CN120183604A

CN120183604A - Remote collaboration and adaptive control system of ICU-AW rehabilitation robot based on 5G network

Info

Publication number: CN120183604A
Application number: CN202510151352.5A
Authority: CN
Inventors: 李霞; 黄丽萍; 王刚; 王宁; 周明; 张攻孜
Original assignee: First Medical Center of PLA General Hospital
Current assignee: First Medical Center of PLA General Hospital
Priority date: 2025-02-11
Filing date: 2025-02-11
Publication date: 2025-06-20
Anticipated expiration: 2045-02-11
Also published as: CN120183604B

Abstract

The present invention provides an ICU-AW rehabilitation robot remote collaboration and adaptive control system based on a 5G network, which belongs to the field of visual detection technology. The system comprises: an operation simulation state evaluation module, an ICU environment monitoring module, an initial plan simulation execution module and an initial plan judgment and adjustment module. The patient state monitoring component collects the initial muscle oxygen state data, and the rehabilitation robot simulates and verifies the muscle oxygen state evaluation value based on the data, and uploads it to the rehabilitation control platform; at the same time, the ICU environment monitoring component obtains the environmental state data to evaluate the environmental characteristic influence coefficient and then uploads it, the rehabilitation control platform matches the initial plan according to the muscle oxygen simulation state evaluation value, remotely collaborates with the rehabilitation robot to simulate the execution, obtains the execution data, comprehensively evaluates the execution quality index of the environmental characteristic influence coefficient, compares it with the expected index, determines whether to adjust adaptively, and realizes the remote collaboration and adaptive control of the ICU-AW rehabilitation robot.

Description

ICU-AW rehabilitation robot remote cooperation and self-adaptive control system based on 5G network

Technical Field

The invention relates to the technical field of visual detection, in particular to a remote collaboration and self-adaptation control system of an ICU-AW rehabilitation robot based on a 5G network.

Background

The bed braking/mechanical ventilation of critical patients exceeds 5-7 days, the severe intensive care unit (ICU-AW) is extremely easy to get weak, the incidence rate can reach 85%, and the hospitalization mortality rate can reach 27%. The existing clinical intervention effect is poor, and the long-term prognosis is poor. The united states pays for it $ 160 billion annually. The ICU-AW patients have multiple system weakness, including cerebral weakness, heart lung weakness, and trunk and limb weakness, which lead to cognitive dysfunction, intolerance of activity, and impaired executive function. Therefore, the recovery needs to be covered by the whole body of the problem, which is indispensable.

The existing rehabilitation evaluation/intervention means can not adapt to clinical requirements and mainly has three problems of deficiency, deficiency and difference. The lack refers to the lack of an evaluation system and the difficulty in early diagnosis. Less refers to ICU-AW patients in particular states of illness and ICU environment, such as sedation, intubation, etc., and less rehabilitation means adapted to particular conditions. The difference means that the rehabilitation prescription of the patient has poor coordination, the existing splice-type prescription has miscellaneous content, depends on manpower mostly and has low efficiency.

Disclosure of Invention

The invention provides a remote cooperative and self-adaptive control system of an ICU-AW rehabilitation robot based on a 5G network, which solves the problems of lack of an evaluation system, environment specificity and low efficiency due to more dependence on manpower in the prior art.

In order to solve the above-mentioned purpose, the technical scheme provided by the invention is as follows:

An ICU-AW rehabilitation robot remote cooperative and adaptive control system based on a 5G network comprises a myooxygen simulation state evaluation module, an ICU environment monitoring module, an initial rehabilitation control platform and a rehabilitation control platform, wherein the myooxygen simulation state evaluation module is used for monitoring the ICU environment of the rehabilitation robot through a 5G network, acquiring initial myooxygen state data of the patient, simulating the myooxygen state of the patient according to the initial myooxygen state data by the rehabilitation robot, judging the myooxygen simulation state evaluation value of the rehabilitation robot, checking with a predefined myooxygen simulation state evaluation threshold value, judging whether to optimize the myooxygen simulation state of the rehabilitation robot or not according to the judgment, uploading the myooxygen simulation state evaluation value of the rehabilitation robot to the rehabilitation control platform through the 5G network, the ICU environment monitoring module is used for monitoring the ICU environment of the rehabilitation robot, acquiring ICU environment state data of the rehabilitation robot, evaluating the environmental characteristic influence coefficient of the ICU of the rehabilitation robot, uploading the initial rehabilitation control platform to the rehabilitation control platform through the 5G network, matching the myooxygen simulation state evaluation value of the rehabilitation robot to the pre-rehabilitation control platform, judging the quality of the rehabilitation control initial rehabilitation platform according to the initial rehabilitation control platform, comprehensively comparing the initial rehabilitation control platform with the initial rehabilitation control platform by the initial rehabilitation control platform, performing the initial rehabilitation control platform, and carrying out the initial rehabilitation control platform to adjust the initial rehabilitation control parameters, accordingly, and carrying out the initial rehabilitation control platform has a comprehensive control function, and finally, the remote coordination and self-adaptive control of the ICU-AW rehabilitation robot are completed.

The method comprises the steps of obtaining myooxygen simulation state data of a rehabilitation robot by simulating a myooxygen state of a patient according to initial myooxygen state data, obtaining output power of the rehabilitation robot at each myooxygen simulation time point, setting action repetition times of the rehabilitation robot in the myooxygen simulation period, response delay time of the rehabilitation robot in the myooxygen simulation period and sensor linearity error average value of the rehabilitation robot in the myooxygen simulation period, carrying out average value processing on the output power of the rehabilitation robot at each myooxygen simulation time point to obtain an output power average value of the rehabilitation robot in the myooxygen simulation period, obtaining an electromagnetic radiation intensity value of an environment of the rehabilitation robot at each myooxygen simulation time point, extracting an output power adaptation average value and setting action repetition adaptation times from a rehabilitation control information base, and carrying out the integrated treatment on the output power average value of the rehabilitation robot in the myooxygen simulation period, the response delay time of the rehabilitation robot in the myooxygen simulation period and the sensor linearity error average value of the rehabilitation robot in the myooxygen simulation period, and the myooxygen simulation time point of the rehabilitation robot.

Optionally, the determining whether to optimize the myooxygen simulation state of the rehabilitation robot is specifically performed by checking a myooxygen simulation state evaluation value of the rehabilitation robot with a predefined myooxygen simulation state evaluation threshold to obtain a check result, determining whether to optimize the myooxygen simulation state of the rehabilitation robot based on the check result, where the check result is a first check result or a second check result, the first check result is specifically performed by the fact that the myooxygen simulation state evaluation value of the rehabilitation robot is greater than or equal to the myooxygen simulation state evaluation threshold, the second check result is specifically performed by the fact that the myooxygen simulation state evaluation value of the rehabilitation robot is smaller than the myooxygen simulation state evaluation threshold, if the check result is displayed as the first check result, the myooxygen simulation state of the rehabilitation robot is not required to be optimized, and if the check result is displayed as the second check result, the myooxygen simulation state of the rehabilitation robot is required to be optimized.

The method comprises the steps of selecting an ICU environment state data of a rehabilitation robot, wherein the ICU environment state data comprise real-time environment temperature of the ICU environment of the rehabilitation robot in an evaluation period, real-time environment humidity of the ICU environment of the rehabilitation robot in the evaluation period, real-time electromagnetic interference strength of the ICU environment of the rehabilitation robot in the evaluation period, real-time noise decibel value of the ICU environment of the rehabilitation robot in the evaluation period and real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot in the evaluation period, extracting an environment temperature adaptive value, an environment humidity adaptive value and a carbon dioxide concentration adaptive value from a rehabilitation control information base, and comprehensively analyzing the ICU environment state data of the rehabilitation robot by the aid of the comprehensive analysis of the ICU environment state data.

Optionally, the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs is specifically analyzed as follows:

In the formula, HJ is an environmental characteristic influence coefficient of an ICU of a rehabilitation robot, t is a time variable, t epsilon [ t ₀,t₁],t₀ ] is an evaluation period starting time point, t ₁ is an evaluation period ending time point, beta (t) is a real-time environmental temperature of the ICU environment of the rehabilitation robot at the evaluation period t, beta ' is an environmental temperature adaptation value, gamma (t) is a real-time environmental humidity of the ICU environment of the rehabilitation robot at the evaluation period t, gamma ' is an environmental humidity adaptation value, delta (t) is a real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot at the evaluation period t, theta (t) is a real-time noise decibel value of the ICU environment of the rehabilitation robot at the evaluation period t, tau (t) is a real-time carbon dioxide concentration adaptation value of the ICU environment of the rehabilitation robot at the evaluation period t, Y ' is a myooxygen simulation state evaluation value of the rehabilitation robot, b ₁ is an environmental characteristic corresponding to a pre-defined real-time electromagnetic interference intensity in an evaluation period t, delta (t) is a pre-defined environment characteristic corresponding to a pre-defined electromagnetic interference intensity in a rehabilitation control information base, theta (t) is a real-time noise decibel value of the ICU environment corresponding to a pre-defined in a pre-defined parameter in a muscle oxygen control information base, and tau (t) is a real-time carbon dioxide concentration in the evaluation period t.

Optionally, the matching is to obtain a rehabilitation control initial plan, and the specific matching process is to match the myooxygen simulation state evaluation value of the rehabilitation robot with the rehabilitation control initial plan corresponding to each predefined myooxygen simulation state evaluation value interval, and the specific matching process is to extract a mapping set between the myooxygen simulation state evaluation value of the rehabilitation robot and the rehabilitation control initial plan from a rehabilitation control information base, determine the section of the myooxygen simulation state evaluation value of the rehabilitation robot, obtain the rehabilitation control initial plan corresponding to the section, and obtain the rehabilitation control initial plan by matching.

The method comprises the steps of obtaining the initial simulated myooxygen saturation of a rehabilitation robot in a myooxygen simulation period, obtaining the preset times of rehabilitation training of the initial rehabilitation control plan in the execution period from a rehabilitation control information base, and carrying out ratio processing on the completion times of the rehabilitation training of the initial rehabilitation control plan in the execution period and the preset times of the rehabilitation training of the initial rehabilitation control plan in the execution period to obtain the simulated completion rate of the rehabilitation training of the initial rehabilitation control plan in the execution period.

The method comprises the steps of determining the action repetition times of a rehabilitation robot in a myooxygen simulation period, matching fault definition times corresponding to a predefined action repetition time interval to obtain the fault definition times of the rehabilitation robot, matching response delay time of the rehabilitation robot in the myooxygen simulation period with feedback response definition time corresponding to a predefined response delay time interval to obtain feedback response definition time of the rehabilitation control initial plan, and comprehensively analyzing the completion rate of the rehabilitation training simulation of the rehabilitation control initial plan in the execution period, the feedback response definition time of the rehabilitation control initial plan in the execution period, the fault definition times of the rehabilitation robot, the final simulated myooxygen saturation of the rehabilitation robot in the execution period, the initial simulated myooxygen saturation of the rehabilitation robot in the myooxygen simulation period and the environmental characteristic influence coefficient of an ICU (information and communication unit) to which the rehabilitation robot belongs to obtain the performance indexes of the rehabilitation control initial plan.

Optionally, the step of judging whether to adaptively adjust the initial rehabilitation control plan is specifically performed, and the step of judging includes executing a predefined optimal adjustment plan on the initial rehabilitation control plan if the execution quality index of the initial rehabilitation control plan is equal to the execution quality default index of the initial rehabilitation control plan, comparing the execution quality index of the initial rehabilitation control plan with a predefined execution quality reference index if the execution quality index of the initial rehabilitation control plan is not equal to the execution quality default index of the initial rehabilitation control plan, and not adaptively adjusting the initial rehabilitation control plan if the execution quality index of the initial rehabilitation control plan is greater than the execution quality reference index, and adaptively adjusting the initial rehabilitation control plan if the execution quality index of the initial rehabilitation control plan is less than or equal to the execution quality reference index.

Optionally, the self-adaptive adjustment is performed on the initial rehabilitation control plan, and the specific adjustment process is that the execution quality index of the initial rehabilitation control plan and the execution quality reference index are subjected to difference processing to obtain the execution quality deviation value of the initial rehabilitation control plan, and the execution quality deviation value is matched with the self-adaptive adjustment plan corresponding to each predefined execution quality deviation value interval, so that the self-adaptive adjustment plan of the initial rehabilitation control plan is obtained through the matching, and finally the initial rehabilitation control plan is subjected to self-adaptive adjustment.

Compared with the prior art, the technical scheme provided by the invention has at least the following beneficial effects:

In the scheme, the patient state monitoring component is used for collecting initial myooxygen state data, the rehabilitation robot simulates and verifies a myooxygen state evaluation value according to the initial myooxygen state data and uploads the myooxygen state evaluation value to the rehabilitation control platform, meanwhile, the ICU environment monitoring component is used for acquiring environment state data to evaluate environment characteristic influence coefficients and uploading the environment characteristic influence coefficients, the rehabilitation control platform is matched with an initial plan according to the myooxygen simulation state evaluation value, the remote cooperative rehabilitation robot performs simulation, execution data are acquired, the comprehensive environment characteristic influence coefficients evaluate and execute quality indexes, and the comprehensive environment characteristic influence coefficients are compared with expected indexes to judge whether the self-adaption adjustment is performed or not, so that the remote cooperative and self-adaption control of the ICU-AW rehabilitation robot is realized.

By collecting initial myooxygen state data of a patient, the rehabilitation robot simulates the myooxygen state of the patient according to the initial myooxygen state data, judges a myooxygen simulation state evaluation value of the rehabilitation robot, helps the rehabilitation robot to accurately simulate the oxygenation condition of the muscle of the patient, and can simulate the optimal rehabilitation scheme for the patient as accurately as possible according to individual differences of the patient, so that the adaptability and the effectiveness of the rehabilitation robot are improved.

By acquiring the ICU environment state data of the rehabilitation robot, the environmental characteristic influence coefficient of the ICU of the rehabilitation robot is evaluated, the environmental factors which possibly influence the performance of the rehabilitation robot can be identified in advance, and corresponding guarantee measures can be timely taken, so that the rehabilitation robot works in a proper environment, the performance of the rehabilitation robot is kept stable, the robot faults caused by the environmental factors are reduced, and the successful development of subsequent rehabilitation simulation training is ensured.

By evaluating the execution quality index of the initial rehabilitation control plan and comparing the execution quality index with the predefined execution quality expected index, whether the initial rehabilitation control plan is subjected to self-adaptive adjustment is judged, and possible problems in the rehabilitation simulation plan can be found in time, so that the self-adaptive adjustment plans with different degrees are performed, the simulation effect of the rehabilitation robot is improved, the initial rehabilitation control plan can be more attached to the state of a patient, and the feasibility of the plan is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a system module according to an embodiment of the present invention;

fig. 2 is a graph of sensor linearity curves.

Reference numeral 1, reference straight line, 2, sensor linearity curve.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.

Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a," "an," or "the" and similar terms do not denote a limitation of quantity, but rather denote the presence of at least one. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

It should be noted that "upper", "lower", "left", "right", "front", "rear", and the like are used in the present invention only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.

Aiming at the problems of lack of the existing evaluation system, environment specificity and low efficiency due to manpower, the invention provides the remote collaboration and self-adaptation control system of the ICU-AW rehabilitation robot based on the 5G network, which can effectively perfect the evaluation system, reduce negative effects caused by the special environment and improve the control efficiency.

As shown in FIG. 1, the embodiment of the invention provides a remote collaboration and self-adaptation control system of an ICU-AW rehabilitation robot based on a 5G network, which comprises a myooxygen simulation state evaluation module, a patient state monitoring module and a rehabilitation control platform, wherein the myooxygen simulation state evaluation module is used for carrying out initial state monitoring on an ICU-AW patient, collecting initial myooxygen state data of the patient, simulating the myooxygen state of the patient according to the initial myooxygen state data by the rehabilitation robot, judging a myooxygen simulation state evaluation value of the rehabilitation robot, checking with a predefined myooxygen simulation state evaluation threshold value, judging whether the myooxygen simulation state of the rehabilitation robot is optimized or not, and uploading the myooxygen simulation state evaluation value of the rehabilitation robot to the rehabilitation control platform through the 5G network.

The patient state monitoring component is a device system for comprehensively monitoring the physical state of an ICU-AW (ICU with weak availability) patient, and has the main functions of accurately acquiring initial myooxygen state data of the patient, and also can monitor other physiological parameters of the patient, such as heart rate, blood pressure, respiratory rate, body temperature and the like, so as to provide basic data support for a follow-up rehabilitation simulation plan, and can be wearable muscle oxygen content real-time monitoring equipment, and the data is sent to a control system of a rehabilitation robot by adopting a wireless communication technology.

The ICU-AW rehabilitation robot is intelligent equipment specially used for helping an ICU-AW patient to perform rehabilitation therapy, combines the technologies of multiple fields such as mechanical engineering, electronic technology, computer science and rehabilitation medicine, can provide accurate and personalized rehabilitation training aiming at weak conditions such as muscle weakness and limited joint movement of the patient caused by factors such as long-term bedridden, diseases or drug therapy in an ICU environment, has a mechanical structure design capable of simulating natural movement of a human body and is provided with a sensor for monitoring the myooxygen state of the patient, and the robot can strictly provide customized rehabilitation service for the patient according to simulation programs such as training time, training frequency, movement mode combination and the like set by a rehabilitation control platform.

The rehabilitation control platform is an integrated intelligent system, plays a core control and coordination role in the whole rehabilitation process, is like a 'brain center' of rehabilitation treatment, integrates data from various aspects including state data of a patient, data of a rehabilitation robot, ICU environment data and the like, performs analysis and decision making based on the data, performs remote cooperative control on the work of the rehabilitation robot, and can adaptively adjust a rehabilitation plan to achieve the optimal rehabilitation effect.

The initial myooxygen status data of the patient can be specifically the myooxygen saturation of the patient at each acquisition time point, the oxygen uptake rate of the patient at each acquisition time point and the muscle oxygen metabolism rate of the patient at each acquisition time point.

The acquisition time points are specifically a plurality of acquisition time points for dividing the acquisition period into time points, wherein the dividing mode can be 30 seconds, and the acquisition period is determined by comprehensively analyzing factors such as the state of a patient, the monitoring state of equipment, specific acquisition requirements and the like by a control manager.

The rehabilitation robot simulates the myooxygen state of a patient according to initial myooxygen state data, particularly a machine learning model driven by data, such as a neural network model, trains the neural network model according to a training set of a patient's historical myooxygen state data crop model, adjusts the weight and bias of the network through a back propagation algorithm to enable the model to learn a complex relationship between input parameters and the myooxygen state, cleans the data by taking the initial myooxygen state data of the patient as the input of the neural network model, removes noise and abnormal values which possibly exist, for example, transient extremely high or extremely low myooxygen saturation data points caused by poor contact of a sensor or external interference can be identified and corrected or eliminated, performs data normalization processing, converts myooxygen data in different ranges into a standard interval, facilitates subsequent model processing, for example, converts the value range of the myooxygen saturation from 0-100% into a value interval of 0-1 to enable the data to have comparability and consistency, calculates the data through the forward propagation algorithm according to the learned weight and bias, calculates the linear transformation of the data between the input parameters and the neural network, and the linear transformation of the data in the neural network, and the linear transformation of the data between the input parameters and the myooxygen saturation layers is obtained.

The method comprises the steps that a rehabilitation robot simulates the myooxygen state of a patient according to initial myooxygen state data, and myooxygen simulation state data of the rehabilitation robot are obtained, wherein the myooxygen simulation state data specifically comprise output power of the rehabilitation robot at each myooxygen simulation time point, set action repetition times of the rehabilitation robot in a myooxygen simulation period, response delay time of the rehabilitation robot in the myooxygen simulation period and a sensor linearity error mean value of the rehabilitation robot in the myooxygen simulation period.

The myooxygen simulation period is a period of time for the rehabilitation robot to simulate the state of the patient according to the patient data, and the myooxygen simulation time points are a plurality of myooxygen simulation time points for dividing the myooxygen simulation period according to the time points, wherein the dividing mode can be 30 seconds, and the determination of the myooxygen simulation period is obtained by comprehensively analyzing factors such as the simulation state, the simulation environment, the actual simulation requirement and the like of the rehabilitation robot by a control manager.

The myooxygen simulation state data of the rehabilitation robot are extracted from a myooxygen simulation report of the rehabilitation robot.

In a specific embodiment, the sensor linearity error mean value may be obtained by applying a series of input physical quantities with different magnitudes to the sensor in a myooxygen simulation period, such as applying myooxygen saturation with different magnitudes to the myooxygen sensor, recording output signals of the sensor, such as current signals, drawing the input-output data into curves, and constructing a sensor linearity curve, as shown in fig. 2, in which the abscissa is the myooxygen saturation, the unit is a percentage, the ordinate is the current signal, and the unit is ampere, and the sensor linearity curve should be ideally a straight line, so that a reference straight line 1 is located in the sensor linearity curve.

And carrying out average processing on the output power of the rehabilitation robot at each myooxygen simulation time point to obtain the average value of the output power of the rehabilitation robot in the myooxygen simulation period.

And acquiring electromagnetic radiation intensity values of the environment of the rehabilitation robot at each myooxygen simulation time point, wherein the electromagnetic radiation intensity values can be detected by an electromagnetic radiation detector.

The environment to which the rehabilitation robot in this embodiment belongs may be the same as or different from the ICU environment to which the rehabilitation robot described below belongs.

And extracting an output power adaptation average value from the rehabilitation control information base, and setting action repetition adaptation times.

Wherein, the myooxygen simulation state evaluation value of the rehabilitation robot is judged, and the specific judgment process is as follows:

the method comprises the steps of comprehensively processing an average value of output power of a rehabilitation robot in a myooxygen simulation period, a set action repetition number of the rehabilitation robot in the myooxygen simulation period, a response delay time of the rehabilitation robot in the myooxygen simulation period, an average value of sensor linearity errors of the rehabilitation robot in the myooxygen simulation period and electromagnetic radiation intensity values of an environment of the rehabilitation robot at each myooxygen simulation time point to obtain a myooxygen simulation state evaluation value of the rehabilitation robot, and specifically comprises the following steps of:

Wherein YX is a myooxygen simulation state evaluation value of the rehabilitation robot, GL is an output power average value of the rehabilitation robot in a myooxygen simulation period, GL ^′ is an output power adaptation average value, DZ is a set action repetition number of the rehabilitation robot in the myooxygen simulation period, DZ ^′ is a set action repetition adaptation number, WY is a response delay time of the rehabilitation robot in the myooxygen simulation period, WC is a sensor linearity error average value of the rehabilitation robot in the myooxygen simulation period, DF _n is an electromagnetic radiation intensity value of an environment of the rehabilitation robot at an nth myooxygen simulation time point, N is a number of each myooxygen simulation time point, n=1, 2,3, N is a total amount of the myooxygen simulation time points, max is a maximum value, a ₂ is a myooxygen simulation influence factor corresponding to a predefined response delay time in a rehabilitation control information base, a ₃ is a myooxygen simulation influence factor corresponding to a predefined sensor linearity error in the rehabilitation control information base, a ₄ is a myooxygen simulation influence factor corresponding to a maximum value in a control information base, and a natural influence factor is a predefined to a myooxygen simulation factor corresponding to a maximum value in the myooxygen simulation information base.

In this embodiment, the evaluation value of the myooxygen simulation state of the rehabilitation robot is used to measure the accuracy and effectiveness of the rehabilitation robot in simulating the limb movement of the patient, and the higher the evaluation value of the myooxygen simulation state, the more accurate the rehabilitation robot grasps the myooxygen state of the patient, and the more helps the follow-up operation.

It should be explained that the average value of the output power refers to the average value of the power output by the rehabilitation robot to the outside in the myooxygen simulation period, and for the rehabilitation robot, the output power represents the energy output by the rehabilitation robot in unit time in the process of simulating the movement of a patient; the output power adaptation average value refers to a preset reference average value corresponding to output power, the set action repetition number refers to a reference value corresponding to the preset action repetition number of the rehabilitation robot in a myooxygen simulation period, for example, the preset action of the rehabilitation robot is the bending and stretching movement of a simulated arm in the myooxygen simulation period of an upper limb rehabilitation simulation training, the palm is close to the shoulder from the state of the straightened arm to the state of the bent arm and returns to the straightened state to be a complete action, if the robot is set to complete such arm bending and stretching action 30 times in the simulation period, the set action repetition number refers to a preset reference value corresponding to the set action repetition number, the response delay time refers to a time interval from the moment when the rehabilitation robot receives a simulation instruction sent by a rehabilitation control platform in the myooxygen simulation period to the moment when the rehabilitation robot actually makes the simulation action, the linearity error average value of a sensor carried by the rehabilitation robot in the myooxygen simulation period is the measurement of a myooxygen related physical quantity, the output signal of the rehabilitation robot has a large electromagnetic radiation intensity in an ideal environment, and the electromagnetic radiation intensity of the environment is large in the real time when the output signal is an ideal environment, this electromagnetic radiation includes electromagnetic radiation generated from the operation of the rehabilitation robot's own electronic equipment, as well as electromagnetic radiation emitted by other surrounding medical equipment, electrical equipment, etc.

The myooxygen simulation influence factors corresponding to the response delay time length, the myooxygen simulation influence factors corresponding to the sensor linearity error mean value and the myooxygen simulation influence factors corresponding to the maximum electromagnetic radiation intensity value are obtained in advance in a rehabilitation control information base, wherein the mapping relation can be one-to-one or one-to-one relation, for example, the myooxygen simulation influence factors corresponding to the response delay time length and the preset response delay time length in the rehabilitation control information base form a mapping set, the real-time response delay time length is brought into the mapping set to obtain the myooxygen simulation influence factors corresponding to the response delay time length, the myooxygen simulation influence factors corresponding to the sensor linearity error mean value and the preset sensor linearity error mean value in the rehabilitation control information base form a mapping set, the myooxygen simulation influence factors corresponding to the sensor linearity error mean value and the maximum electromagnetic radiation intensity value are brought into the mapping set, and the myooxygen simulation influence factors corresponding to the maximum electromagnetic radiation intensity value and the maximum electromagnetic radiation intensity value is obtained in the corresponding to the myooxygen simulation influence factor corresponding to the maximum electromagnetic radiation intensity value in the rehabilitation control information base, and the myooxygen simulation influence value corresponding to the myooxygen simulation influence factor corresponding to the maximum electromagnetic radiation intensity value is taken as the myooxygen simulation influence factor corresponding to the maximum electromagnetic radiation value of the linear influence value of 1.

It should be noted that, in the process of simulating the myooxygen state of the patient by the rehabilitation robot, if the output power of the rehabilitation robot in the process is too high and deviates from the adaptive output power, the rehabilitation robot may repeatedly execute the set action, so that the set action execution times are greatly increased, and the adaptive execution times are also deviated, so that the myooxygen simulation state of the rehabilitation robot is greatly reduced, otherwise, if the output power is low and smaller than the adaptive output power, there may be a situation that the output power is insufficient to support the set action execution, so that the set action execution times are far lower than the adaptive execution times, and the myooxygen simulation state of the rehabilitation robot is also reduced, and if the average value of the linearity error of the sensor is large, the set action repetition times according to the inaccurate data may not be suitable for simulating the actual situation of the patient, for example, if the myooxygen sensor error is large, the oxygen metabolism capacity of the muscle of the patient may be erroneously judged, so that the set too high or too low action repetition times affect the simulation effect of the rehabilitation robot, the simulation state is reduced, the electromagnetic radiation intensity value is too high, the response speed of the robot may be delayed, the myooxygen simulation system may be prolonged, and the response speed of the rehabilitation robot may be prolonged, and the response of the system may be prolonged, so that the myooxygen simulation system may be affected by the rehabilitation robot is prolonged.

And judging whether to optimize the myooxygen simulation state of the rehabilitation robot or not, specifically, checking the myooxygen simulation state evaluation value of the rehabilitation robot with a predefined myooxygen simulation state evaluation threshold value to obtain a check result, and judging whether to optimize the myooxygen simulation state of the rehabilitation robot or not based on the check result.

The checking result is a first checking result or a second checking result.

The first verification result is specifically that the myooxygen simulation state evaluation value of the rehabilitation robot is larger than or equal to a myooxygen simulation state evaluation threshold value predefined in a rehabilitation control information base.

The second test result is specifically that the assessment value of the myooxygen simulation state of the rehabilitation robot is smaller than the assessment threshold value of the myooxygen simulation state.

If the check result is displayed as the first check result, the myooxygen simulation state of the rehabilitation robot does not need to be optimized, and if the check result is displayed as the second check result, the myooxygen simulation state of the rehabilitation robot needs to be optimized.

The method comprises the steps of optimizing myooxygen simulation states of a rehabilitation robot, specifically using professional calibration equipment such as a high-precision oxygen content standard gas source, a simulation tissue sample with known myooxygen saturation and the like to calibrate a myooxygen sensor carried by the rehabilitation robot so as to improve initial accuracy of myooxygen data acquisition, adding electromagnetic shielding materials such as nickel-plated copper mesh, electromagnetic shielding foil and the like to key parts of an electronic equipment cabin of the rehabilitation robot, a sensor shell and the like to block invasion of external electromagnetic radiation and reduce negative effects caused by the electromagnetic radiation, adopting a redundant communication link design, when a main link fails, automatically switching a network system of the rehabilitation robot into a standby link within millisecond level to avoid myooxygen simulation interruption caused by network interruption, introducing a data verification and retransmission mechanism, carrying out integrity verification on myooxygen data of each packet at a receiving end, and immediately requesting retransmission to a transmitting end once an error or lost data packet is found, so as to ensure that data received by a control platform is accurate.

The ICU environment monitoring module is used for monitoring the ICU environment of the rehabilitation robot by the ICU environment monitoring component, acquiring the ICU environment state data of the rehabilitation robot, evaluating the environmental characteristic influence coefficient of the ICU of the rehabilitation robot, and uploading the environmental characteristic influence coefficient to the rehabilitation control platform through the 5G network.

The ICU environment monitoring component is a device system specially used for monitoring the environment condition of an Intensive Care Unit (ICU), and has the main functions of acquiring various physical and chemical parameters in the ICU environment in real time, providing data support for evaluating the influence of the environment on rehabilitation robots and rehabilitation control initial plans, receiving various analog signals from various sensors, converting the analog signals into digital signals, and transmitting the data to a rehabilitation control platform in a wireless communication mode.

The various sensors can be specifically a temperature sensor, a humidity sensor, an electromagnetic interference detection sensor, a noise sensor, a gas sensor and the like.

The method comprises the following specific evaluation processes of evaluating the environmental characteristic influence coefficient of the ICU of the rehabilitation robot:

The ICU environment state data of the rehabilitation robot specifically comprises real-time environment temperature of the ICU environment of the rehabilitation robot in an evaluation period, real-time environment humidity of the ICU environment of the rehabilitation robot in the evaluation period, real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot in the evaluation period, real-time noise decibel value of the ICU environment of the rehabilitation robot in the evaluation period and real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot in the evaluation period.

The evaluation period is specifically used for a period of time for the ICU environment monitoring assembly to monitor the ICU environment of the rehabilitation robot, and the determination of the evaluation period is obtained by comprehensively analyzing factors such as environmental states, monitoring states of the assembly, actual monitoring requirements and the like by control management staff.

The ICU environment state data can be extracted from a monitoring report of an ICU environment monitoring component.

And extracting an environment temperature adaptation value, an environment humidity adaptation value and a carbon dioxide concentration adaptation value from the rehabilitation control information base.

Comprehensively analyzing the real-time environmental temperature of the ICU environment of the rehabilitation robot in an evaluation period, the real-time environmental humidity of the ICU environment of the rehabilitation robot in the evaluation period, the real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot in the evaluation period, the real-time noise decibel value of the ICU environment of the rehabilitation robot in the evaluation period and the real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot in the evaluation period to obtain the environmental characteristic influence coefficient of the ICU of the rehabilitation robot, wherein the method comprises the following steps:

In the formula, HJ is an environmental characteristic influence coefficient of an ICU of a rehabilitation robot, t is a time variable, t epsilon [ t ₀,t₁],t₀ ] is an evaluation period starting time point, t ₁ is an evaluation period ending time point, beta (t) is a real-time environmental temperature of the ICU environment of the rehabilitation robot at the evaluation period t, beta ' is an environmental temperature adaptation value, gamma (t) is a real-time environmental humidity of the ICU environment of the rehabilitation robot at the evaluation period t, gamma ' is an environmental humidity adaptation value, delta (t) is a real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot at the evaluation period t, theta (t) is a real-time noise decibel value of the ICU environment of the rehabilitation robot at the evaluation period t, tau (t) is a real-time carbon dioxide concentration adaptation value of the ICU environment of the rehabilitation robot at the evaluation period t, Y ' is a myooxygen simulation state evaluation value of the rehabilitation robot, b _′ is an environmental characteristic corresponding to a pre-defined real-time electromagnetic interference intensity in an evaluation period t, delta (t) is a pre-defined environment characteristic corresponding to a pre-defined electromagnetic interference intensity in a rehabilitation control information base, theta (t) is a real-time noise decibel value of the ICU environment corresponding to a pre-defined in a pre-defined parameter in a muscle oxygen control information base, and tau (t) is a real-time carbon dioxide concentration in the evaluation period t.

The environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs in this embodiment is used to measure the degree to which various factors in the ICU environment, such as temperature, humidity, electromagnetic interference, air quality, noise, etc., affect the performance of the rehabilitation robot and the process of the rehabilitation robot to simulate and execute the initial plan of rehabilitation control.

The real-time environment temperature is an instant temperature value of a surrounding space of a position where the rehabilitation robot is located in an evaluation period of evaluating the ICU environment, the environment temperature adaptation value is a preset reference value corresponding to the environment temperature, the real-time environment humidity is an instant humidity value of the surrounding space of the position where the rehabilitation robot is located in the evaluation period of evaluating the ICU environment, the environment humidity adaptation value is a preset reference value corresponding to the environment humidity, the real-time electromagnetic interference intensity is an instant electromagnetic interference intensity value of the surrounding space of the position where the rehabilitation robot is located in the evaluation period of the ICU environment, the real-time noise value is a decibel value of instant noise of the surrounding space of the position where the rehabilitation robot is located in the evaluation period of the ICU environment, the real-time carbon dioxide concentration is an instant concentration value of the surrounding space of the position where the rehabilitation robot is located in the evaluation period of the ICU environment, and the carbon dioxide concentration adaptation value is a preset reference value corresponding to the preset carbon dioxide concentration.

The environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity, the environmental characteristic parameters corresponding to the real-time noise decibel values and the environmental characteristic parameters corresponding to the myooxygen simulation state evaluation values are extracted from the rehabilitation control information base, wherein the mapping relation can be one-to-one or one-to-many, for example, the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity preset in the rehabilitation control information base form a mapping set, the real-time electromagnetic interference intensity is brought into the mapping set to obtain the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity, the environmental characteristic parameters corresponding to the real-time noise decibel values preset in the rehabilitation control information base form a mapping set, the environmental characteristic parameters corresponding to the real-time noise decibel values are brought into the mapping set to obtain the environmental characteristic parameters corresponding to the real-time noise decibel values, and the myooxygen simulation state evaluation values preset in the rehabilitation control information base form a mapping set, and the environmental characteristic parameters corresponding to the real-time electromagnetic interference intensity, the real-time noise decibel values and the myooxygen simulation state evaluation values are all equal to 0.

In this embodiment, the temperature affects the performance of the electronic device, and the electronic device is one of the main sources of electromagnetic interference, if the environmental temperature is higher and deviates from and adapts to the environmental temperature, for example, various medical instruments in the ICU and the performance inside the electronic components of the rehabilitation robot may change, so that the electromagnetic emission frequency of the electronic devices drifts, increasing the complexity of the electromagnetic interference intensity, and meanwhile, the high temperature may reduce the anti-interference capability of the electronic device, so that the electronic device is more easily affected by the external electromagnetic interference, and likewise, the higher humidity may also cause the electromagnetic radiation frequency of the electronic device to change, or the device is more easily absorbed by the external electromagnetic interference, thereby affecting the distribution of the electromagnetic interference intensity, so as to increase the environmental characteristic influence coefficient, and if the stronger electromagnetic interference exists, the electronic device may have an abnormal working state, thereby generating additional noise, resulting in an increase in the environmental characteristic influence coefficient, and in addition, the carbon dioxide concentration in the ICU environment may be too high or too low, so that the carbon dioxide concentration in the carbon dioxide environment may have too high or too low influence on the performance of the robot, and the carbon dioxide concentration may also cause the carbon dioxide concentration in the environment to change, and the carbon dioxide concentration may be more negative than the carbon dioxide concentration in the environment, and the carbon dioxide ventilation system may be generated when the carbon dioxide is in the high carbon dioxide is in the environment, and the carbon dioxide is more than normal, and the carbon dioxide is highly stressed, and the carbon dioxide is more likely to be degraded, and the carbon dioxide is highly and highly stressed, and the carbon dioxide is highly stressed, and further affects the stability of the rehabilitation robot, thereby negatively affecting the execution quality of the rehabilitation robot.

The larger the myooxygen simulation state evaluation value of the rehabilitation robot in the embodiment is, the more accurate the simulation of the myooxygen state of the rehabilitation robot on the patient is, the more practical requirements can be met, the higher the myooxygen simulation state evaluation value indicates that the system operation of the rehabilitation robot is more stable and accurate, this means that the electromagnetic signal interference generated by the robot in the data acquisition, transmission and processing processes is smaller, the stable operation state also enables the sensitivity of the rehabilitation robot to external electromagnetic interference to be reduced, because the robot can better resist interference, the accuracy of the myooxygen simulation is kept, the rehabilitation robot works in a better myooxygen simulation state, the electronic components of the robot can also operate in a more suitable temperature environment, the influence of environmental temperature fluctuation on the robot is reduced, and when the myooxygen simulation state evaluation value is larger, the rehabilitation robot can perform state simulation in a more reasonable motion mode and rhythm, and the extra mechanical noise generated due to unsmooth motion or frequent adjustment motion can be avoided.

The initial plan simulation execution module is used for matching to obtain a rehabilitation control initial plan according to the myooxygen simulation state evaluation value of the rehabilitation robot, so that the rehabilitation control platform carries out simulation execution on the rehabilitation control initial plan through the remote cooperative rehabilitation robot, and execution data of the rehabilitation control initial plan are obtained.

Wherein, the matching obtains an initial plan of rehabilitation control, and the specific matching process is as follows:

The method comprises the steps of extracting a mapping set between the myooxygen simulation state evaluation value of the rehabilitation robot and the rehabilitation control initial plan from a rehabilitation control information base, determining the section of the myooxygen simulation state evaluation value of the rehabilitation robot, and obtaining the rehabilitation control initial plan corresponding to the section.

The initial rehabilitation control plan is specifically based on the myooxygen saturation degree of the rehabilitation robot simulation, when the myooxygen simulation state evaluation value shows that the myooxygen saturation degree is at a higher level, such as 75% -85%, the myooxygen simulation state evaluation value shows that the muscle oxygenation of a patient is good, the current bearing capacity of the muscle is strong, the initial rehabilitation control plan can set the rehabilitation robot to moderately increase the assistance force when performing limb simulation auxiliary movement, otherwise, if the myooxygen saturation degree is lower, such as 60% -70%, the myooxygen simulation state can mean that the muscle is possibly in a relative hypoxia state, the assistance force or the resistance of the simulated movement of the robot is reduced at the moment, the acceptance degree of the patient is avoided being exceeded, if the oxygen uptake rate is in a normal range, such as 20% -30%, the efficiency of the muscle of the patient using oxygen is normal, the rehabilitation robot can be propelled according to the standard rehabilitation simulation training strength, if the myooxygen metabolism rate shows that the muscle consumes oxygen at a moderate speed, such as 2-3 ml/100 g of the muscle is set to 30-40 minutes in a single simulation training period, and if each simulation myooxygen index of the rehabilitation robot is stable in the myooxygen simulation period, the rehabilitation robot is set to resume, and the simulation interval can be set to 1-2 days.

The initial plan judging and adjusting module is used for comprehensively analyzing the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs according to the execution data of the initial plan of the rehabilitation control by the rehabilitation control platform, evaluating the execution quality index of the initial plan of the rehabilitation control, comparing the execution quality index with a predefined execution quality expected index, judging whether to carry out self-adaptive adjustment on the initial plan of the rehabilitation control or not, and finally completing remote cooperation and self-adaptive control of the ICU-AW rehabilitation robot.

The execution data of the initial rehabilitation control plan specifically comprises the number of times of completion of rehabilitation training of the initial rehabilitation control plan in an execution period, the feedback response time of the initial rehabilitation control plan in the execution period, the failure number of the rehabilitation robot of the initial rehabilitation control plan in the execution period and the final simulated myooxygen saturation of the rehabilitation robot of the initial rehabilitation control plan in the execution period.

The execution period is specifically a period of time for the rehabilitation robot to perform simulation execution on the rehabilitation control initial plan, and the execution period is specifically a period of time from when the rehabilitation robot starts to execute the first operation of the rehabilitation control initial plan until the rehabilitation robot performs all operations.

The execution data of the initial plan of rehabilitation control can be extracted from a control report of a rehabilitation control platform.

And obtaining the initial simulated myooxygen saturation of the rehabilitation robot in a myooxygen simulation period, wherein the initial simulated myooxygen saturation can be extracted from a simulation report of the rehabilitation robot.

The method comprises the steps of extracting a mapping set between the set action repetition number and the fault definition number of the rehabilitation robot in the myooxygen simulation period from a rehabilitation control information base, determining a specific interval of the set action repetition number of the rehabilitation robot in the myooxygen simulation period, and distributing the fault definition number corresponding to the interval to the rehabilitation robot corresponding to the set action repetition number, so that the fault definition number of the rehabilitation robot is obtained through matching.

The response delay time length of the rehabilitation robot in the myooxygen simulation period is matched with the feedback response definition time length corresponding to each response delay time length interval predefined in the rehabilitation control information base, and the specific matching process comprises the steps of extracting a mapping set between the response delay time length of the rehabilitation robot in the myooxygen simulation period and the feedback response definition time length from the rehabilitation control information base, determining a specific interval of the response delay time length of the rehabilitation robot in the myooxygen simulation period, obtaining the feedback response definition time length of the interval, and accordingly obtaining the feedback response definition time length of the rehabilitation control initial plan in a matching mode.

And acquiring rehabilitation training preset times of the rehabilitation control initial plan in the execution period from the rehabilitation control information base.

And carrying out ratio processing on the number of times of completion of the rehabilitation training of the initial rehabilitation control plan in the execution period and the preset number of times of rehabilitation training of the initial rehabilitation control plan in the execution period to obtain the simulation completion rate of the rehabilitation training of the initial rehabilitation control plan in the execution period.

The number of times of completion of the rehabilitation training specifically refers to the actual number of complete set of rehabilitation training actions or processes successfully completed according to the requirement of the initial rehabilitation control plan in the execution period, for example, the initial rehabilitation control plan prescribes that rehabilitation simulation training of upper limb strength and joint activity is performed once a day, and the rehabilitation robot completes the simulation training for 5 days completely in a week, so that the number of times of completion of the rehabilitation training is 5 times in the week.

Wherein, the execution quality index of the initial plan of rehabilitation control, the concrete analysis process is:

The method comprises the steps of comprehensively analyzing the simulation completion rate of the rehabilitation training of the initial rehabilitation control plan in an execution period, the feedback response time length of the initial rehabilitation control plan in the execution period, the feedback response defining time length of the initial rehabilitation control plan, the fault times of the rehabilitation robot of the initial rehabilitation control plan in the execution period, the fault defining times of the rehabilitation robot, the final simulation myooxygen saturation of the rehabilitation robot of the initial rehabilitation control plan in the execution period, the initial simulation myooxygen saturation of the rehabilitation robot in the myooxygen simulation period and the environmental characteristic influence coefficient of an ICU (information and communication unit) to which the rehabilitation robot belongs, and obtaining the execution quality index of the initial rehabilitation control plan, wherein the specific method comprises the following steps:

Wherein ZX is an execution quality index of the initial plan of rehabilitation control, ZX ^′ is an execution quality default index of the initial plan of rehabilitation control predefined in a rehabilitation control information base, smO is a final simulated myooxygen saturation of the rehabilitation robot of the initial plan of rehabilitation control in an execution period, smO ^′ is an initial simulated myooxygen saturation of the rehabilitation robot in the myooxygen simulation period, CL is a simulated completion rate of rehabilitation training of the initial plan of rehabilitation control in the execution period, FK is a feedback response time length of the initial plan of rehabilitation control in the execution period, FK ^′ is a feedback response defining time length of the initial plan of rehabilitation control, GZ is a failure frequency of the rehabilitation robot of the initial plan of rehabilitation control in the execution period, GZ ^′ is a failure defining time of the rehabilitation robot, HJ is an environmental characteristic influence factor of the ICU to which the rehabilitation robot belongs, d ₁ is an execution quality influence factor corresponding to the predefined simulated completion rate of rehabilitation training in the initial plan of rehabilitation control information base, d ₂ is an execution quality influence factor corresponding to the environmental characteristic influence factor in the initial plan of rehabilitation control information base, and e is a natural constant.

The default execution quality index of the initial rehabilitation control plan is a minimum allowable value corresponding to the preset execution quality index of the initial rehabilitation control plan, and the minimum allowable value of the execution quality index is set to ensure that the rehabilitation simulation training meets certain basic requirements, so that the requirement of a patient is met, and when the actual execution quality index is equal to the minimum allowable value, the default execution quality index prompts that the initial rehabilitation control plan has a larger problem in the simulation execution process and needs to be adjusted in time, so that the initial rehabilitation control plan is more fit with the actual condition of the patient, and the use effect of the rehabilitation robot is improved.

The environmental characteristic influence coefficient of the ICU of the rehabilitation robot indicates that the environmental characteristic influence coefficient of the ICU of the rehabilitation robot is obtained by comprehensively analyzing the real-time environmental temperature of the ICU of the rehabilitation robot in an evaluation period, the real-time environmental humidity of the ICU of the rehabilitation robot in the evaluation period, the real-time electromagnetic interference intensity of the ICU of the rehabilitation robot in the evaluation period, the real-time noise decibel value of the ICU of the rehabilitation robot in the evaluation period and the real-time carbon dioxide concentration of the ICU of the rehabilitation robot in the evaluation period.

In this embodiment, the execution quality index of the initial plan for rehabilitation control is a quantitative or an evaluable standard for measuring whether the initial plan for rehabilitation control reaches an expected target or not and whether the execution effect is good or not in the actual execution process, and the larger the execution quality index of the initial plan for rehabilitation control is, the higher the coincidence degree of the plan for rehabilitation control and a patient in the execution process is, and the larger the effect on the patient is.

It should be explained that, the final simulated muscle oxygen saturation of the rehabilitation robot means the final value of the simulated muscle oxygen saturation of the rehabilitation robot at the moment when a complete cycle of performing rehabilitation simulation training according to the initial plan of rehabilitation control is finished; the method comprises the steps of simulating muscle oxygen saturation, simulating the muscle oxygen saturation of a rehabilitation robot before simulating and executing a rehabilitation control initial plan, simulating the completion rate of the rehabilitation training, namely the ratio of the actual simulated rehabilitation training amount of the rehabilitation robot to the planned simulated rehabilitation training amount set by the initial plan in the execution period of the rehabilitation control initial plan, feeding back response time length, namely the condition needing feedback in the rehabilitation training process, such as abnormal working state of the rehabilitation robot, unexpected training effect and the like, to the rehabilitation control center, effectively feeding back according to the conditions, such as the time interval of adjusting parameters of the rehabilitation robot, changing the training plan and the like, feeding back response defining time length, namely the maximum value corresponding to the preset feedback response time length, and the accumulated times of the condition that the rehabilitation robot cannot work normally in the process of performing the rehabilitation simulation training according to the rehabilitation control initial plan, wherein the fault times possibly comprise various types of mechanical part damage, electronic element faults, software system faults, sensor faults and the like, and the fault defining time length of the rehabilitation robot is the maximum value corresponding to the preset fault times of the rehabilitation robot.

The execution quality influence factors corresponding to the rehabilitation training simulation completion rate and the execution quality influence factors corresponding to the environmental characteristic influence coefficients are extracted from a rehabilitation control information base, wherein the mapping relation can be one-to-one or a many-to-one relation, for example, the execution quality influence factors corresponding to the rehabilitation training simulation completion rate preset in the rehabilitation control information base form a mapping set, the real-time rehabilitation training simulation completion rate is brought into the mapping set to obtain the execution quality influence factors corresponding to the rehabilitation training simulation completion rate, the execution quality influence factors corresponding to the environmental characteristic influence coefficients and the execution quality influence factors corresponding to the environmental characteristic influence coefficients preset in the rehabilitation control information base form the mapping set, the real-time environmental characteristic influence coefficients are brought into the mapping set to obtain the execution quality influence factors corresponding to the environmental characteristic influence coefficients, and the value ranges of the execution quality influence factors corresponding to the environmental characteristic influence coefficients are (0, 1).

In this embodiment, the feedback response time length has a direct influence on the simulation completion rate of the rehabilitation training, if the feedback response time length is shorter, problems in the rehabilitation training process, such as deviation of running simulation parameters of the rehabilitation robot, etc., can be quickly adjusted and solved, the rehabilitation robot can continue to complete the simulation training, thereby improving the simulation completion rate of the rehabilitation training and improving the execution quality of the initial plan of the rehabilitation control, otherwise, if the feedback response time length is too long, the simulation completion rate of the rehabilitation training may be reduced because the problems cannot be timely solved, and the rehabilitation robot may be forced to interrupt the simulation training, resulting in the reduction of the simulation completion rate of the rehabilitation training; in addition, when the myooxygen saturation of the initial rehabilitation control plan is not increased or decreased after the initial rehabilitation control plan is executed, the current initial rehabilitation control plan is indicated to be possibly in a state of excessively consuming oxygen, the execution quality is set to be the minimum quality permission value, the training intensity of the rehabilitation model can be adjusted in time, larger deviation of the initial rehabilitation control plan is prevented, and the situation indicates that the current initial rehabilitation control plan is possibly unsuitable for the physical condition of a patient and parameter adjustment is needed for the initial rehabilitation control plan.

The smaller the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs in the embodiment is, the smaller the interference of the ICU environment to the rehabilitation robot is, for example, the lower electromagnetic interference can ensure that a motor control system of the rehabilitation robot stably operates, so that the rehabilitation robot can accurately work according to set power assistance or resistance parameters during simulation training, the adaptability of the rehabilitation robot in the ICU environment is good, the rehabilitation control initial plan can be continuously and stably simulated and executed, the execution quality of the rehabilitation control initial plan is effectively improved, the smaller the interference of the environmental factors to the rehabilitation robot is, the more accurate the rehabilitation robot is when the rehabilitation robot performs motion coordination simulation training, the sensor and the control system of the rehabilitation robot can accurately sense the simulation action of the rehabilitation robot, and the applicability of the rehabilitation control initial plan is improved.

Wherein, whether the decision carries out self-adaptive adjustment to the initial plan of rehabilitation control or not is determined by the specific decision process:

If the execution quality index of the rehabilitation control initial plan is equal to the execution quality default index of the rehabilitation control initial plan, executing a predefined optimization adjustment plan on the rehabilitation control initial plan;

If the execution quality index of the initial plan of the rehabilitation control is not equal to the default execution quality index of the initial plan of the rehabilitation control, the execution quality index of the initial plan of the rehabilitation control is compared with the predefined execution quality reference index in the rehabilitation control information base, if the execution quality index of the initial plan of the rehabilitation control is greater than the execution quality reference index, the initial plan of the rehabilitation control is not required to be adaptively adjusted, and if the execution quality index of the initial plan of the rehabilitation control is less than or equal to the execution quality reference index, the initial plan of the rehabilitation control is required to be adaptively adjusted.

The default execution quality index of the initial rehabilitation control plan is expressed as a minimum allowable value corresponding to the execution quality index of the initial rehabilitation control plan and is smaller than the reference execution quality index.

In this embodiment, when the final simulated myooxygen saturation of the rehabilitation robot in the execution period of the initial rehabilitation control plan is greater than the initial simulated myooxygen saturation of the rehabilitation robot in the myooxygen simulation period, the execution quality index of the initial rehabilitation control plan is greater than the default execution quality index of the initial rehabilitation control plan, and when the final simulated myooxygen saturation of the rehabilitation robot in the execution period of the initial rehabilitation control plan is less than or equal to the initial simulated myooxygen saturation of the rehabilitation robot in the myooxygen simulation period, the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, i.e., no condition exists that the execution quality index of the initial rehabilitation control plan is less than the default execution quality index of the initial rehabilitation control plan.

Wherein, the self-adaptive adjustment is carried out on the initial plan of rehabilitation control, and the specific adjustment process is as follows:

Performing difference processing on the execution quality index and the execution quality reference index of the initial rehabilitation control plan to obtain an execution quality deviation value of the initial rehabilitation control plan, and matching the execution quality deviation value with the self-adaptive adjustment plan corresponding to each execution quality deviation value interval predefined in the rehabilitation control information base, wherein the specific matching process is as follows: and extracting a mapping set between the execution quality deviation value of the initial rehabilitation control plan and the adaptive adjustment plan from the rehabilitation control information base, determining a specific interval of the execution quality deviation value of the initial rehabilitation control plan, distributing the adaptive adjustment plan corresponding to the interval to the initial rehabilitation control plan corresponding to the execution quality deviation value, obtaining the adaptive adjustment plan of the initial rehabilitation control plan through matching, and finally carrying out adaptive adjustment on the initial rehabilitation control plan.

The optimization adjustment plan may specifically mean that when the execution quality index is equal to the execution quality default index, the current simulation training strength is too high or too low, optimization is required, for the situation that the simulation training strength is too high, simulation assistance or resistance of the rehabilitation robot can be reduced, the repetition number of each group of simulation training is reduced, if the training strength is too low, the simulation assistance or resistance can be increased, the difficulty and strength of the simulation training are improved, the duration of single simulation training or the frequency of the simulation training may need to be adjusted, if the situation that the simulation training completion rate of the rehabilitation robot is lower occurs in the simulation training process, the single simulation training time may need to be shortened, otherwise, if the simulation training time is too short, the initial plan effect of the rehabilitation control is not obvious, the single simulation training time may be properly prolonged, meanwhile, the simulation training strength is adjusted, each parameter of the rehabilitation robot is calibrated, the accuracy and stability of the rehabilitation robot are ensured, and the calibration of the simulation training system comprises calibration of a myooxygen sensor, a force sensor and the like, so that physiological data and motion data in the simulation process can be more accurately perceived, and a reliable basis is provided for the simulation training.

The adaptive adjustment plan may specifically be that when the execution quality index is smaller than or equal to the execution quality reference index, the intensity adjustment is performed to different degrees according to the magnitude of the deviation value, if the deviation value is smaller, the simulation assistance or resistance of the rehabilitation robot can be properly adjusted, for example, by increasing or decreasing 5% -10%, and the simulation training time is adjusted, for example, by increasing or decreasing 5 minutes, if the deviation value is in the middle deviation range, the simulation training intensity may need to be adjusted to a larger extent, for example, the simulation assistance or resistance is changed by 15% -30%, and meanwhile, the training action mode is adjusted, some targeted simulation training actions are added to make up for the deficiency of the effect of the initial plan of the rehabilitation control, for the severe deviation range, the current training simulation plan may need to be suspended, the physical condition and the rehabilitation requirement of the patient are re-evaluated, the initial plan of the rehabilitation control is formulated, the simulation training is gradually resumed from the lower simulation intensity, and the simulation training is started, and if the influence of the environmental factors on the training is considered, for example, the electromagnetic interference of the environmental factors on the training is greatly, for example, the electromagnetic interference is greatly increased, the electromagnetic interference is reduced, and the adaptive adjustment apparatus may be adapted to improve the performance of the rehabilitation robot.

The optimization adjustment plan and the self-adaptive adjustment plan can be specifically the adjustment of the initial plan of the rehabilitation control by monitoring myooxygen simulation state data of the rehabilitation robot, action execution accuracy data of the rehabilitation robot, ICU environment data and the like in the adjustment process, and the data can be extracted from a rehabilitation control platform.

The following points need to be described:

(1) The drawings of the embodiments of the present invention relate only to the structures related to the embodiments of the present invention, and other structures may refer to the general designs.

(2) In the drawings for describing embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced for clarity, i.e., the drawings are not drawn to actual scale. It will be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, it can be "directly on" or "under" the other element or intervening elements may be present.

(3) The embodiments of the invention and the features of the embodiments can be combined with each other to give new embodiments without conflict.

The present invention is not limited to the above embodiments, but the scope of the invention is defined by the claims.

Claims

1. ICU-AW rehabilitation robot remote collaboration and adaptive control system based on 5G network, characterized by:

The muscle oxygen simulation state evaluation module is used for the patient state monitoring component to perform initial state monitoring on ICU-AW patients, collect the initial muscle oxygen state data of the patients, and the rehabilitation robot simulates the muscle oxygen state of the patients according to the initial muscle oxygen state data, determines the muscle oxygen simulation state evaluation value of the rehabilitation robot, verifies it with the predefined muscle oxygen simulation state evaluation threshold, so as to determine whether to optimize the muscle oxygen simulation state of the rehabilitation robot, and uploads the muscle oxygen simulation state evaluation value of the rehabilitation robot to the rehabilitation control platform through the 5G network;

ICU environment monitoring module, which is used by the ICU environment monitoring component to monitor the ICU environment of the rehabilitation robot, obtain the ICU environment status data of the rehabilitation robot, evaluate the environmental characteristic influence coefficient of the ICU of the rehabilitation robot, and upload it to the rehabilitation control platform through the 5G network;

The initial plan simulation execution module is used for the rehabilitation control platform to match the rehabilitation control initial plan according to the muscle oxygen simulation state evaluation value of the rehabilitation robot, so that the rehabilitation control platform can simulate and execute the rehabilitation control initial plan through the remote collaborative rehabilitation robot and obtain the execution data of the rehabilitation control initial plan;

The initial plan judgment and adjustment module is used for the rehabilitation control platform to evaluate the execution quality indicators of the initial rehabilitation control plan based on the execution data of the initial rehabilitation control plan and the environmental characteristics influence coefficient of the ICU to which the rehabilitation robot belongs, and compare them with the predefined expected execution quality indicators to determine whether to make adaptive adjustments to the initial rehabilitation control plan, and finally complete the remote collaboration and adaptive control of the ICU-AW rehabilitation robot.

2. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system according to claim 1, it is characterized in that the muscle oxygen simulation state evaluation value of the rehabilitation robot is determined by the specific determination process:

The rehabilitation robot simulates the patient's muscle oxygen state according to the initial muscle oxygen state data, and obtains the muscle oxygen simulation state data of the rehabilitation robot, specifically including the output power of the rehabilitation robot at each muscle oxygen simulation time point, the number of set action repetitions of the rehabilitation robot within the muscle oxygen simulation cycle, the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle, and the average value of the sensor linearity error of the rehabilitation robot within the muscle oxygen simulation cycle;

The output power of the rehabilitation robot at each muscle oxygen simulation time point is averaged to obtain the average output power of the rehabilitation robot during the muscle oxygen simulation period;

Obtain the electromagnetic radiation intensity value of the environment of the rehabilitation robot at each muscle oxygen simulation time point;

Extract the output power adaptation average value and the set action repetition adaptation times from the rehabilitation control information library;

The average output power of the rehabilitation robot during the muscle oxygen simulation cycle, the number of set action repetitions of the rehabilitation robot during the muscle oxygen simulation cycle, the response delay duration of the rehabilitation robot during the muscle oxygen simulation cycle, the average sensor linearity error of the rehabilitation robot during the muscle oxygen simulation cycle, and the electromagnetic radiation intensity value of the environment to which the rehabilitation robot belongs at each muscle oxygen simulation time point are comprehensively processed to obtain the muscle oxygen simulation state evaluation value of the rehabilitation robot.

3. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system of claim 1, it is characterized in that the determination of whether to optimize the muscle oxygen simulation state of the rehabilitation robot is specifically to verify the muscle oxygen simulation state evaluation value of the rehabilitation robot with a predefined muscle oxygen simulation state evaluation threshold to obtain a verification result, and determine whether to optimize the muscle oxygen simulation state of the rehabilitation robot based on the verification result;

The verification result is the first verification result or the second verification result;

The first verification result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is greater than or equal to the muscle oxygen simulation state evaluation threshold;

The second test result is specifically that the muscle oxygen simulation state evaluation value of the rehabilitation robot is less than the muscle oxygen simulation state evaluation threshold;

If the verification result shows the first verification result, there is no need to optimize the muscle oxygen simulation state of the rehabilitation robot. If the verification result shows the second verification result, the muscle oxygen simulation state of the rehabilitation robot needs to be optimized.

4. The ICU-AW rehabilitation robot remote collaborative and adaptive control system based on 5G network according to claim 1 is characterized in that the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs is evaluated, and the specific evaluation process is:

The ICU environment status data of the rehabilitation robot specifically includes the real-time ambient temperature of the ICU environment of the rehabilitation robot during the evaluation period, the real-time ambient humidity of the ICU environment of the rehabilitation robot during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot during the evaluation period, the real-time noise decibel value of the ICU environment of the rehabilitation robot during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot during the evaluation period;

Extracting the ambient temperature adaptation value, the ambient humidity adaptation value and the carbon dioxide concentration adaptation value from the rehabilitation control information database;

The real-time ambient temperature of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time ambient humidity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time electromagnetic interference intensity of the ICU environment to which the rehabilitation robot belongs during the evaluation period, the real-time noise decibel value of the ICU environment to which the rehabilitation robot belongs during the evaluation period, and the real-time carbon dioxide concentration of the ICU environment to which the rehabilitation robot belongs during the evaluation period are comprehensively analyzed to obtain the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs.

5. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system of claim 4, it is characterized in that the environmental characteristics influence coefficient of the ICU to which the rehabilitation robot belongs is specifically analyzed by the following method:

where HJ is the environmental characteristic influence coefficient of the ICU to which the rehabilitation robot belongs, t is the time variable, t∈[t ₀ , t ₁ ], t ₀ is the start time of the evaluation period, t ₁ is the end time of the evaluation period, β(t) is the real-time ambient temperature of the ICU environment of the rehabilitation robot at the time of the evaluation period t, β′ is the ambient temperature adaptation value, γ(t) is the real-time ambient humidity of the ICU environment of the rehabilitation robot at the time of the evaluation period t, γ′ is the ambient humidity adaptation value, δ(t) is the real-time electromagnetic interference intensity of the ICU environment of the rehabilitation robot at the time of the evaluation period t, θ(t) is the real-time noise decibel value of the ICU environment of the rehabilitation robot at the time of the evaluation period t, τ(t) is the real-time carbon dioxide concentration of the ICU environment of the rehabilitation robot at the time of the evaluation period t, τ′ is the carbon dioxide concentration adaptation value, YX is the muscle oxygen simulation state evaluation value of the rehabilitation robot, b ₁ is the environmental characteristic parameter corresponding to the real-time electromagnetic interference intensity predefined in the rehabilitation control information library, b ₂ is the environmental characteristic parameter corresponding to the real-time noise decibel value predefined in the rehabilitation control information library, and k ₁ Environmental characteristic parameters corresponding to the muscle oxygen simulation state evaluation values predefined in the rehabilitation control information library.

6. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system of claim 1, it is characterized in that the matching obtains the initial rehabilitation control plan, and the specific matching process is:

The muscle oxygen simulation state evaluation value of the rehabilitation robot is matched with the initial rehabilitation control plan corresponding to each predefined muscle oxygen simulation state evaluation value interval. The specific matching process is: extracting the mapping set between the muscle oxygen simulation state evaluation value of the rehabilitation robot and the initial rehabilitation control plan from the rehabilitation control information library, determining the interval to which the muscle oxygen simulation state evaluation value of the rehabilitation robot belongs, and obtaining the initial rehabilitation control plan corresponding to the interval, so as to obtain the initial rehabilitation control plan by matching.

7. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system of claim 1, it is characterized in that the execution data of the initial rehabilitation control plan specifically includes the number of rehabilitation training completions of the initial rehabilitation control plan within the execution cycle, the feedback response duration of the initial rehabilitation control plan within the execution cycle, the number of rehabilitation robot failures of the initial rehabilitation control plan within the execution cycle, and the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan;

Obtaining the initial simulated muscle oxygen saturation of the rehabilitation robot during the muscle oxygen simulation cycle;

Obtaining the preset number of rehabilitation trainings of the rehabilitation control initial plan within the execution cycle from the rehabilitation control information database;

The number of completed rehabilitation trainings of the initial rehabilitation control plan within the execution cycle is ratioed with the preset number of rehabilitation trainings of the initial rehabilitation control plan within the execution cycle to obtain the rehabilitation training simulation completion rate of the initial rehabilitation control plan within the execution cycle.

8. According to the 5G network-based ICU-AW rehabilitation robot remote collaborative and adaptive control system of claim 7, it is characterized in that the execution quality index of the initial rehabilitation control plan is specifically analyzed as follows:

The number of action repetitions of the rehabilitation robot in the muscle oxygen simulation cycle is matched with the number of fault definition corresponding to each predefined interval of action repetitions, thereby obtaining the number of fault definition of the rehabilitation robot;

Matching the response delay duration of the rehabilitation robot within the muscle oxygen simulation cycle with the feedback response limit duration corresponding to each predefined response delay duration interval, thereby obtaining the feedback response limit duration of the initial rehabilitation control plan;

A comprehensive analysis was conducted on the completion rate of rehabilitation training simulation within the execution cycle of the initial rehabilitation control plan, the feedback response time of the initial rehabilitation control plan within the execution cycle, the feedback response definition time of the initial rehabilitation control plan, the number of rehabilitation robot failures within the execution cycle of the initial rehabilitation control plan, the number of rehabilitation robot failure definitions, the final simulated muscle oxygen saturation of the rehabilitation robot within the execution cycle of the initial rehabilitation control plan, the initial simulated muscle oxygen saturation of the rehabilitation robot within the muscle oxygen simulation cycle, and the influence coefficient of environmental characteristics of the ICU to which the rehabilitation robot belongs, to obtain the execution quality indicators of the initial rehabilitation control plan.

9. The ICU-AW rehabilitation robot remote collaboration and adaptive control system based on 5G network according to claim 1 is characterized in that the determination of whether to adaptively adjust the initial rehabilitation control plan is carried out by:

If the execution quality index of the initial rehabilitation control plan is equal to the default execution quality index of the initial rehabilitation control plan, a predefined optimization adjustment plan is executed for the initial rehabilitation control plan;

If the execution quality index of the initial rehabilitation control plan is not equal to the default execution quality index of the initial rehabilitation control plan, compare the execution quality index of the initial rehabilitation control plan with the predefined execution quality reference index. If the execution quality index of the initial rehabilitation control plan is greater than the execution quality reference index, there is no need to make adaptive adjustments to the initial rehabilitation control plan. If the execution quality index of the initial rehabilitation control plan is less than or equal to the execution quality reference index, it is necessary to make adaptive adjustments to the initial rehabilitation control plan.

10. The ICU-AW rehabilitation robot remote collaboration and adaptive control system based on 5G network according to claim 9 is characterized in that the initial rehabilitation control plan is adaptively adjusted, and the specific adjustment process is:

The execution quality index of the initial rehabilitation control plan is differenced with the execution quality reference index to obtain the execution quality deviation value of the initial rehabilitation control plan, which is matched with the adaptive adjustment plan corresponding to each predefined execution quality deviation value interval, so as to obtain the adaptive adjustment plan of the initial rehabilitation control plan, and finally the initial rehabilitation control plan is adaptively adjusted.