A face recognition application method and system combined with smart glasses

Abstract

The present invention discloses a face recognition application method and system combined with smart glasses. Among them, the method includes: collecting multi-dimensional data based on smart glasses, where the multi-dimensional data includes a sequence of facial expressions, an emotion curve, and voice keywords; judging the acquisition frequency difference of each dimension of data through a preset threshold to obtain a frequency adjustment coefficient; performing sampling rate normalization processing on the multi-dimensional data based on this adjustment coefficient; tagging the normalized multi-dimensional data with a timestamp label and a scene label, and dispersedly storing data segments through the indexing mechanism of a distributed database to obtain a unique identifier after storage, etc. This method of the present invention collects multi-dimensional data such as facial expressions, emotions, and voices through smart glasses, and performs intelligent adjustment and normalization processing on the sampling frequency. By using a distributed database and a timeline alignment algorithm, efficient storage and retrieval of massive data are realized.

Description

Technical Field

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

[0002] Smart glasses face a complex technical challenge in multi-dimensional 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 simultaneously capture multi-dimensional information such as facial expressions, emotions, and voices. These data types and collection frequencies are different. How to coordinate and standardize these data is a difficult problem. Second, the storage and rapid retrieval of massive multi-modal data pose another challenge, especially how to achieve efficient data management under limited device resources. Moreover, the time alignment and correlation analysis between different types of data are also a thorny issue, especially when extracting the interaction records of specific individuals. 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: under limited computing and storage resources, how to balance the comprehensiveness of data, the real-time nature of processing, and the accuracy of display to maximize the application value of smart glasses in interpersonal interaction analysis and auxiliary decision-making. 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. This method collects multi-dimensional data such as facial expressions, emotions, and voices through smart glasses, and performs intelligent adjustment and standardization processing on 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, there is provided a face recognition application method combined with smart glasses, the method comprising:

[0005] Collecting multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotion curves, and voice keywords; judging the acquisition frequency difference of each dimension data through a preset threshold to obtain a frequency adjustment coefficient;

[0006] Performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient;

[0007] Attaching a timestamp label and a scene label to the standardized multi-dimensional data, and dispersedly storing data segments through the indexing mechanism of a distributed database to obtain a unique identifier after storage;

[0008] If the retrieval request is triggered, relevant data fragments are queried in parallel from the distributed database according to the tag combinations specified by the user to obtain a preliminary retrieval set;

[0009] According to the timestamp tags in the preliminary retrieval set, the start point and end point offsets of each data fragment are calculated through the timeline alignment algorithm, and it is judged whether the offsets exceed the preset threshold to determine the data fragment sequence after synchronization adjustment, and the data fragment sequence is displayed to the user through the smart glasses.

[0010] In the above technical solution, the smart glasses collect multi-source data such as facial expression sequences, emotion curves, and voice keywords with the help of built-in sensors such as cameras and microphones. These data depict the characteristics of the identified object from different angles and can reflect its characteristics more comprehensively than a single data dimension. To keep the multi-dimensional data consistent on the timeline, the system judges the acquisition frequency differences of each dimension data through the preset threshold, generates a frequency adjustment coefficient, and then performs sampling rate standardization processing on the multi-dimensional data to lay a foundation for subsequent data analysis.

[0011] The multi-dimensional data after standardization processing will be marked with timestamp tags and scene tags. Subsequently, the system uses the indexing mechanism of the distributed database to disperse and store the data fragments with tags and obtain the unique identifier after storage. The distributed database can efficiently store and manage a large amount of multi-dimensional data with high scalability, high availability, and high performance. When the retrieval request is triggered, the system queries relevant data fragments in parallel from the distributed database according to the tag combinations specified by the user to quickly obtain a preliminary retrieval set. To further improve the efficiency of data retrieval, the system calculates the start point and end point offsets of each data fragment through the timeline alignment algorithm according to the timestamp tags in the preliminary retrieval set, and judges whether the offsets exceed the preset threshold, so as to determine the data fragment sequence after synchronization adjustment. Finally, the system displays the data fragment sequence to the user through the smart glasses, and the timeline alignment algorithm ensures the synchronization of different dimension data in time, making the data displayed to the user more accurate and complete.

[0012] Compared with traditional face recognition methods, this application method combines multiple data dimensions such as facial expression sequences, emotion curves, and voice keywords, enabling a more comprehensive capture and analysis of the features of the recognized object, thereby improving the accuracy and robustness of face recognition. By performing sampling rate normalization and time axis alignment on multi-dimensional data, different dimensional data can be analyzed and presented within a unified time framework, enhancing the flexibility and efficiency of data processing. Utilizing the indexing mechanism of a distributed database, a large number of multi-dimensional data segments can be quickly stored and retrieved, meeting the requirements of real-time and large-scale data processing. By presenting the data segment sequence to the user through smart glasses, an intuitive and convenient interaction method is provided, enhancing the user's perception and understanding of the face recognition results.

[0013] In some embodiments, multi-dimensional data is collected based on smart glasses, and the multi-dimensional data includes facial expression sequences, emotion curves, and voice keywords; by judging the acquisition frequency difference of each dimensional data through a preset threshold, a frequency adjustment coefficient is obtained, including:

[0014] The expression sequence, emotion curve, and voice keywords are respectively processed through an eigenvalue extraction method to obtain the eigenvalues of each dimensional data;

[0015] By comparing the acquisition frequencies of each dimensional data through a preset threshold, the frequency differences between the expression sequence, emotion curve, and voice keywords are judged to obtain a frequency difference value;

[0016] The support vector machine algorithm is used to classify the frequency difference value to determine the frequency adjustment directions of the expression sequence, emotion curve, and voice keywords, and a preliminary adjustment coefficient is obtained;

[0017] Regarding the acquisition frequency of multi-dimensional data and the frequency difference value, the random forest algorithm is used to analyze the correlation between the expression sequence, emotion curve, and voice keywords to obtain an optimized value of the frequency adjustment coefficient;

[0018] If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the acquisition frequencies of the expression sequence, emotion curve, and voice keywords are dynamically adjusted through a linear regression algorithm to obtain the adjusted acquisition frequency values;

[0019] Based on the adjusted acquisition frequency values and the eigenvalue extraction results, the multi-dimensional data is reprocessed to determine whether the data acquisition of the smart glasses meets the preset threshold requirements, and an updated eigenvalue set is obtained;

[0020] Through the updated eigenvalue set and the frequency adjustment coefficient, the final acquisition frequencies of the expression sequence, emotion curve, and voice keywords are determined to obtain an optimized multi-dimensional data acquisition scheme.

[0021] In the above technical solution, the smart glasses collect multi-source data such as facial expression sequences, emotion curves, and speech keywords with the help of built-in sensors such as cameras and microphones. These data are processed respectively by eigenvalue extraction methods to obtain the eigenvalues of each dimension data, providing a basis for subsequent data processing and analysis. To judge the frequency differences among the expression sequences, emotion curves, and speech keywords, the system compares the acquisition frequencies of each dimension data through a preset threshold to obtain a frequency difference value. Then, the support vector machine algorithm is used to classify the frequency difference value to determine the frequency adjustment direction of each dimension data and obtain a preliminary adjustment coefficient. For the acquisition frequencies of multi-dimensional data and the frequency difference value, the random forest algorithm is used to analyze the correlation among the expression sequences, emotion curves, and speech keywords to obtain an optimized value of the frequency adjustment coefficient. This step takes into account the mutual influence among different dimension data and improves the rationality of frequency adjustment. If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, the linear regression algorithm is used to dynamically adjust the acquisition frequencies of the expression sequences, emotion curves, and speech keywords to obtain the adjusted acquisition frequency values. According to the adjusted acquisition frequency values and the eigenvalue extraction results, the multi-dimensional data are reprocessed to judge whether the data acquisition of the smart glasses meets the requirements of the preset threshold, and an updated eigenvalue set is obtained. Through the updated eigenvalue set and the frequency adjustment coefficient, the final acquisition frequencies of the expression sequences, emotion curves, and speech keywords are determined, and an optimized multi-dimensional data acquisition scheme is obtained.

[0022] Compared with traditional face recognition application methods, this application method collects multi-dimensional data such as facial expression sequences, emotion curves, and speech keywords, and can capture and analyze the features of the recognized object more comprehensively, providing richer information for subsequent data processing and analysis. By combining multiple algorithms such as support vector machine, random forest, and linear regression, the data acquisition frequency is dynamically adjusted and optimized, improving the flexibility and efficiency of data processing. Using the index mechanism of the distributed database, a large number of multi-dimensional data segments can be stored and retrieved quickly, meeting the requirements of real-time and large-scale data processing.

[0023] In some embodiments, based on this adjustment coefficient, the sampling rate of multi-dimensional data is standardized, including:

[0024] Obtain multi-dimensional data and extract the acquisition frequencies of each dimension data, and standardize the sampling rate through the frequency adjustment coefficient to obtain a data set with a unified frequency;

[0025] Identify the dimension data with a higher acquisition frequency from the data set with a unified frequency, and use the interpolation generation technology to calculate and add supplementary data points to the data set;

[0026] Merge the supplementary data points after interpolation generation with the original data points to obtain a new data set containing the data point generation results;

[0027] Perform a consistency determination operation on the timestamp tags in the new dataset. If the timestamp deviates from the preset threshold, adjust it to be consistent.

[0028] According to the timestamp tags after consistency determination, perform pre-storage processing on the dataset to generate structured data suitable for fragmented storage.

[0029] Obtain the structured data and verify the matching degree after standardizing the acquisition frequency and sampling rate to determine whether the generation of data points meets the requirements.

[0030] Perform fragmented storage on the structured data in the order of timestamp tags to obtain the final processed data sharding result.

[0031] In the above technical solution, first, collect multi-dimensional data and determine the acquisition frequency of each dimension of data. Then, use the frequency adjustment coefficient to standardize the sampling rate to generate a dataset with a unified frequency. For the dimension data with a higher acquisition frequency, use the interpolation generation technology to calculate and supplement data points to ensure the alignment of each dimension of data on the time axis. Merge the supplemented data points generated by interpolation with the original data points to form a new dataset containing the data point generation result. Perform a consistency determination operation on the timestamp tags in the new dataset. If the timestamp deviates from the preset threshold, make corresponding adjustments to ensure the temporal accuracy of the data. According to the timestamp tags after consistency determination, perform pre-storage processing on the dataset to generate structured data suitable for fragmented storage. By performing fragmented storage on the structured data in the order of timestamp tags, the final processed data sharding result is obtained, effectively improving the efficiency of data storage and retrieval.

[0032] Compared with the traditional face recognition application method, 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 basis for subsequent data analysis and processing. Processing the data into structured data suitable for fragmented storage and storing it in the order of timestamp tags not only improves the efficiency of data storage but also speeds up the retrieval speed, meeting 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 handle various complex data acquisition scenarios.

[0033] In some embodiments, timestamp tags and scenario tags are added to the standardized multi-dimensional data, and the data fragments are scattered and stored through the indexing mechanism of the distributed database to obtain the unique identifier after storage, including:

[0034] Execute decentralized storage on the labeled data fragments using the sharding strategy of the distributed database to generate a preliminary mapping table of storage locations;

[0035] For the storage locations in the preliminary mapping table, obtain the unique identifiers generated by the indexing mechanism to determine the positioning addresses of each data fragment;

[0036] If the number of data fragments exceeds the preset threshold, reallocate the storage nodes in the distributed database through the consistent hashing algorithm to obtain an adjusted mapping table;

[0037] According to the adjusted mapping table, obtain the storage node addresses corresponding to each unique identifier and determine whether the data fragments are evenly distributed;

[0038] Perform classification verification on the label assignment of the data fragments through the support vector machine algorithm, obtain the classification results, and determine the accuracy of the timestamp label and the scenario label;

[0039] For the classification results, use the logging mechanism to store the unique identifiers and the corresponding data fragment information to obtain a complete storage index record.

[0040] In the above technical solution, decentralized storage is performed on the labeled data fragments, and a preliminary mapping table of storage locations is generated. For the storage locations in the preliminary mapping table, unique identifiers are generated through the indexing mechanism to determine the precise positioning addresses of each data fragment. Multiple methods can be used to generate 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, thereby obtaining an adjusted mapping table. The consistent hashing algorithm can minimize data migration when nodes are dynamically added or removed, ensuring the even distribution of data. According to the adjusted mapping table, obtain the storage node addresses corresponding to each unique identifier and determine 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 where some nodes are overloaded while others are idle. Perform classification verification on the label assignment of the data fragments through the support vector machine algorithm, obtain the classification results, and determine the accuracy of the timestamp label and the scenario label. As an efficient supervised learning algorithm, the support vector machine can perform data classification and regression analysis tasks, thereby ensuring the correctness of label assignment. For the classification results, use the logging mechanism to store the unique identifiers and their corresponding data fragment information to generate a complete storage index record. Logging helps to track the storage and access history of data, facilitating subsequent data management and maintenance operations.

[0041] Compared with traditional face recognition application methods, this application method utilizes the sharding strategy and indexing mechanism of a distributed database to enable rapid storage and retrieval of data fragments, significantly enhancing the system's response speed and processing efficiency. The application of the consistent hashing algorithm ensures the balanced distribution of data in the distributed database, effectively avoiding data skew and node overload problems, and enhancing the system's stability and reliability. This method can adapt to the growth of data volume and dynamic changes in nodes. By reallocating storage nodes and adjusting the mapping table, it realizes the flexible expansion and optimization of the system. The classification verification of label assignment by the support vector machine algorithm and the application of the logging mechanism ensure the integrity of data and the accuracy of label assignment, providing a solid and reliable data foundation for subsequent data analysis and applications.

[0042] In some embodiments, if a retrieval request is triggered, relevant data fragments are queried in parallel from the distributed database according to the user-specified label combination to obtain a preliminary retrieval set, including:

[0043] If a retrieval request is triggered, according to the user-specified label combination, relevant data fragments are queried in parallel from the distributed database, and the storage identifiers of each dimension of data are extracted using multi-threaded loading technology to obtain the complete data of the preliminary retrieval set;

[0044] Through the complete data of the preliminary retrieval set, the storage identifiers corresponding to each dimension of data are obtained. Regarding the matching degree between the storage identifier and the label combination, the cosine similarity algorithm is used to judge the relevance of the data fragments to obtain a filtered data subset;

[0045] Data fragments are extracted from the filtered data subset. Regarding the multi-dimensional characteristics of the data fragments, the K-means clustering algorithm is used to group the data fragments to determine the grouped data fragment set;

[0046] The grouped data fragment set is obtained. According to the relevance between the storage identifiers of each dimension of data and the user-specified label combination, the decision tree algorithm is used to judge the priority order of the data fragments to obtain a sorted data fragment list;

[0047] Through the sorted data fragment list, regarding the matching degree between the grouped characteristics of the data fragments and the label combination, multi-threaded loading technology is used to perform parallel verification on the data fragments to determine the verified data fragment set;

[0048] Complete data is extracted from the verified data fragment set. According to the query log of the distributed database and the triggering frequency of the retrieval request, the access popularity of the data fragments is judged to obtain a subset of high-popularity data fragments;

[0049] Obtain a subset of high - popularity data segments, update the storage identifiers in the distributed database through parallel query technology, and synchronize the latest status of data in each dimension using multi - thread loading technology to obtain an optimized retrieval set.

[0050] In the above technical solution, when a retrieval request is triggered, relevant data segments are queried in parallel from the distributed database according to the tag combination specified by the user. The storage identifiers of data in each dimension are extracted using multi - thread loading technology to obtain the complete data of the preliminary retrieval set. Through the complete data of the preliminary retrieval set, the storage identifiers corresponding to the data in each dimension are obtained. Regarding the matching degree between the storage identifier and the tag combination, the cosine similarity algorithm is used to judge the relevance of the data segments, and a filtered data subset is obtained. The cosine similarity algorithm can effectively measure the similarity between two vectors and is used here to judge the relevance between the data segments and the tag combination specified by the user. The data segments are extracted from the filtered data subset. Considering the multi - dimensional characteristics of the data segments, the K - means clustering algorithm is used to group the data segments to determine the set of grouped data segments. Then, according to the relevance between the storage identifiers of data in each dimension and the tag combination specified by the user, the decision tree algorithm is used to judge the priority order of the data segments to obtain a sorted list of data segments. The K - means clustering algorithm can divide the data into different groups for subsequent classification processing; the decision tree algorithm can sort the data according to certain rules to improve the pertinence of the retrieval results. Through the sorted list of data segments, considering the matching degree between the grouping characteristics of the data segments and the tag combination, the multi - thread loading technology is used to verify the data segments in parallel to determine the set of verified data segments. The complete data is extracted from the set of verified data segments. According to the query log of the distributed database and the triggering frequency of the retrieval request, the access popularity of the data segments is judged to obtain a subset of high - popularity data segments. This step ensures the accuracy and timeliness of the retrieval results, presenting data that better meets user needs and is more popular to the user. Obtain a subset of high - popularity data segments, update the storage identifiers in the distributed database through parallel query technology, and synchronize the latest status of data in each dimension using multi - thread loading technology to obtain an optimized retrieval set. This step ensures the dynamic update and optimization of the retrieval set, enabling users to obtain the latest data information.

[0051] Compared with traditional face recognition methods, this application method can quickly retrieve relevant data segments from a distributed database through parallel query and multi-threaded loading techniques, improving the system's response speed and retrieval efficiency, and meeting users' requirements for real-time retrieval. By using various data processing and analysis methods such as cosine similarity algorithm, K-means clustering algorithm, and decision tree algorithm, the retrieval results are screened, grouped, and sorted to ensure the accuracy and relevance of the retrieval results, and can better meet users' personalized needs. This retrieval method can adapt to the retrieval needs of large-scale data, and can flexibly handle different application scenarios and data types by adjusting algorithm parameters and strategies, with strong scalability and adaptability. By judging the access popularity of data segments, high-popularity data is presented to users first, improving the practicality and timeliness of the retrieval results, and enabling users to obtain hot and important information faster.

[0052] In some embodiments, according to the timestamp tags in the preliminary retrieval set, the start point and end point offsets of each data segment are calculated through a timeline alignment algorithm, and it is judged whether the offsets exceed a preset threshold to determine the data segment sequence after synchronization adjustment, including:

[0053] Obtain the timestamp tags from the preliminary retrieval set, determine the time positions of each data segment by parsing the timestamp tags, and obtain an initial data segment set containing time information;

[0054] For the initial data segment set, use the timeline alignment algorithm to calculate the start point offset and end point offset of each data segment, and obtain an offset calculation result set;

[0055] Obtain the offset calculation result set, compare it with the preset threshold, and judge whether the start point offset and end point offset of each data segment exceed the preset threshold to obtain an offset exceeding flag set;

[0056] According to the offset exceeding flag set, use a synchronization adjustment method to process the data segments that exceed the preset threshold, determine the time positions of the adjusted data segments, and obtain a data segment set after synchronization adjustment;

[0057] Extract the timestamp tags and adjusted time positions from the data segment set after synchronization adjustment, judge the continuity between data segments, and obtain a continuity verification result set;

[0058] For the continuity verification result set, use a sequence recombination algorithm to integrate each data segment, determine the final segment sequence, and obtain an ordered data segment sequence set;

[0059] Apply the support vector machine algorithm to verify the sequence integrity through the ordered data segment sequence set, judge whether there is a time alignment anomaly, and obtain the final data segment sequence after synchronization adjustment.

[0060] In the above technical solution, when a retrieval request is triggered, the system obtains timestamp tags from the preliminary retrieval set, and determines the time positions of each data segment by parsing the timestamp tags, obtaining an initial data segment set containing time information. For the initial data segment set, a timeline alignment algorithm is used to calculate the start point offset and end point offset of each data segment, obtaining an offset calculation result set. The timeline alignment algorithm can accurately calculate the offsets of different data segments on the timeline, providing a basis for subsequent synchronization adjustment. After obtaining the offset calculation result set, by comparing with a preset threshold, it is judged whether the start point offset and end point offset of each data segment exceed the preset threshold, obtaining an offset exceeding limit identification set. This step determines which data segments need to be synchronously adjusted. According to the offset exceeding limit identification set, a synchronization adjustment method is used to process the data segments exceeding the preset threshold, determining the time positions of the adjusted data segments, obtaining a synchronously adjusted data segment set. Through adjustment, the time synchronization of each data segment is achieved on the timeline, improving the data consistency.

[0061] From the synchronously adjusted data segment set, the timestamp tags and the adjusted time positions are extracted, and the continuity between data segments is judged, obtaining a continuity verification result set. For the continuity verification result set, a sequence recombination algorithm is used to integrate each data segment, determining the final segment sequence, obtaining an ordered data segment sequence set. This step ensures the temporal continuity and logic of the data segments. Through the ordered data segment sequence set, a support vector machine algorithm is applied to verify the sequence integrity, judging whether there is a time alignment anomaly, obtaining the finally synchronously adjusted data segment sequence. The support vector machine algorithm can effectively detect possible anomalies in the sequence, ensuring the integrity and reliability of the final data segment sequence.

[0062] Compared with the traditional face recognition application method, this application method can accurately calculate and adjust the start point and end point offsets of each data segment through the timeline alignment algorithm and the synchronization adjustment method, ensuring the temporal synchronization of multi-dimensional data, and improving the data consistency and accuracy. Using the sequence recombination algorithm to integrate the data segments can effectively judge the continuity between data segments and determine the final segment sequence, making the data logical and coherent in time, facilitating subsequent analysis and display. Applying the support vector machine algorithm to verify the sequence integrity can accurately detect time alignment anomalies, ensuring the integrity and reliability of the finally synchronously adjusted data segment sequence, and providing high-quality data information for users. By using a preset threshold to judge whether the offset exceeds the limit, the threshold can be flexibly set and adjusted according to different application scenarios and requirements, improving the adaptability and flexibility of the system.

[0063] In some embodiments, according to the timestamp tags in the preliminary retrieval set, the start point and end point offsets of each data segment are calculated through a timeline alignment algorithm, it is determined whether the offset exceeds a preset threshold, a data segment sequence after synchronization adjustment is determined, and the data segment sequence is displayed to the user through smart glasses. After that, it further includes:

[0064] Extract the interaction records of a specific person from multi-dimensional data, use a linked list data structure to concatenate audio segments, image snapshots, and text annotations in chronological order, and record the start time and end time of each node through a hash index mechanism to ensure the synchronization of different types of data and determine the timeline storage structure;

[0065] Regularly scan the linked list nodes according to the timeline storage structure, obtain a numerical sequence of changes in interaction frequency and duration, fit the change trend through a linear regression algorithm, and generate a summary of interaction data for a specific time period;

[0066] If a specific person is recognized, load the latest node from the end of the linked list, generate a timeline summary in combination with the change trend, display it through the display module of the smart glasses, and determine whether the displayed content matches according to the keywords and context information of the current scene to obtain the final output sequence.

[0067] In the above technical solution, first, extract the interaction records of a specific person from multi-dimensional data, and use a linked list data structure to concatenate audio segments, image snapshots, and text annotations in chronological order. Record the start time and end time of each node 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, facilitating subsequent analysis and processing.

[0068] Next, regularly scan the linked list nodes according to the timeline storage structure to obtain a numerical sequence of changes in interaction frequency and duration. Fit the change trend through a linear regression algorithm to generate a summary of interaction data for a specific time period. The linear regression algorithm can effectively analyze the change trend of the data and provide a concise summary of the interaction data for the user. If a specific person is recognized, load the latest node from the end of the linked list, generate a timeline summary in combination with the change trend. Display it through the display module of the smart glasses, and determine whether the displayed content matches according to the keywords and context information of the current scene to obtain the final output sequence. This step displays the generated timeline summary to the user through the smart glasses and performs content matching judgment according to the current scene information to ensure the accuracy and relevance of the displayed content.

[0069] Through the linked list data structure and the hash index mechanism, different types of data are concatenated and indexed in chronological order, ensuring the synchronization and integration of the data, which is convenient for subsequent analysis and display.

[0070] 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, and can quickly generate a summary of interaction data for a specific time period, providing users with a concise and clear data summary, improving the readability and usability of information. After identifying a specific person, this method can promptly load the latest node from the end of the linked list, generate a timeline summary in combination with the change trend, and display it through the display module of the smart glasses, providing users with real-time interaction information and enhancing the user experience. By matching and judging the display content according to the keywords and context information of the current scene, the accuracy and relevance of the displayed content are ensured, avoiding the display of irrelevant or inappropriate information, and improving the intelligence level of the system.

[0071] In some embodiments, the interaction records of specific persons are extracted from multi-dimensional data, and a linked list data structure is used to concatenate audio segments, image snapshots, and text annotations in chronological order. 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 a timeline storage structure is determined, including:

[0072] Obtain the interaction records of specific persons from multi-dimensional data, filter out irrelevant data using a preset threshold, and concatenate audio segments, image snapshots, and text annotations in chronological order through a linked list structure to obtain a preliminary time series;

[0073] For the preliminary time series, the start time and end time of each linked list node are recorded through a hash index mechanism to generate an index table containing timestamps, and the synchronization of each data segment is determined;

[0074] According to the start time and end time in the index table, obtain the time span of the audio segment. If the time span exceeds the preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence;

[0075] Through the adjusted audio sequence, obtain the corresponding image snapshots and text annotations in chronological order, and use hash index to match the start time and end time to generate a synchronized multi-modal data stream;

[0076] For the synchronized multi-modal data stream, use a linked list structure to rearrange the audio segments, image snapshots, and text annotations, and record the complete sequence through a timeline storage structure to obtain an ordered data set;

[0077] Extract the interaction records of specific persons from the ordered data set, use a support vector machine algorithm to classify the text annotations, judge the interaction type, and generate a classified interaction timeline;

[0078] According to the classified interaction timeline, analyze the correlation between the interaction records and the chronological order through a random forest algorithm to determine the final timeline storage structure and output a multi-dimensional synchronized sequence.

[0079] In the above technical solution, first, interaction records of a specific person are obtained from multi-dimensional data. Irrelevant data is filtered using a preset threshold, and audio segments, image snapshots, and text annotations are concatenated in chronological order through a linked list structure to obtain a preliminary time series. For the preliminary time series, a hash index mechanism is used to record the start time and end time of each linked list node, generating an index table containing timestamps to determine the synchronization of each data segment. The hash index mechanism can quickly locate and retrieve data segments, ensuring the synchronization of different types of multi-modal data in time.

[0080] Next, 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 the preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain an adjusted audio sequence. From the adjusted audio sequence, the corresponding image snapshots and text annotations in chronological order are obtained, and the start time and end time are matched using hash index to generate a synchronized multi-modal data stream. For the synchronized multi-modal data stream, the audio segments, image snapshots, and text annotations are rearranged using a linked list structure, and the complete sequence is recorded through a timeline storage structure to obtain an ordered data set. The timeline storage structure can clearly display the time order and change process of the data, facilitating subsequent analysis and display.

[0081] Then, interaction records of a specific person are extracted from the ordered data set, and a support vector machine algorithm is used to classify the text annotations to determine the interaction type, generating a classified interaction timeline. The support vector machine algorithm can effectively classify the text annotations to 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 order to determine the final timeline storage structure and output a multi-dimensional synchronized sequence. The random forest algorithm can comprehensively consider various factors, analyze the correlation between the interaction records and the time order, and improve the rationality and accuracy of the timeline storage structure.

[0082] Compared with traditional face recognition application methods, this application method synchronizes and integrates different types of multimodal data in chronological order through a hash index mechanism and a linked list structure, ensuring data consistency and integrity, which is convenient for subsequent analysis and processing. By adopting a linked list structure and a timeline storage structure, it 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 out irrelevant data and judging the time span of audio segments, the thresholds can be flexibly set and adjusted according to different application scenarios and requirements, improving the adaptability and flexibility of the system. Applying support vector machine algorithms and random forest algorithms can accurately classify interaction types and analyze the relevance between interaction records and chronological order, providing users with more in-depth data analysis results and enhancing the intelligence level of the system.

[0083] In some embodiments, the linked list nodes are scanned regularly according to the timeline storage structure to obtain a numerical sequence of interaction frequency and duration changes, and the change trend is fitted by a linear regression algorithm to generate a summary of interaction data for a specific time period, including:

[0084] 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 from them, obtaining an initial numerical sequence;

[0085] According to the initial numerical sequence, a linear regression algorithm is used to fit the change trend of interaction frequency over time to determine the first trend sequence;

[0086] For the initial numerical sequence, a linear regression algorithm is used to fit the change trend of duration change over time to obtain the second trend sequence;

[0087] From the first trend sequence and the second trend sequence, the trend slope and intercept within a specific time period are obtained to judge the correlation strength of the interaction frequency and duration changes;

[0088] Through the correlation strength, combined with the time period constraint, the first trend sequence and the second trend sequence are weighted and fused to obtain a comprehensive trend sequence;

[0089] Using the comprehensive trend sequence, the peaks and means of the interaction frequency and duration changes within the time period are extracted to generate a preliminary data summary;

[0090] According to the preliminary data summary, combined with the distribution of linked list nodes in the timeline structure, the interaction density for a specific time period is calculated to obtain the final data summary.

[0091] In the above technical solution, first, through the storage structure in the timeline structure, the linked list nodes are scanned regularly to obtain the original data of the interaction frequency and duration changes from them, and an initial numerical sequence is obtained. This step provides the basic data for subsequent trend fitting and data summary generation.

[0092] Next, according to the initial numerical sequence, a linear regression algorithm is used to fit the change 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, providing the change trend of the interaction frequency for subsequent analysis. For the initial numerical sequence, the linear regression algorithm is also used to fit the change 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, providing the trend information of the duration change.

[0093] Then, from the first trend sequence and the second trend sequence, the trend slopes and intercepts within a specific time period are obtained to judge the correlation strength of the interaction frequency and duration changes. Through the correlation strength and combined with 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 trend of the interaction frequency and duration changes, and obtains a more comprehensive comprehensive trend through weighted fusion.

[0094] Finally, using the comprehensive trend sequence, the peak values and mean values of the interaction frequency and duration changes within the time period are extracted to generate a preliminary data summary. According to the preliminary data summary and 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 through 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.

[0095] Compared with the traditional face recognition application method, this application method not only considers the change trend of the interaction frequency, but also analyzes the trend of the duration change at the same time, and obtains a comprehensive trend sequence through weighted fusion, which can more comprehensively reflect the change of the interaction data and provide richer information for users. Using the linear regression algorithm to perform trend fitting on the interaction frequency and duration change respectively can accurately find the linear relationship in the data and determine the trend sequence, providing a reliable basis for subsequent data summary generation. Considering the constraint of a specific time period in the analysis process, it can flexibly select and analyze the interaction data within a specific time period according to the user's needs or application scenarios, improving the flexibility and practicability of the system. By combining the linked list node distribution 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, providing more in-depth data analysis results for users.

[0096] According to another aspect of the present invention, there is provided a face recognition application system combined with smart glasses, based on the above method; the system includes:

[0097] A data acquisition and feature extraction module, configured to collect multi-dimensional data based on smart glasses, where the multi-dimensional data includes a face expression sequence, an emotion curve, and voice keywords; judge the acquisition frequency difference of each dimension data through a preset threshold to obtain a frequency adjustment coefficient;

[0098] A frequency adjustment coefficient calculation module, configured to perform sampling rate normalization processing on the multi-dimensional data based on the adjustment coefficient;

[0099] A sampling rate normalization processing module, configured to attach a timestamp label and a scene label to the normalized multi-dimensional data, disperse and store the data segments through the indexing mechanism of a distributed database, and obtain a unique identifier after storage;

[0100] A preliminary retrieval module, configured to, if a retrieval request is triggered, query relevant data segments in parallel from a distributed database according to a user-specified label combination to obtain a preliminary retrieval set;

[0101] A data retrieval and display module, configured to calculate the start point and end point offsets of each data segment through a timeline alignment algorithm according to the timestamp label in the preliminary retrieval set, judge whether the offset exceeds a preset threshold, determine a data segment sequence after synchronous adjustment, and display the data segment sequence to the user through smart glasses.

[0102] 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. Each module corresponds to each step of the above method, and its specific principle has been described above and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0104] Figure 1 is a schematic flowchart of an embodiment of a face recognition application method combined with smart glasses according to the present invention;

[0105] Figure 2 is a schematic structural diagram of an embodiment of a face recognition application system combined with smart glasses according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0106] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be specifically noted that the following embodiments are only used to illustrate the present invention, but do not limit the scope of the present invention. Similarly, the following embodiments are only partial embodiments of the present invention rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present invention.

[0107] 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. Using a distributed database and a time-axis alignment algorithm, efficient storage and retrieval of massive data are achieved.

[0108] Embodiment 1

[0109] Please refer to Figure 1 , a face recognition application method combined with smart glasses, the method comprising:

[0110] S1. Collect multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotion curves, and voice keywords; judge the acquisition frequency difference of each dimension data through a preset threshold to obtain a frequency adjustment coefficient;

[0111] In this embodiment, collecting multi-dimensional data based on smart glasses, the multi-dimensional data including facial expression sequences, emotion curves, and voice keywords; judging the acquisition frequency difference of each dimension data through a preset threshold to obtain a frequency adjustment coefficient, including:

[0112] S11. Process the expression sequence, emotion curve, and voice keywords respectively through eigenvalue extraction methods to obtain the eigenvalues of each dimension data; (For the collection of facial expression sequences, the smart glasses can be used to take images through the built-in camera, and then use which image processing algorithms or models (such as convolutional neural networks based on deep learning, etc.) to identify and extract expression features to obtain facial expression sequence data. For the collection of emotion curves, the smart glasses can be used to take images through the built-in camera, and the expression can be extracted through the images. For the collection of voice keywords, it is stated 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.)

[0113] S12. Compare the acquisition frequencies of each dimension data through a preset threshold, judge the frequency differences between the expression sequence, emotion curve, and voice keywords to obtain a frequency difference value;

[0114] S13. Classify and process the frequency difference value using a support vector machine algorithm to determine the frequency adjustment directions of the expression sequence, emotion curve, and voice keywords to obtain a preliminary adjustment coefficient;

[0115] S14. For the acquisition frequencies and frequency difference values of multi-dimensional data, analyze the correlation between the expression sequence, emotion curve, and speech keywords through the random forest algorithm to obtain the optimized value of the frequency adjustment coefficient.

[0116] S15. If the optimized value of the frequency adjustment coefficient exceeds the preset threshold, dynamically adjust the acquisition frequencies of the expression sequence, emotion curve, and speech keywords through the linear regression algorithm to obtain the adjusted acquisition frequency values.

[0117] S16. According to the adjusted acquisition frequency values and the eigenvalue extraction results, reprocess the multi-dimensional data, determine whether the data acquisition of the smart glasses meets the requirements of the preset threshold, and obtain the updated eigenvalue set.

[0118] S17. Determine the final acquisition frequencies of the expression sequence, emotion curve, and speech keywords through the updated eigenvalue set and the frequency adjustment coefficient, and obtain the optimized multi-dimensional data acquisition scheme.

[0119] In this embodiment, multi-dimensional data such as the expression sequence, emotion curve, and speech keywords collected by the smart glasses are obtained. The eigenvalue extraction method is used to process the expression sequence, emotion curve, and speech keywords respectively to obtain the eigenvalues of each dimension of data. The acquisition frequencies of each dimension of data are compared through the preset threshold, the frequency differences between the expression sequence, emotion curve, and speech keywords are judged, and the frequency difference values are obtained. The support vector machine algorithm is used to classify and process the frequency difference values to determine the frequency adjustment directions of the expression sequence, emotion curve, and speech keywords, and the preliminary adjustment coefficients are obtained. For the acquisition frequencies and frequency difference values of multi-dimensional data, analyze the correlation between the expression sequence, emotion curve, and speech keywords 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, dynamically adjust the acquisition frequencies of the expression sequence, emotion curve, and speech keywords through the linear regression algorithm to obtain the adjusted acquisition frequency values. According to the adjusted acquisition frequency values and the eigenvalue extraction results, reprocess the multi-dimensional data, determine whether the data acquisition of the smart glasses meets the requirements of the preset threshold, and obtain the updated eigenvalue set. Determine the final acquisition frequencies of the expression sequence, emotion curve, and speech keywords through the updated eigenvalue set and the frequency adjustment coefficient, and obtain the optimized multi-dimensional data acquisition scheme.

[0120] Specifically, for the multi-dimensional data collected by the smart glasses, first, deep learning algorithms are used to analyze the expression sequence to extract key expression feature values. For example, the probability values of expressions such as smiling and frowning are extracted through a convolutional neural network, and a threshold is set at 0.7. When the probability of a certain expression exceeds this threshold, it is recorded as valid expression data. Next, for the emotion curve, a time series analysis method is adopted to calculate the emotion fluctuation frequency and amplitude. For example, the frequency characteristics of the emotion curve are extracted through fast Fourier transform, and the emotion fluctuation frequency threshold is set at 0.5 times per second. If it exceeds this threshold, it is considered that the emotion fluctuation is large. For voice keywords, natural language processing techniques are used to extract the frequency and emotional tendency of key sentences. For example, the occurrence frequency of keywords is calculated through the TF-IDF algorithm, and the frequency threshold is set at 5 times per minute. If it exceeds this threshold, the keyword is considered a high-frequency word. Finally, the acquisition frequency differences of each dimension of data are judged through a preset threshold. For example, the acquisition frequency of the expression sequence is 10 times per second, the emotion curve is 5 times per second, and the voice keyword is 60 times per minute. The differences between the frequencies of each dimension and the preset threshold are calculated to obtain the frequency adjustment coefficients. If the frequency difference of the expression sequence is 3 times per second, the frequency difference of the emotion curve is 0 times per second, and the frequency difference of the voice keyword is 0 times per minute, the adjustment coefficients are 1.3, 1.0, and 1.0 respectively, which are used for the dynamic adjustment of the subsequent data acquisition frequency.

[0121] S2. Perform sampling rate standardization processing on the multi-dimensional data based on this adjustment coefficient;

[0122] In this embodiment, performing sampling rate standardization processing on the multi-dimensional data based on this adjustment coefficient includes:

[0123] S21. Obtain the multi-dimensional data and extract the acquisition frequency of each dimension of data, and perform sampling rate standardization processing through the frequency adjustment coefficient to obtain a data set with a unified frequency;

[0124] S22. Identify the dimension data with a higher acquisition frequency from the data set with a unified frequency, and use interpolation generation technology to calculate supplementary data points and add them to the data set;

[0125] S23. Merge the supplementary data points after interpolation generation with the original data points to obtain a new data set containing the data point generation results;

[0126] S24. Perform a consistency determination operation on the timestamp tags in the new data set, and judge that if the timestamp deviates from the preset threshold, adjust it to be consistent;

[0127] S25. According to the timestamp tags after consistency determination, perform pre-storage processing on the data set to generate structured data suitable for fragmented storage;

[0128] S26. Obtain structured data and verify the matching degree after standardizing the acquisition frequency and sampling rate to determine whether the generation of data points meets the requirements;

[0129] S27. Perform fragmented storage on the structured data in the order of timestamp tags to obtain the final processed data sharding result.

[0130] Specifically, in multi-dimensional data processing, first determine the adjustment coefficient 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, use the linear interpolation algorithm to generate supplementary data points. For example, at timestamp 0 seconds, the value of dimension A is 10, and at timestamp 2 seconds, the value is 12. By linear interpolation, the value at 1 second is calculated to be 11. Similarly, for dimension B, at timestamps 0 seconds and 4 seconds, the values are 20 and 24 respectively. By interpolation, the value at 2 seconds is calculated to be 22. After ensuring that the data points of all dimensions are aligned at the same timestamp, perform fragmented storage. For example, store the data points at timestamp 0 seconds as [10, 20, 5], at 1 second as [11, 21, 5], and at 2 seconds as [12, 22, 6]. In this way, the consistency of timestamp tags is ensured, and at the same time, the standardized processing of multi-dimensional data is achieved.

[0131] S3. Add timestamp tags and scenario tags to the standardized multi-dimensional data, and disperse and store the data fragments through the index mechanism of the distributed database to obtain the unique identifier after storage;

[0132] In this embodiment, adding timestamp tags and scenario tags to the standardized multi-dimensional data, and dispersing and storing the data fragments through the index mechanism of the distributed database to obtain the unique identifier after storage includes:

[0133] S31. Use the sharding strategy of the distributed database to perform dispersed storage on the marked data fragments to generate a preliminary mapping table of storage locations;

[0134] S32. For the storage locations in the preliminary mapping table, obtain the unique identifier generated by the index mechanism to determine the positioning address of each data fragment;

[0135] S33. If the number of data fragments exceeds the preset threshold, reallocate the storage nodes in the distributed database through the consistent hashing algorithm to obtain the adjusted mapping table;

[0136] 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;

[0137] S35. Perform classification verification on the label assignment of data segments through the support vector machine algorithm, obtain the classification results, and determine the accuracy of the timestamp label and the scenario label;

[0138] S36. For the classification results, adopt a logging mechanism to store the unique identifier and the corresponding data segment information, and obtain a complete storage index record.

[0139] Specifically, in the multi-dimensional data after normalization processing, first mark it with the timestamp label and the scenario label. For example, use the timestamp label to record that the time point when the data is generated is 14:30 on October 15, 2023, and the scenario label is marked as "urban traffic monitoring". Then, use the index mechanism of the distributed database to store the data segments dispersedly. Specifically, adopt the consistent hashing algorithm to map the data segments to different storage nodes. For example, use the hash function SHA-256 to perform hash calculation on the data, and obtain the hash value "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0", and store the data segment to the corresponding node according to this hash value. After the storage is completed, the system will automatically generate a unique identifier, such as "UUID-1234567890abcdef", to identify the location of this data segment in the distributed database. In this way, not only the efficient storage and fast retrieval of data are realized, but also the security and reliability of the data are ensured.

[0140] S4. If a retrieval request is triggered, parallelly query relevant data segments from the distributed database according to the user-specified label combination, and obtain a preliminary retrieval set;

[0141] In this embodiment, if a retrieval request is triggered, parallelly query relevant data segments from the distributed database according to the user-specified label combination, and obtain a preliminary retrieval set, including:

[0142] S41. If a retrieval request is triggered, according to the user-specified label combination, parallelly query relevant data segments from the distributed database, and use the multi-threaded loading technology to extract the storage identifiers of each dimension data, and obtain the complete data of the preliminary retrieval set;

[0143] S42. Through the complete data of the preliminary retrieval set, obtain the storage identifiers corresponding to each dimension data. For the matching degree between the storage identifier and the label combination, adopt the cosine similarity algorithm to judge the relevance of the data segments, and obtain a filtered data subset;

[0144] S43. Extract data segments from the filtered data subset. For the multi-dimensional characteristics of the data segments, adopt the K-means clustering algorithm to group the data segments, and determine the grouped data segment set;

[0145] S44. Obtain the set of data segments after grouping. According to the relevance between the storage identifiers of each dimension of data and the combination of user-specified tags, use the decision tree algorithm to judge the priority order of the data segments, and obtain the sorted list of data segments;

[0146] S45. Through the sorted list of data segments, for the matching degree between the grouping characteristics of the data segments and the tag combination, use the multi-threaded loading technology to perform parallel verification on the data segments to determine the set of verified data segments;

[0147] S46. Extract the complete data from the set of verified data segments. According to the query log of the distributed database and the trigger frequency of the retrieval request, judge the access popularity of the data segments to obtain the subset of high-popularity data segments;

[0148] S47. Obtain the subset of high-popularity data segments, update the storage identifiers in the distributed database through the parallel query technology, and use the multi-threaded loading technology to synchronize the latest status of each dimension of data to obtain the optimized retrieval set.

[0149] Specifically, when the retrieval request is triggered, the system first queries the relevant data segments in parallel from the distributed database according to the combination of user-specified tags, such as "number of outings in 2023" and "North China region". Using the multi-threaded loading technology, each thread is responsible for extracting the data storage identifiers of different dimensions. For example, one thread extracts the outing time and another thread extracts the geographical location to ensure efficient processing of large-scale data. Through multi-threaded parallel processing, the system can complete data extraction within milliseconds. For example, it can complete the extraction of identifiers for 1 million records within 500 milliseconds. Next, the system aggregates these identifiers to form a preliminary retrieval set. For example, the system combines the extracted outing time identifiers and geographical location identifiers to generate a set containing all eligible outing records. During this process, the system uses the hash algorithm to quickly match the identifiers to ensure the accuracy and efficiency of data retrieval. For example, use the SHA-256 algorithm to perform hash processing on the identifiers to ensure the uniqueness and quick 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 a machine learning model for outing trend prediction and regional outing analysis to generate a detailed outing report.

[0150] S5. According to the timestamp tags in the preliminary retrieval set, calculate the start point and end point offsets of each data segment through the time axis alignment algorithm, judge whether the offsets exceed the preset threshold, determine the data segment sequence after synchronization adjustment, and display the data segment sequence to the user through the smart glasses.

[0151] In this embodiment, according to the timestamp tags in the preliminary retrieval set, the start point and end point offsets of each data segment are calculated through the timeline alignment algorithm, and it is judged whether the offsets exceed the preset threshold to determine the data segment sequence after synchronization adjustment, including:

[0152] S51. Obtain the timestamp tags from the preliminary retrieval set, determine the time positions of each data segment by parsing the timestamp tags, and obtain the initial data segment set containing time information;

[0153] S52. For the initial data segment set, use the timeline alignment algorithm to calculate the start point offset and end point offset of each data segment to obtain the offset calculation result set;

[0154] S53. Obtain the offset calculation result set, compare it with the preset threshold, and judge whether the start point offset and end point offset of each data segment exceed the preset threshold to obtain the offset exceeding flag set;

[0155] S54. According to the offset exceeding flag set, use the synchronization adjustment method to process the data segments that exceed the preset threshold, determine the time positions of the adjusted data segments, and obtain the data segment set after synchronization adjustment;

[0156] S55. Through the data segment set after synchronization adjustment, extract the timestamp tags and the adjusted time positions, and judge the continuity between the data segments to obtain the continuity verification result set;

[0157] S56. For the continuity verification result set, use the sequence recombination algorithm to integrate each data segment, determine the final segment sequence, and obtain the ordered data segment sequence set;

[0158] S57. Through the ordered data segment sequence set, apply the support vector machine algorithm to verify the sequence integrity, and judge whether there is a time alignment anomaly to obtain the final data segment sequence after synchronization adjustment.

[0159] Specifically, in the preliminary retrieval set, each data segment is tagged with a timestamp that records the start time and end time of the segment. Through the timeline alignment algorithm, the timelines of each data segment are first normalized. Assume that the normalized time range is from 0 to 1000 milliseconds. Then, the start point and end point offsets of each data segment are calculated. For example, the start time of segment A is 200 milliseconds, the end time is 500 milliseconds, the start time of segment B is 210 milliseconds, the end time is 510 milliseconds, and the start time of segment C is 205 milliseconds, the end time is 505 milliseconds. Through calculation, the start point offset of segment B is +10 milliseconds, the end point offset is +10 milliseconds, the start point offset of segment C is +5 milliseconds, and the end point offset is +5 milliseconds. Next, it is determined whether these offsets exceed the preset threshold. Assume that the preset threshold is ±15 milliseconds. The offsets of segment B and segment C do not exceed the threshold. Finally, synchronous adjustment is performed according to the offsets to generate an adjusted data segment sequence. For example, the start time of segment B is adjusted to 200 milliseconds, the end time is adjusted to 500 milliseconds, the start time of segment C is adjusted to 200 milliseconds, and the end time is adjusted to 500 milliseconds, so as to ensure that all data segments are aligned on the timeline and form a consistent synchronization sequence.

[0160] In this embodiment, the adjusted data segment sequence is obtained, the priority is determined according to the importance of the data and user requirements, and the information segment with the highest priority is loaded through the display module of the smart glasses at a refresh rate of 30 frames per second. The user's line of sight position is captured in real time through the eye tracking technology, and the projection area coordinates are recalculated to obtain the dynamically adjusted display content.

[0161] Step 1: Obtain the sequence of original data segments, and determine the priority order according to user requirements and data importance through a preset sorting algorithm to obtain the sorted sequence of data segments. Step 2: For the sorted sequence of data segments loaded by the display module of the smart glasses at a frame rate of 30 frames per second, use buffer storage technology to obtain the data stream of the initial projection. Step 3: Use eye-tracking technology to capture the user's line-of-sight position in real time. If the line-of-sight position exceeds the preset threshold range, generate new line-of-sight coordinate data to obtain the set of real-time adjusted line-of-sight positions. Step 4: Recalculate the dynamic coordinates of the projection area according to the set of real-time adjusted line-of-sight positions, and use geometric transformation algorithms to obtain the adjusted sequence of projection coordinates. Step 5: Obtain the adjusted sequence of projection coordinates and the data stream of the initial projection, and use content mapping technology to obtain the sequence of dynamically adjusted display content. Step 6: For the sequence of dynamically adjusted display content, use a convolutional neural network algorithm to detect whether the content boundary matches the coordinates. If not, readjust it through interpolation algorithms to obtain the optimized sequence of display content. Step 7: Load the optimized sequence of display content by the projection device at a frame rate of 30 frames per second, and use real-time rendering technology to obtain the final output of the dynamic display content.

[0162] Specifically, first, the system obtains the sequence of data segments after synchronous adjustment of the display module of the smart glasses through the real-time data acquisition module. After these data segments are preprocessed, the system performs priority sorting according to the importance of the data and user requirements. For example, using a weighted algorithm, the system comprehensively analyzes the user's historical behavior data, current environmental data, and preset user preference parameters to calculate the priority score of each data segment. Suppose the priority score of a certain data segment A is 95 points, while that of another data segment B is 80 points. The system will give priority to loading A. Next, the system loads the information segment with the highest priority at a frame rate of 30 frames per second to ensure the real-time and smoothness of the projection content. During the loading process, the system uses eye-tracking technology to capture the user's line-of-sight position in real time. For example, 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 within the projection area. Suppose the current coordinates of the user's line-of-sight focus are (x = 120, y = 80). The system will recalculate the display content of the projection area based on this coordinate and use interpolation algorithms to dynamically adjust the image to ensure that the information within the user's line of sight is clearly visible. For example, increase the image resolution near the focus to 1080p, while reduce the area far from the focus to 720p to optimize resource allocation. Finally, the system generates the dynamically adjusted display content and updates the projection screen in real time to ensure that the user can obtain the most important information at any time.

[0163] In this embodiment, according to the timestamp tags in the preliminary retrieval set, the start point and end point offsets of each data segment are calculated through the timeline alignment algorithm, it is determined whether the offsets exceed the preset threshold, the data segment sequence after synchronization adjustment is determined, and the data segment sequence is displayed to the user through the smart glasses. After that, it further includes:

[0164] S6. Extract the interaction records of a specific person from the multi-dimensional data, use the linked list data structure to concatenate the audio segments, 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;

[0165] In this embodiment, extracting the interaction records of a specific person from the multi-dimensional data, using the linked list data structure to concatenate the audio segments, image snapshots, and text annotations in chronological order, recording the start time and end time of each node through the hash index mechanism, ensuring the synchronization of different types of data, and determining the timeline storage structure includes:

[0166] S61. Obtain the interaction records of a specific person from the multi-dimensional data, filter out irrelevant data using the preset threshold, and concatenate the audio segments, image snapshots, and text annotations in chronological order through the linked list structure to obtain the preliminary time series;

[0167] S62. For the preliminary time series, record the start time and end time of each linked list node through the hash index mechanism, generate an index table containing timestamps, and determine the synchronization of each data segment;

[0168] S63. According to the start time and end time in the index table, obtain the time span of the audio segment. If the time span exceeds the preset threshold, it is decomposed into multiple sub-segments through segmentation processing to obtain the adjusted audio sequence;

[0169] S64. Through the adjusted audio sequence, obtain the corresponding image snapshots and text annotations in chronological order, and use hash index to match the start time and end time to generate a synchronized multi-modal data stream;

[0170] S65. For the synchronized multi-modal data stream, rearrange the audio segments, image snapshots, and text annotations using the linked list structure, and record the complete sequence through the timeline storage structure to obtain an ordered data set;

[0171] S66. Extract the interaction records of a specific person from the ordered data set, classify the text annotations using the support vector machine algorithm, judge the interaction type, and generate a classified interaction timeline;

[0172] S67. According to the classified interaction timeline, analyze the correlation between the interaction records and the chronological order through the random forest algorithm, determine the final timeline storage structure, and output a multi-dimensional synchronization sequence.

[0173] Specifically, when extracting the interaction records of a specific person from multi-dimensional data, first concatenate the audio segments, image snapshots, and text annotations in chronological order through a hash index mechanism. Assume that the sampling rate of the audio segment 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 segment starts at 10:00:00 on October 1, 2023, lasts for 5 seconds, and the hash value is "a1b2c3d4", and its end time is 10:00:05 on October 1, 2023. The image snapshot is captured at 10:00:02 on October 1, 2023, and the hash value is "e5f6g7h8", and its end time is 10:00:0033 on October 1, 2023. The text annotation is generated at 10:00:03 on October 1, 2023, and the hash value is "i9j0k1l2", and its end time is 10:00:0001 on October 1, 2023. Concatenate these nodes 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 segment "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, when querying 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.

[0174] S7. Regularly scan the linked list nodes according to the timeline storage structure, obtain the numerical sequence of the interaction frequency and duration changes, fit the change trend through the linear regression algorithm, and generate the interaction data summary for a specific time period;

[0175] In this embodiment, the regularly scanning the linked list nodes according to the timeline storage structure, obtaining the numerical sequence of the interaction frequency and duration changes, fitting the change trend through the linear regression algorithm, and generating the interaction data summary for a specific time period includes:

[0176] S71. Through the storage structure in the timeline structure, regularly scan the linked list nodes, obtain the original data of the interaction frequency and duration changes from them, and obtain the initial numerical sequence;

[0177] S72. According to the initial numerical sequence, use the linear regression algorithm to fit the changing trend of the interaction frequency over time, and determine the first trend sequence;

[0178] S73. For the initial numerical sequence, use the linear regression algorithm to fit the changing trend of the duration change over time, and obtain the second trend sequence;

[0179] S74. From the first trend sequence and the second trend sequence, obtain the trend slope and intercept within a specific time period, and judge the correlation strength between the interaction frequency and the duration change;

[0180] S75. Through the correlation strength, combined with the time period constraint, perform weighted fusion on the first trend sequence and the second trend sequence to obtain the comprehensive trend sequence;

[0181] S76. Use the comprehensive trend sequence to extract the peak and mean values of the interaction frequency and the duration change within the time period, and generate a preliminary data summary;

[0182] S77. According to the preliminary data summary, combined with the distribution of linked list nodes in the timeline structure, calculate the interaction density within a specific time period to obtain the final data summary.

[0183] Specifically, in the timeline storage structure, the system will regularly scan the linked list nodes to obtain the numerical sequences of the interaction frequency and the duration change. For example, the system scans the linked list nodes once every hour, records the interaction frequency and the duration of each node, and generates a sequence data containing timestamps and corresponding values. Suppose that within a day, the system records 24 groups of data, and 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 the linear regression algorithm to fit these data to predict the future interaction trend. By calculating the least squares method, the system obtains the regression equation of the interaction frequency as y = 5x + 5, and the regression equation of the 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 the interaction data summary within a specific time period. For example, the predicted interaction frequencies and durations for the next three hours are [75, 70, 75] and [20, 30, 30] minutes respectively. These summary data can be used for further analysis of user behavior, optimization of system performance, or providing data support for decision-making.

[0184] S8. If a specific person is recognized, the latest node is loaded from the end of the linked list, a timeline summary is generated in combination with the change trend, and is displayed through the display module of the smart glasses. It is judged whether the displayed content matches according to the keywords and context information of the current scene, and the final output sequence is obtained.

[0185] In this embodiment, if a specific person is recognized, the latest node is loaded from the end of the linked list, a timeline summary is generated in combination with the change trend, and is displayed through the display module of the smart glasses. It is judged whether the displayed content matches according to the keywords and context information of the current scene, and the final output sequence is obtained, including:

[0186] S81. Face recognition is performed through the smart glasses to obtain the identity identifier of the specific person, the corresponding latest node data is loaded from the end of the preset database linked list, the change trend is calculated by using the time series analysis method and the initial timeline summary is generated to obtain the first sequence data;

[0187] S82. 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 by using the image rendering technology to obtain the second sequence data;

[0188] S83. The real-time audio and video streams of the current scene are obtained, keywords are extracted through natural language processing technology and a scene description vector is generated, and the matching degree with the second sequence data is calculated in combination with the context information to obtain the third sequence data;

[0189] S84. If the matching degree in the third sequence data exceeds the preset threshold, target detection is performed on the video frames of the current scene through a convolutional neural network, and the relevant position information of the specific person is extracted to obtain the fourth sequence data;

[0190] S85. The position information of the specific person is obtained from the fourth sequence data, and in combination with the change trend in the first sequence data, the timeline summary of the next moment is predicted through a long short-term memory network to obtain the fifth sequence data;

[0191] S86. 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 by using the keyword extraction result to obtain the sixth sequence data;

[0192] S87. 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 to obtain the final sequence data.

[0193] In this embodiment, first, face recognition is performed through smart glasses to obtain the identity identifier 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 the initial timeline summary, obtaining the first sequence of data. This step realizes the recognition of a specific person and the preliminary acquisition and processing of relevant data.

[0194] Next, the timeline summary is extracted from the first sequence of data, and the first sequence of data is transmitted to the display module of the smart glasses. The image rendering technology is used to adjust the display content, obtaining the second sequence of data. The image rendering technology can display the data in an intuitive image form to the user, improving the readability and visualization effect of the information.

[0195] Then, the real-time audio and video streams of the current scene are obtained. Keywords are extracted through natural language processing technology and a scene description vector is generated. The matching degree with the second sequence of data is calculated in combination with the context information, obtaining the third sequence of data. This step describes and analyzes the current scene through natural language processing technology, providing a basis for subsequent matching calculations.

[0196] If the matching degree in the third sequence of data exceeds the preset threshold, then object detection is performed on the video frames of the current scene through a convolutional neural network, and the relevant position information of the specific person is extracted, obtaining the fourth sequence of data. The convolutional neural network can effectively detect and locate the objects in the image, providing accurate position information for subsequent prediction and display.

[0197] The position information of the specific person is obtained from the fourth sequence of data. Combining with the change trend in the first sequence of data, the timeline summary at the next moment is predicted through a long short-term memory network, obtaining the fifth sequence of data. The long short-term memory network can model and predict time series data, providing a preview of interactive information at future moments for the user.

[0198] The content display parameters of the projection device are updated through the fifth sequence of data, and the display priority of the display module of the smart glasses is adjusted using the keyword extraction results, obtaining the sixth sequence of data. This step dynamically adjusts the priority and parameters of the display content according to the prediction results and keyword extraction, ensuring that the user can obtain the most important information.

[0199] Finally, the final display sequence is extracted from the sixth sequence of data, and the matching degree is verified through the context information. If the verification result meets the preset conditions, the sixth sequence of data is converted into an output sequence and transmitted to the display module of the smart glasses for display, obtaining the final sequence of data. This step ensures the accuracy and relevance of the display content, providing the user with the final display result that meets the requirements.

[0200] Compared with traditional face recognition application methods, this application method performs face recognition through smart glasses and combines linked list data structures and time series analysis methods to quickly obtain and process relevant data of specific individuals, improving the system's response speed and processing efficiency. By using natural language processing technology and convolutional neural networks, it accurately describes the current scene and detects targets, ensuring a high degree of matching between the displayed content and the actual scene, and improving the system's intelligence level and user experience. Using long short-term memory networks to predict timeline summaries can provide users with previews of interactive information at future times, enhancing the system's forward-looking and practicality. By dynamically updating the display parameters of the projection device and adjusting the display priority of the smart glasses, it can flexibly display the most important information according to different scenarios and user needs, improving the readability and practicality of the information.

[0201] 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:

[0202] S91. Detect the matching degree between the displayed content and the current scene by obtaining real-time data. If the matching degree is lower than the preset threshold, extract key time points from the real-time data to determine the deviation degree, and obtain a preliminary basis for adjusting the timeline summary;

[0203] S92. Use the random forest algorithm to perform feature classification for key time points, extract high-weight time features from the classification results, and determine the part of the content sequence that needs to be adjusted to obtain adjusted timeline summary data;

[0204] S93. Process the adjusted timeline summary data through a convolutional neural network to obtain the temporal dependence relationship between the data, rearrange the content sequence according to the dependence relationship, and determine the structure of the new output sequence;

[0205] S94. If the deviation degree of the new output sequence is still higher than the preset threshold, optimize the weight distribution of the timeline summary through the gradient boosting algorithm, extract more accurate key time points from the optimization results, and obtain further adjusted content sequence data;

[0206] S95. Generate corresponding displayed content according to the adjusted content sequence data, use a real-time evaluation model to calculate the matching degree between the displayed content and the current scene, and determine whether the preset real-time standard is met;

[0207] S96. By comparing the output sequence structures before and after adjustment, obtain the distribution characteristics of the changing time points, update the parameters of the preset threshold according to the distribution characteristics, and determine the final displayed content sequence;

[0208] 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 the display content with optimized real-time performance.

[0209] In this embodiment, first, the matching degree between the display content and the current scene is detected by obtaining real-time data. If the matching degree is lower than the preset threshold, key time points are extracted from the real-time data to determine the deviation degree, and a preliminary basis for adjusting the timeline summary is obtained. This step can timely detect the mismatch between the display content and the actual scene and provide basic data for subsequent adjustments.

[0210] Next, the random forest algorithm is used to classify the features of the key time points, high-weight time features are extracted from the classification results, and the part that needs to be adjusted in the content sequence is judged to obtain the adjusted timeline summary data. The random forest algorithm can effectively classify the features of the key time points, determine the content part that needs to be adjusted, and improve the accuracy of the adjustment.

[0211] Then, the adjusted timeline summary data is processed by a convolutional neural network to obtain the timing dependence relationship between the data, and the content sequence is rearranged according to the dependence relationship to determine the new output sequence structure. The convolutional neural network can capture the timing dependence relationship between the data, provide a basis for rearranging the content sequence, and make the output sequence more in line with the actual time logic and scene requirements.

[0212] 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 the gradient boosting algorithm, and more accurate key time points are extracted from the optimization result to obtain the content sequence data for further adjustment. The gradient boosting algorithm can improve the accuracy and adjustment effect of the timeline summary by optimizing the weight distribution.

[0213] Generate the corresponding display content according to the adjusted content sequence data, and use the real-time performance evaluation model to calculate the matching degree between the display content and the current scene to judge whether the preset real-time performance standard is met. This step can ensure that the adjusted display content meets the requirements in terms of real-time performance and provide timely and accurate information for users.

[0214] By comparing the output sequence structures before and after 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 result, improves the adaptive ability of the system, and ensures that the final display content sequence is more in line with the actual scene and user requirements.

[0215] Finally, 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 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.

[0216] Compared with the traditional face recognition application method, this application method can timely detect the mismatch between the display content and the current scene through real-time data detection and key time point extraction, and make rapid adjustments, ensuring the accuracy and real-time performance of the output sequence. Using the random forest algorithm to classify the features of key time points can accurately extract high-weight time features, judge the content part that needs to be adjusted, and improve the pertinence and accuracy of the adjustment. Utilizing the convolutional neural network to obtain the temporal dependence relationship between data can reasonably rearrange the content sequence, making the output sequence more in line with the actual time logic and scene requirements, and improving the coherence and readability of the display content. According to the structural changes in the output sequence before and after adjustment, update the parameters of the preset threshold, enabling the system to adapt to different scene and data changes, and improving the robustness and flexibility of the system. Synchronize the final display content with the current scene data through the timing alignment technology to ensure the optimized real-time performance of the display content and provide the user with information most in line with the current scene.

[0217] Embodiment 2

[0218] Please refer to Figure 2 , a face recognition application system combined with smart glasses, based on the method described in Embodiment 1; the system includes:

[0219] A data acquisition and feature extraction module, used to collect multi-dimensional data based on smart glasses, where the multi-dimensional data includes a face expression sequence, an emotion curve, and speech keywords; judge the acquisition frequency difference of each dimension data through a preset threshold to obtain a frequency adjustment coefficient;

[0220] A frequency adjustment coefficient calculation module, used to perform sampling rate normalization processing on the multi-dimensional data based on this adjustment coefficient;

[0221] A sampling rate normalization processing module, used to label the normalized multi-dimensional data with time stamps and scene labels, and disperse the storage of data segments through the indexing mechanism of a distributed database to obtain the unique identifier after storage;

[0222] A preliminary retrieval module, used to, if a retrieval request is triggered, query relevant data segments in parallel from the distributed database according to the user-specified label combination to obtain a preliminary retrieval set;

[0223] The data retrieval and display module is used to calculate the start point and end point offsets of each data segment through the timeline alignment algorithm according to the timestamp tags in the preliminary retrieval set, determine whether the offsets exceed the preset threshold, determine the data segment sequence after synchronous adjustment, and display the data segment sequence to the user through the smart glasses.

[0224] 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. Each module corresponds to each step of the above method, and its specific principle has been described above and will not be elaborated here.

[0225] The above are only partial embodiments of the present invention, and thus do not limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be 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 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 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; 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.

2. 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 to obtain a new data set; 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.

3. 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.

4. 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.

5. 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 used to verify the sequence integrity, determine whether there is a time alignment anomaly, and obtain the final synchronized data fragment sequence.

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, 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.

7. The face recognition application method combined with smart glasses as claimed in claim 6, 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 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.

8. The face recognition application method combined with smart glasses as claimed in claim 6, characterized in that: The method of periodically scanning the linked list nodes according to the timeline storage structure to obtain the numerical sequence of interaction frequency and duration changes, fitting the change trend through a linear regression algorithm, and generating a summary of interaction data for a specific time period includes: Through the timeline storage 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 and the linked list node distribution in the timeline storage structure, the interaction density in a specific time period is calculated to obtain the final data summary.

9. A face recognition application system combined with smart glasses, characterized in that: Based on the method described in any one of claims 1 to 8; 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 through a time axis alignment algorithm based on the timestamp tags in the preliminary retrieval set, 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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