radar feature matrix one by one to describe the injury description information until the rating features of all injury description information are traversed and the description fitting is completed, and finally generate the lower limb hip and knee injury rating radar chart of the target training object.

[0009] More specifically, the step S106 includes the following steps: Obtain electromyographic fatigue sensor data and the arrangement array of electromyographic sensors of lower limb hip and knee rehabilitation training equipment, obtain a standard human body structure diagram of lower limb hip and knee joints based on a big data network, construct a multidimensional thermal density framework of lower limb hip and knee joints according to the standard human body structure diagram, and preset a density kernel function and bandwidth smoothing parameters based on the electromyographic fatigue sensor data; Calculating the density contribution function of each electromyographic fatigue sensing data based on the density kernel function and the bandwidth smoothing parameter to obtain a plurality of density contribution functions, and performing thermal summation on the plurality of density contribution functions in a multidimensional thermal density framework according to the arrangement array to obtain a multidimensional electromyographic fatigue thermal model; The current walking posture of the target training object is identified through the human motion knowledge graph to obtain the muscle movement distribution of the target training object's lower limb hip and knee when walking, and the muscle fatigue area is delineated in the standard human body structure diagram according to the muscle movement distribution, and marked as the fatigue key point; Construct an electromyographic fatigue thermal color gamut table of the target training object's fatigue-prone key points in a non-fatigue state, and obtain the electromyographic fatigue thermal chromaticity of the fatigue-prone key points through a multi-dimensional electromyographic fatigue thermal model; If the electromyography fatigue thermal color gamut table can query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally; if the electromyography fatigue thermal color gamut table cannot query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally.

[0010] More specifically, the step S108 includes the following steps: Acquire the real-time electromyographic fatigue signal of the lower limb hip and knee rehabilitation training equipment at the current training time sequence, introduce the wavelet transform algorithm to perform wavelet basis transform on the electromyographic fatigue signal and reconstruct the electromyographic state display, and obtain the training electromyogram of the lower limb hip and knee of the target training object at the current training time sequence, which is defined as the current training electromyogram; Acquire a training electromyogram of a lower limb hip and knee rehabilitation training device located at an adjacent training sequence compared to the current training sequence, which is defined as an adjacent training electromyogram; Constructing a multidimensional space according to the time series change from the adjacent time series to the current time series, taking the current training electromyogram as the objective function, planning a simplex transferred from the adjacent training electromyogram to the current training electromyogram based on the multidimensional space, and calculating the objective function value of each simplex vertex on the simplex; Only the simple vertex corresponding to the maximum objective function value is extracted and marked as the worst solution simple vertex, and the reflection point positioning formula is introduced to calculate the reflection point of the worst solution simple vertex to obtain the objective function value of the reflection point; If the objective function value of the reflection point is less than the objective function value of the simple vertex of the worst solution, the reflection point is accepted and the simple vertex of the worst solution is replaced; if the objective function value of the reflection point is greater than the objective function value of the simple vertex of the worst solution, a contraction operation is performed on the reflection point to generate a contraction point, and the contraction point is accepted to replace the simple vertex of the worst solution; Based on the preset iteration frequency of the current training electromyogram, repeat the above steps of calculating and analyzing the objective functions of the reflection points and the contraction points to replace the simple vertex of the worst solution until the replacement iteration of the simple vertex of the worst solution reaches the iteration frequency, and obtain the lower limb hip and knee time sequence state variables of the target training object transferred from the adjacent training electromyogram to the current training electromyogram; The lower limb hip and knee timing state variables are input into the lower limb hip and knee rehabilitation training device to control and adjust the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training device.

[0011] A second aspect of the present invention provides an intelligent control system for lower limb hip and knee rehabilitation training equipment. The intelligent control system for lower limb hip and knee rehabilitation training equipment includes a memory and a processor. The memory stores an intelligent control method program for lower limb hip and knee rehabilitation training equipment. When the intelligent control method program is executed by the processor, any step of the intelligent control method is implemented.

[0012] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are: Obtain the static data of the lower limb hip and knee joints and the force distribution data of the thigh and calf dynamic training of the target training object, use the static data of the lower limb hip and knee joints and the force distribution data to perform Gaussian distribution calculation on the gait walking of the target training object, and obtain the current walking posture of the target training object; based on the lower limb hip and knee injury rating radar chart of the target training object, perform variational inference learning on the recognition of the activity interval to the rehabilitation training amplitude of the preset rehabilitation training strategy of the lower limb hip and knee rehabilitation training equipment, generate a rehabilitation training amplitude pattern recognition model, and obtain the current rehabilitation training amplitude control of the current walking posture through the rehabilitation training amplitude pattern recognition model. Lower limb hip and knee rehabilitation training equipment; identifying and obtaining the fatigue-prone key points of the target training object in the current walking posture, constructing a multidimensional electromyographic fatigue thermal model based on the electromyographic fatigue sensor data of the lower limb hip and knee rehabilitation training equipment, and using the multidimensional electromyographic fatigue thermal model to perform thermal analysis on the fatigue-prone key points to control the rate and frequency of the lower limb hip and knee rehabilitation training equipment; calculating the lower limb hip and knee time series state variables of the lower limb hip and knee rehabilitation training equipment transferred from the adjacent training electromyogram to the current training electromyogram through the reflex contraction replacement of the worst solution simple vertex, and controlling the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training equipment according to the change in the lower limb hip and knee time series state. The present invention can accurately and intelligently control the dynamic joint training amplitude, training rate and frequency when the target training object uses the lower limb hip and knee rehabilitation training equipment, as well as the electromyographic detection-based adjustment, replacing the phenomenon that traditional manual rehabilitation training is difficult to achieve the expected, and significantly improving the recovery rate and effect of hip and knee joint injuries. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A first method flow chart of an intelligent control method for lower limb hip and knee rehabilitation training equipment is shown; Figure 2 A second method flow chart of an intelligent control method for lower limb hip and knee rehabilitation training equipment is shown; Figure 3 A system framework diagram of an intelligent control system for lower limb hip and knee rehabilitation training equipment is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] The first aspect of the present invention provides an intelligent control method for a lower limb hip and knee rehabilitation training device, such as Figure 1 As shown, the following steps are included: S102: Obtain static data of the lower limb hip and knee joints and force distribution data of the thigh and calf dynamic training of the target training object, and use the static data of the lower limb hip and knee joints and the force distribution data to perform Gaussian distribution calculation on the gait walking of the target training object to obtain the current walking posture of the target training object; S104: Based on the lower limb hip and knee injury rating radar chart of the target training object, the preset rehabilitation training strategy of the lower limb hip and knee rehabilitation training device is subjected to recognition variational inference learning from the activity interval to the rehabilitation training amplitude, and a rehabilitation training amplitude pattern recognition model is generated. The current rehabilitation training amplitude of the current walking posture is obtained through the rehabilitation training amplitude pattern recognition model to control the lower limb hip and knee rehabilitation training device; S106: Identify and obtain the fatigue-prone key points of the target training object when performing the current walking posture, build a multi-dimensional electromyographic fatigue thermal model based on the electromyographic fatigue sensor data of the lower limb hip and knee rehabilitation training equipment, and use the multi-dimensional electromyographic fatigue thermal model to perform thermal analysis on the fatigue-prone key points to control the rate and frequency of the lower limb hip and knee rehabilitation training equipment; S108: Calculate the lower limb hip and knee timing state variables of the lower limb hip and knee rehabilitation training equipment transferred from the adjacent training electromyogram to the current training electromyogram through the reflex contraction replacement of the worst solution simple vertex, and control the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training equipment according to the change in the lower limb hip and knee timing state.

[0018] More specifically, the step S102 is as follows: Figure 2 As shown, the specific steps include: S202: monitoring static data of the lower limb hip and knee joints of the target training subject through an inertial measurement unit sensor on a lower limb hip and knee rehabilitation training device, and defining an initial static gait of the target training subject according to the static data of the lower limb hip and knee joints; S204: acquiring monitoring data of a force sensor of a lower limb hip and knee rehabilitation training device, extracting force distribution data of a target training subject's thigh and calf during dynamic training on the lower limb hip and knee rehabilitation training device through the force sensor monitoring data, and presetting a gait covariance function of the target training subject's dynamic training starting from an initial static gait based on the force distribution data; S206: Acquire a human motion knowledge graph based on a big data network, identify the force distribution data through the human motion knowledge graph, and output gait force characteristics of the target training object and a standard force movement law of dynamic walking starting from an initial static gait; S208: constructing a Gaussian process regression model for gait transfer, performing covariance calculation on a gait covariance function in the Gaussian process regression model based on the gait force characteristics, generating a covariance matrix, and determining a Gaussian gait prior distribution describing the target training subject's training walking from an initial static gait according to the covariance matrix; S210: Construct a probability likelihood function for dynamic training starting from an initial static gait based on the standard force movement law, calculate the logarithm of the probability likelihood function and solve the partial derivative to obtain the gradient of the probability likelihood function, use the gradient to infer the Gaussian gait prior distribution to obtain the Gaussian gait posterior distribution, and determine the current walking posture of the target training object according to the Gaussian gait posterior distribution description.

[0019] It should be noted that the lower limb hip and knee rehabilitation training equipment mainly uses power joints to achieve power output and the movement of the exoskeleton robot. The change in the angle of the exoskeleton robot's knee joint causes the movement of the exoskeleton thigh and calf, changes the inclination angle of the thigh and calf, and achieves the rehabilitation training effect of the lower limb hip and knee joints. However, due to the differences in the gait postures formed between the thighs and calves of different patients, this means that the maximum range of active inclination angles that different patients can adapt to or withstand is also inconsistent. If the control of the equipment is difficult to adaptively and accurately control the straight and straight hip and knee joints of the patient's walking gait, it may cause discomfort or even excessive stretching and pain in the patient's hip and knee joints during training, which is not conducive to the rapid recovery of the patient's hip and knee joints. Therefore, it is crucial to analyze and adapt the patient's walking posture for the control of the lower limb hip and knee rehabilitation training equipment. In this regard, this method obtains the joint posture habits of the target training object's thighs and calves when bound to the lower limb hip and knee rehabilitation training equipment contained in the static data of the lower limb hip and knee joints to copy the patient's static gait habits, that is, the initial static gait, and then sets the gait covariance function of the patient's dynamic training from the initial static gait according to the force distribution data of the patient's dynamic training. The force distribution data reveals the force trend of the patient's leg when walking, which can be correspondingly identified from the human body movement knowledge map, that is, the gait force characteristics and the standard force movement law of dynamic walking from the initial static gait, where the gait force characteristics include the gait force amplitude, gait force direction and gait force. The gait covariance function determines the smoothness and periodic coherence of the random deduction of the walking posture, can effectively capture the potential law of learning gait force changes, and affects the accuracy of the corresponding walking posture formed by different gait habits of different patients. Therefore, it is necessary to set a corresponding gait covariance function according to the leg force points of different patients to deduce the walking posture.

[0020] It should be noted that the covariance of the gait covariance function is calculated in the Gaussian process regression model based on the gait force characteristics. The generated covariance matrix provides the force distribution deduction and coordination information under the premise of gait force characteristics, so that the Gaussian process can effectively and accurately perform walking inference between different force distribution data points, making the Gaussian gait prior distribution of walking posture more accurate and reliable. Then, based on the standard force motion law, a dynamic training is constructed from the initial static gait to obtain a gradient about the probability likelihood function. The gradient is used to infer the Gaussian gait prior distribution to obtain the Gaussian gait posterior distribution of the walking posture. The probability likelihood function is a bridge between the Gaussian process regression model and the force distribution data. It can more accurately explain the probability of the corresponding walking process of the gait following the patient's leg force trend based on the standard force motion law, and reduce the control deviation caused by the randomness of the gait walking. The gradient represents the gait walking trajectory that best matches the patient's leg force trend. According to the gradient, the Gaussian gait prior distribution can be inferred to accurately obtain the dynamic walking posture of the patient's initial static gait binding with the corresponding leg force. The lower limb hip and knee rehabilitation training equipment can adaptively control the appropriate straight and curved range of motion for different patients based on this walking posture. This method can realize the deduction and description of the walking morphology and posture of different patients, and provide a more accurate and reliable control basis for the lower limb hip and knee rehabilitation training equipment to adjust the rehabilitation training for different patients, thereby realizing intelligent adaptive control.

[0021] More specifically, the step S104 includes the following steps: Obtaining a preset rehabilitation training strategy for a lower limb hip and knee rehabilitation training device, obtaining preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings through the preset rehabilitation training strategy, and obtaining the rehabilitation training amplitude corresponding to each preset activity interval; The step of obtaining a lower limb hip and knee case report of a target training subject and a criterion for rating the lower limb hip and knee injuries, overwriting and adding descriptions to each injury description information in the lower limb hip and knee case report through the description conditions of the criterion for rating the injuries, and generating a radar chart for rating the lower limb hip and knee injuries of the target training subject; A variational autoencoder is constructed based on the preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings, and a variational autodecoder is constructed based on the rehabilitation training amplitude corresponding to each preset activity interval; Constructing a latent recognition space of lower limb hip and knee walking posture, inputting the lower limb hip and knee injury rating radar chart into a variational autoencoder and mapping it to the latent recognition space to obtain an implicit activity interval coding sequence, and performing variational inference learning on the latent variable distribution of the implicit activity interval coding sequence in a variational autodecoder; Through variational inference learning, the reconstruction loss difference of the rehabilitation training range planning under the constraint of the lower limb hip and knee injury rating radar chart is obtained, and the allowable reconstruction loss threshold is preset based on the current walking posture of the target training object; If the reconstruction loss difference is greater than the allowable reconstruction loss threshold, the reconstruction loss difference is minimized to train the rehabilitation training amplitude corresponding to the activity interval until the reconstruction loss difference is less than the allowable reconstruction loss threshold, thereby obtaining a rehabilitation training amplitude pattern recognition model of the lower limb hip and knee rehabilitation training equipment under the constraints of the lower limb hip and knee injury rating radar chart; The current walking posture is identified through a rehabilitation training amplitude pattern recognition model to obtain a current rehabilitation training amplitude suitable for the target training object, and the power joints of the lower limb hip and knee rehabilitation training equipment are intelligently controlled according to the current rehabilitation training amplitude.

[0022] It should be noted that, since the degree of hip and knee injury in different patients may be severe or mild, this requires the lower limb hip and knee rehabilitation training equipment to have a reasonable range of activity to meet the corresponding injury training, and the lower limb hip and knee rehabilitation training equipment is preset with multiple adjustable rehabilitation training amplitudes or angles for each activity interval, so as to meet the training needs of different strengths and flexibility of the hip and knee joints of patients during the bending and straightening of the legs. However, the control method of the existing rehabilitation training equipment is difficult to accurately control the appropriate rehabilitation training amplitude for the patient according to the patient's walking posture habits and the severity of the injury, which may cause the patient's hip and knee joints to increase more load intensity and resistance during the extension and flexion process, which is easy to aggravate the patient's hip and knee injury. Therefore, it is necessary for the lower limb hip and knee rehabilitation training equipment to be able to adapt the leg activity interval for the patient in advance according to the degree of hip and knee injury, and further identify and limit the appropriate bending and straightening amplitude. To this end, this method evaluates lower limb hip and knee case reports according to the lower limb hip and knee injury rating criteria to generate a lower limb hip and knee injury rating radar chart for the target training object, so as to more quickly and meticulously clarify the injury status of the patient's hip and knee joints; then, a set of variational autoencoders and variational autodecoders are constructed for preset activity intervals with different injury ratings and the rehabilitation training amplitude corresponding to each preset activity interval. The variational autoencoder can map the lower limb hip and knee injury rating radar chart to the potential recognition space of the lower limb hip and knee walking posture with the preset activity interval as the constraint premise, and then the variational inference learning of the implicit activity interval coding sequence can quickly determine the range of activity that the patient can walk under the current injury degree, so as to achieve the effect of planning the range of reasonable activity for the patient based on the constraints of different hip and knee injury degrees of the patient's hip and knee joints.

[0023] It should be noted that through variational inference learning, a variety of rehabilitation training amplitude patterns corresponding to the preset activity intervals suitable for the patient's hip and knee joint injuries can be obtained. Since the walking posture determines the maximum rehabilitation training amplitude that the patient can bear, the selection and control of these rehabilitation training amplitude patterns need to be determined based on the patient's current walking posture. In this regard, this method obtains the reconstruction loss difference of the rehabilitation training amplitude value planning under the constraint of the lower limb hip and knee injury rating radar map through variational inference learning. The reconstruction loss measures the difference between the sample reconstructed by the variational self-decoder and the original input, reflecting the model's ability and accuracy in recognition generation. Excessive reconstruction loss may cause the recognition result of the recognition model to be difficult to meet the input premise to the maximum extent. Therefore, it is necessary to minimize the reconstruction loss to ensure that the rehabilitation training amplitude pattern generated by the variational self-decoder is as consistent as possible with the current walking posture of the input, improve the accuracy and reliability of the rehabilitation training amplitude recognition that meets the current walking posture based on the patient's injury constraint, and finally generate a rehabilitation training amplitude pattern recognition model for the lower limb hip and knee rehabilitation training equipment under the constraint of the lower limb hip and knee injury rating radar map. The current rehabilitation training amplitude identified by the rehabilitation training amplitude pattern recognition model for the current walking posture is the patient's optimal rehabilitation training amplitude, which can avoid the risk of excessive stretching and excessive load resistance of the patient due to improper control of the lower limb hip and knee rehabilitation training equipment, and ensure the comfort and adaptability of the patient's rehabilitation training, improve the rehabilitation training effect and rehabilitation rate of the patient's hip and knee joints, and realize the intelligent control of rehabilitation training of the power joints of the lower limb hip and knee rehabilitation training equipment.

[0024] More specifically, the step of obtaining a lower limb hip and knee case report of a target training subject and a criterion for rating the lower limb hip and knee injuries, overwriting and adding descriptions of rating indicators to each injury description information in the lower limb hip and knee case report through the description conditions of the injury rating criterion, and generating a radar chart for rating the lower limb hip and knee injuries of the target training subject specifically includes the following steps: Obtaining a lower limb hip and knee case report of a target training subject, and extracting a plurality of injury description information about the lower limb hip and knee of the target training subject through the lower limb hip and knee case report; Based on the big data network, the injury rating criteria for the lower limb hip and knee are obtained, and a descriptive condition set for each different injury rating is constructed according to the injury rating criteria, and each descriptive condition is defined as a conditional predicate; A gain trade-off algorithm is introduced to calculate the information gain between each injury description information and each condition predicate, and the coverage of the injury rating to the injury description information is determined according to the information gain, and the rating index of each injury rating in the injury rating criteria is obtained, and the radar feature matrix of the lower limb hip and knee injury description is established based on the rating index of each injury rating; If the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is not added and ignored; if the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is added to describe the rating feature of the injury description information, and the described rating feature is fitted into the radar feature matrix; Repeat the above steps of adding the injury ratings corresponding to the conditional predicates to the radar feature matrix one by one to describe the injury description information until the rating features of all injury description information are traversed and the description fitting is completed, and finally generate the lower limb hip and knee injury rating radar chart of the target training object.

[0025] It should be noted that the existing control methods for lower limb hip and knee rehabilitation training equipment do not have the corresponding effect of assessing the degree of hip and knee joint injuries of patients based on the diagnosed case reports, making it difficult to identify and obtain the control range suitable for patient rehabilitation training, reducing the control diversity and accuracy of the equipment. Therefore, this method aims to maintain the maximum reasonable degree of injury assessment for patients based on the case reports provided by patients, and provide a more powerful control basis for the intelligent rehabilitation training control of subsequent lower limb rehabilitation training equipment. This method covers and adds the descriptive conditions of different injury ratings in the injury rating criteria of the lower limb hip and knee to each injury description in the case report in the form of conditional predicates, wherein the information gain represents the support of the conditional predicate for the injury description, reflects the coverage of the injury rating to the injury description information, and is the key to accurate assessment; if the coverage is greater than the preset coverage, it means that the injury rating corresponding to the conditional predicate cannot accurately assess the injury description in an all-round manner, and other conditional predicates need to be selected for assessment, so the injury rating corresponding to the conditional predicate is not added and ignored; otherwise, it means that the injury rating corresponding to the conditional predicate can accurately assess the injury degree of the hip and knee joints corresponding to the injury description in an all-round manner, ensuring the credibility of the injury assessment of the hip and knee joints based on the patient's diagnosed case report, so the conditional predicate is added to describe the characteristics of the injury information and fit it into the radar feature matrix. The comprehensive presentation of radar charts can improve the recognition rate of lower limb hip and knee rehabilitation training equipment for multi-index injury assessment, replace the traditional tedious steps of manual analysis of different indicators one by one, reduce the amount of information data calculation, and optimize equipment control efficiency and accuracy.

[0026] More specifically, the step S106 includes the following steps: Obtain electromyographic fatigue sensor data and the arrangement array of electromyographic sensors of lower limb hip and knee rehabilitation training equipment, obtain a standard human body structure diagram of lower limb hip and knee joints based on a big data network, construct a multidimensional thermal density framework of lower limb hip and knee joints according to the standard human body structure diagram, and preset a density kernel function and bandwidth smoothing parameters based on the electromyographic fatigue sensor data; Calculating the density contribution function of each electromyographic fatigue sensing data based on the density kernel function and the bandwidth smoothing parameter to obtain a plurality of density contribution functions, and performing thermal summation on the plurality of density contribution functions in a multidimensional thermal density framework according to the arrangement array to obtain a multidimensional electromyographic fatigue thermal model; The current walking posture of the target training object is identified through the human motion knowledge graph to obtain the muscle movement distribution of the target training object's lower limb hip and knee when walking, and the muscle fatigue area is delineated in the standard human body structure diagram according to the muscle movement distribution, and marked as the fatigue key point; Construct an electromyographic fatigue thermal color gamut table of the target training object's fatigue-prone key points in a non-fatigue state, and obtain the electromyographic fatigue thermal chromaticity of the fatigue-prone key points through a multi-dimensional electromyographic fatigue thermal model; If the electromyography fatigue thermal color gamut table can query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally; if the electromyography fatigue thermal color gamut table cannot query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally.

[0027] It should be noted that patients with different degrees of hip and knee joint injuries will experience muscle fatigue after a period of training. Therefore, lower limb hip and knee rehabilitation training equipment is usually equipped with electromyographic sensors to detect the fatigue of rehabilitation training of patients' calf muscles; however, existing lower limb hip and knee rehabilitation training equipment can only make simple early warning responses based on sensor detection results to remind patients to adjust the training rate and frequency by themselves, and there is no corresponding control decision to perform intelligent control based on muscle fatigue, which greatly reduces the intelligent degree of rehabilitation training of the equipment, increases the risk of patients' activities aggravating injuries, and is not conducive to the rapid recovery of patients' hip and knee joints. In view of this, this method presets the density kernel function and bandwidth smoothing parameters through electromyographic fatigue sensor data, and then fits the density contribution of electromyographic fatigue sensor data according to the density kernel function and bandwidth smoothing parameters, so as to construct a multidimensional electromyographic fatigue thermal model of lower limb hip and knee joints. The thermal chromaticity displayed in the multidimensional electromyographic fatigue thermal model can quickly determine the leg area where the patient has muscle fatigue, and then further conduct thermal query analysis on the fatigue-prone key points that drive the joint distribution of muscle movement under the patient's current walking posture. If the electromyographic fatigue thermal color gamut table can query the electromyographic fatigue thermal chromaticity, it means that the muscle at the fatigue-prone key point is not in a fatigue state, and rehabilitation training can still be carried out according to the current equipment operation rate and frequency, and can even be appropriately increased, so the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are incrementally controlled; if the electromyographic fatigue thermal color gamut table does not query the electromyographic fatigue thermal chromaticity, it means that the muscle at the fatigue-prone key point on the leg is in a fatigue state after long-term training, which may be accompanied by soreness and pain, and it is no longer suitable to continue the rehabilitation training at the current equipment operation rate and frequency. Therefore, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment need to be decreased to reduce the rehabilitation training intensity, so that the patient's fatigued muscles can get proper relaxation and rest. Through this method, the electromyographic fatigue sensor data of the patient's rehabilitation training can be fitted into the expression form of the thermal model to analyze whether the patient's muscles are over-fatigued, so as to accurately control the training intensity of the lower limb hip and knee rehabilitation training equipment, improve muscle training fatigue and strength, and help promote the recovery effect and rate of the patient's hip and knee joints.

[0028] More specifically, the step S108 includes the following steps: Acquire the real-time electromyographic fatigue signal of the lower limb hip and knee rehabilitation training equipment at the current training time sequence, introduce the wavelet transform algorithm to perform wavelet basis transform on the electromyographic fatigue signal and reconstruct the electromyographic state display, and obtain the training electromyogram of the lower limb hip and knee of the target training object at the current training time sequence, which is defined as the current training electromyogram; Acquire a training electromyogram of a lower limb hip and knee rehabilitation training device located at an adjacent training sequence compared to the current training sequence, which is defined as an adjacent training electromyogram; Constructing a multidimensional space according to the time series change from the adjacent time series to the current time series, taking the current training electromyogram as the objective function, planning a simplex transferred from the adjacent training electromyogram to the current training electromyogram based on the multidimensional space, and calculating the objective function value of each simplex vertex on the simplex; Only the simple vertex corresponding to the maximum objective function value is extracted and marked as the worst solution simple vertex, and the reflection point positioning formula is introduced to calculate the reflection point of the worst solution simple vertex to obtain the objective function value of the reflection point; If the objective function value of the reflection point is less than the objective function value of the simple vertex of the worst solution, the reflection point is accepted and the simple vertex of the worst solution is replaced; if the objective function value of the reflection point is greater than the objective function value of the simple vertex of the worst solution, a contraction operation is performed on the reflection point to generate a contraction point, and the contraction point is accepted to replace the simple vertex of the worst solution; Based on the preset iteration frequency of the current training electromyogram, the above steps of calculating and analyzing the objective functions of the reflection points and the contraction points to replace the simple vertex of the worst solution are repeated until the replacement iteration of the simple vertex of the worst solution reaches the iteration frequency, and the muscle fatigue time series state variables of the lower limb hip and knee of the target training object transferred from the adjacent training electromyogram to the current training electromyogram are obtained; The lower limb hip and knee timing state variables are input into the lower limb hip and knee rehabilitation training device to control and adjust the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training device.

[0029] It should be noted that the electromyographic sensor can monitor the degree of muscle fatigue of the patient's thigh and calf during hip and knee joint rehabilitation training in real time. Therefore, there are differences in the electromyographic fatigue sensor data at different training time sequences. The lower limb hip and knee rehabilitation training equipment should be controlled in real time according to the patient's muscle fatigue state during the rehabilitation training process, so as to make the equipment's rehabilitation training intensity regulation more real-time and accurate. However, the existing lower limb hip and knee rehabilitation training equipment does not have this function, and still relies on manual judgment of muscle conditions for manual adjustment and control, which is time-consuming and labor-intensive, and it is easy for the equipment to reduce the accuracy of the patient's hip and knee joint rehabilitation training, and it is difficult to ensure the rehabilitation training effect. Therefore, this method first obtains two training EMGs on adjacent training time series, namely the current training EMG and the adjacent training EMG, and plans the EMG state change from the adjacent training EMG to the current training EMG as a simplex in the multidimensional space based on the time series. Since the training EMGs are mostly presented in two-dimensional form, the simplex can be a triangle. The simplex reveals the state exploration range of the EMG signal when the adjacent training EMG changes to the current training EMG, and quantifies the specific amplitude of the state migration change. Then calculate the objective function value of each simple vertex on the simplex. The objective function value determines the quality of the simple vertex, which provides reliable information about the fatigue change of the EMG state of each vertex of the current simplex during migration.

[0030] It should be noted that the simple vertex corresponding to the maximum objective function value is the worst solution for the change of electromyographic state, which is a position to be optimized that is difficult to reflect the time series change of muscle fatigue. The reflection calculation can explore a better vertex about the simple vertex of the worst solution, but not every reflection point explored can replace the simple vertex of the worst solution. If it is replaced blindly, it may cause errors in the expression of muscle fatigue changes from the state change of the adjacent training electromyogram to the current training electromyogram. Therefore, this method determines whether the objective function value of the reflection point is less than the objective function value of the simple vertex of the worst solution. If it is less than, it means that the objective function value of the reflection point is better than that of the worst point. The objective function value is better, making the described muscle fatigue state change more linear and accurate, so the reflection point is accepted and the worst solution simple vertex is replaced; otherwise, it means that the objective function value of the reflection point is still not good compared with the worst solution simple vertex, and the objective function of the reflection point can be contracted to further express the muscle fatigue state change of the worst solution simple vertex. Finally, through continuous iteration, the muscle fatigue time series state variable of the target training object's lower limb hip and knee transferred from the adjacent training electromyogram to the current training electromyogram can be obtained, and the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training equipment can be controlled and adjusted according to the muscle fatigue time series state variable. Through this method, the state update changes of muscle fatigue of the training electromyograms on two adjacent training time series can be analyzed and calculated, so that the lower limb hip and knee rehabilitation training equipment can maintain the real-time nature of the electromyographic fatigue monitoring data for the patient's rehabilitation training amplitude control, so as to reasonably adjust and control the optimal rehabilitation training amplitude according to the muscle fatigue state of the patient during the rehabilitation training process, improve the rehabilitation training effect and recovery rate of the patient's hip and knee joints, and reduce the risk of patient training injuries.

[0031] The second aspect of the present invention provides an intelligent control system for a lower limb hip and knee rehabilitation training device, such as Figure 3 As shown, the intelligent control system of a lower limb hip and knee rehabilitation training device includes a memory 31 and a processor 32. The memory 31 stores an intelligent control method program for the lower limb hip and knee rehabilitation training device. When the intelligent control method program is executed by the processor 32, any step of the intelligent control method is implemented.

[0032] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent control method for lower limb hip and knee rehabilitation training equipment, characterized in that: The following steps are involved: S102: Obtain static data of the lower limb hip and knee joints and force distribution data of the thigh and calf dynamic training of the target training object, and use the static data of the lower limb hip and knee joints and the force distribution data to perform Gaussian distribution calculation on the gait walking of the target training object to obtain the current walking posture of the target training object; S104: Based on the lower limb hip and knee injury rating radar chart of the target training object, the preset rehabilitation training strategy of the lower limb hip and knee rehabilitation training device is subjected to recognition variational inference learning from the activity interval to the rehabilitation training amplitude, and a rehabilitation training amplitude pattern recognition model is generated. The current rehabilitation training amplitude of the current walking posture is obtained through the rehabilitation training amplitude pattern recognition model to control the lower limb hip and knee rehabilitation training device; S106: Identify and obtain the fatigue-prone key points of the target training object when performing the current walking posture, build a multi-dimensional electromyographic fatigue thermal model based on the electromyographic fatigue sensor data of the lower limb hip and knee rehabilitation training equipment, and use the multi-dimensional electromyographic fatigue thermal model to perform thermal analysis on the fatigue-prone key points to control the rate and frequency of the lower limb hip and knee rehabilitation training equipment; S108: Calculate the lower limb hip and knee timing state variables of the lower limb hip and knee rehabilitation training equipment transferred from the adjacent training electromyogram to the current training electromyogram through the reflex contraction replacement of the worst solution simple vertex, and control the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training equipment according to the change in the lower limb hip and knee timing state.

2. The intelligent control method of a lower limb hip and knee rehabilitation training device according to claim 1, characterized in that: The step S102 specifically includes the following steps: Monitoring the static data of the lower limb hip and knee joints of the target training subject through an inertial measurement unit sensor on a lower limb hip and knee rehabilitation training device, and defining the initial static gait of the target training subject according to the static data of the lower limb hip and knee joints; Acquire force sensor monitoring data of a lower limb hip and knee rehabilitation training device, extract force distribution data of a target training subject's thigh and calf during dynamic training on the lower limb hip and knee rehabilitation training device through the force sensor monitoring data, and preset a gait covariance function of the target training subject's dynamic training starting from an initial static gait based on the force distribution data; Acquire a human motion knowledge graph based on a big data network, identify the force distribution data through the human motion knowledge graph, and output the gait force characteristics of the target training object and the standard force movement law of dynamic walking starting from the initial static gait; Constructing a Gaussian process regression model for gait transfer, performing covariance calculation on a gait covariance function in the Gaussian process regression model based on the gait force characteristics, generating a covariance matrix, and determining a Gaussian gait prior distribution describing the target training subject's walking training from an initial stationary gait according to the covariance matrix; Based on the standard force movement law, a probability likelihood function for dynamic training starting from an initial static gait is constructed, the logarithm of the probability likelihood function is calculated and the partial derivative is solved to obtain the gradient of the probability likelihood function, the gradient is used to infer the Gaussian gait prior distribution to obtain the Gaussian gait posterior distribution, and the current walking posture of the target training object is determined according to the Gaussian gait posterior distribution description.

3. The intelligent control method of a lower limb hip and knee rehabilitation training device according to claim 1, characterized in that: The step S104 specifically includes the following steps: Obtaining a preset rehabilitation training strategy for a lower limb hip and knee rehabilitation training device, obtaining preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings through the preset rehabilitation training strategy, and obtaining the rehabilitation training amplitude corresponding to each preset activity interval; Obtain the lower limb hip and knee case reports of the target training object and the injury rating criteria for the lower limb hip and knee, and add descriptions to the rating indicators of each injury description information in the lower limb hip and knee case reports through the description conditions of the injury rating criteria, so as to generate a radar chart of the lower limb hip and knee injury rating of the target training object; A variational autoencoder is constructed based on the preset activity intervals of the lower limb hip and knee rehabilitation training device for different injury ratings, and a variational autodecoder is constructed based on the rehabilitation training amplitude corresponding to each preset activity interval; Constructing a latent recognition space of lower limb hip and knee walking posture, inputting the lower limb hip and knee injury rating radar chart into a variational autoencoder and mapping it to the latent recognition space to obtain an implicit activity interval coding sequence, and performing variational inference learning on the latent variable distribution of the implicit activity interval coding sequence in a variational autodecoder; Through variational inference learning, the reconstruction loss difference of the rehabilitation training range planning under the constraint of the lower limb hip and knee injury rating radar chart is obtained, and the allowable reconstruction loss threshold is preset based on the current walking posture of the target training object; If the reconstruction loss difference is greater than the allowable reconstruction loss threshold, the reconstruction loss difference is minimized to train the rehabilitation training amplitude corresponding to the activity interval until the reconstruction loss difference is less than the allowable reconstruction loss threshold, thereby obtaining a rehabilitation training amplitude pattern recognition model of the lower limb hip and knee rehabilitation training equipment under the constraints of the lower limb hip and knee injury rating radar chart; The current walking posture is identified through a rehabilitation training amplitude pattern recognition model to obtain a current rehabilitation training amplitude suitable for the target training object, and the power joints of the lower limb hip and knee rehabilitation training equipment are intelligently controlled according to the current rehabilitation training amplitude.

4. The intelligent control method of a lower limb hip and knee rehabilitation training device according to claim 3, characterized in that: The step of obtaining a lower limb hip and knee case report of a target training subject and a criterion for rating the lower limb hip and knee injuries, overwriting and adding descriptions of rating indicators to each injury description information in the lower limb hip and knee case report through the description conditions of the injury rating criterion, and generating a radar chart for rating the lower limb hip and knee injuries of the target training subject specifically includes the following steps: Obtaining a lower limb hip and knee case report of a target training subject, and extracting a plurality of injury description information about the lower limb hip and knee of the target training subject through the lower limb hip and knee case report; Based on the big data network, the injury rating criteria for the lower limb hip and knee are obtained, and a descriptive condition set for each different injury rating is constructed according to the injury rating criteria, and each descriptive condition is defined as a conditional predicate; A gain trade-off algorithm is introduced to calculate the information gain between each injury description information and each condition predicate, and the coverage of the injury rating to the injury description information is determined according to the information gain, and the rating index of each injury rating in the injury rating criteria is obtained, and the radar feature matrix of the lower limb hip and knee injury description is established based on the rating index of each injury rating; If the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is not added and ignored; if the coverage degree is greater than the preset coverage degree, the injury rating corresponding to the conditional predicate is added to describe the rating feature of the injury description information, and the described rating feature is fitted into the radar feature matrix; Repeat the above steps of adding the injury ratings corresponding to the conditional predicates to the radar feature matrix one by one to describe the injury description information until the rating features of all injury description information are traversed and the description fitting is completed, and finally generate the lower limb hip and knee injury rating radar chart of the target training object.

5. The intelligent control method of a lower limb hip and knee rehabilitation training device according to claim 1, characterized in that: The step S106 specifically includes the following steps: Obtain electromyographic fatigue sensor data and the arrangement array of electromyographic sensors of lower limb hip and knee rehabilitation training equipment, obtain a standard human body structure diagram of lower limb hip and knee joints based on a big data network, construct a multidimensional thermal density framework of lower limb hip and knee joints according to the standard human body structure diagram, and preset a density kernel function and bandwidth smoothing parameters based on the electromyographic fatigue sensor data; Calculating the density contribution function of each electromyographic fatigue sensing data based on the density kernel function and the bandwidth smoothing parameter to obtain a plurality of density contribution functions, and performing thermal summation on the plurality of density contribution functions in a multidimensional thermal density framework according to the arrangement array to obtain a multidimensional electromyographic fatigue thermal model; The current walking posture of the target training object is identified through the human motion knowledge graph to obtain the muscle movement distribution of the target training object's lower limb hip and knee when walking, and the muscle fatigue area is delineated in the standard human body structure diagram according to the muscle movement distribution, and marked as the fatigue key point; Construct an electromyographic fatigue thermal color gamut table of the target training object's fatigue-prone key points in a non-fatigue state, and obtain the electromyographic fatigue thermal chromaticity of the fatigue-prone key points through a multi-dimensional electromyographic fatigue thermal model; If the electromyography fatigue thermal color gamut table can query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally; if the electromyography fatigue thermal color gamut table cannot query the electromyography fatigue thermal chromaticity, the training rate and frequency of the lower limb hip and knee rehabilitation training equipment are controlled incrementally.

6. The intelligent control method of a lower limb hip and knee rehabilitation training device according to claim 1, characterized in that: The step S108 specifically includes the following steps: Acquire the real-time electromyographic fatigue signal of the lower limb hip and knee rehabilitation training equipment at the current training time sequence, introduce the wavelet transform algorithm to perform wavelet basis transform on the electromyographic fatigue signal and reconstruct the electromyographic state display, and obtain the training electromyogram of the lower limb hip and knee of the target training object at the current training time sequence, which is defined as the current training electromyogram; Acquire a training electromyogram of a lower limb hip and knee rehabilitation training device located at an adjacent training sequence compared to the current training sequence, which is defined as an adjacent training electromyogram; Constructing a multidimensional space according to the time series change from the adjacent time series to the current time series, taking the current training electromyogram as the objective function, planning a simplex transferred from the adjacent training electromyogram to the current training electromyogram based on the multidimensional space, and calculating the objective function value of each simplex vertex on the simplex; Only the simple vertex corresponding to the maximum objective function value is extracted and marked as the worst solution simple vertex, and the reflection point positioning formula is introduced to calculate the reflection point of the worst solution simple vertex to obtain the objective function value of the reflection point; If the objective function value of the reflection point is less than the objective function value of the simple vertex of the worst solution, the reflection point is accepted and the simple vertex of the worst solution is replaced; if the objective function value of the reflection point is greater than the objective function value of the simple vertex of the worst solution, a contraction operation is performed on the reflection point to generate a contraction point, and the contraction point is accepted to replace the simple vertex of the worst solution; Based on the preset iteration frequency of the current training electromyogram, repeat the above steps of calculating and analyzing the objective functions of the reflection points and the contraction points to replace the simple vertex of the worst solution until the replacement iteration of the simple vertex of the worst solution reaches the iteration frequency, and obtain the lower limb hip and knee time sequence state variables of the target training object transferred from the adjacent training electromyogram to the current training electromyogram; The lower limb hip and knee timing state variables are input into the lower limb hip and knee rehabilitation training device to control and adjust the current rehabilitation training amplitude of the lower limb hip and knee rehabilitation training device.

7. An intelligent control system for lower limb hip and knee rehabilitation training equipment, characterized in that: The intelligent control system of a lower limb hip and knee rehabilitation training device includes a memory and a processor. The memory stores an intelligent control method program for the lower limb hip and knee rehabilitation training device. When the intelligent control method program is executed by the processor, the steps of the intelligent control method as described in any one of claims 1-6 are implemented.

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