Digital twin data synchronization method and system fusing deep learning and internet of things

Abstract

The application discloses a digital twin data synchronization method and system fusing deep learning and Internet of Things, and relates to the technical field of data synchronization. The method comprises the following steps: based on the digital twin information of the Internet of Things equipment, calling the time stamp and data sequence identifier through a sliding window, collecting the time span, calculating the characteristic value change amplitude of adjacent time slices in the data sequence, and performing transmission data sparsification processing to reduce the communication burden and obtain a sparse feature set. In the application, the sliding window technology and time series analysis are adopted to efficiently extract and compress data features, significantly reduce the redundancy in data transmission, improve the transmission rate and efficiency, the dynamic segmented decoding and feature mapping structure application enable stronger response capability and prediction accuracy to data fluctuations, intelligently adjust the data flow direction and node connection weight, optimize the data synchronization path according to the real-time network state and bandwidth allocation, and ensure the efficient synchronization of data among multiple nodes and multiple platforms.

Description

Technical Field

[0001] This invention relates to the field of data synchronization technology, and in particular to a digital twin data synchronization method and system that integrates deep learning and the Internet of Things. Background Technology

[0002] The field of data synchronization technology involves achieving data consistency, coordination, and efficient transmission between multiple systems, devices, or platforms. In the era of the Internet of Things, data synchronization is not limited to the traditional synchronization between databases and applications. Its core goal is the seamless flow and consistency of data between different nodes, reducing latency, minimizing data conflicts, and improving the overall system reliability and performance. Data synchronization is widely used in scenarios such as distributed computing, real-time monitoring, smart device interaction, cloud computing, and edge computing.

[0003] Among them, the digital twin data synchronization method that integrates deep learning and the Internet of Things refers to combining deep learning technology with the Internet of Things to optimize the data synchronization process in the digital twin system. Through the intelligent analysis capabilities of deep learning models, it processes and predicts the data streams generated by IoT devices, improving the real-time mapping accuracy and dynamic response capability of the digital twin system to real physical objects. Its purpose is to solve the complex multi-source data coordination problem in the digital twin system, optimize system performance, support applications in fields such as industrial manufacturing, smart cities, and precision medicine, and achieve more efficient and intelligent digital management and control.

[0004] Existing technologies have failed to effectively solve the problem of coordinating multi-source data. They exhibit low synchronization efficiency in complex network environments, slow response to changes in device status, and conventional synchronization, without optimized data processing strategies, cannot effectively manage high-frequency data changes, easily leading to data delays or loss. Because they do not consider real-time analysis of network status and bandwidth changes, they limit adaptability to changing network conditions. Non-optimized synchronization paths result in inefficient data transmission under network congestion, thereby increasing the synchronization burden and limiting performance in real-time monitoring and IoT interaction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, such as ineffective coordination of multi-source data, low synchronization efficiency in complex network environments, slow response to device status changes, inability of conventional synchronization to effectively manage high-frequency data changes without optimized data processing strategies leading to data delays or loss, and limited adaptability to changing network conditions due to the lack of real-time analysis of network status and bandwidth changes, non-optimized synchronization paths result in inefficient data transmission under network congestion, increasing the synchronization burden and limiting performance in real-time monitoring and IoT interaction, this invention provides a digital twin data synchronization method and system integrating deep learning and IoT. The technical solution is as follows:

[0006] On the one hand, a method for synchronizing digital twin data that integrates deep learning and the Internet of Things is provided, including the following steps:

[0007] S1. Based on the digital twin information of IoT devices, the time span is collected by calling the timestamp and data sequence identifier through a sliding window, the change amplitude of feature values ​​of adjacent time slices in the data sequence is calculated, and the data is sparsified to obtain a sparse feature set.

[0008] S2. Based on the sparse feature set, calculate the difference between each feature value according to the time series feature value, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and perform dynamic segmented decoding according to the analysis results to obtain the fluctuation feature mapping structure.

[0009] S3. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and adjust the data flow direction and node connection weight to obtain the spatiotemporal consistency result.

[0010] S4. Based on the spatiotemporal consistency results, collect the network status and bandwidth allocation during the synchronization process, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and obtain the optimized configuration of the transmission channel according to the communication efficiency of the path and the current load.

[0011] S5. Based on the optimized configuration of the transmission channel, according to the service data synchronization rate in the path, analyze the characteristic values ​​of the synchronization rate change within the time window, filter the data sequences with high-frequency fluctuation characteristics in the synchronization path, and obtain the data synchronization path optimization result according to the data synchronization rate in the path.

[0012] On the other hand, a digital twin data synchronization system integrating deep learning and the Internet of Things is provided. This system is applied to a digital twin data synchronization method integrating deep learning and the Internet of Things, including:

[0013] The time feature sparsity module is used for digital twin information based on IoT devices. It uses a sliding window to call timestamps and data sequence identifiers to collect data over a time span, calculates the change amplitude of feature values ​​between adjacent time slices in the data sequence, and performs data sparsity processing to obtain a sparse feature set.

[0014] The feature fluctuation decoding module is used to calculate the difference between each feature value based on the sparse feature set and the time series feature values, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and obtain the fluctuation feature mapping structure.

[0015] The fluctuation path mapping module is used to collect data synchronization paths between IoT devices based on the fluctuation feature mapping structure, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and obtain spatiotemporal consistency results.

[0016] The path load optimization module is used to collect network status and bandwidth allocation during the synchronization process based on the spatiotemporal consistency results, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and obtain the optimized configuration of the transmission channel based on the communication efficiency of the path and the current load.

[0017] The task synchronization allocation module is used to optimize the configuration based on the transmission channel, analyze the characteristic values ​​of the synchronization rate change within the time window according to the service data synchronization rate in the path, filter the data sequence with high-frequency fluctuation characteristics in the synchronization path, and obtain the data synchronization path optimization result according to the data synchronization rate in the path.

[0018] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0019] By employing sliding window technology and time series analysis, it can efficiently extract and compress data features, significantly reduce redundancy in data transmission, lower network burden, and improve transmission rate and efficiency. The application of dynamic segmented decoding and feature mapping structure enables stronger responsiveness and prediction accuracy to data fluctuations. It intelligently adjusts data flow direction and node connection weights, optimizes data synchronization paths based on real-time network status and bandwidth allocation, ensures efficient data synchronization across multiple nodes and platforms, improves the real-time performance of data synchronization, reduces data conflicts, and enhances overall reliability and performance. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a digital twin data synchronization method that integrates deep learning and the Internet of Things, provided by an embodiment of the present invention;

[0022] Figure 2 This is a block diagram of a digital twin data synchronization system integrating deep learning and the Internet of Things, provided by an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a digital twin data synchronization device that integrates deep learning and the Internet of Things, provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0025] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0026] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0027] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0028] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0029] This invention provides a method for synchronizing digital twin data by integrating deep learning and the Internet of Things, such as... Figure 1 As shown, it includes the following steps:

[0030] S1. Based on the digital twin information of IoT devices, timestamps and data sequence identifiers are called through a sliding window to collect data over a time span, calculate the change amplitude of feature values ​​of adjacent time slices in the data sequence, and perform data sparsification processing to reduce communication burden and obtain a sparse feature set.

[0031] Optionally, the sparse feature set includes time slice sequences, feature value variation ranges, and sparse data point sets; the fluctuation feature mapping structure includes feature fluctuation ranges, time series difference distributions, and dynamic segmentation structures; the spatiotemporal consistency results include node flow intensity values, synchronization path dependency indicators, and communication node weight allocations; the transmission channel optimization configuration includes traffic allocation results, path load distributions, and bandwidth utilization; and the data synchronization path optimization results include high-frequency fluctuation paths, synchronization task allocation tables, and path priority results.

[0032] Optionally, the specific operation steps of S1 may include the following S101-S103:

[0033] S101. Based on the digital twin information of IoT devices, call the timestamp and data sequence identifier, collect data segments within the corresponding time period by sliding a fixed time window, and detect the data range and content integrity within the time window to obtain time series segments.

[0034] By sliding a fixed time window, timestamps and data sequence identifiers are extracted, the span of the sliding window is determined, and multiple time periods are divided according to the timestamps. Raw data points within each time period are collected, and the time range and content integrity of each data segment are analyzed. The data content is checked one by one to see if there are any missing or abnormal values, and abnormal values ​​are removed. Then, the time series data of each window are sorted and merged in chronological order to ensure the order and integrity of the data in each time period in the collected sequence, thus obtaining time series segments.

[0035] S102. Based on time series segments, compare the feature values ​​of adjacent time segments, calculate the range of feature value changes in the time segments, extract the data fluctuation amplitude, and statistically analyze the change amplitude of the segments and the corresponding time points to obtain a set of feature value change amplitudes.

[0036] Feature values ​​are extracted from adjacent time segments one by one. By analyzing the specific numerical changes of each data segment, the feature values ​​are compared numerically. The range of feature value changes in the two data segments is calculated, and the time points corresponding to the changes are recorded. The fluctuation amplitude of feature values ​​in each time segment is statistically analyzed according to the order of time segments. The time segments with significant fluctuations and their corresponding ranges of change are extracted. The mapping relationship between feature values ​​and time points is established. The change amplitude data of all time segments are summarized to obtain the set of feature value change amplitudes.

[0037] S103. Based on the set of feature value change amplitudes, filter time segments with low change amplitudes, remove data content with low change amplitudes, and reorganize the remaining feature values ​​according to sparsity logic to optimize the transmission data load and obtain a sparse feature set.

[0038] Extract time segments with fluctuations below a set threshold, mark the data content of the corresponding time period and remove segments with low fluctuations, reorganize the time series by parsing the feature values ​​in the remaining time segments, filter data points with low feature value change frequency according to sparsity logic, reorganize the remaining feature values ​​to reduce unnecessary data volume, compress data load and optimize bandwidth usage during transmission, and obtain a sparse feature set.

[0039] S2. Based on the sparse feature set, calculate the difference between each feature value according to the time series feature value, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and perform dynamic segmented decoding based on the analysis results to obtain the fluctuation feature mapping structure.

[0040] Optionally, the specific operation steps of S2 may include the following S201-S203:

[0041] S201. Based on the sparse feature set, the feature values ​​are arranged according to the time series. By analyzing the difference data of the feature values ​​at each time point, the difference between adjacent feature values ​​is determined, and a feature difference sequence is obtained.

[0042] Based on the time series, feature values ​​are arranged. By analyzing the feature values ​​at each time point in the time series, the difference in feature values ​​between adjacent time points is calculated point by point. By comparing the data change amplitude between every two consecutive time points, the specific numerical difference between feature values ​​is extracted, and the corresponding time point information is recorded. The feature difference values ​​of all time points are organized and arranged in chronological order. A mapping relationship is established between the difference values ​​and time points, and a difference change matrix in the time series is constructed to obtain the feature difference sequence.

[0043] S202. Based on the feature difference sequence, analyze the data fluctuation range of the feature value difference, count the numerical changes of the difference value, identify the time period of the numerical change, mark the start and end time of the segment, and obtain the set of fluctuation segments.

[0044] The fluctuation range of the eigenvalue difference is analyzed. The fluctuation interval is extracted by calculating the maximum and minimum values ​​of the difference in segments. Each difference value in the time series is segmented and statistically analyzed. The time points with large value changes are recorded. The high fluctuation interval is extracted by comparing the fluctuation changes between time periods. The start and end times of these time periods are marked step by step. The time range and difference value distribution of each fluctuation segment are sorted out. All fluctuation intervals are merged and summarized to obtain a set of fluctuation segments.

[0045] Analyze the data fluctuation range of the eigenvalue difference, and calculate the fluctuation range R of the eigenvalue difference according to the following formula (1):

[0046] (1)

[0047] The root mean square fluctuation of the eigenvalues ​​is calculated according to the following formula (2). :

[0048] (2)

[0049] Among them, D i Represents each difference value in the feature difference sequence, max(D) i) represents the maximum value of the difference in the feature difference sequence, min(D) i () represents the minimum value of the difference in the feature difference sequence, and n represents the total number of data points in the feature difference sequence. The average value of the sequence with distinctive differences is calculated using the following formula:

[0050] ;

[0051] Detailed explanation of the formula and its calculation derivation:

[0052] The difference value D in the characteristic difference sequence i This is achieved by comparing data at different time points, specifically by calculating the differences in feature values ​​between adjacent time points and forming a sequence. Assuming the feature difference sequence is D={2.4,3.6,1.8,4.2,3.0}, the specific steps are as follows:

[0053] Calculate the fluctuation range R;

[0054] Find the maximum and minimum differences in the sequence:

[0055] max(D i =4.2;

[0056] min(D i =1.8;

[0057] Substitute into the formula to calculate the fluctuation range:

[0058] ;

[0059] Calculate root mean square fluctuation ;

[0060] Calculate the average of the sequence :

[0061] ;

[0062] Calculate the squared deviation of each difference from the mean, and sum them:

[0063] ;

[0064] Substitute into the formula to calculate the root mean square fluctuation:

[0065] ;

[0066] The fluctuation range of the characteristic difference sequence is 2.4, the root mean square fluctuation is 0.849, and the fluctuation range R represents the maximum amplitude of the difference value. This reflects the stability and degree of deviation of the overall fluctuations, and these two results will be used for subsequent time segment annotation and analysis.

[0067] S203. Based on the set of fluctuation segments, identify the feature values ​​of the time series of fluctuation segments, calculate the numerical trend of the feature values ​​changing with time, sort the feature values ​​according to the time point features, and obtain the fluctuation feature mapping structure.

[0068] Extract the corresponding time series feature values ​​from each fluctuation segment, analyze the distribution of feature values ​​on the time axis, calculate the change amplitude of each feature value at different time points, record the trend information of feature values ​​changing with time segment by segment, arrange the feature values ​​in order of time points, construct the mapping relationship between time series and feature values, and extract the trend model of feature values ​​changing with time. Summarize and organize the trend models extracted from all segments to obtain the fluctuation feature mapping structure.

[0069] S3. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and adjust the data flow direction and node connection weight to obtain the spatiotemporal consistency result.

[0070] Optionally, the specific operation steps of S3 may include the following S301-S303:

[0071] S301. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, parse the communication records between devices, identify the data flow and transmission times between nodes in the path, analyze the synchronization relationship between nodes, and obtain the data synchronization path set.

[0072] The data synchronization path between IoT devices is collected. By analyzing the communication records between devices, the data interaction information between nodes is extracted. The source node and target node information in each record is parsed one by one. The data traffic and transmission frequency of each node on the path are counted. The synchronization relationship is determined by comparing the transmission volume between nodes. The transmission ratio of each node in the path is calculated based on the transmission frequency and traffic distribution. The synchronization pattern of the path is marked by combining the time series between nodes. The interaction relationship of all nodes in the path is merged and organized to obtain the data synchronization path set.

[0073] S302. Based on the data synchronization path set, evaluate the connection efficiency and load status of communication nodes, analyze the path traffic distribution and node load data between nodes, calculate the transmission rate and path dependency value of nodes, identify the data flow direction and intensity between nodes, and obtain the node load evaluation results.

[0074] The connection efficiency and load status of communication nodes are analyzed. By extracting the transmission rate and path traffic distribution data of each node, the load ratio of nodes in the path is calculated for each node. The efficiency of each node is evaluated by comparing the distribution pattern of node load and path traffic. High-load nodes are marked by analyzing the load fluctuation and path dependency relationship of nodes. The transmission strength between nodes is calculated according to the data flow direction in the path. A path efficiency evaluation table is established by combining the load and path dependency characteristics between nodes to obtain the node load evaluation results.

[0075] S303. Based on the node load assessment results, adjust the node connection weights in the synchronization path, calculate the load balancing situation between nodes, redistribute the data flow direction, and adjust the node connection relationship to obtain the spatiotemporal consistency result.

[0076] The node connection weights in the synchronization path are adjusted. The traffic adjustment ratio between nodes is calculated by analyzing the load balancing of nodes in the path. The connection status and data flow characteristics of each node are compared one by one. The traffic distribution direction is replanned. Redundant transmission tasks are removed from the traffic of high-load nodes and redistributed to low-load nodes to optimize the transmission efficiency between paths. A time synchronization model is constructed by combining the traffic distribution characteristics formed after the node connection relationship is adjusted, and the spatiotemporal consistency result is obtained.

[0077] Adjust the node connection weights in the synchronization path and calculate the load balancing among nodes according to the formula:

[0078] ;

[0079] Calculate the weights between nodes ;

[0080] in, This represents the data flow from node i to node j. This represents the communication efficiency from node i to node j. This represents the load between node i and node j.

[0081] Detailed explanation of the formula and its calculation derivation:

[0082] The node connection weight is calculated by combining traffic, communication efficiency, and load. The definitions and calculations of each parameter are as follows:

[0083] Data traffic ;

[0084] Data traffic is obtained by collecting the number of data packets transmitted between nodes, recording the amount of data from node i to node j per unit time.

[0085] Let the data packet sizes from node i to node j per unit time be respectively ,but:

[0086] ;

[0087] Assumption (Unit: MB), then:

[0088] ;

[0089] Communication efficiency ;

[0090] Communication efficiency is obtained by measuring the success rate of transmission, and the calculation formula is:

[0091] ;

[0092] in The amount of data successfully transferred (MB). This represents the total amount of data transferred (MB).

[0093] Assumption , ,but:

[0094] ;

[0095] load ;

[0096] The load between nodes is obtained by collecting the number of data packets per unit time and the node response time. The load formula is:

[0097] ;

[0098] in Indicates the node response time (in seconds). This indicates the number of data packets per unit of time.

[0099] when , ,but:

[0100] ;

[0101] Calculate node connection weights ;

[0102] Substitute the above calculation results into the formula:

[0103] ;

[0104] The connection weight between node i and node j is 1.045. A higher weight value indicates higher communication efficiency and lower load between nodes. This weight value will be used to adjust the node connection relationship in the synchronization path, thereby optimizing the data flow direction and ultimately obtaining a spatiotemporal consistency result.

[0105] S4. Based on the spatiotemporal consistency results, collect the network status and bandwidth allocation during the synchronization process, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and reallocate the data transmission channel according to the communication efficiency of the path and the current load to obtain the optimized configuration of the transmission channel.

[0106] Optionally, the specific operation steps of S4 may include the following S401-S403:

[0107] S401. Based on the spatiotemporal consistency results, collect the network status and bandwidth allocation data of multiple nodes during the synchronization process, analyze the bandwidth occupancy between nodes, extract the traffic distribution characteristics in the synchronization path, and classify the transmission rate of multiple nodes and the path data traffic to obtain the load distribution of the synchronization path.

[0108] The system collects network status and bandwidth allocation data of multiple nodes during the synchronization process. By analyzing the communication traffic between nodes, it extracts the bandwidth usage of each node. It then performs correlation analysis with time periods to determine the resource usage ratio of each node in different time periods. Based on the actual transmission rate of the nodes and the path data traffic, it classifies each path and calculates the average traffic distribution of each path. By comparing the load characteristics of high-traffic and low-traffic nodes in the path, it organizes the relationship between node and path load to obtain the synchronization path load distribution.

[0109] S402. Based on the load distribution of the synchronous path, calculate the traffic distribution characteristics of the synchronous path, analyze the communication efficiency and load between multiple paths to locate the load path, organize the traffic distribution and load status between paths, and obtain the path communication efficiency evaluation results.

[0110] The system calculates the traffic distribution characteristics between paths, analyzes the transmission ratio of traffic on each path, extracts the traffic occupancy differences between multiple paths, compares the data transmission efficiency of high-load and low-load paths, marks the load balance status between paths in combination with node load, determines path dependencies by analyzing the fluctuation amplitude and transmission direction of path traffic data, organizes the traffic distribution and node load of each path, marks the paths with high load one by one, and merges them to generate a complete evaluation table of communication efficiency between paths, thus obtaining the path communication efficiency evaluation results.

[0111] Calculate the traffic distribution characteristics of the synchronization path, analyze the communication efficiency and load between multiple paths to locate the load path, according to the formula;

[0112] ;

[0113] Calculate the traffic distribution characteristics of the path ;

[0114] in, This represents the communication time for path i. The number of paths represents the data load of path i, and n represents the total number of paths.

[0115] Detailed explanation of the formula and its calculation derivation:

[0116] Communication time;

[0117] Communication time is calculated by monitoring the start and end times of data transmission between nodes, using the following formula;

[0118] ;

[0119] in It is the transmission end time of path i. It is the start time of transmission for path i;

[0120] Data load;

[0121] Data load is the total amount of data transmitted along path i within a specified time, measured in MB. It is calculated by summing the sizes of data packets along the path, using the following formula:

[0122] ;

[0123] in It is the size of the j-th data packet on path i. It is the total number of data packets for path i;

[0124] Formula derivation process:

[0125] According to the formula;

[0126] ;

[0127] Communication time for each path and load By monitoring and collecting data, a weighted average of the path traffic distribution is calculated. This reflects the comprehensive characteristics of all paths in terms of load and time;

[0128] Substitute the example:

[0129] Assuming there are 3 paths, the collected parameters are as follows:

[0130] Path 1: Second, MB;

[0131] Path 2: Second, MB;

[0132] Path 3: Second, MB;

[0133] Calculate the traffic distribution characteristics of each path:

[0134] Molecular calculations:

[0135] ;

[0136] Denominator calculation:

[0137] ;

[0138] Flow distribution characteristics calculation:

[0139] ;

[0140] This indicates the traffic distribution characteristics of the path. The result of 66.67 MB / s indicates that the combined performance of traffic and time across all paths is a data flow capacity of approximately 66.67 MB per second. This result is used for path load and communication efficiency analysis in subsequent steps.

[0141] S403. Based on the path communication efficiency evaluation results, the data traffic allocation is adjusted in combination with the path load status. The traffic distribution of low-load paths and balanced load paths is selected, the traffic transmission direction is optimized, the synchronous path load structure is improved, and the optimized configuration of the transmission channel is obtained.

[0142] The data traffic allocation is adjusted based on the path load status. The priority allocation path for optimized traffic transmission is determined by screening the path with lower load. The traffic distribution ratio is adjusted according to the connection relationship of the nodes. Redundant data traffic is removed from each path and redistributed to low-load paths to balance the data pressure in high-load paths. The transmission direction between nodes is optimized to form a balanced load distribution. The traffic distribution data after the path adjustment is sorted out to obtain the optimized configuration of the transmission channel.

[0143] S5. Based on the optimized configuration of the transmission channel, the characteristic values ​​of the synchronization rate change within the time window are analyzed according to the service data synchronization rate in the path. Data sequences with high-frequency fluctuation characteristics in the synchronization path are selected, and the path allocation of the synchronization task is optimized according to the data synchronization rate in the path to obtain the data synchronization path optimization result.

[0144] Optionally, the specific operation steps of S5 may include the following S501-S503:

[0145] S501. Based on the optimized configuration of the transmission channel, the synchronization rate of service data in the path is collected, the rate change within the time window is analyzed, and the rate feature sequence is constructed by comparing the time point and the corresponding rate data according to the key feature value of the synchronization rate, so as to obtain the synchronization rate feature sequence.

[0146] The synchronization rate of business data in the acquisition path is obtained by recording the transmission rate data at different time points, analyzing the rate changes within the time window, comparing the key feature values ​​of the synchronization rate point by point, extracting the matching relationship between the time point and the corresponding rate data, marking the time period with obvious changes according to the rate fluctuation range, organizing the rate data within the time period into a sequence according to the time sequence, and combining the correlation structure between feature values ​​and time to generate a complete rate change model and obtain the synchronization rate feature sequence.

[0147] S502. Based on the synchronous rate feature sequence, analyze the rate fluctuation frequency and amplitude within the time window, calculate the frequency of rate change within the time period, filter out data paths with abnormal frequencies to mark abnormal fluctuation paths, and obtain the abnormal fluctuation path feature set.

[0148] The frequency and amplitude of rate fluctuations within a time window are analyzed, and the frequency data of rate changes are calculated segment by segment. By classifying the fluctuation amplitude of rate data within a time period, data segments with significantly higher fluctuation frequencies than other paths are selected, and data paths with abnormal frequencies are marked. By comparing the temporal distribution of fluctuation amplitude and frequency of each path, the rate change characteristics of time periods in abnormal paths are analyzed. The rate fluctuation characteristics of abnormal data paths and corresponding time periods are summarized to obtain the abnormal fluctuation path feature set.

[0149] Analyze the frequency and amplitude of rate fluctuations within the time window, calculate the frequency of rate changes within the time period, according to the formula:

[0150] ;

[0151] Calculate the frequency fluctuation characteristic F;

[0152] in, This represents the total length of the time window, in seconds. This represents the change in data rate within the i-th time period, in units of M. bps , The time span represents the i-th time period in seconds, and n represents the total number of segments within the time window.

[0153] Detailed explanation of the formula and its calculation derivation:

[0154] Parameter acquisition method:

[0155] The total length of the time window is directly recorded by the actual transmission time period of the monitoring device to ensure coverage of the complete synchronization cycle, for example, the acquisition window is 10 seconds;

[0156] The calculation is based on the difference in instantaneous rate of the data acquisition device within each time period, and the formula is as follows;

[0157] ;

[0158] in and Time periods and Data rate;

[0159] The duration of each segment can be calculated directly from the time record, for example, a time period. The start and end time difference;

[0160] n: Total number of time segments, based on the time window. and the length of the interval between segments The ratio is obtained by the formula:

[0161] ;

[0162] Specific numerical examples:

[0163] Second;

[0164] Second;

[0165] Data rate acquisition value R:

[0166] ;

[0167] M bps ;

[0168] Segmented calculation process:

[0169] ;

[0170] Calculate the rate change for each segment. :

[0171] Paragraph 1: M bps ;

[0172] Paragraph 2: M bps ;

[0173] Paragraph 3: M bps;

[0174] Paragraph 4: M bps ;

[0175] Calculate the frequency of rate change for each segment:

[0176] Paragraph 1: ;

[0177] Paragraph 2: ;

[0178] Paragraph 3: ;

[0179] Paragraph 4: ;

[0180] Calculate the square value of each segment:

[0181] Paragraph 1: ;

[0182] Paragraph 2: ;

[0183] Paragraph 3: ;

[0184] Paragraph 4: ;

[0185] Final calculation:

[0186] ;

[0187] This indicates that the frequency response value of the rate fluctuation is 6.25, representing the intensity of the rate fluctuation within the time window. By comparing the data with a set frequency threshold, paths with abnormal frequencies can be filtered out and marked as abnormal fluctuation paths, thus providing support for the next step of optimizing the data synchronization path.

[0188] S503. Based on the abnormal fluctuation path feature set, combined with the synchronization rate data and node load status in the path, the synchronization task is redistributed to the path with lower fluctuation amplitude, and the synchronization configuration between paths is optimized to obtain the data synchronization path optimization result.

[0189] By combining the synchronization rate data and node load status in the path, the rate change range of the nodes is analyzed for each path. Based on the path load data, the path with low fluctuation amplitude is selected, the synchronization tasks of the high fluctuation path are removed, and the tasks are redistributed to the low fluctuation amplitude path. By adjusting the task distribution between paths, the overall synchronization configuration is optimized, and the load relationship between nodes and paths and the task distribution are updated to obtain the data synchronization path optimization result.

[0190] In this embodiment of the invention, sliding window technology and time series analysis are used to efficiently extract and compress data features, significantly reduce redundancy in data transmission, reduce network burden, and improve transmission rate and efficiency. The application of dynamic segmented decoding and feature mapping structure enables stronger responsiveness and prediction accuracy to data fluctuations. It intelligently adjusts data flow direction and node connection weights, optimizes data synchronization paths based on real-time network status and bandwidth allocation, ensures efficient data synchronization across multiple nodes and platforms, improves the real-time performance of data synchronization, reduces data conflicts, and enhances overall reliability and performance.

[0191] Figure 2 This is a block diagram of a digital twin data synchronization system integrating deep learning and the Internet of Things (IoT) according to an embodiment of the present invention. This system is used for a digital twin data synchronization method integrating deep learning and the Internet of Things. (Refer to...) Figure 2 The system includes a time feature sparsity module 210, a feature fluctuation decoding module 220, a fluctuation path mapping module 230, a path load optimization module 240, and a task synchronization and allocation module 250, wherein:

[0192] The time feature sparsity module 210 is used for digital twin information based on IoT devices. It calls the timestamp and data sequence identifier through a sliding window to collect data over a time span, calculates the change amplitude of feature values ​​of adjacent time slices in the data sequence, and performs data sparsity processing to obtain a sparse feature set.

[0193] The feature fluctuation decoding module 220 is used to calculate the difference between each feature value based on the sparse feature set and the time series feature values, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and perform dynamic segmented decoding based on the analysis results to obtain the fluctuation feature mapping structure.

[0194] The fluctuation path mapping module 230 is used to collect data synchronization paths between IoT devices based on the fluctuation feature mapping structure, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and adjust the data flow direction and node connection weight to obtain spatiotemporal consistency results.

[0195] The path load optimization module 240 is used to collect network status and bandwidth allocation during the synchronization process based on the spatiotemporal consistency results, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and obtain the optimized configuration of the transmission channel based on the communication efficiency of the path and the current load.

[0196] The task synchronization allocation module 250 is used to optimize the configuration based on the transmission channel, analyze the characteristic values ​​of the synchronization rate change within the time window according to the business data synchronization rate in the path, filter the data sequence with high-frequency fluctuation characteristics in the synchronization path, and obtain the data synchronization path optimization result according to the data synchronization rate in the path.

[0197] Optionally, the sparse feature set includes a time slice sequence, a range of feature value changes, and a sparse data point set; the fluctuation feature mapping structure includes a feature fluctuation range, a time series difference distribution, and a dynamic segmentation structure; the spatiotemporal consistency result includes node flow intensity values, synchronization path dependency indicators, and communication node weight allocation; the transmission channel optimization configuration includes traffic allocation results, path load distribution, and bandwidth utilization; and the data synchronization path optimization result includes high-frequency fluctuation paths, a synchronization task allocation table, and path priority results.

[0198] Optionally, the time feature sparsity module 210 is used to:

[0199] S101. Based on the digital twin information of IoT devices, call the timestamp and data sequence identifier, collect data segments within the corresponding time period by sliding a fixed time window, and detect the data range and content integrity within the time window to obtain time series segments;

[0200] S102. Based on the time series segment, compare the feature values ​​of adjacent time segments, calculate the range of feature value changes in the time segment, extract the data fluctuation amplitude, and statistically analyze the change amplitude of the segment and the corresponding time point to obtain a set of feature value change amplitudes.

[0201] S103. Based on the set of feature value change amplitudes, filter time segments with low change amplitudes, remove data content with low change amplitudes, and reorganize the remaining feature values ​​according to sparsity logic to optimize the transmission data load and obtain a sparse feature set.

[0202] Optionally, the feature fluctuation decoding module 220 is used for:

[0203] S201. Based on the sparse feature set, arrange the feature values ​​according to the time series, and determine the amount of difference between adjacent feature values ​​by parsing the difference data of the feature values ​​at each time point to obtain the feature difference sequence.

[0204] S202. Based on the feature difference sequence, analyze the data fluctuation range of the feature value difference, count the numerical changes of the difference values, identify the time period of the numerical changes, mark the start and end time of the segment, and obtain the set of fluctuation segments.

[0205] S203. Based on the set of fluctuation segments, identify the feature values ​​of the time series of the fluctuation segments, calculate the numerical trend of the feature values ​​changing with time, sort the feature values ​​according to the time point features, and obtain the fluctuation feature mapping structure.

[0206] Optionally, the feature fluctuation decoding module 220 is used for:

[0207] The fluctuation range R of the characteristic value difference is calculated according to the following formula (1):

[0208] (1)

[0209] The root mean square fluctuation of the eigenvalues ​​is calculated according to the following formula (2). :

[0210] (2)

[0211] Among them, D i Represents each difference value in the feature difference sequence, max(D) i ) represents the maximum value of the difference in the feature difference sequence, min(D) i () represents the minimum value of the difference in the feature difference sequence, and n represents the total number of data points in the feature difference sequence. This represents the average value of the sequence with distinctive differences.

[0212] Optionally, the wave path mapping module 230 is used for:

[0213] S301. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, parse the communication records between devices, identify the data flow and transmission times between nodes in the path, analyze the synchronization relationship between nodes, and obtain the data synchronization path set.

[0214] S302. Based on the data synchronization path set, evaluate the connection efficiency and load status of the communication nodes, analyze the path traffic distribution and node load data between nodes, calculate the transmission rate and path dependency value of the nodes, identify the data flow direction and intensity between nodes, and obtain the node load evaluation result.

[0215] S303. Based on the node load assessment results, adjust the node connection weights in the synchronization path, calculate the load balancing between nodes, redistribute the data flow direction, and adjust the node connection relationships to obtain the spatiotemporal consistency results.

[0216] Optionally, the path load optimization module 240 is used to:

[0217] S401. Based on the spatiotemporal consistency results, collect network status and bandwidth allocation data of multiple nodes during the synchronization process, analyze the bandwidth occupancy between nodes, extract the traffic distribution characteristics in the synchronization path, and classify the transmission rate of multiple nodes and the path data traffic to obtain the load distribution of the synchronization path.

[0218] S402. Based on the load distribution of the synchronization path, calculate the traffic distribution characteristics of the synchronization path, analyze the communication efficiency and load between multiple paths to locate the load path, organize the traffic distribution and load status between paths, and obtain the path communication efficiency evaluation results.

[0219] S403. Based on the path communication efficiency evaluation results, the data traffic allocation is adjusted in combination with the path load status. The traffic distribution of low-load paths and balanced load paths is selected, the traffic transmission direction is optimized, the synchronous path load structure is improved, and the optimized configuration of the transmission channel is obtained.

[0220] Optionally, the task synchronization and allocation module 250 is used for:

[0221] S501. Based on the optimized configuration of the transmission channel, the synchronization rate of service data in the path is collected, the rate change within the time window is analyzed, and the rate feature sequence is constructed by comparing the time point and the corresponding rate data according to the key feature value of the synchronization rate, so as to obtain the synchronization rate feature sequence.

[0222] S502. Based on the synchronous rate feature sequence, analyze the rate fluctuation frequency and amplitude within the time window, calculate the frequency of rate change within the time period, filter out data paths with abnormal frequencies, mark abnormal fluctuation paths, and obtain an abnormal fluctuation path feature set.

[0223] S503. Based on the abnormal fluctuation path feature set, combined with the synchronization rate data and node load status in the path, the synchronization task is redistributed to the path with lower fluctuation amplitude, and the synchronization configuration between paths is optimized to obtain the data synchronization path optimization result.

[0224] Optionally, the task synchronization and allocation module 250 is used for:

[0225] The frequency and amplitude of the rate fluctuation within the analytical time window are used to calculate the frequency fluctuation characteristic F according to the following formula (3):

[0226] (3)

[0227] in, Represents the total length of the time window. This represents the change in data rate within the i-th time period. represents the time span of the i-th time period, and n represents the total number of segments within the time window.

[0228] In this embodiment of the invention, sliding window technology and time series analysis are used to efficiently extract and compress data features, significantly reduce redundancy in data transmission, reduce network burden, and improve transmission rate and efficiency. The application of dynamic segmented decoding and feature mapping structure enables stronger responsiveness and prediction accuracy to data fluctuations. It intelligently adjusts data flow direction and node connection weights, optimizes data synchronization paths based on real-time network status and bandwidth allocation, ensures efficient data synchronization across multiple nodes and platforms, improves the real-time performance of data synchronization, reduces data conflicts, and enhances overall reliability and performance.

[0229] Figure 3 This is a schematic diagram of the structure of a digital twin data synchronization device integrating deep learning and the Internet of Things, provided by an embodiment of the present invention. Figure 3 As shown, a digital twin data synchronization device integrating deep learning and the Internet of Things may include the above-mentioned Figure 2 The illustrated digital twin data synchronization system integrates deep learning and the Internet of Things. Optionally, the digital twin data synchronization device 310 integrating deep learning and the Internet of Things may include a first processor 2001.

[0230] Optionally, the digital twin data synchronization device 310 integrating deep learning and the Internet of Things may also include a memory 2002 and a transceiver 2003.

[0231] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0232] The following is combined Figure 3 A detailed introduction to each component of the digital twin data synchronization device 310, which integrates deep learning and the Internet of Things:

[0233] The first processor 2001 is the control center of the digital twin data synchronization device 310 that integrates deep learning and the Internet of Things. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0234] Optionally, the first processor 2001 can perform various functions of the digital twin data synchronization device 310 that integrates deep learning and the Internet of Things by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0235] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0236] In a specific implementation, as one example, the digital twin data synchronization device 310 integrating deep learning and the Internet of Things may also include multiple processors, such as... Figure 3 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0237] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0238] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the digital twin data synchronization device 310 integrating deep learning and the Internet of Things (IoT). Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0239] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0240] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0241] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be connected to the interface circuit of the digital twin data synchronization device 310 that integrates deep learning and the Internet of Things. Figure 3 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0242] It should be noted that, Figure 3 The structure of the digital twin data synchronization device 310 that integrates deep learning and the Internet of Things shown in the figure does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0243] Furthermore, the technical effects of the digital twin data synchronization device 310 that integrates deep learning and the Internet of Things can be referred to the technical effects of the digital twin data synchronization method that integrates deep learning and the Internet of Things described in the above method embodiments, and will not be repeated here.

[0244] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0245] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0246] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0247] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0248] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0249] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0250] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0251] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, systems, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0252] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0253] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0254] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0255] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A digital twin data synchronization method integrating deep learning and the Internet of Things, characterized in that, The method includes: S1. Based on the digital twin information of IoT devices, the time span is collected by calling the timestamp and data sequence identifier through a sliding window, the change amplitude of feature values ​​of adjacent time slices in the data sequence is calculated, and the data is sparsified to obtain a sparse feature set. S2. Based on the sparse feature set, calculate the difference between each feature value according to the time series feature value, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and perform dynamic segmented decoding according to the analysis results to obtain the fluctuation feature mapping structure. S3. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and adjust the data flow direction and node connection weight to obtain the spatiotemporal consistency result. S4. Based on the spatiotemporal consistency results, collect the network status and bandwidth allocation during the synchronization process, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and obtain the optimized configuration of the transmission channel according to the communication efficiency of the path and the current load. S5. Based on the optimized configuration of the transmission channel, according to the service data synchronization rate in the path, analyze the characteristic values ​​of the synchronization rate change within the time window, filter the data sequences with high-frequency fluctuation characteristics in the synchronization path, and obtain the data synchronization path optimization result according to the data synchronization rate in the path. S2, based on the sparse feature set and according to the time series feature values, calculates the difference between each feature value, analyzes the fluctuation range and change characteristics in IoT device data synchronization, and performs dynamic segmented decoding based on the analysis results to obtain a fluctuation feature mapping structure, including: S201. Based on the sparse feature set, arrange the feature values ​​according to the time series, and determine the amount of difference between adjacent feature values ​​by parsing the difference data of the feature values ​​at each time point to obtain the feature difference sequence. S202. Based on the feature difference sequence, analyze the data fluctuation range of the feature value difference, count the numerical changes of the difference values, identify the time period of the numerical changes, mark the start and end time of the segment, and obtain the set of fluctuation segments. S203. Based on the set of fluctuation segments, identify the feature values ​​of the time series of the fluctuation segments, calculate the numerical trend of the feature values ​​changing with time, sort the feature values ​​according to the time point features, and obtain the fluctuation feature mapping structure.

2. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, The sparse feature set includes time slice sequences, feature value variation ranges, and sparse data point sets. The fluctuation feature mapping structure includes feature fluctuation ranges, time series difference distributions, and dynamic segmentation structures. The spatiotemporal consistency results include node flow intensity values, synchronization path dependency indicators, and communication node weight allocations. The transmission channel optimization configuration includes traffic allocation results, path load distributions, and bandwidth utilization. The data synchronization path optimization results include high-frequency fluctuation paths, synchronization task allocation tables, and path priority results.

3. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, The digital twin information based on the IoT device in S1 is collected over a time span by using a sliding window to call timestamps and data sequence identifiers. The variation amplitude of feature values ​​between adjacent time slices in the data sequence is calculated, and data sparsity processing is performed to obtain a sparse feature set, including: S101. Based on the digital twin information of IoT devices, call the timestamp and data sequence identifier, collect data segments within the corresponding time period by sliding a fixed time window, and detect the data range and content integrity within the time window to obtain time series segments; S102. Based on the time series segment, compare the feature values ​​of adjacent time segments, calculate the range of feature value changes in the time segment, extract the data fluctuation amplitude, and statistically analyze the change amplitude of the segment and the corresponding time point to obtain a set of feature value change amplitudes. S103. Based on the set of feature value change amplitudes, filter time segments with low change amplitudes, remove data content with low change amplitudes, and reorganize the remaining feature values ​​according to sparsity logic to optimize the transmission data load and obtain a sparse feature set.

4. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, The analysis of the characteristic value difference range in S202, and the statistical change of the difference value, include: The fluctuation range R of the characteristic value difference is calculated according to the following formula (1): R=max(D i )-min(D i ) (1) The root mean square fluctuation R of the eigenvalues ​​is calculated according to the following formula (2). rms : Among them, D i Represents each difference value in the feature difference sequence, max(D) i ) represents the maximum value of the difference in the feature difference sequence, min(D) i () represents the minimum value of the difference in the feature difference sequence, and n represents the total number of data points in the feature difference sequence. This represents the average value of the sequence with distinctive differences.

5. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, The S3 method, based on the fluctuation feature mapping structure, collects data synchronization paths between IoT devices, evaluates the connection efficiency and load status of IoT device communication nodes, calculates the data flow intensity and synchronization path dependency value between nodes, and simultaneously adjusts the data flow direction and node connection weights to obtain spatiotemporal consistency results, including: S301. Based on the fluctuation feature mapping structure, collect the data synchronization path between IoT devices, parse the communication records between devices, identify the data flow and transmission times between nodes in the path, analyze the synchronization relationship between nodes, and obtain the data synchronization path set. S302. Based on the data synchronization path set, evaluate the connection efficiency and load status of the communication nodes, analyze the path traffic distribution and node load data between nodes, calculate the transmission rate and path dependency value of the nodes, identify the data flow direction and intensity between nodes, and obtain the node load evaluation result. S303. Based on the node load assessment results, adjust the node connection weights in the synchronization path, calculate the load balancing between nodes, redistribute the data flow direction, and adjust the node connection relationships to obtain the spatiotemporal consistency results.

6. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, S4, based on the spatiotemporal consistency result, collects network status and bandwidth allocation during the synchronization process, calculates the traffic distribution of the synchronization path according to the current transmission path load status, and obtains the optimized configuration of the transmission channel based on the path's communication efficiency and current load, including: S401. Based on the spatiotemporal consistency results, collect network status and bandwidth allocation data of multiple nodes during the synchronization process, analyze the bandwidth occupancy between nodes, extract the traffic distribution characteristics in the synchronization path, and classify the transmission rate of multiple nodes and the path data traffic to obtain the load distribution of the synchronization path. S402. Based on the load distribution of the synchronization path, calculate the traffic distribution characteristics of the synchronization path, analyze the communication efficiency and load between multiple paths to locate the load path, organize the traffic distribution and load status between paths, and obtain the path communication efficiency evaluation results. S403. Based on the path communication efficiency evaluation results, the data traffic allocation is adjusted in combination with the path load status. The traffic distribution of low-load paths and balanced load paths is selected, the traffic transmission direction is optimized, the synchronous path load structure is improved, and the optimized configuration of the transmission channel is obtained.

7. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 1, characterized in that, The S5 configuration based on the transmission channel optimization analyzes the characteristic values ​​of synchronization rate changes within a time window according to the service data synchronization rate in the path, filters data sequences with high-frequency fluctuation characteristics in the synchronization path, and obtains the data synchronization path optimization result based on the data synchronization rate in the path, including: S501. Based on the optimized configuration of the transmission channel, the synchronization rate of service data in the path is collected, the rate change within the time window is analyzed, and the rate feature sequence is constructed by comparing the time point and the corresponding rate data according to the key feature value of the synchronization rate, so as to obtain the synchronization rate feature sequence. S502. Based on the synchronous rate feature sequence, analyze the rate fluctuation frequency and amplitude within the time window, calculate the frequency of rate change within the time period, filter out data paths with abnormal frequencies, mark abnormal fluctuation paths, and obtain an abnormal fluctuation path feature set. S503. Based on the abnormal fluctuation path feature set, combined with the synchronization rate data and node load status in the path, the synchronization task is redistributed to the path with lower fluctuation amplitude, and the synchronization configuration between paths is optimized to obtain the data synchronization path optimization result.

8. The digital twin data synchronization method integrating deep learning and the Internet of Things according to claim 7, characterized in that, The frequency and amplitude of rate fluctuations within the analytical time window are calculated, including the frequency of rate changes within the time period: The frequency and amplitude of the rate fluctuation within the analytical time window are analyzed, and the frequency fluctuation characteristic F is calculated according to the following formula (3): Where T represents the total length of the time window, ΔR i Δt represents the change in data rate during the i-th time period. i represents the time span of the i-th time period, and n represents the total number of segments within the time window.

9. A digital twin data synchronization system integrating deep learning and the Internet of Things, wherein the digital twin data synchronization system integrating deep learning and the Internet of Things is used to implement the digital twin data synchronization method integrating deep learning and the Internet of Things as described in any one of claims 1-8, characterized in that, The system includes: The time feature sparsity module is used for digital twin information based on IoT devices. It uses a sliding window to call timestamps and data sequence identifiers to collect data over a time span, calculates the change amplitude of feature values ​​between adjacent time slices in the data sequence, and performs data sparsity processing to obtain a sparse feature set. The feature fluctuation decoding module is used to calculate the difference between each feature value based on the sparse feature set and the time series feature values, analyze the fluctuation range and change characteristics in the data synchronization of IoT devices, and perform dynamic segmented decoding based on the analysis results to obtain the fluctuation feature mapping structure. The fluctuation path mapping module is used to collect data synchronization paths between IoT devices based on the fluctuation feature mapping structure, evaluate the connection efficiency and load status of IoT device communication nodes, calculate the data flow intensity and synchronization path dependency value between nodes, and adjust the data flow direction and node connection weight to obtain spatiotemporal consistency results. The path load optimization module is used to collect network status and bandwidth allocation during the synchronization process based on the spatiotemporal consistency results, calculate the traffic distribution of the synchronization path according to the current transmission path load status, and obtain the optimized configuration of the transmission channel based on the communication efficiency of the path and the current load. The task synchronization allocation module is used to optimize the configuration based on the transmission channel, analyze the characteristic values ​​of the synchronization rate change within the time window according to the business data synchronization rate in the path, filter the data sequences with high-frequency fluctuation characteristics in the synchronization path, and obtain the data synchronization path optimization result according to the data synchronization rate in the path.

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