Self-adaptive balance adjusting method and system for animal postoperative walking aid

Abstract

The invention provides a self-adaptive balance adjusting method and system for an animal postoperative walking assist device, and relates to the technical field of balance adjusting.The method comprises the steps that a constraint matrix and a demand matrix are established according to the animal preoperative state and operation content; adjusting parameters of the limb adjusting units are obtained; sequentially mapping the adjustment parameter set into the constraint matrix and the demand matrix, obtaining a super-bounded parameter, and cutting from the adjustment parameter set; four-limb coordination balance evaluation is performed according to the feasible adjustment parameter set, adjustment parameter screening is performed according to balance evaluation, and target four-limb adjustment parameters are sent to a four-limb adjustment unit for four-limb adjustment according to four-limb positioning. The technical problem that in the prior art, due to the fact that standardized design is adopted for postoperative walking assistance, postoperative recovery requirements of individual animals cannot be accurately met, and the adaptability of a postoperative walking assist device is insufficient is solved, and the adaptability of the postoperative walking assist device is improved by accurately adapting to the recovery requirements.

Description

Technical Field

[0001] This application relates to the field of balance regulation technology, and in particular to an adaptive balance regulation method and system for postoperative walking aids in animals. Background Technology

[0002] Currently, most existing postoperative walking aids for animals adopt a fixed structure, uniformly immobilizing the animal's limbs. However, different animals have significant differences in body size, and postoperative recovery is a dynamic process with varying recovery progress among animals. Fixed-structure aids have limited adjustment capabilities and lack personalized adaptation mechanisms, making it difficult to accurately address each animal's unique postoperative condition and constantly changing recovery process. This can easily lead to over-restriction hindering recovery or insufficient support increasing secondary risks. Because generic designs fail to fully consider individual animal differences and dynamic changes, postoperative walking aids lack adaptability.

[0003] In summary, existing technologies suffer from the problem that standardized designs for postoperative walking aids make it difficult to accurately adapt to the individual postoperative recovery needs of animals, resulting in insufficient adaptability of postoperative walking aids. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive balance adjustment method and system for postoperative walking aids in animals, in order to solve the technical problem that the existing postoperative walking aids are not adaptable enough due to the standardized design of the design, which makes it difficult to accurately adapt to the postoperative recovery needs of individual animals.

[0005] In view of the above problems, this application provides an adaptive balance adjustment method and system for postoperative walking aids in animals.

[0006] Firstly, this application provides an adaptive balance adjustment method for an animal postoperative walking aid. This method is implemented through an adaptive balance adjustment system for the animal postoperative walking aid. The method includes: analyzing the animal's preoperative limb movement restrictions and rehabilitation exercise needs based on the animal's preoperative state and surgical procedures, and establishing a constraint matrix and a demand matrix; obtaining adjustment parameters for the limb adjustment units, performing a grid traversal on the adjustment parameters to construct an adjustment parameter set; sequentially mapping the adjustment parameter set to the constraint matrix and the demand matrix, obtaining overbound parameters through matrix verification, and trimming the overbound parameters from the adjustment parameter set to obtain a feasible adjustment parameter set; performing a limb coordination balance evaluation based on the feasible adjustment parameter set, filtering adjustment parameters based on the balance evaluation to obtain target limb adjustment parameters, and sending the target limb adjustment parameters to the limb adjustment units according to limb positioning for limb adjustment.

[0007] Optionally, a standard motor support relationship for the animal's limbs is established; based on the animal's preoperative state and surgical content, the standard motor support relationship for the animal's limbs is repositioned to obtain an abnormal motor support relationship for the limbs postoperatively; based on the abnormal motor support relationship for the limbs postoperatively, the range and amplitude of motor restriction constraints are analyzed; using the standard motor support relationship for the animal's limbs as a benchmark, rehabilitation motor support goals are analyzed based on the abnormal motor support relationship for the limbs postoperatively, and rehabilitation motor needs are obtained; the range and amplitude of motor restriction constraints and rehabilitation motor needs are decomposed into motor-related influencing factors to obtain constraint factors and demand factors, and the constraint matrix and demand matrix are constructed.

[0008] Optionally, using animal movement experiment data, the factors influencing movement are decomposed according to the range and amplitude of limb movement restrictions and the rehabilitation exercise requirements, to obtain the movement constraint characteristics corresponding to the range and amplitude of limb movement restrictions and the target balance movement characteristics corresponding to the rehabilitation exercise requirements; based on the movement constraint characteristics and the target balance movement characteristics, the associated influencing factors of the movement joints and the movement threshold are obtained, and the constraint factors and the requirement factors are obtained.

[0009] Optionally, the adjustment angle, angle adjustment range, and adjustment height of each limb adjustment unit are obtained to construct an adjustment parameter grid for the limbs. Two limb adjustment units are randomly selected from the limb adjustment units, one as the starting limb and the other as the second limb, and a linkage constraint relationship is established between the starting limb and the second limb. Based on the linkage constraint relationship, the adjustment parameter grid of the second limb is eliminated and closed. The adjustment parameter grid of the starting limb and the adjustment parameter grid of the second limb are combined through grid traversal to obtain a first combined parameter grid. The linkage constraint relationship between the third limb and the starting limb and the second limb is obtained. Based on the linkage constraint relationship, the adjustment parameter grid of the third limb is eliminated and closed. The first combined parameter grid and the third limb adjustment parameter grid are combined through grid traversal to obtain a second combined parameter grid. The second combined parameter grid and the fourth limb adjustment parameter grid are iteratively combined through grid traversal to obtain a final combined parameter grid. The adjustment parameter combinations in the final combined parameter grid are extracted to obtain the adjustment parameter set.

[0010] Optionally, a rehabilitation cycle is set, including at least the initial acute phase, the intermediate subacute phase, and the late functional phase; the width threshold of the analytical adjustment parameters is set according to the surgical content, and the grid density of each rehabilitation cycle is determined; based on the grid density, a preliminary screening is performed on the set of adjustment parameters to obtain a set of adjustment parameters that are matched for the preliminary screening.

[0011] Optionally, the constraint matrix and demand matrix are written into the verification firmware as verification matrices; the set of adjustment parameters matched in the initial screening are sequentially mapped to the verification matrix of the verification firmware to obtain the out-of-bounds parameters; the set of adjustment parameters is out-of-bounds removed according to the out-of-bounds parameters to obtain the feasible set of adjustment parameters.

[0012] Optionally, a dual closed-loop balancing mechanism is established, including an inner loop for balancing local safety and an outer loop for preventing the entire machine from overturning; using the dual closed-loop balancing mechanism, the feasible set of adjustment parameters is evaluated for inner and outer loop balance to obtain balance evaluation values; and adjustment parameters are screened based on the balance evaluation values ​​to obtain the target limb adjustment parameters.

[0013] Optionally, the limiting threshold of each limb and joint is used as the inner loop sub-gate of each adjustment unit, and the balance dependency relationship between the four limb joints is used as the inner loop parent gate; an inner loop balance mechanism is constructed using the inner loop sub-gate and the inner loop parent gate; an outer loop gate is determined according to the balance dependency relationship of the four limb adjustment units and the relative influence position of the animal's center of gravity, wherein the outer loop gate includes an outer loop balance constraint parameter, and an outer loop balance mechanism is constructed; the inner loop balance mechanism and the outer loop balance mechanism are merged to establish the double closed-loop balance mechanism.

[0014] Optionally, the kinematic data and weight distribution of the animal's limbs during walking assistance are collected through a flexible pressure sensor network; the kinematic data and weight distribution are matched with the constraint matrix and demand matrix to obtain difference data; adaptive balance adjustment compensation is performed based on the difference data to reset the limb adjustment parameters.

[0015] Secondly, this application also provides an adaptive balance adjustment system for an animal postoperative walking aid, used to execute the adaptive balance adjustment method for an animal postoperative walking aid as described in the first aspect, wherein the adaptive balance adjustment system for an animal postoperative walking aid includes: a demand analysis module, used to analyze the animal's postoperative limb movement restrictions and rehabilitation movement needs based on the animal's preoperative state and surgical content, and establish a constraint matrix and a demand matrix; an adjustment parameter traversal module, used to obtain the adjustment parameters of the limb adjustment units, perform grid traversal on the adjustment parameters of the limb adjustment units, and construct an adjustment parameter set; a parameter mapping and pruning module, used to sequentially map the adjustment parameter set to the constraint matrix and the demand matrix, obtain overbound parameters through matrix verification, prune the overbound parameters from the adjustment parameter set, and obtain a feasible adjustment parameter set; and a limb coordination balance evaluation module, used to perform limb coordination balance evaluation based on the feasible adjustment parameter set, filter adjustment parameters based on the balance evaluation, obtain target limb adjustment parameters, and send the target limb adjustment parameters to the limb adjustment units according to limb positioning for limb adjustment.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects:

[0017] Based on the animal's preoperative condition and surgical content, the postoperative limb movement restrictions and rehabilitation exercise needs are analyzed to establish a constraint matrix and a demand matrix. The adjustment parameters of the limb adjustment units are obtained, and a set of adjustment parameters is constructed by performing a grid traversal. This set of adjustment parameters is then sequentially mapped to the constraint matrix and the demand matrix. Overbound parameters are obtained through matrix verification and then pruned from the adjustment parameter set to obtain a feasible set of adjustment parameters. Based on this feasible set of adjustment parameters, limb coordination and balance are evaluated, and adjustment parameters are selected based on the balance evaluation to obtain target limb adjustment parameters. These target limb adjustment parameters are then sent to the limb adjustment units according to limb positioning for limb adjustment. In other words, based on the animal's preoperative condition and surgical content, the postoperative limb movement limitations and rehabilitation needs are analyzed, and personalized constraint and demand matrices are established. The adjustment parameters of the limb adjustment unit are traversed in a grid to construct a set of adjustment parameters. Out-of-bounds parameters that do not meet the rehabilitation needs are eliminated. The adjustment parameters are dynamically adjusted according to the recovery progress of the animal at each postoperative stage to ensure that the recovery needs at each stage can be met with appropriate parameters. This avoids the problem of insufficient adaptability caused by general design and improves the adaptability of the postoperative walking aid.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the adaptive balance adjustment method for postoperative walking aids in animals, as described in this application.

[0021] Figure 2 This is a schematic diagram of the adaptive balance adjustment system for an animal postoperative walking aid, as described in this application.

[0022] Figure labeling: Requirement analysis module 11, adjustment parameter traversal module 12, parameter mapping and trimming module 13, limb coordination and balance evaluation module 14. Detailed Implementation

[0023] This application provides an adaptive balance adjustment method and system for postoperative walking aids in animals, solving the technical problem of insufficient adaptability in existing technologies due to the standardized design of postoperative walking aids, which makes it difficult to accurately adapt to the individual postoperative recovery needs of animals. Based on the animal's preoperative condition and surgical content, the method analyzes postoperative limb movement limitations and rehabilitation needs, establishes personalized constraint and demand matrices, performs a grid traversal of the adjustment parameters of the limb adjustment units, constructs a set of adjustment parameters, eliminates out-of-bounds parameters that do not meet rehabilitation needs, and dynamically adjusts the adjustment parameters according to the animal's recovery progress at each stage. This ensures that the recovery needs at each stage are met with appropriate parameters, avoiding the insufficient adaptability problem caused by generic designs and improving the adaptability of postoperative walking aids.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides an adaptive balance adjustment method for an animal postoperative walking aid, wherein the adaptive balance adjustment method for an animal postoperative walking aid is executed by an adaptive balance adjustment system for an animal postoperative walking aid, and the adaptive balance adjustment method for an animal postoperative walking aid specifically includes the following steps:

[0026] Based on the animal's preoperative condition and surgical procedure, we analyzed the animal's postoperative limb movement restrictions and rehabilitation exercise needs, and established a constraint matrix and a demand matrix.

[0027] Furthermore, this application also includes the following steps: establishing standard motor support relationships for animal limbs; changing the positioning status of the standard motor support relationships for animal limbs according to the preoperative state and surgical content of the animal to obtain abnormal motor support relationships for postoperative limbs; analyzing the range and amplitude of motor restriction constraints for limbs based on the abnormal motor support relationships for postoperative limbs; using the standard motor support relationships for animal limbs as a benchmark, analyzing the rehabilitation motor support goals based on the abnormal motor support relationships for postoperative limbs to obtain rehabilitation motor needs; and decomposing the range and amplitude of motor restriction constraints for limbs and rehabilitation motor needs into motor-related influencing factors to obtain constraint factors and demand factors, and constructing the constraint matrix and demand matrix.

[0028] Furthermore, this application also includes the following steps: using animal movement experiment data, decomposing the factors influencing movement according to the range and amplitude of the limb movement restrictions and the rehabilitation exercise requirements, and obtaining the movement constraint characteristics corresponding to the range and amplitude of the limb movement restrictions and the target balance movement characteristics corresponding to the rehabilitation exercise requirements; obtaining the correlation influencing factors of the movement joints and the movement threshold based on the movement constraint characteristics and the target balance movement characteristics, and obtaining the constraint factor and the requirement factor.

[0029] Specifically, this involves establishing standard limb support relationships in animals, that is, the relationship between the range of motion, motor capacity, and support requirements of each limb in a healthy animal under standardized walking (e.g., at a specific speed), i.e., movement data when standing. By measuring the gait data of healthy animals, key data such as limb joint angles, gait cycles, and weight distribution are obtained for each animal to form standard limb support relationships. For example, gait analysis of a pre-operatively healthy animal revealed that during walking, the left foreleg and right hind leg each contributed 40% and 40% of the support force respectively (a total of 80% support force), with the remaining 20% ​​coming from the other legs. The limb angles ranged from 0° to 50° (the angle between the limb and the ground when standing), and the weight distribution of each limb was uniform in a healthy state.

[0030] The process involves obtaining the animal's preoperative condition and surgical content. The preoperative condition refers to the animal's health status, activity level, weight, and body shape before surgery. Preoperative imaging and physical examinations are used to identify the surgical site and potential motor injuries. The surgical content includes the specific surgical site (e.g., left hind limb knee joint, lumbar spine), surgical type (e.g., joint replacement, internal fixation of fractures, nerve exploration), surgical extent, and expected impact on limb function. Based on the preoperative condition and surgical content, the standard motor support relationship of the animal's limbs is repositioned and modified. Specific parameters in the standard motor support relationship are adjusted or modified to simulate or predict changes in movement patterns in specific limbs or joints directly caused by the surgery. Postoperative abnormal limb motor support relationships are modeled after repositioning and modification. They reflect deviations from normal standard movement patterns that may occur in the early postoperative period due to surgical trauma, pain, tissue repair needs, or the presence of fixation devices. These patterns are no longer considered a healthy baseline but rather a prediction of abnormal (e.g., restricted, compensatory) movement patterns bearing the imprint of surgery.

[0031] Standard models, whether general or species-level, are personalized for specific surgical cases, requiring careful analysis of surgical records and preoperative assessment information. For example, if a 25 kg animal undergoes surgery on its right hind limb, the right hind knee joint needs stabilization, but early postoperative pain and swelling may restrict movement. Based on the normal flexion-extension range of the hind limb knee joint in the standard model (e.g., 0 to 130 degrees), combined with the surgical procedure and the surgeon's experience, the positioning status is modified, setting the predictive abnormality support relationship for the early postoperative period (e.g., the first week) as follows: the right hind knee joint flexion-extension range is limited to 0 to 60 degrees, possibly accompanied by a slight hyperextension protective posture (e.g., up to 5 degrees). Meanwhile, considering the pain and decreased weight-bearing capacity, it is predicted that the peak ground reaction force of the right hind limb will be significantly reduced, such as from the normal 1.0×BW (245N) to 0.3 to 0.5×BW (74 to 123N), and there may be limping, which will lead to compensatory changes in the force distribution pattern of the left hind limb and both forelimbs (such as the peak force of the left hind limb may increase to 1.2 to 1.5×BW (294 to 368N), and the force distribution of both forelimbs will be more uniform, but the peak value may decrease slightly), which is the support relationship for postoperative limb movement abnormalities.

[0032] Based on the support relationship of postoperative limb movement abnormalities, the range and amplitude of limb movement restriction were analyzed. The range of limb movement restriction is the interval within which the mobility of each joint or limb is restricted during postoperative movement, i.e., the maximum range of motion that the limbs can perform. For example, the normal range of motion of a joint is 0 to 130 degrees, but the postoperative restriction range may be 0 to 60 degrees, so the restriction range is the interval of 0 to 60 degrees. The amplitude of restriction is the amount or degree to which the mobility of each joint or limb is reduced in various movement dimensions (such as flexion and extension, inversion and vaulting, adduction and abduction), which is the maximum allowable deviation of limb movement. For example, the restriction range of the right posterior knee joint in flexion is 0-60 degrees; the amplitude of restriction is 70-80 degrees; in the right posterior knee joint in extension: if the standard allows hyperextension to -10 degrees, the postoperative restriction is 0 degrees; the restriction range is 0 degrees; the amplitude of restriction is 0 - (-10) = 10 degrees. Flexion-extension speed: If the standard maximum is 30-50 degrees / second, the postoperative predicted maximum is 25 degrees / second; constraint amplitude = 50-25 = 25 degrees / second.

[0033] Based on the standard limb support relationship of the animal, and considering any abnormal limb support relationships after surgery, combined with the rehabilitation plan provided by the veterinarian (such as limiting weight-bearing in the first week post-surgery and initiating gentle activities in the second week), rehabilitation movement needs are determined. Rehabilitation movement support goals are typically set by the veterinarian based on the animal's surgery, aiming to help the animal gradually regain normal mobility. These goals represent the ideal mobility or gait goals the animal needs to achieve during the post-operative recovery phase. Rehabilitation goals might include gradually restoring the weight-bearing capacity of the left hind limb, enabling it to withstand support forces comparable to the right hind limb. For example, gradually increasing weight-bearing from 20% initially to 40%, and gradually expanding the angle range of the left hind limb from 0° to 30° to 0° to 45°.

[0034] Animal movement experimental data are kinematic (joint angles, velocity, acceleration) and dynamic (ground reaction force, electromyographic activity, etc.) data of animals under specific conditions, collected through experimental methods (such as high-speed photography, inertial sensors, force platforms, etc.). This data reflects the animal's body state and movement performance when performing specific actions (such as walking, standing). Target animals (healthy controls, models simulating post-operative conditions, or real post-operative animals) are instructed to perform a series of standardized movements, such as walking in a straight line, turning, going uphill and downhill, and single-limb weight-bearing. During the experiment, motion capture data, ground reaction force data, and electromyographic data are collected simultaneously to obtain animal movement experimental data. Using this animal movement experimental data, combined with the range and amplitude of limb movement constraints and rehabilitation exercise requirements, an analysis of factors influencing movement is conducted to obtain the movement constraint characteristics corresponding to the range and amplitude of limb movement constraints and the target balance movement characteristics corresponding to rehabilitation exercise requirements. Movement constraint characteristics are the physical or physiological limitations imposed on limb movement based on the restricted range and amplitude of limb movement. These limitations include restrictions on joint range of motion, gait patterns, and weight-bearing distribution during the postoperative recovery phase, such as narrowing of the animal's range of motion and reduction in joint angles. Target balance movement characteristics are derived from rehabilitation exercise needs. They are balance-related movement patterns with specific biomechanical and kinematic characteristics that the animal needs to perform to achieve rehabilitation goals (such as restoring a specific gait or improving balance). For example, to increase weight-bearing on the affected limb, the animal may need to learn a more stable standing posture with its center of gravity more shifted towards the affected limb. The joint angles and muscle exertion patterns of this posture constitute target balance movement characteristics.

[0035] The motion constraint features and target balance motion features are decomposed to obtain the key correlation influencing factors and motion thresholds of the motion. The correlation influencing factors of the joints in the motion are the interactions and influences between different joints of the limbs. For example, the movement of the knee joint may affect the movement of the hip and ankle joints, and the restriction of the knee joint will affect the entire gait cycle. By decomposing the motion constraint features and target balance motion features, the influence of each joint on the overall motion pattern is further analyzed to establish the correlation between joints. For example, assuming that the range of motion of the knee joint is restricted to 30°, the model calculation shows that the restriction of the knee joint has a 10% influence on the movement of the hip joint and a 15% influence on the ankle joint.

[0036] The movement threshold refers to the maximum intensity of movement that each joint or limb can withstand. By analyzing postoperative recovery data from animals, combined with their physiological characteristics and rehabilitation needs, the movement threshold for each joint can be determined. For example, the hip joint's range of motion can extend up to 45°, but in the early postoperative period, it can only withstand a range of motion from 0° to 30°. By decomposing movement constraint features and target balance movement features, constraint factors and demand factors are further obtained. The constraint factor is typically used to describe the intensity of movement restriction, while the demand factor is used to describe the intensity of the target to be achieved during rehabilitation. For example, if the knee joint's range of motion is restricted to 30°, the constraint factor can be set to 0.6, indicating the degree of movement restriction in that joint; if the postoperative goal is to restore the weight-bearing capacity of the left hind limb to 40%, the demand factor can be set to 0.7, indicating a higher intensity of that goal.

[0037] Based on constraint and demand factors, constraint and demand matrices are established. This involves organizing the decomposed constraint and demand factors into a matrix according to certain rules (such as joint / limb, movement dimension, and type of influence) to describe the limitations of postoperative motor function and rehabilitation needs in animals. By establishing standard motor support relationships and postoperative motor abnormality support relationships, personalized rehabilitation plans can be developed for each animal. Detailed analysis of motor limitations and rehabilitation needs, combined with the analysis of constraint and demand factors, ensures coordination and balance during limb recovery.

[0038] The walking aid includes a limb adjustment unit. The adjustment parameters of the limb adjustment unit are obtained, and the adjustment parameters of the limb adjustment unit are traversed through a grid to construct an adjustment parameter set.

[0039] Furthermore, this application also includes the following steps: obtaining the adjustment angle, angle adjustment range, and adjustment height of the limb adjustment units respectively, and constructing an adjustment parameter grid for the limbs; randomly selecting two limb adjustment units from the limb adjustment units, one as the starting limb and the other as the second limb, and establishing a linkage constraint relationship between the starting limb and the second limb; based on the linkage constraint relationship, eliminating and closing the adjustment parameter grid of the second limb, and performing grid traversal combination of the adjustment parameter grid of the initial limb and the adjustment parameter grid of the second limb to obtain a first combined parameter grid; obtaining the linkage constraint relationship between the third limb and the starting limb and the second limb, eliminating and closing the adjustment parameter grid of the third limb based on the linkage constraint relationship, and performing grid traversal combination of the first combined parameter grid and the adjustment parameter grid of the third limb to obtain a second combined parameter grid; iteratively completing the grid traversal combination of the second combined parameter grid and the adjustment parameter grid of the fourth limb to obtain a final combined parameter grid; extracting the adjustment parameter combinations from the final combined parameter grid to obtain the adjustment parameter set.

[0040] Specifically, in postoperative walking aids for animals, the limb adjustment unit refers to the unit used to adjust the movement and support of the animal's limbs. Each adjustment unit is responsible for adjusting the range of motion, angle, or height of each limb to adapt to the animal's rehabilitation needs. The adjustment angle, angle adjustment range, and adjustment height of the limb adjustment unit are obtained separately. The adjustment angle is the range of angles that the limb adjustment unit can adjust, such as the range of motion of the knee or hip joint; the angle adjustment range is the maximum angle deviation that the limb adjustment unit can adjust, defining the range of motion variation. For example, the range of motion of the knee joint is from 0° to 50°, and the adjustment range is ±5°, meaning the range of motion of the knee joint can vary within a 5° range; the adjustment height is the height of the limbs that the limb adjustment unit can adjust, usually used to adjust the distance between the limbs and the ground when the animal is standing, simulating the raising and lowering of the animal's joints.

[0041] Based on the adjustment angle, angle adjustment range, and adjustment height of the limb adjustment unit, a grid of adjustment parameters for the limbs is constructed. This grid represents a data structure of all possible adjustment parameters of the limb adjustment unit. Each point represents a possible adjustment configuration, such as different combinations of angle, range, and height, describing all positions and postures that the limb adjustment unit can reach in three-dimensional space.

[0042] Two limb control units are randomly selected from the four limb control units, one as the starting limb and the other as the second limb, such as the forelimb and hindlimb, or any two of the left and right limbs. The selection is random, meaning two units can be randomly chosen for coordinated control. A linkage constraint relationship is established between the starting and second limbs to ensure that adjustments to one control unit do not affect the animal's overall balance and stability, but rather work synergistically to restore normal gait. For example, a change in the knee angle of the left forelimb may affect the weight distribution or gait cycle of the right hindlimb. By establishing a linkage constraint relationship, it is ensured that when the angle of the left forelimb is adjusted, the adjustment of the right hindlimb can be synchronized within a reasonable range. If the knee angle of the left forelimb changes significantly, it may cause a shift in the center of gravity, requiring the right hindlimb to adjust its weight distribution accordingly to maintain overall balance. Sensor data is used to monitor the state of each limb (such as angle, weight, and movement status), and then control algorithms are used to adjust the parameters of each limb. For example, if the angle of the left forelimb knee joint is adjusted between 0° and 50°, and the load on the right hindlimb is adjusted between 20% and 40%, then according to physical constraints and gait analysis, when the angle of the left forelimb knee joint is 30°, the load on the right hindlimb should only be 30% to avoid excessive load.

[0043] The linkage constraint relationship between the four limb control units indicates how the parameters of other control units are affected when the parameters of one control unit change. Based on the linkage constraint relationship, the control parameter mesh of the second limb is eliminated or closed. Due to the linkage constraint relationship between the left forelimb and the right hindlimb, certain combinations of angles or heights do not meet physiological or rehabilitation needs and must be eliminated. The control parameter mesh of the initial limb (e.g., the left forelimb) is combined with the control parameter mesh of the second limb (e.g., the right hindlimb) through a mesh traversal. For example, combining the control parameters of the left forelimb and the right hindlimb individually yields the first combined parameter mesh. The first combined parameter mesh is a new control parameter mesh obtained through mesh traversal combination; it contains all possible positions and postures of the initial and second limbs under the linkage constraint relationship.

[0044] The third limb refers to another limb added to the adjustment process after the initial and second limbs. Similarly, it's crucial to understand the linkage constraints between the third limb and the initial and second limbs. For example, there may be coordination requirements between the first and second limbs regarding range of motion and weight distribution; the adjustment of the third limb also needs to maintain a certain degree of coordination with the first two limbs. Analyze the linkage constraints between the third limb (e.g., the left hind limb), the initial limb (e.g., the left forelimb), and the second limb (e.g., the right hind limb). The adjustment of each limb may affect the weight distribution, angle, or gait pattern of other limbs; therefore, the pre-set parameters of the initial and second limbs must be considered when adjusting the third limb.

[0045] Based on the linkage constraint relationship, the third limb adjustment parameter mesh is eliminated and closed. Parameter combinations that violate physiological constraints (such as excessive load, excessively large or small angles, etc.) need to be closed. For example, if the third limb adjustment parameter mesh represents the knee joint angle range of the left hind limb as 0° to 50°, but since the load distribution of the initial limb (left forelimb) and the second limb (right hind limb) has already reached its maximum value, the angle adjustment of the third limb exceeding a certain threshold may lead to the collapse of the entire balance. Therefore, these mesh points need to be eliminated. The first combined parameter mesh (derived from the combination of the adjustment meshes of the initial limb and the second limb) is combined with the third limb adjustment parameter mesh through mesh traversal to obtain a combined mesh with multiple parameter configurations, reflecting the joint adjustment effect of the three adjustment units. The second combined parameter mesh is a new adjustment parameter mesh obtained through mesh traversal combination, containing all possible positions and postures of the initial limb, second limb, and third limb under the linkage constraint relationship.

[0046] The fourth limb is acquired, and the aforementioned process is repeated to obtain the linkage constraint relationship between the fourth limb and the first, second, and third limbs. The fourth limb parameter mesh is then removed and closed. The second combined parameter mesh and the fourth limb adjustment parameter mesh are then combined through mesh traversal to obtain the final combined parameter mesh, which contains all possible positions and postures of the initial, second, third, and fourth limbs under the linkage constraint relationship. Adjustment parameter combinations are extracted from the final combined parameter mesh to control the limb adjustment units. For example, the optimal adjustment parameter combination in the final combined parameter mesh is: left forelimb knee angle 30°, right hindlimb weight-bearing 35%, left hindlimb angle 25°, and right forelimb height 35cm. The limbs are adjusted according to these parameters to ensure the animal's gait balance and meet rehabilitation needs. The adjustment parameter set is a set of the most suitable adjustment parameters extracted from the combined parameter mesh, used to adjust the limbs.

[0047] By constructing and iterating through parameter grids, the range of motion, angles, and weight distribution of the limbs can be precisely controlled. Through the establishment of linkage constraints and coordinated adjustment among multiple limbs, the movement of each limb is ensured to be coordinated, avoiding unbalanced loads or unnatural gait. Through continuous iteration and elimination of inappropriate parameter combinations, the most suitable adjustment scheme is ultimately obtained, improving the adaptability of postoperative walking aids in animals.

[0048] The set of adjustment parameters is mapped sequentially to the constraint matrix and the demand matrix. The outbound parameters are obtained through matrix verification. The outbound parameters are then trimmed from the set of adjustment parameters to obtain a set of feasible adjustment parameters.

[0049] Furthermore, this application also includes the following steps: setting a rehabilitation cycle, including at least an initial acute phase, a mid-term subacute phase, and a late functional phase; setting a width threshold for the analytical adjustment parameters based on the surgical content, and determining the grid density for each rehabilitation cycle; performing initial screening based on the grid density in the set of adjustment parameters to obtain a set of adjustment parameters that are matched for the initial screening.

[0050] Specifically, a rehabilitation cycle is established, which is the process from surgery to full recovery for the animal. This cycle is typically divided into multiple phases, each with different rehabilitation goals and treatment methods. The rehabilitation cycle includes the initial acute phase, the intermediate subacute phase, and the late functional phase. The initial acute phase is the initial stage after surgery, beginning immediately after the operation. This phase primarily addresses postoperative swelling, pain, and the initial functional recovery. The intermediate subacute phase involves the animal gradually regaining mobility, but some movement limitations remain, occurring several days to several weeks post-surgery. The late functional phase is the final stage of rehabilitation, where the animal can engage in more movement, regaining a normal gait and weight-bearing capacity, usually occurring several weeks after surgery.

[0051] Each rehabilitation cycle has different adjustment requirements. Therefore, within each cycle, the range of adjustment for the limbs (e.g., angle, weight-bearing, amplitude of movement, etc.) will have different limitations. The width threshold of the adjustment parameters represents the parameter adjustment range of each adjustment unit. For example, in the initial acute phase, the range of motion of the limbs is restricted to a smaller extent, while in the later functional phase, a larger range of motion is allowed. Grid density refers to the number or fineness of the combinations of adjustment parameters within the adjustment parameter range. Higher density means more possible combinations within each adjustment parameter range, and a more refined adjustment process. Because the joint movement and weight-bearing of animals are greatly restricted in the initial acute phase, the range of adjustment for the limbs is small, and the amplitude of adjustment also needs more precise control. A high-density grid is set, that is, more discrete values ​​are set within the adjustment range to ensure meticulous adjustment and recovery. In the intermediate subacute phase, the animal gradually recovers its range of motion, and the adjustment range is appropriately increased. A medium-density grid is set to allow parameters to vary within a wider range to achieve moderate recovery. In the later functional phase, the animal's rehabilitation goal is to restore a near-normal gait and motor ability, so a larger range of adjustment can be accepted. A low-density grid is set, and the fineness of adjustment is appropriately reduced. For example, the rehabilitation phase is the initial acute phase (1-7 days), with a high-density grid and a clinical goal of preventing micromotion injury (±3°); the rehabilitation phase is the intermediate subacute phase (7-28 days), with a medium-density grid and a clinical goal of promoting joint mobility (±5°); and the rehabilitation phase is the late functional phase (greater than 28 days), with a low-density grid and clinical goals of gait reconstruction, joint angles ±5°, and gait symmetry ±10%.

[0052] Based on the set grid density and width thresholds, adjustment parameters that meet the needs of the current rehabilitation cycle are selected from the set of adjustment parameters. This initial screening removes parameters that do not meet the current rehabilitation goals, resulting in a preliminary set of adjustment parameters that meet the constraints and requirements. By adjusting the grid density, a set of adjustment parameters that meets the needs of each rehabilitation stage is preliminarily selected. Based on the characteristics of each rehabilitation cycle, by setting different grid densities and adjustment ranges, fine-grained adjustment parameters can be provided for each stage, avoiding over-recovery or inappropriate workload.

[0053] Furthermore, this application also includes the following steps: writing the constraint matrix and demand matrix as verification matrices into the verification firmware; mapping the initially screened and matched set of adjustment parameters sequentially to the verification matrix of the verification firmware to obtain out-of-bounds parameters; and removing out-of-bounds parameters from the set of adjustment parameters to obtain the feasible set of adjustment parameters.

[0054] Specifically, the constraint matrix and demand matrix are used as verification matrices to determine whether the set of adjustment parameters meets physiological constraints and rehabilitation goals. The verification matrix verifies the adjustment parameters, checking whether they conform to recovery goals and physical limitations. The verification firmware is a program or logic embedded in the animal's postoperative walking aid control unit. It checks and verifies the input adjustment parameters in real time or offline based on the verification matrix, ensuring that these parameters do not exceed safe ranges or deviate from rehabilitation goals. The verification matrix represents the allowed movement boundaries of the animal at the current rehabilitation stage that conform to the rehabilitation goals. The verification firmware uses the verification matrix to check the input set of adjustment parameters item by item, ensuring that these parameters are within reasonable ranges. For example, if an element in the constraint matrix indicates that the maximum range of motion of the left hind limb knee joint is 30°, and the demand matrix indicates that the target range of motion of the left hind limb knee joint is 35°, the verification matrix will check whether the range of motion of the left hind limb knee joint meets the requirements, that is, whether its actual angle does not exceed 30° and conforms to the rehabilitation goal of 35°.

[0055] The initial set of matched adjustment parameters is mapped to the validation matrix of the firmware. The actual value of each adjustment parameter is compared with the constraints in the validation matrix. Any adjustment parameter exceeding the constraints is marked as an out-of-bounds parameter. For example, if the initial set of matched adjustment parameters has a left hind limb knee angle of 40°, while the constraint in the validation matrix is ​​a maximum of 30°, then 40° is an out-of-bounds parameter. When the validation matrix detects out-of-bounds parameters in the adjustment parameter set, these parameters need to be removed. The purpose of out-of-bounds removal is to eliminate adjustment combinations that do not meet rehabilitation needs and physiological limitations, ensuring that subsequent adjustment plans only include reasonable and feasible parameter combinations.

[0056] From the initial set of matched adjustment parameters, all parameter combinations containing out-of-bounds parameters are removed, resulting in a feasible set of adjustment parameters that meets all physical limitations and rehabilitation goals. This set can be used to perform limb adjustments, ensuring balance and safety during the animal's gait recovery process. The introduction of a verification matrix ensures that the adjustment parameter set consistently meets physiological limitations and rehabilitation needs throughout the recovery process. All parameters are thoroughly validated before adjustment to avoid adverse effects caused by inappropriate parameters. Out-of-bounds removal eliminates adjustment parameters that may lead to maladaptation or overload, thereby improving safety during rehabilitation and preventing injuries caused by excessive weight-bearing or overactivity.

[0057] Based on the set of feasible adjustment parameters, a limb coordination and balance evaluation is performed. Based on the balance evaluation, adjustment parameters are screened to obtain target limb adjustment parameters. The target limb adjustment parameters are then sent to the limb adjustment unit according to the limb positioning for limb adjustment.

[0058] Furthermore, this application also includes the following steps: establishing a dual closed-loop balance mechanism, including an inner loop for balancing local safety and an outer loop for preventing the entire machine from overturning; using the dual closed-loop balance mechanism to perform inner and outer loop balance evaluations on the feasible set of adjustment parameters to obtain balance evaluation values; and filtering adjustment parameters based on the balance evaluation values ​​to obtain the target limb adjustment parameters.

[0059] Furthermore, this application also includes the following steps: using the limiting threshold of each limb and joint as the inner loop sub-gate of each adjustment unit, and using the balance dependency relationship between the joints of the four limbs as the inner loop parent gate; constructing an inner loop balance mechanism using the inner loop sub-gate and the inner loop parent gate; determining an outer loop gate based on the balance dependency relationship of the limb adjustment units and the relative influence position of the animal's center of gravity, wherein the outer loop gate includes an outer loop balance constraint parameter, and constructing an outer loop balance mechanism; merging the inner loop balance mechanism and the outer loop balance mechanism to establish the dual closed-loop balance mechanism.

[0060] Specifically, the limiting thresholds for each limb and joint are obtained, representing the maximum range or angle of safe movement for each limb and joint. During rehabilitation, the movement of limbs and joints must remain within these limiting thresholds; exceeding these thresholds will lead to overload or injury to the joints. The limiting thresholds for each limb and joint are used as inner-loop sub-gates for each adjustment unit. A gate can be understood as a control logic or judgment condition. The inner-loop sub-gates concretize the limiting thresholds for each limb and joint into control logic, used to monitor and limit the movement or load of individual adjustment units in real time, preventing them from exceeding safe limits; they belong to the inner layer. The inner-loop sub-gates control the range of motion of each limb or joint, ensuring that it does not exceed the limiting thresholds and maintaining the safety of limb movement.

[0061] Based on the balance dependencies between the limbs—that is, the interaction between the movement of each joint and other joints—an inner-loop parent gate is established. The parent gate is generally a higher-level control mechanism that ensures overall body balance by coordinating the movements of limbs and joints. The inner-loop parent gate, on the other hand, refers to adjusting and limiting the coordination and range of motion of each limb and joint based on the dependencies between them. For example, if the knee angle of the left forelimb is 30° and the weight distribution of the right hindlimb is set to 30%, the inner-loop child gate will ensure that the knee angle of the left forelimb does not exceed 30°, while the inner-loop parent gate will ensure that the weight distribution of the right hindlimb does not exceed a safe range.

[0062] By linking the inner loop sub-gates and the inner loop parent gate, an inner loop balance mechanism is constructed. Each time the parameters of a limb are adjusted, the inner loop parent gate coordinates the movement of other limbs according to the balance dependency relationship to ensure the overall balance of movement. The main function of the inner loop balance mechanism is to ensure that each adjustment unit does not exceed its own limit (sub-gate), and that the movement between units is coordinated and meets the basic balance requirements (parent gate).

[0063] Based on the balance dependency of the limb coordination units and the relative influence position of the animal's center of gravity, a more macroscopic regulatory control, namely the outer ring gate, is set. The outer ring control mechanism typically affects the movement of the entire body, ensuring the body does not tip over and maintaining balance. The balance dependency of the limb coordination units refers to the roles and interrelationships of the limbs as a whole in maintaining the animal's balance. For example, the forelimbs mainly bear propulsion and some weight-bearing, while the hind limbs mainly bear the main weight-bearing and propulsion; the limbs work together to stabilize the center of gravity. The relative influence position of the animal's center of gravity is the positional relationship of the animal's center of gravity relative to the four coordination units (simulating the limb's landing points). Even small movements of the center of gravity significantly affect the force on each limb through the lever principle. The outer ring gate includes outer ring balance constraint parameters, which are parameters set within the outer ring gate to limit and constrain the adjustment range of the limb coordination units, ensuring they do not lead to inappropriate weight-bearing or center of gravity shift, thus guaranteeing the animal maintains balance throughout the rehabilitation process. The outer ring gate is designed based on the balance dependency of the limb coordination units and the influence position of the animal's center of gravity. The outer ring gate is responsible for ensuring the overall balance of the body, regulating the coordination between limbs, and preventing the whole body from becoming unbalanced due to local adjustments.

[0064] The outer-loop balance constraint parameters are used to limit and control the adjustment range of the outer-loop gate, ensuring that limb adjustments do not lead to unstable center of gravity shifts or overload adjustments. For example, when limb adjustments cause a shift in the center of gravity, the outer-loop balance constraint parameters can adjust the load or angle of other limbs to maintain body stability. Based on the outer-loop gate and the outer-loop balance constraint parameters, an outer-loop balance mechanism is constructed. By adjusting parameters such as the range of motion and load distribution of the limb adjustment units, it ensures that the overall body balance and stability are guaranteed during each adjustment. The outer-loop balance mechanism ensures that when adjusting local limbs, the overall center of gravity remains within the ideal range, avoiding imbalance caused by improper adjustment.

[0065] By combining the inner-loop and outer-loop balance mechanisms, a dual-closed-loop balance mechanism is formed. The inner-loop mechanism controls the local coordination of the limb regulatory units, while the outer-loop mechanism ensures overall body balance. Through their interaction, the dual-closed-loop mechanism ensures that during the animal's recovery process, not only is the regulation of each limb reasonable, but overall body stability is also maintained. This combination of inner and outer-loop balance mechanisms ensures the balance of limb regulation and the overall body during postoperative recovery, improving the coordination of gait recovery.

[0066] The inner loop of the dual-closed-loop balance mechanism is used for local safety and to control the coordination between limbs, ensuring that the adjustment of each limb does not lead to inappropriate weight-bearing or excessive movement, and avoiding adverse effects of individual limb adjustments on the overall gait. The outer loop of the dual-closed-loop balance mechanism is used to prevent the entire machine from tipping over and to control the balance of the whole body, ensuring that the adjustment of each limb does not cause the animal's center of gravity to shift, and avoiding overall imbalance or tilting caused by local adjustments.

[0067] A dual-loop balance mechanism is used to evaluate the balance of feasible adjustment parameters using inner and outer loops. The inner loop monitors the adjustment parameters of each limb to determine whether the adjustment of each limb is within a safe range and whether it will affect the movement of other joints. The inner loop balance evaluation value is usually based on the rationality and coordination of local adjustments. The outer loop assesses the impact of the adjustment parameter set on the overall body, especially the change in the center of gravity. The outer loop balance evaluation ensures that the entire body does not tilt or become unstable during adjustment. For example, suppose the feasible adjustment parameter set has the following initial adjustment parameters: left forelimb knee angle: 30°, maximum allowable angle 35°; right hindlimb weight-bearing: 35%, maximum allowable weight-bearing 40%; left hindlimb knee angle: 40°, maximum allowable angle 50°; right forelimb knee angle: 30°, maximum allowable angle 45°. Each limb adjustment is within a safe range, therefore the inner loop balance evaluation value is 4. The calculated center-of-gravity offset is 4cm (this value indicates that the center of gravity has deviated from its original position). Assuming the offset exceeds 3cm, the outer ring evaluation value is 0.6; if the offset is less than 3cm, the outer ring evaluation value is 1. Since the offset is 4cm, the outer ring evaluation value is 0.6. Combining the inner and outer ring evaluations, the inner ring evaluation is assigned 80% weight, and the outer ring evaluation is assigned 20% weight. The calculated comprehensive balance evaluation value is 3.32. This high comprehensive evaluation value indicates that the current set of adjustment parameters performs well in terms of local safety and overall balance, but there is still room for optimization, especially regarding the center-of-gravity offset issue.

[0068] The balance evaluation value is a quantitative value calculated through inner and outer loop balance evaluations, reflecting the merits of the current set of regulatory parameters in terms of local safety and overall balance. A higher balance evaluation value indicates that the regulatory parameters are more aligned with the rehabilitation goals, and that the animal maintains better balance and stability during the adjustment process. Based on the balance evaluation value, the optimal set of regulatory parameters is selected from the feasible parameter set to ensure the animal receives the most appropriate support during recovery; this is the target limb regulatory parameters. The limb regulatory parameters represent the regulatory scheme that provides optimal support and ensures local safety and overall balance during rehabilitation. Through the inner and outer loop balance evaluations of the dual closed-loop balance mechanism, precise and personalized limb regulation schemes can be provided to the animal, ensuring both the safety of local regulation and overall balance.

[0069] Furthermore, this application also includes the following steps: collecting animal limb kinematic data and load distribution during walking assistance through a flexible pressure sensor network; performing matrix mapping matching between the limb kinematic data and load distribution and the constraint matrix and demand matrix to obtain difference data; and performing adaptive balance adjustment compensation based on the difference data to reset the limb adjustment parameters.

[0070] Specifically, the target limb adjustment parameter set is transmitted to specific limb adjustment units, each of which performs adjustments according to the animal's specific needs and position. For example, the left forelimb knee joint angle is 30°, and the right hindlimb bears 35% of the weight. These adjustment parameters are transmitted to the adjustment units of the left forelimb and right hindlimb to ensure that they are adjusted according to the specified angle and weight.

[0071] A flexible pressure sensor network is a sensor network deployed on the limbs of an animal to collect real-time kinematic data (such as joint angles and gait) and weight distribution data of the limbs during walking. It typically consists of multiple sensors and can accurately record the force applied to each limb. Using a flexible pressure sensor network to monitor the kinematic data (such as knee joint angles and gait) and weight distribution (the load on each limb) of each limb during walking allows for the assessment of whether the current limb regulation is as expected. For example, real-time data collected by the flexible sensor network shows that the knee joint angle of the left forelimb is 28°, the weight distribution of the right hindlimb is 34%, and the weight distribution of the left hindlimb is 36%, reflecting the animal's current movement state. Limb kinematic data refers to the characteristics of limb movement during animal movement, such as joint angles and gait. Weight distribution refers to the load borne by each limb during walking. Both together determine the stability and balance of the animal while walking.

[0072] The kinematic data and weight distribution of the limbs are matched with pre-defined constraint and demand matrices to determine whether the current adjustment meets physical limitations and rehabilitation goals. The matrix mapping matching process compares the actual limb data with preset values ​​in these matrices and calculates the differences. For example, if the target angle of the left forelimb knee joint is 30° (demand matrix), while the current angle is 28° (collected kinematic data), there is a 2° difference; the target weight-bearing capacity of the right hindlimb is 35% (demand matrix), while the actual weight-bearing capacity is 34% (collected weight-bearing data), there is a 1% difference. Through matrix mapping matching, the calculated difference data represents the deviation between the current adjustment and the expected goals, including differences in joint angles, weight distribution, etc.

[0073] Based on the calculated differential data, adaptive balance adjustment and compensation are performed, automatically adjusting limb adjustment parameters according to deviations to ensure the balance of gait recovery. Adaptive balance adjustment and compensation is a process of dynamically adjusting adjustment parameters based on differential data, automatically correcting unbalanced or uncoordinated adjustment parameters to ensure that the animal's gait always conforms to the rehabilitation goals. Through adaptive balance adjustment and compensation, the parameters of the limb adjustment units are updated to ensure that subsequent adjustments can meet the animal's rehabilitation needs and overall balance. Through real-time data collected by a flexible pressure sensor network and differential compensation adjustments, the kinematic data and weight distribution of the limbs can be dynamically adjusted to ensure that the animal maintains a stable gait throughout the rehabilitation process.

[0074] In summary, the adaptive balance adjustment method for postoperative walking aids in animals provided in this application has the following beneficial effects:

[0075] Based on the animal's preoperative condition and surgical content, the postoperative limb movement restrictions and rehabilitation exercise needs are analyzed to establish a constraint matrix and a demand matrix. The adjustment parameters of the limb adjustment units are obtained, and a set of adjustment parameters is constructed by performing a grid traversal. This set of adjustment parameters is then sequentially mapped to the constraint matrix and the demand matrix. Overbound parameters are obtained through matrix verification and then pruned from the adjustment parameter set to obtain a feasible set of adjustment parameters. Based on this feasible set of adjustment parameters, limb coordination and balance are evaluated, and adjustment parameters are selected based on the balance evaluation to obtain target limb adjustment parameters. These target limb adjustment parameters are then sent to the limb adjustment units according to limb positioning for limb adjustment. In other words, based on the animal's preoperative condition and surgical content, the postoperative limb movement limitations and rehabilitation needs are analyzed, and personalized constraint and demand matrices are established. The adjustment parameters of the limb adjustment unit are traversed in a grid to construct a set of adjustment parameters. Out-of-bounds parameters that do not meet the rehabilitation needs are eliminated. The adjustment parameters are dynamically adjusted according to the recovery progress of the animal at each postoperative stage to ensure that the recovery needs at each stage can be met with appropriate parameters. This avoids the problem of insufficient adaptability caused by general design and improves the adaptability of the postoperative walking aid.

[0076] Example 2: Based on the same inventive concept as the adaptive balance adjustment method for postoperative walking aids in animal embodiment 1, this application also provides an adaptive balance adjustment system for postoperative walking aids in animals. Please refer to the appendix. Figure 2 The adaptive balance adjustment system for the postoperative walking aid in animals includes:

[0077] The requirement analysis module 11 is used to analyze the postoperative limb movement restriction constraints and rehabilitation movement requirements of the animal based on the animal's preoperative state and surgical content, and establish a constraint matrix and a requirement matrix. The adjustment parameter traversal module 12 is used to obtain the adjustment parameters of the limb adjustment unit, perform grid traversal on the adjustment parameters of the limb adjustment unit, and construct an adjustment parameter set. The parameter mapping and pruning module 13 is used to map the adjustment parameter set sequentially to the constraint matrix and the requirement matrix, obtain the overbound parameters through matrix verification, prune the overbound parameters from the adjustment parameter set, and obtain a feasible adjustment parameter set. The limb coordination and balance evaluation module 14 is used to evaluate the limb coordination and balance based on the feasible adjustment parameter set, filter the adjustment parameters based on the balance evaluation, obtain the target limb adjustment parameters, and send the target limb adjustment parameters to the limb adjustment unit according to the limb positioning for limb adjustment.

[0078] Furthermore, the demand analysis module 11 in the adaptive balance adjustment system for postoperative walking aids in animals is also used for: establishing standard movement support relationships for animal limbs; changing the positioning state of the standard movement support relationships for animal limbs according to the preoperative state and surgical content of the animal to obtain abnormal movement support relationships for postoperative limbs; analyzing the range and amplitude of movement restrictions for limbs based on the abnormal movement support relationships for postoperative limbs; using the standard movement support relationships for animal limbs as a benchmark, analyzing rehabilitation movement support goals based on the abnormal movement support relationships for postoperative limbs to obtain rehabilitation movement needs; and decomposing the range and amplitude of movement restrictions for limbs and rehabilitation movement needs into movement-related influencing factors to obtain constraint factors and demand factors, and constructing the constraint matrix and demand matrix.

[0079] Furthermore, the demand analysis module 11 in the adaptive balance adjustment system for postoperative walking aids in animals is also used to: decompose the influencing factors of movement according to the range and amplitude of the limb movement restrictions and the rehabilitation movement requirements using animal movement experimental data, and obtain the movement constraint features corresponding to the range and amplitude of the limb movement restrictions and the target balance movement features corresponding to the rehabilitation movement requirements; and obtain the associated influencing factors of the movement joints and the movement thresholds based on the movement constraint features and the target balance movement features, thereby obtaining the constraint factors and demand factors.

[0080] Furthermore, the adjustment parameter traversal module 12 in the adaptive balance adjustment system for postoperative walking aids in animals is also used for: obtaining the adjustment angle, angle adjustment range, and adjustment height of the four limb adjustment units respectively, and constructing an adjustment parameter grid for the four limbs; randomly selecting two limb adjustment units from the four limb adjustment units, one as the initial limb and the other as the second limb, and establishing a linkage constraint relationship between the initial limb and the second limb; eliminating and closing the adjustment parameter grid of the second limb based on the linkage constraint relationship, and combining the adjustment parameter grid of the initial limb with the adjustment parameter grid of the second limb through grid traversal to obtain a first combined parameter grid; obtaining the linkage constraint relationship between the third limb and the initial limb and the second limb, eliminating and closing the adjustment parameter grid of the third limb based on the linkage constraint relationship, and combining the first combined parameter grid with the adjustment parameter grid of the third limb through grid traversal to obtain a second combined parameter grid; iteratively completing the combination of the second combined parameter grid with the adjustment parameter grid of the fourth limb through grid traversal to obtain a final combined parameter grid; and extracting the adjustment parameter combinations from the final combined parameter grid to obtain the adjustment parameter set.

[0081] Furthermore, the parameter mapping and trimming module 13 in the adaptive balance adjustment system for postoperative walking aids in animals is also used for: setting rehabilitation cycles, including at least the initial acute phase, the intermediate subacute phase, and the late functional phase; setting the width threshold of the analytical adjustment parameters according to the surgical content, and determining the grid density of each rehabilitation cycle; and performing preliminary screening based on the grid density in the adjustment parameter set to obtain a preliminary screening matching adjustment parameter set.

[0082] Furthermore, the parameter mapping and pruning module 13 in the adaptive balance adjustment system for postoperative walking aids in animals is also used to: write the constraint matrix and demand matrix as verification matrices into the verification firmware; map the initially screened and matched set of adjustment parameters sequentially to the verification matrix of the verification firmware to obtain out-of-bounds parameters; and perform out-of-bounds removal on the set of adjustment parameters according to the out-of-bounds parameters to obtain the feasible set of adjustment parameters.

[0083] Furthermore, the limb coordination balance evaluation module 14 in the adaptive balance adjustment system for postoperative walking aids in animals is also used to: establish a dual closed-loop balance mechanism, including an inner loop for local balance safety and an outer loop for preventing the entire device from tipping over; use the dual closed-loop balance mechanism to perform inner and outer loop balance evaluations on the set of feasible adjustment parameters to obtain balance evaluation values; and screen adjustment parameters based on the balance evaluation values ​​to obtain the target limb adjustment parameters.

[0084] Furthermore, the limb coordination balance evaluation module 14 in the adaptive balance adjustment system for postoperative walking aids in animals is also used for: using the limiting threshold of each limb and joint as the inner loop sub-gate of each adjustment unit, and using the balance dependency relationship between the limb joints as the inner loop parent gate; constructing an inner loop balance mechanism using the inner loop sub-gate and the inner loop parent gate; determining an outer loop gate based on the balance dependency relationship of the limb adjustment units and the relative influence position of the animal's center of gravity, wherein the outer loop gate includes an outer loop balance constraint parameter, and constructing an outer loop balance mechanism; merging the inner loop balance mechanism and the outer loop balance mechanism to establish the dual closed-loop balance mechanism.

[0085] Furthermore, the limb coordination balance evaluation module 14 in the adaptive balance adjustment system for postoperative walking aids in animals is also used for: collecting limb kinematic data and weight distribution of the animal during walking assistance through a flexible pressure sensor network; performing matrix mapping matching between the limb kinematic data and weight distribution and the constraint matrix and demand matrix to obtain difference data; and performing adaptive balance adjustment compensation based on the difference data to reset the limb adjustment parameters.

[0086] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The adaptive balance adjustment method and specific examples for animal postoperative walking aids in Example 1 are also applicable to the adaptive balance adjustment system for animal postoperative walking aids in this embodiment. Through the foregoing detailed description of the adaptive balance adjustment method for animal postoperative walking aids, those skilled in the art can clearly understand the adaptive balance adjustment system for animal postoperative walking aids in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0087] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0088] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An adaptive balance adjustment method for postoperative walking aids in animals, characterized in that, The adaptive balance adjustment method for postoperative walking aids in animals is applied to the walking aid, which includes a limb adjustment unit, comprising: Based on the animal's preoperative condition and surgical content, analyze the animal's postoperative limb movement restrictions and rehabilitation exercise needs, and establish a constraint matrix and a demand matrix. Obtain the adjustment parameters of the limb adjustment units, perform mesh traversal on the adjustment parameters of the limb adjustment units, and construct the adjustment parameter set; The set of adjustment parameters is sequentially mapped to the constraint matrix and the demand matrix. The outbound parameters are obtained through matrix verification. The outbound parameters are then trimmed from the set of adjustment parameters to obtain a set of feasible adjustment parameters. Based on the set of feasible adjustment parameters, a limb coordination and balance evaluation is performed. Based on the balance evaluation, adjustment parameters are screened to obtain target limb adjustment parameters. The target limb adjustment parameters are then sent to the limb adjustment unit according to the limb positioning for limb adjustment.

2. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 1, characterized in that, Based on the animal's preoperative condition and surgical procedures, the postoperative limb movement restrictions and rehabilitation exercise needs of the animal were analyzed, and a constraint matrix and a demand matrix were established, including: Establish standard motor support relationships for animal limbs; Based on the animal's preoperative condition and surgical procedure, the standard motor support relationship of the animal's limbs was repositioned and altered to obtain the abnormal motor support relationship of the limbs after surgery. Based on the support relationship of the postoperative limb movement abnormalities, the range and amplitude of limb movement restriction were analyzed. Based on the standard motor support relationship of the animal's limbs, the rehabilitation motor support goals are analyzed according to the abnormal motor support relationship of the limbs after surgery, and the rehabilitation motor needs are obtained. The range and amplitude of limb movement restrictions and the rehabilitation exercise requirements are decomposed into movement-related influencing factors to obtain constraint factors and demand factors, and the constraint matrix and demand matrix are constructed.

3. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 2, characterized in that, The range and amplitude of limb movement restrictions and the rehabilitation exercise requirements are decomposed into movement-related influencing factors to obtain constraint factors and requirement factors, including: Using animal movement experiment data, the factors influencing movement are decomposed according to the range and amplitude of limb movement restrictions and the rehabilitation exercise requirements, respectively obtaining the movement constraint characteristics corresponding to the range and amplitude of limb movement restrictions and the target balance movement characteristics corresponding to the rehabilitation exercise requirements. Based on the decomposition of the action constraint features and target balance action features, the associated influencing factors and motion thresholds of the action joints are obtained, and the constraint factors and demand factors are obtained.

4. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 1, characterized in that, Obtain the adjustment parameters of the limb adjustment units, perform mesh traversal on the adjustment parameters of the limb adjustment units, and construct an adjustment parameter set, including: The adjustment angle, angle adjustment range, and adjustment height of the limb adjustment units are obtained respectively, and the adjustment parameter grid of the limbs is constructed. Two limb adjustment units are randomly selected from the limb adjustment units, one as the starting limb and the other as the second limb, and a linkage constraint relationship is established between the starting limb and the second limb. Based on the aforementioned linkage constraint relationship, the adjustment parameter mesh of the second limb is eliminated and closed. The adjustment parameter mesh of the initial limb and the adjustment parameter mesh of the second limb are combined by mesh traversal to obtain the first combined parameter mesh. Obtain the linkage constraint relationship between the third limb, the starting limb, and the second limb. Based on the linkage constraint relationship, remove and close the third limb adjustment parameter mesh. Then, use the first combined parameter mesh and the third limb adjustment parameter mesh to perform mesh traversal and combination to obtain the second combined parameter mesh. The second combined parameter grid and the fourth limb adjustment parameter grid are iteratively combined to obtain the final combined parameter grid. Extract the adjustment parameter combinations from the final combined parameter grid to obtain the adjustment parameter set.

5. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 1, characterized in that, The set of adjustment parameters is sequentially mapped to the constraint matrix and the demand matrix, and the overbound parameters are obtained through matrix verification. This process includes: The rehabilitation cycle should include at least the initial acute phase, the intermediate subacute phase, and the later functional phase. Based on the surgical procedure, the width threshold of the analytical adjustment parameters is set to determine the grid density for each rehabilitation cycle; Based on the grid density, a preliminary screening is performed on the set of adjustment parameters to obtain a set of adjustment parameters that matches the preliminary screening.

6. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 5, characterized in that, The process of obtaining a feasible set of adjustable parameters includes: The constraint matrix and requirement matrix are written into the verification firmware as verification matrices. The set of adjustment parameters matched in the initial screening is sequentially mapped to the verification matrix of the verification firmware to obtain the over-boundary parameters; The set of adjustment parameters is subjected to out-of-bounds rejection based on the out-of-bounds parameter to obtain the set of feasible adjustment parameters.

7. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 1, characterized in that, Based on the feasible set of adjustable parameters, a limb coordination and balance evaluation is performed. Adjustment parameters are then selected based on the balance evaluation to obtain target limb adjustment parameters, including: Establish a dual closed-loop balance mechanism, including an inner loop for local safety and an outer loop to prevent the entire machine from tipping over. Using the aforementioned dual-closed-loop balance mechanism, the feasible set of adjustable parameters is evaluated for inner and outer loop balance to obtain balance evaluation values. The adjustment parameters for the target limbs are obtained by screening the adjustment parameters based on the balance evaluation value.

8. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 7, characterized in that, The establishment of the dual closed-loop balance mechanism includes: The limiting threshold of each limb and joint is used as the inner ring sub-gate of each adjustment unit, and the balance dependency relationship between the four limb joints is used as the inner ring parent gate. An inner ring balancing mechanism is constructed using the inner ring sub-gate and the inner ring parent gate; Based on the balance dependence of the limb regulatory units and the relative influence position of the animal's center of gravity, the outer ring gate is determined, which includes the outer ring balance constraint parameters, and the outer ring balance mechanism is constructed. The inner-loop balancing mechanism is merged with the outer-loop balancing mechanism to establish the dual-closed-loop balancing mechanism.

9. The adaptive balance adjustment method for an animal postoperative walking aid according to claim 1, characterized in that, The target limb adjustment parameters are sent to the limb adjustment unit according to the limb positioning for limb adjustment, and then the process further includes: Kinematic data of animal limbs and weight distribution during walking assistance were collected using a flexible pressure sensor network. By using the limb kinematics data and load distribution to perform matrix mapping and matching with the constraint matrix and demand matrix, difference data can be obtained. Based on the difference data, adaptive balance adjustment compensation is performed, and the adjustment parameters of the limbs are reset.

10. An adaptive balance adjustment system for postoperative walking aids in animals, characterized in that, The step of implementing the adaptive balance adjustment method for an animal postoperative walking aid according to any one of claims 1 to 9, wherein the adaptive balance adjustment system for the animal postoperative walking aid comprises: The requirements analysis module is used to analyze the animal's postoperative limb movement restrictions and rehabilitation exercise requirements based on the animal's preoperative condition and surgical content, and to establish a constraint matrix and a requirements matrix. The adjustment parameter traversal module is used to obtain the adjustment parameters of the limb adjustment units, perform grid traversal on the adjustment parameters of the limb adjustment units, and construct the adjustment parameter set. The parameter mapping and pruning module is used to map the set of adjustment parameters sequentially to the constraint matrix and the demand matrix, obtain the outbound parameters through matrix verification, and prune the outbound parameters from the set of adjustment parameters to obtain a feasible set of adjustment parameters. The limb coordination and balance evaluation module is used to evaluate the limb coordination and balance based on the set of feasible adjustment parameters, filter adjustment parameters based on the balance evaluation, obtain target limb adjustment parameters, and send the target limb adjustment parameters to the limb adjustment unit according to the limb positioning for limb adjustment.

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