Artificial intelligence-based rehabilitation training assistance method, system and terminal device

Abstract

The embodiment of the present invention provides a rehabilitation training assistance method, system and terminal device based on artificial intelligence, which belongs to the field of artificial intelligence technology. The method includes: obtaining the preoperative physical data corresponding to the target patient before the operation; obtaining the surgical record data corresponding to the target patient during the operation; obtaining the targeted rehabilitation data of the target patient after the operation; analyzing and generating an initial postoperative rehabilitation plan based on the preoperative physical data, targeted rehabilitation data and surgical record data; obtaining the dynamic rehabilitation monitoring data of the target patient during the preliminary rehabilitation training according to the initial postoperative rehabilitation plan; based on the postoperative management plan of the preset rehabilitation management model, predicting the rehabilitation progress of the target patient according to the dynamic rehabilitation monitoring data to generate a rehabilitation progress prediction result; adjusting the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, until the target patient meets the discharge conditions of the preset rehabilitation management model.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based rehabilitation training auxiliary method, system and terminal device. Background Art

[0002] With the advancement of modern medicine, perioperative management has gradually gained attention, especially the promotion of the concept of accelerated recovery after surgery, which marks a major shift in the management of surgical patients. Furthermore, the concept of accelerated recovery after surgery has further transformed the concepts and practices of traditional surgery. These new pre-set rehabilitation management models significantly accelerate patients' recovery and improve their quality of life after surgery by combining advanced minimally invasive surgical techniques with refined management measures. Pre-set rehabilitation models can effectively shorten patients' recovery time, reduce complications, and improve overall treatment outcomes. However, under pre-set rehabilitation management models, medical staff cannot promptly determine and evaluate whether the postoperative rehabilitation plan truly meets the patient's actual needs. This lag can result in the inability to make timely adjustments to rehabilitation strategies, thereby affecting the patient's recovery. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide an artificial intelligence-based rehabilitation training assistance method, system and terminal device, aiming to solve the problem that in the preset rehabilitation management mode, medical staff are unable to timely judge and evaluate whether the postoperative rehabilitation plan truly meets the patient's actual needs. This lag may lead to the inability to make timely adjustments to the rehabilitation strategy, thereby affecting the patient's recovery effect.

[0004] In a first aspect, an embodiment of the present invention provides an artificial intelligence-based rehabilitation training assistance method, comprising:

[0005] Obtaining preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results;

[0006] Obtaining surgical record data corresponding to the target patient during surgery, wherein the surgical record data is surgical process data obtained during the process of adjusting the target patient's physical condition to meet the inclusion conditions of the preset rehabilitation management model and excluding contraindications for surgery;

[0007] Obtaining specific rehabilitation data of the target patient after surgery; wherein the specific rehabilitation data includes movement status, pain score, and functional recovery;

[0008] Analyze the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate an initial postoperative rehabilitation plan; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training;

[0009] Acquiring dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan;

[0010] Based on the postoperative management plan of the preset rehabilitation management model, the rehabilitation progress of the target patient is predicted according to the dynamic rehabilitation monitoring data, and a rehabilitation progress prediction result is generated;

[0011] The initial postoperative rehabilitation plan is adjusted according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, which is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

[0012] In a second aspect, an embodiment of the present invention provides an artificial intelligence-based rehabilitation training assistance system, comprising:

[0013] A first acquisition module is used to obtain preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results;

[0014] The second acquisition module is used to obtain the surgical record data corresponding to the target patient during the operation. The surgical record data is the surgical process data obtained during the process of adjusting the target patient's physical condition to meet the inclusion conditions of the preset rehabilitation management model and excluding contraindications for the operation;

[0015] A third acquisition module is used to obtain the target patient's post-operative rehabilitation data; wherein the rehabilitation data includes movement status, pain score, and functional recovery;

[0016] a plan generation module, configured to generate an initial postoperative rehabilitation plan based on the preoperative physical data, the specific rehabilitation data, and the surgical record data; the initial postoperative rehabilitation plan is used to guide the target patient in performing preliminary rehabilitation training;

[0017] a rehabilitation monitoring module, configured to obtain dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan;

[0018] A progress prediction module is used to predict the rehabilitation progress of the target patient based on the postoperative management plan of the preset rehabilitation management model and the dynamic rehabilitation monitoring data, and generate a rehabilitation progress prediction result;

[0019] The plan adjustment module is used to adjust the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan. The target postoperative rehabilitation plan is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

[0020] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the artificial intelligence-based rehabilitation training assistance methods provided in the specification of the present invention are implemented.

[0021] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the artificial intelligence-based rehabilitation training assistance methods provided in the specification of the present invention.

[0022] Embodiments of the present invention provide an artificial intelligence-based rehabilitation training assistance method, system, and terminal device. The method includes: obtaining preoperative physical data corresponding to a target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results; obtaining surgical record data corresponding to the target patient during surgery, wherein the surgical record data is data obtained after the target patient's physical condition is adjusted to meet the inclusion criteria of a preset rehabilitation management model and contraindications are eliminated and surgery is performed; obtaining targeted rehabilitation data of the target patient after surgery, wherein the targeted rehabilitation data includes movement status, pain score, and functional recovery; analyzing the preoperative physical data, targeted rehabilitation data, and surgical record data to generate an initial postoperative rehabilitation plan; the initial postoperative rehabilitation plan is used to guide the target patient in performing preliminary rehabilitation training; obtaining dynamic rehabilitation monitoring data of the target patient during the preliminary rehabilitation training according to the initial postoperative rehabilitation plan; predicting the target patient's rehabilitation progress according to the dynamic rehabilitation monitoring data based on a postoperative management plan of the preset rehabilitation management model, and generating a rehabilitation progress prediction result; adjusting the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, which is used to guide the target patient in performing targeted rehabilitation training until the target patient meets the discharge criteria of the preset rehabilitation management model. This method relies on dynamic rehabilitation monitoring data to automatically and promptly predict the rehabilitation progress of target patients. With accurate rehabilitation progress prediction results, medical staff can quickly adjust the postoperative rehabilitation plan of the target patient. This flexible management method enables the medical team to efficiently implement rehabilitation programs, thereby optimizing the patient's recovery process, reducing the risk of re-hospitalization, and ultimately achieving more ideal clinical results. In addition, this method also solves the problem that in the preset rehabilitation management model, medical staff are unable to promptly judge and evaluate whether the postoperative rehabilitation plan truly meets the patient's actual needs. This lag may lead to the inability to make timely adjustments to the rehabilitation strategy, thereby affecting the patient's recovery effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A flowchart of an artificial intelligence-based rehabilitation training assistance method provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the module structure of an artificial intelligence-based rehabilitation training assistance system provided in an embodiment of the present invention;

[0026] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0028] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0029] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] Embodiments of the present invention provide an artificial intelligence-based rehabilitation training assistance method, system, and terminal device. The artificial intelligence-based rehabilitation training assistance method can be applied to a terminal device, which can be an electronic device such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The terminal device can also be a server or a server cluster.

[0031] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0032] Please refer to Figure 1 , Figure 1 A flowchart of an artificial intelligence-based rehabilitation training assistance method provided in an embodiment of the present invention.

[0033] like Figure 1 As shown, the artificial intelligence-based rehabilitation training assistance method includes steps S101 to S107.

[0034] Step S101: Obtain preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results.

[0035] For example, the target patient is a patient who requires surgery, and the preoperative physical data includes the target patient's health data, medical history, and preoperative examination results before the surgery. For example, the target patient's medical history data can be obtained through questionnaires, interviews, or electronic medical record systems. The target patient is then arranged to undergo necessary preoperative examinations, such as imaging examinations (e.g., X-rays, CT scans, MRIs), laboratory tests (e.g., blood tests, urine tests), and physiological indicator measurements (e.g., blood pressure, heart rate), thereby obtaining the target patient's preoperative examination results collected during the preoperative examination.

[0036] Step S102: Obtain surgical record data corresponding to the target patient during the operation. The surgical record data is surgical process data obtained when the target patient adjusts his physical condition to meet the inclusion conditions of the preset rehabilitation management model and eliminates contraindications during the operation.

[0037] For example, after the target patient meets the inclusion criteria of the preset rehabilitation management model and undergoes surgery after excluding contraindications, the corresponding surgical record data of the target patient during the surgery is obtained.

[0038] For example, surgical record data includes the following: basic information of the target patient such as name, gender, department, ward, bed number, inpatient medical record number or medical record number; record the date and time of the start and end of the operation; clearly record the type of operation performed to facilitate the subsequent medical team to understand the details of the operation and carry out corresponding treatment; include the names of the surgeon, first assistant and other personnel involved in the operation to ensure clear surgical responsibilities; record the anesthesia method used, anesthetic drugs and dosage, anesthesia provider and other information to evaluate the effect and safety of anesthesia; describe the surgical process in detail, including patient position, skin disinfection method, incision site, exploration situation and main lesion site, surgical method and steps, and inventory of dressing instruments after the operation; record any unexpected situations that occur during the operation (such as bleeding, injury, etc.) and corresponding treatment measures to ensure patient safety and surgical quality.

[0039] In some embodiments, before obtaining the surgical record data corresponding to the target patient during the operation, the method further includes: using a risk prediction model to perform risk prediction on the preoperative physical data to obtain a perioperative risk prediction result; generating preoperative intervention recommendation information based on the perioperative risk prediction result, and the preoperative intervention recommendation information is used to guide the target patient to adjust his physical condition to meet the inclusion conditions of a preset rehabilitation management model, wherein the preoperative intervention recommendation information includes lifestyle adjustment information, psychological support information, and a pre-rehabilitation plan.

[0040] For example, a suitable risk prediction model (such as a classification model, a machine learning model, etc.) is selected based on the type of surgery and data characteristics, and the model is trained using historical data and known surgical risks to ensure that the risk prediction model can accurately predict risks.

[0041] For example, preoperative physical data is input into the relationship recognition layer of the risk prediction model. This layer analyzes the correlations and potential relationships between various factors in the preoperative physical data, which are then used as target relationships as output by the relationship recognition layer. For example, the relationship between certain physiological indicators or patterns associated with surgical risk can be identified. Based on the target relationships, the key relationships between various factors in the preoperative physical data are determined, such as which indicators are most important for risk prediction.

[0042] For example, statistical methods (such as Pearson correlation coefficient and Spearman rank correlation coefficient) are used to calculate the correlation between any two physiological indicators in the preoperative physical data, determine which physiological indicators have significant linear or nonlinear relationships, and thus determine the correlation coefficients between the various physiological indicators, and then determine the target relationship between any two physiological indicators in the preoperative physical data based on the correlation coefficients.

[0043] Exemplarily, preoperative body data is input into the feature representation layer of the risk prediction model. This layer converts the preoperative body data into initial feature vectors using a feature representation method (such as embedding, transformation, or mapping). The feature representation layer extracts key features from the data and encodes these features. Based on the processing results of the feature representation layer, an initial feature vector corresponding to the preoperative body data is obtained.

[0044] Exemplarily, the initial feature vector and the identified target relationship are input into the relationship fusion layer of the risk prediction model, so that the relationship fusion layer combines the target relationship in the preoperative body data, further processes and fuses the initial feature vector, and obtains the target feature vector.

[0045] For example, a feature fusion strategy is developed based on the target relationship, determining how to apply the target relationship to the initial feature vector. Feature fusion strategies include weighted fusion and feature interaction. Based on the predefined feature fusion strategy, possible operations include weighting features, generating new interactive features, or transforming using a relational mapping function. The initial feature vector is then combined with the feature fusion strategy corresponding to the target relationship to obtain the corresponding target feature vector. The target feature vector contains feature information processed by the target relationship and can better reflect potential risk factors in preoperative body data.

[0046] Exemplarily, the obtained target feature vector is input into the risk prediction layer of the risk prediction model for further risk assessment and prediction, thereby obtaining the perioperative risk prediction result corresponding to the target patient.

[0047] Specifically, the relationship identification layer provides a deeper understanding of the relationships between various physiological features in preoperative body data, which helps improve the explanatory power and accuracy of the prediction model. The relationship fusion layer integrates the initial feature vectors using the identified target relationships to generate target feature vectors that better reflect the complex interactions of preoperative body data. This effectively improves model performance and provides support for subsequent accurate perioperative risk prediction results.

[0048] For example, an acceptable risk threshold is set based on the risk requirements of the target surgery. Preoperative intervention recommendation information is generated based on the predicted perioperative risk prediction results. The generated preoperative intervention recommendation information may include lifestyle adjustment information, psychological support information, and pre-rehabilitation plan. The risk threshold is compared with the perioperative risk prediction results, and then the preoperative intervention recommendation information corresponding to the target patient is searched in the preset rules based on the comparison results. That is, personalized preoperative medical intervention recommendations and preparation plans are provided to the target patient based on the perioperative risk prediction results, including but not limited to lifestyle adjustment information, psychological support information, and pre-rehabilitation plan.

[0049] For example, based on historical experience, a detailed preset rule is compiled, which includes intervention strategies corresponding to different risk levels. For example, the risk level can be divided into three levels: low, medium, and high, and specific intervention measures can be defined for each level. The preset rule should include a detailed description of each risk level and the corresponding intervention strategy (such as lifestyle adjustment, drug treatment, further examination, psychological intervention, etc.). In this way, the preset rules are classified according to the risk level to ensure that each risk level has corresponding preoperative intervention recommendation information. Based on the comparison results, the preoperative intervention recommendation information corresponding to the perioperative risk prediction result of the target patient in the preset rule is searched. For example, if the perioperative risk prediction result of the target patient is medium, the intervention measures corresponding to the "medium risk" defined in the preset rule are searched, such as enhanced monitoring, adjustment of drug dosage, increase of regular examinations, etc.

[0050] Exemplarily, after the target patient executes the preoperative intervention recommendation information for a period of time, the third physical data corresponding to the target patient under the preoperative intervention recommendation information is obtained. When the perioperative risk prediction result corresponding to the third physical data meets the corresponding risk requirements during the operation, the target patient can be operated on; when the perioperative risk prediction result corresponding to the third physical data does not meet the corresponding risk requirements during the operation, the preoperative intervention recommendation information is continued to be used to adjust the target patient's body until the perioperative risk prediction result corresponding to the third physical data meets the corresponding risk requirements during the operation.

[0051] Specifically, by analyzing preoperative physical data through a risk prediction model, each target patient's surgical risk can be accurately assessed, avoiding a one-size-fits-all risk assessment approach. Preoperative intervention recommendations, developed based on perioperative risk prediction results and pre-set rules, are highly personalized and can provide appropriate preoperative intervention recommendations tailored to the target patient's specific risk level, ensuring that each target patient receives the most appropriate intervention approach. Effective preoperative intervention recommendations and improved health status can enhance target patients' confidence in surgery and improve their overall medical experience.

[0052] Step S103: Obtaining the targeted rehabilitation data of the target patient after surgery; wherein the targeted rehabilitation data includes movement status, pain score, and functional recovery.

[0053] For example, the specific rehabilitation data that needs to be collected after surgery is determined. This data includes movement status, pain scores, and functional recovery. After surgery, the target patient undergoes necessary postoperative examinations according to the physician's recommendations and rehabilitation plan. For example, imaging examinations, laboratory tests, and monitoring of physiological indicators are performed to obtain the target patient's postoperative rehabilitation data. This data includes movement status, pain scores, and functional recovery.

[0054] Step S104: Analyze the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate an initial postoperative rehabilitation plan; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training.

[0055] For example, based on expert experience or the attending physician's analysis of preoperative physical data, specific rehabilitation data, and surgical record data, an initial postoperative rehabilitation plan corresponding to the target patient is generated; the initial postoperative rehabilitation plan is used to guide the target patient in preliminary rehabilitation training.

[0056] For example, data with common physiological characteristics, such as blood pressure, heart rate, and body temperature, are extracted from preoperative physical data, specific rehabilitation data, and surgical record data. The extracted common physiological characteristic data is compared using the same standards and time points to ensure data consistency and accuracy. Change detection methods, such as differential analysis or time series analysis, are used to identify changes in the same physiological characteristics in preoperative physical data, specific rehabilitation data, and surgical record data. Based on the data changes, change indicators, such as rate of change and trend analysis, are then calculated to obtain target characteristic data. The target characteristic data reflects changes in physiological characteristics before and after surgery.

[0057] For example, the target feature data is fed into a strategy classification model, which then uses the trained model to predict the outcome based on the preoperative physical data, specific rehabilitation data, and surgical record data. The model then outputs one or more rehabilitation strategy types, such as "physical therapy," "medication management," or "nutritional support."

[0058] For example, the obtained rehabilitation type is combined with the target feature data to analyze the relationship between the rehabilitation type and the target feature data. For example, certain rehabilitation types may require different rehabilitation frequencies or intensities, depending on the physiological state and needs of the target patient. Therefore, based on existing clinical standards or guidelines, combined with the target feature data, the rehabilitation frequency or intensity corresponding to each rehabilitation type is determined. For example, physical therapy may require a certain number of training sessions per day, while drug management may involve specific dosages and frequencies of medication, and then personalized adjustments are made based on the target feature data.

[0059] For example, the determined rehabilitation type is integrated with the corresponding rehabilitation frequency or intensity to form a comprehensive initial postoperative rehabilitation plan. For example, the rehabilitation type for a target patient is physical therapy, and the rehabilitation frequency is set to three times per week, each session lasting 30 minutes. The initial postoperative rehabilitation plan includes the rehabilitation type, rehabilitation frequency, or intensity.

[0060] In order to be able to formulate a more personalized rehabilitation plan based on the target patient's own situation, the target patient can be analyzed by the influencing factors that produce the event that requires rehabilitation. In this embodiment, the target patient's preoperative physical condition, intraoperative record data and postoperative fixed rehabilitation data can be used as three dimensions for evaluating the rehabilitation plan for comprehensive consideration, so as to formulate a rehabilitation plan that will help the target patient to recover quickly and meet the discharge criteria. In this embodiment, preoperative intervention recommendation information can be determined based on preoperative physical data, and the preoperative intervention recommendation information is used to guide the target patient to adjust his physical condition to meet the inclusion conditions of the preset rehabilitation management model. The preoperative intervention recommendation information includes lifestyle adjustment information, psychological support information and pre-rehabilitation plan. Among them, the pre-rehabilitation plan can be a preset rehabilitation plan matched by a doctor or expert based on the target patient's physical data. For example, it is possible to establish a corresponding table for a certain type of surgery corresponding to a pre-rehabilitation plan, and match the corresponding pre-rehabilitation plan according to the target patient's surgical type.

[0061] The dynamic data of the adjustment process and the adjustment result data of the target patient's physical condition based on the preoperative intervention recommendation information are obtained. The corresponding physical physiological indicators or survey indicators (for example, psychological questionnaire survey results, lifestyle survey results, etc.) recorded in real time during the process of the target patient adjusting his physical condition, for example, weight data, weight change data, test data, diet data, sleep data, etc. recorded during the adjustment of malnutrition, are used to construct the dynamic data of the adjustment process. After the target patient adjusts his physical condition to meet the inclusion conditions of the preset rehabilitation management model, the corresponding adjustment result data is obtained. In this embodiment, the strength of the target patient's own physical function can be characterized by the preoperative physical data, the dynamic data of the adjustment process, and the adjustment result data, which is of great significance for how to more personalized implement the corresponding rehabilitation plan during the postoperative rehabilitation process, such as diet, exercise intensity, physical rehabilitation tolerance, psychological acceptance, etc.

[0062] The target patient's recovery ability is assessed based on the dynamic data of the adjustment process and the adjustment result data to obtain a recovery ability assessment result. In this embodiment, recovery ability may include physiological recovery ability and psychological recovery willingness or psychological recovery ability. Different target patients may have similar preoperative physical data, but due to individual differences, their recovery abilities may be different. Therefore, in this embodiment, the target patient's recovery ability can be assessed based on the preoperative physical adjustment process and the results obtained after the adjustment. The dynamic data of the adjustment process can characterize the physical adjustment rate, thereby more accurately understanding the recovery ability of the individual target patient.

[0063] Key surgical data from the surgical record data is extracted, and discrepancy information is determined between the theoretical surgical data corresponding to the target patient. In this embodiment, a certain type of surgery often has theoretical surgical data. A description of the surgical data can be found in the description of the above embodiment and is not repeated in this embodiment. However, during actual surgery, the physical conditions of different target patients, the surgeons who perform the surgery, and the coordination between the surgeon and the anesthesiologist often differ, resulting in discrepancies between the actual key surgical data and the theoretical surgical data for each target patient during surgery. Therefore, in this embodiment, after obtaining the discrepancy information, the discrepancy information can be analyzed to assess its relevance to, or importance to, the rehabilitation plan, including rehabilitation exercises and rehabilitation items. The target patient's postoperative physical condition is assessed based on the specific rehabilitation data to obtain a postoperative physical condition assessment result. An initial postoperative rehabilitation plan is generated based on the recovery capacity assessment result, the discrepancy information, and the postoperative physical condition assessment result. The recovery capacity assessment result, the discrepancy information, and the postoperative physical condition assessment result enable a comprehensive and personalized rehabilitation plan to be developed based on the target patient's individual condition, the surgical process, and postoperative performance.

[0064] Step S105: obtaining dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan.

[0065] For example, a high-resolution camera or video capturer is selected to ensure clear capture of the target patient's rehabilitation activities. A static camera or dynamic video camera can be selected as needed. The camera or video capturer is then positioned to fully cover the rehabilitation activity area, ensuring dynamic rehabilitation monitoring data is captured during the target patient's initial rehabilitation training.

[0066] In some embodiments, after analyzing the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate an initial postoperative rehabilitation plan, the method further includes: generating auxiliary rehabilitation training instructions, sending the auxiliary rehabilitation training instructions to a rehabilitation robot to instruct the rehabilitation robot to perform video and voice interaction actions according to the auxiliary rehabilitation training instructions to assist the target patient in conducting preliminary rehabilitation training.

[0067] For example, abnormalities are identified based on the dynamic rehabilitation monitoring data of the target patient performing the initial postoperative rehabilitation plan to obtain the corresponding abnormal behavior of the target patient. The abnormal behavior is compared with the preset rules. The preset rules may include descriptions of various abnormal movements or postures, such as "incorrect posture" or "excessive exercise intensity". Based on the identified abnormal behaviors and preset rules, a detailed abnormal description text is written. These descriptions should clearly and accurately describe the abnormal conditions of the target patient and include specific guidance. For example, "your range of motion is insufficient" or "your posture deviates from the standard."

[0068] For example, select a suitable speech synthesis model, which can convert text into speech. Modern speech synthesis technology can generate natural, fluent speech in multiple languages ​​and intonations. The generated abnormality description text is then input into the speech synthesis model. The speech synthesis model will generate corresponding speech information based on the text. The intonation, speed, and volume of the speech can be adjusted as needed to ensure that the speech information is clearly conveyed and suited to the target patient's auditory habits.

[0069] For example, the generated target voice information is played to the target patient through an appropriate device (such as a smartphone, tablet, or voice assistant). Ensure that the target patient can clearly hear and understand the voice guidance content. The voice information should include an explanation of the abnormal behavior and specific rehabilitation suggestions. For example, the voice can instruct the target patient on how to adjust his posture, how to perform the movements correctly, or how to adjust the intensity of the exercise. The target patient can then adjust his movement behavior according to the voice guidance and implement the corresponding rehabilitation plan, and then generate auxiliary rehabilitation training instructions based on the dynamic rehabilitation monitoring data, and then transmit the auxiliary rehabilitation training instructions to the rehabilitation robot to instruct the rehabilitation robot to perform video and voice interactive actions according to the rehabilitation training instructions to assist the target patient in preliminary rehabilitation training.

[0070] Specifically, by combining abnormal behavior with pre-set rules, a precise description of the abnormality is generated, providing a definitive diagnosis of the patient's motor performance issues. This diagnosis can accurately identify specific abnormalities, such as substandard range of motion or incorrect posture. These descriptions are not only comprehensive but also provide clear feedback to the patient regarding their motor deficits, significantly improving the accuracy and efficiency of problem-solving. A speech synthesis model is then used to convert these descriptions into spoken messages. This approach not only makes the guidance more natural and easy to understand, but also helps the patient gain a deeper understanding of the problem and the appropriate improvement methods. Compared to traditional text-based guidance, spoken messages are significantly more approachable and easier for the patient to accept and absorb. Furthermore, the speech messages generated by the speech synthesis model provide immediate feedback to the patient, enabling them to make real-time adjustments during rehabilitation training, thereby avoiding the long-term accumulation of incorrect movements. This immediate voice guidance method clearly and specifically guides the patient on how to correct abnormal behaviors and optimize movements, thereby helping them complete rehabilitation training more efficiently.

[0071] Step S106 : Based on the postoperative management plan of the preset rehabilitation management model, the rehabilitation progress of the target patient is predicted according to the dynamic rehabilitation monitoring data, and a rehabilitation progress prediction result is generated.

[0072] For example, data analysis technology and algorithms are used to identify the movement accuracy of the target patient in the dynamic rehabilitation monitoring data, so that the movement accuracy predicts the rehabilitation progress of the target patient, and then a rehabilitation progress prediction result corresponding to the target patient is generated according to the rehabilitation progress.

[0073] In some embodiments, predicting the rehabilitation progress of the target patient based on the dynamic rehabilitation monitoring data and generating a rehabilitation progress prediction result includes: obtaining first physical data corresponding to the target patient before executing the initial postoperative rehabilitation plan; performing abnormality identification on the dynamic rehabilitation monitoring data to obtain abnormal behavior corresponding to the target patient; generating the auxiliary rehabilitation training instructions corresponding to the target patient based on the abnormal behavior; obtaining second physical data corresponding to the target patient after performing rehabilitation training under the auxiliary rehabilitation training instructions; determining a matching degree between the initial postoperative rehabilitation plan and the target patient based on the first physical data and the second physical data; predicting the rehabilitation progress of the target patient based on the matching degree and generating the rehabilitation progress prediction result.

[0074] For example, the first physical data is the target patient's preoperative examination results, for example, the target patient's medical history data obtained through a questionnaire, interview, or electronic medical record system. The target patient is then scheduled to undergo necessary preoperative examinations, such as imaging examinations (e.g., X-rays, CT scans, MRIs), laboratory tests (e.g., blood tests, urine tests), and physiological indicator measurements (e.g., blood pressure, heart rate).

[0075] For example, data analysis techniques and algorithms are used to identify abnormal behaviors in dynamic rehabilitation monitoring data. These abnormal behaviors may include deviations in movement, incorrect posture, and abnormal exercise intensity. Identified abnormal behaviors are categorized into categories, such as irregular movement execution, excessive or insufficient exercise intensity, and insufficient range of motion. Different abnormality types may require different interventions.

[0076] Exemplarily, the causes of abnormal behavior are analyzed, including technical problems, physical condition problems, external environmental influences, etc. For example, if the target patient's range of motion is insufficient, it may be due to insufficient muscle strength or unclear movement instructions. Therefore, specific auxiliary rehabilitation training instructions are set according to the abnormal behavior and the cause of the abnormality. For example, if it is found that the target patient has incorrect posture during exercise, a goal of improving the posture can be set, and then corresponding auxiliary rehabilitation training instructions can be designed according to the type of abnormal behavior. The auxiliary rehabilitation training instructions include: movement correction: providing specific movement correction suggestions to help the target patient adjust the movement posture, enhanced training: designing targeted training programs to enhance the target patient's muscle strength or flexibility, improved technology: providing technical guidance and training to help the target patient master the correct exercise skills, and adjusted intensity: adjusting the intensity and frequency of exercise according to the actual situation of the target patient to avoid overtraining or insufficient training.

[0077] Exemplarily, during the implementation of the rehabilitation guidance or after the rehabilitation guidance is completed, the second body data of the target patient corresponding to the auxiliary rehabilitation training instruction is retrieved.

[0078] For example, the second body data is used as a baseline, and the second body data is compared with the first body data to observe the change trend of each physiological indicator, and then the change trend of each physiological indicator is integrated to determine the matching degree between the initial postoperative rehabilitation plan and the target patient.

[0079] For example, the difference between the second body data and the first body data is calculated for each physiological indicator, that is, the amount of change before and after the initial postoperative rehabilitation plan is implemented, so as to evaluate whether the initial postoperative rehabilitation plan matches the physical response of the target patient based on the change trend of each physiological indicator. When the change trend is positive and the change amplitude is large, the matching value should be set to a large positive value; when the change trend is positive but the change amplitude is small, the matching value should be set to a small positive value; when the change trend is negative and the change amplitude is large, the matching value should be set to a large negative value; when the change trend is negative and the change amplitude is small, the matching value should be set to a small negative value; thus, the matching values ​​of each physiological indicator are added together to obtain the matching degree between the initial postoperative rehabilitation plan and the target patient.

[0080] For example, based on the matching results, deficiencies in the initial postoperative rehabilitation plan are identified. If the matching is low, it indicates that the initial postoperative rehabilitation plan does not fully match the target patient's physical condition, and the rehabilitation progress prediction result is exceeded expectations. If the matching is high, it indicates that the initial postoperative rehabilitation plan matches the target patient's physical condition, and the rehabilitation progress prediction result is in line with expectations.

[0081] In some embodiments, the performing abnormality identification on the dynamic rehabilitation monitoring data to obtain the abnormal behavior corresponding to the target patient includes: performing keyword extraction according to the initial postoperative rehabilitation plan to obtain the movement movements corresponding to the target patient, and obtaining the body parts associated with the movement movements; performing target identification on the dynamic rehabilitation monitoring data according to a target recognition model to obtain target association data corresponding to the body parts in the dynamic rehabilitation monitoring data; obtaining the target contour corresponding to the body parts in the dynamic rehabilitation monitoring data, and determining the running trajectory corresponding to the target patient based on the target contour and the movement movements; performing abnormality identification on the target patient based on the running trajectory to obtain the abnormal behavior corresponding to the target patient.

[0082] For example, the named entity model is used to extract keywords from the initial postoperative rehabilitation plan to obtain the corresponding movement movements of the target patient, such as leg lifting, walking, grasping, squatting, shoulder rotation, etc. It should be noted that the named entity model can be trained based on data corresponding to the annotated movement movements.

[0083] For example, information query is performed based on the mapping table to obtain the body parts associated with each movement. For example, "leg raise" mainly involves the thigh and knee, and "squat" involves the whole body.

[0084] For example, an appropriate target recognition model is selected based on the task requirements. Common models include YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN. Dynamic rehabilitation monitoring data is input into the target recognition model for inference, and the location of the target part in the dynamic rehabilitation monitoring data (e.g., the coordinates of the bounding box) is obtained. The location of the target part in the dynamic rehabilitation monitoring data is then determined as the target association data corresponding to the body part.

[0085] For example, an edge detection algorithm is used to perform edge detection on the target-related data to obtain the corresponding target contour in the body part target-related data. The target contour is then matched with the posture corresponding to each stage of the movement to obtain the target movement corresponding to the target contour. The continuous changes in the target movement are then combined with the timestamp information corresponding to the dynamic rehabilitation monitoring data to reconstruct the target patient's movement trajectory along the timeline.

[0086] For example, if the movement action is squatting, then splitting the movement action can obtain the target patient standing, target patient knee bending (multiple bending degrees), target patient squatting, target patient standing up (multiple standing up degrees), target patient standing, etc., then match the target contour with the posture corresponding to each stage of squatting (target patient standing, target patient knee bending (multiple bending degrees), target patient squatting, target patient standing up (multiple standing up degrees), target patient standing), so as to obtain the different movement stages represented by the target contour, and then use the continuous change of the target contour, combined with the timestamp information corresponding to the dynamic rehabilitation monitoring data, to reconstruct the movement trajectory of the target patient on the time axis. When the target contour is in the target patient squatting stage, it is determined as the peak of the movement trajectory. When the target contour is in the target patient standing stage, it is determined as the trough of the movement trajectory; when the target contour is in other stages, it is determined as the data between the peak and the trough.

[0087] For example, key features such as velocity peaks, acceleration changes, and trajectory curvature are extracted from the reconstructed trajectory. Machine learning or statistical learning methods, such as support vector machines (SVMs), random forests, or anomaly detection algorithms (such as isolation forests), can be used to train an anomaly detection model. This model should be able to distinguish between normal and abnormal trajectories. The extracted trajectory features are input into the anomaly detection model, which outputs a judgment on whether each trajectory point or movement is abnormal. Based on the model's output, it determines which parts of the trajectory or specific movements are identified as abnormal. This can include sudden speed changes, incoherent movement transitions, and other factors, thereby identifying abnormal behaviors corresponding to the target patient.

[0088] For example, in a movement trajectory, a peak represents the end point of a target patient's motor activity. This refers to the highest point or key location in the trajectory during movement, typically marking the completion or transition of the movement. A trough represents the start point of a movement. This refers to the lowest point or key location in the trajectory, typically marking the initiation or recovery phase of the movement. By measuring the time intervals between adjacent peaks and troughs, the time it takes the target patient to perform the movement can be calculated. Specifically, the length of time between a peak and the adjacent trough represents the duration of a complete movement. By counting all peaks and adjacent troughs, the frequency of the target patient's movement can be determined. This is the total number of times the target patient performed the movement within a specific time period. Anomaly detection can then be performed based on the calculated movement time and frequency. For example, if movement time or frequency deviates from expected standards, it may indicate that the target patient's movement does not meet the requirements. If a movement lasts an unusually long time, it may indicate that the target patient is experiencing difficulty or errors in performing the movement. If the movement frequency is lower than expected, it may indicate that the target patient is having problems completing the movement task. By analyzing the anomaly detection results, abnormal behavior in the target patient can be identified. These abnormal behaviors may include substandard movement quality, excessive exercise time, or insufficient frequency.

[0089] For example, by analyzing the running trajectory and target contour, abnormal behaviors that occur during exercise can be accurately detected. Abnormal identification based on the running trajectory can provide an objective basis, avoid reliance on subjective judgment, and improve the accuracy of abnormality detection. Abnormal identification can promptly detect risky behaviors or irregular movements that may occur in the target patient during exercise, thereby preventing potential sports injuries and improving the safety of the target patient. Furthermore, by continuously monitoring the target patient's movement trajectory and analyzing abnormal behaviors, the rehabilitation plan can be continuously optimized. Adjust the exercise intensity, frequency or movement details according to the actual performance of the target patient to make the rehabilitation plan more personalized and targeted.

[0090] Specifically, by quantifying the target profile into a running trajectory based on the movement, the target patient's actual performance can be more accurately captured. This allows for the timely identification of any deficiencies in the target patient's movement execution, as well as any abnormalities in exercise intensity, frequency, or movement details. The quantified running trajectory accurately records the path, speed, and timing of each movement, comprehensively reflecting the target patient's performance. This allows for objective assessment of the target patient's movement quality, rather than relying solely on subjective judgment. This helps improve the accuracy and reliability of the assessment. By analyzing the running trajectory, deficiencies in the target patient's movement execution can be identified early. The running trajectory can reveal abnormalities in exercise intensity and frequency. For example, if the trajectory indicates that the exercise intensity is too high or too low, or the exercise frequency is not as expected, this information can be used to adjust the exercise program to avoid overtraining or undertraining. The refined trajectory data facilitates the examination of movement details, such as whether the range of motion meets the target and whether the movements are coherent. Quantifying the target profile into a running trajectory not only more accurately captures the target patient's actual exercise performance, but also allows for the timely identification of deficiencies, optimizing rehabilitation plans, improving safety, and providing strong support for data-driven decision-making, which is crucial for improving the effectiveness and efficiency of subsequent exercise rehabilitation.

[0091] In some embodiments, determining the running trajectory corresponding to the target patient based on the target contour and the motion action includes: determining the motion direction corresponding to the target patient based on the motion action; when the motion direction is up and down motion, obtaining the user width and user height corresponding to the target contour, and determining the user ratio corresponding to the target patient based on the user width and the user height; obtaining the first time corresponding to the target contour from the target-associated data, and determining the running trajectory corresponding to the target patient based on the first time and the user ratio; when the motion direction is left and right motion, obtaining the area ratio corresponding to the target contour in the target-associated data; obtaining the second time corresponding to the target contour from the target-associated data, and determining the running trajectory corresponding to the target patient based on the second time and the area ratio.

[0092] For example, when the target patient performs various posture exercises in a fixed position, the corresponding movement direction of the target patient can be determined to be up and down movement; when the target patient exercises in different positions but with the same body posture, the corresponding movement direction of the target patient can be determined to be left and right movement. For example, when the movement action is squatting, the movement direction is up and down movement, and when the movement action is walking, the movement direction is left and right movement.

[0093] For example, when the motion direction is up and down, the user width and user height corresponding to the target patient in the target outline are obtained, and the ratio between the user width and the user height is determined as the user ratio corresponding to the target patient. The first time corresponding to the target outline is then obtained from the target-related data, and the first time is determined as the horizontal axis and the user ratio is determined as the vertical axis. The running trajectory corresponding to the target patient is then determined based on the changing trajectory of the user ratio at the first time.

[0094] First, the user width and user height of the target patient are extracted from the target outline. The user width generally refers to the horizontal size of the target patient's body parts in the target-associated data, while the user height generally refers to the vertical size of the target patient's body parts in the target-associated data.

[0095] For example, the user proportion is calculated based on the ratio of user width to user height. This ratio is the ratio of the target patient's body width to body height at a specific time point. The target patient's user proportion reflects the stage of the target patient's exercise execution, and the first time corresponding to the target outline is extracted from the target-related data.

[0096] For example, the first time is set as the horizontal axis. This represents the change in time, that is, the evolution of the action or posture. The calculated user ratio is set as the vertical axis. This represents the ratio of the user's body width to height, that is, the stage of the target patient's motor action execution. By plotting the relationship between the user ratio and time, the target patient's running trajectory during the execution of the motor action can be obtained. Specifically, this trajectory shows the changes in the target patient's motor action execution stage at the first time.

[0097] For example, when the exercise action is squatting, the user ratio will change according to the target patient's various stages of squatting. By drawing a relationship graph between the user ratio and the first time, the running trajectory of the target patient during the squatting process can be obtained.

[0098] For example, when the direction of motion is left-right motion, valuable information about the user's motion can be obtained by analyzing the area ratio of the target contour in the target-associated data. Since the direction of motion is left-right motion, the data size of the target patient in the target-associated data will gradually change as the motion progresses. For example, when the motion action is walking, as the target patient moves further and further forward during the motion, the area ratio of the target contour in the target-associated data will gradually decrease. This is because as the target patient moves, the relative position of the target contour and other elements in the data frame changes, causing the data area it occupies to gradually shrink.

[0099] For example, the second time corresponding to the target outline is obtained from the target-associated data, and the second time is set as the horizontal axis. This represents the change in time, that is, the evolution of the action or posture. The calculated area percentage is set as the vertical axis. By plotting the relationship between the area percentage and the second time, the trajectory of the target patient during the execution of the movement can be obtained. Specifically, this trajectory shows the changes in the target patient's movement execution stage at the second time.

[0100] For example, when the movement action is walking, the area ratio will change according to the target patient's various stages of walking. Therefore, by drawing a relationship graph between the area ratio and the second time, the running trajectory of the target patient during the walking process can be obtained.

[0101] Specifically, determining the target patient's specific movement direction (up and down or side to side) helps further analyze the user's body reactions and dynamic performance when performing specific movements. For up and down movement, by obtaining the user's width and height and calculating their ratio (i.e., user proportion), the stability of the user's body posture and movement can be understood. For side to side movement, by obtaining the area proportion, the amount of space occupied by the user during lateral movement can be tracked, helping to analyze the user's lateral movement efficiency and body coordination. Combining the first time and user proportion, the user's trajectory during up and down movement can be plotted, visualizing the user's vertical movement changes. Combining the second time and area proportion, the user's trajectory during side to side movement can be analyzed to observe the user's dynamic performance during lateral movement. Quantifying the target patient's performance at a specific time point can improve the accuracy and objectivity of the assessment, helping medical staff understand the target patient's exercise status and improve strategies, and also helping the target patient understand their own exercise status and effectiveness. Real-time monitoring and analysis of the movement trajectory can provide immediate feedback on the target patient's movement, enabling the user to adjust their movements and exercise plans promptly, avoiding sports injuries and irregular movements, thereby improving exercise efficiency and rehabilitation outcomes.

[0102] In some embodiments, the first body data and the second body data are collected at the same time, and determining the matching degree between the initial postoperative rehabilitation plan and the target patient based on the first body data and the second body data includes: determining the body index corresponding to the target patient, obtaining first data corresponding to the body index at the target time from the first body data and obtaining second data corresponding to the body index at the target time from the second body data; determining the first state corresponding to the body index at the target time based on the first data and preset data, and determining the second state corresponding to the body index at the target time based on the second data and the preset data; fusing the first state and the second state corresponding to the body index at the target time to obtain the state similarity between the first body data and the second body data; determining the first conformity between the initial postoperative rehabilitation plan and the target patient based on the state similarity; calculating the data gap between the first body data and the second body data, and determining the second conformity between the initial postoperative rehabilitation plan and the target patient based on the data gap; fusing the first conformity and the second conformity to determine the matching degree between the initial postoperative rehabilitation plan and the target patient; wherein, the state similarity is obtained according to the following formula:

[0103] ;

[0104] in, represents the state similarity, N represents the total number of moments corresponding to the target moment, m represents the total number of indicators corresponding to the physical indicators, represents the value corresponding to the first state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the second state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the first state under the j-th physical indicator at the t-th target moment, Represents the value corresponding to the second state under the kth physical indicator at the tth target moment.

[0105] For example, the first and second body data are collected at the same time, that is, the second and first body data are collected at the same time on different dates. For example, the first body data is collected at 12:00 on July 1, and the second body data is collected at 12:00 on July 9.

[0106] Exemplarily, the physical indicators corresponding to the target patient are determined based on historical experience or expert experience, thereby obtaining first data corresponding to the physical indicators at the target time from the first physical data and obtaining second data corresponding to the physical indicators at the target time from the second physical data.

[0107] Exemplarily, the first data is compared with preset data to determine a first state of the physical indicator at the target moment. For example, if the preset data is set to a normal range, the first data is within the normal range, and the first state is determined to be good; if the first data is outside the normal range, the first state is determined to be poor.

[0108] For example, the second data is compared with the same preset data to determine the second state of the physical indicator at the target moment. Similarly, if the second data is within a normal range, the second state is determined to be good; if it is outside the normal range, the second state is determined to be poor.

[0109] Exemplarily, the first state and the second state corresponding to the physical indicator at the target moment are fused according to the following formula to obtain the state similarity between the first physical data and the second physical data:

[0110] ;

[0111] in, represents the state similarity, N represents the total number of moments corresponding to the target moment, and m represents the total number of indicators corresponding to the physical indicators. represents the value corresponding to the first state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the second state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the first state of the j-th physical indicator at the t-th target moment, Indicates the value corresponding to the second state of the k-th physical indicator at the t-th target moment.

[0112] For example, if the preset data is set to a normal range, and the first data is within this normal range, the first status is determined to be good, and the corresponding value of the first status is 1; if the first data is outside this normal range, the first status is determined to be poor, and the corresponding value of the first status is 0. Similarly, if the second data is within the normal range, the second status is determined to be good, and the corresponding value of the second status is 1; if it is outside this normal range, the second status is determined to be poor, and the corresponding value of the second status is 0.

[0113] For example, by combining the first and second states of all physical indicators at the target moment through the above formula, a comprehensive assessment of the target patient's physical condition can be made. This comprehensive analysis helps to more accurately understand the changes in physical indicators at different time points.

[0114] For example, when the state similarity is small, the match between the initial postoperative rehabilitation plan and the target patient is greater, and when the state similarity is large, the match between the initial postoperative rehabilitation plan and the target patient is smaller. Thus, the state similarity is normalized to between 0 and 1, and then the normalized state similarity is subtracted from 1 to determine the first degree of conformity between the initial postoperative rehabilitation plan and the target patient.

[0115] For example, calculating the data gap between the first and second body data sets aims to quantify the physical changes experienced by the user after undergoing the rehabilitation strategy. This involves comparing the values ​​of the same physical indicators in the two sets of data and calculating the difference between them. This difference analysis helps reveal the actual impact of the rehabilitation strategy on the user's body. After determining the data gap, the next step is to analyze the impact trends reflected by these data gaps. By comparing the first and second body data sets, it is possible to determine whether the rehabilitation strategy has had a positive impact on the user, i.e., whether the physical indicators have improved in the expected direction, or whether a negative impact has occurred, i.e., whether the physical indicators have not improved or have even worsened. Based on the data gap and the impact trends revealed, the second level of compliance between the initial postoperative rehabilitation plan and the target patient can be further assessed. If the data gap indicates significant improvement in physical indicators and is consistent with the rehabilitation goals, the rehabilitation strategy can be considered highly compatible with the user. Conversely, if the data gap indicates no significant improvement in physical indicators or is inconsistent with the rehabilitation goals, the rehabilitation strategy is considered less compatible with the user.

[0116] For example, the first degree of conformity (based on state similarity) and the second degree of conformity (based on data gap) are combined to obtain the final degree of conformity. This can be achieved by taking both degrees of conformity into account through weighted averaging, scoring systems, etc., to determine the degree of conformity between the initial postoperative rehabilitation plan and the target patient.

[0117] Specifically, fusing the first and second states and calculating state similarity facilitates a comprehensive comparison of the health status of two different datasets at the same time point, resulting in a more accurate assessment of physical indicator status. State similarity is used to assess the effectiveness of the initial postoperative rehabilitation plan. This helps understand whether the rehabilitation strategy is adapted to the user's actual health status and whether adjustments are needed. By calculating data gaps, the actual impact of the rehabilitation strategy is assessed. The first and second degrees of conformity are combined to form a comprehensive fit. This comprehensive assessment provides more comprehensive feedback on the effectiveness of the rehabilitation strategy, ensuring that the strategy best meets the user's health needs.

[0118] Step S107: adjusting the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, wherein the target postoperative rehabilitation plan is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

[0119] For example, when the predicted result of rehabilitation progress exceeds expectations, the parts of the initial postoperative rehabilitation plan that perform well are strengthened, such as increasing the frequency or increasing the intensity. The parts of the initial postoperative rehabilitation plan that do not perform well are improved, such as adjusting the treatment methods or changing the sports. The adjusted parts are integrated into a new postoperative rehabilitation plan, namely the target postoperative rehabilitation plan. The target postoperative rehabilitation plan is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model. Therefore, through this method, artificial intelligence can provide important support in the perioperative rehabilitation process, improve rehabilitation effects, shorten rehabilitation time, and at the same time reduce the workload of medical staff, and can promote and popularize remote, multi-center, homogeneous preset rehabilitation technologies.

[0120] See also Figure 2 , Figure 2An artificial intelligence-based user rehabilitation training assistance system 200 is provided in an embodiment of the present application. The artificial intelligence-based user rehabilitation training assistance system 200 includes a first acquisition module 201, a second acquisition module 202, a third acquisition module 203, a plan generation module 204, a rehabilitation monitoring module 205, a progress prediction module 206, and a plan adjustment module 207. The first acquisition module 201 is used to obtain the preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history and preoperative examination results; the second acquisition module 202 is used to obtain the surgical record data corresponding to the target patient during surgery, wherein the surgical record data is the data obtained after the target patient adjusts his physical condition to meet the inclusion conditions of the preset rehabilitation management model and excludes contraindications for surgery; the third acquisition module 203 is used to obtain the targeted rehabilitation data of the target patient after surgery; wherein the targeted rehabilitation data includes Movement status, pain score and functional recovery; a plan generation module 204, used to analyze the preoperative physical data, the fixed rehabilitation data and the surgical record data to generate an initial postoperative rehabilitation plan; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training; a rehabilitation monitoring module 205, used to obtain dynamic rehabilitation monitoring data of the target patient during the preliminary rehabilitation training according to the initial postoperative rehabilitation plan; a progress prediction module 206, used to predict the rehabilitation progress of the target patient according to the dynamic rehabilitation monitoring data based on the postoperative management plan of the preset rehabilitation management model, and generate a rehabilitation progress prediction result; a plan adjustment module 207, used to adjust the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, and the target postoperative rehabilitation plan is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

[0121] In some embodiments, after analyzing the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate the initial postoperative rehabilitation plan, the plan generation module 204 further performs:

[0122] Generate auxiliary rehabilitation training instructions, and send the auxiliary rehabilitation training instructions to the rehabilitation robot to instruct the rehabilitation robot to perform video and voice interactive actions according to the auxiliary rehabilitation training instructions to assist the target patient in performing preliminary rehabilitation training.

[0123] In some embodiments, before obtaining the surgical record data corresponding to the target patient during surgery, the second acquisition module 202 further performs:

[0124] Using a risk prediction model to perform risk prediction on the preoperative physical data to obtain a perioperative risk prediction result;

[0125] Preoperative intervention recommendation information is generated based on the perioperative risk prediction results. The preoperative intervention recommendation information is used to guide the target patient to adjust his or her physical condition to meet the inclusion criteria of a preset rehabilitation management model, wherein the preoperative intervention recommendation information includes lifestyle adjustment information, psychological support information, and a pre-rehabilitation plan.

[0126] In some embodiments, during the process of predicting the target patient's rehabilitation progress based on the dynamic rehabilitation monitoring data and generating a rehabilitation progress prediction result, the progress prediction module 206 executes:

[0127] obtaining first physical data corresponding to the target patient before executing the initial postoperative rehabilitation plan;

[0128] Performing abnormality identification on the dynamic rehabilitation monitoring data to obtain abnormal behavior corresponding to the target patient;

[0129] generating the auxiliary rehabilitation training instruction corresponding to the target patient according to the abnormal behavior;

[0130] Obtaining second body data corresponding to the target patient after the target patient performs rehabilitation training according to the auxiliary rehabilitation training instruction;

[0131] determining a matching degree between the initial postoperative rehabilitation plan and the target patient according to the first body data and the second body data;

[0132] The rehabilitation progress of the target patient is predicted according to the matching degree, and the rehabilitation progress prediction result is generated.

[0133] In some embodiments, during the process of identifying abnormalities in the dynamic rehabilitation monitoring data and obtaining abnormal behaviors corresponding to the target patient, the progress prediction module 206 performs:

[0134] Extract keywords based on the initial postoperative rehabilitation plan to obtain the movement actions corresponding to the target patient and the body parts associated with the movement actions;

[0135] Performing target recognition on the dynamic rehabilitation monitoring data according to a target recognition model to obtain target association data corresponding to the body part in the dynamic rehabilitation monitoring data;

[0136] Obtaining a target contour corresponding to the body part in the dynamic rehabilitation monitoring data, and determining a movement trajectory corresponding to the target patient based on the target contour and the movement action;

[0137] Abnormalities of the target patient are identified according to the running trajectory to obtain the abnormal behavior corresponding to the target patient.

[0138] In some embodiments, during the process of determining the running trajectory corresponding to the target patient according to the target profile and the movement action, the progress prediction module 206 executes:

[0139] determining a movement direction corresponding to the target patient according to the movement action;

[0140] When the movement direction is up and down movement, the user width and user height corresponding to the target outline are obtained, and the user ratio corresponding to the target patient is determined according to the user width and the user height;

[0141] Obtaining a first time corresponding to the target profile from the target association data, and determining the running trajectory corresponding to the target patient according to the first time and the user ratio;

[0142] When the movement direction is left-right movement, the area ratio corresponding to the target outline in the target association data is obtained;

[0143] A second time corresponding to the target contour is obtained from the target association data, and the running trajectory corresponding to the target patient is determined according to the second time and the area proportion.

[0144] In some embodiments, the first body data and the second body data are collected at the same time. In the process of determining the matching degree between the initial postoperative rehabilitation plan and the target patient based on the first body data and the second body data, the progress prediction module 206 performs:

[0145] Determining a physical indicator corresponding to the target patient, obtaining first data corresponding to the physical indicator at a target time from the first physical data, and obtaining second data corresponding to the physical indicator at the target time from the second physical data;

[0146] determining a first state of the physical indicator corresponding to the target time according to the first data and the preset data, and determining a second state of the physical indicator corresponding to the target time according to the second data and the preset data;

[0147] fusing the first state and the second state corresponding to the physical indicator at the target moment to obtain a state similarity between the first physical data and the second physical data;

[0148] determining a first degree of compliance between the initial postoperative rehabilitation plan and the target patient according to the state similarity;

[0149] calculating a data gap between the first body data and the second body data, and determining a second degree of compliance between the initial postoperative rehabilitation plan and the target patient based on the data gap;

[0150] fusing the first compliance and the second compliance to determine the matching degree between the initial postoperative rehabilitation plan and the target patient;

[0151] The state similarity is obtained according to the following formula:

[0152] ;

[0153] in, represents the state similarity, N represents the total number of moments corresponding to the target moment, m represents the total number of indicators corresponding to the physical indicators, represents the value corresponding to the first state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the second state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the first state under the j-th physical indicator at the t-th target moment, Represents the value corresponding to the second state under the kth physical indicator at the tth target moment.

[0154] In some embodiments, the artificial intelligence-based user rehabilitation training assistance system 200 can be applied to a terminal device.

[0155] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process of the user rehabilitation training assistance system 200 based on artificial intelligence described above can refer to the corresponding process in the aforementioned artificial intelligence-based rehabilitation training assistance method embodiment, and will not be repeated here.

[0156] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0157] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I 2 C (Inter-Integrated Circuit) bus.

[0158] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0159] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0160] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0161] The processor is configured to run a computer program stored in a memory, and implement any one of the artificial intelligence-based rehabilitation training assistance methods provided in the embodiments of the present invention when executing the computer program.

[0162] In one embodiment, the processor is configured to run a computer program stored in the memory, and implement the following steps when executing the computer program:

[0163] Obtaining preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results;

[0164] Obtaining surgical record data corresponding to the target patient during surgery, wherein the surgical record data is surgical process data obtained during the process of adjusting the target patient's physical condition to meet the inclusion conditions of the preset rehabilitation management model and excluding contraindications for surgery;

[0165] Obtaining specific rehabilitation data of the target patient after surgery; wherein the specific rehabilitation data includes movement status, pain score, and functional recovery;

[0166] Analyze the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate an initial postoperative rehabilitation plan; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training;

[0167] Acquiring dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan;

[0168] Based on the postoperative management plan of the preset rehabilitation management model, the rehabilitation progress of the target patient is predicted according to the dynamic rehabilitation monitoring data, and a rehabilitation progress prediction result is generated;

[0169] The initial postoperative rehabilitation plan is adjusted according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, which is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

[0170] In some embodiments, after analyzing the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate the initial postoperative rehabilitation plan, the processor 301 further performs:

[0171] Determining preoperative intervention recommendation information based on preoperative physical data, wherein the preoperative intervention recommendation information is used to guide the target patient to adjust their physical condition to meet the inclusion criteria of a preset rehabilitation management model, and the preoperative intervention recommendation information includes lifestyle adjustment information, psychological support information, and a pre-rehabilitation plan;

[0172] Acquiring dynamic data and adjustment result data of the target patient's adjustment process of adjusting the physical condition based on the preoperative intervention recommendation information;

[0173] Performing a recovery ability assessment on the target patient based on the adjustment process dynamic data and the adjustment result data to obtain a recovery ability assessment result;

[0174] Extracting key surgical data from the surgical record data and determining difference information between the theoretical surgical data corresponding to the target patient;

[0175] Evaluate the postoperative physical condition of the target patient based on the specific rehabilitation data to obtain a postoperative physical condition evaluation result;

[0176] The initial postoperative rehabilitation plan is generated based on the recovery ability assessment result, the difference information and the postoperative physical condition assessment result.

[0177] In some embodiments, before obtaining the surgical record data corresponding to the target patient during surgery, the processor 301 further executes:

[0178] The risk prediction model is used to perform risk prediction on the preoperative physical data to obtain a perioperative risk prediction result. The risk prediction model includes a relationship recognition layer, a feature representation layer, a relationship fusion layer, and a risk prediction layer, wherein the relationship recognition layer is used to identify the target relationship between various factors in the preoperative physical data; the feature representation layer is used to convert the preoperative physical data into an initial feature vector; the relationship fusion layer is used to fuse the initial feature vector with the target relationship to obtain a target feature vector; the risk prediction layer is used to perform risk assessment and prediction using the target feature vector to obtain a perioperative risk prediction result;

[0179] Preoperative intervention recommendation information is generated based on the perioperative risk prediction results.

[0180] In some embodiments, during the process of predicting the target patient's rehabilitation progress based on the dynamic rehabilitation monitoring data and generating a rehabilitation progress prediction result, the processor 301 executes:

[0181] obtaining first physical data corresponding to the target patient before executing the initial postoperative rehabilitation plan;

[0182] Performing abnormality identification on the dynamic rehabilitation monitoring data to obtain abnormal behavior corresponding to the target patient;

[0183] generating the auxiliary rehabilitation training instruction corresponding to the target patient according to the abnormal behavior;

[0184] Obtaining second body data corresponding to the target patient after the target patient performs rehabilitation training according to the auxiliary rehabilitation training instruction;

[0185] determining a matching degree between the initial postoperative rehabilitation plan and the target patient according to the first body data and the second body data;

[0186] The rehabilitation progress of the target patient is predicted according to the matching degree, and the rehabilitation progress prediction result is generated.

[0187] In some embodiments, during the process of identifying abnormalities in the dynamic rehabilitation monitoring data and obtaining abnormal behaviors corresponding to the target patient, the processor 301 executes:

[0188] Extract keywords based on the initial postoperative rehabilitation plan to obtain the movement actions corresponding to the target patient and the body parts associated with the movement actions;

[0189] Performing target recognition on the dynamic rehabilitation monitoring data according to a target recognition model to obtain target association data corresponding to the body part in the dynamic rehabilitation monitoring data;

[0190] Obtaining a target contour corresponding to the body part in the dynamic rehabilitation monitoring data, and determining a movement trajectory corresponding to the target patient based on the target contour and the movement action;

[0191] Abnormalities of the target patient are identified according to the running trajectory to obtain the abnormal behavior corresponding to the target patient.

[0192] In some embodiments, during the process of determining the movement trajectory corresponding to the target patient according to the target profile and the movement action, the processor 301 executes:

[0193] determining a movement direction corresponding to the target patient according to the movement action;

[0194] When the movement direction is up and down movement, the user width and user height corresponding to the target outline are obtained, and the user ratio corresponding to the target patient is determined according to the user width and the user height;

[0195] Obtaining a first time corresponding to the target profile from the target association data, and determining the running trajectory corresponding to the target patient according to the first time and the user ratio;

[0196] When the movement direction is left-right movement, the area ratio corresponding to the target outline in the target association data is obtained;

[0197] A second time corresponding to the target contour is obtained from the target association data, and the running trajectory corresponding to the target patient is determined according to the second time and the area proportion.

[0198] In some embodiments, the first body data and the second body data are collected at the same time. In the process of determining the matching degree between the initial postoperative rehabilitation plan and the target patient based on the first body data and the second body data, the processor 301 executes:

[0199] Determining a physical indicator corresponding to the target patient, obtaining first data corresponding to the physical indicator at a target time from the first physical data, and obtaining second data corresponding to the physical indicator at the target time from the second physical data;

[0200] determining a first state of the physical indicator corresponding to the target time according to the first data and the preset data, and determining a second state of the physical indicator corresponding to the target time according to the second data and the preset data;

[0201] fusing the first state and the second state corresponding to the physical indicator at the target moment to obtain a state similarity between the first physical data and the second physical data;

[0202] determining a first degree of compliance between the initial postoperative rehabilitation plan and the target patient according to the state similarity;

[0203] calculating a data gap between the first body data and the second body data, and determining a second degree of compliance between the initial postoperative rehabilitation plan and the target patient based on the data gap;

[0204] fusing the first compliance and the second compliance to determine the matching degree between the initial postoperative rehabilitation plan and the target patient;

[0205] The state similarity is obtained according to the following formula:

[0206] ;

[0207] in, represents the state similarity, N represents the total number of moments corresponding to the target moment, m represents the total number of indicators corresponding to the physical indicators, represents the value corresponding to the first state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the second state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the first state under the j-th physical indicator at the t-th target moment, Represents the value corresponding to the second state under the kth physical indicator at the tth target moment.

[0208] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned embodiment of the artificial intelligence-based rehabilitation training assistance method, and will not be repeated here.

[0209] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the artificial intelligence-based rehabilitation training assistance methods provided in the description of the embodiment of the present invention.

[0210] The storage medium may be an internal storage unit of the terminal device described in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal device.

[0211] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0212] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0213] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A rehabilitation training assistance method based on artificial intelligence, characterized in that: The method comprises: Obtaining preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results; Obtaining surgical record data corresponding to the target patient during surgery, wherein the surgical record data is surgical process data obtained during the process of adjusting the target patient's physical condition to meet the inclusion conditions of the preset rehabilitation management model and excluding contraindications for surgery; Obtaining specific rehabilitation data of the target patient after surgery; wherein the specific rehabilitation data includes movement status, pain score, and functional recovery; Analyzing the preoperative physical data, the specific rehabilitation data, and the surgical record data to generate an initial postoperative rehabilitation plan, including: determining preoperative intervention recommendation information based on the preoperative physical data, the preoperative intervention recommendation information being used to guide the target patient to adjust their physical condition to meet the inclusion criteria of a preset rehabilitation management model, the preoperative intervention recommendation information including lifestyle adjustment information, psychological support information, and a pre-rehabilitation plan; Acquiring dynamic data and adjustment result data of the target patient's adjustment process of adjusting the physical condition based on the preoperative intervention recommendation information; Performing a recovery ability assessment on the target patient based on the adjustment process dynamic data and the adjustment result data to obtain a recovery ability assessment result; Extracting key surgical data from the surgical record data and determining difference information between the theoretical surgical data corresponding to the target patient; Evaluate the postoperative physical condition of the target patient based on the specific rehabilitation data to obtain a postoperative physical condition evaluation result; generating the initial postoperative rehabilitation plan based on the recovery ability assessment result, the difference information, and the postoperative physical condition assessment result; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training; Acquiring dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan; Based on the postoperative management plan of the preset rehabilitation management model, the rehabilitation progress of the target patient is predicted according to the dynamic rehabilitation monitoring data, and a rehabilitation progress prediction result is generated; The initial postoperative rehabilitation plan is adjusted according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan, which is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

2. The method according to claim 1, characterized in that Determining preoperative intervention recommendation information based on preoperative physical data includes: The risk prediction model is used to perform risk prediction on the preoperative physical data to obtain a perioperative risk prediction result. The risk prediction model includes a relationship recognition layer, a feature representation layer, a relationship fusion layer, and a risk prediction layer, wherein the relationship recognition layer is used to identify the target relationship between various factors in the preoperative physical data; the feature representation layer is used to convert the preoperative physical data into an initial feature vector; the relationship fusion layer is used to fuse the initial feature vector with the target relationship to obtain a target feature vector; the risk prediction layer is used to perform risk assessment and prediction using the target feature vector to obtain a perioperative risk prediction result; Preoperative intervention recommendation information is generated based on the perioperative risk prediction results.

3. The method according to claim 1, characterized in that The step of predicting the target patient's rehabilitation progress based on the dynamic rehabilitation monitoring data and generating a rehabilitation progress prediction result includes: obtaining first physical data corresponding to the target patient before executing the initial postoperative rehabilitation plan; Performing abnormality identification on the dynamic rehabilitation monitoring data to obtain abnormal behavior corresponding to the target patient; generating auxiliary rehabilitation training instructions corresponding to the target patient according to the abnormal behavior; Obtaining second body data corresponding to the target patient after the target patient performs rehabilitation training according to the auxiliary rehabilitation training instruction; determining a matching degree between the initial postoperative rehabilitation plan and the target patient according to the first body data and the second body data; The rehabilitation progress of the target patient is predicted according to the matching degree, and the rehabilitation progress prediction result is generated.

4. The method according to claim 3, characterized in that The performing abnormality identification on the dynamic rehabilitation monitoring data to obtain abnormal behavior corresponding to the target patient includes: Extract keywords based on the initial postoperative rehabilitation plan to obtain the movement actions corresponding to the target patient and the body parts associated with the movement actions; Performing target recognition on the dynamic rehabilitation monitoring data according to a target recognition model to obtain target association data corresponding to the body part in the dynamic rehabilitation monitoring data; Obtaining a target contour corresponding to the body part in the dynamic rehabilitation monitoring data, and determining a movement trajectory corresponding to the target patient based on the target contour and the movement action; Abnormalities of the target patient are identified according to the running trajectory to obtain the abnormal behavior corresponding to the target patient.

5. The method according to claim 4, characterized in that The determining the movement trajectory corresponding to the target patient according to the target contour and the movement action includes: determining a movement direction corresponding to the target patient according to the movement action; When the movement direction is up and down movement, the target patient width and the target patient height corresponding to the target contour are obtained, and the target patient ratio corresponding to the target patient is determined according to the target patient width and the target patient height; Obtaining a first time corresponding to the target profile from the target association data, and determining the running trajectory corresponding to the target patient according to the first time and the target patient ratio; When the movement direction is left-right movement, the area ratio corresponding to the target outline in the target association data is obtained; A second time corresponding to the target contour is obtained from the target association data, and the running trajectory corresponding to the target patient is determined according to the second time and the area proportion.

6. The method according to claim 4, characterized in that The first body data and the second body data are collected at the same time, and determining the matching degree between the initial postoperative rehabilitation plan and the target patient based on the first body data and the second body data includes: Determining a physical indicator corresponding to the target patient, obtaining first data corresponding to the physical indicator at a target time from the first physical data, and obtaining second data corresponding to the physical indicator at the target time from the second physical data; determining a first state of the physical indicator corresponding to the target time according to the first data and the preset data, and determining a second state of the physical indicator corresponding to the target time according to the second data and the preset data; fusing the first state and the second state corresponding to the physical indicator at the target moment to obtain a state similarity between the first physical data and the second physical data; determining a first degree of compliance between the initial postoperative rehabilitation plan and the target patient according to the state similarity; calculating a data gap between the first body data and the second body data, and determining a second degree of compliance between the initial postoperative rehabilitation plan and the target patient based on the data gap; fusing the first compliance and the second compliance to determine the matching degree between the initial postoperative rehabilitation plan and the target patient; The state similarity is obtained according to the following formula: ； in, represents the state similarity, N represents the total number of moments corresponding to the target moment, m represents the total number of indicators corresponding to the physical indicators, represents the value corresponding to the first state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the second state of the i-th physical indicator at the t-th target moment, represents the value corresponding to the first state under the j-th physical indicator at the t-th target moment, Represents the value corresponding to the second state under the kth physical indicator at the tth target moment.

7. A rehabilitation training auxiliary system based on artificial intelligence, characterized in that: include: A first acquisition module is used to obtain preoperative physical data corresponding to the target patient before surgery, wherein the preoperative physical data includes health data, medical history, and preoperative examination results; The second acquisition module is used to obtain the surgical record data corresponding to the target patient during the operation. The surgical record data is the surgical process data obtained during the process of adjusting the target patient's physical condition to meet the inclusion conditions of the FRAS management model and excluding contraindications for the operation; A third acquisition module is used to obtain the target patient's post-operative rehabilitation data; wherein the rehabilitation data includes movement status, pain score, and functional recovery; a plan generation module, configured to generate an initial postoperative rehabilitation plan based on the preoperative physical data, the specific rehabilitation data, and the surgical record data. The plan generation module is further configured to determine preoperative intervention recommendation information based on the preoperative physical data. The preoperative intervention recommendation information is used to guide the target patient to adjust their physical condition to meet the enrollment criteria of a preset rehabilitation management model. The preoperative intervention recommendation information includes lifestyle adjustment information, psychological support information, and a pre-rehabilitation plan. Acquiring dynamic data and adjustment result data of the target patient's adjustment process of adjusting the physical condition based on the preoperative intervention recommendation information; Performing a recovery ability assessment on the target patient based on the adjustment process dynamic data and the adjustment result data to obtain a recovery ability assessment result; Extracting key surgical data from the surgical record data and determining difference information between the theoretical surgical data corresponding to the target patient; Evaluate the postoperative physical condition of the target patient based on the specific rehabilitation data to obtain a postoperative physical condition evaluation result; generating the initial postoperative rehabilitation plan based on the recovery ability assessment result, the difference information, and the postoperative physical condition assessment result; the initial postoperative rehabilitation plan is used to guide the target patient to perform preliminary rehabilitation training; a rehabilitation monitoring module, configured to obtain dynamic rehabilitation monitoring data of the target patient during the initial rehabilitation training according to the initial postoperative rehabilitation plan; A progress prediction module is used to predict the rehabilitation progress of the target patient based on the postoperative management plan of the preset rehabilitation management model and the dynamic rehabilitation monitoring data, and generate a rehabilitation progress prediction result; The plan adjustment module is used to adjust the initial postoperative rehabilitation plan according to the rehabilitation progress prediction result to obtain a target postoperative rehabilitation plan. The target postoperative rehabilitation plan is used to guide the target patient to perform target rehabilitation training until the target patient meets the discharge conditions of the preset rehabilitation management model.

8. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the artificial intelligence-based rehabilitation training assistance method according to any one of claims 1 to 6 when executing the computer program.

9. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the artificial intelligence-based rehabilitation training assistance method according to any one of claims 1 to 6.

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