Control method and system of intelligent physiotherapy health care bed

Abstract

The present invention discloses a control method and system for an intelligent physiotherapy health care bed, which relates to the technical field of equipment control. The method takes reducing the abnormality of physiological sign data as an optimization goal, uses a pre-trained genetic algorithm to optimize the massage path, and the physiotherapy bed executes the optimized massage path. A risk value is generated according to the abnormal state of the user's physiological sign data. If the risk value exceeds the risk threshold, the current massage intensity and rhythm are optimized. After collecting the user's feedback data, the existing massage mode library is updated with a new massage mode. The user's health status is evaluated by the 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 constrained; the physiotherapy bed fault maintenance knowledge map provides a corresponding maintenance plan for the physiotherapy bed. The verification and evaluation of the effectiveness of the massage therapy of the physiotherapy bed are realized, and targeted treatment can be made when the expected effect is not achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to a control method and system for an intelligent physiotherapy health-care bed. Background Art

[0002] The built-in intelligent control system of the smart physiotherapy bed can be set and adjusted according to the user's physical condition, physiotherapy needs and personal preferences. Whether it is chronic pain problems such as lumbar and cervical discomfort, arthritis, or daily body relaxation and health care, it can provide customized physiotherapy solutions to ensure that every use can achieve the best effect.

[0003] In the Chinese invention patent with authorization announcement number CN113018064B, an intelligent control method for a physiotherapy bed for endocrinology department includes modeling the operation space of the physiotherapy lamp in the physiotherapy bed for endocrinology department to construct the sensing area of ​​the physiotherapy bed; setting the operation direction of the physiotherapy lamp to obtain the position coordinates of the central action point in each sensing area; designing the operation turning point based on the symptom characteristics of the patient's part corresponding to the central action point and the meridian extension direction; obtaining the optimal turning point between two adjacent central action points of the physiotherapy lamp, obtaining the sequence and path planning of the physiotherapy lamp for this time, combining the patient's personal data, the data monitored by the temperature sensor and the pressure sensor, providing the current patient with intelligent physiotherapy planning, greatly improving the physiotherapy effect, reducing operation errors, avoiding local burns caused by long-term irradiation of the same position, and reducing the manual operation process of medical staff.

[0004] Combined with the above application and the contents of the prior art:

[0005] When the user's current mental state is poor, for example, when he is very tired, he can use a massage therapy bed for massage relaxation. Existing massage therapy beds usually have several fixed massage modes. The user can pre-select the mode during the massage, and can adjust the various control parameters of the current massage mode through his own perception and feedback of the massage effect, so that the massage therapy effect is more in line with the user's expected effect.

[0006] The intelligence of the existing control methods for physiotherapy health care beds usually has certain deficiencies, which is mainly reflected in that it usually cannot actively provide feedback and optimization for the current massage process and massage effect, but relies more on manual control, especially when the user is currently in a poor state, such as being tired or in poor health. In the existing control methods, it is impossible to provide feedback and adjustment based on the user's current state in real time, and it is also difficult to judge the current massage effect based on changes in the user's vital sign data. This easily leads to the control method being difficult to achieve targeted massage for users when using physiotherapy health care beds, difficult to achieve the expected massage therapy improvement effect, and difficult to ensure the health of users.

[0007] To this end, the present invention provides a control method and system for an intelligent physiotherapy health-care bed. Summary of the invention

[0008] 1. Technical issues to be resolved

[0009] In view of the deficiencies in the prior art, the present invention provides a control method and system for an intelligent physiotherapy health care bed. By taking reducing the abnormality of physiological sign data as the optimization goal, a pre-trained genetic algorithm is used to optimize the massage path, and the physiotherapy bed executes the optimized massage path. A risk value is generated according to the abnormal state of the user's physiological sign data. If the risk value exceeds the risk threshold, the current massage intensity and rhythm are optimized. After collecting the user's feedback data, the existing massage mode library is updated with a new massage mode. The user's health status is evaluated by the 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 constrained. The effectiveness of the massage therapy on the physiotherapy bed is verified and evaluated, and targeted treatment can be made when the expected effect is not achieved, thereby solving the technical problems recorded in the background.

[0010] (II) Technical solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0012] The control method of the intelligent physiotherapy health care bed includes: identifying the current physiotherapy state of the user according to the state data, making the physiotherapy bed select the corresponding massage mode for the user according to the physiotherapy state, and controlling the massage scene conditions in the identified area;

[0013] 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.

[0014] 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;

[0015] 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:

[0016] ;

[0017] in, nis the number of times the first-level alarm command is received, It is i The next level of alarm command goes to j The time interval, is the mean of the time interval; the weight coefficient, , ,and ;

[0018] Build improvement degree 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.

[0019] Furthermore, 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 status for treatment.

[0020] Furthermore, based on the user's current treatment status and historical preference data, a corresponding massage mode is matched for the user in the existing massage mode library; facial recognition and voice recognition are used to verify the user's identity. After the user's identity verification is correct, the therapy bed is started and put into the massage state. The therapy bed executes the matched massage mode, including the massage path, strength and rhythm, and the massage cycle is preset by the user.

[0021] Furthermore, a 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.

[0022] Furthermore, 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;

[0023] 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.

[0024] Furthermore, within 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.

[0025] Furthermore, a risk value is generated based on the time point of the abnormal reminder and the degree of abnormality each time. The way is as follows:

[0026] ;

[0027] 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 point; is a constant correction factor.

[0028] Furthermore, after receiving the first-level alarm command, historical data is used as feedback to reduce the risk value. 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;

[0029] 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.

[0030] Furthermore, after the massage therapy bed finishes the massage therapy process for the user, the user's physical sign feedback data is uploaded to the cloud, and 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;

[0031] 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.

[0032] Furthermore, after receiving the report generation instruction, a health report is generated for the user based on the user status data and vital sign feedback data, and the user is provided with diet, exercise and lifestyle adjustment suggestions, and the push interval of the health report is restricted. , after a push interval that meets the constraints, a health report is sent to the user.

[0033] Furthermore, the user's health score is retrieved, the continuous health scores are sorted according to the time axis, and the improvement degree is constructed according to the change of the 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 :

[0034] ;

[0035] in, For the i Health scores at each time point, is the corresponding mean, is its qualified standard value;

[0036] For the i Improve the median value, is its mean value, , is the number of time nodes, weight coefficient: , and .

[0037] Furthermore, after receiving the secondary alarm command, the operation data of the physiotherapy bed is continuously monitored and collected; the operation data is used as input, and the trained fault recognition model is used to perform fault detection, and the corresponding fault characteristics are obtained when a fault occurs, and a fault alarm command is issued to the outside;

[0038] 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; according to 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.

[0039] The control system of the intelligent physiotherapy health care bed includes a scene control unit, which identifies the user's current physiotherapy state according to the state data, enables the physiotherapy bed to select a corresponding massage mode for the user according to the physiotherapy state, and controls the massage scene conditions in the identified area;

[0040] 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.

[0041] 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;

[0042] A 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;

[0043] 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.

[0044] (III) Beneficial effects

[0045] The present invention provides a control method and system for an intelligent physiotherapy health care bed, which has the following beneficial effects:

[0046] 1. When users need massage and physical therapy, a more suitable massage mode can be matched for the user, so that the output massage method is more compatible with the user, improving the user experience and feedback.

[0047] 2. Massage scene condition data is collected in the identification area through a pre-arranged sensor network. When the user is massaged on a therapy bed, the massage scene conditions in the identification area can be adjusted and controlled to select and execute a personalized massage environment for the user.

[0048] 3. By detecting abnormalities in the user's vital sign data, timely processing can be carried out when there may be abnormalities in the user's health status, thus ensuring the user's health status; the massage path can be optimized to achieve preliminary optimization and improvement of the current massage method.

[0049] 4. Monitor and collect users’ vital signs data and then build risk values , based on the risk value Chao will further evaluate the user's health status and the current massage feedback effect to make the current massage method more suitable for the user and improve the user's fatigue state. After optimizing the current matching massage mode, the optimized massage mode will be added to the mode library to expand the massage mode.

[0050] 5. Evaluate the user's health status in real time or periodically to perform health analysis on the user, generate health reports and reference suggestions on this basis, and describe and characterize the user's health status; constraining the push interval of health reports can adjust the push frequency, increase the reporting frequency when the user's health status deteriorates, enable users to pay attention to their own health changes in a timely manner, and indirectly provide a health alarm mechanism.

[0051] 6. Build improvement through changes in user's vital signs feedback data , to verify and evaluate the effectiveness of massage therapy on the therapy bed. When the expected effect is not achieved, targeted treatment can be made to make the massage therapy effect better.

[0052] 7. Through detecting the operational faults of the physiotherapy bed and matching the corresponding inspection and maintenance plans based on the acquired fault features, the inspection and maintenance of the physiotherapy bed can be realized. When using the physiotherapy bed to perform massage therapy on the user, better results can be achieved by combining the user's physical sign feedback data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a control method of the intelligent physiotherapy health care bed of the present invention;

[0054] Figure 2 It is a schematic diagram of the control system structure of the intelligent physiotherapy health care bed of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 The present invention provides a control method for an intelligent physiotherapy health care bed, comprising:

[0057] Step 1: 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 in the identified area;

[0058] The step 1 includes the following contents:

[0059] Step 101: Pre-set an identification area with the physiotherapy bed as the center. After the user enters the identification area, a data acquisition device, such as an imaging device and a temperature monitoring device, collects the user's image data, body temperature data, posture data, expression data, etc., and aggregates and generates a user status data set;

[0060] The machine learning algorithm is trained with the labeled sample data to obtain a trained state recognition model; the user state data is used as input, and the trained state recognition model is used to identify the user's current state to be treated;

[0061] When the user uses the physiotherapy bed for massage, by identifying the user's current state, such as fatigue or relaxation, when the user needs massage and physiotherapy, a more suitable massage mode can be matched for the user, so that the output massage method is more compatible with the user, improving the user's experience and feedback;

[0062] Step 102: According to the user's current treatment status and historical preference data, a corresponding massage mode is matched for the user in the existing massage mode library; the user's identity is verified by using facial recognition and voice recognition, and after the user's identity verification is correct, a massage start instruction is issued to the outside;

[0063] After receiving the massage start command, the physiotherapy bed is started and enters the massage state, and the physiotherapy bed executes the matching massage mode, including the massage path, strength and rhythm, and the massage cycle is preset by the user;

[0064] Step 103: A sensor network arranged around the physiotherapy bed, such as a temperature and humidity sensor, a light sensor, and a sound sensor, collects massage scene condition data, including temperature, humidity, light, and sound, and trains a fuzzy control algorithm with the labeled sample data to obtain a trained conditional automatic control model;

[0065] Taking the massage scene condition data in the identified area as input and combining it with the user preference data, the trained conditional automatic control model is used to control the massage scene conditions in the identified area, such as adjusting the lighting or humidification and temperature control. As a further content, the user's favorite music can also be played based on the user's preference information;

[0066] When using, combine the contents in steps 101 to 103:

[0067] As a further content, massage scene condition data is collected in the identification area through a pre-arranged sensor network. When the user is massaged on a therapy bed, the massage scene conditions in the identification area can be adjusted and controlled to select and execute a personalized massage environment for the user.

[0068] The intelligence of the existing control methods for physiotherapy health care beds usually has certain deficiencies, which is mainly reflected in that it usually cannot actively provide feedback and optimization for the current massage process and massage effect, but relies more on manual control, especially when the user is currently in a poor state, such as being tired or in poor health. In the existing control methods, it is impossible to provide feedback and adjustment based on the user's current state in real time, and it is also difficult to judge the current massage effect based on changes in the user's vital sign data. This easily leads to the control method being difficult to achieve targeted massage for users when using physiotherapy health care beds, difficult to achieve the expected massage therapy improvement effect, and difficult to ensure the health of users.

[0069] Step 2: Collect the user's physiological sign data in real time. If the physiological sign data is abnormal, reduce the abnormality of the physiological sign data as the optimization goal, use the pre-trained genetic algorithm to optimize the massage path, and the physiotherapy bed executes the optimized massage path;

[0070] The step 2 includes the following contents:

[0071] Step 201: After the user enters the massage therapy state, the health wearable device associated with the therapy bed, such as a wearable device and a blood glucose meter, collects the user's real-time physiological sign data, such as heart rate, electrocardiogram, electromyography, respiration, blood oxygen and blood pressure, in real time, and aggregates the acquired physiological sign data to generate a physiological sign data set;

[0072] Step 202: train the convolutional network with the labeled sample data to obtain a trained abnormal data recognition model; use the user's physiological sign data as input, and use the trained abnormal data recognition model to perform abnormal recognition and evaluation. If there is abnormal data and the corresponding abnormality exceeds expectations, it means that the user's current health status may have certain abnormalities, and issue an abnormal reminder instruction to the outside;

[0073] When in use, after the user enters the massage state, the health collection device associated with the physiotherapy bed monitors and collects the user's various physical sign data, and judges and verifies the user's current health status by detecting abnormalities in the user's physical sign data. When there may be abnormalities in the user's health status, it can be processed in time to ensure the user's health status;

[0074] Step 203: After receiving the abnormal reminder, the contact pressure between the user and the mattress is monitored by using pressure sensors distributed at key positions of the physiotherapy bed, and the massage path is optimized by a pre-trained genetic algorithm in combination with relevant historical data to reduce abnormal physical sign data as an optimization target. The physiotherapy bed executes the optimized massage path and iteratively optimizes the massage path.

[0075] When using, combine the contents in steps 201 to 203:

[0076] As further feedback, when the user's health status becomes abnormal, the massage path is optimized to achieve preliminary optimization and improvement of the current massage method, thereby improving the current massage therapy effect.

[0077] Step 3: Generate 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;

[0078] The step three includes the following contents:

[0079] Step 301: within 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 under dimensionless conditions. , as follows:

[0080] ;

[0081] 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, and its value falls between 0 and 1; For time arrive Time point; is a constant correction coefficient, whose value falls between 0 and 1;

[0082] Based on historical data and management expectations for massage therapy, risk thresholds are set in advance; if the risk value obtained If the risk threshold is exceeded, it means that the current massage therapy has not achieved the expected effect. On the basis of optimizing the massage path, further adjustments need to be made. At this time, a first-level alarm command is issued to the outside.

[0083] When using it, as a further content, when the initial optimization of the current massage method does not achieve the expected effect, continue to monitor and collect the user's vital sign data and then construct a risk value , based on the risk value The user's health status can be further evaluated, and the current massage feedback effect can also be evaluated. When the current massage effect cannot achieve the expected effect, further optimization can be made.

[0084] Step 302: After receiving the first-level alarm instruction, relevant historical data is used as feedback, such as historical massage control parameters and user vital signs feedback data, to reduce the risk value. 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;

[0085] When in use, as further content, the massage method is further optimized to make the current massage method more compatible with the user, so as to improve the user's current state, for example, adjusting the user's vital sign data, improving the user's experience, improving the user's fatigue state, etc.;

[0086] Step 303: Execute the optimized massage mode on the user, adjust the massage parameters in real time through voice control and collect the user's 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;

[0087] When using, combine the contents in steps 301 to 303:

[0088] As a further content, after optimizing the currently matched massage mode with the user's various vital signs data and other data as feedback, the optimized massage mode can be added to the mode library to achieve the expansion of the massage mode.

[0089] Step 4: Evaluate the user's health status based on the vital sign feedback data. If the health status is on a downward trend, generate a health report for the user and constrain the push interval of the health report;

[0090] The step 4 includes the following contents:

[0091] Step 401: train a convolutional neural network with the labeled sample data to obtain a trained health evaluation model; after the massage therapy process for the user is completed on the therapy bed, upload the user's physical sign feedback data to the cloud, use the user's physical sign feedback data as input, use the trained health evaluation model to perform health assessment, and obtain a corresponding health score;

[0092] Arrange several health scores obtained continuously along the time. If the health score is in a downward trend, it means that the user's health status is gradually declining and needs to be paid attention to in time. At this time, send a report generation instruction to the outside;

[0093] When in use, based on the collection and detection of the user's vital signs, the user's health status is evaluated in real time or periodically to achieve a health analysis of the user. On this basis, a health report and reference suggestions are generated. After the user receives them, they can be used as a feedback method for massage therapy, and can also be used to describe and characterize the user's health status.

[0094] Step 402: After receiving the report generation instruction, generate a health report for the user based on the user status data and physical sign feedback data, and provide the user with diet, exercise and lifestyle adjustment suggestions, and limit the push interval of the health report After a push interval that meets the constraints, a health report is sent to the user. The constraints are as follows:

[0095] ;

[0096] in, n is the number of times the first-level alarm command is received, It is i The next level of alarm command goes to j The time interval, is the mean of the time interval; the weight coefficient, , ,and ;

[0097] When using, combine the contents in steps 401 and 402:

[0098] As a further content, considering that the user's health status will not change significantly in the short term, there is no need to regenerate the health report every time a small change occurs. Therefore, the push interval of the health report can be constrained, and the push frequency can be adjusted. When the user's health status deteriorates, the reporting frequency can be increased, so that the user can pay attention to his or her own health changes in time, and indirectly provide a health alarm mechanism.

[0099] Step 5: Build improvement measures based on changes in user feedback data If the improvement If the failure does not exceed expectations, the physiotherapy bed is detected and the physiotherapy bed fault maintenance knowledge graph is used to provide a corresponding maintenance plan for the physiotherapy bed;

[0100] The step five includes the following contents:

[0101] Step 501: After the massage therapy process for the user is completed on the therapy bed, the user's vital sign feedback data, including heart rate, electrocardiogram, electromyography, blood oxygen, blood pressure, etc., is collected, and the health assessment is performed using the trained health assessment model, and the user's health score is re-obtained. The improvement degree of the current massage therapy is analyzed by the improvement degree, and the continuous health scores are sorted according to the time axis, and the improvement degree is constructed according to the change of the health score. , and then follow the following method:

[0102] ;

[0103] in, For the i Health scores at each time point, is the corresponding mean, is its qualified standard value;

[0104] For the i Improve the median value, is its mean value, , is the number of time nodes, weight coefficient: , and ;The weight coefficient remains consistent with the previous value;

[0105] Based on historical data and management expectations of massage therapy effects, improvement thresholds are set in advance; if the improvement degree obtained is If the improvement threshold is not exceeded, it means that the current massage therapy has not achieved the expected effect. This may be due to the incorrect massage method or some key components cannot be used effectively. At this time, a secondary alarm command is issued to the outside.

[0106] When used, as further feedback content, the improvement degree is constructed through the changes in the user's vital signs feedback data , to verify and evaluate the effectiveness of massage therapy on the therapy bed. When the expected effect is not achieved, targeted treatment can be made to make the massage therapy effect better;

[0107] Step 502: After receiving the secondary alarm instruction, if the user still needs to continue the massage, the user can switch the massage mode and then resume the massage, or continue to monitor and collect the operation data of the physiotherapy bed;

[0108] The machine learning algorithm is trained with the labeled sample data to obtain the trained fault recognition model. The operating data is used as input, and the trained fault recognition model is used to perform fault detection. When a fault occurs, the corresponding fault features are obtained, and a fault alarm instruction is issued to the outside.

[0109] Step 503: Taking the fault maintenance of the physiotherapy bed as the target word, after deep retrieval and entity relationship construction, a knowledge graph of fault maintenance of the physiotherapy bed is pre-built; according to the correspondence between the fault characteristics and the maintenance plan, the knowledge graph of fault maintenance of the physiotherapy bed provides a corresponding maintenance plan for the physiotherapy bed, and the maintenance plan is executed to maintain the key components of the physiotherapy bed, such as the adjustable massage head, the hot and cold compress modules, and the air pressure massage system;

[0110] When using, combine the contents in steps 501 to 503:

[0111] As further feedback, considering that effective improvement cannot be achieved after adjusting the massage mode and massage method of the physiotherapy bed, the operating fault of the physiotherapy bed is detected to confirm whether there is a fault, resulting in the expected massage effect cannot be effectively achieved. Therefore, as further content, the corresponding inspection and maintenance plan can be matched by the knowledge graph based on the acquired fault characteristics to realize the inspection and maintenance of the physiotherapy bed. When using the physiotherapy bed to perform massage therapy on users, better results can be achieved when combined with the user's physical sign feedback data.

[0112] The construction method of the physiotherapy bed fault maintenance knowledge graph can refer to the following:

[0113] Clarify the construction goal - First, it is necessary to clarify the construction goal of the physiotherapy bed fault maintenance knowledge graph, that is, to achieve efficient diagnosis and maintenance of physiotherapy bed faults. This requires the knowledge graph to cover common physiotherapy bed faults, fault causes, repair methods, and related maintenance knowledge.

[0114] Collect and organize data - Fault case collection: Collect failure cases of physiotherapy beds from professional maintenance records, user feedback, after-sales service reports and other channels, including failure phenomena, causes of failures, maintenance steps and other information.

[0115] Knowledge organization: Organize the collected fault cases and extract key information, such as fault name, fault location, fault phenomenon, fault cause, maintenance method, etc., to form a structured knowledge base.

[0116] Select the construction method - The construction methods of knowledge graphs are mainly bottom-up, top-down and a mixture of the two. For the knowledge graph of physiotherapy bed fault maintenance, the following methods can be used:

[0117] Bottom-up method: extract entities (such as fault name, fault location, etc.), attributes and relationships (such as fault cause, repair method, etc.) from the collected fault cases, and then gradually summarize and organize them to form the underlying data of the knowledge graph.

[0118] Top-down method: first define the top-level concepts in the field of physiotherapy bed fault maintenance (such as fault type, repair method, etc.), and then gradually refine the concepts and relationships to form a well-structured concept hierarchy tree.

[0119] Hybrid method: Combining bottom-up and top-down methods, first extract the underlying data through the bottom-up method, then construct the top-level concepts through the top-down method, and finally perform knowledge fusion and processing.

[0120] Constructing a knowledge graph - Entity extraction: Using natural language processing technology, automatically extract entities related to the maintenance of physiotherapy bed faults, such as fault name, fault location, etc. from the text. Relationship extraction: Using linguistics, statistics and other methods, discover the semantic relationship between entities from the text, such as the relationship between the cause of the fault and the fault name, the relationship between the maintenance method and the fault location, etc. Knowledge fusion: Fusion of the extracted entities and relationships, elimination of duplication and ambiguity, and formation of a unified knowledge representation. Knowledge processing: Perform conceptual abstraction and pattern layer construction on the constructed data to form a structured knowledge graph.

[0121] Verification and optimization - Verify the knowledge graph: Ensure the accuracy and completeness of the knowledge graph through expert review, actual case verification, etc. Optimize the knowledge graph: Based on the verification results, revise and optimize the knowledge graph to improve its practicality and reliability.

[0122] Application and maintenance—Application development: Based on the constructed knowledge graph, develop intelligent applications for physiotherapy bed fault maintenance, such as fault diagnosis system, maintenance guidance system, etc. Continuous maintenance: With the continuous development of physiotherapy bed technology and the continuous accumulation of fault cases, the knowledge graph needs to be updated and maintained regularly to maintain its timeliness and accuracy.

[0123] See also Figure 2 The present invention provides a control method for an intelligent physiotherapy health care bed, comprising:

[0124] 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;

[0125] 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.

[0126] 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;

[0127] A 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;

[0128] Inspection and maintenance unit, build improvement degree according to the changes of user's vital signs feedback data , if the improvement obtained is If the failure does not exceed expectations, the physiotherapy bed is detected and the physiotherapy bed fault maintenance knowledge graph is used to provide a corresponding maintenance plan for the physiotherapy bed;

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control method for an intelligent physiotherapy health care bed, characterized in that: include, 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: ； in, is the number of times the first-level alarm command is received, It is The next level of alarm command to The time interval, is the mean of the time interval; the weight coefficient, , ,and ; Build improvement degree based on changes in user vital signs feedback data , if the improvement obtained is If the failure does not exceed expectations, the physiotherapy bed is detected and the physiotherapy bed fault maintenance knowledge graph is used to provide a corresponding maintenance plan for the physiotherapy bed; The sensor network arranged around the physiotherapy 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 trained conditional automatic control model is used to control the massage scene conditions in the identified area. 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.

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; Taking the user status data as input, 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: 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.

5. The control method of the intelligent physiotherapy health care bed according to claim 4 is 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, The corresponding abnormal threshold, is the indicator function of the abnormality degree being higher than the abnormal threshold, where , and is the weight coefficient, For time arrive Time point; is a constant correction factor.

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

7. The control method of the intelligent physiotherapy health care bed according to claim 6, characterized in that: After the massage therapy bed finishes the massage therapy process for the user, 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.

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

9. The control method of the intelligent physiotherapy health care bed according to claim 8, 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; For the Improve the median value, is its mean value, , is the number of time nodes, weight coefficient: , and .

10. The control method of the intelligent physiotherapy health care bed according to claim 9, 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; according to 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.

11. A control system for an intelligent physiotherapy health-care bed, the system being applied to the control method for the intelligent physiotherapy health-care bed as claimed in claim 1, characterized in that: include, 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; A 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.

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