CN119650026A

CN119650026A - Control method and system of intelligent physiotherapy health-care bed

Info

Publication number: CN119650026A
Application number: CN202510171840.2A
Authority: CN
Inventors: 杨和娟; 杨东润; 王志成
Original assignee: Guangdong Renkang Technology Co ltd
Current assignee: Guangdong Renkang Technology Co ltd
Priority date: 2025-02-17
Filing date: 2025-02-17
Publication date: 2025-03-18

Abstract

The invention discloses a control method and a control system of an intelligent physiotherapy health care bed, which relate to the technical field of equipment control, take abnormality of physiological sign data as an optimization target, optimize a massage path by using a pre-trained genetic algorithm, execute the optimized massage path by the physiotherapy bed, generate a risk value according to the abnormal state of the physiological sign data of a user, optimize the current massage force and rhythm if the risk value exceeds a risk threshold, update the current massage mode library by a new massage mode after collecting feedback data of the user, evaluate the health state of the user by the physical sign feedback data, generate a health report for the user if the health state is in a descending trend and restrict the pushing interval of the health report, and give a corresponding maintenance scheme for the physiotherapy bed by a physiotherapy bed fault maintenance knowledge graph. The method realizes the verification and evaluation of the effectiveness of massage physiotherapy of the physiotherapy couch, and can make targeted treatment when the expected effect cannot be obtained.

Description

Control method and system of intelligent physiotherapy health-care bed

Technical Field

The invention relates to the technical field of equipment control, in particular to a control method and a control system of an intelligent physiotherapy health-care bed.

Background

The intelligent control system arranged in the intelligent physiotherapy health care bed can be set and adjusted in a personalized way according to the physical condition, physiotherapy requirements and personal preferences of a user. Whether the problems of chronic pain such as discomfort of lumbar and cervical vertebra, arthritis and the like or daily body relaxation and health care, the device can provide a customized physiotherapy scheme, and ensure that the best effect can be achieved after each use.

In the Chinese patent of the invention with the publication number of CN113018064B, an intelligent control method of a physiotherapy couch for endocrinology is provided, which comprises modeling the running space of physiotherapy lamps in the physiotherapy couch for endocrinology to construct sensing areas of the physiotherapy couch, setting the running directions of the physiotherapy lamps to calculate the position coordinates of central action points in the sensing areas, designing running inflection points based on the disease characteristics and the meridian extending directions of the central action points corresponding to the parts of a patient, calculating the optimal inflection points of the physiotherapy lamps between two adjacent central action points to obtain the sequence and path planning of the physiotherapy lamps, providing intelligent physiotherapy planning for the current patient by combining the personal data of the patient, the data monitored by a temperature sensor and a pressure sensor, greatly improving the physiotherapy effect, reducing the running errors, avoiding local scalds caused by irradiating the same position for a long time, and reducing the manual operation flow of medical staff.

Combining the contents of the above applications and prior art:

When the current state of mind of the user is poor, such as tired, the user can use the massage physiotherapy bed to massage and relax, the existing massage physiotherapy bed usually has a plurality of fixed massage modes, the user can pre-select the modes when massaging, and can adjust various control parameters of the current massage modes through the self perception feedback of the massage effect, so that the massage physiotherapy effect is more matched with the expected effect of the user.

The control method of the existing physiotherapy health care bed has certain defects in the intelligence, mainly in that the control method cannot actively perform feedback optimization on the current massage process and massage effect, is more dependent on manual control, particularly when a user is in a poor state, such as tired state or poor health state, the control method cannot perform feedback adjustment in real time according to the current state of the user, and is also difficult to judge the current massage effect according to the change of physical sign data of the user, so that the control method is easy to cause difficulty in realizing targeted massage on the user, difficulty in realizing the expected massage physiotherapy improvement effect and difficulty in guaranteeing the health of the user when the physiotherapy health care bed is applied.

Therefore, the invention provides a control method and a control system of an intelligent physiotherapy health-care bed.

Disclosure of Invention

(One) solving the technical problems

Aiming at the defects of the prior art, the invention provides a control method and a system of an intelligent physiotherapy health care bed, which take the abnormality of physiological sign data as an optimization target, optimize a massage path by using a pre-trained genetic algorithm, execute the optimized massage path by the physiotherapy bed, generate a risk value according to the abnormal state of the physiological sign data of a user, optimize the current massage strength and rhythm if the risk value exceeds a risk threshold value, update the current massage mode library by a new massage mode after collecting feedback data of the user, evaluate the health state of the user by the physical sign feedback data, generate a health report for the user if the health state is in a descending trend and restrict the pushing interval of the health report, and verify and evaluate the effectiveness of the physiotherapy bed, and can make targeted treatment when the expected effect cannot be obtained, thereby solving the technical problems recorded in the background.

(II) technical scheme

In order to achieve the above purpose, the invention is realized by the following technical scheme:

The control method of the intelligent physiotherapy health care bed comprises the steps of identifying the current physiotherapy waiting state of a user according to state data, enabling the physiotherapy bed to select a corresponding massage mode for the user according to the physiotherapy waiting state, and controlling massage scene conditions in an identification area;

Collecting physiological sign data of a user in real time, and if the physiological sign data are abnormal, optimizing a massage path by using a pre-trained genetic algorithm and taking the abnormality of the physiological sign data as an optimization target, wherein the optimized massage path is executed by a physiotherapy couch;

Generating risk values according to abnormal states of physiological sign data of users If the risk valueThe current massage force and rhythm are optimized when the risk threshold value is exceeded, and after feedback data of a user are collected, the existing massage mode library is updated in a new massage mode;

Estimating the health state of the user by the physical sign feedback data, and generating a health report for the user and restricting the pushing interval of the health report if the health state is in a descending trend, wherein the pushing interval is used for pushing the health report The constraint mode of (2) is as follows:

;

wherein n is the number of times of receiving the first-level alarm instruction, Is the time interval from the ith level alarm instruction to the jth level,Is the average value of the time interval, and the weight coefficient,,And (2) and;

Constructing an improvement degree according to the change of the user sign feedback dataIf the obtained improvement degreeAnd (3) performing fault detection on the physiotherapy couch without exceeding the expectation, and giving a corresponding maintenance scheme for the physiotherapy couch by using a physiotherapy couch fault maintenance knowledge graph.

Further, after the user enters the recognition area, collecting image data, body temperature data, gesture data and expression data of the user, summarizing and generating a user state data set, and recognizing the current state to be physiotherapeutic of the user by using the trained state recognition model by taking the user state data as input.

Further, according to the current state to be treated and the historical preference data of the user, a corresponding massage mode is matched for the user in the existing massage mode library, the identity of the user is verified by adopting facial recognition and voice recognition, after the identity of the user is verified, the physiotherapy couch is started and enters the massage state, the physiotherapy couch executes the matched massage mode comprising massage path, force and rhythm, and the massage period is preset by the user.

Furthermore, the sensor network arranged on the whole body of the physiotherapy couch is used for collecting massage scene condition data comprising temperature, humidity, illumination and sound, the massage scene condition data in the identification area is used as input, and the trained condition automatic control model is used for controlling the massage scene condition in the identification area in combination with user preference data.

Further, real-time physiological sign data of the user are collected in real time by healthy wearing equipment associated with the physiotherapy couch, and a physiological sign data set is generated after the physiological sign data are summarized;

And taking physiological sign data of the user as input, carrying out abnormality identification and evaluation by using a trained abnormal data identification model, and monitoring the contact pressure of the user and the mattress by using pressure sensors distributed at each key part of the physiotherapy bed if abnormal data exist and the corresponding abnormality degree exceeds the expected value.

Further, in a preset massage observation period, if the number of times of sending the abnormal reminding is received to exceed the expected value, generating a risk value according to the sending time node of the abnormal reminding and the degree of abnormality of each timeIf the risk valueAnd when the risk threshold value is exceeded, a primary alarm instruction is sent to the outside.

Further, generating a risk value according to the sending time node of the abnormal reminding and the degree of each abnormalityThe way of (2) is as follows:

;

Wherein: for anomalies in the user's sign data at time t, For the corresponding anomaly threshold value,Is an indicator function that the degree of anomaly is above an anomaly threshold, wherein,,AndAs the weight coefficient of the light-emitting diode,Is time ofTo the point ofIs a time node of (a); Is a constant correction coefficient.

Further, after receiving the first-level alarm instruction, historical data is used as feedback to reduce the risk valueAs an optimization target, optimizing the current massage force and rhythm by a pretrained multi-target optimization algorithm to obtain an optimized massage mode;

The optimized massage mode is executed for the user, the massage parameters are regulated in real time through voice control, feedback data of the user are collected, the massage path, the massage force and the rhythm of the regulated massage mode are recorded in real time, and the existing massage mode library is updated after a new massage mode is generated.

Further, after the massage physiotherapy bed finishes the massage physiotherapy process of the user, uploading physical sign feedback data of the user to the cloud, taking the physical sign feedback data of the user as input, and carrying out health assessment by using the trained health assessment model to obtain corresponding health scores;

And arranging a plurality of continuously acquired health scores along the time, and if the health scores are in a descending trend, sending a report generation instruction to the outside.

Further, after receiving the report generation instruction, generating a health report for the user according to the user state data and the sign feedback data, providing diet, exercise and life style adjustment suggestions for the user, and restricting the pushing interval of the health reportAnd after the pushing interval conforming to the constraint condition is passed, sending a health report to the user.

Further, the health scores of the users are re-acquired, the continuous health scores are ordered according to a time axis, and the improvement degree is built according to the change of the health scoresIf the obtained improvement degreeSending out a secondary alarm instruction to the outside when the improvement threshold value is not exceeded, and constructing the improvement degree according to the following mode:

;

Wherein, For the health score on the ith time node,As a result of the corresponding mean value,The standard value is qualified;

For the ith improvement intermediate value, As a mean value thereof,,Weight coefficient is the number of time nodes:, And is also provided with 。

Further, after receiving the secondary alarm instruction, continuously monitoring and collecting operation data of the physiotherapy couch, taking the operation data as input, performing fault detection by using the trained fault identification model, acquiring corresponding fault characteristics when generating a fault, and sending a fault alarm instruction to the outside;

And according to the correspondence between the fault characteristics and the maintenance schemes, giving a corresponding maintenance scheme for the physiotherapy couch by the physiotherapy couch fault maintenance knowledge map, and executing the maintenance scheme to maintain key parts of the physiotherapy couch.

The control system of the intelligent physiotherapy health care bed comprises a scene control unit, a control unit and a control unit, wherein the scene control unit identifies the current physiotherapy waiting state of a user according to state data, enables the physiotherapy bed to select a corresponding massage mode for the user according to the physiotherapy waiting state, and controls massage scene conditions in an identification area;

the massage path optimization unit is used for collecting physiological sign data of a user in real time, and if the physiological sign data are abnormal, optimizing the massage path by using a pre-trained genetic algorithm and taking the abnormality of the physiological sign data as an optimization target, wherein the optimized massage path is executed by the physiotherapy couch;

the feedback updating unit generates a risk value according to the abnormal state of the physiological sign data of the user If the risk valueThe current massage force and rhythm are optimized when the risk threshold value is exceeded, and after feedback data of a user are collected, the existing massage mode library is updated in a new massage mode;

The health report generating unit is used for evaluating the health state of the user by the physical sign feedback data, and generating a health report for the user and restraining the pushing interval of the health report if the health state is in a descending trend;

The overhaul maintenance unit is used for constructing improvement degree according to the change of the sign feedback data of the user If the obtained improvement degreeAnd (3) performing fault detection on the physiotherapy couch without exceeding the expectation, and giving a corresponding maintenance scheme for the physiotherapy couch by using a physiotherapy couch fault maintenance knowledge graph.

(III) beneficial effects

The invention provides a control method and a control system of an intelligent physiotherapy health care bed, which have the following beneficial effects:

1. When the user needs to perform massage and physiotherapy, a more adaptive massage mode can be matched for the user, so that the output massage method is more matched with the user, and the user experience and feedback are improved.

2. The massage scene condition data are acquired in the identification area through the prearranged sensor network, and when the physiotherapy couch is used for massaging the user, the massage scene condition in the identification area can be adjusted and controlled, and the personalized massage environment is selected and executed for the user.

3. By detecting the abnormality of the physical sign data of the user, the method can timely process the physical sign data of the user when the health state of the user possibly has abnormality, ensure the health state of the user, optimize the massage path and realize preliminary optimization and improvement of the current massage mode.

4. Monitoring and collecting sign data of a user and further constructing risk valuesAccording to the risk valueThe method comprises the steps of carrying out further evaluation on the health state of a user, evaluating the current massage feedback effect, enabling the current massage mode to be more matched with the user, improving the tired state of the user, optimizing the current matched massage mode, and adding the optimized massage mode into a mode library, so that the amplification of the massage mode can be realized.

5. The method comprises the steps of evaluating the health state of a user in real time or periodically, realizing the health analysis of the user, generating a health report and a reference suggestion on the basis, realizing the description and characterization of the health state of the user, restricting the pushing interval of the health report, realizing the adjustment of pushing frequency, improving the reporting frequency when the health state of the user is poor, enabling the user to pay attention to the health change of the user in time, and indirectly providing a health alarm mechanism.

6. Constructing a degree of improvement by user's sign feedback data changesThe method realizes verification and evaluation of massage physiotherapy effectiveness of the physiotherapy couch, and can make targeted treatment when the expected effect cannot be obtained, so that the massage physiotherapy effect is better.

7. Through detecting the operation trouble of physiotherapy couch, match the maintenance and maintenance scheme that corresponds by the knowledge graph on the basis of the trouble feature of acquisition, realize the maintenance to physiotherapy couch, when using physiotherapy couch to massage physiotherapy to the user, can obtain better effect when cooperating with user's physical sign feedback data.

Drawings

FIG. 1 is a schematic flow chart of a control method of the intelligent physiotherapy health care bed of the invention;

FIG. 2 is a schematic diagram of the control system of the intelligent physiotherapy health care bed of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, the present invention provides a control method of an intelligent physiotherapy health care bed, comprising,

Step one, identifying the current state to be physiotherapeutic of a user according to state data, enabling a physiotherapy couch to select a corresponding massage mode for the user according to the state to be physiotherapeutic, and controlling massage scene conditions in an identification area;

the first step comprises the following steps:

Step 101, setting an identification area with a physiotherapy couch as a center in advance, and collecting image data, body temperature data, posture data, expression data and the like of a user by a data collecting device, such as an imaging device, a temperature monitoring device and the like after the user enters the identification area, and summarizing to generate a user state data set;

Training a machine learning algorithm by using the marked sample data to obtain a trained state recognition model, taking user state data as input, and recognizing the current state to be physiotherapeutic of the user by using the trained state recognition model;

when the physiotherapy couch is used, the user can be matched with a more adaptive massage mode when the user needs to perform massage and physiotherapy by identifying the current state of the user, such as fatigue or relaxation, so that the output massage method is more matched with the user, and the user experience and feedback are improved;

Step 102, according to the current state to be treated and the historical preference data of the user, matching a corresponding massage mode for the user in the existing massage mode library, adopting facial recognition and voice recognition to verify the identity of the user, and sending a massage starting instruction to the outside after the identity of the user is verified without errors;

after receiving the massage starting instruction, starting the physiotherapy couch and enabling the physiotherapy couch to enter a massage state, executing a matched massage mode by the physiotherapy couch, wherein the massage mode comprises a massage path, a massage force and a massage rhythm, and presetting a massage period by a user;

Step 103, collecting massage scene condition data including temperature, humidity, illumination, sound and the like by a sensor network, such as a temperature and humidity sensor, a light sensor, a sound sensor and the like, which is arranged on the whole body of the physiotherapy couch, training a fuzzy control algorithm by using the marked sample data, and obtaining a trained condition automatic control model;

The massage scene condition data in the identification area is taken as input, the trained condition automatic control model is used for controlling the massage scene condition in the identification area in combination with the user preference data, for example, light adjustment, humidification temperature control and the like are used as further contents, and music and the like liked by the user can be played according to the preference information of the user;

In use, the contents of steps 101 to 103 are combined:

As a further matter, the massage scene condition data is collected in the identification area through the prearranged sensor network, and when the physiotherapy couch is used for massaging the user, the massage scene condition in the identification area can be adjusted and controlled, and a personalized massage environment is selected and executed for the user.

Step two, acquiring physiological sign data of a user in real time, and if the physiological sign data are abnormal, optimizing a massage path by using a pre-trained genetic algorithm and taking the abnormality of the physiological sign data as an optimization target, wherein the optimized massage path is executed by a physiotherapy couch;

The second step comprises the following steps:

Step 201, after a user enters a massage physiotherapy state, real-time physiological sign data of the user, such as heart rate, electrocardio, myoelectricity, respiration, blood oxygen, blood pressure and the like, are collected in real time by healthy wearing equipment, such as wearing equipment, a blood glucose meter and the like, which are associated with a physiotherapy bed, and the obtained physiological sign data are summarized to generate a physiological sign data set;

step 202, training a convolutional network by using marked sample data to obtain a trained abnormal data identification model, taking physiological sign data of a user as input, carrying out abnormal identification and evaluation by using the trained abnormal data identification model, and if abnormal data exist and the corresponding degree of abnormality exceeds the expected value, indicating that a certain abnormality may exist in the current health state of the user and sending an abnormality reminding instruction to the outside;

When the physiotherapy couch is used, after a user enters a massage state, health acquisition equipment associated with the physiotherapy couch monitors and acquires various sign data of the user, and the current health state of the user is judged and verified by detecting abnormality of the sign data of the user, so that the user can be treated in time when the health state of the user possibly has abnormality, and the health state of the user is guaranteed;

Step 203, after an abnormal prompt is received, monitoring the contact pressure between a user and a mattress by using pressure sensors distributed at key parts of the physiotherapy couch, combining relevant historical data to reduce abnormal sign data as an optimization target, optimizing a massage path by a pre-trained genetic algorithm, executing the optimized massage path by the physiotherapy couch, and performing iterative optimization on the massage path;

in use, the contents of steps 201 to 203 are combined:

As further feedback content, when the health state of the user is abnormal, the massage path is optimized, so that the current massage mode is primarily optimized and improved, and the current massage physiotherapy effect is improved.

Step three, generating a risk value according to the abnormal state of the physiological sign data of the userIf the risk valueThe current massage force and rhythm are optimized when the risk threshold value is exceeded, and after feedback data of a user are collected, the existing massage mode library is updated in a new massage mode;

The third step comprises the following steps:

Step 301, if the number of times of sending out the abnormal reminding is received in a preset massage observation period exceeds the expected number, generating a risk value according to the sending-out time node of the abnormal reminding and the degree of each abnormality under the dimensionless condition The mode is as follows:

;

Wherein: for anomalies in the user's sign data at time t, For the corresponding anomaly threshold value,Is an indicator function that the degree of anomaly is above an anomaly threshold, wherein,,AndThe value of the weight coefficient is between 0 and 1; Is time of To the point ofIs a time node of (a); Is a constant correction coefficient, and the value of the constant correction coefficient falls between 0 and 1;

presetting a risk threshold according to historical data and management expectation of massage physiotherapy, if the obtained risk value When the risk threshold value is exceeded, the current massage physiotherapy fails to achieve the expected effect, further adjustment is needed on the basis of optimizing a massage path, and at the moment, a primary alarm instruction is sent to the outside;

When the massage device is used, as a further content, when the current massage mode is initially optimized and does not have the expected effect, the sign data of the user are continuously monitored and collected, and then a risk value is constructed According to the risk valueThe health state of the user can be further evaluated, the current massage feedback effect can be evaluated, and when the current massage effect cannot reach the expected effect, further optimization can be performed.

Step 302, after receiving the first-level alarm command, taking the relevant historical data as feedback, for example, historical massage control parameters and user sign feedback data, etc., to reduce the risk valueAs an optimization target, optimizing the current massage force and rhythm by a pretrained multi-target optimization algorithm to obtain an optimized massage mode;

When the massage device is used, as further content, the massage mode is further optimized, so that the current massage mode is more matched with a user, and the current state of the user is improved, for example, the physical sign data of the user is regulated, the user experience is improved, the tired state of the user is improved and the like;

Step 303, executing an optimized massage mode on a user, adjusting massage parameters in real time through voice control and collecting feedback data of the user, recording massage paths, massage forces and rhythms of the adjusted massage modes in real time, generating new massage modes, and updating an existing massage mode library;

in use, the contents of steps 301 to 303 are combined:

As a further content, after the current matching massage mode is optimized by taking various sign data and other data of the user as feedback, the optimized massage mode can be added into the mode library, so that the amplification of the massage mode is realized.

Step four, evaluating the health state of the user by the physical sign feedback data, and if the health state is in a descending trend, generating a health report for the user and restraining the pushing interval of the health report;

the fourth step comprises the following steps:

step 401, training a convolutional neural network by using the marked sample data to obtain a trained health evaluation model, uploading physical sign feedback data of a user to a cloud after a massage physiotherapy process of the user is finished by a physiotherapy couch, taking the physical sign feedback data of the user as input, and carrying out health evaluation by using the trained health evaluation model to obtain a corresponding health score;

arranging a plurality of continuously acquired health scores along time, if the health scores are in a descending trend, indicating that the health state of the user is gradually descending and attention needs to be paid in time, and at the moment, sending a report generation instruction to the outside;

When the massage physiotherapy instrument is used, the health state of a user is evaluated in real time or periodically on the basis of collecting and detecting the physical sign indexes of the user, so that the health analysis of the user is realized, the health report and the reference suggestion are generated on the basis, and after the massage physiotherapy instrument is received by the user, the massage physiotherapy instrument is used as a feedback mode for massage physiotherapy, and the description and the characterization of the health state of the user can also be realized.

Step 402, after receiving the report generation instruction, generating a health report for the user according to the user status data and the sign feedback data, providing diet, exercise and lifestyle adjustment advice for the user, and restricting the pushing interval of the health reportAfter passing the pushing interval meeting the constraint condition, sending a health report to the user, wherein the pushing intervalThe constraint mode of (2) is as follows:

;

In use, the contents of steps 401 and 402 are combined:

As a further matter, considering that the health status of the user does not generate large variation in a short period, and the health report does not need to be regenerated each time a small variation is generated, the push interval of the health report can be restrained, the push frequency can be adjusted, the report frequency is increased when the health status of the user is poor, the user can pay attention to the health variation of the user in time, and a health alarm mechanism is indirectly provided.

Step five, constructing improvement degree according to the change of the user sign feedback dataIf the degree of improvement isPerforming fault detection on the physiotherapy couch without exceeding the expectation, and giving a corresponding maintenance scheme for the physiotherapy couch by using a physiotherapy couch fault maintenance knowledge graph;

the fifth step comprises the following steps:

Step 501, collecting physical sign feedback data of a user, including heart rate, electrocardio, myoelectricity, blood oxygen, blood pressure and the like, after a massage physiotherapy process of the user is finished by a physiotherapy couch, performing health evaluation by using a trained health evaluation model, re-acquiring health scores of the user, analyzing the current improvement degree of the massage physiotherapy with the improvement degree, sequencing continuous health scores according to a time axis, and constructing the improvement degree according to the change of the health scores Then according to the following mode:

;

For the ith improvement intermediate value, As a mean value thereof,,Weight coefficient is the number of time nodes:, And is also provided with The weight coefficient and the previous value are kept consistent;

Presetting an improvement threshold according to historical data and management expectation of massage physiotherapy effects, and if the obtained improvement degree If the improvement threshold is not exceeded, the current massage physiotherapy fails to achieve the expected effect, which may be that the current massage mode is not right, or that part of key components cannot be effectively used, and at the moment, a secondary alarm instruction is sent to the outside;

when in use, as further feedback content, the improvement degree is constructed through the physical sign feedback data change of the user The method realizes verification and evaluation of massage physiotherapy effectiveness of the physiotherapy couch, and can make targeted treatment when the expected effect cannot be obtained, so that the massage physiotherapy effect is better;

step 502, after receiving the secondary alarm instruction, if the user still needs to continue massaging, massaging again after switching the massaging mode, or continuously monitoring and collecting operation data of the physiotherapy couch;

training a machine learning algorithm by using the marked sample data to obtain a trained fault recognition model; the operation data is used as input, the trained fault recognition model is used for fault detection, corresponding fault characteristics are obtained when faults are generated, and a fault alarm instruction is sent to the outside;

Step 503, constructing a physical therapy bed fault maintenance knowledge graph in advance by taking the fault maintenance of the physical therapy bed as a target word after the deep retrieval and the entity relation construction, giving a corresponding maintenance scheme for the physical therapy bed by the physical therapy bed fault maintenance knowledge graph according to the correspondence between the fault characteristics and the maintenance scheme, and executing the maintenance scheme to maintain key components of the physical therapy bed, such as an adjustable massage head, a hot compress and cold compress module, an air pressure massage system and the like;

In use, the contents of steps 501 to 503 are combined:

as a further feedback, after considering the adjustment of the massage mode and the massage method of the physiotherapy couch, effective improvement cannot be achieved, so that by detecting the operation failure of the physiotherapy couch, it is confirmed whether there is a failure, resulting in that the intended massage effect cannot be effectively achieved, and therefore, as a further content, the corresponding overhaul and maintenance scheme can be matched by the knowledge graph on the basis of the acquired fault characteristics, so that overhaul and maintenance of the physiotherapy couch are realized, and a better effect can be achieved when the physiotherapy couch is used for massaging physiotherapy of a user and the physical sign feedback data of the user are matched.

The construction method of the fault maintenance knowledge graph of the physiotherapy couch can refer to the following contents:

The method comprises the steps of definitely constructing a target, namely firstly, the construction target of the maintenance knowledge graph of the physical therapy bed fault is required to be definitely constructed, namely, the efficient diagnosis and maintenance of the physical therapy bed fault are realized. This requires knowledge maps that can cover common faults of the physiotherapy couch, fault causes, maintenance methods and related maintenance knowledge.

And collecting and arranging data, namely collecting fault cases of the physiotherapy couch from the channels of professional maintenance records, user feedback, after-sales service reports and the like, wherein the fault cases comprise information of fault phenomena, fault reasons, maintenance steps and the like.

And (3) knowledge arrangement, namely arranging the collected fault cases, extracting key information such as fault names, fault positions, fault phenomena, fault reasons, maintenance methods and the like, and forming a structured knowledge base.

The construction mode of the selection-knowledge graph mainly comprises a bottom-up method, a top-down method and a mixed method of the two. For the fault maintenance knowledge graph of the physiotherapy couch, the following modes can be adopted:

And (3) extracting entities (such as fault names, fault parts and the like), attributes and relations (such as fault reasons, maintenance methods and the like) from the collected fault cases, and gradually inducing and organizing to form the bottom data of the knowledge graph.

The top-down method is to define the top concept (such as fault type, maintenance method, etc.) of the fault maintenance field of the physiotherapy couch, and then refine the concept and the relationship step by step to form a concept hierarchical tree with good structure.

The mixing method comprises the steps of combining a bottom-up method and a top-down method, extracting bottom data through the bottom-up method, constructing a top concept through the top-down method, and finally carrying out knowledge fusion and processing.

And constructing a knowledge graph, namely extracting entities, such as fault names, fault parts and the like, related to the fault maintenance of the physiotherapy couch by utilizing a natural language processing technology. And extracting the relation, namely finding the semantic relation between the entities from the text by using methods such as linguistics, statistics and the like, such as the relation between the fault reason and the fault name, the relation between the maintenance method and the fault part and the like. And (3) knowledge fusion, namely fusing the extracted entities and relations, and eliminating repetition and ambiguity to form a unified knowledge representation. And (3) knowledge processing, namely carrying out concept abstraction and pattern layer construction on the constructed data to form a structured knowledge graph.

And (3) verifying and optimizing-verifying the knowledge graph, namely ensuring the accuracy and the integrity of the knowledge graph through expert auditing, actual case verification and other modes. Optimizing the knowledge graph, namely correcting and optimizing the knowledge graph according to the verification result, and improving the practicability and reliability of the knowledge graph.

Application and maintenance-application development, namely developing intelligent application of fault maintenance of the physiotherapy couch, such as a fault diagnosis system, a maintenance guidance system and the like, based on the constructed knowledge graph. And (3) continuous maintenance, namely along with the continuous development of physiotherapy couch technology and continuous accumulation of fault cases, the knowledge graph needs to be updated and maintained regularly so as to keep timeliness and accuracy.

Referring to fig. 2, the present invention provides a control method of an intelligent physiotherapy health care bed, comprising,

The scene control unit is used for identifying the current state to be physiotherapeutic of the user according to the state data, enabling the physiotherapy couch to select a corresponding massage mode for the user according to the state to be physiotherapeutic, and controlling massage scene conditions in the identification area;

The overhaul maintenance unit is used for constructing improvement degree according to the change of the sign feedback data of the user If the obtained improvement degreePerforming fault detection on the physiotherapy couch without exceeding the expectation, and giving a corresponding maintenance scheme for the physiotherapy couch by using a physiotherapy couch fault maintenance knowledge graph;

those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a division of some logic functions, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A control method for an intelligent physiotherapy health care bed, characterized in that: it includes:

Identify the user's current treatment status based on the status data, so that the therapy bed selects a corresponding massage mode for the user based on the treatment status, and controls the massage scene conditions within the identified area;

The user's physiological sign data is collected in real time. If there is an abnormality in the physiological sign data, the massage path is optimized using a pre-trained genetic algorithm with the goal of reducing the abnormality of the physiological sign data. The massage path is then executed by the physiotherapy bed.

Generate risk values based on abnormal status of user's physiological sign data , if the risk value If the risk threshold is exceeded, the current massage intensity and rhythm will be optimized, and after collecting user feedback data, the existing massage mode library will be updated with the new massage mode;

The user's health status is evaluated by the vital signs feedback data. If the health status is on a downward trend, a health report is generated for the user and the push interval of the health report is restricted; the push interval The constraints are as follows:

;

Where n is the number of times the first-level alarm command is received. is the time interval from the i -th level 1 alarm command to the j- th level 1 alarm command, is the mean of the time interval; the weight coefficient, , ,and ;

Build improvement based on changes in user vital signs feedback data , if the improvement obtained is No more than expected, the physiotherapy bed is fault-detected, and the physiotherapy bed fault maintenance knowledge graph provides a corresponding maintenance plan for the physiotherapy bed.

2. The control method of the intelligent physiotherapy health care bed according to claim 1, characterized in that:

After the user enters the recognition area, the user's image data, body temperature data, posture data and expression data are collected and summarized to generate a user status data set; the user status data is used as input, and the trained status recognition model is used to identify the user's current treatment status.

3. The control method of the intelligent physiotherapy health care bed according to claim 2 is characterized in that:

According to the user's current treatment status and historical preference data, the corresponding massage mode is matched for the user in the existing massage mode library, including massage path, strength and rhythm;

Facial recognition and voice recognition are used to verify the user's identity. After the user's identity is verified, the therapy bed is started and put into massage state. The therapy bed executes the matching massage mode and the user presets the massage cycle.

4. The control method of the intelligent physiotherapy health care bed according to claim 3 is characterized in that:

The sensor network arranged around the therapy bed collects massage scene condition data, including temperature, humidity, light and sound; the massage scene condition data in the identified area is used as input, combined with user preference data, and the massage scene conditions in the identified area are controlled using the trained conditional automatic control model.

5. The control method of the intelligent physiotherapy health care bed according to claim 4 is characterized in that:

The health wearable device associated with the physiotherapy bed collects the user's real-time physiological sign data in real time, and generates a physiological sign data set after aggregation;

The user's physiological sign data is used as input, and the trained abnormal data recognition model is used to perform abnormality recognition and evaluation. If abnormal data exists and the corresponding abnormality exceeds expectations, pressure sensors distributed at key positions of the physiotherapy bed are used to monitor the contact pressure between the user and the mattress.

6. The control method of the intelligent physiotherapy health care bed according to claim 5, characterized in that:

During the preset massage observation period, if the number of abnormal reminders received exceeds the expected number, a risk value is generated based on the time node of the abnormal reminder and the degree of abnormality each time. , if the risk value When the risk threshold is exceeded, a first-level alarm command will be issued to the outside.

7. The control method of the intelligent physiotherapy health care bed according to claim 6, characterized in that:

Generate risk values based on the time node of abnormal reminder issuance and the degree of abnormality each time The way is as follows:

;

Where: is the abnormality of the user's vital sign data at time t, is the corresponding abnormal threshold, is the indicator function of the abnormality being higher than the abnormal threshold, where , and is the weight coefficient, For time arrive Time node; is a constant correction factor.

8. The control method of the intelligent physiotherapy health care bed according to claim 7, characterized in that:

After receiving the first level alarm command, the risk value is reduced As the optimization target, the pre-trained multi-objective optimization algorithm is used to optimize the current massage intensity and rhythm to obtain the optimized massage method;

Execute the optimized massage method for the user, adjust the massage parameters in real time through voice control and collect user feedback data, record the massage path, massage intensity and rhythm of the adjusted massage mode in real time, and update the existing massage mode library after generating a new massage mode.

9. The control method of the intelligent physiotherapy health care bed according to claim 8, characterized in that:

After the massage therapy process for the user is completed on the therapy bed, the user's physical sign feedback data is uploaded to the cloud. The user's physical sign feedback data is used as input to perform health assessment using the trained health evaluation model to obtain the corresponding health score;

Several health scores obtained continuously are arranged along time. If the health score is on a downward trend, a report generation instruction is issued to the outside.

10. The control method of the intelligent physiotherapy health care bed according to claim 9, characterized in that:

After receiving the report generation instruction, generate a health report for the user based on the user's status data and vital sign feedback data, and provide the user with diet, exercise and lifestyle adjustment suggestions; limit the push interval of the health report , after a push interval that meets the constraints, a health report is sent to the user.

11. The control method of the intelligent physiotherapy health care bed according to claim 10, characterized in that:

Re-obtain the user's health score, sort the consecutive health scores by timeline, and build the improvement degree based on the change of health score , if the improvement obtained is If the improvement threshold is not exceeded, a secondary alarm command is issued to the outside; the improvement degree is constructed as follows :

;

in, is the health score at the i -th time node, is the corresponding mean, is its qualified standard value;

is the i- th improved intermediate value, is its mean value, , is the number of time nodes, weight coefficient: , and .

12. The control method of the intelligent physiotherapy health care bed according to claim 11, characterized in that:

After receiving the second-level alarm command, the system continuously monitors and collects the operation data of the physiotherapy bed, uses the operation data as input, uses the trained fault recognition model to perform fault detection, obtains the corresponding fault features when a fault occurs, and issues a fault alarm command to the outside;

Taking the fault maintenance of the physiotherapy bed as the target word, a knowledge graph of the fault maintenance of the physiotherapy bed is pre-built; based on the correspondence between the fault characteristics and the maintenance plan, the knowledge graph of the physiotherapy bed fault maintenance provides a corresponding maintenance plan for the physiotherapy bed, and the maintenance plan is executed to maintain the key components of the physiotherapy bed.

13. The control system of the intelligent physiotherapy health care bed is characterized by: including:

The scene control unit identifies the user's current treatment state based on the state data, enables the therapy bed to select a corresponding massage mode for the user based on the treatment state, and controls the massage scene conditions within the identified area;

The massage path optimization unit collects the user's physiological sign data in real time. If the physiological sign data is abnormal, the massage path is optimized using a pre-trained genetic algorithm with the goal of reducing the abnormality of the physiological sign data. The massage path is then executed by the physiotherapy bed.

Feedback update unit generates risk value based on abnormal status of user's physiological sign data , if the risk value If the risk threshold is exceeded, the current massage intensity and rhythm will be optimized, and after collecting user feedback data, the existing massage mode library will be updated with the new massage mode;

The health report generation unit evaluates the user's health status based on the vital sign feedback data. If the health status is on a downward trend, a health report is generated for the user and the push interval of the health report is restricted;

Inspection and maintenance unit, build improvement degree according to the changes of user's vital signs feedback data , if the improvement obtained is No more than expected, the physiotherapy bed is fault-detected, and the physiotherapy bed fault maintenance knowledge graph provides a corresponding maintenance plan for the physiotherapy bed.