Intelligent exhibition hall management platform based on VR technology

Abstract

The invention relates to the technical field of smart exhibition halls, in particular to a smart exhibition hall management platform based on VR (virtual reality) technology, which comprises an observation connection module, a data display module, an emotion analysis module, a character analysis module and a multi-user interaction module. Meanwhile, the emotion state of the user is detected, different backgrounds are sorted, the user selects the backgrounds through voice, in the watching process of the user, the emotion of the user is monitored in real time, the backgrounds are adjusted in real time according to the emotion changes of the user, and the character of the user is determined according to the time when the user selects the backgrounds and the emotion changes of the user; when the multi-user interaction module establishes communication rooms, rooms are distributed according to different characters of different users, and the time required by the users to switch backgrounds is used for determining the endurance degree of the users, the time of robot-assisted communication putting in the rooms and the sound made by robot-assisted communication are determined through the sound of selecting the background time for the users.

Description

Technical Field

[0001] The present invention relates to the technical field of smart exhibition halls, and in particular to a smart exhibition hall management platform based on VR technology. Background Art

[0002] Virtual reality (VR), also known as a virtual environment, a spiritual environment, or an artificial environment, refers to the use of computers to generate a virtual world that directly exerts visual, auditory, and tactile sensations on participants, allowing them to observe and manipulate it interactively. VR technology possesses a virtuality that transcends reality.

[0003] When users explore the smart exhibition hall through VR technology, their emotions will fluctuate. At this time, the appropriate data background of the smart exhibition hall makes users feel more immersive and enhances their involvement in the exhibition hall content. At the same time, the smart exhibition hall needs to adjust the background of the displayed data in real time, so that users can immerse themselves more deeply in the exhibition hall's data environment and obtain the ultimate immersive experience.

[0004] During data display interactions, users' emotions will fluctuate with changes in the displayed data. To ensure continuous optimization of the user experience, we need to track and analyze users' emotional changes in real time so that we can promptly and synchronously adjust the data display background of the exhibition hall to form an emotional resonance with it. In addition, when users pay too much attention to a certain data point for too long, they may experience attention fatigue. At this time, users should be encouraged to communicate and discuss with each other to stimulate their interest and maintain the freshness and participation of observation. At the same time, when users watch the smart exhibition hall for a long time, long-term individual viewing will reduce their patience. Summary of the Invention

[0005] The purpose of the present invention is to provide a smart exhibition hall management platform based on VR technology to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is a smart exhibition hall management platform based on VR technology, which includes an observation and connection module, a data display module, a sentiment analysis module, a personality analysis module and a multi-person interaction module;

[0007] The observation connection module includes a data connection unit, a user interaction unit and a performance optimization unit;

[0008] The data connection unit is used to wirelessly connect the VR technology to the smart exhibition hall platform via Bluetooth technology;

[0009] The working principle of Bluetooth technology to wirelessly connect VR technology with the smart exhibition hall platform is based on the Bluetooth protocol, which is a wireless communication standard used for data exchange between devices within a short distance;

[0010] Initialization and pairing: The VR device and the smart exhibition hall platform must first be initialized and paired. During this process, the devices communicate via Bluetooth signals to confirm each other's identities and establish a connection.

[0011] Data transmission: Once paired successfully, the VR device sends data to the smart exhibition hall platform via Bluetooth. This data includes the user's location information, action instructions, environmental information, etc.

[0012] Signal modulation and demodulation: Bluetooth devices use radio waves to communicate. At the sending end, data is converted into radio waves through a modulation process; at the receiving end, radio waves are converted back into original data through a demodulation process.

[0013] The calculation formula in Bluetooth technology is related to data transmission rate and signal strength:

[0014] Data transfer rate: The data transfer rate of a Bluetooth device is expressed by the following formula:

[0015]

[0016] The amount of data is usually measured in bits and the time in seconds;

[0017] Signal strength: The strength of the Bluetooth signal is expressed by the following formula:

[0018]

[0019] The power is in watts, the reference power is in watts), and the result is in decibels;

[0020] Transmission distance: The transmission distance of a Bluetooth signal is related to the signal strength, but it is not a simple linear relationship. The transmission distance is estimated by the following formula:

[0021]

[0022] The intensity transmission is the signal strength of the transmitting end, and the intensity reception is the signal strength of the receiving end. The reference distance is in meters;

[0023] The user interaction unit is used to interact with the user's voice and the smart exhibition hall platform through a voice recognition method;

[0024] Voice recognition method: Users directly control the exhibition hall's display content through voice commands, such as "Play the next exhibit" or "Zoom in on this exhibit." The user speaks, and the system understands the question through voice recognition and responds. The calculation formula is as follows:

[0025] Suppose we have an audio signal x with length T and sampling rate S;

[0026] Preprocessing:

[0027] X preprocessed =f(x,θ)

[0028] Feature extraction:

[0029] features = g(X preprocessed ,θ)

[0030] Model Inference:

[0031] predicted t ext=model(featuers,θ)

[0032] Post-processing:

[0033] final t ext=postprocess(predicted t ext)

[0034] θ represents the parameters of the model;

[0035] After the user outputs the speech, the user's speech features are calculated, and then the mapping between sound and language is modeled;

[0036] Capture user actions and commands and convert them into corresponding responses in the virtual environment, enhancing user immersion and participation;

[0037] The performance optimization unit is used to monitor the performance of the connection between the VR technology and the smart exhibition hall platform via Bluetooth technology through the amplifier to ensure smooth data transmission under different network conditions;

[0038] The amplifier calculation formula is as follows:

[0039]

[0040] G is the gain of the amplifier, P out is the output power, P in is the input power;

[0041] The data display module includes a data display unit and a customized background selection unit;

[0042] The data display unit is used to display the background of use to different users before they use the smart exhibition hall platform, and the background is sound and accompaniment;

[0043] Use the autocorrelation function to detect the overall beat of the music. The calculation formula is as follows:

[0044]

[0045] x[n] is the sample value of the music signal at time n, t is the time delay, and N is the length of the signal;

[0046] Then, based on the beat of the music, it classifies different types of music into recommended background music for users;

[0047] The customized background selection unit is used to allow the user to select different backgrounds by voice, sort the backgrounds that meet the user's needs, and record the time when the background is selected;

[0048] Background weight = emotional factors × background content

[0049] Then the calculated background weights are sorted from large to small for the user to choose;

[0050] The data display module also includes a real-time data update unit, which is used to update VR technical data in real time through a data caching method to ensure that the information seen by the user is the latest smart exhibition hall platform data);

[0051] The data caching method is used to improve the efficiency of data transmission, reduce network delay, and save the amount of data transmitted over the network;

[0052] Step 1: Before the data request arrives, pre-retrieve the data from the storage and store it in the cache;

[0053] Step 2: Data reuse: When a user requests data, if the data already exists in the cache, it is directly provided from the cache, avoiding data retransmission;

[0054] Step 3: Reduce duplicate requests: For frequently requested data, cache multiple copies to reduce the network overhead of duplicate requests;

[0055] The hit rate indicates the ratio of the number of times the cache successfully provides data from the cache to the total number of requests. The calculation formula is as follows:

[0056]

[0057] Miss rate: indicates the ratio of the number of times the cache fails to provide data and needs to obtain data from the source to the total number of requests. The calculation formula is as follows:

[0058]

[0059] Cache hit rate: This is the same concept as the hit rate, but sometimes refers specifically to the proportion of data successfully provided by the cache;

[0060] Cache miss rate: This concept is similar to miss rate, but sometimes refers to the percentage of times the cache fails to provide data and needs to obtain data from the source.

[0061] Cache hit count: The number of times the cache successfully retrieves data from the cache;

[0062] Cache misses: The number of times the cache fails to provide data and needs to obtain data from the source.

[0063] The sentiment analysis module includes a user experience evaluation unit, a priority sorting unit and an automatic switching unit;

[0064] The user experience evaluation unit is used to analyze the user's emotional state during the VR technology experience, such as excitement, satisfaction, boredom or frustration, through physiological detection methods;

[0065] Physiological detection methods analyze user emotions by inferring emotional states through measuring physiological signals, including heart rate, skin conductance, and respiratory rate. The inference method is emotion recognition.

[0066] Heart rate detection monitors changes in blood flow through changes in light absorption and reflection, thereby calculating the heart rate. The calculation formula is as follows:

[0067]

[0068] The calculation formula for skin conductance is as follows:

[0069]

[0070] I is the current passing through the skin, V is the voltage applied to the skin, G skin is skin conductance;

[0071] The respiratory rate is calculated as follows:

[0072]

[0073] The calculation formula of the emotion recognition method is as follows:

[0074] First, set weight coefficients for heart rate, skin conductance, and respiratory rate respectively. Assume that the weight of heart rate is w1, the weight of skin conductance is w2, and the weight of respiratory rate is w3, and w1+w2+w3=1;

[0075] Each physiological indicator is standardized. Assuming that the original heart rate value is H, the standardized heart rate value is in is the mean heart rate, σ H is the standard deviation of heart rate, and similar normalization processing is performed on skin conductance S and respiratory rate R to obtain S' and R';

[0076] E=w1×H'+w2×S'+w3×R'

[0077] Current calculated emotional state E = w1×H'+w2×S'+w3×R'

[0078] And the criteria for emotional states are manually defined in advance;

[0079] The priority sorting unit is used to push the backgrounds in the customized background selection unit according to the user's emotions;

[0080] Establish a mapping relationship between emotions and data types: First, identify the data categories corresponding to various emotions. For example, associate joy with entertaining and relaxing content; associate sadness with warm and encouraging content. The calculation formula is as follows:

[0081]

[0082] Among them, the emotional word weight is pre-defined in the dictionary and indicates the degree of association between the word and a specific emotion;

[0083] Rule-based push notifications: Set a series of clear rules. When a specific emotion is detected, the corresponding data is pushed according to the predetermined rules. For example, if the emotion is anxiety, relaxation techniques and psychological adjustment background are pushed;

[0084] Set an anxiety threshold A. When P exceeds this threshold, the user is considered to be in an anxious state.

[0085] Calculation example:

[0086] P>A, execute push rules;

[0087] The automatic switching unit is used to automatically switch the background in the customized background selection unit according to the emotion of the user when viewing the smart exhibition hall platform data in the user experience evaluation unit, and record the frequency of background switching;

[0088] First, an emotion recognition system is established to convert the emotional state into a numerical range, for example, 0-100, where 0 represents the most negative and 100 represents the most positive;

[0089] When the mood value is 0-30: play natural sounds, such as rain and waves; when the mood value is 31-60: play light music, such as piano and classical music; when the mood value is 61-100: play pop music or dance music.

[0090] The sentiment analysis module further includes an early warning unit, which is used to issue an early warning in a timely manner when the user experience evaluation unit detects that a large number of users have negative emotions, so that management personnel can take measures to make adjustments and interventions;

[0091] Step 1: Staff set the negative emotion alarm threshold;

[0092] Step 2: When negative emotions reach the threshold, a remote alarm is issued;

[0093] The calculation formula for remote alarm distance is as follows:

[0094]

[0095] I is the signal strength at distance d, I0 is the signal strength at reference distance d0, d is the current distance, and n is the path loss exponent.

[0096] The personality analysis module includes a user analysis unit, a personality analysis unit and an imitation unit;

[0097] The user analysis unit is used to determine the patience of different users according to the background switching time in the automatic switching unit through a patience calculation method;

[0098] The patience calculation method uses the following formula: A longer period without background switching indicates a higher level of patience, while frequent background switching indicates a lower level of patience;

[0099]

[0100] T total Refers to the total time the user spends during the task, T switch Refers to the total time the user takes to switch backgrounds, N switch Refers to the number of times the user switches the background, T avg It refers to the average time users spend switching backgrounds each time, and λ refers to the patience decay coefficient;

[0101] The personality analysis unit is used to determine the user's personality based on the user's emotions analyzed by the user experience evaluation unit and the user's patience level in the customized background selection unit;

[0102] First, staff analyze the scores of different personalities, and then establish personality standards. Based on the comparison of personality scores with personality standards, the personality of different users is determined. The personality standards are divided into five personality traits: openness, conscientiousness, extroversion, agreeableness and neuroticism.

[0103] Personality score = (patience level × patience weight) + (emotion score × emotion weight)

[0104] The imitation unit is used to analyze the voice output by the user in the customized background selection unit using the acoustic model of the GMM, and then output the voice analyzed by the acoustic model of the GMM to the multi-person interaction module;

[0105] The GMM acoustic model is a commonly used acoustic model that represents the distribution of each acoustic feature vector by modeling it as a set of Gaussian distributions.

[0106] The calculation formula is as follows;

[0107] Gaussian distribution: Each Gaussian distribution is described by a mean (μ) and a variance (Σ);

[0108]

[0109] x is the eigenvector, D is the dimension of the eigenvector;

[0110] GMM model: The entire model consists of C Gaussian distributions, each corresponding to a mixture component

[0111]

[0112] π c is the weight of the Cth Gaussian distribution.

[0113] The multi-person interaction module includes a multi-person interaction unit, a real-time communication unit, an information analysis unit and an auxiliary communication unit;

[0114] The multi-person interaction unit is used to allocate different communication rooms according to the user personalities in the personality analysis unit through a chat allocation method, and the people in the communication rooms have active personalities and calm personalities;

[0115] The description of the chat assignment method is as follows:

[0116] For users with high openness, adopt a more free and creative chat method;

[0117] For users with a strong sense of responsibility, provide structured and detailed answers;

[0118] For extroverted users, use a more friendly and interactive chat style;

[0119] For users with high agreeableness, adopt a more considerate and caring chat style;

[0120] For users with higher neuroticism, provide more stable and soothing communication;

[0121] The calculation formula is as follows:

[0122] Chat method selection: Select the most appropriate chat method based on the user's personality score;

[0123] Character score = (patience level × patience level weight) + (emotion score × emotion weight)

[0124] ChatMethod

[0125] =SELECT(Openness,Conscientiousness,Extraversion,Agreeableness,Neuroticism)

[0126] Openness refers to openness, Conscientiousness refers to responsibility, Extraversion refers to extroversion, Agreeableness refers to agreeableness, and Neuroticism refers to neuroticism.

[0127] SELECT is a function that selects the appropriate chat method based on the user's personality score;

[0128] The real-time communication unit is used to deploy a chatbot into the communication room of the multi-person interaction unit using a generative adversarial network method. After the robot is deployed, it outputs voice to the communication room in the multi-person interaction unit based on the voice analyzed by the personality analysis module, and explains the smart exhibition hall platform data to users in the communication room;

[0129] The generative adversarial network method is an unsupervised learning technology used to generate data. It is used to generate complex data with a distribution similar to real data. In the scenario of automatic generation of chatbots, it is used to generate high-quality conversation data. The calculation formula is as follows:

[0130] G(z)=D(z)

[0131] G(z) is the generator that transforms random noise z into data samples X;

[0132] D(x) is the discriminator, which outputs a real number D(x)∈[0,1], indicating the probability that X is real data or generated data;

[0133] During use, the calculation formula of the optimization target is as follows:

[0134] The loss function of the generator is:

[0135] LG=-Ez~pz(z)[logD(G(z))]

[0136] pz(z) is the noise distribution, the generator tries to maximize the probability that the discriminator considers its generated samples to be real;

[0137] The information analysis unit is used to compare the data in the real-time communication unit by establishing a comparison model library, and modify the background according to the comparison data;

[0138] The calculation formula for comparison of the model library is as follows:

[0139] A and B are two vectors, · represents the dot product of the vectors, and ||·|| represents the modulus of the vectors;

[0140]

[0141] Euclidean distance:

[0142]

[0143] A i and B i are the i-th elements of the two vectors respectively;

[0144] Background replacement:

[0145]

[0146] modelBackground is the background with the highest similarity in the model library, and threshold is the set threshold;

[0147] The auxiliary communication unit is used to determine the time to deploy the robot into the room established by the multi-person interaction unit according to the user patience analyzed by the user analysis unit;

[0148] The calculation formula is as follows:

[0149]

[0150] T initial is the initial delivery time threshold, which is the initial setting of the delivery time of the robot without considering the patience level; P current is the current patience value, P total is the total patience value, and R is the patience consumption rate.

[0151] First, the user wirelessly connects the VR technology to the smart exhibition hall platform through the data connection unit. After the user puts on the VR, the data display unit shows the user the background selected when using the smart exhibition hall platform;

[0152] During the process of the user selecting a background, the user analysis unit determines the patience of different users based on the background switching time in the automatic switching unit, and the personality analysis unit determines the user's personality based on the user's emotions analyzed by the user experience evaluation unit and the user's background selection time in the customized background selection unit;

[0153] The user experience evaluation unit analyzes the user's emotional state, and prioritizes the backgrounds displayed to the user when using the smart exhibition hall platform based on the user's emotional state and the priority sorting unit, so as to facilitate the user to select a background;

[0154] When the user experience evaluation unit causes discomfort when watching the smart exhibition hall platform, the background is automatically switched by the automatic switching unit. The data changed by the user experience evaluation unit will be recorded, and the frequency of switching the background determines the user's patience;

[0155] Synchronously, the multi-person interaction unit allocates different communication rooms according to the user's personality, and deploys robots into the room through the auxiliary communication unit. The time of deploying the robot is determined according to the user's patience, so that different users can communicate in a friendly manner about the data of the smart exhibition hall platform. At the same time, the imitation unit robot imitates the background sound selected by the user, and the robot assists the user in understanding the data in the smart exhibition hall platform.

[0156] Compared with the prior art, the present invention has the following beneficial effects:

[0157] 1. In the smart exhibition hall management platform based on VR technology, the data display unit shows the user the background selected when using the smart exhibition hall platform. Before that, the user's emotional state is detected and different backgrounds are sorted to facilitate the user's subsequent selection of backgrounds through voice. During the user's viewing process, the user's emotions are monitored in real time and the background is adjusted in real time according to the user's emotional changes;

[0158] Then, the user's personality is determined based on the time when the user selects the background and the user's emotional changes. When establishing a communication room, the multi-person interaction module allocates rooms according to the different personalities of different users. The time required for the user to switch the background determines the user's patience and the time for the robot-assisted communication in the room. The sound emitted by the robot-assisted communication is determined by the sound of the time when the user selects the background. BRIEF DESCRIPTION OF THE DRAWINGS

[0159] Figure 1 This is the overall module principle diagram of the present invention;

[0160] Figure 2 This is a schematic diagram of the observation connection module of the present invention;

[0161] Figure 3 This is a schematic diagram of the data display module of the present invention;

[0162] Figure 4 This is a schematic diagram of the sentiment analysis module of the present invention;

[0163] Figure 5 This is a schematic diagram of the personality analysis module of the present invention;

[0164] Figure 6 Schematic diagram of the multi-person interaction module of the present invention;

[0165] Figure 7 It is a flow chart of the working principle of the present invention.

[0166] The meaning of each number in the figure is:

[0167] 100. Observation connection module; 110. Data connection unit; 120. User interaction unit; 130. Performance optimization unit; 200. Data display module; 210. Data display unit; 220. Customized background selection unit; 230. Real-time data update unit; 300. Sentiment analysis module; 310. User experience evaluation unit; 320. Priority sorting unit; 330. Automatic switching unit; 340. Early warning unit; 400. Personality analysis module; 410. User analysis unit; 420. Personality analysis unit; 430. Imitation unit; 500. Multi-person interaction module; 510. Multi-person interaction unit; 520. Real-time communication unit; 530. Information analysis unit; 540. Auxiliary communication unit. DETAILED DESCRIPTION

[0168] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0169] The smart exhibition hall management platform based on VR technology includes an observation and connection module 100, a data display module 200, a sentiment analysis module 300, a personality analysis module 400, and a multi-person interaction module 500;

[0170] The observation connection module 100 includes a data connection unit 110, a user interaction unit 120 and a performance optimization unit 130;

[0171] The data connection unit 110 is used to wirelessly connect the VR technology to the smart exhibition hall platform via Bluetooth technology;

[0172] The working principle of Bluetooth technology to wirelessly connect VR technology with the smart exhibition hall platform is based on the Bluetooth protocol, which is a wireless communication standard used for data exchange between devices within a short distance;

[0173] Initialization and pairing: The VR device and the smart exhibition hall platform must first be initialized and paired. During this process, the devices communicate via Bluetooth signals to confirm each other's identities and establish a connection.

[0174] Data transmission: Once paired successfully, the VR device sends data to the smart exhibition hall platform via Bluetooth. This data includes the user's location information, action instructions, environmental information, etc.

[0175] Signal modulation and demodulation: Bluetooth devices use radio waves to communicate. At the sending end, data is converted into radio waves through a modulation process; at the receiving end, radio waves are converted back into original data through a demodulation process.

[0176] The calculation formula in Bluetooth technology is related to data transmission rate and signal strength:

[0177] Data transfer rate: The data transfer rate of a Bluetooth device is expressed by the following formula:

[0178]

[0179] The amount of data is usually measured in bits and the time in seconds;

[0180] Signal strength: The strength of the Bluetooth signal is expressed by the following formula:

[0181]

[0182] The power is measured in watts, with a reference power of 1 watt, and the result is measured in decibels;

[0183] Transmission distance: The transmission distance of a Bluetooth signal is related to the signal strength, but it is not a simple linear relationship. The transmission distance is estimated by the following formula:

[0184]

[0185] The intensity transmission is the signal strength of the transmitting end, and the intensity reception is the signal strength of the receiving end. The reference distance is 1 meter.

[0186] The user interaction unit 120 is used to interact with the user's voice and the smart exhibition hall platform through a voice recognition method;

[0187] Voice recognition method: Users directly control the exhibition hall's display content through voice commands, such as "Play the next exhibit" or "Zoom in on this exhibit." The user speaks, and the system understands the question through voice recognition and responds. The calculation formula is as follows:

[0188] Suppose we have an audio signal x with length T and sampling rate S;

[0189] Preprocessing:

[0190] X preprocessed =f(x,θ)

[0191] Feature extraction:

[0192] features = g(X preprocessed ,θ)

[0193] Model Inference:

[0194] predicted t ext=model(featuers,θ)

[0195] Post-processing:

[0196] final t ext=postprocess(predicted t ext)

[0197] θ represents the parameters of the model;

[0198] After the user outputs the speech, the user's speech features are calculated, and then the mapping between sound and language is modeled;

[0199] Capture user actions and commands and convert them into corresponding responses in the virtual environment, enhancing user immersion and participation;

[0200] The performance optimization unit 130 is used to monitor the performance of the connection between the VR technology and the smart exhibition hall platform via Bluetooth technology through the amplifier to ensure smooth data transmission under different network conditions;

[0201] The amplifier calculation formula is as follows:

[0202]

[0203] G is the gain of the amplifier, P out is the output power, P in is the input power.

[0204] The data display module 200 includes a data display unit 210 and a customized background selection unit 220;

[0205] The data display unit 210 is used to display the background of the smart exhibition hall platform to different users before they use it, and the background is sound and accompaniment;

[0206] Use the autocorrelation function to detect the overall beat of the music. The calculation formula is as follows:

[0207]

[0208] x[n] is the sample value of the music signal at time n, t is the time delay, and N is the length of the signal;

[0209] Then, based on the beat of the music, it classifies different types of music into recommended background music for users;

[0210] The customized background selection unit 220 is used to allow the user to select different backgrounds by voice, sort the backgrounds that meet the user's needs, and record the time when the background is selected;

[0211] Background weight = emotional factors × background content

[0212] The calculated background weights are then sorted from large to small for the user to choose.

[0213] The data display module 200 also includes a real-time data update unit 230, which is used to update VR technical data in real time through a data caching method to ensure that the information viewed by the user is the latest smart exhibition hall platform data;

[0214] The data caching method is used to improve the efficiency of data transmission, reduce network delay, and save the amount of data transmitted over the network;

[0215] Step 1: Before the data request arrives, pre-retrieve the data from the storage and store it in the cache;

[0216] Step 2: Data reuse: When a user requests data, if the data already exists in the cache, it is directly provided from the cache, avoiding data retransmission;

[0217] Step 3: Reduce duplicate requests: For frequently requested data, cache multiple copies to reduce the network overhead of duplicate requests;

[0218] The hit rate indicates the ratio of the number of times the cache successfully provides data from the cache to the total number of requests. The calculation formula is as follows:

[0219]

[0220] Miss rate: indicates the ratio of the number of times the cache fails to provide data and needs to obtain data from the source to the total number of requests. The calculation formula is as follows:

[0221]

[0222] Cache hit rate: This is the same concept as the hit rate, but sometimes refers specifically to the proportion of data successfully provided by the cache;

[0223] Cache miss rate: This concept is similar to miss rate, but sometimes refers to the percentage of times the cache fails to provide data and needs to obtain data from the source.

[0224] Cache hit count: The number of times the cache successfully retrieves data from the cache;

[0225] Cache misses: The number of times the cache fails to provide data and needs to obtain data from the source.

[0226] The sentiment analysis module 300 includes a user experience evaluation unit 310, a priority ranking unit 320, and an automatic switching unit 330;

[0227] The user experience evaluation unit 310 is used to analyze the user's emotional state during the VR technology experience, such as excitement, satisfaction, boredom or frustration, through physiological detection methods;

[0228] Physiological detection methods analyze user emotions by inferring emotional states through measuring physiological signals, including heart rate, skin conductance, and respiratory rate. The inference method is emotion recognition.

[0229] Heart rate detection monitors changes in blood flow through changes in light absorption and reflection, thereby calculating the heart rate. The calculation formula is as follows:

[0230]

[0231] The calculation formula for skin conductance is as follows:

[0232]

[0233] I is the current passing through the skin, V is the voltage applied to the skin, G skin is skin conductance;

[0234] The respiratory rate is calculated as follows:

[0235]

[0236] The calculation formula of the emotion recognition method is as follows:

[0237] First, set weight coefficients for heart rate, skin conductance, and respiratory rate respectively. Assume that the weight of heart rate is w1, the weight of skin conductance is w2, and the weight of respiratory rate is w3, and w1+w2+w3=1;

[0238] Each physiological indicator is standardized. Assuming that the original heart rate value is H, the standardized heart rate value is in is the mean heart rate, σ H is the standard deviation of heart rate, and similar normalization processing is performed on skin conductance S and respiratory rate R to obtain S' and R';

[0239] E=w1×H'+w2×S'+w3×R'

[0240] Current calculated emotional state E = w1×H'+w2×S'+w3×R'

[0241] And the criteria for emotional states are manually defined in advance;

[0242] The priority sorting unit 320 is used to push the backgrounds in the customized background selection unit 220 according to the user's emotions;

[0243] Establish a mapping relationship between emotions and data types: First, identify the data categories corresponding to various emotions. For example, associate joy with entertaining and relaxing content; associate sadness with warm and encouraging content. The calculation formula is as follows:

[0244] Sentiment score = ∑(sentiment word weight × word frequency)

[0245] worde text

[0246] Among them, the emotional word weight is pre-defined in the dictionary and indicates the degree of association between the word and a specific emotion;

[0247] Rule-based push notifications: Set a series of clear rules. When a specific emotion is detected, the corresponding data is pushed according to the predetermined rules. For example, if the emotion is anxiety, relaxation techniques and psychological adjustment background are pushed;

[0248] Set an anxiety threshold A. When P exceeds this threshold, the user is considered to be in an anxious state.

[0249] Calculation example:

[0250] P>A, execute push rules;

[0251] The automatic switching unit 330 is used to automatically switch the background in the customized background selection unit 220 according to the user's emotion when viewing the smart exhibition hall platform data in the user experience evaluation unit 310, and record the frequency of background switching;

[0252] First, an emotion recognition system is established to convert the emotional state into a numerical range, for example, 0-100, where 0 represents the most negative and 100 represents the most positive;

[0253] When the mood value is 0-30: play natural sounds, such as rain and waves; when the mood value is 31-60: play light music, such as piano and classical music; when the mood value is 61-100: play pop music or dance music.

[0254] The sentiment analysis module 300 also includes an early warning unit 340, which is used to issue an early warning in a timely manner when the user experience evaluation unit 310 detects that a large number of users have negative emotions, so that management personnel can take measures to adjust and intervene;

[0255] Step 1: Staff set the negative emotion alarm threshold;

[0256] Step 2: When negative emotions reach the threshold, a remote alarm is issued;

[0257] The calculation formula for remote alarm distance is as follows:

[0258]

[0259] I is the signal strength at distance d, I0 is the signal strength at reference distance d0, d is the current distance, and n is the path loss exponent.

[0260] The personality analysis module 400 includes a user analysis unit 410 , a personality analysis unit 420 , and an imitation unit 430 ;

[0261] The user analysis unit 410 is used to determine the patience of different users based on the background switching time in the automatic switching unit 330 by using a patience calculation method;

[0262] The patience calculation method uses the following formula: A longer period without background switching indicates a higher level of patience, while frequent background switching indicates a lower level of patience;

[0263]

[0264] T total Refers to the total time the user spends during the task, T switch Refers to the total time the user takes to switch backgrounds, N switch Refers to the number of times the user switches the background, T avg It refers to the average time users spend switching backgrounds each time, and λ refers to the patience decay coefficient;

[0265] The personality analysis unit 420 is used to determine the user's personality based on the user's emotions analyzed by the user experience evaluation unit 310 and the user's patience level in the customized background selection unit 220;

[0266] First, staff analyze the scores of different personalities, and then establish personality standards. Based on the comparison of personality scores with personality standards, the personality of different users is determined. The personality standards are divided into five personality traits: openness, conscientiousness, extroversion, agreeableness and neuroticism.

[0267] Personality score = (patience level × patience weight) + (emotion score × emotion weight)

[0268] The imitation unit 430 uses the acoustic model of the GMM to analyze the voice output by the user in the customized background selection unit 220, and then outputs the voice analyzed by the acoustic model of the GMM to the multi-person interaction module 500;

[0269] The GMM acoustic model is a commonly used acoustic model that represents the distribution of each acoustic feature vector by modeling it as a set of Gaussian distributions.

[0270] The calculation formula is as follows;

[0271] Gaussian distribution: Each Gaussian distribution is described by a mean (μ) and a variance (Σ);

[0272]

[0273] x is the eigenvector, D is the dimension of the eigenvector;

[0274] GMM model: The entire model consists of C Gaussian distributions, each corresponding to a mixture component

[0275]

[0276] π c is the weight of the Cth Gaussian distribution.

[0277] The multi-person interaction module 500 includes a multi-person interaction unit 510 , a real-time communication unit 520 , an information analysis unit 530 , and an auxiliary communication unit 540 ;

[0278] The multi-person interaction unit 510 is used to allocate different communication rooms according to the user's personality in the personality analysis unit 420 through a chat allocation method. The people in the communication room have active personalities and calm personalities.

[0279] The description of the chat assignment method is as follows:

[0280] For users with high openness, adopt a more free and creative chat method;

[0281] For users with a strong sense of responsibility, provide structured and detailed answers;

[0282] For extroverted users, use a more friendly and interactive chat style;

[0283] For users with high agreeableness, adopt a more considerate and caring chat style;

[0284] For users with higher neuroticism, provide more stable and soothing communication;

[0285] The calculation formula is as follows:

[0286] Chat method selection: Select the most appropriate chat method based on the user's personality score;

[0287] Character score = (patience level × patience level weight) + (emotion score × emotion weight)

[0288] ChatMethod

[0289] =SELECT(Openness,Conscientiousness,Extraversion,Agreeableness,Neuroticism)

[0290] Openness refers to openness, Conscientiousness refers to responsibility, Extraversion refers to extroversion, Agreeableness refers to agreeableness, and Neuroticism refers to neuroticism.

[0291] SELECT is a function that selects the appropriate chat method based on the user's personality score;

[0292] The real-time communication unit 520 is used to deploy a chatbot into the communication room of the multi-person interaction unit 510 using a generative adversarial network method. After the chatbot is deployed, it outputs speech to the communication room in the multi-person interaction unit 510 based on the speech analyzed by the personality analysis module 400, and explains the smart exhibition hall platform data to the users in the communication room.

[0293] The generative adversarial network method is an unsupervised learning technology used to generate data. It is used to generate complex data with a distribution similar to real data. In the scenario of automatic generation of chatbots, it is used to generate high-quality conversation data. The calculation formula is as follows:

[0294] G(z)=D(z)

[0295] G(z) is the generator that transforms random noise z into data samples X;

[0296] D(x) is the discriminator, which outputs a real number D(x)∈[0,1], indicating the probability that X is real data or generated data;

[0297] During use, the calculation formula of the optimization target is as follows:

[0298] The loss function of the generator is:

[0299] LG=-Ez~pz(z)[logD(G(z))]

[0300] pz(z) is the noise distribution, the generator tries to maximize the probability that the discriminator considers its generated samples to be real;

[0301] The information analysis unit 530 is used to compare the data in the real-time communication unit 520 by establishing a comparison model library, and modify the background according to the comparison data;

[0302] The calculation formula for comparison of the model library is as follows:

[0303] A and B are two vectors, · represents the dot product of the vectors, and ||·|| represents the modulus of the vectors;

[0304]

[0305] Euclidean distance:

[0306]

[0307] A i and B i are the i-th elements of the two vectors respectively;

[0308] Background replacement:

[0309]

[0310] modelBackground is the background with the highest similarity in the model library, and threshold is the set threshold;

[0311] The auxiliary communication unit 540 is used to determine the time to deploy the robot into the room established by the multi-person interaction unit 510 according to the user's patience analyzed by the user analysis unit 410;

[0312] The calculation formula is as follows:

[0313]

[0314] T initial is the initial delivery time threshold, which is the initial setting of the delivery time of the robot without considering the patience level; P current is the current patience value, P total is the total patience value, and R is the patience consumption rate.

[0315] First, the user wirelessly connects the VR technology to the smart exhibition hall platform through the data connection unit 110. After the user puts on the VR, the data display unit 210 shows the user the background selected when using the smart exhibition hall platform;

[0316] During the process of users selecting backgrounds, the user analysis unit 410 determines the patience of different users based on the background switching time in the automatic switching unit 330, and the personality analysis unit 420 determines the user's personality based on the user's emotions analyzed by the user experience evaluation unit 310 and the user's background selection time in the customized background selection unit 220;

[0317] The user experience evaluation unit 310 analyzes the user's emotional state and prioritizes the backgrounds displayed to the user when using the smart exhibition hall platform based on the user's emotional state and the priority sorting unit 320, so as to facilitate the user to select the background;

[0318] When the user experience evaluation unit 310 causes discomfort when viewing the smart exhibition hall platform, the background is automatically switched by the automatic switching unit 330. The data changed by the user experience evaluation unit 310 will be recorded, and the frequency of switching the background determines the user's patience;

[0319] Synchronously, the multi-person interaction unit 510 allocates different communication rooms according to the user's personality, and deploys robots into the room through the auxiliary communication unit 540. The time of deploying the robot is determined according to the user's patience, so that different users can communicate in a friendly manner about the data of the smart exhibition hall platform. At the same time, the imitation unit 430 robot imitates the background sound selected by the user, and the robot assists the user in understanding the data in the smart exhibition hall platform.

[0320] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. The smart exhibition hall management platform based on VR technology is characterized by: include: An observation connection module (100) includes at least a data connection unit (110), a user interaction unit (120) and a performance optimization unit (130); A data display module (200) comprising at least a data display unit (210) and a customized background selection unit (220); A sentiment analysis module (300) comprising at least a user experience evaluation unit (310), a priority ranking unit (320), and an automatic switching unit (330); A personality analysis module (400) comprising at least a user analysis unit (410), a personality analysis unit (420) and an imitation unit (430); A multi-person interaction module (500) comprising at least a multi-person interaction unit (510), a real-time communication unit (520), an information analysis unit (530), and an auxiliary communication unit (540); The user wirelessly connects the VR technology to the smart exhibition hall platform through the data connection unit (110). After wearing the VR device, the data display unit (210) displays the background selection. The user analysis unit (410) determines the user's patience based on the background switching time in the automatic switching unit (330). The personality analysis unit (420) determines the user's personality based on the user experience evaluation unit (310) and the background selection time. The user experience evaluation unit (310) analyzes the user's emotional state and sorts the backgrounds according to the priority sorting unit (320). If the user feels uncomfortable while watching, the automatic switching unit (330) switches the background and records data to determine the user's patience. The multi-person interaction unit (510) allocates a communication room according to the user's personality. The auxiliary communication unit (540) deploys a robot according to the user's patience level. The robot imitates the sound of the background selected by the user to assist the user in understanding the platform data.

2. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The data connection unit (110) is used to wirelessly connect the VR technology with the smart exhibition hall platform via Bluetooth technology, comprising the following steps: Initialization and pairing: The VR device and the smart exhibition hall platform must first be initialized and paired. During this process, the devices communicate via Bluetooth signals to confirm each other's identities and establish a connection. Data transmission: Once paired successfully, the VR device sends data to the smart exhibition hall platform via Bluetooth. This data includes the user's location information, action instructions, and environmental information. Signal modulation and demodulation: Bluetooth devices use radio waves to communicate. At the sending end, data is converted into radio waves through a modulation process; at the receiving end, radio waves are converted back into original data through a demodulation process. The user interaction unit (120) is used to perform interactive operations between the user's voice and the smart exhibition hall platform through a voice recognition method; Voice recognition method: Users directly control the exhibition hall's display content through voice commands; users speak, the system understands the question content through voice recognition, and gives a response.

3. The smart exhibition hall management platform based on VR technology according to claim 1 is characterized by: The data display unit (210) is used to display the background of the smart exhibition hall platform to different users before they use it, and the background is sound and accompaniment; Then, based on the beat of the music, it classifies different types of music into recommended background music for users; The customized background selection unit (220) is used to allow the user to select different backgrounds by voice, sort the backgrounds that meet the user's needs, and record the time when the background is selected; Background weight = emotional factors × background content The calculated background weights are sorted from large to small for user selection.

4. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The user experience evaluation unit (310) is used to analyze the user's emotional state during the VR technology experience process through physiological detection methods; Physiological detection methods analyze user emotions by inferring emotional states through measuring physiological signals, including heart rate, skin conductance, and respiratory rate. The inference method is emotion recognition. The automatic switching unit (330) is used to automatically switch the background in the customized background selection unit (220) according to the emotion of the user in the user experience evaluation unit (310) when viewing the smart exhibition hall platform data, and record the frequency of background switching.

5. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The user analysis unit (410) is used to determine the patience of different users according to the background switching time in the automatic switching unit (330) through a patience calculation method; The personality analysis unit (420) is used to determine the user's personality based on the user's emotions analyzed by the user experience evaluation unit (310) and the user's patience in the customized background selection unit (220).

6. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The multi-person interaction unit (510) is used to allocate different communication rooms according to the user personalities in the personality analysis unit (420) through a chat allocation method; The real-time communication unit (520) is used to deploy a chat robot into the communication room of the multi-person interaction unit (510) by using a generative adversarial network method. After the robot is deployed, it outputs voice to the communication room in the multi-person interaction unit (510) based on the voice analyzed by the personality analysis module (400), and explains the smart exhibition hall platform data to users in the communication room; The information analysis unit (530) is used to compare the data in the real-time communication unit (520) by establishing a comparison model library, and modify the background according to the comparison data.

7. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The data display module (200) further includes a real-time data updating unit (230), wherein the real-time data updating unit (230) is used to update VR technical data in real time through a data caching method, thereby ensuring that the information viewed by the user is the latest smart exhibition hall platform data; The data caching method is used to improve the efficiency of data transmission, reduce network delay, and save the amount of data transmitted over the network; Step 1: Before the data request arrives, pre-retrieve the data from the storage and store it in the cache; Step 2: Data reuse: When a user requests data, if the data already exists in the cache, it is directly provided from the cache, avoiding data retransmission; Step 3: Reduce duplicate requests: For frequently requested data, cache multiple copies to reduce the network overhead of duplicate requests.

8. The VR-based smart exhibition hall management platform according to claim 1 is characterized by: The sentiment analysis module (300) further includes an early warning unit (340), which is configured to issue an early warning in a timely manner when the user experience evaluation unit (310) detects that a large number of users have negative emotions.

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