Face recognition application method and system combined with intelligent glasses

Abstract

The invention discloses a face recognition application method and system combined with intelligent glasses. The method comprises the following steps: collecting multi-dimensional data based on intelligent glasses, the multi-dimensional data comprising a facial expression sequence, an emotion curve and a voice keyword; judging an acquisition frequency difference of the data of each dimension through a preset threshold value to obtain a frequency adjustment coefficient; performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient; and marking a timestamp label and a scene label on the standardized multi-dimensional data, carrying out scattered storage on data fragments through an index mechanism of a distributed database, and obtaining a unique identifier after storage and the like. According to the method, multi-dimensional data such as facial expressions, emotions and voices are collected through the intelligent glasses, and intelligent adjustment and standardization processing are performed on the sampling frequency. And efficient storage and retrieval of mass data are realized by utilizing a distributed database and a time axis alignment algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of smart glasses, and in particular to a face recognition application method and system combined with smart glasses. Background Art

[0002] Smart glasses face a complex technical challenge in multidimensional data processing: how to effectively collect, process and display a large amount of heterogeneous data to enhance the user's interpersonal interaction experience. This problem involves multiple aspects: First, smart glasses need to capture multi-dimensional information such as facial expressions, emotions and voice at the same time. These data types and collection frequencies are different. How to coordinate and standardize these data is a difficult problem. Secondly, the storage and rapid retrieval of massive multimodal data pose another challenge, especially how to achieve efficient data management under limited device resources. Furthermore, time alignment and correlation analysis between different types of data is also a thorny issue, especially when extracting interaction records of specific people. In addition, how to extract meaningful interaction trends and summary information from these complex data and present them in real time and appropriately on the glasses lens is also an important technical difficulty. These sub-problems together constitute a larger technical contradiction: how to balance the comprehensiveness of data, the real-time processing and the accuracy of display under limited computing and storage resources to maximize the application value of smart glasses in interpersonal interaction analysis and decision-making assistance. Summary of the invention

[0003] In view of this, the purpose of the present invention is to propose a face recognition application method and system combined with smart glasses, which collects multi-dimensional data such as facial expressions, emotions and voices through smart glasses, and intelligently adjusts and standardizes the sampling frequency. Using a distributed database and a time axis alignment algorithm, efficient storage and retrieval of massive data are achieved.

[0004] According to one aspect of the present invention, a face recognition application method combined with smart glasses is provided, the method comprising: Collect multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotional curves, and voice keywords; determine the collection frequency difference of each dimensional data through a preset threshold to obtain a frequency adjustment coefficient; Performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient; The standardized multi-dimensional data is labeled with timestamps and scene tags, and the data fragments are stored in a dispersed manner through the index mechanism of the distributed database to obtain a unique identifier after storage; If a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set; According to the timestamp labels in the preliminary retrieval set, the offsets of the starting and ending points of each data segment are calculated through the timeline alignment algorithm, and it is determined whether the offset exceeds the preset threshold. The sequence of data segments after synchronization adjustment is determined and displayed to the user through smart glasses.

[0005] In the above technical solution, smart glasses use built-in cameras, microphones and other sensors to collect multi-source data such as facial expression sequences, emotional curves, and voice keywords. These data depict the characteristics of the identified object from different angles and can more comprehensively reflect its characteristics compared to a single data dimension. In order to keep the multi-dimensional data consistent on the time axis, the system determines the difference in the collection frequency of each dimensional data through a preset threshold, generates a frequency adjustment coefficient, and then implements sampling rate standardization processing on the multi-dimensional data, laying the foundation for subsequent data analysis.

[0006] After standardized processing, the multi-dimensional data will be labeled with timestamps and scene tags. Subsequently, the system uses the indexing mechanism of the distributed database to disperse the data fragments with tags and obtain the unique identifier after storage. With high scalability, high availability and high performance, the distributed database can efficiently store and manage massive multi-dimensional data. When a retrieval request is triggered, the system queries the relevant data fragments from the distributed database in parallel according to the tag combination specified by the user, and quickly obtains the preliminary retrieval set. In order to further improve the efficiency of data retrieval, the system will calculate the offset of the starting point and the ending point of each data fragment according to the timestamp tag in the preliminary retrieval set through the timeline alignment algorithm, and determine whether the offset exceeds the preset threshold, so as to determine the sequence of data fragments after synchronization adjustment. Finally, the system displays the sequence of data fragments to the user through smart glasses. The timeline alignment algorithm ensures the synchronization of data of different dimensions in time, making the data displayed to the user more accurate and complete.

[0007] Compared with traditional face recognition methods, this application method combines multiple data dimensions such as facial expression sequences, emotional curves, and voice keywords, and can more comprehensively capture and analyze the characteristics of the identified object, thereby improving the accuracy and robustness of face recognition. By standardizing the sampling rate and aligning the time axis of multi-dimensional data, data of different dimensions can be analyzed and displayed in a unified time frame, improving the flexibility and efficiency of data processing. By using the indexing mechanism of the distributed database, a large number of multi-dimensional data fragments can be quickly stored and retrieved, meeting the requirements of real-time and large-scale data processing. The data fragment sequence is displayed to the user through smart glasses, providing an intuitive and convenient interaction method, enhancing the user's perception and understanding of the face recognition results.

[0008] In some embodiments, multi-dimensional data is collected based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotional curves, and voice keywords; the collection frequency difference of each dimensional data is determined by a preset threshold to obtain a frequency adjustment coefficient, including: The expression sequence, emotion curve and voice keywords are processed respectively by the feature value extraction method to obtain the feature value of each dimension data; By comparing the collection frequency of each dimension data through a preset threshold, the frequency difference between the expression sequence, the emotion curve and the voice keyword is determined to obtain the frequency difference value; The support vector machine algorithm is used to classify the frequency difference values, determine the frequency adjustment direction of expression sequences, emotional curves and voice keywords, and obtain the preliminary adjustment coefficients; Based on the collection frequency and frequency difference values ​​of multi-dimensional data, the correlation between expression sequences, emotional curves and voice keywords is analyzed through the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient; If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the collection frequency of the expression sequence, the emotion curve and the voice keyword is dynamically adjusted through the linear regression algorithm to obtain the adjusted collection frequency value; Reprocess the multi-dimensional data according to the adjusted acquisition frequency value and the feature value extraction result, determine whether the data acquisition of the smart glasses meets the preset threshold requirements, and obtain an updated feature value set; Through the updated feature value set and frequency adjustment coefficient, the final collection frequency of expression sequence, emotion curve and voice keyword is determined to obtain the optimized multi-dimensional data collection plan.

[0009] In the above technical solution, the smart glasses use built-in sensors such as cameras and microphones to collect multi-source data such as facial expression sequences, emotional curves, and voice keywords. These data are processed separately by the feature value extraction method to obtain the feature values ​​of each dimension of data, which provides a basis for subsequent data processing and analysis. In order to determine the frequency difference between the expression sequence, emotional curve and voice keyword, the system compares the acquisition frequency of each dimension of data by a preset threshold to obtain a frequency difference value. Then the support vector machine algorithm is used to classify the frequency difference value, determine the frequency adjustment direction of each dimension of data, and obtain a preliminary adjustment coefficient. For the acquisition frequency and frequency difference value of multi-dimensional data, the correlation between the expression sequence, emotional curve and voice keyword is analyzed by the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient. This step takes into account the mutual influence between data of different dimensions and improves the rationality of frequency adjustment. If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the acquisition frequency of the expression sequence, emotional curve and voice keyword is dynamically adjusted by the linear regression algorithm to obtain the adjusted acquisition frequency value. According to the adjusted acquisition frequency value and the feature value extraction result, the multi-dimensional data is reprocessed to determine whether the data acquisition of the smart glasses meets the preset threshold requirements, and an updated feature value set is obtained. Through the updated feature value set and frequency adjustment coefficient, the final collection frequency of expression sequence, emotion curve and voice keyword is determined to obtain the optimized multi-dimensional data collection plan.

[0010] Compared with traditional face recognition application methods, this application method collects data of multiple dimensions such as facial expression sequences, emotional curves, and voice keywords, which can more comprehensively capture and analyze the characteristics of the identified objects, providing richer information for subsequent data processing and analysis. Through the combined use of multiple algorithms, such as support vector machines, random forests, linear regression, etc., the frequency of data collection is dynamically adjusted and optimized, which improves the flexibility and efficiency of data processing. Using the index mechanism of distributed databases, a large number of multi-dimensional data fragments can be quickly stored and retrieved, meeting the needs of real-time and large-scale data processing.

[0011] In some embodiments, performing sampling rate normalization processing on the multi-dimensional data based on the adjustment coefficient includes: Acquire multi-dimensional data and extract the acquisition frequency of each dimensional data, and standardize the sampling rate through the frequency adjustment coefficient to obtain a data set with uniform frequency; Identify dimensional data with a higher collection frequency from a data set with uniform frequency, use interpolation generation technology to calculate supplementary data points and add them to the data set; The supplementary data points generated by interpolation are merged with the original data points to obtain a new data set containing the data point generation results; For the timestamp labels in the new data set, a consistency determination operation is performed to determine if the timestamp deviates from the preset threshold and then adjust it to be consistent; According to the timestamp label after consistency is determined, the data set is processed before storage to generate structured data suitable for fragmented storage; Acquire structured data and verify the matching degree between the acquisition frequency and the standardized sampling rate to determine whether the data point generation meets the requirements; The structured data is fragmented and stored in the order of timestamp labels to obtain the final processed data sharding result.

[0012] In the above technical solution, first, multi-dimensional data is collected and the collection frequency of each dimensional data is determined, and then the sampling rate is standardized using the frequency adjustment coefficient to generate a data set with a uniform frequency. For dimensional data with a higher collection frequency, interpolation generation technology is used to calculate and supplement data points to ensure the alignment of each dimensional data on the time axis. The supplementary data points generated by interpolation are merged with the original data points to form a new data set containing the data point generation results. For the timestamp labels in the new data set, a consistency determination operation is performed. If the timestamp deviates from the preset threshold, a corresponding adjustment is made to ensure the accuracy of the data in time. According to the timestamp labels after consistency determination, the data set is pre-processed for storage to generate structured data suitable for fragmented storage. By fragmenting the structured data in the order of the timestamp labels, the final processed data sharding result is obtained, which effectively improves the efficiency of data storage and retrieval.

[0013] Compared with traditional face recognition application methods, this application method ensures the consistency and integrity of multi-dimensional data on the time axis through frequency adjustment and interpolation generation technology, providing a reliable data foundation for subsequent data analysis and processing. Processing data into structured data suitable for fragmented storage and storing them in timestamp tag order not only improves the efficiency of data storage, but also speeds up retrieval, and can meet the real-time processing and query requirements of large-scale data. In addition, this method can adapt to multi-dimensional data with different acquisition frequencies. By dynamically adjusting the sampling rate and interpolating to generate data points, the system has good flexibility and adaptability, and can cope with various complex data acquisition scenarios.

[0014] In some embodiments, the standardized multi-dimensional data is labeled with a timestamp and a scene label, and the data segments are stored in a dispersed manner through an index mechanism of a distributed database to obtain a unique identifier after storage, including: The sharding strategy of the distributed database is used to perform decentralized storage on the marked data fragments and generate a preliminary mapping table of storage locations; For the storage location in the preliminary mapping table, obtain the unique identifier generated by the index mechanism to determine the location address of each data fragment; If the number of data fragments exceeds a preset threshold, the storage nodes in the distributed database are reallocated through the consistent hashing algorithm to obtain an adjusted mapping table; According to the adjusted mapping table, the storage node address corresponding to each unique identifier is obtained to determine whether the data fragments are evenly distributed; Perform classification verification on the label assignment of data segments through the support vector machine algorithm, obtain classification results, and determine the accuracy of timestamp labels and scene labels; For the classification results, a logging mechanism is used to store the unique identifier and the corresponding data fragment information to obtain a complete storage index record.

[0015] In the above technical solution, the marked data fragments are stored in a decentralized manner, and a preliminary mapping table of storage locations is generated. For the storage locations in the preliminary mapping table, a unique identifier is generated through an index mechanism to determine the precise location address of each data fragment. There are many methods for generating unique identifiers, such as ULID (Universally Unique Lexicographically Sortable Identifier) ​​based on timestamps and randomness, which has excellent sorting characteristics and uniqueness in distributed systems. If the number of data fragments exceeds the preset threshold, the consistent hashing algorithm is used to reallocate the storage nodes in the distributed database to obtain an adjusted mapping table. The consistent hashing algorithm can minimize data migration when nodes are dynamically increased or decreased, ensuring balanced distribution of data. According to the adjusted mapping table, the storage node address corresponding to each unique identifier is obtained, and it is determined whether the data fragments are evenly distributed. This process ensures the reasonable layout of data in the distributed database and effectively avoids the waste of resources caused by overloading some nodes and idleness of other nodes. The label allocation of the data fragments is classified and verified by the support vector machine algorithm to obtain the classification results to determine the accuracy of the timestamp label and the scene label. As an efficient supervised learning algorithm, support vector machine can perform data classification and regression analysis tasks to ensure the correctness of label assignment. For the classification results, a logging mechanism is used to store unique identifiers and their corresponding data fragment information to generate complete storage index records. Logging helps track the storage and access history of data, facilitating subsequent data management and maintenance operations.

[0016] Compared with traditional face recognition application methods, this application method uses the sharding strategy and indexing mechanism of distributed databases to enable data fragments to be quickly stored and retrieved, significantly improving the system's response speed and processing efficiency. The application of consistent hashing algorithm ensures the balanced distribution of data in distributed databases, effectively avoids the problems of data skew and node overload, and enhances the stability and reliability of the system. This method can adapt to the growth of data volume and the dynamic changes of nodes, and realizes flexible expansion and optimization of the system by reallocating storage nodes and adjusting mapping tables. The classification verification of label allocation by support vector machine algorithm and the use of logging mechanism ensure the integrity of data and the accuracy of label allocation, providing a solid and reliable data foundation for subsequent data analysis and application.

[0017] In some embodiments, if a search request is triggered, relevant data segments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set, including: If a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination, and the storage identifiers of each dimension of data are extracted using multi-threaded loading technology to obtain the complete data of the initial search set; By initially retrieving the complete data of the collection, the storage identifiers corresponding to the data of each dimension are obtained. According to the matching degree between the storage identifiers and the label combination, the cosine similarity algorithm is used to determine the relevance of the data fragments, and the filtered data subset is obtained. Extract data fragments from the filtered data subsets, group the data fragments using the K-means clustering algorithm based on the multi-dimensional characteristics of the data fragments, and determine the grouped data fragment set; Obtain a set of grouped data segments, and use a decision tree algorithm to determine the priority order of the data segments based on the correlation between the storage identifiers of each dimension of data and the user-specified label combination, to obtain a sorted data segment list; Through the sorted data fragment list, multi-threaded loading technology is used to perform parallel verification on the data fragments according to the matching degree between the grouping characteristics of the data fragments and the label combination, and the verified data fragment set is determined; Extract complete data from the verified data fragment set, determine the access popularity of the data fragment based on the query log of the distributed database and the trigger frequency of the retrieval request, and obtain a subset of high-popularity data fragments; Obtain a subset of high-frequency data fragments, update the storage identifiers in the distributed database through parallel query technology, and use multi-threaded loading technology to synchronize the latest status of data in each dimension to obtain an optimized retrieval set.

[0018] In the above technical solution, when a search request is triggered, the relevant data fragments are queried in parallel from the distributed database according to the tag combination specified by the user. The storage identifiers of the data of each dimension are extracted by multi-thread loading technology to obtain the complete data of the preliminary search set. The storage identifiers corresponding to the data of each dimension are obtained by the complete data of the preliminary search set. According to the matching degree between the storage identifier and the tag combination, the cosine similarity algorithm is used to judge the relevance of the data fragments to obtain the filtered data subset. The cosine similarity algorithm can effectively measure the similarity between two vectors, and is used here to judge the relevance between the data fragments and the tag combination specified by the user. The data fragments are extracted from the filtered data subset, and the K-means clustering algorithm is used to group the data fragments according to the multi-dimensional characteristics of the data fragments to determine the grouped data fragment set. Then, according to the correlation between the storage identifiers of the data of each dimension and the tag combination specified by the user, the decision tree algorithm is used to judge the priority order of the data fragments to obtain a sorted data fragment list. The K-means clustering algorithm can divide the data into different groups, which is convenient for subsequent classification processing; the decision tree algorithm can sort the data according to certain rules to improve the pertinence of the search results. Through the sorted data fragment list, multi-threaded loading technology is used to perform parallel verification of the data fragments according to the matching degree between the grouping characteristics of the data fragments and the label combination, and the verified data fragment set is determined. The complete data is extracted from the verified data fragment set, and the access popularity of the data fragment is judged according to the query log of the distributed database and the trigger frequency of the retrieval request to obtain a subset of high-hot data fragments. This step ensures the accuracy and timeliness of the retrieval results, and presents more user-friendly and popular data to users. Obtain a subset of high-hot data fragments, update the storage identifiers in the distributed database through parallel query technology, and use multi-threaded loading technology to synchronize the latest status of data in each dimension to obtain an optimized retrieval set. This step ensures the dynamic update and optimization of the retrieval set, so that users can obtain the latest data information.

[0019] Compared with traditional face recognition methods, this application method can quickly retrieve relevant data fragments from distributed databases through parallel query and multi-threaded loading technology, improve the system's response speed and retrieval efficiency, and meet users' needs for real-time retrieval. A variety of data processing and analysis methods such as cosine similarity algorithm, K-means clustering algorithm and decision tree algorithm are used to screen, group and sort the retrieval results, ensuring the accuracy and relevance of the retrieval results, and better meeting users' personalized needs. This retrieval method can adapt to the retrieval needs of large-scale data, and can flexibly respond to different application scenarios and data types by adjusting algorithm parameters and strategies, with strong scalability and adaptability. By judging the access popularity of data fragments, high-heat data is presented to users first, which improves the practicality and timeliness of retrieval results, allowing users to obtain popular and important information more quickly.

[0020] In some embodiments, according to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by a time axis alignment algorithm, and it is determined whether the offsets exceed a preset threshold, and the sequence of data segments after synchronization adjustment is determined, including: Obtaining a timestamp tag from the preliminary search set, determining the time position of each data segment by parsing the timestamp tag, and obtaining an initial data segment set containing time information; For the initial data segment set, a time axis alignment algorithm is used to calculate the start point offset and the end point offset of each data segment to obtain an offset calculation result set; Obtain a set of offset calculation results, and determine whether the start point offset and the end point offset of each data segment exceed the preset threshold by comparing them with the preset threshold, and obtain an offset overlimit mark set; According to the offset overlimit identification set, a synchronous adjustment method is used to process the data segments exceeding the preset threshold, and the time position of the adjusted data segments is determined to obtain a synchronously adjusted data segment set; By synchronizing the adjusted data segment set, extracting the timestamp label and the adjusted time position, determining the continuity between the data segments, and obtaining a continuity verification result set; For the continuity verification result set, a sequence recombination algorithm is used to integrate the data fragments, determine the final fragment sequence, and obtain an ordered data fragment sequence set; Through the ordered data fragment sequence set, the support vector machine algorithm is applied to verify the sequence integrity, determine whether there is a time alignment anomaly, and obtain the final synchronized data fragment sequence.

[0021] In the above technical solution, when a search request is triggered, the system obtains a timestamp tag from the preliminary search set, and determines the time position of each data segment by parsing the timestamp tag, and obtains an initial data segment set containing time information. For the initial data segment set, the time axis alignment algorithm is used to calculate the starting point offset and the ending point offset of each data segment to obtain an offset calculation result set. The time axis alignment algorithm can accurately calculate the offset of different data segments on the time axis, providing a basis for subsequent synchronization adjustment. After obtaining the offset calculation result set, by comparing with the preset threshold, it is determined whether the starting point offset and the ending point offset of each data segment exceed the preset threshold, and an offset overlimit identification set is obtained. This step determines which data segments need to be synchronized. According to the offset overlimit identification set, the synchronization adjustment method is used to process the data segments that exceed the preset threshold, determine the time position of the adjusted data segments, and obtain a synchronized adjusted data segment set. Through adjustment, each data segment is synchronized on the time axis, which improves the consistency of the data.

[0022] Through the synchronized and adjusted data fragment set, the timestamp label and the adjusted time position are extracted, the continuity between the data fragments is judged, and the continuity verification result set is obtained. For the continuity verification result set, the sequence reorganization algorithm is used to integrate the data fragments, determine the final fragment sequence, and obtain the ordered data fragment sequence set. This step ensures the continuity and logic of the data fragments in time. Through the ordered data fragment sequence set, the support vector machine algorithm is applied to verify the sequence integrity, determine whether there is a time alignment anomaly, and obtain the final synchronized and adjusted data fragment sequence. The support vector machine algorithm can effectively detect possible anomalies in the sequence, ensuring the integrity and reliability of the final data fragment sequence.

[0023] Compared with traditional face recognition application methods, this application method can accurately calculate and adjust the offset of the starting and ending points of each data segment through the time axis alignment algorithm and synchronization adjustment method, ensuring the synchronization of multi-dimensional data in time and improving the consistency and accuracy of the data. The sequence recombination algorithm is used to integrate the data segments, which can effectively determine the continuity between data segments and determine the final segment sequence, so that the data is logical and coherent in time, which is convenient for subsequent analysis and display. The support vector machine algorithm is used to verify the sequence integrity, which can accurately detect time alignment anomalies, ensure the integrity and reliability of the data segment sequence after the final synchronization adjustment, and provide users with high-quality data information. By presetting the threshold to determine whether the offset exceeds the limit, the threshold can be flexibly set and adjusted according to different application scenarios and needs, which improves the adaptability and flexibility of the system.

[0024] In some embodiments, according to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by a time axis alignment algorithm, it is determined whether the offset exceeds a preset threshold, a sequence of data segments after synchronization adjustment is determined, and the sequence of data segments is displayed to the user through smart glasses, and then the following is further included: Extract the interaction records of specific people from multi-dimensional data, use linked list data structure to connect audio clips, image snapshots and text annotations in chronological order, record the start and end time of each node through hash index mechanism, ensure the synchronization of different types of data, and determine the timeline storage structure; According to the timeline storage structure, the linked list nodes are scanned regularly to obtain the numerical sequence of interaction frequency and duration changes, and the change trend is fitted through the linear regression algorithm to generate a summary of interaction data for a specific time period; If a specific person is recognized, the latest node is loaded from the end of the linked list, and a timeline summary is generated based on the change trend. This is displayed through the display module of the smart glasses. The display content is judged based on the keywords and context information of the current scene to obtain the final output sequence.

[0025] In the above technical solution, first, the interaction records of specific characters are extracted from the multi-dimensional data, and the audio clips, image snapshots and text annotations are connected in chronological order using a linked list data structure. The start time and end time of each node are recorded through a hash index mechanism to ensure the synchronization of different types of data and determine the timeline storage structure. This step integrates different types of interaction data in chronological order to facilitate subsequent analysis and processing.

[0026] Next, the linked list nodes are scanned regularly according to the timeline storage structure to obtain the numerical sequence of interaction frequency and duration changes. The linear regression algorithm is used to fit the change trend and generate a summary of the interaction data for a specific time period. The linear regression algorithm can effectively analyze the change trend of the data and provide users with a concise summary of the interaction data. If a specific person is identified, the latest node is loaded from the end of the linked list, and a timeline summary is generated in combination with the change trend. The display module of the smart glasses is used to display the content, and the keywords and context information of the current scene are used to determine whether the displayed content matches, and the final output sequence is obtained. In this step, the generated timeline summary is displayed to the user through the smart glasses, and the content matching judgment is performed based on the current scene information to ensure the accuracy and relevance of the displayed content.

[0027] Through the linked list data structure and hash index mechanism, different types of data are connected and indexed in chronological order, ensuring the synchronization and integration of the data, facilitating subsequent analysis and display.

[0028] Compared with traditional face recognition application methods, this application method uses a linear regression algorithm to fit the numerical sequence of interaction frequency and duration changes, which can quickly generate a summary of interaction data for a specific time period, provide users with a concise and clear data summary, and improve the readability and practicality of information. After identifying a specific person, this method can load the latest node from the end of the linked list in a timely manner, generate a timeline summary based on the change trend, and display it through the display module of smart glasses, providing users with real-time interactive information and enhancing the user experience. Matching and judging the displayed content based on the keywords and contextual information of the current scene ensures the accuracy and relevance of the displayed content, avoids the display of irrelevant or inappropriate information, and improves the intelligence level of the system.

[0029] In some embodiments, the interaction records of specific characters are extracted from multi-dimensional data, and the audio clips, image snapshots, and text annotations are connected in chronological order using a linked list data structure. The start time and end time of each node are recorded through a hash index mechanism to ensure the synchronization of different types of data, and determine the timeline storage structure, including: Obtain the interaction records of specific people from multi-dimensional data, use a preset threshold to filter irrelevant data, and concatenate audio clips, image snapshots, and text annotations in chronological order through a linked list structure to obtain a preliminary time series; For the preliminary time series, the start and end time of each linked list node is recorded through the hash index mechanism, and an index table containing timestamps is generated to determine the synchronization of each data fragment; According to the start time and end time in the index table, the time span of the audio segment is obtained. If the time span exceeds a preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence; Through the adjusted audio sequence, we obtain the image snapshots and text annotations in the corresponding time order, and use the hash index to match the start time and end time to generate a synchronized multimodal data stream. For synchronized multimodal data streams, a linked list structure is used to rearrange audio clips, image snapshots, and text annotations, and the complete sequence is recorded through a timeline storage structure to obtain an ordered data set; Extract interaction records of specific people from ordered data sets, use support vector machine algorithms to classify text annotations, determine interaction types, and generate classified interaction timelines; According to the classified interaction timeline, the correlation between the interaction records and the time sequence is analyzed through the random forest algorithm, the final timeline storage structure is determined, and a multi-dimensional synchronization sequence is output.

[0030] In the above technical solution, first, the interaction records of specific people are obtained from the multi-dimensional data, and irrelevant data is filtered using a preset threshold. The audio clips, image snapshots and text annotations are connected in chronological order through a linked list structure to obtain a preliminary time series. For the preliminary time series, the start time and end time of each linked list node are recorded through a hash index mechanism, and an index table containing timestamps is generated to determine the synchronization of each data fragment. The hash index mechanism can quickly locate and retrieve data fragments, ensuring the synchronization of different types of multimodal data in time.

[0031] Next, the time span of the audio clip is obtained according to the start time and end time in the index table. If the time span exceeds the preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence. Through the adjusted audio sequence, image snapshots and text annotations of the corresponding time sequence are obtained, and the hash index is used to match the start time and end time to generate a synchronized multimodal data stream. For the synchronized multimodal data stream, the linked list structure is used to rearrange the audio clips, image snapshots and text annotations, and the complete sequence is recorded through the timeline storage structure to obtain an ordered data set. The timeline storage structure can clearly display the time sequence and change process of the data, which is convenient for subsequent analysis and display.

[0032] Then, the interaction records of specific people are extracted from the ordered data set, and the support vector machine algorithm is used to classify the text annotations, determine the interaction type, and generate the classified interaction timeline. The support vector machine algorithm can effectively classify the text annotations and determine the type of interaction, providing more detailed information for subsequent analysis. According to the classified interaction timeline, the random forest algorithm is used to analyze the correlation between the interaction records and the time sequence, determine the final timeline storage structure, and output a multi-dimensional synchronization sequence. The random forest algorithm can comprehensively consider multiple factors, analyze the correlation between the interaction records and the time sequence, and improve the rationality and accuracy of the timeline storage structure.

[0033] Compared with traditional face recognition application methods, this application method synchronizes and integrates different types of multimodal data in chronological order through hash index mechanism and linked list structure, ensuring data consistency and integrity, and facilitating subsequent analysis and processing. The use of linked list structure and timeline storage structure can efficiently store and manage multi-dimensional data, improve data access and retrieval efficiency, and meet the needs of large-scale data processing. By presetting thresholds to filter irrelevant data and judge the time span of audio clips, the thresholds can be flexibly set and adjusted according to different application scenarios and needs, improving the adaptability and flexibility of the system. The application of support vector machine algorithm and random forest algorithm can accurately classify interaction types and analyze the correlation between interaction records and chronological order, providing users with more in-depth data analysis results and enhancing the intelligence level of the system.

[0034] In some embodiments, the method of periodically scanning the linked list nodes according to the timeline storage structure, obtaining the numerical sequence of interaction frequency and duration changes, fitting the change trend through a linear regression algorithm, and generating an interaction data summary for a specific time period includes: Through the storage structure in the timeline structure, the linked list nodes are scanned regularly to obtain the original data of interaction frequency and duration changes, and the initial value sequence is obtained; According to the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of the interaction frequency over time to determine the first trend sequence; For the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of duration over time to obtain the second trend sequence; From the first trend sequence and the second trend sequence, obtain the trend slope and intercept in a specific time period to determine the correlation strength of the interaction frequency and duration changes; Through the correlation strength and the time period constraint, the first trend sequence and the second trend sequence are weightedly fused to obtain a comprehensive trend sequence; Using a comprehensive trend series, we extracted the peak and mean values ​​of the interaction frequency and duration changes within the time period and generated a preliminary data summary; Based on the preliminary data summary, combined with the linked list node distribution in the timeline structure, the interaction density in a specific time period is calculated to obtain the final data summary.

[0035] In the above technical solution, firstly, the storage structure in the timeline structure is used to periodically scan the linked list nodes to obtain the raw data of interaction frequency and duration changes and obtain the initial numerical sequence. This step provides basic data for subsequent trend fitting and data summary generation.

[0036] Next, based on the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of the interaction frequency over time to determine the first trend sequence. The linear regression algorithm can find the linear relationship between the interaction frequency and time in the data, and provide the changing trend of the interaction frequency for subsequent analysis. For the initial numerical sequence, the linear regression algorithm is also used to fit the changing trend of the duration change over time to obtain the second trend sequence. This step is used to find the linear relationship between the duration change and time, and provide the trend information of the duration change.

[0037] Then, from the first trend sequence and the second trend sequence, the trend slope and intercept in a specific time period are obtained to determine the correlation strength between the interaction frequency and duration changes. By combining the correlation strength and the time period constraint, the first trend sequence and the second trend sequence are weighted and fused to obtain a comprehensive trend sequence. This step comprehensively considers the trends of interaction frequency and duration changes, and obtains a more comprehensive comprehensive trend through weighted fusion.

[0038] Finally, the peak and mean values ​​of the interaction frequency and duration changes within the time period are extracted using the comprehensive trend sequence to generate a preliminary data summary. Based on the preliminary data summary, combined with the linked list node distribution in the timeline structure, the interaction density of a specific time period is calculated to obtain the final data summary. This step generates a preliminary data summary by combining the key indicators in the comprehensive trend sequence and calculates the interaction density in combination with the linked list node distribution to obtain a more detailed and accurate final data summary.

[0039] Compared with traditional face recognition application methods, this application method not only considers the changing trend of interaction frequency, but also analyzes the trend of duration change, and obtains a comprehensive trend sequence through weighted fusion, which can more comprehensively reflect the changes in interaction data and provide users with richer information. The linear regression algorithm is used to perform trend fitting on the interaction frequency and duration changes respectively, which can accurately find the linear relationship in the data, determine the trend sequence, and provide a reliable basis for the subsequent data summary generation. The constraints of a specific time period are considered in the analysis process, and the interaction data within a specific time period can be flexibly selected and analyzed according to user needs or application scenarios, which improves the flexibility and practicality of the system. By combining the distribution of linked list nodes in the timeline structure and calculating the interaction density of a specific time period, it can more accurately reflect the frequency of interaction and the distribution of data, and provide users with more in-depth data analysis results.

[0040] According to another aspect of the present invention, a face recognition application system combined with smart glasses is provided, based on the above method; the system comprises: The data collection and feature extraction module is used to collect multi-dimensional data based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotional curves, and voice keywords; the collection frequency difference of each dimensional data is determined by a preset threshold to obtain a frequency adjustment coefficient; A frequency adjustment coefficient calculation module, used for performing sampling rate standardization processing on multi-dimensional data based on the adjustment coefficient; The sampling rate standardization processing module is used to add timestamp tags and scene tags to the standardized multi-dimensional data, store the data fragments in a dispersed manner through the index mechanism of the distributed database, and obtain the unique identifier after storage; A preliminary search module is used to query relevant data fragments from the distributed database in parallel according to the user-specified tag combination to obtain a preliminary search set if a search request is triggered; The data retrieval and display module is used to calculate the starting and ending point offsets of each data segment based on the timestamp tags in the preliminary retrieval set through the time axis alignment algorithm, determine whether the offset exceeds a preset threshold, determine the data segment sequence after synchronization adjustment, and display the data segment sequence to the user through smart glasses.

[0041] In the above technical solution, in order to better use the above method, the present application proposes a face recognition application system combined with smart glasses, and each module corresponds to each step of the above method. The specific principle has been described above and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of an embodiment of a face recognition application method combined with smart glasses of the present invention; Figure 2 It is a structural schematic diagram of an embodiment of a face recognition application system combined with smart glasses of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0045] The present invention provides a face recognition application method and system combined with smart glasses. The method collects multi-dimensional data such as facial expressions, emotions and voices through smart glasses, and intelligently adjusts and standardizes the sampling frequency. The distributed database and timeline alignment algorithm are used to achieve efficient storage and retrieval of massive data.

[0046] Embodiment 1 See also Figure 1 , a face recognition application method combined with smart glasses, the method comprising: S1. Collect multi-dimensional data based on smart glasses, where the multi-dimensional data includes facial expression sequences, emotion curves, and voice keywords; determine the collection frequency difference of each dimensional data by a preset threshold to obtain a frequency adjustment coefficient; In this embodiment, multi-dimensional data is collected based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotional curves, and voice keywords; the collection frequency difference of each dimensional data is determined by a preset threshold to obtain a frequency adjustment coefficient, including: S11. The expression sequence, emotion curve and voice keyword are processed respectively by the feature value extraction method to obtain the feature value of each dimension data; (For the collection of facial expression sequence, the built-in camera of the smart glasses can be used to capture images, and then the image processing algorithm or model (such as convolutional neural network based on deep learning, etc.) is used to identify and extract expression features to obtain facial expression sequence data. For the collection of emotion curves, the built-in camera of the smart glasses captures images and extracts expressions through images. For the collection of voice keywords, it is explained that the voice signal is collected through the microphone of the smart glasses, and then the voice recognition technology and keyword extraction algorithm are used to obtain the voice keyword data.) S12, comparing the collection frequency of each dimension data through a preset threshold, determining the frequency difference between the expression sequence, the emotion curve and the voice keyword, and obtaining a frequency difference value; S13, using a support vector machine algorithm to classify the frequency difference values, determine the frequency adjustment direction of the expression sequence, the emotion curve and the voice keyword, and obtain a preliminary adjustment coefficient; S14, for the collection frequency and frequency difference value of the multi-dimensional data, the correlation between the expression sequence, the emotion curve and the voice keyword is analyzed by the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient; S15. If the optimized value of the frequency adjustment coefficient exceeds a preset threshold, the collection frequency of the expression sequence, the emotion curve, and the voice keyword is dynamically adjusted by a linear regression algorithm to obtain an adjusted collection frequency value; S16, reprocessing the multi-dimensional data according to the adjusted collection frequency value and the feature value extraction result, determining whether the data collection of the smart glasses meets the preset threshold requirements, and obtaining an updated feature value set; S17. Determine the final collection frequency of the expression sequence, emotion curve and voice keyword through the updated feature value set and frequency adjustment coefficient, and obtain an optimized multi-dimensional data collection plan.

[0047] In this embodiment, multi-dimensional data such as expression sequence, emotion curve, and voice keywords collected by smart glasses are obtained, and the expression sequence, emotion curve, and voice keywords are processed respectively by the feature value extraction method to obtain the feature value of each dimensional data. The collection frequency of each dimensional data is compared by a preset threshold, and the frequency difference between the expression sequence, emotion curve, and voice keywords is determined to obtain the frequency difference value. The frequency difference value is classified and processed by the support vector machine algorithm to determine the frequency adjustment direction of the expression sequence, emotion curve, and voice keywords, and obtain the preliminary adjustment coefficient. For the collection frequency and frequency difference value of the multi-dimensional data, the correlation between the expression sequence, emotion curve, and voice keywords is analyzed by the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient. If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the collection frequency of the expression sequence, emotion curve, and voice keywords is dynamically adjusted by the linear regression algorithm to obtain the adjusted collection frequency value. According to the adjusted collection frequency value and the feature value extraction result, the multi-dimensional data is reprocessed to determine whether the data collection of the smart glasses meets the preset threshold requirements, and the updated feature value set is obtained. Through the updated feature value set and frequency adjustment coefficient, the final collection frequency of expression sequence, emotion curve and voice keyword is determined to obtain the optimized multi-dimensional data collection plan.

[0048] Specifically, the multi-dimensional data collected by smart glasses is first analyzed using a deep learning algorithm to analyze the expression sequence and extract key expression feature values. For example, the probability values ​​of expressions such as smiles and frowns are extracted through a convolutional neural network, and the threshold is set to 0.7. When the probability of a certain expression exceeds the threshold, it is recorded as valid expression data. Next, for the emotion curve, a time series analysis method is used to calculate the frequency and amplitude of emotion fluctuations. For example, the frequency characteristics of the emotion curve are extracted through fast Fourier transform, and the emotion fluctuation frequency threshold is set to 0.5 times per second. If the threshold is exceeded, it is considered that the emotion fluctuation is large. For voice keywords, natural language processing technology is used to extract the frequency and emotional tendency of key sentences. For example, the TF-IDF algorithm is used to calculate the frequency of keywords, and the frequency threshold is set to 5 times per minute. If the threshold is exceeded, the keyword is considered to be a high-frequency word. Finally, the collection frequency difference of each dimensional data is judged by the preset threshold. For example, the collection frequency of expression sequence is 10 times per second, emotion curve is 5 times per second, and voice keyword is 60 times per minute. The difference between the frequency of each dimension and the preset threshold is calculated to obtain the frequency adjustment coefficient. If the frequency difference of expression sequence is 3 times per second, the frequency difference of emotion curve is 0 times per second, and the frequency difference of voice keyword is 0 times per minute, the adjustment coefficients are 1.3, 1.0, and 1.0 respectively, which are used for dynamic adjustment of subsequent data collection frequency.

[0049] S2, performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient; In this embodiment, the sampling rate of the multi-dimensional data is normalized based on the adjustment coefficient, including: S21, acquiring multi-dimensional data and extracting the acquisition frequency of each dimensional data, and standardizing the sampling rate by a frequency adjustment coefficient to obtain a data set with a uniform frequency; S22, identifying dimensional data with a higher collection frequency from a data set with a uniform frequency, using interpolation generation technology to calculate supplementary data points and adding them to the data set; S23, merging the supplementary data points generated by interpolation with the original data points to obtain a new data set containing data point generation results; S24, performing consistency determination operation for the timestamp labels in the new data set, and adjusting to be consistent if the timestamp deviates from a preset threshold; S25. Perform storage pre-processing on the data set according to the timestamp tag after consistency is determined, and generate structured data suitable for fragmented storage; S26, obtaining structured data and verifying the matching degree between the acquisition frequency and the standardized sampling rate to determine whether the data point generation meets the requirements; S27. Perform fragmented storage on the structured data in the order of timestamp tags to obtain the final processed data fragmentation result.

[0050] Specifically, in multi-dimensional data processing, the adjustment coefficient is first determined according to the acquisition frequency of each dimension. Assume that the acquisition frequency of dimension A is 10 times per second, dimension B is 5 times per second, and dimension C is 2 times per second. Taking dimension C as the benchmark, its adjustment coefficient is 1, dimension B is 5, and dimension A is 5. For dimension A, a linear interpolation algorithm is used to generate supplementary data points. For example, at timestamp 0 seconds, the value of dimension A is 10, at timestamp 2 seconds, the value is 12, and the value at 1 second is 11 calculated by linear interpolation. Similarly, for dimension B, at timestamps 0 seconds and 4 seconds, the values ​​are 20 and 24 respectively, and the value at 2 seconds is 22 calculated by interpolation. After ensuring that the data points of all dimensions are aligned at the same timestamp, they are stored in fragments. For example, the data points at timestamp 0 seconds are stored as [10, 20, 5], 1 second as [11, 21, 5], and 2 seconds as [12, 22, 6]. In this way, the consistency of timestamp labels is ensured, and standardized processing of multi-dimensional data is achieved.

[0051] S3: Add timestamp tags and scene tags to the standardized multi-dimensional data, store the data fragments in a decentralized manner through the index mechanism of the distributed database, and obtain the unique identifier after storage; In this embodiment, the standardized multi-dimensional data is labeled with a timestamp and a scene label, and the data fragments are dispersedly stored through the index mechanism of the distributed database to obtain a unique identifier after storage, including: S31, using a sharding strategy of a distributed database to perform decentralized storage on the marked data fragments, and generating a preliminary mapping table of storage locations; S32, for the storage location in the preliminary mapping table, obtain a unique identifier generated by the index mechanism to determine the location address of each data fragment; S33, if the number of data fragments exceeds a preset threshold, reallocate the storage nodes in the distributed database through a consistent hashing algorithm to obtain an adjusted mapping table; S34. According to the adjusted mapping table, obtain the storage node address corresponding to each unique identifier, and determine whether the data fragments are evenly distributed; S35, performing classification verification on the label assignment of the data segments through a support vector machine algorithm, obtaining classification results, and determining the accuracy of the timestamp label and the scene label; S36. For the classification result, a log recording mechanism is used to store the unique identifier and the corresponding data segment information to obtain a complete storage index record.

[0052] Specifically, in the standardized multi-dimensional data, it is first marked by a timestamp label and a scene label. For example, the time point when the data is generated is recorded as 14:30 on October 15, 2023, and the scene label is marked as "urban traffic monitoring". Then, the index mechanism of the distributed database is used to store the data fragments in a decentralized manner. Specifically, the consistent hashing algorithm is used to map the data fragments to different storage nodes. For example, the hash function SHA-256 is used to hash the data, and the hash value is "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0". According to the hash value, the data fragments are stored on the corresponding nodes. After the storage is completed, the system automatically generates a unique identifier, such as "UUID-1234567890abcdef", which is used to identify the location of the data fragment in the distributed database. In this way, not only the efficient storage and fast retrieval of data are achieved, but also the security and reliability of the data can be ensured.

[0053] S4. If a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set; In this embodiment, if a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set, including: S41. If a search request is triggered, then the relevant data fragments are queried in parallel from the distributed database according to the tag combination specified by the user, and the storage identifiers of the data of each dimension are extracted using multi-threaded loading technology to obtain the complete data of the preliminary search set; S42, by preliminarily searching the complete data of the set, obtaining the storage identifier corresponding to each dimension of data, and using the cosine similarity algorithm to determine the relevance of the data fragments according to the matching degree between the storage identifier and the label combination, to obtain a filtered data subset; S43, extracting data segments from the screened data subset, grouping the data segments using a K-means clustering algorithm based on the multi-dimensional characteristics of the data segments, and determining a set of grouped data segments; S44, obtaining a grouped set of data segments, and using a decision tree algorithm to determine the priority order of the data segments according to the association between the storage identifier of each dimensional data and the tag combination specified by the user, to obtain a sorted list of data segments; S45, using the sorted data segment list, according to the matching degree between the grouping characteristics of the data segments and the tag combination, the data segments are verified in parallel using a multi-threaded loading technology to determine a verified data segment set; S46, extracting complete data from the verified data fragment set, judging the access popularity of the data fragment according to the query log of the distributed database and the trigger frequency of the retrieval request, and obtaining a subset of high-popularity data fragments; S47. Obtain a subset of high-temperature data fragments, update the storage identifiers in the distributed database through parallel query technology, and use multi-threaded loading technology to synchronize the latest status of data in each dimension to obtain an optimized retrieval set.

[0054] Specifically, when a retrieval request is triggered, the system first queries the relevant data fragments in parallel from the distributed database according to the user-specified tag combination, such as "number of outings in 2023" and "North China". Using multi-threaded loading technology, each thread is responsible for extracting data storage identifiers of different dimensions, such as one thread extracting out-of-town time and another thread extracting geographic location, to ensure efficient processing of large-scale data. Through multi-threaded parallel processing, the system can complete data extraction within milliseconds, such as extracting identifiers for 1 million records within 500 milliseconds. Next, the system aggregates these identifiers to form a preliminary retrieval set. For example, the system merges the extracted out-of-town time identifier and geographic location identifier to generate a set containing all eligible out-of-town records. In this process, the system uses a hash algorithm to quickly match the identifiers to ensure the accuracy and efficiency of data retrieval. For example, the SHA-256 algorithm is used to hash the identifiers to ensure the uniqueness and fast search of each identifier. Finally, the system passes the preliminary retrieval set to the subsequent data processing module for further analysis and mining. For example, the system can pass the preliminary retrieval set to the machine learning model to predict out-of-town trends and regional out-of-town analysis, and generate a detailed out-of-town report.

[0055] S5. According to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated through the time axis alignment algorithm, and it is determined whether the offset exceeds a preset threshold, and the sequence of data segments after synchronization adjustment is determined, and the sequence of data segments is displayed to the user through the smart glasses.

[0056] In this embodiment, according to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by a time axis alignment algorithm, and it is determined whether the offset exceeds a preset threshold, and the sequence of data segments after synchronization adjustment is determined, including: S51, obtaining a timestamp tag from the preliminary search set, determining the time position of each data segment by parsing the timestamp tag, and obtaining an initial data segment set containing time information; S52, for the initial data segment set, using a time axis alignment algorithm to calculate the start point offset and the end point offset of each data segment to obtain an offset calculation result set; S53, obtaining a set of offset calculation results, comparing with a preset threshold, determining whether the start point offset and the end point offset of each data segment exceed the preset threshold, and obtaining a set of offset excess flags; S54, according to the offset overlimit identification set, using a synchronous adjustment method to process the data segments exceeding the preset threshold, determining the time position of the adjusted data segments, and obtaining a synchronously adjusted data segment set; S55, extracting the timestamp label and the adjusted time position through the synchronized adjusted data segment set, determining the continuity between the data segments, and obtaining a continuity verification result set; S56. For the continuity verification result set, a sequence recombination algorithm is used to integrate the data fragments, determine the final fragment sequence, and obtain an ordered data fragment sequence set; S57. Using the ordered data segment sequence set, apply the support vector machine algorithm to verify the sequence integrity, determine whether there is a time alignment anomaly, and obtain the final synchronized data segment sequence.

[0057] Specifically, in the preliminary retrieval set, each data fragment is labeled with a timestamp, which records the start time and end time of the fragment. Through the timeline alignment algorithm, the timeline of each data fragment is first normalized, assuming that the normalized time range is 0 to 1000 milliseconds. Then, the start and end point offsets of each data fragment are calculated. For example, the start time of fragment A is 200 milliseconds and the end time is 500 milliseconds, the start time of fragment B is 210 milliseconds and the end time is 510 milliseconds, and the start time of fragment C is 205 milliseconds and the end time is 505 milliseconds. Through calculation, the start point offset of fragment B is +10 milliseconds and the end point offset is +10 milliseconds, and the start point offset of fragment C is +5 milliseconds and the end point offset is +5 milliseconds. Next, it is determined whether these offsets exceed the preset threshold. Assuming that the preset threshold is ±15 milliseconds, the offsets of fragments B and C do not exceed the threshold. Finally, synchronization adjustment is performed according to the offset to generate an adjusted data segment sequence. For example, the start time of segment B is adjusted to 200 milliseconds and the end time is adjusted to 500 milliseconds, and the start time of segment C is adjusted to 200 milliseconds and the end time is adjusted to 500 milliseconds, thereby ensuring that all data segments are aligned on the timeline to form a consistent synchronization sequence.

[0058] In this embodiment, a synchronously adjusted sequence of data segments is obtained, and the priority is determined according to the importance of the data and user needs. The highest priority information segment is loaded through the display module of the smart glasses at a refresh rate of 30 frames per second, and the user's line of sight is captured in real time through eye tracking technology, and the projection area coordinates are recalculated to obtain dynamically adjusted display content.

[0059] Step 1: Get the original data fragment sequence, determine the priority order according to user needs and data importance through a preset sorting algorithm, and get the sorted data fragment sequence. Step 2: Load the sorted data fragment sequence through the display module of the smart glasses at a refresh rate of 30 frames per second, and use the buffer storage technology to get the data stream of the initial projection. Step 3: Capture the user's line of sight in real time through the eye tracking technology. If the line of sight position exceeds the preset threshold range, generate new line of sight coordinate data to get a real-time adjusted line of sight position set. Step 4: Recalculate the dynamic coordinates of the projection area according to the real-time adjusted line of sight position set, and use the geometric transformation algorithm to get the adjusted projection coordinate sequence. Step 5: Get the adjusted projection coordinate sequence and the data stream of the initial projection, and get the dynamically adjusted display content sequence through the content mapping technology. Step 6: For the dynamically adjusted display content sequence, use the convolutional neural network algorithm to detect whether the content boundary matches the coordinate. If not, readjust it through the interpolation algorithm to get the optimized display content sequence. Step 7: Load the optimized display content sequence through the projection device at a refresh rate of 30 frames per second, and use the real-time rendering technology to get the final output dynamic display content.

[0060] Specifically, first, the system obtains the synchronized and adjusted data fragment sequence of the display module of the smart glasses through the real-time data acquisition module. After preprocessing, the system will prioritize the data according to the importance of the data and user needs. For example, using a weighted algorithm, the user's historical behavior data, current environment data and preset user preference parameters are comprehensively analyzed to calculate the priority score of each data fragment. Assuming that the priority score of a data fragment A is 95 points, and the score of another data fragment B is 80 points, the system will prioritize A for loading. Then, the system loads the information fragment with the highest priority at a refresh rate of 30 frames per second to ensure the real-time and smoothness of the projected content. During the loading process, the system captures the user's line of sight in real time through eye tracking technology, such as using an image processing algorithm based on a convolutional neural network to analyze the trajectory of the user's eye movement and accurately calculate the coordinates of the line of sight focus in the projection area. Assuming that the current user's line of sight focus coordinates are (x=120, y=80), the system will recalculate the display content of the projection area based on this coordinate, and use the interpolation algorithm to dynamically adjust the image to ensure that the information within the user's line of sight is clearly visible. For example, the image resolution near the focus is increased to 1080p, while the area away from the focus is reduced to 720p to optimize resource allocation. Ultimately, the system generates dynamically adjusted display content and updates the projected image in real time to ensure that users can get the most important information at any time.

[0061] In this embodiment, according to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by a time axis alignment algorithm, it is determined whether the offset exceeds a preset threshold, a sequence of data segments after synchronization adjustment is determined, and the sequence of data segments is displayed to the user through smart glasses, and then the following is further included: S6. Extract the interaction records of specific characters from the multi-dimensional data, use the linked list data structure to connect the audio clips, image snapshots and text annotations in chronological order, record the start time and end time of each node through the hash index mechanism, ensure the synchronization of different types of data, and determine the timeline storage structure; In this embodiment, the interaction records of specific characters are extracted from the multi-dimensional data, and the audio clips, image snapshots and text annotations are connected in chronological order using a linked list data structure. The start time and end time of each node are recorded through a hash index mechanism to ensure the synchronization of different types of data and determine the timeline storage structure, including: S61, obtaining the interaction records of a specific person from the multi-dimensional data, filtering irrelevant data using a preset threshold, and concatenating the audio clips, image snapshots, and text annotations in chronological order through a linked list structure to obtain a preliminary time series; S62. For the preliminary time series, the start time and end time of each linked list node are recorded through a hash index mechanism, an index table containing a timestamp is generated, and synchronization of each data fragment is determined; S63, obtaining the time span of the audio segment according to the start time and the end time in the index table, and if the time span exceeds a preset threshold, decomposing it into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence; S64, obtaining image snapshots and text annotations in corresponding time order through the adjusted audio sequence, using hash indexes to match start time and end time, and generating a synchronized multimodal data stream; S65. For the synchronized multimodal data stream, the linked list structure is used to rearrange the audio clips, image snapshots and text annotations, and the complete sequence is recorded through the timeline storage structure to obtain an ordered data set; S66, extracting the interaction records of specific characters from the ordered data set, classifying the text annotations using a support vector machine algorithm, determining the interaction type, and generating a classified interaction timeline; S67. According to the classified interaction timeline, the correlation between the interaction record and the time sequence is analyzed by the random forest algorithm, the final timeline storage structure is determined, and a multi-dimensional synchronization sequence is output.

[0062] Specifically, when extracting the interaction records of a specific person from multi-dimensional data, the audio clip, image snapshot, and text annotation are first concatenated in chronological order through a hash index mechanism. Assume that the sampling rate of the audio clip is 44100Hz, the timestamp of the image snapshot is 33ms per frame, and the time accuracy of the text annotation is 1ms. Generate a unique identifier for each node through a hash algorithm (such as SHA-256), and record its start time and end time. For example, an audio clip starts at 10:00:00 on October 1, 2023, lasts for 5 seconds, has a hash value of "a1b2c3d4", and its end time is October 1, 2023, 10:00:05. The image snapshot was captured at 10:00:02 on October 1, 2023, has a hash value of "e5f6g7h8", and its end time is October 1, 2023, 10:00:0033. The text annotation is generated at 10:00:03 on October 1, 2023, with a hash value of "i9j0k1l2" and an end time of 10:00:0001 on October 1, 2023. These nodes are linked in chronological order through a linked list data structure to ensure the synchronization of different types of data. For example, the first node in the linked list is the audio clip "a1b2c3d4", the second node is the image snapshot "e5f6g7h8", and the third node is the text annotation "i9j0k1l2". The timeline storage structure uses a B+ tree index to support fast query and insertion operations. For example, if you query the time period from 10:00:02 on October 1, 2023 to 10:00:04 on October 1, 2023, the B+ tree index will return the image snapshot "e5f6g7h8" and the text annotation "i9j0k1l2". In this way, the synchronization and query efficiency of multi-dimensional data are ensured.

[0063] S7. Regularly scan the linked list nodes according to the timeline storage structure to obtain the numerical sequence of interaction frequency and duration changes, fit the change trend through a linear regression algorithm, and generate a summary of interaction data for a specific time period; In this embodiment, the linked list nodes are scanned regularly according to the timeline storage structure to obtain the numerical sequence of interaction frequency and duration changes, and the change trend is fitted by a linear regression algorithm to generate an interaction data summary for a specific time period, including: S71. Scan the linked list nodes regularly through the storage structure in the timeline structure to obtain the original data of the interaction frequency and duration change, and obtain an initial value sequence; S72. According to the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of the interaction frequency over time to determine a first trend sequence; S73, for the initial numerical sequence, using a linear regression algorithm to fit the changing trend of the duration over time, to obtain a second trend sequence; S74, obtaining the trend slope and intercept in a specific time period from the first trend sequence and the second trend sequence, and determining the correlation strength of the interaction frequency and duration changes; S75, weightedly merging the first trend sequence and the second trend sequence by combining the association strength and the time period constraint to obtain a comprehensive trend sequence; S76. Use the comprehensive trend sequence to extract the peak and mean values ​​of the interaction frequency and duration changes within the time period and generate a preliminary data summary; S77. Based on the preliminary data summary and the linked list node distribution in the timeline structure, the interaction density in a specific time period is calculated to obtain a final data summary.

[0064] Specifically, in the timeline storage structure, the system will periodically scan the linked list nodes to obtain the numerical sequence of changes in interaction frequency and duration. For example, the system scans the linked list nodes once every hour, records the interaction frequency and duration of each node, and generates a sequence data containing a timestamp and a corresponding value. Suppose in one day, the system records 24 sets of data, the interaction frequencies are [10, 12, 15, 18, 20, 22, 25, 28, 30, 32, 35, 38, 40, 42, 45,48, 50, 52, 55, 58, 60, 62, 65, 68], and the corresponding durations are [5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28] minutes. Next, the system uses a linear regression algorithm to fit these data to predict future interaction trends. By calculating the least squares method, the system obtains the regression equation of interaction frequency as y = 5x + 5, and the regression equation of duration as y = 0x + 0, where x is the timestamp and y is the predicted value. Based on these regression equations, the system can generate a summary of interaction data for a specific time period. For example, the predicted interaction frequency and duration for the next three hours are [75, 70, 75] and [20, 30, 30] minutes respectively. These summary data can be used to further analyze user behavior, optimize system performance, or provide data support for decision-making.

[0065] S8. If a specific person is recognized, the latest node is loaded from the end of the linked list, and a timeline summary is generated in combination with the change trend. The summary is displayed through the display module of the smart glasses, and the display content is judged whether it matches based on the keywords and context information of the current scene to obtain the final output sequence.

[0066] In this embodiment, if a specific person is identified, the latest node is loaded from the end of the linked list, and a timeline summary is generated in combination with the change trend. The summary is displayed through the display module of the smart glasses, and the display content is judged whether it matches according to the keywords and context information of the current scene to obtain the final output sequence, including: S81, using smart glasses to perform face recognition to obtain the identity of a specific person, loading the corresponding latest node data from the end of a preset database linked list, using a time series analysis method to calculate the change trend and generate an initial timeline summary to obtain a first sequence of data; S82, extracting a timeline summary from the first sequence data, transmitting the first sequence data to a display module of the smart glasses, and adjusting the display content using image rendering technology to obtain a second sequence data; S83, obtaining real-time audio and video streams of the current scene, extracting keywords and generating a scene description vector through natural language processing technology, and calculating the degree of matching with the second sequence data in combination with context information to obtain a third sequence data; S84, if the matching degree in the third sequence data exceeds a preset threshold, target detection is performed on the video frame of the current scene through a convolutional neural network, and relevant position information of the specific person is extracted to obtain a fourth sequence data; S85, obtaining the location information of the specific person from the fourth sequence data, combining the change trend in the first sequence data, and predicting the timeline summary of the next moment through the long short-term memory network to obtain the fifth sequence data; S86, updating the content display parameters of the projection device through the fifth sequence data, and adjusting the display priority of the display module of the smart glasses using the keyword extraction result to obtain a sixth sequence data; S87, extracting the final display sequence from the sixth sequence data, verifying the matching degree through context information, and if the verification result meets the preset conditions, converting the sixth sequence data into an output sequence and transmitting it to the display module of the smart glasses for display, thereby obtaining the final sequence data.

[0067] In this embodiment, first, face recognition is performed through smart glasses to obtain the identity of a specific person, the corresponding latest node data is loaded from the end of the preset database linked list, and the time series analysis method is used to calculate the change trend and generate an initial timeline summary to obtain the first sequence data. This step realizes the identification of a specific person and the preliminary acquisition and processing of related data.

[0068] Next, the timeline summary is extracted from the first sequence data, the first sequence data is transmitted to the display module of the smart glasses, and the display content is adjusted using image rendering technology to obtain the second sequence data. Image rendering technology can display data to users in an intuitive image form, improving the readability and visualization of information.

[0069] Then, the real-time audio and video streams of the current scene are obtained, and keywords are extracted and a scene description vector is generated through natural language processing technology. The matching degree with the second sequence data is calculated in combination with context information to obtain the third sequence data. This step describes and analyzes the current scene through natural language processing technology, providing a basis for subsequent matching calculations.

[0070] If the matching degree in the third sequence of data exceeds the preset threshold, the convolutional neural network is used to detect the target in the video frame of the current scene, extract the relevant position information of the specific person, and obtain the fourth sequence of data. The convolutional neural network can effectively detect and locate the target in the image, providing accurate position information for subsequent prediction and display.

[0071] The location information of a specific person is obtained from the fourth sequence of data, combined with the change trend in the first sequence of data, and the timeline summary of the next moment is predicted through the long short-term memory network to obtain the fifth sequence of data. The long short-term memory network can model and predict time series data and provide users with interactive information previews at future moments.

[0072] The content display parameters of the projection device are updated through the fifth sequence data, and the display priority of the display module of the smart glasses is adjusted using the keyword extraction results to obtain the sixth sequence data. This step dynamically adjusts the priority and parameters of the displayed content based on the prediction results and keyword extraction to ensure that users can obtain the most important information.

[0073] Finally, the final display sequence is extracted from the sixth sequence data, and the matching degree is verified through the context information. If the verification result meets the preset conditions, the sixth sequence data is converted into an output sequence and transmitted to the display module of the smart glasses for display, thereby obtaining the final sequence data. This step ensures the accuracy and relevance of the displayed content, and provides users with a final display result that meets the requirements.

[0074] Compared with traditional face recognition application methods, this application method uses smart glasses for face recognition, and combines linked list data structure and time series analysis methods to quickly obtain and process relevant data of specific people, thereby improving the response speed and processing efficiency of the system. Natural language processing technology and convolutional neural networks are used to accurately describe and detect targets in the current scene, ensuring that the displayed content is highly matched with the actual scene, and improving the intelligence level of the system and user experience. The long short-term memory network is used to predict the timeline summary, which can provide users with interactive information previews at future moments, enhancing the foresight and practicality of the system. By dynamically updating the display parameters of the projection device and adjusting the display priority of smart glasses, the most important information can be flexibly displayed according to different scenarios and user needs, improving the readability and practicality of the information.

[0075] In this embodiment, S9, if the displayed content does not match the current scene, recalculate the timeline summary and adjust the displayed content to ensure the accuracy and real-time performance of the output sequence, including: S91, detecting the matching degree between the displayed content and the current scene by acquiring real-time data, and if the matching degree is lower than a preset threshold, extracting key time points from the real-time data to determine the degree of deviation, and obtaining a preliminary basis for adjusting the timeline summary; S92. Use a random forest algorithm to perform feature classification on key time points, extract high-weight time features from the classification results, determine the parts of the content sequence that need to be adjusted, and obtain the adjusted timeline summary data; S93, processing the adjusted timeline summary data through a convolutional neural network, obtaining the temporal dependency relationship between the data, rearranging the content sequence according to the dependency relationship, and determining a new output sequence structure; S94. If the deviation degree of the new output sequence is still higher than the preset threshold, the weight distribution of the timeline summary is optimized by a gradient boosting algorithm, and more accurate key time points are extracted from the optimization results to obtain further adjusted content sequence data; S95, generating corresponding display content according to the adjusted content sequence data, using a real-time evaluation model to calculate the matching degree between the display content and the current scene, and determining whether a preset real-time standard is met; S96, obtaining distribution characteristics of the changed time points by comparing the output sequence structures before and after the adjustment, updating parameters of the preset thresholds according to the distribution characteristics, and determining a final display content sequence; S97. Extract the output result with the highest real-time performance from the final display content sequence, and synchronize the result with the current scene data through the timing alignment technology to obtain display content with optimized real-time performance.

[0076] In this embodiment, first, the matching degree between the displayed content and the current scene is detected by acquiring real-time data. If the matching degree is lower than a preset threshold, key time points are extracted from the real-time data to determine the degree of deviation, and a preliminary basis for adjusting the timeline summary is obtained. This step can timely discover the mismatch between the displayed content and the actual scene, and provide basic data for subsequent adjustments.

[0077] Next, the random forest algorithm is used to classify the features of key time points, extract high-weight time features from the classification results, determine the parts of the content sequence that need to be adjusted, and obtain the adjusted timeline summary data. The random forest algorithm can effectively classify the features of key time points, determine the parts of the content that need to be adjusted, and improve the accuracy of the adjustment.

[0078] Then, the adjusted timeline summary data is processed through the convolutional neural network to obtain the temporal dependencies between the data, rearrange the content sequence according to the dependencies, and determine the new output sequence structure. The convolutional neural network can capture the temporal dependencies between the data, provide a basis for the rearrangement of the content sequence, and make the output sequence more in line with the actual time logic and scenario requirements.

[0079] If the deviation of the new output sequence is still higher than the preset threshold, the gradient boosting algorithm is used to optimize the weight distribution of the timeline summary, extract more accurate key time points from the optimization results, and obtain further adjusted content sequence data. The gradient boosting algorithm can improve the accuracy and adjustment effect of the timeline summary by optimizing the weight distribution.

[0080] The corresponding display content is generated according to the adjusted content sequence data, and the real-time evaluation model is used to calculate the matching degree between the display content and the current scene to determine whether it meets the preset real-time standard. This step can ensure that the adjusted display content meets the requirements in terms of real-time performance and provide users with timely and accurate information.

[0081] By comparing the output sequence structure before and after the adjustment, the distribution characteristics of the changed time points are obtained, and the parameters of the preset threshold are updated according to the distribution characteristics to determine the final display content sequence. This step optimizes the preset threshold according to the adjustment results, improves the system's adaptive ability, and ensures that the final display content sequence is more in line with the actual scene and user needs.

[0082] Finally, the output results with the highest real-time performance are extracted from the final display content sequence, and the results are synchronized with the current scene data through the timing alignment technology to obtain the display content with optimized real-time performance. The timing alignment technology ensures the temporal consistency between the display content and the current scene data, improving the real-time performance and accuracy of the display content.

[0083] Compared with traditional face recognition application methods, this application method can timely detect the mismatch between the displayed content and the current scene through real-time data detection and key time point extraction, and make rapid adjustments to ensure the accuracy and real-time performance of the output sequence. The random forest algorithm is used to classify the key time points, which can accurately extract high-weight time features and determine the content parts that need to be adjusted, thereby improving the pertinence and accuracy of the adjustment. The convolutional neural network is used to obtain the temporal dependencies between data, and the content sequence can be rearranged reasonably, so that the output sequence is more in line with the actual time logic and scene requirements, and the coherence and readability of the displayed content are improved. According to the changes in the output sequence structure before and after the adjustment, the parameters of the preset threshold are updated, so that the system can adapt to different scenes and data changes, thereby improving the robustness and flexibility of the system. The final display content is synchronized with the current scene data through the timing alignment technology, ensuring the real-time optimization of the display content and providing users with the information that best suits the current scene.

[0084] Embodiment 2 See also Figure 2 , a face recognition application system combined with smart glasses, based on the method described in one of the embodiments; the system comprises: The data collection and feature extraction module is used to collect multi-dimensional data based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotional curves, and voice keywords; the collection frequency difference of each dimensional data is determined by a preset threshold to obtain a frequency adjustment coefficient; A frequency adjustment coefficient calculation module, used for performing sampling rate standardization processing on multi-dimensional data based on the adjustment coefficient; The sampling rate standardization processing module is used to add timestamp tags and scene tags to the standardized multi-dimensional data, store the data fragments in a dispersed manner through the index mechanism of the distributed database, and obtain the unique identifier after storage; A preliminary search module is used to query relevant data fragments from the distributed database in parallel according to the user-specified tag combination to obtain a preliminary search set if a search request is triggered; The data retrieval and display module is used to calculate the starting and ending point offsets of each data segment based on the timestamp tags in the preliminary retrieval set through the time axis alignment algorithm, determine whether the offset exceeds a preset threshold, determine the data segment sequence after synchronization adjustment, and display the data segment sequence to the user through smart glasses.

[0085] In this embodiment, in order to better use the method described in one of the embodiments, the present application proposes a face recognition application system combined with smart glasses, and each module corresponds to each step of the above method. The specific principles have been described above and will not be repeated here.

[0086] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A face recognition application method combined with smart glasses, characterized in that: The method comprises: Collect multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotional curves, and voice keywords; determine the collection frequency difference of each dimensional data through a preset threshold to obtain a frequency adjustment coefficient; Performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient; The standardized multi-dimensional data is labeled with timestamps and scene tags, and the data fragments are stored in a dispersed manner through the index mechanism of the distributed database to obtain a unique identifier after storage; If a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set; According to the timestamp labels in the preliminary retrieval set, the offsets of the starting and ending points of each data segment are calculated through the timeline alignment algorithm, and it is determined whether the offset exceeds the preset threshold. The sequence of data segments after synchronization adjustment is determined and displayed to the user through smart glasses.

2. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: Collect multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotional curves, and voice keywords; The frequency difference of data collection in each dimension is determined by the preset threshold, and the frequency adjustment coefficient is obtained, including: The expression sequence, emotion curve and voice keywords are processed respectively by the feature value extraction method to obtain the feature value of each dimension data; By comparing the collection frequency of each dimension data through a preset threshold, the frequency difference between the expression sequence, the emotion curve and the voice keyword is determined to obtain the frequency difference value; The support vector machine algorithm is used to classify the frequency difference values, determine the frequency adjustment direction of expression sequences, emotional curves and voice keywords, and obtain the preliminary adjustment coefficients; Based on the collection frequency and frequency difference values ​​of multi-dimensional data, the correlation between expression sequences, emotional curves and voice keywords is analyzed through the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient; If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the collection frequency of the expression sequence, the emotion curve and the voice keyword is dynamically adjusted through the linear regression algorithm to obtain the adjusted collection frequency value; Reprocess the multi-dimensional data according to the adjusted acquisition frequency value and the feature value extraction result, determine whether the data acquisition of the smart glasses meets the preset threshold requirements, and obtain an updated feature value set; Through the updated feature value set and frequency adjustment coefficient, the final collection frequency of expression sequence, emotion curve and voice keyword is determined to obtain the optimized multi-dimensional data collection plan.

3. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: Based on the adjustment coefficient, the sampling rate of the multi-dimensional data is standardized, including: Acquire multi-dimensional data and extract the acquisition frequency of each dimensional data, and standardize the sampling rate through the frequency adjustment coefficient to obtain a data set with uniform frequency; Identify dimensional data with a higher collection frequency from a data set with uniform frequency, use interpolation generation technology to calculate supplementary data points and add them to the data set; The supplementary data points generated by interpolation are merged with the original data points to obtain a new data set containing the data point generation results; For the timestamp labels in the new data set, a consistency determination operation is performed to determine if the timestamp deviates from the preset threshold and then adjust it to be consistent; According to the timestamp label after consistency is determined, the data set is processed before storage to generate structured data suitable for fragmented storage; Acquire structured data and verify the matching degree between the acquisition frequency and the standardized sampling rate to determine whether the data point generation meets the requirements; The structured data is fragmented and stored in the order of timestamp labels to obtain the final processed data sharding result.

4. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: The standardized multi-dimensional data is labeled with timestamps and scene tags, and the data fragments are stored in a decentralized manner through the index mechanism of the distributed database to obtain the unique identifier after storage, including: The sharding strategy of the distributed database is used to perform decentralized storage on the marked data fragments and generate a preliminary mapping table of storage locations; For the storage location in the preliminary mapping table, obtain the unique identifier generated by the index mechanism to determine the location address of each data fragment; If the number of data fragments exceeds a preset threshold, the storage nodes in the distributed database are reallocated through the consistent hashing algorithm to obtain an adjusted mapping table; According to the adjusted mapping table, the storage node address corresponding to each unique identifier is obtained to determine whether the data fragments are evenly distributed; Perform classification verification on the label assignment of data segments through the support vector machine algorithm, obtain classification results, and determine the accuracy of timestamp labels and scene labels; For the classification results, a logging mechanism is used to store the unique identifier and the corresponding data fragment information to obtain a complete storage index record.

5. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: If a search request is triggered, the relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination to obtain a preliminary search set, including: If a search request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified tag combination, and the storage identifiers of each dimension of data are extracted using multi-threaded loading technology to obtain the complete data of the initial search set; By initially retrieving the complete data of the collection, the storage identifiers corresponding to the data of each dimension are obtained. According to the matching degree between the storage identifier and the label combination, the cosine similarity algorithm is used to determine the relevance of the data fragments, and the filtered data subset is obtained; Extract data fragments from the filtered data subsets, group the data fragments using the K-means clustering algorithm based on the multi-dimensional characteristics of the data fragments, and determine the grouped data fragment set; Obtain a set of grouped data segments, and use a decision tree algorithm to determine the priority order of the data segments based on the correlation between the storage identifiers of each dimension of data and the user-specified label combination, to obtain a sorted data segment list; Through the sorted data fragment list, the multi-threaded loading technology is used to perform parallel verification on the data fragments according to the matching degree between the grouping characteristics of the data fragments and the label combination, and the verified data fragment set is determined; Extract complete data from the verified data fragment set, determine the access popularity of the data fragment based on the query log of the distributed database and the trigger frequency of the retrieval request, and obtain a subset of high-popularity data fragments; Obtain a subset of high-frequency data fragments, update the storage identifiers in the distributed database through parallel query technology, and use multi-threaded loading technology to synchronize the latest status of data in each dimension to obtain an optimized retrieval set.

6. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: According to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by the time axis alignment algorithm, and it is determined whether the offset exceeds the preset threshold, and the sequence of data segments after synchronization adjustment is determined, including: Obtaining a timestamp tag from the preliminary search set, determining the time position of each data segment by parsing the timestamp tag, and obtaining an initial data segment set containing time information; For the initial data segment set, a time axis alignment algorithm is used to calculate the start point offset and the end point offset of each data segment to obtain an offset calculation result set; Obtain a set of offset calculation results, and determine whether the start point offset and the end point offset of each data segment exceed the preset threshold by comparing them with the preset threshold, and obtain an offset overlimit mark set; According to the offset overlimit identification set, a synchronous adjustment method is used to process the data segments exceeding the preset threshold, and the time position of the adjusted data segments is determined to obtain a synchronously adjusted data segment set; By synchronizing the adjusted data segment set, extracting the timestamp label and the adjusted time position, determining the continuity between the data segments, and obtaining a continuity verification result set; For the continuity verification result set, a sequence recombination algorithm is used to integrate the data fragments, determine the final fragment sequence, and obtain an ordered data fragment sequence set; Through the ordered data fragment sequence set, the support vector machine algorithm is applied to verify the sequence integrity, determine whether there is a time alignment anomaly, and obtain the final synchronized data fragment sequence.

7. The face recognition application method combined with smart glasses as claimed in claim 1, characterized in that: According to the timestamp tags in the preliminary search set, the offsets of the starting and ending points of each data segment are calculated by the time axis alignment algorithm, and it is determined whether the offset exceeds the preset threshold, and the sequence of data segments after synchronization adjustment is determined, and the sequence of data segments is displayed to the user through the smart glasses, and then it also includes: Extract the interaction records of specific people from multi-dimensional data, use linked list data structure to connect audio clips, image snapshots and text annotations in chronological order, record the start and end time of each node through hash index mechanism, ensure the synchronization of different types of data, and determine the timeline storage structure; According to the timeline storage structure, the linked list nodes are scanned regularly to obtain the numerical sequence of interaction frequency and duration changes, and the change trend is fitted through the linear regression algorithm to generate a summary of interaction data for a specific time period; If a specific person is recognized, the latest node is loaded from the end of the linked list, and a timeline summary is generated based on the change trend. This is displayed through the display module of the smart glasses. The display content is judged based on the keywords and context information of the current scene to obtain the final output sequence.

8. The face recognition application method combined with smart glasses as claimed in claim 7, characterized in that: Extract the interaction records of specific people from multi-dimensional data, use linked list data structure to connect audio clips, image snapshots and text annotations in chronological order, record the start time and end time of each node through hash index mechanism, ensure the synchronization of different types of data, and determine the timeline storage structure, including: Obtain the interaction records of specific people from multi-dimensional data, use a preset threshold to filter irrelevant data, and concatenate audio clips, image snapshots, and text annotations in chronological order through a linked list structure to obtain a preliminary time series; For the preliminary time series, the start and end time of each linked list node is recorded through the hash index mechanism, and an index table containing timestamps is generated to determine the synchronization of each data fragment; According to the start time and end time in the index table, the time span of the audio segment is obtained. If the time span exceeds a preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence; Through the adjusted audio sequence, we obtain the image snapshots and text annotations in the corresponding time order, and use the hash index to match the start time and end time to generate a synchronized multimodal data stream. For synchronized multimodal data streams, a linked list structure is used to rearrange audio clips, image snapshots, and text annotations, and the complete sequence is recorded through a timeline storage structure to obtain an ordered data set; Extract interaction records of specific people from ordered data sets, use support vector machine algorithms to classify text annotations, determine interaction types, and generate classified interaction timelines; According to the classified interaction timeline, the correlation between the interaction records and the time sequence is analyzed through the random forest algorithm, the final timeline storage structure is determined, and a multi-dimensional synchronization sequence is output.

9. The face recognition application method combined with smart glasses as claimed in claim 7, characterized in that: The method of periodically scanning the linked list nodes according to the timeline storage structure, obtaining the numerical sequence of interaction frequency and duration changes, fitting the change trend through a linear regression algorithm, and generating an interaction data summary for a specific time period includes: Through the storage structure in the timeline structure, the linked list nodes are scanned regularly to obtain the original data of interaction frequency and duration changes, and the initial value sequence is obtained; According to the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of the interaction frequency over time to determine the first trend sequence; For the initial numerical sequence, a linear regression algorithm is used to fit the changing trend of duration over time to obtain the second trend sequence; From the first trend sequence and the second trend sequence, obtain the trend slope and intercept in a specific time period to determine the correlation strength of the interaction frequency and duration changes; Through the correlation strength and the time period constraint, the first trend sequence and the second trend sequence are weightedly fused to obtain a comprehensive trend sequence; Using a comprehensive trend series, we extracted the peak and mean values ​​of the interaction frequency and duration changes within the time period and generated a preliminary data summary; Based on the preliminary data summary, combined with the linked list node distribution in the timeline structure, the interaction density in a specific time period is calculated to obtain the final data summary.

10. A face recognition application system combined with smart glasses, characterized in that: Based on the method described in any one of claims 1 to 9; the system comprises: The data collection and feature extraction module is used to collect multi-dimensional data based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotional curves, and voice keywords; the collection frequency difference of each dimensional data is determined by a preset threshold to obtain a frequency adjustment coefficient; A frequency adjustment coefficient calculation module, used for performing sampling rate standardization processing on multi-dimensional data based on the adjustment coefficient; The sampling rate standardization processing module is used to add timestamp tags and scene tags to the standardized multi-dimensional data, store the data fragments in a dispersed manner through the index mechanism of the distributed database, and obtain the unique identifier after storage; A preliminary search module is used to query relevant data fragments from the distributed database in parallel according to the user-specified tag combination to obtain a preliminary search set if a search request is triggered; The data retrieval and display module is used to calculate the starting and ending point offsets of each data segment based on the timestamp tags in the preliminary retrieval set through the time axis alignment algorithm, determine whether the offset exceeds a preset threshold, determine the data segment sequence after synchronization adjustment, and display the data segment sequence to the user through smart glasses.

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