CN117519486B - Meta-universe digital person interaction method, device, equipment and storage medium - Google Patents
Meta-universe digital person interaction method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN117519486B CN117519486B CN202410001622.XA CN202410001622A CN117519486B CN 117519486 B CN117519486 B CN 117519486B CN 202410001622 A CN202410001622 A CN 202410001622A CN 117519486 B CN117519486 B CN 117519486B
- Authority
- CN
- China
- Prior art keywords
- data
- universe
- environment
- meta
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000003993 interaction Effects 0.000 title claims abstract description 205
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000004458 analytical method Methods 0.000 claims abstract description 62
- 230000002452 interceptive effect Effects 0.000 claims abstract description 54
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000000605 extraction Methods 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 12
- 230000006399 behavior Effects 0.000 claims description 95
- 239000013598 vector Substances 0.000 claims description 82
- 230000007613 environmental effect Effects 0.000 claims description 81
- 230000002159 abnormal effect Effects 0.000 claims description 43
- 230000000007 visual effect Effects 0.000 claims description 38
- 230000004927 fusion Effects 0.000 claims description 37
- 230000008859 change Effects 0.000 claims description 36
- 238000005286 illumination Methods 0.000 claims description 31
- 238000004364 calculation method Methods 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 21
- 230000006870 function Effects 0.000 claims description 18
- 230000010354 integration Effects 0.000 claims description 18
- 230000004044 response Effects 0.000 claims description 17
- 230000003190 augmentative effect Effects 0.000 claims description 16
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 230000008649 adaptation response Effects 0.000 claims description 15
- 230000002068 genetic effect Effects 0.000 claims description 14
- 238000012937 correction Methods 0.000 claims description 13
- 238000012544 monitoring process Methods 0.000 claims description 11
- 230000000694 effects Effects 0.000 claims description 8
- 230000002457 bidirectional effect Effects 0.000 claims description 7
- 230000035945 sensitivity Effects 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000003542 behavioural effect Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 description 14
- 230000005856 abnormality Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 8
- 230000008447 perception Effects 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 238000013178 mathematical model Methods 0.000 description 3
- JJWKPURADFRFRB-UHFFFAOYSA-N carbonyl sulfide Chemical compound O=C=S JJWKPURADFRFRB-UHFFFAOYSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
- 230000008303 genetic mechanism Effects 0.000 description 2
- 238000007654 immersion Methods 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 238000009877 rendering Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003334 potential effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
- G06F2203/01—Indexing scheme relating to G06F3/01
- G06F2203/012—Walk-in-place systems for allowing a user to walk in a virtual environment while constraining him to a given position in the physical environment
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application relates to the technical field of metauniverse, and discloses a metauniverse digital person interaction method, a device, equipment and a storage medium. The method comprises the following steps: performing environment scanning and feature extraction on the initial meta-universe environment to obtain a target environment feature set and a first meta-universe digital person; performing self-adaptive tracking adjustment to obtain a second universe digital person and performing interactive consistency analysis to obtain interactive consistency data; carrying out digital human interaction strategy analysis to obtain a first digital human interaction strategy; performing virtual object display and interactive dynamic adjustment to obtain interactive adjustment index data; performing anomaly detection to obtain meta-universe environment anomaly data and generating target environment anomaly processing data; and (3) performing meta-universe behavior and preference analysis to obtain a user behavior preference mode and performing policy updating to generate a second digital person interaction policy, thereby improving the interaction intelligence of the meta-universe digital person.
Description
Technical Field
The present application relates to the field of meta-cosmic technologies, and in particular, to a method, an apparatus, a device, and a storage medium for interaction of a meta-cosmic digital person.
Background
In recent years, with the rapid development of virtual reality and augmented reality technologies, the metauniverse has become an attractive field. The meta universe is a virtual digital world, integrates virtual reality, augmented reality, artificial intelligence and internet technology, and provides brand-new interactive experience for users. In this digitized world, metauniverse digital people play an important role, which is the primary medium for users to interact with this virtual world. However, to achieve truly realistic interactions, a number of challenges need to be overcome, including issues of environmental awareness, user adaptivity, interaction coherence, environmental change detection, virtual object presentation, and exception handling.
However, the prior art has the following problems: current metauniverse digital people face the problem of environmental perception in user interaction. Digital persons need to accurately perceive the characteristics of the virtual environment in order to provide a consistent and realistic interactive experience. However, how to achieve efficient context awareness and feature extraction remains a challenging problem. Second, digital person adaptation and interactivity is also a problem to be solved. The user's behavior and location in the meta-universe may change and digital persons need to track and adjust in real-time to maintain consistent interactions with the user. This requires powerful adaptive algorithms and coherence analysis techniques. With the continuous change of the virtual environment, the interaction strategy of the digital person and the virtual object display need to be continuously adjusted to adapt to different situations. At the same time, exception handling is also critical, as exceptions in the environment can affect the user's interactive experience. Therefore, how to implement efficient policy updating and exception handling is an area requiring intensive research.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for interaction of metauniverse digital people, and further improves interaction intelligence of the metauniverse digital people.
The first aspect of the application provides a method for interaction of a metauniverse digital person, which comprises the following steps:
performing environment scanning and feature extraction on an initial metauniverse environment to obtain a target environment feature set, and performing metauniverse digital person deployment through the target environment feature set to obtain a first metauniverse digital person;
performing self-adaptive tracking adjustment on a target user and the first universe digital person to obtain a second universe digital person, and performing interaction continuity analysis on the second universe digital person to obtain interaction continuity data;
detecting environmental changes of the initial meta-universe environment to obtain an updated environmental feature set, and analyzing the digital human interaction strategy of the second meta-universe digital human according to the updated environmental feature set and the interaction consistency data to obtain a first digital human interaction strategy;
performing augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and performing virtual object display and interactive dynamic adjustment on the second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data;
Performing anomaly detection on the target meta-universe environment according to the interactive adjustment index data to obtain meta-universe environment anomaly data, and generating corresponding target environment anomaly processing data according to the meta-universe environment anomaly data;
and performing meta space behavior and preference analysis on the target user to obtain a user behavior preference mode, and performing strategy update on the first digital human interaction strategy according to the user behavior preference mode and the target environment exception handling data to generate a second digital human interaction strategy.
A second aspect of the present application provides a metauniverse digital person interaction device, the metauniverse digital person interaction device comprising:
the system comprises a scanning module, a first universe digital person and a second universe digital person, wherein the scanning module is used for carrying out environment scanning and feature extraction on an initial universe environment to obtain a target environment feature set, and carrying out universe digital person deployment through the target environment feature set to obtain a first universe digital person;
the tracking module is used for carrying out self-adaptive tracking adjustment on a target user and the first universe digital person to obtain a second universe digital person, and carrying out interaction continuity analysis on the second universe digital person to obtain interaction continuity data;
The analysis module is used for detecting the environment change of the initial meta-universe environment to obtain an updated environment feature set, and carrying out digital human interaction strategy analysis on the second meta-universe digital human according to the updated environment feature set and the interaction consistency data to obtain a first digital human interaction strategy;
the integration module is used for carrying out augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and carrying out virtual object display and interactive dynamic adjustment on the second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data;
the processing module is used for carrying out anomaly detection on the target meta-universe environment according to the interactive adjustment index data to obtain meta-universe environment anomaly data, and generating corresponding target environment anomaly processing data according to the meta-universe environment anomaly data;
and the updating module is used for carrying out meta-universe behavior and preference analysis on the target user to obtain a user behavior preference mode, carrying out strategy updating on the first digital human interaction strategy according to the user behavior preference mode and the target environment exception handling data, and generating a second digital human interaction strategy.
A third aspect of the present application provides a computer device comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the computer device to perform the method of metauniverse digital person interaction described above.
A fourth aspect of the present application provides a computer-readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the above-described method of interaction of metauniverse digital people.
According to the technical scheme, through environment scanning and feature extraction, the method can accurately capture the features of the initial meta-universe environment, including landmarks and illumination conditions. This accurate environmental perception helps the digital person to better integrate into the virtual environment, providing a more realistic interactive experience. Through self-adaptive tracking adjustment, the method can monitor the position and behavior of the user in real time and adjust the visual angle and position of the digital person so as to keep continuous interaction. This helps to eliminate discontinuities and allows the user to feel a better fusion with the virtual world. Changes in the meta-universe environment can be monitored and the interaction strategy of the digital person updated accordingly. This means that environmental changes occurring in the meta-universe do not lead to interruption or inadaptation of the user experience. Through augmented reality integration, the method can realistically embed the virtual object into the real environment of the user, providing a more immersive experience. This makes the virtual object appear to be truly present around the user, increasing the realism of the interaction. The anomaly of the meta-universe environment can be detected and corresponding processing can be carried out, so that the user can be ensured to enjoy stable interaction experience all the time. This improves the stability of the system and user satisfaction. By analyzing the behaviors and preferences of the user, the method can adjust the interaction strategy of the digital person according to the demands and interests of the user, so that the interaction is more personalized and meets the user expectations, and the interaction intelligence of the meta-universe digital person is further improved.
Drawings
FIG. 1 is a schematic diagram of one embodiment of a method of interaction of metauniverse digital people in an embodiment of the application;
FIG. 2 is a schematic diagram of one embodiment of a metauniverse digital man-interactive device in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method, a device, equipment and a storage medium for interaction of metauniverse digital people, and further improves interaction intelligence of the metauniverse digital people.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, the following describes a specific flow of an embodiment of the present application, referring to fig. 1, an embodiment of a method for interaction between metauniverse digital people in an embodiment of the present application includes:
step 101, carrying out environment scanning and feature extraction on an initial metauniverse environment to obtain a target environment feature set, and carrying out metauniverse digital person deployment through the target environment feature set to obtain a first metauniverse digital person;
it can be understood that the execution body of the application may be a meta-cosmic digital human interaction device, and may also be a terminal or a server, which is not limited herein. The embodiment of the present application will be described by taking a server as an execution body.
Specifically, the environment scanning is performed on the initial metauniverse environment to obtain initial environment scanning data, and physical characteristics of the metauniverse environment are captured through a scanning technology, such as three-dimensional laser scanning or high-resolution image acquisition. These initial environmental scan data are feature extracted to obtain an initial set of environmental features, and key information, such as initial landmark data and initial lighting condition data, is extracted from the scan data, typically by image processing and pattern recognition techniques, such as using edge detection algorithms to identify landmark contours and color analysis algorithms to evaluate lighting conditions. And carrying out vector conversion on the extracted features to generate an initial environment feature vector, and acquiring the feature vector of the target environment. Features such as landmark position, illumination intensity, etc. are converted into numerical data for mathematical operations and comparison. Vector similarity calculation is carried out on the initial environmental feature vector and the target environmental feature vector to obtain vector similarity data, and the similarity degree between the two groups of feature vectors is evaluated through cosine similarity or Euclidean distance and other algorithms. And then, carrying out light condition matching on the initial illumination condition data according to the vector similarity data to obtain illumination condition adjustment data, and carrying out landmark positioning correction on the initial landmark data to obtain landmark positioning correction data. The illumination conditions and landmark positions are adjusted by comparing the difference between the current environment and the target environment, so that the deployment of the metauniverse digital people is more suitable for the current environment. And respectively carrying out characteristic weight adjustment on the illumination condition adjustment data and the landmark positioning correction data to obtain illumination condition weight data and landmark positioning weight data, and adjusting the weights of the illumination condition weight data and the landmark positioning weight data by analyzing the influence degree of each characteristic on the whole environment, so that the whole environment characteristic representation is optimized. And carrying out characteristic weight adjustment on the initial environment characteristic set according to the adjusted weight data to obtain a target environment characteristic set. By comprehensively considering the importance and actual situation of each feature, a feature set which is more accurate and suitable for the current environment is formed. And according to the target environment characteristic set, performing meta-cosmic digital person deployment on the initial meta-cosmic environment to obtain a first meta-cosmic digital person. The environment characteristic set which is adjusted and optimized is used as a guide, so that the metauniverse digital person can better adapt to and respond to the virtual environment in which the metauniverse digital person is located, and a richer and real user interaction experience is provided.
102, carrying out self-adaptive tracking adjustment on a target user and a first universe digital person to obtain a second universe digital person, and carrying out interaction continuity analysis on the second universe digital person to obtain interaction continuity data;
specifically, a state connection relation between a target user and an initial meta-universe environment is established, and dynamic connection between the user and the environment is established by analyzing the behavior and position change of the user in the meta-universe environment. According to the state connection relation, user movement monitoring is carried out, namely the movement track and speed of the user in the meta-universe environment are monitored, so that the future position of the user is predicted. A user position prediction model is used, which takes the current position, the moving speed and the time interval of the user into consideration, and the future reached position of the user is obtained through calculation. And then, according to the user position prediction data, carrying out self-adaptive tracking adjustment on the visual angle of the first universe digital person, ensuring that the digital person can effectively track the movement of the user, and adjusting the visual angle and the position of the digital person so as to keep effective interaction with the user. Through such viewing angle adjustment, digital human viewing angle adjustment data can be obtained. And according to the digital person visual angle adjustment data, determining digital person coordinate data of the first universe digital person, and simultaneously acquiring user coordinate data of a target user. These two sets of data are the basis for achieving efficient interaction between the digital person and the user. And calculating position error data between the target user and the first universe digital person according to the digital person coordinate data and the user coordinate data, and evaluating the response accuracy of the digital person to the user position change. Based on these position error data, an adaptive tracking adjustment is performed on the first universe of digital people to obtain a second universe of digital people. This adjustment process ensures that the digital person can track and respond to the movements of the user more accurately, thereby improving the naturalness and fluency of the interaction. And carrying out interaction distance analysis on the target user and the second universe digital person, namely monitoring and analyzing the distance change between the target user and the second universe digital person in real time to obtain real-time interaction distance data. These data help to evaluate the effectiveness of the interaction. At the same time, the maximum effective interaction distance data between the target user and the initial meta-universe environment is acquired, which is helpful to determine the optimal distance range of the user interaction with the digital human. And carrying out interaction continuity analysis on the second binary universe digital person according to the real-time interaction distance data and the maximum effective interaction distance data so as to evaluate the continuity and naturalness of interaction. Through the steps, interaction consistency data are obtained, the data not only can help judge the interaction effect, but also can provide important basis for further optimizing the interaction strategy of the digital person.
Step 103, detecting environmental changes of the initial meta-universe environment to obtain an updated environmental feature set, and analyzing a digital human interaction strategy of a second meta-universe digital human according to the updated environmental feature set and interaction consistency data to obtain a first digital human interaction strategy;
it should be noted that, environmental change detection is performed on the initial meta-universe environment, and changes in the environment, such as illumination changes, topography changes, etc., are identified through a monitoring technology and an analysis algorithm, so as to obtain an environmental change feature set. The feature update weight analysis is performed on the feature set of the environmental change by presetting a feature update weight function, the core of which is to adjust the update weight of the feature by a sensitivity parameter, the function takes the degree of the environmental change into account, and the changes are converted into feature update weight data by a mathematical model. Thereby quantifying the influence of environmental changes on the meta-universe digital human interaction strategy and providing quantitative basis for subsequent strategy adjustment. And updating the feature weight of the environmental change feature set according to the obtained feature update weight data, so as to obtain an updated environmental feature set. The quantized environmental changes are integrated into the original feature set, and the updated feature set can reflect the current meta-universe environmental state more accurately. And calculating an adaptive response coefficient, and calculating the adaptive coefficient of each feature in the updated environment feature set, so as to evaluate the influence degree of each feature change on the whole environment. The adaptive response coefficient is an index which comprehensively considers the characteristic variation and the characteristic importance, and the influence of the environment variation on the digital human interaction strategy can be more accurately estimated through the coefficient. And carrying out digital human behavior adjustment calculation on the second universe digital person according to the adaptive response coefficient data and the interaction continuity data. And combining the environment change and the interaction continuity among the digital people, and calculating the digital people behavior adjustment rate data through a mathematical model. These data directly influence the behavior and response patterns of the digital person, which is the key to optimizing the interaction strategy. And carrying out digital human interaction strategy analysis on the second universe digital person according to the digital human behavior adjustment rate data so as to obtain strategy optimization index data. The data comprehensively considers environmental changes and interaction consistency. Through the analysis, a corresponding first digital human interaction strategy is generated, and the strategy is based on the real-time environment data and the user interaction data optimization result, so that the digital human can be ensured to perform efficient, natural and consistent interaction with the user in the meta universe.
104, performing augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and performing virtual object display and interactive dynamic adjustment on a second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data;
specifically, the virtual object positioning is performed on the initial metauniverse environment and the second metauniverse digital person through a space positioning technology and a virtual environment modeling technology, so that virtual object positioning data is obtained, and the virtual object can be accurately positioned and displayed in the metauniverse environment. Based on the virtual object positioning data, the augmented reality integration is carried out on the initial meta-universe environment, so that the target meta-universe environment is obtained. By combining advanced image processing techniques and three-dimensional rendering techniques, virtual objects are seamlessly incorporated into a meta-cosmic environment while maintaining the realism of the environment. Thus, not only the interactivity of the environment is enhanced, but also the immersion of the user is improved. Then, the visual fusion coefficient is calculated for the target metauniverse environment and the second metauniverse digital person, and visual coordination and fusion effect between the virtual object and the metauniverse environment are evaluated. Including matching and adjustment of visual elements such as color, illumination, shading, etc. The visual fusion coefficient data obtained through calculation can help the server to more accurately adjust the fusion degree of the virtual object and the environment, and ensure that more natural and real visual experience is provided. And carrying out virtual object display and interactive dynamic adjustment on the second universe digital person according to the visual fusion coefficient data. How virtual objects are presented in the meta-cosmic environment, and how digital people interact with these objects are adjusted. Emphasis on adjustment includes the display position, size, interaction mode, etc. of the virtual object, so as to make interaction between the digital person and the virtual object smoother and more natural. Based on the adjusted information coverage and the interactivity indexes, corresponding interactive adjustment index data are calculated. The data comprehensively considers the display effect of the virtual object and the interaction quality with the digital person.
Step 105, performing anomaly detection on the target meta-universe environment according to the interactive adjustment index data to obtain meta-universe environment anomaly data, and generating corresponding target environment anomaly processing data according to the meta-universe environment anomaly data;
specifically, the anomaly identification is performed on the target meta-universe environment, and the interactive adjustment index data is used as an evaluation tool to detect various anomaly states in the meta-universe environment. Such abnormal states include false displays of virtual objects, malfunctions of the interaction logic, or degradation of the user experience, etc. By analyzing these data, the result of abnormal state recognition can be obtained. And evaluating the abnormal response priority of the target element universe environment according to the identified abnormal state. This evaluation takes into account the extent to which different exception states affect the user experience and system operation, thereby determining which exceptions require preferential treatment. This evaluation process involves not only analysis of the current anomaly but also prediction of the potential impact, ensuring that various anomalies can be responded to efficiently and effectively. Then, meta-cosmic environment anomaly data of the target meta-cosmic environment is generated according to the identified anomaly state and the response priority thereof. These data are a structured representation of the abnormal state, including the nature, severity, impact of the abnormality and urgency of the process. And calculating the self-adaptive adjustment coefficient of the obtained meta-universe environment abnormal data to obtain abnormal self-adaptive adjustment coefficient data. These data take into account the specific circumstances of the meta-universe environment and the interactive habits of the user to ensure that the exception handling scheme is able to accommodate changing environments and user requirements. This calculation process is multivariable and complex, requiring a comprehensive consideration of various factors such as the frequency of occurrence of the abnormality, the sensitivity of the user to the abnormality, the stability of the environment, and the like. And generating target environment exception handling data corresponding to the target meta-universe environment according to the calculated exception influence relieving index data. The data provides a specific processing scheme for the abnormal state in the meta-universe environment, and comprises correction measures of the abnormal state, adjustment suggestions of user interaction and maintenance strategies of system stability.
And 106, performing meta-universe behavior and preference analysis on the target user to obtain a user behavior preference mode, and performing strategy update on the first digital human interaction strategy according to the user behavior preference mode and the target environment exception handling data to generate a second digital human interaction strategy.
Specifically, the behaviors and preferences of the target user in the meta universe are analyzed through a preset behavior analysis function. The behavior analysis function comprehensively calculates the user behavior preference mode by combining various behavior indexes of the user in the meta universe and corresponding weights of the behavior indexes. This pattern is a quantitative representation of the user's behavior, covering the user's activity features, preferences and habits in the meta-universe. And then, vector encoding is carried out on the user behavior preference mode to obtain a user behavior preference vector. And meanwhile, performing feature coding on the exception handling data of the target environment to obtain an environment exception handling vector. User behavior preferences and environmental anomaly handling are converted into numerical forms that can be used for computation and analysis. And calculating a correlation influence coefficient between the user behavior preference vector and the environment exception handling vector, and fusing the two vectors according to the coefficient to obtain a target correlation fusion vector. This process is to identify the association between user behavior preferences and environmental anomaly handling to ensure that the digital human interaction policy updates are fully capable of taking into account user requirements and environmental facts. And inputting the target correlation fusion vector into a preset strategy optimization network. This network includes a bi-directional GRU network, a full connectivity layer, and a genetic optimization layer. The bidirectional GRU network is responsible for extracting hidden features of the fusion vector, and further understanding the complex relationship between user behavior preference and environment exception handling. Then, the full connection layer predicts the strategy updating direction of the extracted hidden feature vector, which provides direction guidance for subsequent strategy updating. Updating the interaction strategy of the first digital person according to the strategy updating direction prediction result by a genetic algorithm in the genetic optimization layer, so as to generate a second digital person interaction strategy. The genetic algorithm optimizes the interaction strategy by simulating natural selection and genetic mechanism, so that the newly generated second digital human interaction strategy can be better adapted to the behavior preference of the user and the actual situation of the meta-universe environment.
According to the embodiment of the application, through environment scanning and feature extraction, the method can accurately capture the features of the initial meta-universe environment, including landmarks and illumination conditions. This accurate environmental perception helps the digital person to better integrate into the virtual environment, providing a more realistic interactive experience. Through self-adaptive tracking adjustment, the method can monitor the position and behavior of the user in real time and adjust the visual angle and position of the digital person so as to keep continuous interaction. This helps to eliminate discontinuities and allows the user to feel a better fusion with the virtual world. Changes in the meta-universe environment can be monitored and the interaction strategy of the digital person updated accordingly. This means that environmental changes occurring in the meta-universe do not lead to interruption or inadaptation of the user experience. Through augmented reality integration, the method can realistically embed the virtual object into the real environment of the user, providing a more immersive experience. This makes the virtual object appear to be truly present around the user, increasing the realism of the interaction. The anomaly of the meta-universe environment can be detected and corresponding processing can be carried out, so that the user can be ensured to enjoy stable interaction experience all the time. This improves the stability of the system and user satisfaction. By analyzing the behaviors and preferences of the user, the method can adjust the interaction strategy of the digital person according to the demands and interests of the user, so that the interaction is more personalized and meets the user expectations, and the interaction intelligence of the meta-universe digital person is further improved.
In a specific embodiment, the process of executing step 101 may specifically include the following steps:
(1) Performing environment scanning on the initial meta-universe environment to obtain initial environment scanning data;
(2) Extracting features of the initial environmental scan data to obtain an initial environmental feature set, wherein the initial environmental feature set comprises: initial landmark data and initial lighting condition data;
(3) Vector conversion is carried out on the initial environment feature set to obtain an initial environment feature vector, and a target environment feature vector of an initial meta-universe environment is obtained;
(4) Vector similarity calculation is carried out on the initial environmental feature vector and the target environmental feature vector, and vector similarity data are obtained;
(5) Performing light condition matching on the initial illumination condition data according to the vector similarity data to obtain illumination condition adjustment data, and performing landmark positioning correction on the initial landmark data to obtain landmark positioning correction data;
(6) Respectively carrying out characteristic weight adjustment on the illumination condition adjustment data and the landmark positioning correction data to obtain illumination condition weight data and landmark positioning weight data;
(7) Performing feature weight adjustment on the initial environment feature set according to the illumination condition weight data and the landmark positioning weight data to obtain a target environment feature set;
(8) And according to the target environment feature set, performing meta-universe digital person deployment on the initial meta-universe environment to obtain a first meta-universe digital person.
Specifically, the environment scanning is performed on the initial metauniverse environment, and physical characteristics of the metauniverse environment are captured through scanning technologies such as three-dimensional laser scanning, high-resolution image acquisition and the like. Including critical data for spatial layout, object position, etc., as initial environmental scan data. Feature extraction is performed on the initial environmental scan data, landmark contours are identified by image processing and pattern recognition techniques, such as edge detection algorithms, and illumination conditions are evaluated using color analysis algorithms. Key information, such as initial landmark data and initial lighting condition data, is extracted from the scanned data, and the data forms an initial environment characteristic set. Then, vector conversion is performed on the initial environmental feature set to generate an initial environmental feature vector. And simultaneously, acquiring a target environment characteristic vector of the initial meta-universe environment. And the characteristics of landmark position, illumination intensity and the like are converted into numerical data, so that subsequent mathematical operation and comparison are facilitated. And carrying out vector similarity calculation on the initial environmental feature vector and the target environmental feature vector, and evaluating the similarity degree between the two groups of feature vectors by using cosine similarity or Euclidean distance and other algorithms. And carrying out light condition matching on the initial illumination condition data according to the obtained vector similarity data to obtain illumination condition adjustment data. And meanwhile, performing landmark positioning correction on the initial landmark data to obtain landmark positioning correction data. The illumination conditions and landmark positions are adjusted by comparing the difference between the current environment and the target environment, so that the deployment of the metauniverse digital people is more suitable for the current environment. And respectively carrying out characteristic weight adjustment on the illumination condition adjustment data and the landmark positioning correction data, so as to obtain illumination condition weight data and landmark positioning weight data. The overall environmental feature representation is optimized by analyzing the extent to which each feature affects the overall environment to adjust their weights. And carrying out characteristic weight adjustment on the initial environment characteristic set according to the adjusted illumination condition weight data and the landmark positioning weight data, thereby obtaining a target environment characteristic set. By comprehensively considering the importance and actual situation of each feature, a feature set which is more accurate and suitable for the current environment is formed. And according to the target environment characteristic set, performing meta-cosmic digital person deployment on the initial meta-cosmic environment to obtain a first meta-cosmic digital person. The environment characteristic set which is adjusted and optimized is used as a guide, so that the metauniverse digital person can better adapt to and respond to the virtual environment in which the metauniverse digital person is located, and a richer and real user interaction experience is provided.
In a specific embodiment, the process of executing step 102 may specifically include the following steps:
(1) Establishing a state connection relation between a target user and an initial meta-universe environment, and carrying out user mobile monitoring on the target user according to the state connection relation to obtain user position prediction data, wherein the user mobile monitoring comprises the following steps:whereinIs the current location of the user and,is the speed at which the velocity of the fluid,is the time interval over which the data is to be stored,is user location prediction data;
(2) Performing visual angle self-adaptive tracking adjustment on the first universe digital person according to the user position prediction data to obtain digital person visual angle adjustment data;
(3) Determining digital person coordinate data of a first universe digital person according to the digital person visual angle adjustment data, and acquiring user coordinate data of a target user;
(4) Calculating quiet position error data of the target user and the first universe digital person according to the digital person coordinate data and the user coordinate data, and carrying out self-adaptive tracking adjustment on the first universe digital person according to the position error data to obtain a second universe digital person;
(5) Performing interaction distance analysis on the target user and the second universe digital person to obtain real-time interaction distance data;
(6) And acquiring the maximum effective interaction distance data between the target user and the initial meta-universe environment, and carrying out interaction consistency analysis on the second meta-universe digital person according to the real-time interaction distance data and the maximum effective interaction distance data to obtain interaction consistency data.
Specifically, a state connection relationship between the target user and the initial meta-universe environment is established, and a dynamic data link is established between the behavior, the position and the environment of the user so as to monitor and analyze the moving track of the user in the meta-universe in real time. And carrying out user mobile monitoring on the target user through the state connection relation to obtain user position prediction data. The next position of the user is predicted using a mathematical model that is calculated based on the current position of the user, the speed of movement and the time interval. Specifically, the current position of the user is added with the product of the speed and the time interval to obtain the position of the user at the next moment. And carrying out visual angle self-adaptive tracking adjustment on the first universe digital person according to the user position prediction data, ensuring that the digital person can effectively track the movement of the user, and adjusting the visual angle and the position of the digital person so as to keep effective interaction with the user. Such viewing angle adjustment is realized based on an algorithm, and the viewing angle and the position of the digital person are correspondingly adjusted in real time in consideration of the moving direction and the moving speed of the user. And determining digital person coordinate data of the first universe digital person according to the digital person visual angle adjustment data, and acquiring user coordinate data of a target user. These two sets of data are the basis for achieving efficient interaction between the digital person and the user. And calculating position error data between the target user and the first universe digital person according to the digital person coordinate data and the user coordinate data, and carrying out self-adaptive tracking adjustment on the first universe digital person according to the position error data so as to obtain a second universe digital person. This adjustment process ensures that the digital person can track and respond to the movements of the user more accurately, thereby improving the naturalness and fluency of the interaction. And carrying out interaction distance analysis between the target user and the second binary cosmic digital person, namely monitoring and analyzing the distance change between the target user and the second binary cosmic digital person in real time to obtain real-time interaction distance data. These data help to evaluate the effectiveness of the interaction. At the same time, the maximum effective interaction distance data between the target user and the initial meta-universe environment is acquired, which is helpful to determine the optimal distance range of the user interaction with the digital human. And carrying out interaction continuity analysis on the second binary universe digital person according to the real-time interaction distance data and the maximum effective interaction distance data so as to evaluate the continuity and naturalness of interaction. Through the steps, interaction continuity data are obtained, and the data can help judge the interaction effect.
In a specific embodiment, the process of executing step 103 may specifically include the following steps:
(1) Detecting environmental change of the initial meta-universe environment to obtain an environmental change feature set;
(2) And carrying out feature updating weight analysis on the environment change feature set by presetting a feature updating weight function to obtain feature updating weight data, wherein the feature updating weight function is as follows:，is a characteristic update sensitivity parameter that is used to update the sensitivity parameters,the feature update weight data is represented as,representing an environmental change feature set, e representing a constant;
(3) According to the feature updating weight data, carrying out feature weight updating on the environment change feature set to obtain an updated environment feature set;
(4) Calculating adaptive response coefficients of the updated environmental feature set to obtain adaptive response coefficient data, whichThe adaptive response coefficient calculation includes:，is to update the first in the environment feature setThe coefficient of adaptability of the individual features,the data representing the adaptive response coefficient is displayed,representing the characteristic variation;
(5) Carrying out digital pedestrian behavior adjustment calculation on the second universe digital person according to the adaptive response coefficient data and the interactive consistency data to obtain digital pedestrian behavior adjustment rate data;
(6) Carrying out digital human interaction strategy analysis on the second universe digital person according to the digital human behavior adjustment rate data to obtain strategy optimization index data;
(7) And generating a corresponding first digital human interaction strategy according to the strategy optimization index data.
Specifically, the environment change detection is performed on the initial meta-universe environment, and any important changes occurring in the environment, such as changes in lighting conditions, physical structures or user activity patterns, are captured and converted into an environment change feature set. And adopting a preset feature updating weight function to perform feature updating weight analysis on the environment change feature set. Corresponding weights are assigned according to the significance of the environmental changes to determine which changes are more important and need to be prioritized. In this function, sensitivity parametersFor regulating the response to environmental changesRepresenting environmental change featuresAnd (5) collecting. From this function, feature update weight data can be obtained. And updating the feature weight of the environment change feature set according to the feature update weight data. The originally captured environmental features are re-weighted to reflect their relative importance in current and future meta-cosmic interactions. The updated environment feature set can more accurately reflect the actual influence of the environment change on the user experience and the digital human interaction. And then, calculating an adaptive response coefficient of the updated environment feature set, evaluating the influence of the change of each environment feature on the whole interaction strategy, wherein the adaptive response coefficient considers the change quantity and the importance of each feature. And based on the adaptive response coefficient data and the interaction continuity data, carrying out digital pedestrian behavior adjustment calculation on the second universe digital person to obtain digital pedestrian behavior adjustment rate data. These data indicate how the digital person should adjust his behavior under different environmental changes in order to better interact with the user. And carrying out digital human interaction strategy analysis according to the digital human behavior adjustment rate data to obtain strategy optimization index data. These data evaluate the potential effects of different policy adjustments on optimizing the user experience and generate a corresponding first digital human interaction policy based on these analysis results.
In a specific embodiment, the process of executing step 104 may specifically include the following steps:
(1) Performing virtual object positioning on the initial meta-universe environment and the second meta-universe digital person to obtain virtual object positioning data;
(2) Performing augmented reality integration on the initial meta-universe environment according to the virtual object positioning data to obtain a target meta-universe environment;
(3) Performing vision fusion coefficient calculation on the target element universe environment and the second element universe digital person to obtain vision fusion coefficient data;
(4) Virtual object display and interactive dynamic adjustment are carried out on the second universe digital person according to the visual fusion coefficient data, so that information coverage and interactive indexes are obtained;
(5) And calculating corresponding interactive adjustment index data according to the information coverage and the interactive index.
Specifically, virtual object positioning is performed on the initial meta-cosmic environment and the second meta-cosmic digital person. Various virtual objects in the meta-universe, including the position of the digital person itself, are captured by spatial positioning techniques, such as three-dimensional laser scanning or high-precision GPS positioning. And carrying out augmented reality integration on the initial meta-universe environment according to the virtual object positioning data. By fusing the virtual objects with the real environment, the virtual objects are seamlessly embedded into the metauniverse environment, for example, using advanced image rendering techniques. The integration not only improves the visual effect of the environment, but also enhances the immersion of the user, and then, the visual fusion coefficient calculation of the target meta-universe environment and the second meta-universe digital person is carried out. Visual coordination between the virtual object and the surrounding environment is assessed by image processing and data analysis techniques. The calculation of the visual fusion coefficients takes into account a number of factors, such as illumination, shading, color, texture, etc., to ensure that the virtual object is visually harmonious with the surrounding environment. And according to the visual fusion coefficient data, performing virtual object display and interactive dynamic adjustment on the second universe digital person. And correspondingly adjusting the interaction mode, the display position and the expression form of the digital person according to the visual fusion coefficient so as to ensure that the behavior and the display of the digital person attract users to the greatest extent and provide effective interaction experience. Such adjustment involves not only visual presentation of the virtual object, but also the way in which it interacts with the user. And calculating to obtain corresponding interactive adjustment index data according to the adjusted information coverage and the interactive index. The height of the interaction regulation index reflects how much the interaction mode of the digital person meets the user requirement, and provides reference for further interaction strategy regulation.
In a specific embodiment, the process of executing step 105 may specifically include the following steps:
(1) Carrying out abnormal recognition on the target element universe environment according to the interactive adjustment index data to obtain an abnormal state recognition result;
(2) According to the abnormal state identification result, carrying out abnormal response priority assessment on the target meta-universe environment to obtain abnormal response priority;
(3) Generating meta-cosmic environment abnormal data of the target meta-cosmic environment according to the abnormal state identification result and the abnormal response priority;
(4) Performing self-adaptive adjustment coefficient calculation on the meta-universe environment abnormal data to obtain abnormal self-adaptive adjustment coefficient data, and calculating abnormal influence relieving index data of the target meta-universe environment according to the abnormal self-adaptive adjustment coefficient data;
(5) And generating target environment exception handling data corresponding to the target meta-universe environment according to the exception influence relieving index data.
Specifically, anomaly identification is performed on the target meta-universe environment according to the interactive adjustment index data. The interactive adjustment index data reflects the effect and fluency of interactions between the user and the digital person in the metauniverse environment. By analyzing the data, abnormal states in the environment that affect the user experience, such as virtual object display errors, broken interaction logic, or user path occlusion, can be identified. This identification process relies on data analysis techniques such as machine learning algorithms that can extract key anomaly characteristics from complex data sets. And carrying out abnormal response priority evaluation on the target meta-universe environment according to the abnormal state identification result, and determining which abnormal states need to be processed preferentially. For example, if an abnormal state directly affects the user's interaction, it may be given a higher priority. The assessment of the response priority of an abnormality takes into account various factors such as the severity of the abnormality, the extent of influence and the ease of repair. Then, meta-cosmic environment anomaly data of the target meta-cosmic environment is generated according to the anomaly state identification result and the response priority thereof. These data detail various abnormal states, including their nature, location and impact. And carrying out self-adaptive adjustment coefficient calculation on the meta-universe environment abnormal data to obtain abnormal self-adaptive adjustment coefficient data. These data provide an adjustment factor for each abnormal state by taking into account the specific conditions of the environment and the interactive habits of the user. For different abnormal states, the system can take response measures with different degrees so as to reduce the influence of the abnormality on the user experience to the greatest extent. And generating target environment exception handling data corresponding to the target meta-universe environment according to the exception influence relieving index data. Based on the results of the previous analysis, this data provides specific exception handling schemes, including technical fixes, user interface adjustments, or other necessary response measures.
In a specific embodiment, the process of executing step 106 may specifically include the following steps:
(1) And performing meta-universe behaviors and preference analysis on the target user through a preset behavior analysis function to obtain a user behavior preference mode, wherein the behavior analysis function is as follows:wherein, the method comprises the steps of, wherein,is the firstThe weight of the individual actions is determined,is an index of the behavior of the vehicle,representing a user behavior preference pattern;
(2) Vector coding is carried out on the user behavior preference mode to obtain a user behavior preference vector, and exception handling feature coding is carried out on the target environment exception handling data to obtain an environment exception handling vector;
(3) Calculating a correlation influence coefficient of the user behavior preference vector and the environment exception handling vector, and carrying out vector fusion on the user behavior preference vector and the environment exception handling vector according to the correlation influence coefficient to obtain a target correlation fusion vector;
(4) Inputting the target correlation fusion vector into a preset strategy optimization network, wherein the strategy optimization network comprises: a bidirectional GRU network, a full connection layer and a genetic optimization layer;
(5) Extracting hidden features of the target correlation fusion vector through a bidirectional GRU network to obtain a target hidden feature vector;
(6) Carrying out strategy updating direction prediction on the target hidden feature vector through the full connection layer to obtain a strategy updating direction prediction result;
(7) And carrying out strategy updating on the first digital human interaction strategy according to the strategy updating direction prediction result by a genetic algorithm in the genetic optimization layer, and generating a second digital human interaction strategy.
Specifically, meta-universe behaviors and preference analysis are performed on the target user through a preset behavior analysis function. The analysis function calculates the user behavior preference mode by integrating various behavior indexes of the user in the meta universe and corresponding weights thereof. In this function, each behavior index has a corresponding weight that indicates the importance of the different behavior in the user preferences. By summarizing these weighted behavior indicators, a comprehensive user behavior preference pattern is obtained, which is a quantitative representation of the user's behavior trends in the meta-universe. The user behavior preference pattern is vector coded and converted into a mathematically processable format, i.e. a user behavior preference vector. The user's behavioral preferences are converted from an abstract concept to a concrete numerical representation. And meanwhile, performing feature coding on the identified anomalies in the target environment to generate environment anomaly handling vectors. Such encoding takes into account various anomaly characteristics, such as anomaly type, severity, and frequency, to translate the anomaly status of the environment into quantifiable data. And calculating a correlation influence coefficient between the user behavior preference vector and the environment abnormality processing vector, and evaluating the degree of correlation between the user behavior preference and the environment abnormality. By analyzing the correlation between these two vectors, it is possible to understand the sensitivity of the user behavior to the environmental anomaly response, and how the anomaly state affects the behavior pattern of the user. Based on the correlation influence coefficient, vector fusion is carried out on the user behavior preference vector and the environment exception handling vector, and a target correlation fusion vector is generated. This fusion vector takes into account both user behavior and environmental conditions. Then, the target correlation fusion vector is input into a preset strategy optimization network, wherein the network comprises a bidirectional GRU network, a full connection layer and a genetic optimization layer. The bidirectional GRU network is used for carrying out deep analysis on the fusion vector, extracting key hidden features, wherein the features comprise complex interaction information between user behaviors and environment states. The full connection layer is used for predicting the strategy updating direction based on the hidden features and determining how to adjust the interaction strategy of the digital person so as to improve the user experience to the greatest extent. And updating the first digital human interaction strategy by a genetic algorithm in the genetic optimization layer according to the strategy updating direction prediction result, so as to generate a second digital human interaction strategy. The genetic algorithm optimizes the interaction strategy by simulating natural selection and genetic mechanism, so that the newly generated second digital human interaction strategy can be better adapted to the behavior preference and meta-universe environment of the user.
The above describes the method for interaction of the metauniverse digital person in the embodiment of the present application, and the following describes the device for interaction of the metauniverse digital person in the embodiment of the present application, please refer to fig. 2, and one embodiment of the device for interaction of the metauniverse digital person in the embodiment of the present application includes:
the scanning module 201 is configured to perform environment scanning and feature extraction on an initial metauniverse environment to obtain a target environment feature set, and perform metauniverse digital person deployment through the target environment feature set to obtain a first metauniverse digital person;
the tracking module 202 is configured to perform adaptive tracking adjustment on a target user and the first universe digital person to obtain a second universe digital person, and perform interaction continuity analysis on the second universe digital person to obtain interaction continuity data;
the analysis module 203 is configured to perform environmental change detection on the initial meta-universe environment to obtain an updated environmental feature set, and perform digital human interaction strategy analysis on the second meta-universe digital human according to the updated environmental feature set and the interaction consistency data to obtain a first digital human interaction strategy;
the integration module 204 is configured to perform augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and perform virtual object display and interactive dynamic adjustment on the second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data;
The processing module 205 is configured to perform anomaly detection on the target meta-cosmic environment according to the interactive adjustment index data to obtain meta-cosmic environment anomaly data, and generate corresponding target environment anomaly processing data according to the meta-cosmic environment anomaly data;
and the updating module 206 is configured to perform meta space behavior and preference analysis on the target user to obtain a user behavior preference mode, and perform policy updating on the first digital human interaction policy according to the user behavior preference mode and the target environment exception handling data to generate a second digital human interaction policy.
Through the cooperation of the components, the method can accurately capture the characteristics of the initial meta-universe environment, including landmarks and illumination conditions, through environment scanning and feature extraction. This accurate environmental perception helps the digital person to better integrate into the virtual environment, providing a more realistic interactive experience. Through self-adaptive tracking adjustment, the method can monitor the position and behavior of the user in real time and adjust the visual angle and position of the digital person so as to keep continuous interaction. This helps to eliminate discontinuities and allows the user to feel a better fusion with the virtual world. Changes in the meta-universe environment can be monitored and the interaction strategy of the digital person updated accordingly. This means that environmental changes occurring in the meta-universe do not lead to interruption or inadaptation of the user experience. Through augmented reality integration, the method can realistically embed the virtual object into the real environment of the user, providing a more immersive experience. This makes the virtual object appear to be truly present around the user, increasing the realism of the interaction. The anomaly of the meta-universe environment can be detected and corresponding processing can be carried out, so that the user can be ensured to enjoy stable interaction experience all the time. This improves the stability of the system and user satisfaction. By analyzing the behaviors and preferences of the user, the method can adjust the interaction strategy of the digital person according to the demands and interests of the user, so that the interaction is more personalized and meets the user expectations, and the interaction intelligence of the meta-universe digital person is further improved.
The application also provides a computer device, which includes a memory and a processor, where the memory stores computer readable instructions that, when executed by the processor, cause the processor to perform the steps of the metauniverse digital person interaction method in the foregoing embodiments.
The present application also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the meta-digital human interaction method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (8)
1. The method for interacting the metauniverse digital people is characterized by comprising the following steps of:
performing environment scanning and feature extraction on an initial metauniverse environment to obtain a target environment feature set, and performing metauniverse digital person deployment through the target environment feature set to obtain a first metauniverse digital person;
performing self-adaptive tracking adjustment on a target user and the first universe digital person to obtain a second universe digital person, and performing interaction continuity analysis on the second universe digital person to obtain interaction continuity data; the method specifically comprises the following steps: establishing a state connection relation between a target user and the initial meta-universe environment, and carrying out user movement monitoring on the target user according to the state connection relation to obtain user position prediction data, wherein the user movement monitoring comprises the following steps: Wherein->Is the current location of the user, < >>Is speed, & lt>Is the time interval over which the data is to be stored,is user location prediction data; performing visual angle self-adaptive tracking adjustment on the first universe digital person according to the user position prediction data to obtain a digital person visual angle adjustmentFinishing data; determining digital person coordinate data of the first universe digital person according to the digital person visual angle adjustment data, and acquiring user coordinate data of the target user; calculating quiet position error data of the target user and the first universe digital person according to the digital person coordinate data and the user coordinate data, and carrying out self-adaptive tracking adjustment on the first universe digital person according to the position error data to obtain a second universe digital person; performing interaction distance analysis on the target user and the second universe digital person to obtain real-time interaction distance data; acquiring maximum effective interaction distance data between the target user and the initial meta-universe environment, and carrying out interaction consistency analysis on the second meta-universe digital person according to the real-time interaction distance data and the maximum effective interaction distance data to obtain interaction consistency data;
Detecting environmental changes of the initial meta-universe environment to obtain an updated environmental feature set, and analyzing the digital human interaction strategy of the second meta-universe digital human according to the updated environmental feature set and the interaction consistency data to obtain a first digital human interaction strategy;
performing augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and performing virtual object display and interactive dynamic adjustment on the second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data; the method specifically comprises the following steps: performing virtual object positioning on the initial meta-universe environment and the second meta-universe digital person to obtain virtual object positioning data; performing augmented reality integration on the initial meta-universe environment according to the virtual object positioning data to obtain a target meta-universe environment; performing visual fusion coefficient calculation on the target universe environment and the second universe digital person to obtain visual fusion coefficient data; performing virtual object display and interactive dynamic adjustment on the second universe digital person according to the visual fusion coefficient data to obtain information coverage and interactive indexes; calculating corresponding interactive adjustment index data according to the information coverage and the interactive index;
Performing anomaly detection on the target meta-universe environment according to the interactive adjustment index data to obtain meta-universe environment anomaly data, and generating corresponding target environment anomaly processing data according to the meta-universe environment anomaly data;
and performing meta space behavior and preference analysis on the target user to obtain a user behavior preference mode, and performing strategy update on the first digital human interaction strategy according to the user behavior preference mode and the target environment exception handling data to generate a second digital human interaction strategy.
2. The method for interaction of metauniverse digital persons according to claim 1, wherein the performing environmental scanning and feature extraction on the initial metauniverse environment to obtain a target environmental feature set, and performing metauniverse digital person deployment through the target environmental feature set to obtain a first metauniverse digital person comprises:
performing environment scanning on the initial meta-universe environment to obtain initial environment scanning data;
extracting features from the initial environmental scan data to obtain an initial environmental feature set, wherein the initial environmental feature set comprises: initial landmark data and initial lighting condition data;
Vector conversion is carried out on the initial environmental feature set to obtain an initial environmental feature vector, and a target environmental feature vector of the initial meta-universe environment is obtained;
vector similarity calculation is carried out on the initial environmental feature vector and the target environmental feature vector, and vector similarity data are obtained;
performing light condition matching on the initial illumination condition data according to the vector similarity data to obtain illumination condition adjustment data, and performing landmark positioning correction on the initial landmark data to obtain landmark positioning correction data;
respectively carrying out characteristic weight adjustment on the illumination condition adjustment data and the landmark positioning correction data to obtain illumination condition weight data and landmark positioning weight data;
performing feature weight adjustment on the initial environmental feature set according to the illumination condition weight data and the landmark positioning weight data to obtain a target environmental feature set;
and according to the target environment characteristic set, performing meta-universe digital person deployment on the initial meta-universe environment to obtain a first meta-universe digital person.
3. The method according to claim 1, wherein the detecting the environmental change of the initial metauniverse environment to obtain an updated environmental feature set, and performing digital human interaction policy analysis on the second metauniverse digital human according to the updated environmental feature set and the interaction consistency data to obtain a first digital human interaction policy, includes:
Detecting the environmental change of the initial meta-universe environment to obtain an environmental change characteristic set;
and carrying out feature updating weight analysis on the environment change feature set through presetting a feature updating weight function to obtain feature updating weight data, wherein the feature updating weight function is as follows:，/>is a characteristic update sensitivity parameter, +.>Representing feature update weight data, ++>Representing an environmental change feature set, e representing a constant;
performing feature weight updating on the environment change feature set according to the feature updating weight data to obtain an updated environment feature set;
adaptively reacting to the updated set of environmental featuresAnd calculating coefficients to obtain adaptive response coefficient data, wherein the adaptive response coefficient calculation comprises the following steps:，/>is to update the +.>Adaptive coefficient of individual features,/->Representing adaptive response index data, < >>Representing the characteristic variation;
performing digital pedestrian adjustment calculation on the second universe digital people according to the adaptive response coefficient data and the interaction consistency data to obtain digital pedestrian adjustment rate data;
carrying out digital human interaction strategy analysis on the second universe digital person according to the digital human behavior adjustment rate data to obtain strategy optimization index data;
And generating a corresponding first digital human interaction strategy according to the strategy optimization index data.
4. The method according to claim 1, wherein the performing anomaly detection on the target metauniverse environment according to the interactive adjustment index data to obtain metauniverse environment anomaly data, and generating corresponding target environment anomaly processing data according to the metauniverse environment anomaly data, includes:
carrying out abnormal recognition on the target meta-universe environment according to the interactive adjustment index data to obtain an abnormal state recognition result;
performing abnormal response priority assessment on the target meta-universe environment according to the abnormal state identification result to obtain abnormal response priority;
generating meta-cosmic environment abnormal data of the target meta-cosmic environment according to the abnormal state identification result and the abnormal response priority;
performing self-adaptive adjustment coefficient calculation on the meta-universe environment abnormal data to obtain abnormal self-adaptive adjustment coefficient data, and calculating abnormal influence relieving index data of the target meta-universe environment according to the abnormal self-adaptive adjustment coefficient data;
and generating target environment exception handling data corresponding to the target meta-universe environment according to the exception effect alleviation index data.
5. The method for interaction of metauniverse digital people according to claim 1, wherein the performing metauniverse behavior and preference analysis on the target user to obtain a user behavior preference mode, performing policy update on the first digital human interaction policy according to the user behavior preference mode and the target environment exception handling data, and generating a second digital human interaction policy includes:
and performing meta-universe behaviors and preference analysis on the target user through a preset behavior analysis function to obtain a user behavior preference mode, wherein the behavior analysis function is as follows:wherein->Is->The weight of the individual actions is determined,is a behavioral index, ->Representing user behavior preference patterns；
Vector coding is carried out on the user behavior preference mode to obtain a user behavior preference vector, and exception handling feature coding is carried out on the target environment exception handling data to obtain an environment exception handling vector;
calculating a correlation influence coefficient of the user behavior preference vector and the environment exception handling vector, and carrying out vector fusion on the user behavior preference vector and the environment exception handling vector according to the correlation influence coefficient to obtain a target correlation fusion vector;
Inputting the target correlation fusion vector into a preset strategy optimization network, wherein the strategy optimization network comprises the following steps: a bidirectional GRU network, a full connection layer and a genetic optimization layer;
extracting hidden features of the target correlation fusion vector through the bidirectional GRU network to obtain a target hidden feature vector;
carrying out strategy updating direction prediction on the target hidden feature vector through the full connection layer to obtain a strategy updating direction prediction result;
and carrying out strategy updating on the first digital human interaction strategy according to the strategy updating direction prediction result by using a genetic algorithm in the genetic optimization layer, and generating a second digital human interaction strategy.
6. A metauniverse digital person interaction device, characterized in that the metauniverse digital person interaction device comprises:
the system comprises a scanning module, a first universe digital person and a second universe digital person, wherein the scanning module is used for carrying out environment scanning and feature extraction on an initial universe environment to obtain a target environment feature set, and carrying out universe digital person deployment through the target environment feature set to obtain a first universe digital person;
the tracking module is used for carrying out self-adaptive tracking adjustment on a target user and the first universe digital person to obtain a second universe digital person, and carrying out interaction continuity analysis on the second universe digital person to obtain interaction continuity data; the method specifically comprises the following steps: establishing a state connection between a target user and the initial meta-universe And performing user movement monitoring on the target user according to the state connection relation to obtain user position prediction data, wherein the user movement monitoring comprises:wherein->Is the current location of the user, < >>Is speed, & lt>Is the time interval over which the data is to be stored,is user location prediction data; performing visual angle self-adaptive tracking adjustment on the first universe digital person according to the user position prediction data to obtain digital person visual angle adjustment data; determining digital person coordinate data of the first universe digital person according to the digital person visual angle adjustment data, and acquiring user coordinate data of the target user; calculating quiet position error data of the target user and the first universe digital person according to the digital person coordinate data and the user coordinate data, and carrying out self-adaptive tracking adjustment on the first universe digital person according to the position error data to obtain a second universe digital person; performing interaction distance analysis on the target user and the second universe digital person to obtain real-time interaction distance data; acquiring maximum effective interaction distance data between the target user and the initial meta-universe environment, and carrying out interaction consistency analysis on the second meta-universe digital person according to the real-time interaction distance data and the maximum effective interaction distance data to obtain interaction consistency data;
The analysis module is used for detecting the environment change of the initial meta-universe environment to obtain an updated environment feature set, and carrying out digital human interaction strategy analysis on the second meta-universe digital human according to the updated environment feature set and the interaction consistency data to obtain a first digital human interaction strategy;
the integration module is used for carrying out augmented reality integration on the initial meta-universe environment to obtain a target meta-universe environment, and carrying out virtual object display and interactive dynamic adjustment on the second meta-universe digital person according to the target meta-universe environment to obtain interactive adjustment index data; the method specifically comprises the following steps: performing virtual object positioning on the initial meta-universe environment and the second meta-universe digital person to obtain virtual object positioning data; performing augmented reality integration on the initial meta-universe environment according to the virtual object positioning data to obtain a target meta-universe environment; performing visual fusion coefficient calculation on the target universe environment and the second universe digital person to obtain visual fusion coefficient data; performing virtual object display and interactive dynamic adjustment on the second universe digital person according to the visual fusion coefficient data to obtain information coverage and interactive indexes; calculating corresponding interactive adjustment index data according to the information coverage and the interactive index;
The processing module is used for carrying out anomaly detection on the target meta-universe environment according to the interactive adjustment index data to obtain meta-universe environment anomaly data, and generating corresponding target environment anomaly processing data according to the meta-universe environment anomaly data;
and the updating module is used for carrying out meta-universe behavior and preference analysis on the target user to obtain a user behavior preference mode, carrying out strategy updating on the first digital human interaction strategy according to the user behavior preference mode and the target environment exception handling data, and generating a second digital human interaction strategy.
7. A computer device, the computer device comprising: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invoking the instructions in the memory to cause the computer device to perform the method of interaction of a metauniverse digital person as claimed in any one of claims 1 to 5.
8. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the metauniverse digital person interaction method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410001622.XA CN117519486B (en) | 2024-01-02 | 2024-01-02 | Meta-universe digital person interaction method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410001622.XA CN117519486B (en) | 2024-01-02 | 2024-01-02 | Meta-universe digital person interaction method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117519486A CN117519486A (en) | 2024-02-06 |
| CN117519486B true CN117519486B (en) | 2024-03-29 |
Family
ID=89766757
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410001622.XA Active CN117519486B (en) | 2024-01-02 | 2024-01-02 | Meta-universe digital person interaction method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117519486B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010093780A2 (en) * | 2009-02-13 | 2010-08-19 | University Of Florida Research Foundation, Inc. | Communication and skills training using interactive virtual humans |
| CN115525144A (en) * | 2022-04-18 | 2022-12-27 | 颜峰 | Multi-object interaction equipment based on virtual reality and interaction method thereof |
| CN116595398A (en) * | 2023-06-12 | 2023-08-15 | 江西开创数码科技有限公司 | Resource intelligent matching method and related device based on meta universe |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100251337A1 (en) * | 2009-03-27 | 2010-09-30 | International Business Machines Corporation | Selective distribution of objects in a virtual universe |
| US11893669B2 (en) * | 2021-01-08 | 2024-02-06 | Samsung Electronics Co., Ltd. | Development platform for digital humans |
-
2024
- 2024-01-02 CN CN202410001622.XA patent/CN117519486B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010093780A2 (en) * | 2009-02-13 | 2010-08-19 | University Of Florida Research Foundation, Inc. | Communication and skills training using interactive virtual humans |
| CN115525144A (en) * | 2022-04-18 | 2022-12-27 | 颜峰 | Multi-object interaction equipment based on virtual reality and interaction method thereof |
| CN116595398A (en) * | 2023-06-12 | 2023-08-15 | 江西开创数码科技有限公司 | Resource intelligent matching method and related device based on meta universe |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117519486A (en) | 2024-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110460837A (en) | With central fovea display and the electronic equipment for watching prediction attentively | |
| EP1768058B1 (en) | Information processing apparatus and control method therefor | |
| JP2021503662A (en) | Neural network model training | |
| US20190220673A1 (en) | People flow estimation device, people flow estimation method, and recording medium | |
| KR20150057424A (en) | A system and method for interaction with augmented reality avatar | |
| EP3392745B1 (en) | Multi-device virtual reality, artifical reality and mixed reality analytics | |
| US20180268738A1 (en) | Systems and methods for augmented reality-based service delivery | |
| CN113661520A (en) | Modifying the appearance of hair | |
| KR20160114996A (en) | personalized sports service providing method and apparatus thereof | |
| US11657334B2 (en) | Techniques for deriving and/or leveraging application-centric model metric | |
| WO2020041893A1 (en) | Image processing methods and systems | |
| CN117519486B (en) | Meta-universe digital person interaction method, device, equipment and storage medium | |
| KR102509698B1 (en) | Dancing smart robot studied dance gesture | |
| Gonzalez et al. | Sensorimotor Simulation of Redirected Reaching Using Stochastic Optimal Feedback Control | |
| CN112085223A (en) | Guidance system and method for mechanical maintenance | |
| JP2005071285A (en) | Collision detection method that change detail degree according to interaction in space and virtual space formation device using its method | |
| CN113176822A (en) | Virtual user detection | |
| JP7164008B2 (en) | Data generation method, data generation device and program | |
| JP2021082000A (en) | Learned model generation method, computer program, information processing method, and information processing device | |
| Fletcher et al. | Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures | |
| JP2020201674A (en) | Video analyzer and control method therefor and program | |
| Larey et al. | Facial Expression Re-targeting from a Single Character | |
| US20230169742A1 (en) | Entirely virtual process for optometric values measurements | |
| RU2788482C2 (en) | Training of neural network model | |
| CN116993929B (en) | Three-dimensional face reconstruction method and device based on human eye dynamic change and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |