Cloud storage method and system for internet of things device data

Abstract

The application provides a cloud storage method and system for Internet of Things device data, and relates to the technical field of Internet of Things data storage and processing, wherein the method comprises the following steps: collecting metadata of a plurality of Internet of Things devices in an Internet of Things environment; performing labelization processing on the metadata to form standardized data packets; performing hierarchical compression processing on the standardized data packets, and extracting multi-dimensional features from the data packets after the hierarchical compression processing; inputting the multi-dimensional features into a multi-modal neural network model based on deep learning, analyzing the multi-dimensional features through the multi-modal neural network model, generating a target storage priority index, and determining a corresponding security level according to the target storage priority index; based on the security level, performing differential encryption processing on the data packets after the hierarchical compression processing by using a lightweight encryption algorithm, and transmitting the data packets after the encryption processing to a corresponding storage area in the cloud. The application improves the efficiency and security of Internet of Things data storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things data storage and processing, and particularly relates to a cloud storage method and system for Internet of Things device data. BACKGROUND

[0002] With the rapid development of Internet of Things technology, the data generated by a large number of heterogeneous devices is growing exponentially. In application scenarios such as smart home and smart city, how to achieve efficient storage and safe management of multi-source heterogeneous data has become a key technical requirement. Such scenarios require the system to adapt to the characteristics of diverse device types, non-uniform data formats, and large network environment fluctuations, while meeting the requirements of prioritized storage and real-time response for high-value data, and balancing storage efficiency and security.

[0003] A current targeted solution adopts a data hierarchical storage mechanism based on fixed rules. This solution simply divides device data through pre-defined data classification rules, combines a unified compression algorithm to reduce data transmission volume, adopts a static encryption method to implement the same level of security protection for all data, and finally distributes the data to different cloud storage areas according to a pre-set storage strategy.

[0004] However, this solution has obvious limitations in dynamic adaptability. Its fixed rules are difficult to cope with real-time changes in device status and network conditions in the Internet of Things environment, resulting in deviations between storage resource allocation and actual needs. At the same time, the unified data processing method cannot distinguish different data value differences, causing the coexistence of insufficient protection of high-value data and waste of low-value data resources. In addition, the static storage strategy lacks deep mining of data characteristics, making the system not flexible enough when facing complex and variable Internet of Things environments. SUMMARY

[0005] The present application provides a cloud storage method and system for Internet of Things device data to solve the problems of low efficiency and poor security in Internet of Things data storage in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a cloud storage method for Internet of Things device data, comprising:

[0007] Collecting metadata of a plurality of Internet of Things devices in an Internet of Things environment;

[0008] Labeling the metadata to form standardized data packets;

[0009] Performing hierarchical compression processing on the standardized data packets, and extracting multi-dimensional features from the data packets after the hierarchical compression processing;

[0010] input the multi-dimensional features into a deep learning-based multi-modal neural network model, analyze the multi-dimensional features through the multi-modal neural network model, generate a target storage priority index, and determine a corresponding security level according to the target storage priority index;

[0011] based on the security level, differentially encrypt the data packets after the hierarchical compression processing using a lightweight encryption algorithm, and transmit the encrypted data packets to a corresponding storage area in the cloud.

[0012] Optionally, the inputting of the multi-dimensional features into the deep learning-based multi-modal neural network model, the analyzing of the multi-dimensional features through the multi-modal neural network model, the generation of the target storage priority index, and the determination of the corresponding security level according to the target storage priority index comprise:

[0013] inputting the multi-dimensional features into the multi-modal neural network model, wherein the multi-modal neural network model comprises a time series feature processing branch, a statistical feature processing branch, and an output layer;

[0014] analyzing the update interval feature and the time series continuity feature in the multi-dimensional features through the time series feature processing branch, and analyzing the capacity feature in the multi-dimensional features through the statistical feature processing branch;

[0015] performing feature fusion on the output results of the time series feature processing branch and the output results of the statistical feature processing branch to obtain a fused feature result;

[0016] generating a target storage priority index based on the fused feature result through the output layer of the multi-modal neural network model in combination with a device importance degree parameter and a network state parameter;

[0017] mapping a numerical interval of the target storage priority index to a preset security level classification library to determine a corresponding security level.

[0018] Optionally, the analyzing of the update interval feature and the time series continuity feature in the multi-dimensional features through the time series feature processing branch, and the analyzing of the capacity feature in the multi-dimensional features through the statistical feature processing branch comprise:

[0019] extracting a time series pattern from the update interval feature using a recurrent neural network structure in the time series feature processing branch to obtain a time series pattern;

[0020] identifying a key time point from the time series continuity feature using an attention mechanism in the time series feature processing branch to obtain a key time point identifier;

[0021] The time sequence mode and the key time point identifier are fused to generate a time sequence feature vector, which is an output result of a time sequence feature processing branch;

[0022] A full connection layer in the statistical feature processing branch is used to perform dimension transformation on the capacity feature;

[0023] A feature scaling layer in the statistical feature processing branch is used to perform normalization processing on the capacity feature;

[0024] The transformation result and the normalization result are spliced to generate a statistical feature vector, which is an output result of the statistical feature processing branch.

[0025] Optionally, the output layer of the multi-modal neural network model generates a target storage priority index based on the fused feature result, in combination with a device importance degree parameter and a network state parameter, including:

[0026] The fused feature result is subjected to dimension regularization processing to form a first feature vector;

[0027] The device importance degree parameter is subjected to standardization encoding processing to form a device importance vector;

[0028] The network state parameter is subjected to real-time quantization processing to form a network state vector;

[0029] The first feature vector, the device importance vector and the network state vector are spliced and combined to form a comprehensive input vector;

[0030] A full connection network in the output layer is used to perform nonlinear transformation on the comprehensive input vector, and the transformed comprehensive input vector is subjected to importance weighting to generate an initial storage priority index;

[0031] The initial storage priority index is subjected to normalization processing to generate a target storage priority index.

[0032] Optionally, the metadata is subjected to labelization processing to form a standardized data packet, including:

[0033] The metadata is parsed to obtain a device unique identifier;

[0034] According to a preset device type classification rule, a corresponding device type code is assigned to each device unique identifier;

[0035] Based on data acquisition time information, a time mark is added to each metadata record, and the byte length of the metadata is calculated to form metadata length information;

[0036] combine the device type code, the time mark and the metadata length information to generate a standardized data packet header;

[0037] encapsulate the metadata and the standardized data packet header to form a standardized data packet.

[0038] Optionally, the standardized data packet is subjected to hierarchical compression processing, and multi-dimensional features are extracted from the data packet after the hierarchical compression processing, including:

[0039] According to the update frequency characteristics of the standardized data packet, the standardized data packet is divided into state data packets and data stream packets;

[0040] The state data packets are subjected to compression processing using a difference encoding mode, and acquisition time stamp information is recorded during the compression processing;

[0041] The data stream packets are subjected to compression processing using a streaming encoding mode, and timing mark information is recorded during the compression processing;

[0042] Based on the acquisition time stamp information, update interval features are generated, and based on the timing mark information, timing continuity features are generated;

[0043] Based on the size information of the state data packets after compression processing and the size information of the data stream packets after compression processing, corresponding capacity features are respectively counted;

[0044] The update interval features, the timing continuity features and the corresponding capacity features are combined to form multi-dimensional features.

[0045] Optionally, based on the security level, the hierarchical compression processed data packet is subjected to differential encryption processing using a lightweight encryption algorithm, and the encrypted data packet is transmitted to a corresponding storage area in the cloud, including:

[0046] According to the security level, a corresponding encryption strength configuration is selected, wherein different security level data in the hierarchical compression processed data packet adopts different encryption strength configurations;

[0047] The corresponding security level data is subjected to encryption processing using the encryption strength configuration;

[0048] During the encryption processing, an initialization vector is generated, and the initialization vector is combined and encapsulated with the encrypted data packet;

[0049] A secure transmission channel with the cloud is established, and the encapsulation result is transmitted to a cloud storage system through the secure transmission channel;

[0050] In the cloud storage system, the encapsulation result is distributed to different storage areas in the cloud according to the target storage priority index.

[0051] In a second aspect, the present application provides a cloud storage system for Internet of Things device data, comprising:

[0052] a collection module configured to collect metadata of a plurality of Internet of Things devices in an Internet of Things environment;

[0053] a processing module configured to labelize the metadata to form standardized data packets;

[0054] a compression module configured to perform hierarchical compression processing on the standardized data packets and extract multi-dimensional features from the data packets after the hierarchical compression processing;

[0055] an input module configured to input the multi-dimensional features into a multi-modal neural network model based on deep learning, analyze the multi-dimensional features through the multi-modal neural network model, generate a target storage priority index, and determine a corresponding security level according to the target storage priority index;

[0056] an encryption module configured to perform differential encryption processing on the data packets after the hierarchical compression processing based on the security level using a lightweight encryption algorithm, and transmit the data packets after the encryption processing to a corresponding storage area in the cloud.

[0057] In a third aspect, the present application provides an electronic device, comprising:

[0058] a memory configured to store a computer program;

[0059] a processor configured to execute the computer program to implement the steps of the cloud storage method for Internet of Things device data according to the first aspect described above.

[0060] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the cloud storage method for Internet of Things device data according to the first aspect described above.

[0061] In the present application, a cloud storage method for Internet of Things device data is provided, which comprises: collecting metadata of a plurality of Internet of Things devices in an Internet of Things environment; performing label processing on the metadata to form a standardized data packet; performing hierarchical compression processing on the standardized data packet, and extracting multi-dimensional features from the data packet after the hierarchical compression processing; inputting the multi-dimensional features into a multi-modal neural network model based on deep learning, analyzing the multi-dimensional features through the multi-modal neural network model, generating a target storage priority index, and determining a corresponding security level according to the target storage priority index; based on the security level, performing differential encryption processing on the data packet after the hierarchical compression processing by using a lightweight encryption algorithm, and transmitting the encrypted data packet to a corresponding storage area in the cloud.

[0062] The technical scheme provided by the present application has the following beneficial effects:

[0063] The present application realizes unified data collection of heterogeneous devices, provides complete data basis for subsequent processing, eliminates device data format differences, improves the standardization and consistency of data processing, reduces data transmission volume while retaining key feature information of data, realizes intelligent evaluation of data value and accurate division of security levels, and realizes accurate matching of security protection strength and storage resource configuration.

[0064] Further, the present application also analyzes the time sequence characteristics and statistical characteristics of data through the time sequence feature processing branch and the statistical feature processing branch of the multi-modal neural network model respectively, fuses the processing results, combines device importance and network state parameters to generate a target storage priority index, and finally maps the target storage priority index to a security level classification library to determine a specific security level.

[0065] Moreover, the multi-branch neural network structure is adopted to realize deep mining and fusion analysis of data features, and more accurate storage priority evaluation results are generated by combining environmental parameters, which provides a reliable basis for data security level division and improves the accuracy and adaptability of storage decision.

[0066] These aspects or other aspects of the present application will be more apparent in the following description of embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0068] Figure 1A flowchart of a cloud storage method for Internet of Things device data provided by an embodiment of the present application;

[0069] Figure 2 A specific implementation schematic diagram of a cloud storage method for Internet of Things device data provided by an embodiment of the present application;

[0070] Figure 3 A structural schematic diagram of a cloud storage system for Internet of Things device data provided by an embodiment of the present application. DETAILED DESCRIPTION

[0071] In the field of cloud storage of Internet of Things device data, the existing data tiered storage scheme based on fixed rules has obvious limitations. This scheme uses pre-defined static rules to handle dynamic and variable Internet of Things environment, which is difficult to adapt to real-time changes in device state and network conditions, resulting in a deviation between storage resource configuration and actual demand. At the same time, the unified data processing method cannot distinguish the difference in data value, which may either cause insufficient protection of high-value data or lead to resource waste of low-value data. Such rigid processing mechanism shows insufficient flexibility and adaptability in the face of high heterogeneity and dynamics of the Internet of Things environment.

[0072] To solve the above problems, the present application provides a cloud storage method for Internet of Things device data. This method analyzes the deep features of data through a multi-modal neural network, combines multi-dimensional parameters such as device importance and network state, and dynamically generates storage priority indicators and security levels. Specifically, the method first standardizes and hierarchically compresses the metadata, then extracts the time series features and statistical features of the data, and finally implements differentiated encryption and storage strategies according to the evaluation results through neural network model fusion analysis. This method breaks through the constraints of fixed rules, realizes the precise matching of storage strategies and data value, guarantees the safe storage of important data, optimizes the utilization efficiency of overall storage resources, and effectively solves the problems of unreasonable storage resource configuration and insufficient data security protection in the prior art.

[0073] In order to enable personnel in the technical field to better understand the present application scheme, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0074] The core of the present application is to provide a cloud storage method for Internet of Things device data, and a flowchart of a specific embodiment thereof is shown in Figure 1 The method comprises:

[0075] Step 101: Collecting metadata of multiple Internet of Things devices in an Internet of Things environment.

[0076] In step 101, metadata refers to a collection of data describing the properties and running status of Internet of Things devices, including device identifiers (encoding uniquely identifying devices), device properties (such as sensor types, measurement ranges), device status (such as online / offline, running mode), and sensor readings (such as temperature values, humidity values). In the Internet of Things environment, metadata is the basis for subsequent data processing and analysis.

[0077] In the embodiments of the present application, the original data of the Internet of Things devices is obtained in real time through the device communication interface, the device identifier, attribute parameter, status indicator and sensor reading contained in the data are parsed, and these data are arranged as structured data sets according to device units to provide complete input data for subsequent processing.

[0078] For example, taking the smart home scenario as an example, the metadata of the temperature sensor (device identifier TS001), door and window sensor (device identifier DS002) and smart socket (device identifier PS003) in the home is collected. The temperature sensor transmits data "device identifier TS001, temperature value 25.6℃, status normal", the door and window sensor transmits "device identifier DS002, status open", and the smart socket transmits "device identifier PS003, power consumption 120W, status online". These data are parsed and arranged as structured metadata sets, including device identifier, attribute parameter, status indicator and sensor reading.

[0079] Step 102: Labeling the metadata to form a standardized data packet.

[0080] In step 102, labeling is the process of adding standardized identification information to metadata, including device type code (encoding classified according to device function) and time marker (time point of data collection). The standardized data packet is a data unit with a unified structure formed by encapsulating metadata and label information, containing packet header (device type code, time marker, metadata length) and data content (original metadata).

[0081] In the embodiments of the present application, the device identifier in the metadata is read, the device type code is matched according to the preset classification rule, the data collection time is obtained and the time marker is generated, the metadata byte length is calculated to form the metadata length information, the device type code, time marker and metadata length are combined into the data packet header, and finally the metadata and data packet header are encapsulated into the standardized data packet.

[0082] For example, the temperature sensor metadata "Device Identifier TS001, Temperature Value 25.6℃, Status Normal" is tagged. The device identifier TS001 matches the device type code TEMP (temperature device), the time marker is set to 20231015143025356 (October 15, 2023, 14:30:25:356), and the metadata length is calculated to be 28 bytes. The data packet header is combined as TEMP20231015143025356028, which is encapsulated with the original metadata as a standardized data packet.

[0083] Step 103: Hierarchical compression processing is performed on the standardized data packet, and multi-dimensional features are extracted from the data packet after hierarchical compression processing.

[0084] In step 103, hierarchical compression processing is performed according to data update frequency, and the standardized data packet is divided into state data packets (data with long update intervals) and data stream packets (data with short update intervals), and difference encoding (storing data changes) and stream encoding (eliminating data redundancy) are used for compression respectively. Multi-dimensional features are a set of indicators reflecting data characteristics extracted from compressed data packets, including update interval features (data update frequency), time series continuity features (data time distribution pattern), and capacity features (data packet size).

[0085] In the embodiments of the present application, the standardized data packet is divided into state data packets and data stream packets according to the data update frequency threshold, the state data packet is compressed using difference encoding and the timestamp is recorded, the data stream packet is compressed using stream encoding and the time series marker is recorded, the timestamp and the time series marker are parsed from the compressed data packet to generate the update interval feature and the time series continuity feature, the capacity feature is extracted from the data packet size information, and finally these features are combined into a multi-dimensional feature set.

[0086] For example, the temperature sensor data packet update interval of 32 seconds (exceeding the 30-second threshold) is divided into a state data packet, compressed using difference encoding, and records the timestamps 20231015143025356 and 20231015142953356. The door and window sensor data packet update interval of 3 seconds (below the 5-second threshold) is divided into a data stream packet, compressed using stream encoding, and records the time series marker (triggered 12 times within 10 minutes). The update interval feature is calculated from the timestamp to be 32 seconds, the time series continuity feature is extracted from the time series marker (average interval 50 seconds, maximum interval 120 seconds, minimum interval 5 seconds), the capacity feature is extracted from the size of the compressed data packet (state data packet 8 bytes, data stream packet 48 bytes), and finally combined into a multi-dimensional feature set 32, 50, 120, 5, 8, 48.

[0087] Step 104: Input the multidimensional features into a deep learning-based multimodal neural network model, analyze the multidimensional features through the multimodal neural network model, generate a target storage priority index, and determine the corresponding security level based on the target storage priority index.

[0088] In step 104, the multimodal neural network model is a deep learning model that includes a temporal feature processing branch (analyzing time-related features), a statistical feature processing branch (analyzing data volume features), and an output layer (generating decision results). The target storage priority index is a numerical score output by the model, reflecting the urgency and importance of data storage. The security level is a data protection level determined based on the target storage priority index, which determines the encryption strength and storage area.

[0089] In this embodiment, multidimensional features are input into a multimodal neural network model, time-series feature processing is used to analyze the update interval features and time-series continuity features, statistical feature processing is used to analyze the capacity features, the branch output results are fused into comprehensive features, and the target storage priority index is generated through the output layer by combining device importance parameters and network status parameters. Finally, the index value range is mapped to a preset security level.

[0090] For example, a multidimensional feature set of 32, 50, 120, 5, 8, 48 is input into a multimodal neural network model. Temporal branch analysis updates the features at 32-second intervals and the temporal continuity features 50, 120, 5. Statistical branch analysis updates the capacity features 8 and 48. The branch outputs are fused into a comprehensive feature. Combining device importance parameters (door and window sensor weight 0.9) and network status parameters (bandwidth 8Mbps), the target storage priority index of 74.9 points is calculated through the output layer and mapped to a security level classification library (a score above 70 indicates a high security level), thus determining the security level as high.

[0091] Step 105: Based on the security level, use a lightweight encryption algorithm to perform differentiated encryption on the data packets after layered compression, and transmit the encrypted data packets to the corresponding storage area in the cloud.

[0092] In step 105, differentiated encryption processing involves selecting different encryption strengths based on the security level. High-security-level data uses strong encryption strategies (such as long-key encryption), while low-security-level data uses efficient encryption strategies (such as short-key encryption). The corresponding cloud storage areas are divided into two main categories based on performance characteristics and cost structure: high-performance storage areas and low-cost storage areas. High-performance storage areas use solid-state drives (SSDs) for high read / write speeds, while low-cost storage areas use hard disk drives (HDDs) for large capacity and low cost. The determination of the corresponding storage area is based on the numerical range of the target storage priority index. When the target storage priority index value is higher than a preset threshold, data is allocated to the high-performance storage area; when the target storage priority index value is lower than the preset threshold, data is allocated to the low-cost storage area. This differentiation method achieves a balance and optimization between data storage performance requirements and storage costs.

[0093] In this embodiment, the encryption strength configuration is selected according to the security level. Strong encryption algorithm is used to process high-security-level data, and efficient encryption algorithm is used to process low-security-level data. During the encryption process, an initialization vector is generated and encapsulated with the encrypted data. The data is transmitted to the cloud through a secure transmission channel. Finally, the data is distributed to the corresponding storage area according to the target storage priority index.

[0094] For example, door and window sensor data has a high security level, using the AES-256 algorithm for encryption, generating an initialization vector of 0x12a4f7c8, which is then encapsulated with the encrypted data. Temperature sensor data has a lower security level, using the ChaCha20 algorithm for encryption, generating an initialization vector of 0x89b3d2e1, which is then encapsulated with the encrypted data. The data is transmitted to the cloud via a TLS channel. Based on the target storage priority index of 74.9 points (greater than 70 points), door and window sensor data is stored in a high-performance storage area (SSD storage), while temperature sensor data is stored in a low-cost storage area (HDD storage).

[0095] This method achieves efficient storage and secure management of IoT device data through intelligent data acquisition, standardized processing, feature extraction, and dynamic decision-making. The method can adapt to different data characteristics and environmental conditions, accurately allocating storage resources and security protection strategies. This improves the security and access efficiency of important data while optimizing overall storage costs, making it suitable for large-scale heterogeneous IoT scenarios.

[0096] To address the issue of dynamic priority assessment for IoT data storage, in some embodiments, step 104 involves inputting the multidimensional features into a deep learning-based multimodal neural network model. The multimodal neural network model analyzes the multidimensional features to generate a target storage priority index, and based on this index, determines the corresponding security level.Figure 2 The above includes:

[0097] Step 201: Input the multidimensional features into the multimodal neural network model, which includes a temporal feature processing branch, a statistical feature processing branch, and an output layer.

[0098] In step 201, the multimodal neural network model is a deep learning model that can process multiple types of features. It includes three core components: a temporal feature processing branch, a statistical feature processing branch, and an output layer. The temporal feature processing branch specifically analyzes time-related features, the statistical feature processing branch specifically analyzes data volume-related features, and the output layer is responsible for integrating all information to generate the final decision result.

[0099] In this embodiment, a multidimensional feature set is input into a multimodal neural network model. The model automatically assigns time-related features to the time-series feature processing branch and data volume-related features to the statistical feature processing branch according to the feature type. The two branches start processing their respective feature data in parallel.

[0100] Step 202: Analyze the update interval feature and temporal continuity feature in the multidimensional features through the temporal feature processing branch, and analyze the capacity feature in the multidimensional features through the statistical feature processing branch.

[0101] In step 202, the update interval feature refers to the time interval characteristic of data packet updates, reflecting the frequency of data changes; the temporal continuity feature refers to the distribution pattern and key point characteristics of data in the time dimension; the capacity feature refers to the size and volume characteristics of data packets; the temporal feature processing branch analyzes the time series pattern through a recurrent neural network structure; and the statistical feature processing branch analyzes the statistical characteristics of data through a fully connected network.

[0102] In this embodiment, the temporal feature processing branch performs in-depth analysis on the update interval feature and temporal continuity feature to extract the time patterns and key change points. The statistical feature processing branch simultaneously processes the capacity feature to extract the distribution characteristics of the data volume. The two branches generate processed feature outputs respectively.

[0103] Step 203: Perform feature fusion on the output results of the temporal feature processing branch and the statistical feature processing branch to obtain the fused feature results.

[0104] In step 203, feature fusion refers to the process of integrating the feature results after processing different branches. The fused feature result is a feature vector containing comprehensive time and statistical information, providing a comprehensive basis for the final decision.

[0105] In this embodiment of the application, the temporal feature vector output by the temporal feature processing branch and the statistical feature vector output by the statistical feature processing branch are concatenated and combined to form a comprehensive feature expression. This fused feature contains both temporal and statistical dimension information.

[0106] Step 204: Based on the fused feature results, the target storage priority index is generated through the output layer of the multimodal neural network model, combined with the device importance parameter and network state parameter.

[0107] In step 204, the device importance parameter refers to the pre-set weight value based on the functional criticality and business value of IoT devices in the system. This parameter is configured and allocated by the device management platform according to factors such as device type and business scenario importance. The network status parameter refers to the network transmission quality indicators obtained through real-time monitoring. This parameter is obtained by collecting real-time performance data such as current network bandwidth availability, transmission latency, and packet loss rate through the network monitoring module of the edge gateway.

[0108] In this embodiment, the fused feature results and the network state parameters of the device importance parameters are input into the output layer. The neural network computing unit in the output layer performs weighted comprehensive calculations to finally generate a quantified target storage priority index value.

[0109] Step 205: Map the numerical range of the target storage priority index to a preset security level classification library to determine the corresponding security level.

[0110] In step 205, the security level classification library is a predefined security level division standard that maps numerical ranges to different security protection levels.

[0111] In this embodiment of the application, the generated target storage priority index value is compared with the value range in the security level classification library to find the corresponding range, thereby determining the security level that the data should correspond to, and completing the mapping process from value to level.

[0112] Here is a specific example:

[0113] In a smart home scenario, a multidimensional feature set of 32, 50, 120, 5, 8, and 48 is input into a multimodal neural network model. This model includes a temporal feature processing branch, a statistical feature processing branch, and an output layer. The temporal feature processing branch analyzes and updates the 32-second interval feature and the temporal continuity features of 50 seconds, 120 seconds, and 5 seconds, extracting time series patterns through a recurrent neural network structure and outputting a temporal feature vector [0.38, 0.25, 0.40]. The statistical feature processing branch analyzes the capacity features of 8 bytes and 48 bytes, performing feature transformation through a fully connected layer and outputting a statistical feature vector [0.15, 0.30]. The temporal feature vector [0.38, 0.25, 0.40] and the statistical feature vector [0.15, 0.30] are concatenated and fused to obtain the fused feature result [0.38, 0.25, 0.40, 0.15, 0.30]. Based on the fused feature results, the output layer generates a target storage priority index by combining the device importance parameter (door / window sensor weight 0.9) and the network status parameter (bandwidth 8Mbps). The specific calculation process is as follows: the fused feature vector, along with the device importance parameter and network status parameter, is input into the fully connected layer, and the calculation formula is... ,in Indicates the target storage priority metric. This represents the weight coefficient vector [0.4, 0.3, 0.2, 0.05, 0.05]. This represents the fused feature vector [0.38, 0.25, 0.40, 0.15, 0.30]. This indicates a bias term of 0.1. Substituting the values, we get S = 0.4 × 0.38 + 0.3 × 0.25 + 0.2 × 0.40 + 0.05 × 0.15 + 0.05 × 0.30 + 0.1 = 0.152 + 0.075 + 0.080 + 0.0075 + 0.015 + 0.1 = 0.4295, which, normalized to a percentage, gives 42.95 points. Mapping the target storage priority index of 42.95 points to a preset security level classification library, where 0-50 points correspond to low security level, 50-70 points to medium security level, and 70-100 points to high security level, the corresponding security level is determined to be low security level.

[0114] In this embodiment, the multimodal neural network model is used to perform in-depth analysis and comprehensive evaluation of data features, which can accurately reflect the storage value and urgency of the data. This provides a precise basis for subsequent differentiated storage and security protection, ensuring that important data is fully protected, avoiding resource waste, and improving the overall intelligence level and operating efficiency of the storage system.

[0115] To address the accuracy issue in multidimensional feature analysis, in some embodiments, step 202 involves analyzing the update interval feature and temporal continuity feature in the multidimensional features through the temporal feature processing branch, and analyzing the capacity feature in the multidimensional features through the statistical feature processing branch, including:

[0116] Step 301: Using the recurrent neural network structure in the temporal feature processing branch, extract the temporal pattern of the update interval feature to obtain the temporal pattern.

[0117] In step 301, the recurrent neural network structure is a type of neural network capable of processing sequential data, capturing temporal dependencies through memory units. Temporal patterns refer to the periodic or trend-like regularities hidden within the data update intervals.

[0118] In this embodiment, the recurrent neural network structure in the time series feature processing branch receives the update interval feature as input, and analyzes the time series data layer by layer through multi-layer neural network units to extract the implicit time series patterns in the data.

[0119] Step 302: Utilize the attention mechanism in the temporal feature processing branch to identify key time points of the temporal continuity features and obtain key time point identifiers.

[0120] In step 302, the attention mechanism is a neural network component that can automatically focus on important features. Key time point identifiers refer to the locations of time points in the continuous characteristics of data time series that exhibit changes or special patterns. This is determined by calculating the first and second difference values ​​of the time series data and setting a threshold for the magnitude of change. When the magnitude of change in consecutive data points exceeds the preset threshold, they are identified as key points. For example, in a temperature sensor data stream, the time points where the temperature value changes drastically are key points.

[0121] In this embodiment of the application, the attention mechanism in the temporal feature processing branch receives temporal continuity features as input, identifies key time points in the sequence by calculating feature importance weights, and generates corresponding identification information.

[0122] Step 303: Fuse the time series pattern and the key time point identifier to generate a time series feature vector, which is the output result of the time series feature processing branch.

[0123] In step 303, the temporal feature vector is a comprehensive feature representation formed by fusing temporal patterns and key time point identifiers, containing complete feature information in the time dimension.

[0124] In this embodiment, the temporal pattern extracted by the recurrent neural network and the key time point identifiers identified by the attention mechanism are concatenated and combined, and the feature is fused through a fully connected layer to generate the final temporal feature vector.

[0125] Step 304: Use the fully connected layer in the statistical feature processing branch to perform dimensional transformation on the capacity feature.

[0126] In step 304, the fully connected layer is a neural network layer that implements feature space transformation. Dimension transformation refers to the process of mapping capacity features from the original space to a new feature space.

[0127] In this embodiment of the application, the fully connected layer in the statistical feature processing branch receives capacity features as input, and maps the original features to a new dimensional space through weight matrix calculation, thereby achieving feature representation enhancement.

[0128] Step 305: Normalize the capacity feature using the feature scaling layer in the statistical feature processing branch.

[0129] In step 305, the feature scaling layer is a neural network layer specifically designed to adjust the range of feature values. Normalization is the process of adjusting feature values ​​of different dimensions to the same numerical range.

[0130] In this embodiment, the feature scaling layer in the statistical feature processing branch standardizes the capacity features and adjusts the feature values ​​to a uniform range using the maximum and minimum value scaling method, thereby eliminating the influence of dimensional differences.

[0131] Step 306: Concatenate the transformation result and the normalization result to generate a statistical feature vector, which is the output result of the statistical feature processing branch.

[0132] In step 306, the statistical feature vector is a comprehensive feature representation formed by fusing the transformed features and the normalized features, containing complete feature information in terms of data volume dimension.

[0133] In this embodiment, the transformation result output by the fully connected layer and the normalization result output by the feature scaling layer are concatenated and combined, and the final statistical feature vector is generated through the feature fusion layer.

[0134] Here is a specific example:

[0135] In the smart home scenario, a recurrent neural network structure in the temporal feature processing branch is used to extract temporal patterns from the 32-second update interval feature. A three-layer recurrent neural network unit analyzes the time series data, where the input layer receives data at 32-second intervals, the hidden layer calculates time dependencies, and the output layer generates a temporal pattern vector [0.38, 0.25, 0.40]. Simultaneously, an attention mechanism in the temporal feature processing branch is used to identify key time points at 50 seconds, 120 seconds, and 5 seconds of temporal continuity. By calculating the attention weight distribution, time point 120 seconds receives the highest weight (0.7), time point 50 seconds receives a weight of 0.2, and time point 5 seconds receives a weight of 0.1, generating a key time point identifier vector [0.2, 0.7, 0.1]. The temporal pattern vector [0.38, 0.25, 0.40] and the key time point identifier vector [0.2, 0.7, 0.1] are fused, and the temporal feature vector [0.38, 0.25, 0.40, 0.2, 0.7, 0.1] is calculated through a fully connected layer. On the other hand, the dimensionality transformation of the 8-byte and 48-byte capacity features is performed using a fully connected layer in the statistical feature processing branch. The calculation is performed using a weight matrix W equal to [0.5, 0.3; 0.2, 0.4]. The 8-byte transformation is 8 × 0.5 + 48 × 0.2 = 4 + 9.6 = 13.6, and the 48-byte transformation is 8 × 0.3 + 48 × 0.4 = 2.4 + 19.2 = 21.6, resulting in the transformed vector [13.6, 21.6]. The capacity feature is then normalized using a feature scaling layer in the statistical feature processing branch, employing the formula... ,in Represents the normalized eigenvalues. Represents the original feature values. This represents the minimum eigenvalue, which is 8. The maximum value of the feature is 48. Normalizing 8 bytes results in (8-8) divided by (48-8) equal to 0, and normalizing 48 bytes results in (48-8) divided by (48-8) equal to 1, yielding the normalized result vector [0,1]. Finally, the transformed result vector [13.6,21.6] and the normalized result vector [0,1] are concatenated to generate the statistical feature vector [13.6,21.6,0,1], completing the processing of time-series and statistical features.

[0136] In this embodiment, by working together with the temporal feature processing branch and the statistical feature processing branch, the temporal and statistical characteristics of the data can be fully explored to generate high-quality feature representations, providing an accurate feature basis for subsequent storage priority evaluation and improving the decision-making accuracy and reliability of the entire system.

[0137] To address the accuracy issue in generating storage priority metrics, in some embodiments, step 204 involves generating a target storage priority metric using the output layer of a multimodal neural network model, based on the fused feature results and combined with device importance parameters and network state parameters.

[0138] Step 401: Perform dimension normalization on the fused feature results to form the first feature vector.

[0139] In step 401, dimension normalization refers to the process of adjusting feature vectors of different lengths to a uniform dimension. The first feature vector is the standardized feature vector formed after the normalization process.

[0140] In this embodiment, the fused feature result is input into the dimension normalization processing layer, and the feature vector is adjusted to a uniform length through padding or truncation operations to form the normalized first feature vector.

[0141] Step 402: Standardize and encode the equipment importance parameters to form an equipment importance vector.

[0142] In step 402, the standardization coding process is the process of converting the device importance parameter into a standard format. The device importance vector is a standardized vector that contains device importance information.

[0143] In this embodiment, the device importance parameter is input into a standardized encoder, and the original importance parameter is mapped to a standard numerical range through linear transformation to form a device importance vector.

[0144] Step 403: Perform real-time quantization of the network state parameters to form a network state vector.

[0145] In step 403, real-time quantization is the process of converting continuous network state parameters into discrete values. The network state vector is a quantized feature vector that represents the network state.

[0146] In this embodiment, network state parameters are input into a real-time quantization processor, and continuous parameters are converted into discrete levels through a piecewise function to form a network state vector.

[0147] Step 404: Concatenate and combine the first feature vector, the device importance vector, and the network state vector to form a comprehensive input vector.

[0148] In step 404, the concatenation and combination is the process of connecting multiple feature vectors into a comprehensive vector. The comprehensive input vector is a complete feature vector containing all input information.

[0149] In this embodiment, the first feature vector, the device importance vector, and the network state vector are concatenated in sequence to form a comprehensive input vector containing all feature information.

[0150] Step 405: Perform a nonlinear transformation on the synthesized input vector through the fully connected network in the output layer, and perform importance weighting on the transformed synthesized input vector to generate an initial storage priority index.

[0151] In step 405, the nonlinear transformation is the process of feature transformation through activation function, the importance weighting is the operation of assigning weights according to the importance of features, and the initial storage priority index is the preliminary score result after weighted calculation.

[0152] In this embodiment, the comprehensive input vector is input into a fully connected network, nonlinear transformation is performed through an activation function, and then an attention mechanism is used to weight the importance of the transformed features to finally generate an initial storage priority index.

[0153] Step 406: Normalize the initial storage priority index to generate the target storage priority index.

[0154] In this embodiment, the initial storage priority index is input into the normalization processor, and the value is mapped to a predetermined range through linear transformation to generate the final target storage priority index.

[0155] Here is a specific example:

[0156] In the smart home scenario, the fused feature result [0.38, 0.25, 0.40, 0.15, 0.30] undergoes dimension normalization, and the vector length is unified to 6 dimensions through zero-padding, forming the first feature vector [0.38, 0.25, 0.40, 0.15, 0.30, 0]. The device importance parameter, the door and window sensor weight of 0.9, is standardized and encoded using the formula... ,in This represents the original weight values. This represents the standardized weight values, which are then substituted into the calculation to obtain... The device importance vector [0.82] is formed. The network state parameter bandwidth of 8Mbps is quantized in real time using a piecewise function mapping, where less than 5Mbps is mapped to 0.3, 5 to 10Mbps is mapped to 0.6, and greater than 10Mbps is mapped to 0.9, resulting in a quantized value of 0.6, forming the network state vector [0.6]. The first feature vector [0.38,0.25,0.40,0.15,0.30,0], the device importance vector [0.82], and the network state vector [0.6] are concatenated to form the comprehensive input vector [0.38,0.25,0.40,0.15,0.30,0,0.82,0.6]. The comprehensive input vector is nonlinearly transformed by the fully connected network in the output layer, and the ReLU activation function is used to calculate the transformed vector [0.38,0.25,0.40,0.15,0.30,0,0.82,0.6]. The transformed integrated input vector is weighted according to importance, and the weight vector [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2] is used for calculation, as shown in the formula. ,in Indicates the initial storage priority metric. Indicates the first The weight coefficients of each feature, Represents the transformed synthesized input vector. Substituting the components into the calculation yields S. init =0.1x0.38+0.1x0.25+0.1x0.40+0.1x0.15+0.1x0.30+0.1x0+0.2x0.82+0.2x0.6=0.038+0.025+0.040+0.015+0.030+0+0.164+0.12=0.432, generating an initial storage priority index of 0.432. The initial storage priority index is then normalized using the formula... ,in This represents the target storage priority index, which is then substituted into the calculation. The target storage priority metric score was 43.2, completing the entire metric generation process.

[0157] In this embodiment, through multi-step feature processing and comprehensive calculation, an index value that accurately reflects the data storage priority can be generated, providing a quantitative basis for subsequent storage decisions and improving the rationality of storage resource allocation and system operating efficiency.

[0158] To address the issue of data standardization in IoT devices, in some embodiments, step 102, which involves tagging the metadata to form a standardized data packet, includes:

[0159] Step 501: Parse the metadata to obtain the device's unique identifier.

[0160] In step 501, the device unique identifier refers to an encoded string that can uniquely identify an IoT device, which usually includes identification information such as the device model serial number.

[0161] In this embodiment of the application, a data parser reads the metadata content, identifies and extracts the unique device identifier string contained therein, and provides a basis for device identification for subsequent processing.

[0162] Step 502: Assign a corresponding device type code to each device unique identifier according to the preset device type classification rules.

[0163] In step 502, the preset device type classification rule refers to a classification system established in advance based on the physical characteristics, functional attributes, and application scenarios of IoT devices. This rule maps different types of devices (such as temperature sensors, humidity sensors, motion detectors, etc.) to unique device type codes, which are used to quickly identify and classify device types during data processing. The device type code is a category identifier assigned based on the functional characteristics of the device.

[0164] In this embodiment of the application, based on the device type classification rule library, the corresponding device type code is matched with each device unique identifier to establish a mapping relationship between device and type.

[0165] Step 503: Based on the data acquisition time information, add a time stamp to each metadata record and calculate the byte length of the metadata to form metadata length information.

[0166] In step 503, the data acquisition time information refers to the precise timestamp provided by the device's built-in clock module or the connected edge gateway at the moment the IoT device generates or sends metadata. This time information reflects the actual point in time when the data was generated. The timestamp is the timestamp information appended to the data, and the metadata length information refers to the byte count of the metadata content.

[0167] In this embodiment of the application, the time information at the time of data collection is obtained, a precise time stamp is added to each metadata record, and the byte length of the metadata is calculated to form complete metadata description information.

[0168] Step 504: Combine the device type code, the timestamp, and the metadata length information to generate a standardized data packet header.

[0169] In step 504, the standardized data packet header is a standardized header structure that includes device type encoding timestamps and metadata length information.

[0170] In this embodiment of the application, the device type encoding timestamp and metadata length information are combined according to a predetermined format to generate a standardized data packet header with a uniform structure.

[0171] Step 505: Encapsulate the metadata with the standardized data packet header to form a standardized data packet.

[0172] In this embodiment of the application, the standardized data packet header and the original metadata are combined according to a specific encapsulation format to form the final standardized data packet.

[0173] Here is a specific example:

[0174] In a smart home scenario, the metadata "Device Identifier DS002, Status On" transmitted from door and window sensors is tagged. First, the metadata is parsed to obtain the unique device identifier DS002. Based on preset device type classification rules, the door and window sensors are assigned a device type code DOOR. Using the data acquisition time information of October 15, 2023, 14:31:05:123, a timestamp 20231015143105123 is added to the metadata record, and the byte length of the metadata is calculated. The original data "Device Identifier DS002, Status On" contains 20 characters, encoded in UTF-8 with each character occupying 1 byte, totaling 20 bytes, forming the metadata length information 20. The device type code DOOR, the timestamp 20231015143105123, and the metadata length information 20 are combined to generate a standardized data packet header DOOR20231015143105123020. Finally, the metadata and standardized data packet header are encapsulated in TLV format, with the type field set to DOOR, the length field set to 20, and the value field containing the original metadata, forming a complete standardized data packet and completing the tagging process. This standardized data packet will be used for subsequent hierarchical compression and feature extraction, maintaining consistency with the temperature sensor data processing workflow and ensuring the consistency of data processing throughout the system.

[0175] In this embodiment of the application, a standardized tagging process is used to achieve unified formatting of heterogeneous device data, providing a standardized data foundation for subsequent data compression feature extraction and intelligent analysis, and improving the efficiency and reliability of the entire data processing process.

[0176] To address the efficiency issues of IoT data compression and feature extraction, in some embodiments, step 103: performing layered compression on the standardized data packets and extracting multidimensional features from the layered compressed data packets includes:

[0177] Step 601: Based on the update frequency characteristics of the standardized data packets, divide the standardized data packets into status data packets and data stream packets.

[0178] In step 601, the update frequency characteristic refers to the time interval characteristic of the data packets. The status data packets are a set of data with a low update frequency, while the data stream packets are a continuous data sequence with a high update frequency.

[0179] In this embodiment of the application, standardized data packets are classified according to their update time interval characteristics based on a preset update frequency threshold. Data packets exceeding the threshold are classified as status data packets, and data packets below the threshold are classified as data stream packets.

[0180] Step 602: Compress the status data packet using differential encoding, and record the collection timestamp information during the compression process.

[0181] In step 602, the difference encoding method is a compression method that stores the differences between adjacent data, and the acquisition timestamp information is a time point marker recorded when the data is acquired.

[0182] In this embodiment of the application, the status data packet is processed using a difference encoding algorithm, which compares the differences in data values ​​at adjacent time points, stores only the change portion, and records the collection timestamp information of each data point during the processing.

[0183] Step 603: Compress the data stream packet using streaming encoding, and record timing marker information during the compression process.

[0184] In step 603, streaming encoding is a real-time compression method for continuous data streams, and time-series marker information is the identification information of important time points in the data stream. The data packets after hierarchical compression refer to two types of data packets after classified compression processing. Specifically, they include two entity concepts: state data packets compressed using differential encoding and data stream packets compressed using streaming encoding. The state data packets contain state data processed by differential compression and its associated timestamp information, while the data stream packets contain the data stream processed by streaming compression and its time-series marker information. These two types of data packets together constitute the set of data packets after hierarchical compression.

[0185] In this embodiment of the application, the data stream packets are processed using a streaming coding algorithm, and data redundancy is eliminated by using sliding window technology. At the same time, key timing marker information of the data stream is recorded during the compression process.

[0186] Step 604: Based on the collected timestamp information, generate update interval features; based on the time sequence marker information, generate time sequence continuity features.

[0187] In step 604, the update interval feature is the statistical feature of the data packet update time interval, and the temporal continuity feature is the distribution pattern of data in the time dimension.

[0188] In this embodiment, based on the recorded collection timestamp information, the statistical value of the time interval between data packets is calculated to generate update interval features. At the same time, based on the time sequence marker information, the time distribution pattern of the data stream is analyzed to generate time sequence continuity features.

[0189] Step 605: Based on the size information of the compressed status data packet and the size information of the compressed data stream packet, respectively, calculate the corresponding capacity characteristics.

[0190] In step 605, the capacity feature is a statistical feature of the data packet size information, reflecting the occupancy of data storage space.

[0191] In this embodiment of the application, the size information of the compressed status data packet and data stream packet are statistically analyzed, their capacity statistical characteristics are calculated, and corresponding capacity characteristic values ​​are generated.

[0192] Step 606: Combine the update interval feature, the temporal continuity feature, and the corresponding capacity feature to form a multidimensional feature.

[0193] In this embodiment of the application, the update interval feature, temporal continuity feature, and capacity feature are combined in a specific order to form a feature vector set containing multi-dimensional information.

[0194] Here is a specific example:

[0195] In a smart home scenario, standardized data packets for smart sockets are processed. These packets contain the device identifier PS003, a power consumption of 120W, and online status information. Based on update frequency characteristics, the smart socket data packet update interval is 15 seconds, falling between the 30-second and 5-second thresholds. Referring to the system's default rules, it is classified as a status data packet. The status data packet is compressed using differential encoding. The current power value of 120W is compared with the previous value of 115W, and the difference of 5W is stored. During compression, the collection timestamps 20231015143045356 and 20231015143030356 are recorded. An update interval feature is generated based on the collection timestamps. Subtracting 20231015143045356 from 20231015143030356 equals 15000 milliseconds, or 15 seconds. Based on the statistical capacity characteristics of the compressed data packet size, the original 25-byte data packet size is reduced to 8 bytes after differential encoding compression. Simultaneously, supplementary processing was performed on the data stream packets from the door and window sensors. Packets with update intervals below the 5-second threshold (3 seconds) were classified as data stream packets and compressed using streaming encoding. The number of triggers increased from 12 to 18 within 10 minutes, and the recorded timing marker information included the sequence of trigger time points. Based on the timing marker information, a timing continuity feature was generated. The average interval time point total of 600 seconds divided by the number of triggers (18) equals 33.33 seconds. The maximum interval was found to be 75 seconds by identifying the maximum difference between adjacent trigger time points, and the minimum interval was found to be 2 seconds by identifying the minimum difference between adjacent trigger time points. Based on the size information of the compressed data stream packets, a capacity feature was statistically analyzed. The original data packet size of 20 bytes was compressed to 12 bytes using streaming encoding. Finally, the update interval feature of 15 seconds, the timing continuity features of 33.33 seconds, 75 seconds, and 2 seconds, and the capacity features of 8 bytes and 12 bytes were combined to form a multi-dimensional feature set: 15, 33.33, 75, 2, 8, 12. This multidimensional feature set will be used for subsequent multimodal neural network analysis, maintaining consistency with the feature processing of temperature sensors and door / window sensors to ensure the integrity and comparability of system feature data.

[0196] In this embodiment, by using layered compression and feature extraction, the amount of data storage and transmission is effectively reduced while retaining the key feature information of the data, providing high-quality feature input for subsequent intelligent analysis and decision-making, and improving the processing efficiency and accuracy of the entire system.

[0197] To address the diverse needs for secure storage and transmission of IoT data, in some embodiments, step 105: based on the security level, performing differentiated encryption processing on the layered compressed data packets using a lightweight encryption algorithm, and transmitting the encrypted data packets to the corresponding storage area in the cloud, includes:

[0198] Step 701: Select the corresponding encryption strength configuration according to the security level, wherein different security level data in the data packet after layered compression process adopt different encryption strength configurations.

[0199] In step 701, encryption strength configuration refers to the combination of encryption parameters set according to the security level, including parameter settings such as key length, encryption mode, and algorithm type. Different security level data represent high-security-level data, which refers to important data requiring strong encryption protection based on storage priority indicators. This type of data typically has high value, high sensitivity, or high-frequency access characteristics. The criteria for judgment include the data's importance score, access frequency threshold, and security requirement level. For example, critical device operating status data and alarm information belong to high-security-level data.

[0200] In this embodiment of the application, based on the security level identifier associated with the data packet, a corresponding encryption strength configuration scheme is selected from a preset encryption configuration library, and appropriate encryption parameter combinations are assigned to data of different security levels.

[0201] Step 702: Encrypt the corresponding security level data using the encryption strength configuration.

[0202] In step 702, encryption processing refers to the process of converting data using encryption algorithms, converting plaintext data into ciphertext data through cryptographic operations.

[0203] In this embodiment of the application, the data is encrypted using the corresponding encryption algorithm according to the selected encryption strength configuration, and the original data is converted into encrypted data that cannot be directly read.

[0204] Step 703: During the encryption process, an initialization vector is generated, and the initialization vector is combined and encapsulated with the encrypted data packet.

[0205] In step 703, the initialization vector is a random number seed used in the encryption process to ensure that the same plaintext produces different ciphertext results after encryption.

[0206] In this embodiment of the application, a random initialization vector is generated during the encryption process, and the initialization vector is combined and encapsulated with the encrypted data packet to form a complete encrypted data unit.

[0207] Step 704: Establish a secure transmission channel with the cloud and transmit the encapsulation result to the cloud storage system through the secure transmission channel.

[0208] In step 704, the secure transmission channel is an encrypted communication link established between the client and the cloud to ensure the security of data transmission.

[0209] In this embodiment, a secure connection channel with the cloud is established using a secure transmission protocol, and the encapsulated encrypted data packets are transmitted to the cloud storage system through this channel.

[0210] Step 705: In the cloud storage system, the encapsulation results are distributed to different storage areas in the cloud according to the target storage priority index.

[0211] In step 705, the cloud storage system is a cloud platform system that provides data storage services and includes storage areas with different performance characteristics.

[0212] In this embodiment of the application, the received encrypted data packets are automatically distributed to the storage area of ​​the corresponding performance level according to the value of the target storage priority index in the cloud storage system.

[0213] Here is a specific example:

[0214] In smart home scenarios, data packets after layered compression are encrypted differently based on their security level. Door and window sensor data has a high security level, so a high-strength encryption configuration is selected, employing the AES256 algorithm and CBC encryption mode with a 256-bit key length. Temperature sensor data has a low security level, so a standard encryption configuration is selected, employing the ChaCha20 algorithm and stream encryption mode with a 128-bit key length. Encryption is performed using data corresponding to the security level configured by encryption strength. Door and window sensor data is encrypted using the AES256 algorithm, generating an initialization vector of 0x12a4f7c8; temperature sensor data is encrypted using the ChaCha20 algorithm, generating an initialization vector of 0x89b3d2e1. During encryption, the initialization vector is combined with the encrypted data packet for encapsulation. The encapsulation format for door and window sensor data is the initialization vector 0x12a4f7c8 plus encrypted data, and the encapsulation format for temperature sensor data is the initialization vector 0x89b3d2e1 plus encrypted data. A secure transmission channel is established with the cloud, employing the TLS 1.3 protocol. Key exchange uses the ECDHE algorithm, and the encryption suite uses TLS_AES_256_GCM_SHA384. The encapsulated result is transmitted to the cloud storage system via this secure channel. In the cloud storage system, based on target storage priority metrics, door and window sensor data (74.9 points, exceeding the 70-point threshold) is allocated to a high-performance storage area using SSD storage media with an access latency of less than 1 millisecond. Temperature sensor data (42.95 points, below the 50-point threshold) is allocated to a low-cost storage area using HDD storage media with an access latency of approximately 10 milliseconds. This secure storage process ensures high-strength protection for critical data while optimizing storage resource utilization efficiency.

[0215] In this embodiment, differentiated encryption processing and intelligent storage allocation ensure both the security of important data and the need for fast access, while also optimizing the overall storage resource utilization efficiency, achieving the best balance between security and economy.

[0216] Figure 3 This application provides a schematic diagram of the structure of a cloud storage system for IoT device data, and the specific implementation details are as follows:

[0217] The acquisition module 31 is used to acquire metadata from multiple IoT devices in the IoT environment.

[0218] The processing module 32 is used to tagged the metadata to form a standardized data packet.

[0219] Compression module 33 is used to perform layered compression processing on the standardized data packets and extract multidimensional features from the data packets after layered compression processing.

[0220] The input module 34 is used to input the multidimensional features into a deep learning-based multimodal neural network model, analyze the multidimensional features through the multimodal neural network model, generate a target storage priority index, and determine the corresponding security level based on the target storage priority index.

[0221] The encryption module 35 is used to perform differentiated encryption processing on the data packet after layered compression based on the security level using a lightweight encryption algorithm, and transmit the encrypted data packet to the corresponding storage area in the cloud.

[0222] The cloud storage system for IoT device data in this application embodiment is used to implement the aforementioned cloud storage method for IoT device data. Therefore, the specific implementation of the cloud storage system for IoT device data can be found in the embodiment section of the cloud storage method for IoT device data above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0223] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the cloud storage method for IoT device data described above.

[0224] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of any of the above-described cloud storage methods for IoT device data.

[0225] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0226] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the cloud storage method for IoT device data.

[0227] Those skilled in the art will further 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, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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 implementation should not be considered beyond the scope of this application.

[0228] The foregoing has provided a detailed description of a cloud storage method, system, electronic device, and storage medium for IoT device data provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A cloud storage method for data from Internet of Things (IoT) devices, characterized in that, include: Collect metadata from multiple IoT devices in an IoT environment; The metadata is tagged to form standardized data packets; The standardized data packets are subjected to hierarchical compression, and multidimensional features are extracted from the hierarchically compressed data packets. The multidimensional features are input into a deep learning-based multimodal neural network model. The multimodal neural network model analyzes the multidimensional features to generate a target storage priority index. Based on the target storage priority index, the corresponding security level is determined. Based on the security level, a lightweight encryption algorithm is used to perform differentiated encryption on the data packets after the layered compression process, and the encrypted data packets are transmitted to the corresponding storage area in the cloud. The step of inputting the multidimensional features into a deep learning-based multimodal neural network model, analyzing the multidimensional features through the multimodal neural network model, generating a target storage priority index, and determining the corresponding security level based on the target storage priority index includes: The multidimensional features are input into the multimodal neural network model, which includes a temporal feature processing branch, a statistical feature processing branch, and an output layer. The update interval feature and temporal continuity feature in the multidimensional features are analyzed through the temporal feature processing branch, and the capacity feature in the multidimensional features is analyzed through the statistical feature processing branch. The outputs of the temporal feature processing branch and the statistical feature processing branch are fused to obtain the fused feature results. Based on the fused feature results, the target storage priority index is generated through the output layer of the multimodal neural network model, combined with the device importance parameter and network state parameter. The numerical range of the target storage priority index is mapped to a preset security level classification library to determine the corresponding security level; The step of analyzing the update interval feature and temporal continuity feature in the multidimensional features through the temporal feature processing branch, and analyzing the capacity feature in the multidimensional features through the statistical feature processing branch, includes: Using the recurrent neural network structure in the temporal feature processing branch, temporal pattern extraction is performed on the update interval feature to obtain the temporal pattern; By utilizing the attention mechanism in the temporal feature processing branch, key time points are identified in the temporal continuity features to obtain key time point identifiers; The time series pattern and the key time point identifier are fused to generate a time series feature vector, which is the output result of the time series feature processing branch; The capacity feature is transformed using the fully connected layer in the statistical feature processing branch. The capacity feature is normalized using the feature scaling layer in the statistical feature processing branch; The transformation result and the normalization result are concatenated to generate a statistical feature vector, which is the output result of the statistical feature processing branch.

2. The method according to claim 1, characterized in that, The step involves generating a target storage priority index through the output layer of the multimodal neural network model, based on the fused feature results and combined with device importance parameters and network state parameters, including: The fused feature results are then subjected to dimension normalization to form a first feature vector. The equipment importance parameters are standardized and encoded to form an equipment importance vector; The network state parameters are quantized in real time to form a network state vector. The first feature vector, the device importance vector, and the network state vector are concatenated and combined to form a comprehensive input vector; The integrated input vector is nonlinearly transformed by a fully connected network in the output layer, and the transformed integrated input vector is weighted by importance to generate an initial storage priority index. The initial storage priority index is normalized to generate the target storage priority index.

3. The method according to claim 1, characterized in that, The step of tagging the metadata to form a standardized data packet includes: Parse the metadata to obtain the device's unique identifier; According to the preset device type classification rules, assign a corresponding device type code to each unique device identifier; Based on the data acquisition time information, a time stamp is added to each metadata record, and the byte length of the metadata is calculated to form metadata length information; The device type code, the timestamp, and the metadata length information are combined to generate a standardized data packet header; The metadata is encapsulated with the standardized data packet header to form a standardized data packet.

4. The method according to claim 1, characterized in that, The step of performing hierarchical compression on the standardized data packets and extracting multidimensional features from the hierarchically compressed data packets includes: Based on the update frequency characteristics of the standardized data packets, the standardized data packets are divided into status data packets and data stream packets; The status data packets are compressed using differential encoding, and the collection timestamp information is recorded during the compression process. The data stream packets are compressed using streaming encoding, and timing marker information is recorded during the compression process. Based on the collected timestamp information, an update interval feature is generated; based on the time series marker information, a time series continuity feature is generated. Based on the size information of the compressed status data packet and the size information of the compressed data stream packet, the corresponding capacity characteristics are statistically analyzed respectively; The update interval feature, the temporal continuity feature, and the corresponding capacity feature are combined to form a multidimensional feature.

5. The method according to claim 1, characterized in that, Based on the security level, the process of applying a lightweight encryption algorithm to the layered compressed data packet for differentiated encryption, and then transmitting the encrypted data packet to the corresponding storage area in the cloud, includes: Based on the security level, select the corresponding encryption strength configuration, wherein different encryption strength configurations are used for data of different security levels in the data packets after layered compression. The corresponding security level data is encrypted using the encryption strength configuration; During the encryption process, an initialization vector is generated, and the initialization vector is combined and encapsulated with the encrypted data packet; Establish a secure transmission channel with the cloud and transmit the encapsulation results to the cloud storage system through the secure transmission channel; In the cloud storage system, the encapsulation results are distributed to different storage areas in the cloud according to the target storage priority index.

6. A cloud storage system for data from Internet of Things (IoT) devices, characterized in that, include: The data acquisition module is used to collect metadata from multiple IoT devices in the IoT environment. The processing module is used to tag the metadata to form standardized data packets; The compression module is used to perform hierarchical compression on the standardized data packets and extract multidimensional features from the hierarchically compressed data packets. The input module is used to input the multidimensional features into a deep learning-based multimodal neural network model, analyze the multidimensional features through the multimodal neural network model, generate a target storage priority index, and determine the corresponding security level based on the target storage priority index. The encryption module is used to perform differentiated encryption processing on the data packets after layered compression based on the security level using a lightweight encryption algorithm, and transmit the encrypted data packets to the corresponding storage area in the cloud. The step of inputting the multidimensional features into a deep learning-based multimodal neural network model, analyzing the multidimensional features through the multimodal neural network model, generating a target storage priority index, and determining the corresponding security level based on the target storage priority index includes: The multidimensional features are input into the multimodal neural network model, which includes a temporal feature processing branch, a statistical feature processing branch, and an output layer. The update interval feature and temporal continuity feature in the multidimensional features are analyzed through the temporal feature processing branch, and the capacity feature in the multidimensional features is analyzed through the statistical feature processing branch. The outputs of the temporal feature processing branch and the statistical feature processing branch are fused to obtain the fused feature results. Based on the fused feature results, the target storage priority index is generated through the output layer of the multimodal neural network model, combined with the device importance parameter and network state parameter. The numerical range of the target storage priority index is mapped to a preset security level classification library to determine the corresponding security level; The step of analyzing the update interval feature and temporal continuity feature in the multidimensional features through the temporal feature processing branch, and analyzing the capacity feature in the multidimensional features through the statistical feature processing branch, includes: Using the recurrent neural network structure in the temporal feature processing branch, temporal pattern extraction is performed on the update interval feature to obtain the temporal pattern; By utilizing the attention mechanism in the temporal feature processing branch, key time points are identified in the temporal continuity features to obtain key time point identifiers; The time series pattern and the key time point identifier are fused to generate a time series feature vector, which is the output result of the time series feature processing branch; The capacity feature is transformed using the fully connected layer in the statistical feature processing branch. The capacity feature is normalized using the feature scaling layer in the statistical feature processing branch; The transformation result and the normalization result are concatenated to generate a statistical feature vector, which is the output result of the statistical feature processing branch.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the cloud storage method for IoT device data as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the cloud storage method for IoT device data as described in any one of claims 1 to 5.

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