A preoperative anxiety relief method and system based on virtual reality technology

Abstract

The present application relates to the technical field of data processing, in particular to a preoperative anxiety relief method and system based on virtual reality technology, comprising: obtaining physiological rhythm data of a patient before operation, the physiological rhythm data at least including respiratory frequency and heart rate variability information; constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the respiratory frequency; wearing a VR headset and a bone conduction earphone for the patient, the device automatically loading a customized virtual scene, and real-time prompting of breathing points through a virtual guide to help the patient establish a perceptual association with the rhythm elements; real-time analysis of the patient's physiological data to obtain respiratory frequency, heart rate variability and skin electrical activity value, and step-by-step adjustment of the light and shadow frequency to approach the respiratory frequency when the difference between the light and shadow fluctuation frequency and the respiratory frequency exceeds a threshold value. The multidimensional evaluation system of the present application more comprehensively reflects the anxiety state of the patient and avoids insufficient intervention caused by evaluation bias.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a preoperative anxiety relief method and system based on virtual reality technology. BACKGROUND

[0002] Preoperative anxiety is a common psychological stress response of surgical patients, mainly manifested as preoperative emotional tension, rapid heart rate, rapid breathing and other physiological and psychological abnormalities. Severe cases may lead to decreased tolerance to surgery, delayed postoperative recovery and even induce cardiovascular complications. Currently, the main methods for relieving preoperative anxiety in clinical practice include three categories:

[0003] Drug intervention: such as benzodiazepine sedative drugs, which can quickly relieve anxiety, but may cause drowsiness, respiratory depression and other side effects, and have the risk of drug allergy for some patients, and affect the metabolic balance of anesthetic drugs during surgery;

[0004] Traditional psychological intervention: including verbal reassurance by medical staff, music therapy, progressive muscle relaxation training, etc. It relies on manual operation and is greatly influenced by the experience of medical staff, and lacks personalized adaptation, so the relief effect is limited;

[0005] Conventional virtual reality technology: some studies use pre-set virtual scenes (such as static natural landscapes) to distract patients, but the scenes are mostly fixed content and cannot respond to changes in the patient's real-time physiological state, and do not combine physiological indicators with psychological state analysis, making it difficult to achieve "rhythm synchronization" deep relaxation, and the effect on patients with moderate to severe anxiety is not good;

[0006] In addition, the existing technology generally has the problem of splitting physiological and psychological data: either relying only on subjective scale to assess anxiety state, ignoring the objective feedback of physiological indicators, or simply monitoring physiological data, lacking dynamic correlation with psychological state, resulting in insufficient targeted intervention measures. SUMMARY

[0007] The present application provides a preoperative anxiety relief method and system based on virtual reality technology to solve the technical problems in the prior art.

[0008] The technical solution of the present application to solve the above technical problems is as follows: a preoperative anxiety relief method based on virtual reality technology, comprising the following steps:

[0009] S101, acquiring physiological rhythm data of a patient before surgery, the physiological rhythm data at least including respiratory rate and heart rate change information;

[0010] S102, constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the respiratory rate;

[0011] S103, wearing VR headsets and bone conduction earphones for patients, the device automatically loads customized virtual scenes, and real-time prompts breathing points through virtual guides to help patients establish perceptual association with rhythm elements;

[0012] S104, real-time analysis of physiological data of patients to obtain respiratory rate, heart rate variability and skin electrical activity value, when the difference between light and shadow fluctuation frequency and respiratory rate exceeds the threshold value, adjust the light and shadow frequency step by step to approach the respiratory rate.

[0013] In a preferred embodiment, in S101, preoperative physiological rhythm data of patients in resting state are continuously collected by integrated physiological monitoring device, including chest belt type respiratory sensor, wrist heart rate monitor and skin electrical sensor, including respiratory rate, heart rate variability and skin electrical activity, and motion interference and device noise abnormal value are removed by data cleaning algorithm to form standardized physiological data set;

[0014] After physiological data collection, subjective anxiety score of patients is collected by anxiety self-assessment scale, which contains n entries, of which positive scoring entries, negative scoring entries, The score calculation formula of the anxiety self-assessment scale is as follows:

[0015] ;

[0016] Among them, the total score of anxiety self-assessment scale, the score of positive scoring entries, the total score conversion coefficient, the specific calculation formula of anxiety self-assessment scale standard score is as follows:

[0017] ;

[0018] Among them, the integer symbol, when , it means that the patient is in normal state, it means that the patient has moderate anxiety tendency, it means that the patient has severe anxiety tendency;

[0019] Combined with physiological data, physiological- psychological two-dimensional anxiety benchmark model is constructed, physiological indicators include respiratory rate standardized value , heart rate variability standardized value , skin electrical activity standardized value , psychological indicators include subjective anxiety score as anxiety self-assessment scale standard score , the specific calculation formula of physiological- psychological two-dimensional anxiety benchmark model is as follows:

[0020] ;

[0021] wherein, is a weight coefficient, satisfying , represents the standardization denominator of the self-rating anxiety scale score, which is used to map the score to the [0, 1] interval, and the anxiety index threshold is set as , when , it indicates that the patient is in a mild anxiety state, when , it indicates that the patient is in a moderate anxiety state, , it indicates that the patient is in a severe anxiety state;

[0022] Determine the initial anxiety state of the patient, collect the patient's type preference, light sensitivity and sound acceptance range of the virtual scene through the preference questionnaire, provide basis for scene customization, and adjust the initial parameters of the virtual scene basic dynamic elements based on the initial anxiety state according to the anxiety level;

[0023] Integrate individual physiological stability value, standard score of anxiety self-rating scale and corresponding physiological interval to generate a physiological- psychological two-dimensional anxiety benchmark model, wherein the physiological stability value and the standard score of the anxiety self-rating scale are the benchmark values of real-time monitoring.

[0024] In a preferred embodiment, in S102, after obtaining the two-dimensional anxiety benchmark model and the scene preference data, the virtual scene matching the patient's preference is first called from the scene material library. The virtual scene is essentially a computer-generated simulation environment. The lighting effect in the scene is adjusted according to the change of time to establish dynamic light and shadow change. The construction process relies on a three-dimensional graphics rendering engine. The basic scene is built by importing a natural environment model and configuring light source attributes. The dynamic light and shadow change is not a preset animation, but is driven by physiological rhythm data, and its change logic is directly related to the patient's individual physiological rhythm data.

[0025] In the virtual scene, light and shadow fluctuation elements corresponding to the breathing frequency are included to establish a mapping relationship between the breathing frequency data and the light and shadow parameters. Let the breathing frequency be , the light and shadow fluctuation element be , and the mapping relationship be as follows:

[0026] ;

[0027] wherein, represents the frequency conversion coefficient, which can be adjusted according to the scene comfort, This represents the frequency offset. The higher the respiratory rate, the faster the light and shadow fluctuation frequency, achieving rhythmic synchronization between breathing and light and shadow. The respiratory rate signal is received in real time and converted into light and shadow fluctuation parameters. The patient's skin conductance activity value is extracted from the physiological-psychological dual-dimensional anxiety benchmark model, and the fluctuation amplitude is dynamically adjusted. If the skin conductance activity value is higher than the benchmark range, the fluctuation amplitude is reduced.

[0028] In a preferred embodiment, in S103, after the patient enters the preoperative preparation room, they wear a VR headset and bone conduction headphones. The bone conduction headphones use the temporal bone vibration sound transmission principle to ensure that the patient can simultaneously receive virtual scene audio and instructions from medical staff in the environment. After the device is worn, a customized virtual scene is automatically loaded. After the virtual scene is activated, a voice guidance instruction is issued through the virtual guide in the scene. This instruction has two technical functions:

[0029] S1. Requires the patient to focus on light and shadow fluctuations that are synchronized with their breathing rate;

[0030] S2. Clearly establish the relationship between the patient's spontaneous breathing behavior and the light and shadow fluctuation cycle. Voice commands are output through bone conduction headphones to ensure that the sound source directionality matches the virtual guide's position space.

[0031] During the initial adaptation phase, a phased control strategy will be implemented:

[0032] S1. Maintain a fluctuation cycle that is completely consistent with the patient's preoperative baseline respiratory rate.

[0033] S2. The virtual guide triggers voice prompts at respiratory cycle nodes. These voice prompts establish a mapping rule between the direction of light and dark changes and the respiratory phase: the process of light and shadow brightness rising from the lowest value to the highest value corresponds to the inspiratory phase, and the process of light and shadow brightness falling from the highest value to the lowest value corresponds to the expiratory phase. Through voice prompts, the guide helps patients establish a perceptual association between changes in visual brightness gradients and respiratory muscle movements.

[0034] In a preferred embodiment, in S104, after physiological monitoring is initiated, a data acquisition time interval T is automatically set. Every T elapses, the patient's current physiological rhythm data, including real-time respiratory rate, real-time heart rate variability, and real-time skin conductance, is synchronously acquired through integrated sensors. The data is then transmitted to the edge computing unit for rapid analysis. During the analysis process, the timestamp of the original data is retained to ensure that the data remains synchronized with the dynamic adjustments of the virtual scene.

[0035] After analysis, calculate the difference between the current light and shadow fluctuation frequency and the breathing frequency. ,like Greater than the preset threshold This triggers a dynamic adjustment mechanism for light and shadow frequency, with a fixed step size. The difference between the light and shadow fluctuation frequency and the real-time breathing frequency is gradually reduced, and the time interval of each adjustment is set to , until , if , it is determined that the current light and shadow frequency and the breathing rhythm have reached the adaptation state, and after reaching the adaptation state, positive feedback is given through the auxiliary dynamic elements in the virtual scene to strengthen the patient's perception of rhythm synchronization.

[0036] The real-time physiological rhythm data is continuously compared with the baseline value in the physiological-mental two-dimensional anxiety baseline model, the improvement ratio of real-time heart rate variability relative to heart rate variability baseline and the change ratio of real-time skin electricity activity relative to skin electricity activity baseline are calculated, if the heart rate variability improvement ratio and the skin electricity activity decline ratio , it is determined that the patient's anxiety state is relieved, the level of light and shadow fluctuation can be automatically increased, if the skin electricity activity improvement ratio , the scene pacification mechanism is started immediately, the light and shadow contrast is reduced to 1-k of the original value, k represents the contrast adjustment coefficient, and pacification voice is issued through the virtual guide built in the scene to reduce sensory stimulation and relieve anxiety escalation.

[0037] The application also provides a preoperative anxiety relief system based on virtual reality technology, comprising:

[0038] The data acquisition and personalized baseline establishment module acquires the physiological rhythm data of the patient before the operation, and the physiological rhythm data at least includes breathing frequency and heart rate variation information.

[0039] The dynamic virtual scene generation module constructs a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, and the virtual natural scene includes light and shadow fluctuation elements corresponding to the breathing frequency.

[0040] The guide and rhythm synchronization module wears a VR head-mounted display and a bone conduction earphone for the patient, the device automatically loads the customized virtual scene, and the virtual guide prompts the breathing points in real time to help the patient establish the perception association of the rhythm elements.

[0041] The dynamic rhythm adjustment module analyzes the physiological data of the patient in real time to obtain the breathing frequency, heart rate variability and skin electricity activity value, and when the difference between the light and shadow fluctuation frequency and the breathing frequency exceeds the threshold value, the light and shadow fluctuation frequency is adjusted step by step to approach the breathing frequency.

[0042] The beneficial effects of the present application are: the present application is based on a physiological-psychological two-dimensional anxiety benchmark model, objective physiological data such as respiratory rate, heart rate variability and skin electrical activity are collected through integrated sensors, combined with subjective anxiety scores of anxiety self-rating scale, a multi-dimensional evaluation system is formed, which can more comprehensively reflect the anxiety state of patients, provide accurate basis for subsequent intervention, avoid insufficient intervention and excessive stimulation caused by evaluation deviation, through preoperative preference questionnaire, the preferences of patients for virtual scene type, light and shadow sensitivity and sound acceptance range are collected, combined with the initial anxiety level, the scene parameters are customized, compared with the fixed scene of the conventional virtual reality technology, the personalized customization can reduce the resistance of patients to the scene, and the scene intensity matching the anxiety degree can improve the immersion and acceptance;

[0043] The respiratory frequency is mapped to the light and shadow fluctuation frequency by real-time driving scene changes through physiological data, the precise synchronization of light and shadow and breathing is realized by dynamically adjusting the step length, and the scene complexity is adjusted based on the real-time changes of heart rate variability and skin electrical activity, this rhythm anchoring mechanism utilizes the instinctive following reaction of human beings to periodic visual stimulation, compared with the simple attention distraction of static scene, it can more effectively induce autonomic nervous relaxation. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The method flowchart of the present application is shown in the figure;

[0045] Figure 2 The system block diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] In the description of the present application, the terms "first", "second" are only used for description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0048] In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration." Any embodiment described as "for example" in this application is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of the application. It will be apparent to one skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated upon in order to avoid obscuring the description of the present application. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0049] As Figure 1 The present embodiment provides a preoperative anxiety relief method based on virtual reality technology, comprising the following steps:

[0050] S101, acquiring physiological rhythm data of a patient before surgery, the physiological rhythm data at least including respiratory rate and heart rate variability information;

[0051] Further, the physiological rhythm data of the patient in a resting state is continuously collected by an integrated physiological monitoring device before surgery, including a chest strap type respiration sensor, a wrist heart rate monitor, and a skin electricity sensor, and specifically including respiratory rate, heart rate variability, and skin electricity activity, and motion interference and device noise outliers are removed through a data cleaning algorithm to form a standardized physiological data set;

[0052] After the physiological data collection is completed, a self-rating anxiety scale is used to collect the subjective anxiety score of the patient, the scale contains n entries, of which n1 entries are positively scored and n2 entries are negatively scored. The score calculation formula of the self-rating anxiety scale is as follows:

[0053] ;

[0054] Wherein, represents the total score of the self-rating anxiety scale, represents the score of the positively scored entry, represents the total score conversion coefficient, and the specific calculation formula of the standard score of the self-rating anxiety scale is as follows:

[0055] ;

[0056] Wherein, represents the rounding symbol, when , it means that the patient is in a normal state, , it means that the patient has a moderate anxiety tendency,​​ indicates that the patient has a severe anxiety tendency;

[0057] A physiological-mental dual-dimension anxiety benchmark model is constructed in combination with physiological data, the physiological indexes including a respiratory frequency standardized value , a heart rate variability standardized value , and a skin electrical activity standardized value , the mental indexes including a subjective anxiety score as a self-rating anxiety scale standard score , and a specific calculation formula of the physiological-mental dual-dimension anxiety benchmark model is as follows:

[0058] ;

[0059] wherein, is a weight coefficient, satisfying , represents a self-rating anxiety scale score standardized score denominator, used for mapping the score to the interval [0, 1], and the anxiety index demarcation value is set as , when , it indicates that the patient is in a mild anxiety state, when , it indicates that the patient is in a moderate anxiety state, , it indicates that the patient is in a severe anxiety state;

[0060] The initial anxiety state of the patient is determined, the type tendency of the patient to the virtual scene, the light and shadow sensitivity, and the sound acceptance range are collected through a preference questionnaire, to provide a basis for scene customization, and based on the initial anxiety state, the initial parameters of the basic dynamic elements of the virtual scene are adjusted according to the anxiety level.

[0061] The physiological stability value, the self-rating anxiety scale standard score, and the corresponding physiological interval are integrated to generate a physiological-mental dual-dimension anxiety benchmark model, wherein the physiological stability value and the self-rating anxiety scale standard score are the benchmark values of real-time monitoring.

[0062] It should be noted that within 12 hours before the procedure, the integrated physiological monitoring equipment should be calibrated to ensure that the tightness of the chest strap respiratory sensor fits the patient's chest, the wrist heart rate monitor is positioned 2cm above the wrist crease and the electrodes are in full contact with the skin, and the skin conductance sensor is fixed to the fingertips of the ring and middle fingers of the patient's non-dominant hand. The equipment is then connected to the data acquisition terminal and a self-test program is initiated to confirm stable signal transmission from each sensor (respiratory signal-to-noise ratio ≥30dB, heart rate signal error <2 bpm). The patient is then guided into a quiet monitoring room, the chair is adjusted to a semi-recumbent position, and the patient is instructed to remain resting (avoid talking or large movements of the limbs). The monitoring procedure is explained to reduce patient anxiety. The equipment is then started for 30 consecutive days. Physiological data is collected in minutes, with the first 5 minutes being an adaptation period during which the data is not included in the analysis. The respiratory rate, heart rate variability (HRV), and electrical skin activity (EDA) data for the next 25 minutes are transmitted to the terminal in real time. The terminal's built-in adaptive filtering algorithm automatically identifies and removes motion interference signals caused by the patient's slight turning over or swallowing movements, as well as noise spikes caused by poor device contact. After standardization, the data is processed to form a structured physiological dataset and stored.

[0063] After the physiological data collection was completed, the Self-Rating Anxiety Scale (SAS) was distributed to the patients. Medical staff assisted in interpreting the scale items (to avoid distorted scoring due to patient misunderstanding). The patients completed the scoring of 20 items based on their anxiety status over the past week. After the scales were collected, the total SAS score was calculated according to the scoring rules (the scores of each item were added together and then multiplied by 1.25 and rounded to the nearest integer). Subsequently, the total score was correlated with the aforementioned physiological data to construct a two-dimensional anxiety benchmark model corresponding to the "subjective rating of physiological indicators" and to clarify the patient's initial anxiety level.

[0064] S102. Construct a virtual natural scene with dynamic light and shadow changes based on physiological rhythm data. The virtual natural scene contains light and shadow fluctuation elements corresponding to the respiratory frequency.

[0065] Furthermore, after obtaining the dual-dimensional anxiety baseline model and scene preference data, a virtual scene matching the patient's preferences is retrieved from the scene material library. The essence of this virtual scene is a computer-generated simulation environment. The lighting effects in the scene are adjusted according to the changes over time to establish dynamic light and shadow changes. The construction process relies on a three-dimensional graphics rendering engine. The basic scene is built by importing a natural environment model and configuring the light source attributes. The dynamic light and shadow changes are not preset animations, but are driven by physiological rhythm data. The change logic is directly related to the patient's individual physiological rhythm data.

[0066] The virtual scene includes light and shadow fluctuation elements corresponding to the breathing frequency. A mapping relationship between breathing frequency data and light and shadow parameters is established. Let the breathing frequency be... Light and shadow fluctuation elements are , the mapping relationship is as follows:

[0067] ;

[0068] wherein, represents a frequency conversion coefficient, which can be adjusted according to scene comfort, represents a frequency offset, the higher the breathing frequency, the faster the light and shadow fluctuation frequency, realizing the rhythm synchronization of breathing-light and shadow, real-time receiving the breathing frequency signal, converting it into a light and shadow fluctuation parameter, extracting the skin electricity activity value of the patient from the physiological- psychological two-dimensional anxiety benchmark model, dynamically adjusting the fluctuation amplitude, if the skin electricity activity value is higher than the benchmark interval, reducing the fluctuation amplitude.

[0069] It should be noted that the construction of the virtual scene loads the basic terrain data of the template through the three-dimensional graphics rendering engine, including water surface height, mountain profile, vegetation distribution and other information, and calls A1 auxiliary modeling tool to optimize the scene details, adjusts the sky tone according to the patient's SAS score, and adopts a softer blue-purple tone for patients with moderate anxiety, and sets the cloud density according to the HRV value, reduces the cloud cover for patients with low HRV, and increases the sense of openness, and then starts the dynamic element configuration program, automatically associates the physical parameters of the basic dynamic elements cloud drift and vegetation shaking with the patient's anxiety level: if it is a severe anxiety patient, reduce the cloud drift speed, reduce the vegetation shaking amplitude, and reduce the appearance frequency of fast moving elements in the scene to avoid visual stimulation and aggravate anxiety;

[0070] After completing the scene framework construction, enter the core rhythm element configuration stage: if the patient prefers a lake scene, the system is positioned to the water area in the scene, generates a lake surface wave effect through the GPU particle system, and sets it as the core fluctuation element; if it prefers a starry sky scene, add a starlight particle group to the dome layer, set the brightness change period of each particle to be independently controllable, then call the patient's preoperative benchmark breathing frequency data, convert it into a light and shadow fluctuation frequency parameter, for example, if the benchmark breathing frequency is 20 times / minute, the corresponding period is calculated as 3 seconds / time, and the frequency parameter is bound to the material properties of the core fluctuation element such as transparency, brightness or position offset through the animation curve editor, to ensure that the light and shadow fluctuation is completely synchronized with the patient's breathing rhythm, at the same time, the skin electricity activity value of the patient is extracted from the anxiety benchmark model, and the fluctuation amplitude is dynamically adjusted: if the skin electricity activity value is higher than the benchmark interval, the fluctuation amplitude is reduced, and the gradual transition time is increased, so that the light and shadow change is more smooth and comfortable, reducing the sensory stimulation, finally, the scene is checked for lighting consistency to ensure that the core fluctuation element is coordinated with the environment light effect, such as matching the starlight flicker intensity with the moonlight brightness in the starry sky scene, and generating a dynamic virtual scene instance that can respond to physiological data changes in real time.

[0071] S103, wearing VR headsets and bone conduction earphones for patients, the device automatically loads customized virtual scenes, and real-time prompts breathing points through virtual guides to help patients establish the perception of rhythm elements;

[0072] Further, after the patient enters the preoperative preparation room, wearing VR headsets and bone conduction earphones, the bone conduction earphones adopt the principle of temporal bone vibration sound transmission to ensure that the patient can receive virtual scene audio and environmental medical staff instructions at the same time. The device automatically loads the customized virtual scene after wearing, and the virtual scene activates to issue voice guidance instructions through the virtual guide in the scene. The instruction contains two technical effects:

[0073] S1, requiring the patient to fixate on the light and shadow fluctuation element synchronized with the breathing frequency;

[0074] S2, clearly establish the following relationship between the patient's spontaneous breathing behavior and the light and shadow fluctuation cycle. The voice instruction is output through the bone conduction earphone to ensure that the sound source direction sense matches the virtual guide position space;

[0075] In the initial adaptation stage, a phased control strategy is implemented:

[0076] S1, maintain the fluctuation cycle completely consistent with the patient's preoperative baseline breathing frequency;

[0077] S2, the virtual guide triggers voice prompts according to the breathing cycle nodes. The voice prompts establish a mapping rule between the light and shadow change direction and the breathing phase: the process of light and shadow brightness rising from the lowest value to the highest value corresponds to the inspiration phase, and the process of light and shadow brightness falling from the highest value to the lowest value corresponds to the expiration phase. Through voice prompts, help patients establish the perception of visual brightness gradient change and respiratory muscle movement.

[0078] S104, real-time analysis of patient physiological data to obtain breathing frequency, heart rate variability, and skin electrical activity values. When the difference between the light and shadow fluctuation frequency and the breathing frequency exceeds the threshold value, adjust the light and shadow frequency step by step to approach the breathing frequency;

[0079] Further, after starting to implement physiological monitoring, automatically set the data collection time interval T. Every T time interval, the current physiological rhythm data of the patient is synchronously collected through the integrated sensor, including real-time breathing frequency, real-time heart rate variability, and real-time skin electrical activity. The data is transmitted to the edge computing unit for rapid analysis. The timestamp of the original data is preserved during the analysis process to ensure that the dynamic adjustment of the virtual scene is time-synchronized;

[0080] After analysis, calculate the difference between the current light and shadow fluctuation frequency and the breathing frequency , if is greater than the preset threshold value ( represents the critical value that needs to be actively adjusted) triggers the light and shadow frequency dynamic adjustment mechanism to fix the step narrowing the gap between the light and shadow fluctuation frequency and the real-time breathing frequency gradually, and the time interval of each adjustment is set to , until , if , it is determined that the current light and shadow frequency and the breathing rhythm have reached the adaptation state, and after reaching the adaptation state, positive feedback is given through auxiliary dynamic elements in the virtual scene to strengthen the patient's perception of rhythm synchronization;

[0081] continuously comparing the real-time physiological rhythm data with the baseline value in the physiological-mental two-dimensional anxiety baseline model, calculating the improvement ratio of real-time heart rate variability relative to heart rate variability baseline and the change ratio of real-time skin electricity activity relative to skin electricity activity baseline, if the heart rate variability improvement ratio and the skin electricity activity decline ratio , it is determined that the patient's anxiety state is relieved, and the level of light and shadow fluctuation can be automatically increased, if the skin electricity activity improvement ratio , represents the deterioration threshold, which represents anxiety aggravation), the scene soothing mechanism is immediately started, the light and shadow contrast is lowered to 1-k of the original value, k represents the contrast adjustment coefficient, and soothing voice (such as breathing in the rhythm of light and shadow) is issued through the virtual guide built in the scene to reduce sensory stimulation and relieve anxiety escalation.

[0082] It should be noted that during the entire adjustment process, all parameters can be dynamically adapted according to the initial anxiety level of the patient, for example, the of a severe anxiety patient has a smaller value, which can trigger frequency adjustment earlier, and the has a lower value, so as to identify anxiety aggravation and start the soothing mechanism earlier, and ensure that the dynamic changes of the virtual scene are always accurately linked with the physiological state of the patient.

[0083] The embodiment of the application also provides a preoperative anxiety relief system based on virtual reality technology, comprising:

[0084] a data acquisition and personalized baseline establishment module: obtaining physiological rhythm data of a patient before surgery, the physiological rhythm data at least including breathing frequency and heart rate variation information;

[0085] a dynamic virtual scene generation module: constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene including light and shadow fluctuation elements corresponding to the breathing frequency;

[0086] Guidance and rhythm synchronization module: wear VR headsets and bone conduction earphones for patients, and the device automatically loads customized virtual scenes, and real-time prompts breathing points through virtual guides to help patients establish perceptual association with rhythm elements;

[0087] Dynamic rhythm adjustment module: real-time analysis of physiological data of patients to obtain respiratory frequency, heart rate variability and skin electrical activity values, and when the difference between light and shadow fluctuation frequency and respiratory frequency exceeds the threshold value, the light and shadow frequency is adjusted step by step to approach the respiratory frequency.

[0088] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0089] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0090] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0091] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0092] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0093] While the preferred embodiments of the application have been described, it should be apparent that a little thought and experimentation can lead to the development of other techniques and approaches that are widely equivalent to those described above. Accordingly, no limitation is intended to the scope of the present application except as it can be defined by the appended claims, and equivalents thereto.

[0094] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A preoperative anxiety relief method based on virtual reality technology, characterized by, The method comprises the following steps: S101、acquire physiological rhythm data of the patient before operation, the physiological rhythm data at least includes respiratory rate and heart rate variation information, collect subjective anxiety score of the patient by adopting anxiety self-rating scale, combine physiological data to construct physiological-mental double-dimension anxiety benchmark model, physiological indexes include respiratory rate standardized value , heart rate variability standardized value , skin electricity activity standardized value , mental indexes include subjective anxiety score is anxiety self-rating scale standard score , the specific calculation formula of the physiological-mental double-dimension anxiety benchmark model is as follows: ； wherein, are weight coefficients satisfying , denotes the standardized denominator of the anxiety self-rating scale score, used to map the score to the [0, 1] interval, and the anxiety index cutoff value is set as ; S102, constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the breathing frequency, the mapping relationship between the light and shadow fluctuation elements and the breathing frequency being: ； Wherein, Identify light and shadow fluctuation frequency, Indicates the breathing frequency, Indicates the frequency conversion coefficient, which can be adjusted according to the scene comfort, Indicates the frequency offset, the higher the breathing frequency, the faster the light and shadow fluctuation frequency, real-time receiving the breathing frequency signal, converting it into light and shadow fluctuation parameters, extracting the skin electricity activity value of the patient from the physiological- psychological two-dimensional anxiety benchmark model, dynamically adjusting the fluctuation amplitude, and if the skin electricity activity value is higher than the benchmark interval, the fluctuation amplitude is reduced. S103, wearing a VR headset and a bone conduction earphone for the patient, the device automatically loading the customized virtual scene, and real-time prompting of breathing points through a virtual guide to help the patient establish a perception association with the rhythm elements; S104, real-time analysis of physiological data of the patient to obtain the breathing frequency, heart rate variability, and skin electrical activity value, and step-by-step adjustment of the light and shadow fluctuation frequency to approach the breathing frequency when the difference between the light and shadow fluctuation frequency and the breathing frequency exceeds a threshold value.

2. The preoperative anxiety relief method based on virtual reality technology according to claim 1, characterized in that, In S101, physiological rhythm data of the patient in a resting state is continuously collected by an integrated physiological monitoring device, including a chest belt type breathing sensor, a wrist heart rate monitor, and a skin electrical sensor, and specifically including: breathing frequency, heart rate variability, and skin electrical activity, and through a data cleaning algorithm, motion interference and device noise abnormal values are removed to form a standardized physiological data set; After the physiological data collection is completed, a subjective anxiety score of the patient is collected by using an anxiety self-evaluation scale, the scale contains n entries, wherein the positive scoring entries are , the negative scoring entries are , the score calculation formula of the anxiety self-evaluation scale is as follows: ； wherein, represents the total score of the anxiety self-rating scale, represents the positive score item score, represents the total score conversion coefficient, the specific calculation formula of the anxiety self-rating scale standard score is as follows: ； wherein, represents an integer symbol, when represents that the patient is in a normal state, represents that the patient has a moderate anxiety tendency, represents that the patient has a severe anxiety tendency. 3.The preoperative anxiety relief method based on virtual reality technology of claim 1, wherein, When , the patient is in a mild state of anxiety, when , the patient is in a moderate state of anxiety, , the patient is in a severe state of anxiety; The initial anxiety state of the patient is determined, the type preference of the patient for the virtual scene, the light and shadow sensitivity, and the sound acceptance range are collected through a preference questionnaire to provide a basis for scene customization, and based on the initial anxiety state, the initial parameters of the basic dynamic elements of the virtual scene are adjusted according to the anxiety level; The individual physiological stability value, the standard score of the anxiety self-assessment scale, and the corresponding physiological interval are integrated to generate a physiological-mental dual-dimension anxiety benchmark model, wherein the physiological stability value and the standard score of the anxiety self-assessment scale are the benchmark values of real-time monitoring. 4.The preoperative anxiety relief method based on virtual reality technology of claim 1, wherein, In S102, after obtaining the dual-dimension anxiety benchmark model and the scene preference data, a virtual scene matching the patient's preference is first called from a scene material library, the virtual scene is essentially a computer-generated simulated environment, the lighting effect in the scene is adjusted according to the time change to establish a dynamic light and shadow change, the construction process relies on a three-dimensional graphics rendering engine, the basic scene is built by importing a natural environment model and configuring light source attributes, the dynamic light and shadow change is not a preset animation, but is driven by physiological rhythm data, and the change logic is directly related to the individual physiological rhythm data of the patient. 5.The preoperative anxiety relief method based on virtual reality technology according to claim 1, characterized in that, In S103, after the patient enters the preoperative preparation room, the VR headset and the bone conduction earphone are worn, the bone conduction earphone adopts a temporal bone vibration sound transmission principle to ensure that the patient can simultaneously receive virtual scene audio and instructions from medical staff in the environment, the device automatically loads the customized virtual scene after wearing is completed, after the virtual scene is activated, a voice guide instruction is issued by a virtual guide in the scene, the instruction includes two technical effects: S1, requiring the patient to fixate on the light and shadow fluctuation elements synchronized with the breathing frequency; S2, clearly establishing a following relationship between the patient's autonomous breathing behavior and the light and shadow fluctuation period, the voice instruction is output through the bone conduction earphone to ensure that the sound source direction sense matches the virtual guide position space; In the initial adaptation stage, a phased regulation strategy is executed: S1, maintaining a fluctuation period completely consistent with the patient's preoperative benchmark breathing frequency; S2, the virtual guide triggers the voice prompt according to the respiratory cycle node, and the voice prompt establishes the mapping rule of the light and dark change direction and the respiratory phase: the process of light and shadow brightness from the minimum value to the maximum value corresponds to the inspiration phase, and the process of light and shadow brightness from the maximum value to the minimum value corresponds to the expiration phase. Through the voice prompt, the patient can help to establish the perception association between the visual brightness gradient change and the respiratory muscle movement. 6.The preoperative anxiety relief method based on virtual reality technology according to claim 1, characterized in that, In S104, after starting to implement physiological monitoring, the data acquisition time interval T is automatically set. The physiological rhythm data of the patient at the current time is synchronously collected through the integrated sensor every T time interval, including real-time respiratory frequency, real-time heart rate variability and real-time skin electrical activity, and the data is transmitted to the edge computing unit for rapid analysis. The time stamp of the original data is retained during the analysis process to ensure that the dynamic adjustment of the virtual scene is time-synchronized.

7. The preoperative anxiety relief method based on virtual reality technology according to claim 6, characterized in that, After the analysis, the difference between the current light fluctuation frequency and the breathing frequency is calculated , if is greater than a preset threshold , a light frequency dynamic adjustment mechanism is triggered to gradually narrow the difference between the light fluctuation frequency and the real-time breathing frequency by a fixed step , and the time interval of each adjustment is set to , until , if , it is determined that the current light frequency and the breathing rhythm have reached the adaptation state. After reaching the adaptation state, positive feedback is given through auxiliary dynamic elements in the virtual scene to strengthen the patient's perception of rhythm synchronization. 8.The preoperative anxiety relief method based on virtual reality technology according to claim 6, characterized in that, The real-time physiological rhythm data is continuously compared with the benchmark value in the physiological- psychological two-dimensional anxiety benchmark model, the improvement ratio of real-time heart rate variability relative to heart rate variability benchmark and the change ratio of real-time skin electricity activity relative to skin electricity activity benchmark are calculated, if the heart rate variability improvement ratio and the skin electricity activity decline ratio , it is determined that the anxiety state of the patient is relieved, the level of light and shadow fluctuation can be automatically increased, if the skin electricity activity improvement ratio , the scene pacification mechanism is immediately started, the light and shadow contrast is reduced to 1-k of the original value, k represents the contrast adjustment coefficient, and the pacification voice is issued through the virtual guide built in the scene to reduce the sensory stimulation and relieve the anxiety escalation.

9. A preoperative anxiety relief system based on virtual reality technology for use in a preoperative anxiety relief method based on virtual reality technology according to any one of claims 1 to 8, characterized in that, Comprise: Data acquisition and personalized benchmark establishment module: obtain the physiological rhythm data of the patient before operation, and the physiological rhythm data at least includes respiratory frequency and heart rate variability information; Dynamic virtual scene generation module: based on the physiological rhythm data, a virtual natural scene with dynamic light and shadow change is constructed, and the virtual natural scene includes light and shadow fluctuation elements corresponding to the respiratory frequency; Guidance and rhythm synchronization module: the patient wears a VR head-mounted display and a bone conduction earphone, the device automatically loads the customized virtual scene, and the virtual guide prompts the respiratory points in real time to help the patient establish the perception association with the rhythm elements; Dynamic rhythm adjustment module: real-time analysis of physiological data of the patient obtains respiratory frequency, heart rate variability and skin electrical activity value, and when the difference between the light and shadow fluctuation frequency and the respiratory frequency exceeds the threshold value, the light and shadow frequency is adjusted step by step to approach the respiratory frequency.

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