Lightweight data encryption processing method driven by edge computing

Abstract

The present invention relates to the field of data encryption technology, and in particular to a lightweight data encryption processing method driven by edge computing. The method comprises the following steps: collecting original input data, combining semantic recognition with field structure analysis, accurately identifying data types, and realizing field-level encryption sensitivity division; formulating differentiated encryption processing strategies based on field encryption priority labels to improve encryption efficiency and resource allocation rationality; during the encryption execution process, evaluating abnormal disturbance conditions in real time, further analyzing the load gradient changes of edge nodes, and extracting the dynamic degradation trend of encryption element structures; monitoring edge node response delays in combination with structural state changes, continuously tracking the growth trend of encryption risks, and optimizing encryption strategy parameter configuration accordingly; the present invention optimizes encryption strategies to achieve higher stability during the encryption process.

Description

Technical Field

[0001] The present invention relates to the field of data encryption technology, and in particular to a lightweight data encryption processing method driven by edge computing. Background Art

[0002] Data collection and transmission are typically characterized by low latency, high frequency, small batches, and multiple nodes. Under the unified processing architecture of traditional cloud computing centers, data must be transmitted back to remote servers for encryption and analysis, resulting in significant bandwidth pressure, high processing latency, and risks associated with sensitive data transmission. Existing methods, particularly in applications requiring high real-time performance and data privacy, are unable to meet the demands of new data processing methods requiring on-site processing, immediate response, and secure reliability. Existing data encryption schemes rely on general encryption algorithms and unified encryption strategies for complete data streams. These schemes are characterized by coarse processing granularity and large algorithmic complexity, making them unsuitable for resource-constrained edge devices. Traditional encryption ignores the contextual structure and semantic characteristics of data, resulting in low encryption efficiency, inflexible adaptation to dynamic field changes, and a lack of a structural hierarchical subdivision mechanism, hindering the independence and adaptability of edge nodes in data encryption processing. However, traditional data encryption suffers from inaccurate assessments of abnormal disturbances in the encrypted data and inaccurate detection of dynamic degradation trends in edge encryption component structures. Summary of the Invention

[0003] Based on this, it is necessary to provide an edge computing-driven lightweight data encryption processing method to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a lightweight data encryption processing method driven by edge computing includes the following steps:

[0005] Step S1: collecting original input data to be encrypted; performing semantic recognition of the original input data based on the original input data to be encrypted, thereby obtaining original data field semantic information; performing field structure parsing processing on the original data field semantic information, thereby obtaining an original data field structure; performing original input data type recognition based on the original data field semantic information and the original data field structure, thereby obtaining an original input data type;

[0006] Step S2: performing field encryption priority label assignment processing based on the original input data type to obtain field encryption priority label assignment data; determining a data encryption processing strategy based on the original input data type and the field encryption priority label assignment data; performing input data encryption processing on the original input data type based on the data encryption processing strategy to obtain encrypted data;

[0007] Step S3: Evaluate the abnormal disturbance condition of the encrypted data according to the data encryption processing condition; evaluate the edge node load gradient growth trend according to the abnormal disturbance condition of the encrypted data; and determine the dynamic degradation trend of the edge encryption element structure according to the edge node load gradient growth trend;

[0008] Step S4: Detect the response delay status of the encryption edge node based on the dynamic degradation trend of the edge encryption element structure; evaluate the data encryption risk trend based on the response delay status of the encryption edge node; optimize the data encryption strategy based on the data encryption risk trend to obtain data encryption strategy optimization data.

[0009] The present invention extracts the semantics and analyzes the structure of the original input data, and finely classifies the data fields to provide a precise matching basis for the encryption strategy. A field encryption priority label mechanism is introduced to allocate encryption resources according to field sensitivity and structural complexity, thereby improving processing efficiency and reducing resource waste. A dynamic feedback mechanism is used to monitor abnormal disturbances in real time and track changes in edge node loads. By evaluating the degradation trend of the node structure, hardware performance degradation is identified in advance, and early warning adjustments are implemented. Finally, a closed-loop optimization system for response delay and encryption risk is constructed to enhance the strategy's adaptive capabilities, which is suitable for efficient encryption processing in edge scenarios. Therefore, the present invention is an optimization of the traditional lightweight data encryption processing method, which solves the problem of inaccurate assessment of abnormal disturbance conditions of encryption processing data and inaccurate detection of dynamic degradation trends of edge encryption element structures in traditional lightweight data encryption processing methods. The accuracy of abnormal disturbance condition assessment and the accuracy of dynamic degradation trend detection of edge encryption element structures are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the steps of a lightweight data encryption processing method driven by edge computing;

[0011] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0012] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0014] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0015] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0016] To achieve this, please refer to Figures 1 to 2 , a lightweight data encryption processing method driven by edge computing, comprising the following steps:

[0017] Step S1: collecting original input data to be encrypted; performing semantic recognition of the original input data based on the original input data to be encrypted, thereby obtaining original data field semantic information; performing field structure parsing processing on the original data field semantic information, thereby obtaining an original data field structure; performing original input data type recognition based on the original data field semantic information and the original data field structure, thereby obtaining an original input data type;

[0018] In this embodiment of the present invention, raw input data to be encrypted is collected. This data is acquired by a data collection module in an edge computing node. This module aggregates the raw data collected based on specific application scenarios (such as industrial equipment, IoT sensors, and communication networks). The collected data includes various types, such as text, images, and sensor values, and comes from a wide range of sources. The collected data is typically a mixture of structured and unstructured data, requiring preprocessing at this stage. Specifically, a data cleaning module performs denoising and standardization to remove irrelevant information. Semantic recognition is then performed based on the raw input data. The semantic recognition module analyzes the input data using natural language processing techniques and machine learning algorithms (such as deep neural networks and support vector machines) to extract its core semantic information. This process is optimized for different data types. For example, for text data, semantic recognition uses techniques such as word segmentation and named entity recognition, while for sensor data, pattern recognition techniques are used to extract key information from the values. These techniques enable accurate identification of semantic information contained in the input data, such as key features such as value type, timestamp, and geographic location. Based on this semantic information, the field structure of the raw data is parsed. The field structure parsing module classifies data fields based on semantic recognition results and extracts structural features such as each field's type, length, and content format. For example, when processing tabular data, the system identifies the relationships between columns and labels the data according to field type (e.g., string, integer, floating decimal, etc.). For text data, field parsing analyzes specific fields (e.g., username, password, date, etc.) based on grammatical rules or predefined data structures. This process utilizes a customized field parsing algorithm that flexibly adapts to different data sources and structural rules. The data type of the original input is identified based on the semantic information and field structure of the original data field. The data type identification module analyzes the structural features of each field to determine its data type (e.g., text, number, date, etc.). For example, if a field's content is numeric, it is classified as an integer; if it is in time format, it is classified as a date. These determinations enable the system to prepare for data encryption and determine the required data processing method.

[0019] Step S2: performing field encryption priority label assignment processing based on the original input data type to obtain field encryption priority label assignment data; determining a data encryption processing strategy based on the original input data type and the field encryption priority label assignment data; performing input data encryption processing on the original input data type based on the data encryption processing strategy to obtain encrypted data;

[0020] In this embodiment of the present invention, field encryption priority tags are assigned based on the original input data type obtained in the previous step. This process is implemented by the encryption priority assignment module. This module assigns encryption priority tags based on each field's sensitivity, frequency of use, and impact on overall data security. Highly sensitive fields are assigned a higher encryption priority, while less sensitive fields are assigned a lower priority. For example, fields with data types such as address and phone number have a higher priority, while public data (such as non-confidential statistical data) have a lower priority. A data encryption processing policy is assigned based on the original input data type and field encryption priority tag. This policy determines the selection of encryption algorithm, encryption strength setting, and the specific data processing flow. The encryption policy generation module selects an appropriate encryption algorithm (such as AES symmetric encryption or RSA asymmetric encryption) and sets the encryption strength based on the field encryption priority tag, data sensitivity, and system resource availability. For fields requiring higher security, more complex and stronger encryption methods are used, and hardware resources are adapted to minimize the impact on system performance. For fields with large data volumes or that require frequent encryption and decryption, the encryption policy selects an efficient stream cipher algorithm to ensure encryption processing efficiency. Based on the determined encryption strategy, the original input data is encrypted. The encryption module encrypts the data according to the selected encryption algorithm and strategy. The encryption operation encrypts each field individually, generating the encrypted data. This encrypted data is then stored or transmitted securely while maintaining a balanced resource consumption.

[0021] Step S3: Evaluate the abnormal disturbance condition of the encrypted data according to the data encryption processing condition; evaluate the edge node load gradient growth trend according to the abnormal disturbance condition of the encrypted data; and determine the dynamic degradation trend of the edge encryption element structure according to the edge node load gradient growth trend;

[0022] In an embodiment of the present invention, abnormal disturbance assessment is performed on encrypted data. The abnormal disturbance assessment module detects distortion or structural anomalies in the encrypted data. It compares the data before and after encryption to check for field loss, data errors, and other issues. If any potential anomalies are detected, the system will flag and record them to provide a basis for subsequent adjustments. Based on the abnormal disturbance conditions in the encrypted data, the load gradient growth trend of the edge node is assessed. This assessment monitors metrics such as edge node processing power, memory usage, and CPU load, and combines them with the complexity of the encryption process to infer the load trend of the edge node. The load assessment module uses resource monitoring and prediction algorithms (such as those based on time series analysis or regression models) to predict future node load changes. If the system detects that the node load is about to exceed a set threshold, it will issue an early warning and provide an optimization strategy for the next encryption process. Based on the load gradient growth trend of the edge node, the dynamic structural degradation trend of the edge encryption component is assessed. This assessment analyzes the node load and the encryption processing intensity to infer thermal effects or hardware failures in the encryption component. The encryption component monitoring module uses hardware diagnostic tools and thermal monitoring algorithms to assess component stability and predict degradation trends over time. If an abnormal trend is detected in an encryption component, the system will take appropriate adjustments to prevent encryption failure or performance degradation caused by hardware failure.

[0023] Step S4: Detect the response delay status of the encryption edge node based on the dynamic degradation trend of the edge encryption element structure; evaluate the data encryption risk trend based on the response delay status of the encryption edge node; optimize the data encryption strategy based on the data encryption risk trend to obtain data encryption strategy optimization data.

[0024] In this embodiment of the present invention, the response delay of encryption edge nodes is detected based on the dynamic degradation trend of edge encryption components. The response delay detection module monitors the response time during the encryption process in real time, capturing any delays caused by device aging or excessive load. By setting thresholds and timeout mechanisms, the system can promptly detect node response delays and record delay data. This data serves as a basis for optimizing encryption policies, further improving encryption processing efficiency. Data encryption risk trends are assessed based on the response delay of encryption edge nodes. The data encryption risk assessment module analyzes potential risks in the data encryption process by comprehensively considering factors such as node load, response time, and hardware stability. For example, excessive node load can lead to reduced encryption processing speed or data loss, while hardware failures can prevent accurate encryption results. Based on these risk factors, this module conducts a comprehensive assessment of the data encryption process and generates an encryption risk trend report. Based on the encryption risk trends, the system optimizes the data encryption policy. The encryption policy optimization module adjusts the encryption algorithm, encryption priority, resource allocation, and other aspects to develop an encryption policy that best suits the current node status and load. This process involves dynamically adjusting the encryption algorithm, such as selecting a lighter encryption method under high load conditions and using a stronger encryption algorithm under low load conditions. The optimized encryption strategy is automatically applied to the encryption process to ensure data security and efficient encryption processing.

[0025] Preferably, step S1 includes the following steps:

[0026] Step S11: collecting the original input data to be encrypted;

[0027] In this embodiment of the present invention, raw input data to be encrypted is acquired through physical or virtual sensors. This raw input data originates from a variety of devices, including sensors, databases, and user input. It is crucial to ensure that the collected raw input data fully reflects the state of the data to be encrypted. High-precision data acquisition tools and equipment are used during the data acquisition process. IoT devices collect real-time sensor data, which is then retrieved from the system or cloud platform via an API. The collected data is stored in data storage media (such as databases and file systems), providing a foundation for subsequent processing and encryption operations.

[0028] Step S12: performing desensitization processing on the original input data to be encrypted, thereby obtaining desensitized data to be encrypted;

[0029] In an embodiment of the present invention, the collected original input data is desensitized, and the desensitization process adopts different processing strategies according to the type and sensitivity of the data. For information data, character replacement, encryption replacement, or desensitization encoding are adopted to hide or obfuscate sensitive information. For numerical data, the desensitization methods adopted include blurring the data, interval processing, etc. This process uses a data desensitization tool or program script to process sensitive information through regular expressions or encryption algorithms to ensure that the desensitized data does not contain the original sensitive information, while retaining the validity of the data to meet subsequent operation requirements. The processed data is saved as desensitized data to be encrypted for subsequent encryption processing.

[0030] Step S13: performing semantic recognition of the original input data based on the desensitized data to be encrypted to obtain semantic information of the original data fields;

[0031] In this embodiment of the present invention, data semantic recognition is performed based on the desensitized data to be encrypted. Semantic recognition involves parsing each field in the desensitized data to identify its corresponding actual meaning. To achieve this goal, natural language processing (NLP) technology is used, combined with tools such as semantic annotation, dictionary matching, and regular expressions, to extract field semantic information from the desensitized data to be encrypted. During this recognition process, the data field type (such as text, number, date, etc.) is accurately identified and its specific business meaning is annotated based on a dictionary library or trained model, resulting in the semantic information of the original data field.

[0032] Step S14: performing field structure parsing on the original data field semantic information to obtain the original data field structure;

[0033] In an embodiment of the present invention, after obtaining the semantic information of the original data field, the field structure parsing process is then performed. The purpose of field structure parsing is to understand the arrangement of data fields, data types, and the relationship between fields. For example, field structure parsing will identify whether the field is a single data item or a multidimensional data item, whether the data has a hierarchical structure (such as nested fields), whether there is an association between fields, etc. In this process, tools such as XML parsing and JSON parsing are used to conduct an in-depth analysis of the data structure based on the data format. If the semantic information of the original data field includes form fields, database table fields, etc., the structure of the field is parsed through SQL queries to identify the primary key, foreign key and other constraints of the data table. After parsing, the complete original data field structure is obtained, and the dependency and hierarchical relationships between fields are also reflected in the parsing results, providing support for subsequent data type identification and encryption processing.

[0034] Step S15: Identify the original input data type based on the original data field semantic information and the original data field structure to obtain the original input data type.

[0035] In an embodiment of the present invention, the semantic information and field structure of the original data field obtained in the previous step are used to identify the data type. The goal of this step is to classify the original input data according to its specific business scenario and data structure, and identify the type of data. Common data types include text, numeric, date, Boolean, binary, etc. Based on the semantic information of the data field and combined with the structural characteristics of the field, the system can determine the type of field data. For example, for a field containing a date format, the system can automatically identify it as a date type; for a field containing an account balance, it can be identified as a numeric type. At this time, the system performs type identification through methods such as type matching and data pattern analysis, and further verifies the correctness of the data type based on the field structure. The identified original input data type will play an important role in the data encryption process, determining the selection of encryption algorithm and the allocation of encryption priority labels.

[0036] Preferably, step S13 includes the following steps:

[0037] Step S131: extracting basic format features of the encrypted data field based on the desensitized data to be encrypted, and obtaining basic format features of the data field to be encrypted;

[0038] In an embodiment of the present invention, in a lightweight pre-processing module deployed at an edge node, after receiving the data input to be encrypted, the data is structured and segmented at the field level. The data content of each field is preliminarily marked with features based on the character encoding category (such as the ASCII code range), byte length, special symbol density (such as non-alphanumeric symbols such as "_", "-", "."), and field length range (for example, limited to 5 to 128 characters). Subsequently, the frequency of character distribution in each field is statistically analyzed to construct a character set distribution matrix to determine whether the composition pattern of the field data presents a linear arrangement, a repeating pattern, or a fixed prefix structure. For example, if the first 4 digits of a field are always fixed in most samples, followed by a sequence of digits, the field is marked as having a prefix-sequence type basic format feature. The format features of all fields will be encapsulated in a JSON structure and output as the basic format feature results of the data field to be encrypted, which will be used as the basic input for subsequent grammatical similarity analysis.

[0039] Step S132: performing similar grammatical pattern matching with a similarity threshold of 0.75 based on the basic format features of the data field to be encrypted, to obtain similar grammatical matching data;

[0040] In an embodiment of the present invention, after receiving the basic format features of the data field to be encrypted output in step S131, the rule matching unit deployed on the edge computing node is used to call the built-in grammatical pattern rule library. The rule library stores more than 50,000 format matching rules, and each rule is a standard format expression composed of a combination of features such as character composition, structural segmentation, numerical interval, and length definition. During the matching process, a structural alignment algorithm is used to perform character-level comparison of the format features of the field to be analyzed to each rule, and the edit distance is used as the basis for the matching score. In order to control the matching accuracy, the similarity threshold is set to 0.75, and rule results below this value are excluded to avoid redundant matching information interfering with downstream analysis. The grammatical rules matched in the above manner are called similar grammatical matching data, and the result serves as the core input for field format classification and context domain analysis.

[0041] Step S133: Analyze data context domain features based on similar syntax matching data and basic format features of the data field to be encrypted;

[0042] In an embodiment of the present invention, similar syntax matching data and original basic format features are used as joint inputs and passed to the context feature analysis unit. The unit adopts a bidirectional scanning strategy in the edge node to analyze context behaviors such as the arrangement relationship between the previous and next fields of the field in the original data set, the frequency of adjacent field combinations, and the use of special separators between fields. For example, by counting whether the field often co-occurs with the previous field, or whether it forms a specific data combination pattern (such as timestamp + location identifier) ​​with the next field, the context semantic boundary of the field can be identified. In this process, the sliding window method and the in-window combination frequency statistics algorithm are used to construct a field context structure map, and the map data is refined into data context domain features and output to the next field combination recognition link.

[0043] Step S134: identifying repeated combinations of field contexts based on data context domain features;

[0044] In an embodiment of the present invention, based on the data context domain features extracted in the previous step, the identification and processing of repeated combinations of field contexts are further performed. The frequency analyzer is started on the edge computing node, and different field combination structures are uniquely encoded using hash compression technology, and then the frequency of occurrence of the combination structure in different data blocks is recorded using bitmap statistics. For example, if the field combination ABC exists in a certain data set and appears in more than 80% of the data segments, the system will mark it as a highly repeated combination structure. In order to enhance statistical accuracy, the edge node uses 5 groups of independent data caches for repeated comparison to avoid interference caused by sample offset, output the repeated combination of field contexts, and mark the repetition rate value of each combination structure to provide a basis for judging whether to perform sequence rearrangement later.

[0045] Step S135: When the field context repetition combination exceeds 86%, a field sequence rearrangement process is performed to obtain field sequence rearrangement data;

[0046] In an embodiment of the present invention, for the field sequence marked as a repeated combination in step S134, the system calls the field rearrangement unit to perform position optimization processing. When the repetition rate of a certain field combination structure exceeds 86%, the rearrangement mechanism is triggered. The rearrangement process adopts a sorting strategy based on position stability priority, and adjusts the fields that appear stably in the combination (defined as fields that appear in more than 90% of samples) to the combination header, and the dynamic fields (with a repetition rate between 86%-90%) are arranged at the end. This operation is completed by rebuilding the field index table and updating the field mapping relationship on the edge node. The processing process records the mapping relationship between the original index of the field and the rearranged index throughout the process, and outputs it as field sequence rearrangement data, providing a clearly structured input basis for subsequent semantic recognition.

[0047] Step S136: Rearrange the data based on the field sequence to perform semantic recognition on the original input data to obtain semantic information of the original data fields.

[0048] In an embodiment of the present invention, the field sequence rearrangement data obtained in step S135 is used as input, and the semantic recognition unit is called to perform the field semantic derivation task. This task performs multi-dimensional classification processing based on information such as the character structure characteristics of the field content, the position of the field in the combination, and the coupling frequency between fields. Then, the meaning of the field is logically attributed through the embedded semantic classification rule set (including: position indication structure, flag structure, time structure, coding structure, etc.). For example, if a field is always arranged in the second position in the combination structure and contains 8 consecutive digits, the system recognizes it as a sequence identification field. The recognition result does not rely on model reasoning, but is based on logical rules to perform field labeling and the output result is the original data field semantic information, where each field is attached with a semantic tag and its derived source field combination path.

[0049] Preferably, step S14 includes the following steps:

[0050] Step S141: identifying physical boundary information of the data field to be encrypted based on the semantic information of the original data field;

[0051] In this embodiment of the present invention, in the original data field semantic information obtained in step S136, each field is assigned a structural position, a semantic label, and a contextual combination path. Based on this information, the edge node activates the locally deployed data structure recognition engine module and calls a field structure location algorithm to identify the physical boundary information of the semantic field. This algorithm constructs a field boundary detection rule table based on parameters such as the field's starting offset and ending offset in the data string, the number of character encoding bits, and the number of separators between fields. In specific operation, the field starting position is defined as the first occurrence of a continuous character sequence, and the ending position is defined as the location of a semantic breakpoint (e.g., a separator, a length mutation, or an encoding mode switch). Taking 128-byte original data as an example, if a field starts at bit 11 and ends at bit 28, its physical boundary is [11, 28]. This boundary information is output and uniformly encapsulated into a physical boundary information structure for the data field to be encrypted, which serves as direct input to step S142.

[0052] Step S142: measuring the length of the data field to be encrypted according to the physical boundary information of the data field to be encrypted;

[0053] In an embodiment of the present invention, the physical boundary information of the data field to be encrypted outputted in step S141 is sent to the length measurement submodule in the edge node. This module has a built-in character width mapping table and a byte counter, and uses a sequential traversal method to calculate the byte span length of each field. For a data source using the UTF-8 encoding format, the system reads the start and end offsets of the field (for example, start 11, end 28), and then reads the byte sequence of the corresponding interval of the original data, compares the encoding width byte by byte, and accumulates the byte length actually occupied by the field. During the process, if the field contains multi-byte symbols (such as Chinese characters or special format marks), the system will identify and accurately measure them one by one according to the encoding table, without performing width uniform simplification. The physical length information of each field is output in the form of bytes, and is encapsulated together with the original boundary information as a record table of the length of the data field to be encrypted, which includes the field number, start position, end position and byte length, providing fixed-length data support for the next step of semantic attribute classification.

[0054] Step S143: performing semantic attribute classification processing on the semantic information of the original data fields to obtain field semantic grouping data;

[0055] In this embodiment of the present invention, the semantic attribute classification processing module built into the edge node is activated based on the obtained raw data field semantic information and the field length record table output in step S142. This module does not rely on model inference, but instead performs field semantic clustering using a hard-coded set of semantic label rules. Specifically, all fields are initially grouped according to their semantic labels (such as location identifiers, time labels, quantity counts, and status descriptions). Subgroups are then further divided based on field length ranges (such as 0-10 bytes, 11-20 bytes, etc.) and grammatical structural features appearing in the context (such as location at the head, middle, or tail of the data). During implementation, fields that share the same location identifier semantic label, have a byte length between 5 and 10, and appear at the tail of the data structure are assigned the same semantic group number, such as Group_03. All field semantic grouping information is uniformly recorded in a field semantic grouping data table, with each record containing parameters such as the field number, group number, classification label, byte length range, and context segment location.

[0056] Step S144: identifying field semantic association coupling attributes based on field semantic grouping data;

[0057] In an embodiment of the present invention, the field semantic grouping data table is used as input and sent to the semantic coupling analysis module deployed at the edge node. The module identifies coupling attributes based on the context combination frequency, common context boundary overlap and field content interaction characteristics between fields in the field group. The operation process includes three parts: (1) context combination frequency analysis: counting the frequency of any two fields co-occurring in the same data segment in the sample data; (2) boundary overlap calculation: analyzing whether the start and end of the field boundaries overlap or have a relatively fixed offset pattern, such as field A and field B are arranged at a 3-byte interval in most samples; (3) interaction feature matching: detecting the correlation between field contents, such as whether there is a numerical cascade or unit conversion relationship. All indicators are standardized to coupling attribute values ​​in the [0,1] interval. If the coupling value between the field pairs is higher than the set threshold (such as 0.6), it is marked as semantic coupling. The output is a field semantic association coupling attribute table, which contains parameters such as field pair number, coupling attribute value, co-occurrence number, boundary offset mean, etc., which is used for subsequent structural nesting determination processing.

[0058] Step S145: determining the field structure nesting when the field semantic association coupling attribute exceeds 0.89, and obtaining the field structure nesting situation;

[0059] In an embodiment of the present invention, the field semantic association coupling attribute table generated in step S144 is screened to select all field pairs with coupling attribute values ​​greater than 0.89. These field pairs are used as nested judgment analysis objects and sent to the field structure nesting judgment unit in the edge node. This unit uses hierarchical nested relationship deduction rules to determine whether there is an inclusion relationship between fields. The specific process includes: checking whether the physical boundary of the field completely contains another field (for example, the boundary of field A is [15,40], and the boundary of field B is [22,35]); at the same time, comparing whether the field group numbers are consistent and whether there is an inheritance relationship between the semantic labels (for example, the status field contains multiple specific status subfields); if all three conditions are met at the same time, the field pair is marked as having a nested structure. All nested structure records are written into the field structure nesting situation table, which contains detailed parameters such as the nested parent field number, child field number, nesting depth, boundary level, etc.

[0060] Step S146: determining data field hierarchy information based on the field structure nesting and field semantic association coupling;

[0061] In an embodiment of the present invention, based on the field structure nesting table and the field semantic association coupling attribute table, the data field hierarchy parser deployed in the edge node is started. The parser maps the nested relationship into a tree-like hierarchical structure, where each node represents a field and each edge represents a nested relationship or a strong coupling connection. The parser reads the nesting table, constructs the root node field and adds subfields layer by layer, and uses a recursive structure to accurately mark the nesting depth of each layer of fields. If there is a field pair whose coupling attribute value exceeds 0.7 but does not reach the nesting threshold, it is marked as a same-level coupling, and the data field hierarchy information formed by identifying the horizontal connection edge in the hierarchical structure diagram is encapsulated as a data field hierarchy map, including fields such as field ID, hierarchical depth, parent field ID, coupling association ID, path identifier, etc., providing full structural information input for field structure parsing.

[0062] Step S147: Based on the data field hierarchy information and when the length of the data field to be encrypted exceeds 10, a field structure parsing process is performed to obtain the original data field structure.

[0063] In this embodiment of the present invention, the data field hierarchy graph output in step S146 and the table of field lengths to be encrypted in step S142 are processed together. The field structure parsing engine is activated, and a parsing threshold is set: parsing is initiated only when the field length exceeds 10 bytes. The parsing engine reads all field numbers that meet the length condition and searches the graph for their corresponding hierarchical structure and nesting path. A depth-first traversal algorithm is then used to expand the nested field structure nodes, recording each field's path level, number of nesting levels, and horizontally coupled field groups, and outputting a structured representation. For example, if field X is a parent field and contains two child fields, fields Y and Z, with Y and Z being horizontally coupled, the output is: field X → field Y (level 2, nested), field X → field Z (level 2, nested), field Y ↔ field Z (coupled). The parsing result is output as a raw data field structure table, which includes detailed data such as the field structure path, field hierarchy ID, coupling flag, nesting depth, and structure path index, providing structural input for subsequent encryption rule generation.

[0064] Preferably, step S2 includes the following steps:

[0065] Step S21: performing field encryption priority label assignment processing according to the original input data type to obtain field encryption priority label assignment data;

[0066] In an embodiment of the present invention, in the initial processing stage of the edge computing node, a field identification matrix needs to be constructed based on the collected original input data type. By identifying the structural features of each field in the input data, the corresponding field structure vector is constructed in combination with multiple dimensions such as the field's character type, symbol distribution density, structural nesting complexity, contextual semantic coupling degree, and field length. Based on the character type as the basic classification basis, pure numeric types, mixed alphanumeric types, special character types, and all-alphabetic types are distinguished; then, based on the field nesting relationship, it is detected whether the field has a multi-layer data combination, such as a list-in-dictionary, a key-value pair nesting, etc., and its structural complexity level is further divided; then, combined with the frequency of occurrence of the field in the original input data and the rules of semantic position (such as the first field, the last field, the middle field), its dependency relationship in the semantic chain is evaluated; and a multidimensional field evaluation parameter group is constructed through the above analysis. Based on the field processing complexity and the expected resource consumption and processing time of encryption implementation as the sorting criteria, the field encryption priority is divided into five levels: the first level (P1) represents low structural complexity, no nesting, simple character types and minimal processing resource requirements; the fifth level (P5) represents high structural complexity, multi-layer nesting, complex character types and high dependence on semantic analysis. Field encryption priority label allocation data is generated, and the label data is saved in key-value pairs. The field name is the key and the corresponding encryption priority level is the value, which serves as the basic input for subsequent encryption strategy formulation and resource scheduling.

[0067] Step S22: allocating data based on the field encryption priority tag to predict data encryption resource consumption;

[0068] In this embodiment of the present invention, after obtaining field encryption priority tag assignment data, the edge node scheduling module initiates a resource consumption prediction subprocess to construct an encryption resource prediction parameter table based on the standard encryption complexity factor corresponding to each level of encryption priority tag. Using static historical data as a reference, levels P1 to P5 are mapped to resource usage estimation intervals. Historical encryption task logs are retrieved from the edge device's local cache, from which the actual resource usage values ​​for each field type during the actual encryption process are extracted. The average resource usage values ​​for the three most recent task cycles are then extracted using a sliding window approach as empirical parameters for the current prediction process. Based on this, the resources for each level of encryption tag fields are summed, combining the number of fields of each encryption level and their total byte length in the original input data, to obtain the total resource estimate corresponding to the field encryption priority. Each priority level is then weighted and combined to generate a total resource prediction value. The resource weight percentage corresponding to each level of tag is also output. This is used as input for the subsequent policy generation step. The generated data encryption resource consumption status includes a field-level resource estimation list, a total resource estimation, the average time consumption per field, the estimated time consumption per task, and a resource distribution weight table.

[0069] Step S23: Determine the data encryption processing strategy based on the data encryption resource consumption status and the field encryption priority label allocation data;

[0070] In an embodiment of the present invention, after receiving the field encryption priority tag allocation data and data encryption resource consumption status, the encryption processing strategy formulation module initiates the encryption scheme configuration process and performs resource adaptation calculations based on the total resource valuation and the current available resource status of the edge node. If the total resource valuation is less than the total value of the remaining allocatable resources of the current node, the full-field instant encryption strategy is enabled, and all fields are directly encrypted in label order. If the total valuation approaches or exceeds the current allocatable resource limit, a batch encryption processing mechanism is initiated. By limiting the encryption priority of the highest priority field (such as P5 level) and delaying the execution of lower priority fields (such as P1 and P2), the scheduling cycle controller divides the encryption execution tasks into time periods based on resource weights, sets field encryption queues, and reduces the peak resource usage per cycle. During the policy generation process, the encryption algorithm type is determined based on the field distribution characteristics. For example, a dual symmetric algorithm and structure nested segmentation processing are used for the P5 field, a standard symmetric encryption algorithm is used for the P3 field, and a lightweight hash scrambling method is used for the P1 field. The execution is performed after the field structure is decoupled. The policy result data includes the encryption queue configuration list, the time window for each field allocation, the corresponding algorithm number, resource constraint conditions and the field execution order, which serve as the control parameter input for the subsequent encryption processing process.

[0071] Step S24: Perform input data encryption processing on the original input data type based on the data encryption processing strategy to obtain encrypted data.

[0072] In an embodiment of the present invention, based on the data encryption processing strategy generated in step S23, the data encryption execution module starts the field encryption process in sequence according to the policy setting parameters, loads the field processing sequence and algorithm number specified in the policy list, and disassembles the P5 tag field into structural segments. Each data unit is processed in blocks using the advanced symmetric encryption standard AES-256. After processing, each block is appended with a data segment check code and a processing number to identify the source field segment structure; then the P4 and P3 tag fields are processed field by field according to the set standard symmetric encryption algorithm (such as AES-128). After the processing is completed, the corresponding field name and ciphertext key value table are generated for subsequent data indexing; then the lightweight encryption mechanism is enabled for the P1 and P2 tag fields, and the hash scrambling function group embedded in the edge encryption engine is called to complete the field value perturbation and encryption through lightweight algorithms such as bit shift scrambling, character reordering, and CRC embedding. After all fields are processed, the encrypted fields are integrated and assembled to restore the original data structure order, generating encrypted processed data. The encrypted processed data keeps the original data structure unchanged, and the field values ​​have been processed according to the encryption strategy. The processed data is accompanied by a field encryption index table and a processing record list for edge node cache scheduling and data chain transmission.

[0073] Preferably, step S23 includes the following steps:

[0074] Step S231: Assessing the encryption difficulty of the data field according to the field encryption priority label allocation data;

[0075] In an embodiment of the present invention, the encryption difficulty of the data field is evaluated based on the field encryption priority label allocation data; in step S231, all fields and their corresponding encryption priority levels are extracted from the field encryption priority label allocation data generated in step S21. The encryption algorithm complexity evaluation parameter set is loaded in the local processing module of the edge node. The parameter set is established in advance through experimental statistics, covering the unit computing resource consumption and average processing time of encryption algorithms such as AES-256, AES-128, SM4, and RC4 under different data lengths, field structure complexity, and character set type conditions. For example, for nested structure fields with a length of more than 512 bytes, the average CPU cycle consumed when using AES-256 is 2600 cycles, and the memory occupancy is 12MB; while for flat structure fields within 64 bytes, only 600 cycles are required when using SM4, and the memory occupancy is about 1.5MB. The edge node control module matches the structural features of the field content one by one based on the field priority label, including the character type (only numbers, mixed numbers and letters, including special symbols), length range (less than 64 bytes, 64 to 512 bytes, greater than 512 bytes), and whether it contains structure nesting (such as JSON nesting, list dictionary combination, etc.). Combining the field structure characteristics with the standard encryption algorithm corresponding to its label, the resource load factor of the corresponding encryption algorithm is retrieved in the processing parameter set to obtain the field encryption difficulty factor score. The scoring standard ranges from 1 to 10, with 1 representing the lowest complexity (such as a flat numeric field with SM4 encryption) and 10 representing the highest complexity (such as a multi-layer nested structure combined with AES-256 processing). Each field is matched one-to-one with its encryption difficulty score result to generate data field encryption difficulty status data.

[0076] Step S232: Calculate the computing power usage of the data encryption edge node based on the data field encryption difficulty;

[0077] In an embodiment of the present invention, the computing power usage of the data encryption edge node is calculated based on the encryption difficulty status of the data field; based on the encryption difficulty status data of the data field generated in step S231, the computing power resource measurement module built into the edge node is called to estimate the resource consumption of all fields in the planned encryption cycle. Based on the number of fields, field length, and encryption difficulty score, the fields of different encryption levels are classified and grouped. Each group of fields corresponds to a specific algorithm processing channel, which has set encryption instruction cycle statistical parameters. Taking the AES-256 encryption channel as an example, for a field group with a score of 10, the system sets a single field processing cycle of 2800 CPU cycles, and the average parallel thread utilization is 75%. The system simulates the processing behavior in a fixed cycle window manner in the computing task scheduler, and performs encryption simulation calculations on each group of fields within a virtual cycle. For each field group, the estimated total CPU cycles, peak memory usage, and average thread scheduling wait time are compared with the available resources in the current edge node resource pool to generate the computing power utilization data required by the edge node to process the data group, including CPU utilization percentage, total memory usage (MB), thread queue depth, and estimated total encryption time (ms). All field group resources are aggregated to form a complete data encryption edge node computing power utilization data.

[0078] Step S233: Perform field weight allocation processing on the computing power usage of the data encryption edge node to obtain field encryption weight data;

[0079] In this embodiment of the present invention, field weights are assigned to the computing power usage of data encryption edge nodes to obtain field encryption weight data. Step S233 performs field weight assignment based on the data encryption edge node computing power usage data obtained in step S232. During execution, the system scheduling module constructs a field encryption impact factor matrix based on the field encryption difficulty score and its actual resource usage. This matrix uses the field name as the index row and the resource usage dimensions (CPU cycles, memory usage, encryption time) as the column. The resource usage values ​​of each field are normalized to a range between 0 and 1 using a normalization process. After the normalization process is completed, the field priority label is introduced as a weight multiplier. For example, the P5 priority field is multiplied by a weight factor of 1.5, and the P1 priority field is multiplied by a weight factor of 0.5. This further determines the comprehensive weighted resource contribution of each field. All field weighted contribution values ​​are normalized so that the sum of the field encryption weights is 1. This generates field encryption weight data. The data format is the encryption weight value corresponding to the field name, which is used to perform node resource capability tag distribution mapping in step S234.

[0080] Step S234: marking the node encryption resource interval according to the encryption resource consumption status to obtain node encryption resource capability marking data;

[0081] In this embodiment of the present invention, node encryption resource intervals are marked based on encryption resource consumption to obtain node encryption resource capability tag data. Step S234 combines the data encryption resource consumption data generated in step S22 with the current physical resource status of the edge node to divide the node's resource carrying capacity into intervals and perform capability tagging. Specifically, the edge node resource status monitoring module extracts metrics such as the current number of schedulable CPU cores, free memory capacity, encryption engine thread idle ratio, and average response delay per encryption task. These metrics are then compared with the total resource demand value from the data encryption resource consumption status to calculate the interval difference. Based on the resource matching degree, the intervals are divided into three zones: a high adaptation zone (resource surplus exceeding estimated demand by more than 20%), a medium adaptation zone (resource surplus within ±20%), and a low adaptation zone (resource surplus less than 20% of estimated demand). Each resource interval is assigned a tag value, A, B, or C, to form the node encryption resource capability tag data. This data structure contains the current remaining value, corresponding interval, and tag for each resource type (CPU, memory, and thread), providing a mapping basis for subsequent encryption strategy combination.

[0082] Step S235: performing policy combination mapping processing on the node encrypted resource capability tag data and the field encrypted weight data to obtain encrypted policy combination mapping data;

[0083] In this embodiment of the present invention, a policy combination mapping process is performed on the node encryption resource capability tag data and the field encryption weight data to obtain encryption policy combination mapping data. The field encryption weight data obtained in step S233 is then subjected to a policy combination mapping process with the node encryption resource capability tag data obtained in step S234. The system calls a policy combination module to read the encryption weight values ​​of all fields and the current node resource capability tag value, and establishes an encryption policy template library based on resource tag categories. For example, when marking interval A, the highest occupied weight field is allowed to be encrypted first, enabling multi-threaded parallel processing; when marking interval B, a resource balancing scheduling policy is activated, prioritizing medium-weight fields and setting a thread delay queue; when marking interval C, a resource conservative policy is activated, processing only low-weight fields and limiting the number of parallel threads to a single thread. During the template mapping process, a mapping matrix is ​​formed between the field weight interval and the resource tag interval. A table lookup is used to determine the encryption execution policy to be adopted for each field, including whether parallel processing is enabled, the algorithm to be used, the start and end times of the processing time window, and other contents. The encryption policy combination mapping data is generated and output as a structured mapping table, providing a basis for evaluating the feasibility of the policy combination in step S236.

[0084] Step S236: Evaluate the feasibility of the data encryption policy combination based on the encryption policy combination mapping data;

[0085] In an embodiment of the present invention, the feasibility of the data encryption policy combination is evaluated based on the encryption policy combination mapping data; by calling the edge node policy feasibility evaluation module, the encryption policy combination mapping data generated in step S235 is subjected to feasibility verification processing item by item. The feasibility evaluation is performed based on the task cycle scheduling simulation engine. The system schedules simulation tasks in sequence according to the encryption algorithm, number of concurrent threads and field encryption time window set in the policy mapping table, records the resource occupancy curve of each round of encryption tasks in the virtual cycle, and determines whether it exceeds the current node resource load. For those who have resource conflicts (such as memory overflow, thread congestion, response delay exceeding expectations), the system marks them as infeasible strategies; for those who can complete the task within the resource upper limit, it marks them as feasible. The feasibility evaluation results generate a policy feasibility Boolean matrix in units of fields, and accompanying explanatory data including the total task execution time prediction, average thread occupancy rate, number of algorithm calls and maximum memory usage peak to form data encryption policy combination feasibility evaluation data, providing a basis for policy adaptation in step S237.

[0086] Step S237: Evaluate the feasibility of the data encryption strategy combination and the adaptability of the data encryption processing strategy;

[0087] In an embodiment of the present invention, the feasibility of the data encryption policy combination is evaluated to determine the adaptation of the data encryption processing policy. The policy adaptation analysis module is used to read the data encryption policy combination feasibility evaluation data generated in step S236, and a comprehensive matching judgment is made on whether the encryption policy of each field is adapted to the current edge node resource status. For all feasible policy fields, they are arranged in descending order of weight, and an attempt is made to arrange the execution order within the available time window. For infeasible policy fields, the system automatically searches for alternative solutions under the corresponding field weight level in the backup policy template library, including replacing the encryption algorithm with one with lower resource consumption or adjusting the thread scheduling strategy, and re-executes step S236 for a second round of evaluation. The adaptation process generates a list of field policy adaptation results, including the encryption policy adopted by each field, the execution time window number, the resource call channel, and whether the downgrade policy mark is enabled. The list data structure is indexed by the field name and the adaptation situation data is the value, which serves as the input for the encryption processing policy determination operation in step S238.

[0088] Step S238: Determine the data encryption processing strategy based on the adaptation of the data encryption processing strategy and the feasibility of the data encryption strategy combination.

[0089] In an embodiment of the present invention, the data encryption processing strategy is determined based on the adaptation of the data encryption processing strategy and the feasibility of the data encryption strategy combination; the field strategy adaptation result list generated in step S237 is integrated with the encryption strategy combination feasibility assessment data provided in step S236, and the encryption processing strategy of all fields is structured and confirmed. Based on the field strategy adaptation list, the encryption algorithm type, encryption execution thread number, processing cycle position and other parameters are selected field by field, and a corresponding execution task queue is established in the edge node scheduler. Subsequently, each field task queue is sorted in order of execution, tasks with resource conflicts or overlapping execution times are excluded, and the remaining resource capacity is verified twice to ensure that all policy sets are fully executable within the target encryption cycle. Finally, a data encryption processing strategy table is generated, which contains information such as the field name, the encryption algorithm number used, the execution thread channel, the processing window number, and the resource quota parameters. This strategy table serves as the only control data source for executing the encryption processing task in step S24.

[0090] Preferably, the evaluation of abnormal disturbance conditions of the encrypted processed data in step S3 includes:

[0091] Detect the node data encryption processing deviation trend based on the data encryption processing status;

[0092] In this embodiment of the present invention, the edge computing node's monitoring system collects data processing information about encryption tasks in real time, including information such as the number of encryption tasks executed on each node, the encryption execution cycle, and node resource usage. The encryption task data for each node is captured by a monitoring module in the edge computing scheduling system. The timestamp, input data size, encryption algorithm type, and output results of all encryption execution tasks are recorded in a dedicated logging system. Based on the collected encryption task logs, a time series data analysis method is used to calculate the encryption processing offset for each node over a period of time. Encryption processing offset refers to the deviation in the processing time of an encryption task at a node within a specified timeframe, typically expressed as the difference between the encryption task start time and the expected processing time. If the deviation between the actual processing time and the expected processing time of an encryption task at a node exceeds a set threshold (e.g., ±10%), it is recorded as an abnormal offset. This offset trend detection embodiment uses a time series data smoothing algorithm (e.g., a sliding average algorithm) to perform trend prediction on historical encryption processing offset data. The resulting offset trend chart for each node is generated and output as a graph, displaying the offset trend of each node's encryption task, providing a basis for subsequent offset anomaly detection.

[0093] Detect encrypted statement field nesting conflicts based on node data encryption processing offset trends;

[0094] In an embodiment of the present invention, further encryption statement analysis is performed based on the obtained node encryption task offset trend data. Encryption statement field nesting conflicts often occur in complex encryption operations, especially when the encryption algorithm involves multiple fields and complex data nesting. This can lead to conflicts in certain encryption operations, thereby affecting processing efficiency. In this step, field nesting hierarchy information is extracted from all encryption statements, especially for multi-level nested encryption statements. The technical approach used for field nesting conflict detection is based on the construction of a field reference tree. Each encryption statement is parsed into a field reference tree. The nodes of the tree represent the fields in the encryption statement, and the edges represent the encryption relationships between fields. A nesting conflict occurs when two encrypted fields are repeatedly encrypted or cross-encrypted in the same encryption task. By parsing and comparing the field reference tree, any nesting conflicts detected are marked and recorded. This detection process uses a graph matching algorithm from graph theory to identify the path of nesting conflicts. The encrypted statement parsing tool performs automated analysis. The nesting conflicts are generated into a conflict report, which is presented in graphical and tabular form, including information such as the field ID, nesting conflict hierarchy, and conflicting field pairs for each encryption statement.

[0095] Determine the misidentification of encryption processing field boundaries based on the encryption statement field nesting conflict;

[0096] In this embodiment of the present invention, the encrypted statement field nesting conflict information obtained in the previous step is used to analyze the problem of field boundary misidentification during the encryption process. Field boundary misidentification often occurs in multi-field encryption operations, particularly when the start and end positions of certain fields are misjudged during the encryption process, resulting in inaccurate encryption results. The key technology used in this step is a field boundary identification algorithm based on the encryption algorithm. This algorithm scans the spacing between fields in the encrypted statement. By analyzing the execution log of the encrypted statement, the consistency between the field boundaries at the beginning and end of the encryption operation and the original data fields is checked. If the actual boundary of the encrypted field deviates from the set boundary position by more than a predetermined range, a field boundary misidentification has occurred. The determination of misidentification is verified by the byte offset of the encrypted field. If inaccurate byte alignment is detected during the field boundary identification process, the field is marked as having a boundary misidentification, and a detailed log record and report are generated. The report includes the field ID, the misidentified byte range, the offset, and the associated encryption task ID.

[0097] Identifying the distortion of encrypted data based on the misidentification of encryption field boundaries;

[0098] In an embodiment of the present invention, the distortion status of the encrypted data is further determined by analyzing the identified field boundary misidentification information. Data distortion is usually manifested as the inability of encrypted data to be restored to the correct original data, mainly due to the loss or duplication of some data caused by field boundary errors. By comparing the original data and the encrypted data, the byte differences between the two are detected, and the encrypted data is decrypted through the inverse operation of the encryption algorithm to obtain the decrypted data, which is then compared byte by byte with the original data. If the bytes between the encrypted data and the decrypted data are inconsistent (for example, zero values ​​or illegal characters appear in certain positions), it is considered that data distortion has occurred. The distortion identification process uses a byte-level difference detection algorithm, which quickly identifies the differences between the data by comparing the hash values ​​(such as SHA256 hash) of the original data and the decrypted data. This step generates a distortion report, which contains the distortion location of each field encryption, the distortion type (such as byte loss, data duplication) and related field identification information.

[0099] Analyze the degree of byte alignment deviation of encrypted data based on the distortion status of encrypted data;

[0100] In an embodiment of the present invention, the degree of data byte alignment deviation is further analyzed based on the obtained data distortion status. During the encryption process, byte alignment deviation prevents the data from being correctly restored during the decryption process, affecting the data availability. A byte-by-byte alignment comparison algorithm is used to evaluate the byte alignment difference between the encrypted and decrypted data. This algorithm evaluates the byte stream alignment deviation of the entire encrypted data by calculating the alignment error of each data unit (such as a character, integer, floating-point number, etc.) in the byte stream. If the alignment error of the byte stream exceeds a specified threshold (such as 1 byte), it is considered that a byte alignment deviation exists. The calculation method of the byte alignment deviation is based on the data block size and memory alignment requirements (for example, data blocks need to be aligned to 64 bytes). If the actual starting position of the data block does not match the alignment position, the deviation value will be recorded and reported. Once this report is generated, it will include the byte alignment deviation value, the affected data block range, and related field identification information.

[0101] Predict the increasing difficulty of encrypted data parsing based on the degree of byte alignment deviation and distortion of encrypted data;

[0102] In an embodiment of the present invention, based on the obtained byte alignment deviation information and the data distortion in step S3.4, the growth trend of the difficulty of parsing encrypted data is further predicted. By comparing the changing trends of the byte alignment deviation and the degree of data distortion, the parsing difficulty encountered in decrypting the encrypted data in the future is predicted. Based on historical data and real-time data of encryption task execution, a time series analysis method is used to calculate the changing trends of the byte alignment deviation and data distortion over time. Specifically, the weighted moving average algorithm is used to smooth the byte alignment deviation value and the degree of data distortion in the historical data, so as to derive the growth trend of the difficulty of parsing encrypted data in the future. Through these trend data, the processing difficulty faced by encrypted data in parsing in the future is predicted, such as the computational overhead during decryption, the extension of decryption time, etc. The prediction results will be recorded in the analysis report, which includes trend charts, predicted values ​​and encryption task adjustment suggestions.

[0103] Determine the degree of consistency difference between data before and after encryption based on the increasing trend of encrypted data parsing difficulty;

[0104] In an embodiment of the present invention, the degree of consistency difference of the data before and after encryption is further analyzed based on the increasing trend of the difficulty of parsing the encrypted data obtained. Data consistency difference generally refers to the difference in logic and structure between the decrypted data and the original data, especially the data error caused by incorrect byte alignment or field nesting during the encryption process. This step compares the hash value, data structure and field integrity of the original data and the decrypted data. Use a data structure validation tool (such as a JSON Schema validator or an XML Schema validator) to compare the structural consistency of the original data and the decrypted data, and calculate the structural difference index. If there is a difference between the structure in the decrypted data and the original data, and this difference exceeds the predetermined tolerance range (for example, missing structural elements, mismatched field types), it is marked as a consistency difference.

[0105] Evaluate the abnormal disturbance of encrypted data based on the degree of data consistency difference before and after encryption and the increasing trend of encrypted data parsing difficulty.

[0106] In an embodiment of the present invention, the abnormal disturbance condition of the encrypted data is evaluated by comprehensively considering the degree of consistency difference of the data before and after encryption and the growth trend of the difficulty of parsing the encrypted data. According to the data processing results in the previous steps, the degree of disturbance of the encrypted data is quantitatively evaluated in combination with the prediction model. By combining the degree of consistency difference with the growth trend of the difficulty of parsing, a disturbance index is generated, which characterizes the degree of abnormal disturbance of the data during the encryption process. The calculation formula of the index is based on historical data and analysis results. By comparing indicators such as consistency difference, byte alignment deviation, and parsing difficulty of the encrypted data, the disturbance condition of the encrypted data is comprehensively evaluated. After the evaluation results are generated, they will be presented in the form of a chart, including the time change trend of the disturbance index, abnormal points, impact range, and corresponding processing suggestions.

[0107] Preferably, the edge node load gradient growth trend assessment in step S3 includes:

[0108] Perform encryption processing abnormality inducement analysis based on abnormal disturbance conditions of encryption processing data to obtain encryption processing abnormality inducement data;

[0109] In an embodiment of the present invention, the encrypted data generated during the encryption process is obtained and compared with the original unencrypted data through a data consistency check tool to analyze the data disturbance during the encryption process. The specific operation includes: using a specially designed data consistency difference engine to compare the encrypted data and the original data, and recording the differences, such as data loss, field changes, data errors, etc. Based on these differences, the sources and influencing factors are analyzed using preset rules and models, and then the abnormal causes of encryption processing are summarized. The analysis results will provide a basis for subsequent abnormal source identification. For example, if it is found that the proportion of data field loss before and after encryption is high, it can be inferred that the encryption algorithm has a loss risk when processing certain data. The output of this step is encryption processing abnormality inducement data, including a list of potential abnormal sources and inducements for further analysis in subsequent steps.

[0110] Identify abnormal edge nodes for encryption processing based on abnormal inducement data for encryption processing, and obtain abnormal data of edge nodes for encryption processing;

[0111] In an embodiment of the present invention, after obtaining the encryption processing abnormality inducement data, the edge node abnormality identification process is entered. Through the network topology information and the encryption processing log data, combined with the change characteristics of the encryption data, the edge node data analysis tool is used to evaluate each edge node. This evaluation process is based on the node's historical performance data, such as CPU usage, memory usage, network latency, etc., combined with the encryption processing abnormality inducement data, and analyzes each edge node one by one whether there is an abnormality. In operation, the abnormality inducement data is matched with the operating status of the edge node (such as CPU load, memory usage, network bandwidth, etc.) to identify nodes that have abnormal loads or resource constraints during the encryption processing process. This process monitors the behavior of edge nodes in real time by configuring abnormality detection rules, ensures a detailed analysis of the encryption processing situation of each node, and obtains encryption processing edge node abnormality data, including the identification of the abnormal node, the type of abnormality, the duration of the abnormality, and the frequency of occurrence.

[0112] Detect excessive memory usage at edge nodes based on encrypted processing of abnormal data at edge nodes;

[0113] In an embodiment of the present invention, edge nodes with excessive memory usage are identified based on the obtained encrypted edge node abnormal data. To this end, it is necessary to monitor and evaluate the memory usage of each node in detail. The specific operation is to collect the memory usage of each node in real time through the performance monitoring tool of the edge computing platform. And based on the preset threshold (such as memory usage exceeding a certain percentage, such as 80%), it is judged whether the node has excessive memory usage. By comparing historical data, the memory usage trend of the node is further analyzed, the peak time period of memory usage is identified, and the extent of excessive memory usage is calculated. The data includes the total memory usage, the usage ratio, and the peak memory usage of each node. Finally, the memory usage of the node is comprehensively analyzed through the memory usage measurement tool to obtain the degree of excessive memory usage and identify specific abnormal nodes.

[0114] Detect excessive CPU usage at edge nodes based on encrypted processing of abnormal data at edge nodes;

[0115] In an embodiment of the present invention, similar to the memory occupancy detection, in this step, the edge nodes in the encryption process are detected for excessive CPU occupancy. Based on the obtained edge node abnormal data and combined with the CPU performance monitoring data of each node, the nodes with abnormal CPU occupancy are identified. The operation steps are to use the CPU monitoring tool of the edge computing platform to collect the CPU usage of each node in real time and compare it with the set threshold (for example, the CPU occupancy exceeds 90%) to determine whether there is a situation of excessive CPU load. The process also includes calculating the peak value and duration of the CPU occupancy of the node, analyzing the abnormal fluctuations of the CPU occupancy during the encryption operation, obtaining data on the excessive CPU occupancy, and marking the edge nodes with abnormalities.

[0116] Statistics on the overlapping distribution of abnormal edge node usage, including excessive CPU usage and excessive memory usage at edge nodes;

[0117] In an embodiment of the present invention, the obtained data on memory occupancy and excessive CPU occupancy are statistically analyzed to calculate the overlapping distribution of edge nodes when the memory and CPU occupancy are excessive. The specific operation is to combine the data on excessive memory occupancy and excessive CPU occupancy, perform cross-statistics, and obtain the occupancy of the same node when the memory and CPU load are too large. The overlapping occupancy of each node under different load conditions is displayed through data visualization methods such as heat maps or bar charts. For example, if a node has a memory occupancy of 80% and a CPU utilization rate of more than 90%, then these two data points belong to an overlapping distribution. The statistical results will help identify which nodes have abnormal memory and CPU occupancy at the same time.

[0118] Determine the concentration trend of edge node encryption operation pressure based on the overlapping distribution of abnormal edge node occupancy;

[0119] In an embodiment of the present invention, overlapping distribution is used to determine the pressure concentration trend of edge node encryption operations. By analyzing abnormally occupied nodes and the corresponding resource usage, it is identified that the nodes are under excessive computing pressure during the encryption process. In certain periods of time, when multiple nodes have excessive memory and CPU usage, it means that the encryption tasks of these nodes are relatively concentrated. Using the computing pressure assessment tool, combined with the node resource usage, the pressure concentration trend of encryption operations is determined, and it is analyzed whether certain nodes have experienced excessive accumulation of encryption tasks. Pressure concentration trend data is obtained, including concentration, duration, and node distribution.

[0120] Detect edge node encryption scheduling imbalance based on edge node computing pressure concentration trends;

[0121] In an embodiment of the present invention, based on the obtained concentration trend of encryption operation pressure of edge nodes, an encryption scheduling imbalance detection process is entered, and the scheduling system of the edge computing platform is monitored to analyze the distribution of encryption tasks. Specifically, it is checked whether the encryption tasks are evenly distributed to each edge node, and nodes with uneven task distribution are identified. By analyzing the relationship between node load and task distribution, it is determined whether there are some nodes with heavy loads and some nodes with light loads. Encryption scheduling imbalance is usually manifested as resource overload of some nodes, affecting the efficiency and stability of the entire system. The data of encryption scheduling imbalance of edge nodes is output, and the nodes with uneven scheduling and the corresponding task types are identified.

[0122] The edge node load gradient growth trend is evaluated based on the imbalance of edge node encryption scheduling and the concentration trend of edge node encryption computing pressure.

[0123] In this embodiment of the present invention, the edge node load gradient growth trend is evaluated by combining encryption scheduling imbalance data and encryption computing pressure concentration trend data. Specifically, the encryption scheduling imbalance and computing pressure concentration trend are comprehensively analyzed to calculate the growth rate and degree of the load gradient. Using data analysis tools, the speed and trend of edge node load growth are calculated, and a load growth curve is plotted to derive the edge node load gradient growth trend. This reflects how encryption tasks are transferred and expanded between different nodes over time, indicating that node load will further increase in the future.

[0124] Preferably, determining the dynamic degradation trend of the edge encryption element structure in step S3 includes:

[0125] Identify the attenuation of node encryption processing stability based on the edge node load gradient growth trend;

[0126] In an embodiment of the present invention, the stability of the node encryption processing is evaluated based on the edge node load gradient growth trend data obtained in the aforementioned steps. The operation process is to identify whether the node has a trend of decreasing encryption processing efficiency when the load increases by analyzing the encryption processing load data of the edge node in different time periods. The specific operation is to use an encryption processing stability detection tool, which monitors key parameters such as node encryption data throughput, delay, and calculation time in real time, records whether the processing time of the encryption task increases during the gradual increase of the load, and calculates the correlation between load growth and encryption processing efficiency. When load growth is detected, the encryption processing throughput decreases or the delay increases, and it is inferred that the stability of the node encryption processing is decaying. Through these parameters, quantitative data of the decay of node encryption processing stability is obtained, including decay rate, impact threshold and duration.

[0127] Detect the increasing risk of edge node cache overflow based on the attenuation of edge encryption processing stability;

[0128] In an embodiment of the present invention, the encryption processing stability decay data obtained is used to further detect the cache overflow risk of the edge node. In a specific implementation, by monitoring the memory and cache occupancy of the edge node, a cache monitoring tool is used to collect data such as the usage rate, remaining space, and overflow times of each node cache. When the encryption processing stability of the node decays, the cache processing capacity is affected, resulting in an increased risk of cache overflow. By setting a cache overflow threshold and combining it with changes in the node encryption task, the degree of growth of the overflow risk is calculated. For example, when the encryption processing delay increases and the throughput decreases, the cache write and read speeds are limited, resulting in frequent overflows of the cache area. Based on the data, the specific degree of growth of the cache overflow risk is obtained, including the rate of risk increase and the time point when the threshold is broken.

[0129] Detect edge encryption component overload based on the increasing risk of edge node cache overflow and the attenuation of node encryption processing stability;

[0130] In an embodiment of the present invention, the cache overflow risk data and the encryption processing stability decay data are combined to determine whether the node has an encryption element operation overload situation, and the degree of increase in the cache overflow risk is linked to factors such as encryption processing delay and processing capacity for analysis. By setting the load threshold of the encryption element, when the node encryption processing stability decays and the cache overflow risk increases, the overload detection algorithm is used to evaluate the operation status of the encryption element in real time. The load of the encryption element exceeds the set threshold, indicating that it cannot effectively complete the encryption task under high load, resulting in an operation overload situation at the node. In this process, the load data of each node, such as CPU, memory, hard disk I / O, etc., is counted by the monitoring system to comprehensively evaluate whether the encryption element of the node is overloaded. The output result is the overload situation of the encryption element, including the overload node identification, overload duration and overload severity.

[0131] Measure the high temperature operation state of the edge encryption element according to the overload condition of the edge encryption element;

[0132] In an embodiment of the present invention, based on the overload condition of the encryption element, a temperature sensor and a thermal analysis tool are used to monitor the temperature status of the edge encryption element, and a real-time temperature monitoring tool is used to obtain the operating temperature data of the edge encryption element, especially the temperature change when the workload is high. When the node is in an encryption element overload condition, the temperature of the element usually rises significantly. By continuously recording the temperature change curve of the encryption element when overloaded, it is detected whether the temperature exceeds the set safety threshold (for example, 80°C). Once the threshold is exceeded, it can be determined that the node is in a high-temperature operating state, and further evaluation is conducted to determine whether the temperature of the element continues to rise and whether there is a risk of overheating to obtain the high-temperature operating state data of the edge encryption element, including information such as the time period when the temperature exceeds the standard and the temperature fluctuation amplitude.

[0133] Detect the accumulation of thermal effects of encryption components based on the high-temperature operating state of edge encryption components;

[0134] In an embodiment of the present invention, when the edge encryption element is in a high-temperature operating state, the cumulative thermal effect of the encryption element is further evaluated. Using a temperature sensor and a thermal effect simulation tool, the working time of the encryption element at high temperature is recorded and compared with the historical temperature data. This process includes analyzing the thermal cycle data of the encryption element, especially the impact of frequent temperature changes and long periods of high-temperature exposure on the internal materials of the element (such as semiconductors, metal contacts, etc.). The thermal effect simulation tool is used to calculate the impact of temperature fluctuations on the encryption element, including problems such as material degradation caused by overheating and aging of internal circuits. Based on the long-term high-temperature operation, the cumulative thermal effect of the encryption element is inferred, such as thermal decay, solder joint fatigue, chip aging, etc., and the cumulative thermal effect data of the encryption element is output, including the degree of impact and duration of the thermal effect.

[0135] Identify the thermal aging status of semiconductor materials in encryption components based on the cumulative thermal effects of encryption components;

[0136] In an embodiment of the present invention, the thermal aging of the semiconductor material in the encryption element is assessed based on the cumulative thermal effects of the encryption element. This involves using a material analysis tool to inspect the semiconductor material in the encryption element and evaluate changes in its performance under high-temperature conditions. The material analysis tool measures the electrical properties of the semiconductor material (such as conductivity and impedance) and, combined with the thermal effect data, identifies signs of material aging, such as decreased conductivity and reduced high-temperature resistance. Furthermore, scanning electron microscopy (SEM) or physical analysis techniques are used to examine microstructural changes in the semiconductor material, such as surface cracks and lattice defects. These analyses reveal the thermal aging status of the encryption element's semiconductor material, including the extent of material aging, the time at which signs of aging appear, and the specific causes of aging.

[0137] The dynamic degradation trend of the edge encryption element structure is determined based on the thermal aging status of the encryption element semiconductor material and the accumulation of thermal effects of the encryption element.

[0138] In an embodiment of the present invention, the dynamic structural degradation trend of the edge encryption element is evaluated in combination with the thermal aging status of the encryption element's semiconductor material and the accumulated data on thermal effects. Based on the thermal aging information of the semiconductor material and the accumulated data on thermal effects, the degradation trend of the overall structure of the element is analyzed. For example, if the material ages significantly, the electrical performance of the encryption element will decline, affecting the processing capability of the encryption task. By constructing a structural degradation model for the encryption element, combining historical operating data and thermal aging analysis, the degree of encryption element degradation and its impact on encryption processing in the future are predicted, and the dynamic degradation trend of the edge encryption element structure is derived, and output information including degradation rate, expected lifespan, and the time point when replacement is required.

[0139] Particularly important, the identification of thermal aging conditions of semiconductor materials in encryption components includes:

[0140] Detect the decrease in electrical conductivity of the encryption element based on the accumulation of thermal effects of the encryption element;

[0141] In an embodiment of the present invention, the decrease in the electrical conductivity of the encryption element is detected based on the accumulation of thermal effects of the encryption element. During specific operations, the monitoring system needs to track the operating temperature of the encryption element in real time and calculate the cumulative thermal effect value in combination with the temperature data obtained by the temperature sensor. The cumulative thermal effect value reflects the heat accumulation of the encryption element during long-term operation. As the temperature rises, the electrical conductivity of the material will gradually decrease. The electrical conductivity of the encryption element can be accurately measured using a conductivity measuring instrument. By comparing the real-time conductivity data with the historical records of the equipment and analyzing the downward trend of the conductivity, the degree of change in the conductivity can be determined, and the correlation analysis with the accumulation of thermal effects can be performed to obtain the specific value of the conductivity decrease.

[0142] Determine the increase in current loss of the encryption element based on the decrease in conductivity of the encryption element;

[0143] In an embodiment of the present invention, based on the data on the decrease in conductivity of the encryption element obtained in the previous step, the current loss growth condition of the encryption element is further determined. The decrease in the conductivity of the encryption element directly affects the efficiency of its current flow, so the decrease in conductivity will lead to an increase in current loss. In order to detect the increase in current loss, it is necessary to monitor the current of the encryption element in real time through a current detection instrument or a current sensor, and calculate the change in current loss in combination with the conductivity data. By comparing the current loss data in different time periods, the trend of loss growth is identified. The degree of current loss growth is determined by analyzing the inverse relationship between current and conductivity. When conductivity decreases, current loss will increase. Based on the current loss growth data, the loss rate is further quantified, and a growth trend of current loss is generated.

[0144] Identify the short circuit trend of the internal circuit of the encryption component based on the growth of the current loss of the encryption component;

[0145] In an embodiment of the present invention, the short-circuit trend of the internal circuit of the encryption element is identified based on the current loss growth status obtained in the step. The rapid growth of current loss is often due to abnormalities in the internal circuit of the encryption element, including the occurrence of short circuits. By installing current sensors and temperature sensors at key circuit points of the encryption element, the current fluctuations and temperature changes of the encryption element under different working conditions are monitored. Especially in the case of a significant increase in current loss, abnormal fluctuations in current and a sharp increase in temperature are often precursors to short circuits. By setting a threshold, when the current exceeds the normal range in real time, it is determined whether it is caused by a circuit short circuit. Based on the joint changes in current and temperature, the health status of the internal circuit is further evaluated, the short-circuit trend is identified, and potential circuit short circuit problems are warned.

[0146] Predict the negative feedback effect of internal heat growth of encryption components based on the short-circuit trend of the internal circuit of the encryption components;

[0147] In an embodiment of the present invention, the negative feedback effect of heat growth inside the encryption element is predicted based on the short-circuit trend of the internal circuit of the encryption element. The short-circuit phenomenon often causes the local temperature of the encryption element to rise sharply. This heat accumulation will form a negative feedback effect, further exacerbating the decrease in conductivity, increased losses and thermal degradation of the equipment. In order to accurately predict this negative feedback effect of thermal growth, it is first necessary to monitor the temperature changes at the short-circuit point and the surrounding area using a thermal analysis instrument, and at the same time use a thermal sensor to record the temperature data of each part of the encryption element. By combining the current and temperature data, the heat accumulation rate is predicted, and it is calculated how the thermal effect caused by the short circuit affects the overall heat distribution and electrical performance of the equipment. Based on this predicted data, it is possible to foresee how the temperature will gradually rise after the short circuit occurs, and further affect the performance and life of the component.

[0148] The thermal aging condition of the semiconductor material of the encryption component is identified based on the negative feedback effect of thermal growth inside the encryption component and the short circuit trend of the internal circuit of the encryption component.

[0149] In an embodiment of the present invention, the thermal aging condition of the semiconductor material of the encryption element is further identified based on the thermal growth negative feedback effect and the short-circuit trend of the circuit of the encryption element. During the long-term operation of the encryption element, the short-circuit phenomenon and heat accumulation will cause the aging of the semiconductor material inside the encryption element. As the material ages, the conductivity, structural stability and overall performance of the semiconductor will gradually decrease, thereby affecting the working efficiency and life of the encryption element. By combining the data of the thermal growth negative feedback effect and the current short-circuit trend, the conductivity change of the semiconductor material is monitored using a conductivity detection device to further determine whether the material has aged. For the identification of thermal aging, the degree and trend of thermal aging are identified by measuring the resistivity change of the semiconductor material, the decrease in carrier mobility and the structural damage of the material. By comparing the data at different time points, the thermal aging condition of the semiconductor material of the encryption element is obtained, including the decrease in conductivity and the degree of degradation of material performance.

[0150] Preferably, step S4 includes the following steps:

[0151] Step S41: detecting the gradient growth of encryption resource loss according to the dynamic degradation trend of the edge encryption element structure;

[0152] In an embodiment of the present invention, the loss of encryption resources is detected and analyzed in combination with the obtained dynamic degradation trend data of the edge encryption element structure. During the specific implementation process, the operation data of the encryption element is obtained through a real-time monitoring system, including the usage of resources during the encryption process (such as CPU, memory, hard disk I / O, etc.). As the edge encryption element structure deteriorates, the resource consumption rate tends to increase. Therefore, by establishing an encryption element resource consumption monitoring system, the resource loss data in different time periods are compared to identify the gradient growth trend of resource loss. Specifically, the monitoring tool can record and analyze the changes in the computing resources consumed by the encryption element (such as CPU usage, memory occupancy and storage space consumption) when the load and the number of encryption tasks increase. By comparing historical data with current monitoring data, the growth rate of encryption resource loss is obtained, including the loss rate and the growth rate of resource consumption.

[0153] Step S42: Detecting the response delay of the encryption edge node according to the encryption resource loss gradient growth;

[0154] In an embodiment of the present invention, the response delay of the encryption edge node is further monitored according to the gradient growth of the encryption resource loss. In the specific implementation process, the response delay of the encryption edge node when processing data is monitored by measuring the response time of the encryption task in real time. With the loss of encryption resources and the degradation of the encryption element structure, the response delay tends to increase. The monitoring system can record the response time of each encryption node in real time, and analyze the impact of encryption resource loss on response delay in combination with the processing load of the encryption task. For example, when the computing power of the encryption element decreases due to degradation, the encryption processing time will increase significantly, resulting in an increase in the response delay of the edge node. By comparing the response delay data at different time points, the relationship between encryption resource loss and response delay is detected, and the response delay status data of the encryption edge node is obtained, including the change trend of the delay and the specific value of the response time.

[0155] Step S43: Evaluate the edge node data encryption risk trend based on the encryption edge node response delay status;

[0156] In an embodiment of the present invention, the risk trend of edge node data encryption is further evaluated in combination with the obtained encryption edge node response delay data. The specific operation is to evaluate the risks arising in the edge node data encryption process by combining the response delay of the encryption edge node with factors such as the node's encryption task volume and data security requirements. For example, an increase in response delay causes a lag in the data flow during the encryption process, affecting the real-time nature of the encrypted data, thereby increasing the risks of data leakage, data loss, etc. The evaluation process requires the use of a risk analysis algorithm to compare the response delay data with historical data to identify the risk growth trend faced by the data encryption process under different delay conditions. By calculating the relationship between response delay and risks such as data loss and leakage, the encryption risk trend of the edge node is obtained, and the data of the risk trend is output, including the risk growth rate and key nodes.

[0157] Step S44: Optimize the data encryption strategy according to the edge node data encryption risk trend and the encryption edge node response delay status to obtain data encryption strategy optimization data.

[0158] In an embodiment of the present invention, the data encryption strategy is further optimized based on the edge node data encryption risk trend obtained and the encrypted edge node response delay data in step S4.2. In this process, it is necessary to analyze the existing data encryption strategy and identify the weak links in the strategy in combination with the response delay and encryption risk trend of the current edge node. For example, if the response delay of the edge node is large, it is necessary to adjust the complexity of the encryption algorithm, or introduce a lightweight encryption algorithm to reduce the load on the node, thereby reducing the delay and risk in the encryption process. At the same time, it is necessary to optimize the resource allocation in the encryption strategy according to the gradient growth of encryption resource loss. The process of data encryption strategy optimization generates an optimized data encryption strategy by adjusting the parameters of the encryption algorithm, the encryption key length, the concurrent processing method, etc., and outputs the optimized strategy data, including the strategy adjustment content and the expected encryption performance improvement.

[0159] It is particularly important that step S41 includes the following steps:

[0160] Step S411: detecting the attenuation of power consumption stability according to the dynamic degradation trend of the edge encryption element structure;

[0161] In an embodiment of the present invention, the attenuation of power consumption stability is detected based on the dynamic structural degradation trend of the edge encryption element. During the specific operation, it is first necessary to monitor the structural condition of the encryption element over a long period of time and record its performance change data at different time points. A structural health monitoring system is used in combination with temperature sensors, strain sensors, and current sensors to collect the operating parameters of the encryption element in real time, including information such as temperature, load, and current. By analyzing these data and using numerical methods (such as fitting algorithms or regression analysis), a relationship model between power consumption and structural degradation is established. On this basis, the power consumption data is regularly compared to determine whether there is a attenuation trend in the power consumption stability of the encryption element. When the structure of the encryption element deteriorates, the power consumption tends to show an unstable growth. Therefore, the fluctuation of power consumption and the continuous increase trend are monitored to identify the impact of structural degradation on the stability of power consumption.

[0162] Step S412: Detecting a decreasing trend of the component energy efficiency ratio based on the power consumption stability decay;

[0163] In an embodiment of the present invention, the downward trend of the energy efficiency ratio of the detection element is based on the attenuation of the stability of power consumption. According to the power consumption data obtained in step S411, the energy efficiency ratio of the element is further calculated by real-time monitoring of the relationship between the input power and the output encryption processing capacity of the element. The energy efficiency ratio represents the amount of encryption processing that can be completed per unit of power consumption. Due to the degradation of the encryption element, the increase in power consumption will lead to a decrease in the energy efficiency ratio. In specific operations, the change in the energy efficiency ratio is calculated by regularly collecting the input power and output data of the element and the processing capacity of the encryption element (such as encryption speed, decryption speed, etc.). By comparing the energy efficiency ratio data in different time periods multiple times, it is analyzed whether it shows a downward trend. If the energy efficiency ratio continues to decline, it means that the energy efficiency of the encryption element is being affected by structural degradation or factors, thereby forming a downward trend in the energy efficiency ratio.

[0164] Step S413: determining the growth of the power consumption of the encryption element according to the downward trend of the element energy efficiency ratio and the attenuation of the power consumption stability;

[0165] In an embodiment of the present invention, the growth of the power consumption of the encryption element is determined based on the downward trend of the energy efficiency ratio of the element and the attenuation of the stability of power consumption. The decline in the energy efficiency ratio indicates that the amount of encryption tasks completed by the element per unit time has decreased, and more electricity is required to maintain the same processing volume. By calculating the decline in the energy efficiency ratio and combining it with the fluctuation data of power consumption, the growth of the power consumption of the encryption element under different working conditions is obtained. By monitoring the power consumption data obtained in real time by the system, the change in the energy efficiency ratio is combined with the change in power consumption to calculate the growth rate of the power consumption of the encryption element. In this process, by comparing the power consumption data of the encryption element in multiple operating cycles, the correlation between power consumption and energy efficiency ratio is identified, and it is determined whether its power consumption increases over time. If the power consumption shows a trend of continuous growth, it is determined that the power consumption of the encryption element has increased significantly.

[0166] Step S414: Detecting the encryption resource consumption gradient growth based on the encryption element power consumption growth.

[0167] In an embodiment of the present invention, the growth of the loss gradient of encryption resources is detected based on the growth of the power consumption of the encryption element. The loss of encryption resources is closely related to the power consumption of the encryption element. As the power consumption of the encryption element increases, the rate of resource consumption will also accelerate. In order to detect the loss gradient of encryption resources, it is necessary to monitor the changes in the power consumption of the encryption element in real time, and analyze the relationship between the power consumption and the usage of encryption resources, such as the number of processing tasks, the amount of data encrypted, etc. Through a high-precision resource monitoring system, the resource consumption of each encryption task is recorded in real time, and compared with the power consumption data for analysis. If the gradient of resource consumption increases significantly when the power consumption increases, it means that the resource consumption efficiency of the encryption element when processing encryption tasks is reduced, and the loss gradient shows an increasing trend. In this way, the growth trend of encryption resource loss can be accurately identified, and corresponding measures can be taken in a timely manner to optimize the efficiency of encryption resource use.

[0168] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A lightweight data encryption processing method driven by edge computing, characterized in that: The following steps are involved: Step S1: collecting original input data to be encrypted; performing semantic recognition of the original input data according to the original input data to be encrypted, thereby obtaining original data field semantic information; performing field structure analysis processing on the original data field semantic information, thereby obtaining the original data field structure; Identify the original input data type based on the original data field semantic information and the original data field structure, thereby obtaining the original input data type; Step S2: performing field encryption priority label assignment processing according to the original input data type to obtain field encryption priority label assignment data; determining a data encryption processing strategy according to the original input data type and the field encryption priority label assignment data; Performing input data encryption processing on the original input data type based on the data encryption processing strategy to obtain encrypted data; Step S3: Evaluate the abnormal disturbance of the encrypted data according to the data encryption processing status; and evaluate the edge node load gradient growth trend according to the abnormal disturbance of the encrypted data; Determine the dynamic degradation trend of the edge encryption component structure based on the edge node load gradient growth trend; Step S4: Detecting the encryption edge node response delay status based on the dynamic degradation trend of the edge encryption element structure; and evaluating the data encryption risk trend based on the encryption edge node response delay status; Optimize the data encryption strategy based on the data encryption risk trend to obtain data encryption strategy optimization data.

2. The edge computing-driven lightweight data encryption processing method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting the original input data to be encrypted; Step S12: performing desensitization processing on the original input data to be encrypted, thereby obtaining desensitized data to be encrypted; Step S13: performing semantic recognition of the original input data based on the desensitized data to be encrypted to obtain semantic information of the original data fields; Step S14: performing field structure parsing on the original data field semantic information to obtain the original data field structure; Step S15: Identify the original input data type based on the original data field semantic information and the original data field structure to obtain the original input data type.

3. The edge computing-driven lightweight data encryption processing method according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: extracting basic format features of the encrypted data field based on the desensitized data to be encrypted, and obtaining basic format features of the data field to be encrypted; Step S132: performing similar grammatical pattern matching with a similarity threshold of 0.75 based on the basic format features of the data field to be encrypted, to obtain similar grammatical matching data; Step S133: Analyze data context domain features based on similar syntax matching data and basic format features of the data field to be encrypted; Step S134: identifying repeated combinations of field contexts based on data context domain features; Step S135: When the field context repetition combination exceeds 86%, a field sequence rearrangement process is performed to obtain field sequence rearrangement data; Step S136: Rearrange the data based on the field sequence to perform semantic recognition on the original input data to obtain semantic information of the original data fields.

4. The edge computing-driven lightweight data encryption processing method according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: identifying physical boundary information of the data field to be encrypted based on the semantic information of the original data field; Step S142: measuring the length of the data field to be encrypted according to the physical boundary information of the data field to be encrypted; Step S143: performing semantic attribute classification processing on the semantic information of the original data fields to obtain field semantic grouping data; Step S144: identifying field semantic association coupling attributes based on field semantic grouping data; Step S145: determining the field structure nesting when the field semantic association coupling attribute exceeds 0.89, and obtaining the field structure nesting situation; Step S146: determining data field hierarchy information based on the field structure nesting and field semantic association coupling; Step S147: Based on the data field hierarchy information and when the length of the data field to be encrypted exceeds 10, a field structure parsing process is performed to obtain the original data field structure.

5. The edge computing driven lightweight data encryption processing method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing field encryption priority label assignment processing according to the original input data type to obtain field encryption priority label assignment data; Step S22: allocating data based on the field encryption priority tag to predict data encryption resource consumption; Step S23: Determine the data encryption processing strategy based on the data encryption resource consumption status and the field encryption priority label allocation data; Step S24: Perform input data encryption processing on the original input data type based on the data encryption processing strategy to obtain encrypted data.

6. The edge computing driven lightweight data encryption processing method according to claim 5 is characterized in that: Step S23 includes the following steps: Step S231: Assessing the encryption difficulty of the data field according to the field encryption priority label allocation data; Step S232: Calculate the computing power usage of the data encryption edge node based on the data field encryption difficulty; Step S233: Perform field weight allocation processing on the computing power usage of the data encryption edge node to obtain field encryption weight data; Step S234: marking the node encryption resource interval according to the encryption resource consumption status to obtain node encryption resource capability marking data; Step S235: performing policy combination mapping processing on the node encrypted resource capability tag data and the field encrypted weight data to obtain encrypted policy combination mapping data; Step S236: Evaluate the feasibility of the data encryption policy combination based on the encryption policy combination mapping data; Step S237: Evaluate the feasibility of the data encryption strategy combination and the adaptability of the data encryption processing strategy; Step S238: Determine the data encryption processing strategy based on the adaptation of the data encryption processing strategy and the feasibility of the data encryption strategy combination.

7. The edge computing driven lightweight data encryption processing method according to claim 1, characterized in that: The evaluation of abnormal disturbance conditions of the encrypted data in step S3 includes: Detect the node data encryption processing deviation trend based on the data encryption processing status; Detect encrypted statement field nesting conflicts based on node data encryption processing offset trends; Determine the misidentification of encryption processing field boundaries based on the encryption statement field nesting conflict; Identifying the distortion of encrypted data based on the misidentification of encryption field boundaries; Analyze the degree of byte alignment deviation of encrypted data based on the distortion status of encrypted data; Predict the increasing difficulty of encrypted data parsing based on the degree of byte alignment deviation and distortion of encrypted data; Determine the degree of consistency difference between data before and after encryption based on the increasing trend of encrypted data parsing difficulty; Evaluate the abnormal disturbance of encrypted data based on the degree of data consistency difference before and after encryption and the increasing trend of encrypted data parsing difficulty.

8. The edge computing-driven lightweight data encryption processing method according to claim 1, characterized in that: The edge node load gradient growth trend evaluation in step S3 includes: Perform encryption processing abnormality inducement analysis based on abnormal disturbance conditions of encryption processing data to obtain encryption processing abnormality inducement data; Identify abnormal edge nodes for encryption processing based on abnormal inducement data for encryption processing, and obtain abnormal data of edge nodes for encryption processing; Detect excessive memory usage at edge nodes based on encrypted processing of abnormal data at edge nodes; Detect excessive CPU usage at edge nodes based on encrypted processing of abnormal data at edge nodes; Statistics on the overlapping distribution of abnormal edge node usage, including excessive CPU usage and excessive memory usage at edge nodes; Determine the concentration trend of edge node encryption operation pressure based on the overlapping distribution of abnormal edge node occupancy; Detect edge node encryption scheduling imbalance based on edge node computing pressure concentration trends; The edge node load gradient growth trend is evaluated based on the imbalance of edge node encryption scheduling and the concentration trend of edge node encryption computing pressure.

9. The edge computing-driven lightweight data encryption processing method according to claim 1, characterized in that: The determination of the dynamic degradation trend of the edge encryption element structure in step S3 includes: Identify the attenuation of node encryption processing stability based on the edge node load gradient growth trend; Detect the increasing risk of edge node cache overflow based on the attenuation of edge encryption processing stability; Detect edge encryption component overload based on the increasing risk of edge node cache overflow and the attenuation of node encryption processing stability; Measure the high temperature operation state of the edge encryption element according to the overload condition of the edge encryption element; Detect the accumulation of thermal effects of encryption components based on the high-temperature operating state of edge encryption components; Identify the thermal aging status of semiconductor materials in encryption components based on the cumulative thermal effects of encryption components; The dynamic degradation trend of the edge encryption element structure is determined based on the thermal aging status of the encryption element semiconductor material and the accumulation of thermal effects of the encryption element.

10. The edge computing driven lightweight data encryption processing method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: detecting the gradient growth of encryption resource loss according to the dynamic degradation trend of the edge encryption element structure; Step S42: Detecting the response delay of the encryption edge node according to the encryption resource loss gradient growth; Step S43: Evaluate the edge node data encryption risk trend based on the encryption edge node response delay status; Step S44: Optimize the data encryption strategy according to the edge node data encryption risk trend and the encryption edge node response delay status to obtain data encryption strategy optimization data.

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