Method and system for optimizing child preoperative anxiety relieving scheme based on intelligent agent

Abstract

The invention discloses a method and system for optimizing a child preoperative anxiety relieving scheme based on an intelligent agent, and relates to the technical field related to medical health, and the method comprises the steps: executing the questionnaire data collection of a child guardian, and building a child questionnaire data set; calling the combined sensor group to collect physiological data of the child, and establishing a time sequence physiological data set; anxiety grade prediction is carried out, and anxiety grade correction is carried out through context awareness and age compensation; performing alleviation strategy optimization according to the anxiety grade correction result and the children preference data; and generating a mitigation scheme optimization result according to the mitigation strategy optimization result. The technical problems that in the prior art, the child preoperative anxiety relies on subjective experience, accuracy is insufficient, personalized relieving strategies cannot be provided according to different medical scenes and individual differences, and consequently the child preoperative anxiety relieving efficiency and effect are poor are solved. The technical effects of improving the efficiency and effect of relieving the preoperative anxiety of the children and achieving personalized services are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of medical health, and in particular to an optimization method and system for an intelligent agent-based preoperative anxiety relief program for children. Background Art

[0002] Preoperative anxiety in children is a critical issue that needs urgent attention in healthcare settings. Children often experience varying degrees of anxiety during medical procedures, such as dental consultations, imaging studies, and surgeries, due to fear of the unknown, concerns about pain, separation from family, and unfamiliarity with the medical environment and procedures. This anxiety not only impacts their own mental well-being but can also interfere with the smooth progress of surgery. For example, anxious children may be extremely resistant during the preoperative preparation phase, making procedures like anesthesia induction difficult, prolonging preoperative preparation time, and even increasing surgical risks. Currently, methods for alleviating preoperative anxiety in children have limitations. Medical staff rely on experience and simple observation to assess children's anxiety levels, which is highly subjective and inaccurate. For example, simply conversing with children and observing their behavior can miss subtle but crucial emotional signals. Common anxiety-relief methods, such as animations and storytelling, lack customization and have varying effectiveness across children with different personalities and preferences. Furthermore, there is a lack of a universally applicable and effective solution for different medical settings, such as dental consultations, imaging studies, and surgery. This, in turn, impacts the efficiency and effectiveness of preoperative anxiety relief in children.

[0003] At present, relevant technologies have the problem that children's preoperative anxiety relies on subjective experience, lacks accuracy, and cannot provide personalized relief strategies based on different medical scenarios and individual differences, which leads to poor efficiency and effectiveness in alleviating children's preoperative anxiety. Summary of the Invention

[0004] This application provides an optimization method and system for an intelligent agent-based preoperative anxiety relief program for children, thereby solving the technical problems in the prior art that preoperative anxiety in children relies on subjective experience, lacks accuracy, and cannot provide personalized relief strategies based on different medical scenarios and individual differences, which leads to poor efficiency and effectiveness in alleviating preoperative anxiety in children. This application achieves the technical effect of improving the efficiency, effectiveness and personalized service of alleviating preoperative anxiety in children.

[0005] The present application provides an agent-based optimization method for a preoperative anxiety relief program for children, comprising: executing questionnaire data collection from a child's guardian to establish a child questionnaire data set, wherein the child questionnaire data set includes questionnaire anxiety data and child preference data; calling a joint sensor group to collect the child's physiological data to establish a time-series physiological data set, wherein the time-series physiological data set includes heart rate data, skin electricity data, facial data, and voice data; predicting anxiety levels based on the time-series physiological data set and the questionnaire anxiety data in the child questionnaire data set by an agent, and correcting the anxiety level through situational perception and age compensation to establish an anxiety level correction result; optimizing a relief strategy based on the anxiety level correction result and the child preference data in the child questionnaire data set to establish a relief strategy optimization result; and generating a relief program optimization result based on the relief strategy optimization result.

[0006] In a possible implementation, the agent-based optimization method for pediatric preoperative anxiety relief program also performs the following processing: calling heart rate data to calculate the low-frequency and high-frequency ratio of heart rate variability to generate a first calculation result; calling skin electricity data to calculate the skin electricity response amplitude to generate a second calculation result; calling facial data to calculate the facial action unit intensity to generate a third calculation result; calling voice data to calculate the voice micro-disturbance rate and voice amplitude disturbance to generate a fourth calculation result; generating the anxiety index at the current moment based on the first calculation result, the second calculation result, the third calculation result, and the fourth calculation result, and predicting the anxiety level based on the anxiety index and the questionnaire anxiety data in the children's questionnaire data set.

[0007] In a possible implementation, the agent-based optimization method for alleviating children's preoperative anxiety further performs the following processing: calculating the anxiety index using the formula as follows: in, Representational Moment The anxiety index, Characterizes the low-frequency and high-frequency ratio of heart rate variability, The cumulative distribution function that characterizes the standard normal distribution, Characterizes the normal value of sympathetic-vagal balance in children, represents the standard deviation of the age group, Characterizes the skin electrical response amplitude, Adaptive thresholds to characterize galvanic skin responses, Characterizes the sensitivity adjustment coefficient of the skin's electrical response, Representing facial action unit index, Characterization The weight coefficient of each facial action unit, Characterization Facial action unit strength values, Characterizes the speech perturbation rate, Characterizes speech amplitude disturbance, They are the low-frequency and high-frequency ratio of heart rate variability, the amplitude of skin electrode response, the intensity of facial action unit, and the weight coefficient of speech interference.

[0008] In a possible implementation, the agent-based optimization method for alleviating children's preoperative anxiety further performs the following processing: age-based normalization of the child's sympathetic-vagal balance and adaptive threshold compensation of the skin electrical response, as follows: ; ; The anxiety index was reconstructed based on the compensated normal value of children's sympathetic-vagal balance and the adaptive threshold of skin electrodermal response.

[0009] In a possible implementation, the agent-based optimization method for alleviating children's preoperative anxiety also performs the following processing: obtaining operating room information, and calculating the child's operating room unfamiliarity coefficient based on the operating room information; obtaining medical instrument information, and calculating the child's instrument impact based on the medical instrument information; and compensating for the anxiety index under situational perception based on the calculation results of the operating room unfamiliarity and the calculation results of the instrument impact.

[0010] In a possible implementation, the agent-based optimization method for relieving children's preoperative anxiety also performs the following processing: using a joint sensor group to read the continuous monitoring results of the child, performing an anxiety relief evaluation on the child based on the continuous monitoring results, and generating evaluation feedback; and optimizing and managing the relief strategy optimization results based on the evaluation feedback.

[0011] In a possible implementation, the agent-based optimization method for relieving children's preoperative anxiety also performs the following processing: creating key nodes based on the optimization results of the relief strategy, and taking the execution time point of the optimization results of the relief strategy as the time zero point, performing time step division according to the time zero point and the key nodes, and arranging important nodes, and the important nodes are distributed within the time zero point and the key nodes; performing anxiety relief evaluation of the continuous monitoring results at the important nodes and key nodes respectively to generate evaluation feedback.

[0012] In a possible implementation, the agent-based optimization method for alleviating children's preoperative anxiety further performs the following processing: the joint sensor group includes PPG, ECG, dry electrode conductivity meter, 3D ToF camera, and array microphone.

[0013] In a possible implementation, the agent-based optimization method for relieving children's preoperative anxiety also performs the following processing: establishing a relief profile for the child, extracting relief preference features and updating them to the relief profile; and optimizing the child's subsequent relief plan based on the relief profile.

[0014] The present application also provides an optimization system for a preoperative anxiety relief program for children based on an intelligent agent, including: a questionnaire data acquisition module, used to execute questionnaire data collection from child guardians and establish a child questionnaire data set, wherein the child questionnaire data set includes questionnaire anxiety data and child preference data; a physiological data acquisition module, used to call a joint sensor group to collect physiological data of the child and establish a time-series physiological data set, wherein the time-series physiological data set includes heart rate data, skin electricity data, facial data, and voice data; an anxiety level prediction module, used to predict the anxiety level through an intelligent agent based on the time-series physiological data set and the questionnaire anxiety data in the child questionnaire data set, and to correct the anxiety level through situational perception and age compensation to establish an anxiety level correction result; a relief strategy optimization module, used to optimize the relief strategy based on the anxiety level correction result and the child preference data in the child questionnaire data set to establish a relief strategy optimization result; and a relief program optimization module, used to generate a relief program optimization result based on the relief strategy optimization result.

[0015] The proposed method and system for optimizing the agent-based preoperative anxiety relief program for children proposed in this application will collect questionnaire data from child guardians and establish a child questionnaire dataset; call a joint sensor group to collect physiological data from children and establish a time-series physiological dataset; predict anxiety levels and correct them through contextual perception and age compensation; optimize relief strategies based on the anxiety level correction results and child preference data to establish relief strategy optimization results; and generate relief program optimization results based on the relief strategy optimization results. This solves the technical problems in the prior art of relying on subjective experience, lacking accuracy, and failing to provide personalized relief strategies based on different medical scenarios and individual differences, which in turn leads to poor efficiency and effectiveness in alleviating children's preoperative anxiety. This achieves the technical effect of improving the efficiency, effectiveness, and personalized service of preoperative anxiety relief for children. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flowchart of an optimization method for an agent-based preoperative anxiety relief program for children provided in an embodiment of the present application.

[0018] Figure 2 Schematic diagram of the optimized system structure of the agent-based children's preoperative anxiety relief program provided in an embodiment of the present application.

[0019] Explanation of the reference numerals: questionnaire data collection module 10 , physiological data collection module 20 , anxiety level prediction module 30 , relief strategy optimization module 40 , relief plan optimization module 50 . DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, systems, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application embodiment provides an optimization method for relieving children's preoperative anxiety based on an agent, such as Figure 1 As shown, the method includes: Step S100 , collecting questionnaire data from children's guardians to establish a children's questionnaire data set, wherein the children's questionnaire data set includes questionnaire anxiety data and children's preference data.

[0024] Preferably, the child's guardian is asked to fill out a questionnaire to collect child-related information. As the information provider, the child's guardian spends time with the child day and night, has a relatively in-depth and comprehensive understanding of the child's daily performance, psychological state, and personal preferences, and can provide relatively accurate and reliable information. Then, a child questionnaire data set is established, which includes all the information obtained from the guardian's questionnaire, mainly including questionnaire anxiety data and child preference data. The questionnaire anxiety data refers to the child's own emotional expression, daily behavioral changes, and cognition and concerns about the surgery. Specifically, the guardian can describe the child's mention of surgery-related information, For example, whether there are obvious emotional reactions such as nervousness, anxiety, fear, crying, etc. during the time, place or process of the operation. For example, some children may start crying when they hear that they are going to have an operation, or show strong resistance and refuse to talk about things related to the operation; observe whether the child's daily behavior is abnormal in the period before the operation, for example, a child who originally had good sleep quality begins to have difficulty falling asleep, has frequent dreams, and is easily awakened; understand the child's understanding of the operation and what they are worried about. For example, children may worry that the operation will be painful, that they will not be able to move normally as before after the operation, or that the operation will leave scars. Child preference data refers to interests, hobbies, and emotional attachments. Specifically, it includes favorite game types, such as puzzles, building blocks, and role-playing games; favorite animated characters, such as Pleasant Goat and Super Wings; and favorite music styles, such as nursery rhymes and classical music. This data can serve as an important basis for alleviating children's anxiety. For example, a child's favorite animations or music can be selected as part of a relief strategy. Some children may be particularly dependent on specific items, such as pacifiers and small blankets. When alleviating preoperative anxiety, it may be appropriate to allow children to bring these items. Based on this questionnaire data, a child's anxiety level can be accurately assessed and appropriate relief strategies can be intelligently recommended based on the assessment results.

[0025] Step S200 , calling a joint sensor group to collect physiological data of the child and establishing a time series physiological data set, wherein the time series physiological data set includes heart rate data, skin electrical data, facial data, and voice data.

[0026] Step S200 further includes step S210, where the combined sensor group includes PPG, ECG, a dry electrode conductivity meter, a 3D ToF camera, and an array microphone.

[0027] Preferably, the combined sensor group is composed of different types of sensors, each of which has its own unique function. The combined sensor group can collect children's physiological data more comprehensively and accurately from different angles. Among them, the PPG (photoplethysmography sensor) is mainly used to detect changes in blood volume, thereby obtaining information related to the cardiovascular system. The PPG sensor emits light of a specific wavelength to the surface of the skin and then detects changes in the intensity of the reflected or transmitted light. When the heart contracts and relaxes, the degree of blood filling in the blood vessels changes, resulting in changes in light absorption and reflection, thereby And be captured by the PPG sensor; ECG (electrocardiograph) is used to record the electrical activity of the heart. The heart generates a weak electrical signal every time it beats, which can be detected by electrodes placed on the surface of the human body. The ECG sensor can accurately record the changes in these electrical signals to form an electrocardiogram; the dry electrode conductivity meter is used to measure the conductivity of the skin. The conductivity of the skin is closely related to the activity of the sweat glands on the surface of the skin, and the activity of the sweat glands is regulated by the autonomic nervous system. When the human body is in an emotional state such as tension and anxiety, the autonomic nervous system will be excited, resulting in increased secretion of sweat glands, thereby changing the conductivity of the skin.

[0028] Preferably, a 3D ToF camera (three-dimensional time-of-flight camera) acquires three-dimensional information about an object by measuring the time it takes for light to be emitted from the camera to hit the object's surface and then reflected back. For collecting physiological data about children, a 3D ToF camera can capture a three-dimensional image of a child's face, including facial shape, contours, and subtle changes in expression, for use in analyzing the child's emotional state, as different emotions are often accompanied by distinct facial expression characteristics. An array microphone, consisting of multiple microphones, can simultaneously collect sound signals from multiple directions. During the physiological data collection process, the array microphone records the child's voice information, including characteristics such as voice volume, pitch, speaking speed, intonation, and micro-perturbations. This voice data can reflect the child's emotional state and psychological stress level. For example, anxiety may cause changes in voice trembling and increased speech speed. The various physiological data collected by the combined sensor group are then organized and stored in chronological order to form a time-series physiological data set, recording changes in the child's physiological state over a period of time and reflecting the dynamic changes in the child's physiological indicators over time.

[0029] Preferably, the time-series physiological data set includes heart rate data, skin electrode data, facial data and voice data. Specifically, heart rate data refers to heart rate information collected from PPG and ECG sensors. When children are anxious before surgery, their heart rate tends to accelerate. By analyzing the changing trend of heart rate data, we can understand the child's emotional state and the degree of physical stress response; skin electrode data is skin conductivity data measured by a dry electrode conductivity meter, reflecting the child's emotional arousal level. When a child feels anxious, the skin conductivity usually increases; facial data refers to the three-dimensional image data of the child's face, which is processed to obtain information such as facial action unit intensity, facial expression characteristics, etc. Different emotions correspond to different facial expression patterns. By analyzing facial data, the child's emotional state can be identified, such as anxiety, fear, calmness, etc.; voice data refers to the child's voice information, including various characteristic parameters of the voice, reflecting the child's emotional state and psychological stress level. For example, anxiety may cause the micro-disturbance rate of the voice to increase, the voice amplitude disturbance to increase, etc. By analyzing the changes in voice data, it is possible to assist in judging the child's anxiety level. By obtaining these children's physiological data and combining them with questionnaire data, we can accurately assess the children's anxiety state and intelligently recommend corresponding relief strategies based on the assessment results.

[0030] In step S300, the intelligent agent predicts the anxiety level based on the time-series physiological data set and the questionnaire anxiety data in the children's questionnaire data set, and corrects the anxiety level through situational awareness and age compensation to establish an anxiety level correction result.

[0031] Step S300 further includes step S310, calling heart rate data to calculate the low-frequency and high-frequency ratio of heart rate variability to generate a first calculation result; step S320, calling skin electricity data to calculate the skin electricity response amplitude to generate a second calculation result; step S330, calling facial data to calculate the facial action unit intensity to generate a third calculation result; step S340, calling voice data to calculate the voice micro-disturbance rate and voice amplitude disturbance to generate a fourth calculation result; step S350, generating the anxiety index at the current moment based on the first calculation result, the second calculation result, the third calculation result, and the fourth calculation result, and predicting the anxiety level based on the anxiety index and the questionnaire anxiety data in the children's questionnaire data set.

[0032] Step S350 further includes calculating the anxiety index using the formula as follows: in, Representational Moment The anxiety index, Characterizes the low-frequency and high-frequency ratio of heart rate variability, The cumulative distribution function that characterizes the standard normal distribution, Characterizes the normal value of sympathetic-vagal balance in children, represents the standard deviation of the age group, Characterizes the skin electrical response amplitude, Adaptive thresholds to characterize galvanic skin responses, Characterizes the sensitivity adjustment coefficient of the skin's electrical response, Representing facial action unit index, Characterization The weight coefficient of each facial action unit, Characterization Facial action unit strength values, Characterizes the speech perturbation rate, Characterizes speech amplitude disturbance, They are the low-frequency and high-frequency ratio of heart rate variability, the amplitude of skin electrode response, the intensity of facial action unit, and the weight coefficient of speech interference.

[0033] Preferably, the intelligent agent is a unit with perception, reasoning and decision-making capabilities, and can predict the child's anxiety level based on the questionnaire anxiety data in the time-series physiological data set and the children's questionnaire data set. Specifically, the intelligent agent first processes and analyzes the various data in the time-series physiological data set. For example, for the heart rate data, the low-frequency to high-frequency ratio of the heart rate variability is calculated, and the first calculation result obtained can reflect the balance state of the child's sympathetic-vagus nerve. Different ratios correspond to different anxiety levels. Among them, heart rate variability (HRV) refers to the change in the difference between successive heartbeat cycles, reflecting the regulatory function of the autonomic nervous system on the heart. The low-frequency (LF) and high-frequency (HF) components are related to the activities of the sympathetic nerves and vagus nerves, respectively. When the child is in an anxious state, the sympathetic nerve activity is enhanced, and the LF / HF ratio tends to increase.

[0034] For the skin electrical data, the skin electrical response amplitude is calculated. The skin electrical response amplitude is related to the child's emotional arousal level. The skin electrical response amplitude tends to increase when anxious. Among them, the skin electrical response (GSR) refers to the change in skin conductivity caused by the activity of sweat glands on the skin surface. When the human body is in an emotionally excited or nervous state, sympathetic nerve excitement will lead to increased sweat gland secretion, thereby enhancing the conductivity of the skin. The skin electrical response amplitude is used to measure the size of the conductivity change. By analyzing the skin electrical data, the maximum change amplitude of the skin electrical response over a period of time is calculated to obtain the second calculation result. Anxiety usually increases the skin electrical response amplitude, which can reflect the child's emotional arousal level.

[0035] For facial data, the intensity of facial action units is calculated. A specific combination of facial action unit intensities may indicate anxiety. Facial expressions are an important way for humans to express emotions. Different emotions correspond to different facial muscle movement patterns. A facial action unit (AU) refers to the smallest observable facial change caused by facial muscle movement. By analyzing facial data (such as facial images collected by a 3D ToF camera), different facial action units are identified and the intensity of each action unit is calculated. For example, the intensity changes of action units such as furrowed brows and drooping mouth corners can reflect the emotional state of children. The intensity calculation results of each facial action unit are then summarized to obtain a third calculation result, which can assess the degree of children's anxiety from the perspective of facial expressions.

[0036] For speech data, the speech micro-disturbance rate and speech amplitude disturbance are calculated. Anxiety may cause changes in speech micro-disturbance and amplitude disturbance. Among them, the speech signal is also affected by the emotional state. The speech micro-disturbance rate refers to the tiny frequency fluctuations in the speech signal, and the speech amplitude disturbance refers to the instability of the amplitude of the speech signal. When a child is in an anxious state, the speech may tremble or be unsmooth, resulting in an increase in the speech micro-disturbance rate and speech amplitude disturbance. By performing acoustic feature analysis on the speech data, the speech micro-disturbance rate and speech amplitude disturbance are calculated to obtain the fourth calculation result, which can be used to judge the degree of child anxiety.

[0037] Preferably, based on the first calculation result, the second calculation result, the third calculation result and the fourth calculation result, the anxiety index is calculated using a formula, that is, corresponding weight coefficients are assigned to the low-frequency and high-frequency ratio of heart rate variability, the amplitude of skin electrode response, the intensity of facial action units and voice interference, so as to reflect the importance of different factors in anxiety assessment. Specifically, the anxiety index calculation formula integrates multi-dimensional physiological data into an anxiety index, which can more comprehensively reflect the child's current anxiety state. The anxiety index calculation formula is as follows: in, Representational Moment The anxiety index, Characterizes the low-frequency and high-frequency ratio of heart rate variability, The cumulative distribution function that characterizes the standard normal distribution, Characterizes the normal value of sympathetic-vagal balance in children, represents the standard deviation of the age group, Characterizes the skin electrical response amplitude, Adaptive thresholds to characterize galvanic skin responses, Characterizes the sensitivity adjustment coefficient of the skin's electrical response, Representing facial action unit index, Characterization The weight coefficient of each facial action unit, Characterization Facial action unit strength values, Characterizes the speech perturbation rate, Characterizes speech amplitude disturbance, The anxiety index is combined with the anxiety data from the children's questionnaire dataset to classify the children's anxiety levels into different levels, such as mild, moderate, and severe.

[0038] Furthermore, step S300 further includes step S360, performing adaptive threshold compensation of the normal value of the child's sympathetic-vagal balance and skin electrical response based on age, as follows: ; ; Step S370 , reconstructing the anxiety index based on the compensated normal value of the child's sympathetic-vagal balance and the adaptive threshold of the skin electrical response.

[0039] Preferably, using the formula and formula , an age-based compensation calculation is performed on the normal value of the children's sympathetic-vagal balance and the adaptive threshold of the skin electrode response, where age is the child's age. When the age changes, the normal value of the children's sympathetic-vagal balance and the adaptive threshold of the skin electrode response will change accordingly; after obtaining the compensated normal value of the children's sympathetic-vagal balance and the adaptive threshold of the skin electrode response, they are substituted into the anxiety index calculation formula to replace the uncompensated corresponding values, thereby reconstructing the anxiety index so that the anxiety index can more accurately reflect the actual anxiety state of children of different ages.

[0040] Step S370 further includes step S371, obtaining operating room information, and calculating the child's operating room unfamiliarity coefficient based on the operating room information; step S372, obtaining medical instrument information, and calculating the child's instrument impact based on the medical instrument information; step S373, compensating for the anxiety index under situational perception based on the calculation results of the operating room unfamiliarity and the calculation results of the instrument impact.

[0041] Preferably, the anxiety level is corrected through situational perception and age compensation. Specifically, children of different age groups have different physiological characteristics and psychological endurance. For example, the normal value of children's sympathetic-vagal balance and the adaptive threshold of skin electrical response will vary with age. Through the formula, the normal value of children's sympathetic-vagal balance and the adaptive threshold of skin electrical response are compensated based on age, so that the calculation of the anxiety index is more in line with the actual situation of children of different age groups. Situational awareness refers to obtaining information about the operating room, such as the layout, environmental atmosphere, and lighting brightness of the operating room, and calculating the child's operating room unfamiliarity coefficient based on this information. That is, the operating room layout, environmental atmosphere, lighting brightness and other information are quantified. For example, the layout is scored according to its complexity, with winding corridors and messy rooms scoring high. The environmental atmosphere can be evaluated from aspects such as color and quietness, with cold colors and loud noise scoring high. The lighting brightness is scored according to whether it is too bright or too dark. Then, based on the weight of each factor's impact on the child's psychology, a weighted calculation is performed to obtain the operating room unfamiliarity coefficient, such as a layout weight of 0.4, an environmental atmosphere weight of 0.3, and a lighting brightness weight of 0.3. The scores of each factor are multiplied by the corresponding weights and then added together to obtain the operating room unfamiliarity coefficient. An unfamiliar environment may aggravate children's anxiety, which is quantified by calculating the operating room unfamiliarity coefficient.

[0042] Preferably, information about medical instruments is obtained, such as the appearance and sound of surgical instruments, and the impact of instruments on children is calculated based on this information. Medical instruments may make children feel afraid, thereby increasing their anxiety level, and the instrument impact calculation can evaluate the magnitude of the impact. For the appearance of surgical instruments, scores can be given based on the sharpness and complexity of their shapes, such as instruments with sharp corners and complex structures with high scores. For sounds, scores can be given based on volume and sharpness, with louder volumes and sharper sounds having higher scores. Similarly, based on the weight of the impact of appearance and sound on children's fear psychology, a weighted calculation is performed to obtain the instrument impact value. Assuming that the external The appearance weight is 0.6, and the sound weight is 0.4. The appearance and sound scores are multiplied by the corresponding weights and then added together to obtain the instrument impact. Then, the anxiety index under situational perception is compensated according to the calculation results of the operating room unfamiliarity and the instrument impact. For example, the operating room unfamiliarity coefficient and the medical instrument threat value are linearly combined with weights of 0.8 and 0.2, and then multiplied by the anxiety index before correction to obtain the final corrected anxiety index. The anxiety level is further corrected to obtain the anxiety level correction result, making the anxiety level assessment more comprehensive, thereby being able to more accurately reflect the actual anxiety level of children before surgery.

[0043] Step S400 , performing a mitigation strategy optimization based on the anxiety level correction result and the children's preference data in the children's questionnaire data set, and establishing a mitigation strategy optimization result.

[0044] Preferably, the anxiety level correction result is obtained by comprehensively considering the child's physiological data (such as heart rate, skin electricity, facial expressions, voice characteristics, etc.), questionnaire anxiety data, and adjusting the initial anxiety level prediction through situational perception (unfamiliarity of the operating room, impact of instruments) and age compensation. It can accurately reflect the child's current anxiety level. For example, if the correction result shows that the child is in severe anxiety, it means that more intense and comprehensive relief measures are needed; children's preference data covers children's interests and hobbies (favorite games, cartoon characters, music styles, etc.) and emotional dependence objects or items, etc., reflecting the child's personality characteristics and psychological needs, and is an important basis for formulating personalized relief strategies. For example, some children particularly like characters in a certain cartoon, or rely on specific comfort items.

[0045] Preferably, a strategy library containing a variety of anxiety relief strategies is constructed, which can cover multiple aspects, such as environment creation, such as adjusting the lighting and temperature of the operating room, and playing soothing music; personnel companionship, arranging people the child trusts to accompany the child; activity participation, providing games and reading materials suitable for children, etc.; then, according to the anxiety level correction results and children's preference data, appropriate strategies are selected from the strategy library for combination. For example, for children who are at a severe anxiety level and like animation, they may choose to play their favorite animation in the operating room, and arrange for their dependent parents to accompany them, and provide activities such as puzzles or picture books related to the animation while waiting for the operation to distract their attention and relieve anxiety; for children who are mildly anxious and like a certain kind of music, they only need to play the corresponding music during the preparation stage of the operation, and then simply arrange the environment to achieve a better relief effect.

[0046] Preferably, these preoperative anxiety relief programs for children can be implemented using intelligent humanoid robots combined with AI agents. Specifically, the robots can provide preoperative anxiety relief services for children through interactive games, emotional comfort, storytelling, and other means, helping them reduce their fear of surgery and improve their psychological comfort. Specifically, the robots can interact with children and parents through voice commands or touchscreen operations, such as using voice dialogue to soothe children's emotions; play stories or music suitable for children and provide an immersive experience through VR technology; provide simple interactive games that can be selected through touchscreens to distract children; and use cameras and sensors to identify children's emotional states and automatically adjust interactive content and comfort strategies based on their emotional state. In addition, medical staff or psychological counselors can remotely control the robots to provide personalized services. Finally, the selected strategy combinations are evaluated to obtain the optimal strategy relief results (i.e., the best combination) that can maximize the relief of children's anxiety. For example, based on the effectiveness feedback of previous similar cases, the strategy combinations can be weighted and fine-tuned. Through automated emotional relief, the effectiveness of the relief strategies can be improved, thereby helping children better cope with preoperative anxiety, thereby improving the efficiency of preoperative preparation and service coverage.

[0047] Step S500: generating a mitigation solution optimization result based on the mitigation strategy optimization result.

[0048] Step S500 further includes step S510, using the joint sensor group to read the continuous monitoring results of the child, performing an anxiety relief evaluation on the child based on the continuous monitoring results, and generating evaluation feedback; step S520, performing optimization management of the relief strategy optimization results based on the evaluation feedback.

[0049] Preferably, the required resources are prepared according to the optimal results of the mitigation strategy, and the mitigation strategy is implemented at an appropriate time point before the operation, such as allowing dependent parents to come to the child's side in advance to accompany the child; playing music, animation, or providing favorite toys, reading materials, etc. At the same time, sensors and other equipment are continuously used to monitor the child's physiological data, including heart rate, skin electrodermal response, facial expressions, voice characteristics, etc., and the child's reaction is closely observed. If the child has no positive response to a certain strategy or the condition is not effectively alleviated or even worsens, the mitigation strategy is re-evaluated and optimized according to the new situation and existing data, and timely adjustments are made to continuously improve and optimize the mitigation plan to effectively alleviate the child's preoperative anxiety.

[0050] Preferably, a combined sensor group (PPG, ECG, dry electrode conductivity meter, 3D ToF Cameras, array microphones, etc.) continuously collect physiological and behavioral data from children to analyze whether their anxiety has improved. For example, by comparing heart rate data before and after the implementation of a relief strategy, if the heart rate gradually decreases from a high level and stabilizes, it indicates that the relief strategy may have alleviated the child's anxiety to some extent. Observing facial expressions: if an expression of tension or fear gradually eases, it is also a sign of anxiety relief. By setting corresponding evaluation indicators, the degree of anxiety relief in children is quantitatively evaluated, and evaluation feedback is generated. The feedback may include the effectiveness of the relief strategy, which aspects are effective, and which aspects are ineffective. Finally, based on the evaluation feedback, the relief strategy optimization results are optimized and managed. That is, the original relief strategy optimization results are adjusted and optimized. For example, if a strategy is found to be ineffective, other potentially effective strategies can be selected from the strategy library to replace or supplement it. For strategies that are effective, consideration can be given to strengthening them. The optimized relief strategy is then applied to subsequent optimization of child anxiety relief plans, and the joint sensor group continues to be used for monitoring and evaluation. Through continuous improvement and refinement, the relief strategy optimization results are better met to effectively alleviate children's preoperative anxiety.

[0051] Furthermore, step S510 also includes step S511, creating key nodes according to the optimization results of the mitigation strategy, and taking the execution time point of the optimization results of the mitigation strategy as the time zero point, performing time step division according to the time zero point and the key nodes, and arranging important nodes, and the important nodes are distributed within the time zero point and the key nodes; step S512, performing anxiety relief evaluation of the continuous monitoring results at the important nodes and the key nodes respectively to generate evaluation feedback.

[0052] Preferably, key nodes are created based on the results of the relief strategy optimization, that is, key time points in the entire anxiety relief process are determined, such as the time to start playing the animation that the child likes, the time when the parents come to accompany the child, the time when the surgical preparation begins, etc. The execution start time of the relief strategy optimization result is set as time zero, and the entire relief process is divided into several time steps based on the key nodes and time zero. For example, if there are 3 key nodes and the time span is 3 hours, these 3 hours may be divided into 6 time steps, each step is 30 minutes; in the time period between time zero and the key nodes, important nodes are reasonably set, such as based on time uniform distribution, It can also be set at time points where large changes in anxiety may occur, based on the characteristics and expected effects of the relief strategy, for more detailed monitoring and evaluation. Then, at important and critical nodes, the continuous monitoring results obtained by the joint sensor group are used to evaluate the child's anxiety relief, including analyzing the child's heart rate, skin electrical response and other physiological data, as well as facial expressions, voice characteristics and other behavioral data at this time to determine the degree of anxiety relief. Finally, based on the evaluation results of each important and critical node, evaluation feedback is formed, including information such as changes in the child's anxiety state at the node, whether the currently implemented relief strategy is effective, and whether the strategy needs to be adjusted.

[0053] Furthermore, step S500 also includes step S530, establishing a remission profile for the child, and extracting remission preference features and updating them to the remission profile; and step S540, optimizing the subsequent remission plan for the child based on the remission profile.

[0054] Preferably, a relief file for children is established to systematically retain detailed information related to the relief of children's preoperative anxiety, mainly including basic information of the child (such as name, age, gender, medical history, etc., which helps to understand the potential impact of individual differences in children on anxiety), surgery-related details (such as surgery type, surgery time, etc., which are important factors associated with anxiety situations), records of the implementation of relief strategies (including specific animations played, accompanying personnel, comfort items used, etc., to intuitively show the relief methods used), physiological data (such as heart rate, skin electrode response, facial expression and voice feature analysis results at each time point), and anxiety relief evaluation feedback at each key node and important node; then, relief preference characteristics (such as interests and hobbies, emotional dependence, etc.) are extracted from the children's questionnaire data and updated to the relief file to make the file more in line with the children's personalized needs; then, subsequent relief plan optimization is carried out based on the relief file. When the child faces a similar medical scenario again, medical staff can give priority to the strategies that have been effective in the past according to the relief preference characteristics in the file, and by analyzing historical data, they can understand the anxiety characteristics of the child in different situations, thereby adjusting the relief strategy in an individualized manner, realizing dynamic optimization of children's anxiety relief services, and effectively alleviating children's preoperative anxiety.

[0055] In the above, refer to Figure 1 The optimization method of the agent-based children's preoperative anxiety relief program according to an embodiment of the present invention is described in detail. Figure 2 An agent-based optimization system for alleviating children's preoperative anxiety according to an embodiment of the present invention is described.

[0056] The agent-based optimization system for alleviating children's preoperative anxiety according to an embodiment of the present invention is used to solve the technical problems in the prior art that children's preoperative anxiety relies on subjective experience, lacks accuracy, and cannot provide personalized relief strategies based on different medical scenarios and individual differences, thereby resulting in poor efficiency and effectiveness of alleviating children's preoperative anxiety. This achieves the technical effect of improving the efficiency, effectiveness and personalized service of alleviating children's preoperative anxiety. Figure 2 As shown, the agent-based optimization system for children's preoperative anxiety relief program includes: a questionnaire data collection module 10, a physiological data collection module 20, an anxiety level prediction module 30, a relief strategy optimization module 40, and a relief program optimization module 50.

[0057] The questionnaire data collection module 10 is used to execute the questionnaire data collection of the child's guardian and establish a child questionnaire data set, wherein the child questionnaire data set includes questionnaire anxiety data and child preference data; the physiological data collection module 20 is used to call the joint sensor group to collect the child's physiological data and establish a time-series physiological data set, wherein the time-series physiological data set includes heart rate data, skin electricity data, facial data, and voice data; the anxiety level prediction module 30 is used to predict the anxiety level through an intelligent agent based on the time-series physiological data set and the questionnaire anxiety data in the child questionnaire data set, and to correct the anxiety level through situational perception and age compensation to establish an anxiety level correction result; the relief strategy optimization module 40 is used to optimize the relief strategy based on the anxiety level correction result and the child preference data in the child questionnaire data set to establish a relief strategy optimization result; the relief plan optimization module 50 is used to generate a relief plan optimization result based on the relief strategy optimization result.

[0058] The specific configuration of the anxiety level prediction module 30 will be described in detail below. The anxiety level prediction module 30 further includes: calling heart rate data to calculate the low-frequency and high-frequency ratio of heart rate variability to generate a first calculation result; calling skin electrodermal data to calculate the skin electrodermal response amplitude to generate a second calculation result; calling facial data to calculate the intensity of facial action units to generate a third calculation result; calling speech data to calculate the speech micro-disturbance rate and speech amplitude disturbance to generate a fourth calculation result; generating an anxiety index at the current moment based on the first calculation result, the second calculation result, the third calculation result, and the fourth calculation result; and predicting the anxiety level based on the anxiety index and the questionnaire anxiety data in the children's questionnaire dataset.

[0059] The specific configuration of the anxiety level prediction module 30 will be described in detail below. The anxiety level prediction module 30 further includes: calculating the anxiety index by the formula as follows: in, Representational Moment The anxiety index, Characterizes the low-frequency and high-frequency ratio of heart rate variability, The cumulative distribution function that characterizes the standard normal distribution, Characterizes the normal value of sympathetic-vagal balance in children, represents the standard deviation of the age group, Characterizes the skin electrical response amplitude, Adaptive thresholds to characterize galvanic skin responses, Characterizes the sensitivity adjustment coefficient of the skin's electrical response, Representing facial action unit index, Characterization The weight coefficient of each facial action unit, Characterization Facial action unit strength values, Characterizes the speech perturbation rate, Characterizes speech amplitude disturbance, They are the low-frequency and high-frequency ratio of heart rate variability, the amplitude of skin electrode response, the intensity of facial action unit, and the weight coefficient of speech interference.

[0060] The specific configuration of the anxiety level prediction module 30 will be described in detail below. The anxiety level prediction module 30 further includes: age-based normal value of the child's sympathetic-vagal balance and adaptive threshold compensation of the skin electrical response, as follows: ; ; The anxiety index was reconstructed based on the compensated normal value of children's sympathetic-vagal balance and the adaptive threshold of skin electrodermal response.

[0061] The specific configuration of anxiety level prediction module 30 will be described in detail below. Anxiety level prediction module 30 further includes: obtaining operating room information and calculating the child's operating room unfamiliarity coefficient based on the operating room information; obtaining medical instrument information and calculating the instrument impact of the child based on the medical instrument information; and performing situational anxiety index compensation based on the calculated operating room unfamiliarity and instrument impact results.

[0062] The specific configuration of the mitigation solution optimization module 50 will be described in detail below. The mitigation solution optimization module 50 may further include: using a joint sensor group to read the continuous monitoring results of the child, performing an anxiety relief evaluation on the child based on the continuous monitoring results, generating evaluation feedback; and optimizing the mitigation strategy optimization results based on the evaluation feedback.

[0063] The specific configuration of the mitigation solution optimization module 50 will be described in detail below. The mitigation solution optimization module 50 may further include: creating key nodes based on the mitigation strategy optimization results, taking the execution time point of the mitigation strategy optimization results as time zero, performing time step segmentation based on time zero and the key nodes, and arranging important nodes, wherein the important nodes are distributed within time zero and the key nodes; and performing anxiety relief evaluation of the continuous monitoring results at the important nodes and key nodes, respectively, to generate evaluation feedback.

[0064] The following will describe in detail the specific configuration of the physiological data acquisition module 20. The physiological data acquisition module 20 may further include: the combined sensor group includes PPG, ECG, dry electrode conductivity meter, 3D ToF camera, array microphone.

[0065] The specific configuration of the mitigation plan optimization module 50 will be described in detail below. The mitigation plan optimization module 50 may further include: establishing a mitigation profile for the child, extracting mitigation preference features and updating the profile; and optimizing the subsequent mitigation plan for the child based on the profile.

[0066] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An agent-based optimization method for alleviating children's preoperative anxiety, characterized by: The method comprises: Execute questionnaire data collection from child guardians to establish a child questionnaire data set, wherein the child questionnaire data set includes questionnaire anxiety data and child preference data; Invoking a joint sensor group to collect physiological data of the child and establish a time series physiological data set, wherein the time series physiological data set includes heart rate data, skin electrode data, facial data, and voice data; An anxiety level is predicted by an intelligent agent based on the time-series physiological data set and the questionnaire anxiety data in the children's questionnaire data set, and the anxiety level is corrected through situational awareness and age compensation to establish an anxiety level correction result; performing a mitigation strategy optimization based on the anxiety level correction result and the children's preference data in the children's questionnaire data set, and establishing a mitigation strategy optimization result; Generate a mitigation solution optimization result based on the mitigation strategy optimization result.

2. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 1, characterized in that: The predicting of the anxiety level by the intelligent agent based on the time-series physiological data set and the questionnaire anxiety data in the children's questionnaire data set includes: Calling the heart rate data to calculate the low-frequency and high-frequency ratio of heart rate variability to generate a first calculation result; Calling the skin electrical data to calculate the skin electrical response amplitude and generate a second calculation result; Calling facial data to perform facial action unit intensity calculation to generate a third calculation result; Calling the voice data to perform voice micro-interference rate and voice amplitude disturbance calculation to generate a fourth calculation result; An anxiety index at the current moment is generated according to the first calculation result, the second calculation result, the third calculation result, and the fourth calculation result, and an anxiety level is predicted according to the anxiety index and the questionnaire anxiety data in the children's questionnaire data set.

3. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 2, characterized in that: Generating the anxiety index at the current moment according to the first calculation result, the second calculation result, the third calculation result, and the fourth calculation result includes: The anxiety index is calculated using the formula as follows: in, Representational Moment The anxiety index, Characterizes the low-frequency and high-frequency ratio of heart rate variability, The cumulative distribution function that characterizes the standard normal distribution, Characterizes the normal value of sympathetic-vagal balance in children, represents the standard deviation of the age group, Characterizes the skin electrical response amplitude, Adaptive thresholds to characterize galvanic skin responses, Characterizes the sensitivity adjustment coefficient of the skin's electrical response, Representing facial action unit index, Characterization The weight coefficient of each facial action unit, Characterization Facial action unit strength values, Characterizes the speech perturbation rate, Characterizes speech amplitude disturbance, They are the low-frequency and high-frequency ratio of heart rate variability, the amplitude of skin electrode response, the intensity of facial action unit, and the weight coefficient of speech interference.

4. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 3, wherein: The anxiety level modification through situational awareness and age compensation includes: The normal value of children's sympathetic-vagal balance and adaptive threshold compensation of skin electrical response are based on age as follows: ； ； The anxiety index was reconstructed based on the compensated normal value of children's sympathetic-vagal balance and the adaptive threshold of skin electrodermal response.

5. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 4, characterized in that: The anxiety index is reconstructed based on the compensated normal value of the child's sympathetic-vagal balance and the adaptive threshold of the skin electrical response, including: Acquiring operating room information, and calculating the child's operating room unfamiliarity coefficient based on the operating room information; obtaining medical device information, and calculating the device impact of the child based on the medical device information; The anxiety index under situational perception is compensated according to the calculation results of the unfamiliarity of the operating room and the impact of the instruments.

6. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 1, wherein: After generating the mitigation solution optimization result according to the mitigation strategy optimization result, the method includes: Using the combined sensor group to read the continuous monitoring results of the child, performing an anxiety relief evaluation on the child based on the continuous monitoring results, and generating evaluation feedback; Optimize and manage the mitigation strategy search results based on the evaluation feedback.

7. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 6, characterized in that: The step of evaluating the child's anxiety relief based on the continuous monitoring results and generating evaluation feedback includes: Creating key nodes according to the mitigation strategy optimization result, and taking the execution time point of the mitigation strategy optimization result as the time zero point, performing time step division according to the time zero point and the key nodes, and arranging important nodes, wherein the important nodes are distributed within the time zero point and the key nodes; Anxiety relief evaluation of the continuous monitoring results is performed at important nodes and key nodes respectively to generate evaluation feedback.

8. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 1, characterized in that: The combined sensor group includes PPG, ECG, dry electrode conductivity meter, 3D ToF camera, and array microphone.

9. The agent-based optimization method for alleviating children's preoperative anxiety according to claim 1, wherein: After generating the mitigation solution optimization result according to the mitigation strategy optimization result, the method further includes: Establishing a remission profile for the child, and extracting remission preference features to update the remission profile; The child's subsequent remission regimen was optimized based on the remission profile.

10. An agent-based optimization system for alleviating children's preoperative anxiety, characterized by: The system is used to implement the agent-based optimization method for alleviating children's preoperative anxiety according to any one of claims 1 to 9, and the system comprises: A questionnaire data collection module is used to collect questionnaire data from children's guardians and establish a children's questionnaire data set, wherein the children's questionnaire data set includes questionnaire anxiety data and children's preference data; A physiological data acquisition module is used to call a joint sensor group to collect physiological data of children and establish a time series physiological data set, wherein the time series physiological data set includes heart rate data, skin electrode data, facial data, and voice data; An anxiety level prediction module is used to predict the anxiety level based on the time-series physiological data set and the questionnaire anxiety data in the children's questionnaire data set through an intelligent agent, and to correct the anxiety level through situational awareness and age compensation to establish an anxiety level correction result; a relief strategy optimization module, configured to optimize the relief strategy based on the anxiety level correction result and the children's preference data in the children's questionnaire data set, and establish a relief strategy optimization result; The mitigation solution optimization module is used to generate a mitigation solution optimization result based on the mitigation strategy optimization result.

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