Power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrix

Abstract

This invention discloses a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices. The method includes: collecting protocol-related parameters and constructing an original data feature set through a smart grid homomorphic signature encryption data transmission platform; forming a three-dimensional original matrix through multi-dimensional matrix mapping; calling a sensor-integrated power semantic short packet communication model to complete semantic extraction and short packet encapsulation; achieving encryption and signature encryption binding using a multi-dimensional matrix homomorphic signature encryption encoding and decoding model; adapting transmission resource allocation based on a multi-timescale heterogeneous resource collaborative optimization algorithm; and restoring the original data at the receiving end through inverse mapping decryption, signature encryption verification, and semantic parsing. The method refines the semantic processing, signature encryption, and resource scheduling processes step-by-step, integrating the core functions of multiple models to achieve semantic adaptation, secure transmission, and resource optimization collaboration in power grid communication protocol encoding and decoding, adapting to the communication needs of multiple devices and scenarios in smart grids.

Description

Technical Field

[0001] This invention relates to the field of smart grid communication technology, and in particular to a grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices. Background Technology

[0002] As the smart grid undergoes a profound transformation towards digitalization and intelligence, the amount of data transmitted in power grid communication scenarios is growing exponentially. Communication protocols must simultaneously meet the requirements of semantic integrity, transmission security, and resource adaptability for power data. Current power grid communication involves the access of massive numbers of heterogeneous devices. These devices exhibit differences in communication protocol frame structures and data field formats, and the transmission process faces challenges such as channel fluctuations and uneven resource allocation. Traditional encoding and decoding methods struggle to simultaneously optimize semantic parsing accuracy, encryption security, and transmission efficiency. The integrated application of technologies such as sensor-based communication and homomorphic signature encryption has become a key direction for solving the bottlenecks in power grid communication protocol encoding and decoding. There is an urgent need to construct an integrated encoding and decoding framework adapted to power grid scenarios, achieving full-process collaboration in protocol parameter acquisition, semantic processing, encryption and signature encryption, resource scheduling, and data restoration.

[0003] Existing power grid communication protocol encoding and decoding technologies have two significant shortcomings: First, traditional encoding and decoding methods lack deep adaptation to the semantic features of power in the context of sensor-communication fusion, and mostly adopt a single-dimensional data processing mode, which cannot effectively integrate communication sensing information with power data semantics. This leads to semantic loss or excessive redundant information during short packet transmission, affecting the accuracy and efficiency of encoding and decoding. Second, existing encryption and encoding / decoding processes are independent of each other, lacking a multi-dimensional matrix-level signature-cryptography binding mechanism, and resource scheduling does not fully consider the dynamic changes of heterogeneous resources at multiple time scales. This makes it easy for resource conflicts and increased transmission delays to occur during encrypted data transmission, making it difficult to meet the dual requirements of smart grids for data transmission security and real-time performance, and failing to achieve deep synergy between encoding / decoding, encryption signature-cryptography, and resource optimization. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices.

[0005] The technical solution adopted in this invention is a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices, comprising the following steps: S1, collecting frame structure parameters, data field identifier parameters, transmission timing parameters, and channel transmission characteristic parameters of the power grid communication protocol through a smart grid homomorphic signature data transmission platform, and establishing an original data feature set adapted to the integrated sensing power semantic short packet communication model; S2, based on the dimension configuration rules of the multi-dimensional matrix homomorphic signature encoding and decoding model, mapping the original data feature set into row vectors and column vectors of a multi-dimensional matrix according to the communication protocol field types, constructing a three-dimensional original matrix including signature dimension, semantic parsing dimension, and transmission adaptation dimension; S3, calling the integrated sensing power semantic short packet communication model to extract semantic features and encapsulate short packets on the three-dimensional original matrix. The process involves: S4, generating a short packet matrix that integrates communication sensing information and power data semantics; S5, performing homomorphic encryption transformation and signature verification operations on the short packet matrix using a multi-dimensional matrix homomorphic signature encoding and decoding model, and binding the encrypted data with signatures through homomorphic mapping operations of matrix elements; S6, performing transmission resource allocation adaptation on the signed encrypted matrix based on a multi-timescale heterogeneous resource collaborative optimization algorithm, adjusting the matrix transmission priority and fragmentation strategy according to the channel resource status, computational resource load, and storage resource capacity under different time scales; and S7, the receiving end performing inverse mapping decryption and signature verification through the multi-dimensional matrix homomorphic signature encoding and decoding model, and restoring the original data fields and frame structure information of the power grid communication protocol by combining the semantic parsing rules of the integrated sensing and power semantic short packet communication model, thus completing the entire encoding and decoding process.

[0006] Furthermore, the expression for the integrated inductive power semantic short packet communication model is as follows: ,in, This is a short packet matrix for electricity semantics. The number of fields in the power grid communication protocol. For each field, the semantic feature dimension, For the first The semantic weight coefficient of each field, It is a homomorphic mapping operator. The third element in the three-dimensional original matrix Line number Column elements, For the homomorphic mapping parameter set, For the first Perceptual adaptation coefficient of each semantic feature For synesthetic fusion operators, For the first Line number Channel-aware data of column elements, For the sensing parameter set, For matrix tensor product operations, For time-scale adaptation functions, For the first Each timescale parameter For the first Channel resource parameters.

[0007] Furthermore, the expression for the multi-dimensional matrix homomorphic signcryption encoding and decoding model is: ,in, For signature encryption matrix, It is a homomorphic encryption function. For the number of signature dimensions, The number of columns in the matrix. To generate operators for signature encryption, For private key parameters, For hash functions, For the public key parameter set, It is a finite cyclic group. For group generators, For the dimension fusion operator, Configure functions for dimensions. For signature dimension parameters, For semantic parsing dimension parameters, To transmit adapted dimension parameters.

[0008] Furthermore, the expression for the multi-timescale heterogeneous resource collaborative optimization algorithm is as follows: ,in, This is the optimal resource allocation scheme. The total number of time scales. The number of heterogeneous resource types. For the first Weight coefficients of resource classes For the first The first time scale Available capacity of the resource class For the first Size of the short package matrix at each time scale For the number of transmission tasks, For the first The first time scale The transmission load of each task For resource coordination operators, For the first Channel state parameters at various time scales For the first Computational resource load parameters at each time scale.

[0009] Furthermore, the transmission adaptation model expression of the smart grid homomorphic signature encryption data transmission platform is as follows: ,in, For transmission adaptation matrix, For the first A matrix of protocol frame structure parameters, For transmission adaptation functions, For platform parameter set, For bandwidth parameters, For frame synchronization parameters, For transmission delay parameters, This is a matrix addition operation. For link monitoring operators, These are link state parameters. This is the bit error rate parameter.

[0010] Furthermore, the frame structure reconstruction model expression for the power grid communication protocol encoding and decoding is as follows: ,in, The restored frame structure data, This is a homomorphic decryption function. For frame parsing functions, For the set of field parameters, Identify parameters for data fields. For field length parameters, For field offset parameters, To receive the adaptation operator, For receiver channel parameters, These are synchronization parameters.

[0011] Further, S3 includes the following sub-steps: S31, calling the semantic feature extraction module of the integrated sensing power semantic short packet communication model, performing semantic correlation analysis on each element in the three-dimensional original matrix, and extracting semantic identification information including voltage, current, and power data features through mapping and matching of matrix elements with the power data semantic library; S32, based on the correlation between communication sensing parameters and semantic identification information, performing dimensional compression processing on the row and column vectors of the three-dimensional original matrix, retaining the core feature dimensions adapted to short packet transmission, and eliminating redundant dimension information; S33, according to the integrated sensing short packet encapsulation rules, rearranging the compressed matrix elements according to the transmission timing requirements of the power grid communication protocol to generate fixed-length short packet data blocks, each short packet data block including a semantic verification identifier and a sensing adaptation field; S34, combining multiple short packet data blocks into a short packet matrix through a short packet fusion algorithm, where the row dimension of the short packet matrix corresponds to the short packet number, and the column dimension corresponds to the data fields within the short packet, performing fusion encapsulation of semantic features and communication sensing features.

[0012] Further, step S4 includes the following sub-steps: S41, based on the private key parameters of the multi-dimensional matrix homomorphic signature encryption and decoding model, perform a hash operation on each element of the short packet matrix to generate element-level hash values ​​and construct a hash matrix; S42, use the private key to perform signature encryption processing on the hash matrix, embedding the signature encryption information into the element bits of the short packet matrix through a homomorphic mapping algorithm to form a signature encryption matrix; S43, call the homomorphic encryption algorithm to perform encryption transformation on the signature encryption matrix, mapping the matrix elements to a specified finite cyclic group to generate an encryption matrix, the encryption process maintaining the dimensional structure and element correlation of the matrix; S44, add dimension identifiers and signature verification identifiers to the encryption matrix, the dimension identifiers are used to label the signature encryption dimension, semantic parsing dimension, and transmission adaptation dimension information of the matrix, and the signature verification identifiers are used for signature validity verification at the receiving end.

[0013] Further, S5 includes the following sub-steps: S51, using the resource awareness module of the multi-timescale heterogeneous resource collaborative optimization algorithm, collecting parameters such as channel resource bandwidth, computing resource processing capability, and storage resource capacity at different time scales, and establishing a resource state matrix; S52, based on the scale of the resource state matrix and the encryption matrix, and the transmission priority requirements, constructing a resource allocation objective function, which is guided by maximizing resource utilization and transmission efficiency; S53, using an optimization algorithm to solve the objective function, obtaining the number of fragments of the encryption matrix, the transmission path of each fragment, and the transmission timing arrangement, while maintaining the logical correlation and integrity of the matrix elements during the fragmentation process; S54, performing fragmentation processing on the encryption matrix according to the solution results, allocating corresponding transmission resources and storage resources to each fragment, and recording the fragment identifier and resource allocation information for fragment reassembly at the receiving end.

[0014] A power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices is proposed. This method is implemented through different units, including: a multi-dimensional acquisition unit for power grid communication protocol parameters, a sensor-integrated power semantic short packet generation unit, a multi-dimensional matrix homomorphic signature encryption and decoding unit, a multi-timescale heterogeneous resource collaborative scheduling unit, an encrypted data transmission adaptation unit, and an original data frame structure restoration unit. The output of the multi-dimensional acquisition unit is connected to the input of the sensor-integrated power semantic short packet generation unit to transmit the acquired protocol parameters to the short packet generation unit. The output of the sensor-integrated power semantic short packet generation unit is connected to the input of the multi-dimensional matrix homomorphic signature encryption and decoding unit to perform short packet encoding and decoding. The packet matrix is ​​transmitted to the signature decoding unit; the output of the multi-dimensional matrix homomorphic signature encoding and decoding unit is connected to the input of the multi-timescale heterogeneous resource collaborative scheduling unit, transmitting the encrypted matrix to the resource scheduling unit; the output of the multi-timescale heterogeneous resource collaborative scheduling unit is connected to the input of the encrypted data transmission adaptation unit, providing a resource allocation scheme for the transmission adaptation unit; the output of the encrypted data transmission adaptation unit is connected to the input of the original data frame structure restoration unit, transmitting the adapted encrypted data to the restoration unit; the original data frame structure restoration unit, through a bidirectional connection with the multi-dimensional matrix homomorphic signature encoding and decoding unit, obtains the signature verification information required for decryption, completing the restoration output of the original data.

[0015] Beneficial Effects: This invention proposes a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices. Using a smart grid homomorphic signature encryption data transmission platform as a foundation, it achieves deep adaptation between power grid protocol data and a semantically integrated short packet model through multi-dimensional parameter acquisition and three-dimensional matrix construction. This addresses the problems of insufficient semantic feature integration, redundancy in short packet transmission, or information loss in traditional methods. By leveraging a multi-dimensional matrix homomorphic signature encryption encoding and decoding model, signature encryption and encryption are deeply bound, enhancing transmission security while maintaining data correlation and overcoming the shortcomings of traditional methods that separate encoding / decoding and encryption processes. Through a multi-timescale heterogeneous resource collaborative optimization algorithm, it dynamically adapts to the state of channel, computing, and storage resources, achieving precise resource scheduling and efficient utilization, solving the problems of rigid resource allocation and high transmission latency in existing technologies. The close integration of each step and unit constructs an integrated framework from parameter acquisition, semantic encapsulation, signature encryption, resource scheduling to data restoration. This ensures both the semantic integrity and signature encryption security of power data transmission, while improving resource adaptability and transmission efficiency throughout the encoding and decoding process, fully meeting the core requirements of smart grid digital transformation for communication protocol encoding and decoding. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention;

[0017] Figure 2 This is a diagram showing the unit composition for implementing the method of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, the power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices includes the following steps:

[0020] S1, collect frame structure parameters, data field identifier parameters, transmission timing parameters and channel transmission characteristic parameters of the power grid communication protocol through the smart grid homomorphic signature data transmission platform, and establish an original data feature set adapted to the integrated power semantic short packet communication model;

[0021] Specifically, step S1 revolves around the multi-dimensional parameter acquisition and feature set construction of the smart grid homomorphic signature data transmission platform. This is achieved by synchronously acquiring key parameters of the power grid communication protocol through the platform's protocol parameter acquisition module and channel sensing module. These parameters include: frame structure parameters (such as protocol frame header length, frame tail identifier, check field position, and data load area boundary); data field identifier parameters (such as voltage data field ID, current data field identifier code, power data field marker, and device status field identifier); transmission timing parameters (such as frame transmission interval, field transmission order, retransmission trigger timing, and synchronization signal period); and channel transmission characteristic parameters (such as channel attenuation coefficient, signal interference intensity, transmission delay fluctuation range, and error distribution pattern). During acquisition, the platform uses a multi-port parallel acquisition mechanism to capture various parameters in real time at a frequency of 10ms / acquisition, covering 1-256 protocol fields, 1-16 transmission timing levels, and 8-64 channel scenario characteristics. After data collection, the parameters are deduplicated, sorted, and standardized in format. Based on the parameter adaptation requirements of the integrated sensing and communication power semantic short packet communication model, effective parameters that match the model's semantic parsing dimensions and short packet encapsulation format are selected. A four-dimensional raw data feature set, including frame structure features, field identification features, timing features, and channel features, is constructed. The feature set data volume is controlled between 1024 and 4096 entries to ensure the efficiency and accuracy of the subsequent matrix mapping process, providing complete and adapted raw data support for the entire encoding and decoding process.

[0022] S2, based on the dimension configuration rules of the multi-dimensional matrix homomorphic signature encryption and decoding model, maps the original data feature set into row vectors and column vectors of a multi-dimensional matrix according to the communication protocol field type, and constructs a three-dimensional original matrix including signature encryption dimension, semantic parsing dimension, and transmission adaptation dimension.

[0023] Specifically, step S2, based on the pre-defined dimensional configuration rules of the multi-dimensional matrix homomorphic signature encryption and decoding model, performs a mapping transformation operation from the original data feature set to a three-dimensional matrix, achieving precise binding between data features and matrix dimensions. First, the three-dimensional dimensions defined by the model are clarified: the signature encryption dimension corresponds to security-related attributes such as key length and encryption level in homomorphic signature encryption operations; the semantic parsing dimension corresponds to semantic attributes such as semantic categories and relationships in power data; and the transmission adaptation dimension corresponds to transmission attributes such as bandwidth requirements and latency thresholds for data transmission. During implementation, classification mapping is performed according to the field types of the power grid communication protocol. Static configuration parameters such as frame header length and check field position in the frame structure parameters are mapped to row vectors of the three-dimensional matrix, with each row vector corresponding to a field type. A total of 8-32 row vectors are set, corresponding to different field categories such as frame structure parameters, voltage data, current data, and power data. Dynamic data parameters in the data field identifier parameters and transmission timing parameters are mapped to column vectors of the matrix, with each column vector corresponding to a specific parameter item. A total of 16-64 column vectors are set, including the specific values ​​and attribute information of various parameters. During the mapping process, a two-way verification mechanism of field type and dimension attribute is adopted to ensure that each parameter item is accurately mapped to the intersection of the corresponding row vector and column vector, and a three-dimensional original matrix with a dimension scale of (8-32)×(16-64) is constructed. Each element of the matrix includes three information: parameter value, field type identifier, and dimension attribute label. This not only preserves the integrity of the original data, but also meets the data structure requirements of the multi-dimensional matrix homomorphic signature encryption and decoding model, laying a structured data foundation for subsequent semantic extraction and signature encryption operations.

[0024] S3, invoke the integrated sensing power semantic short packet communication model to extract semantic features and encapsulate short packets into the three-dimensional original matrix, and generate a short packet matrix that integrates communication sensing information and power data semantics;

[0025] Specifically, step S3 involves calling the integrated power semantic short packet communication model to extract semantic features and encapsulate short packets from the three-dimensional original matrix. The core objective is to achieve deep integration of power data semantics and communication sensing information. After the model starts, the semantic feature extraction module is activated first. Based on the built-in power data semantic library (including 1000-5000 power professional semantic rules), semantic correlation analysis is performed on each element in the three-dimensional original matrix. Through matching operations between element parameter values ​​and semantic rules, core semantic identifier information such as voltage level semantics, current change trend semantics, and power balance semantics is extracted. At the same time, combined with the channel bandwidth, interference intensity, and other sensing data obtained by the model's communication sensing module, a correlation mapping between semantic features and sensing data is established. Subsequently, based on the correlation mapping results, the three-dimensional original matrix is ​​subjected to dimensional compression processing. Principal component analysis is used to retain core feature dimensions with a variance contribution rate higher than 85%, and redundant duplicate information and irrelevant dimensions are removed, compressing the matrix dimension from (8-32)×(16-64) to (4-16)×(8-32). After compression, following the integrated short packet encapsulation rules, the compressed matrix elements are reordered according to the transmission timing requirements of the power grid communication protocol. Each short packet data block contains a fixed 256-1024 matrix elements, and each data block embeds a 16-bit semantic verification identifier and an 8-bit perception adaptation field. The semantic verification identifier is used to verify the integrity of semantic features, and the perception adaptation field is used to adapt to different channel scenarios. Finally, multiple short packet data blocks are combined into a short packet matrix using a short packet fusion algorithm. The matrix row dimension corresponds to the short packet number (numbers 1-64), and the column dimension corresponds to the data fields within the short packet, achieving integrated encapsulation of semantic features and communication perception features, ensuring semantic parsingability and channel adaptability during data transmission.

[0026] S4 employs a multi-dimensional matrix homomorphic signature encryption and decoding model to perform homomorphic encryption transformation and signature verification operations on the short packet matrix, and performs signature binding of encrypted data through homomorphic mapping operations of matrix elements;

[0027] Specifically, step S4 employs a multi-dimensional matrix homomorphic signature encryption and decoding model to perform integrated homomorphic encryption and signature verification on the short packet matrix. The core of this model is the deep binding of encryption and signature verification through homomorphic mapping operations on matrix elements. During implementation, the model first loads preset security parameter configurations, including core parameters such as the size of the finite cyclic group, key length, and hash function type. The key length is set to 256-1024 bits, and the hash function uses algorithms from SHA-256 to SHA-512. First, a hash operation is performed on each element of the short packet matrix to generate a fixed-length element-level hash value. Based on these hash values, a hash matrix with the same dimensions as the short packet matrix is ​​constructed, ensuring a one-to-one correspondence between the hash value of each element and the original element. Subsequently, the hash matrix is ​​signed using a private key. The private key information is embedded into the elements of the hash matrix using a digital signature algorithm, forming a signature matrix that includes signature information. During the signature process, bitwise operations on matrix elements are used to achieve the fusion and storage of the signature information and hash value. Next, a homomorphic encryption algorithm is invoked to perform an encryption transformation on the signature matrix, mapping the matrix elements to a pre-defined finite cyclic group. The encryption process strictly follows the homomorphic mapping rules, ensuring that the results of addition and multiplication operations on the encrypted matrix are consistent with the encrypted values ​​of the original matrix operations, thus achieving "computational feasibility in the encrypted state." After encryption, dimension identifiers and signature verification identifiers are added to the generated encrypted matrix. The dimension identifiers specify the concrete parameters of the signature dimension, semantic parsing dimension, and transmission adaptation dimension of the matrix. The signature verification identifiers include the core verification information of the private key signature. The entire process uses a parallel processing mechanism for matrix operations to complete the encryption and signature operations synchronously, controlling the processing efficiency to 10-100ms / matrix. This ensures the security of data transmission while avoiding the inefficiency caused by separating encryption and signature operations.

[0028] S5, based on the multi-timescale heterogeneous resource collaborative optimization algorithm, performs transmission resource allocation adaptation on the encrypted matrix after signing, and adjusts the matrix transmission priority and fragmentation strategy according to the channel resource status, computational resource load and storage resource capacity under different time scales.

[0029] Specifically, step S5, based on a multi-timescale heterogeneous resource collaborative optimization algorithm, adapts the transmission resource allocation for the encrypted matrix after signature encryption. The core is to achieve dynamic matching of resources and transmission requirements at different time scales. The algorithm first divides the time scale into three levels: short-term (1-10ms), medium-term (10-100ms), and long-term (100-1000ms). A resource-aware module collects heterogeneous resource parameters at each time scale in real time. Channel resource parameters include bandwidth utilization (0%-100%) and channel bit error rate (BER). -6 -10 -2), transmission latency (1-100ms), computing resource parameters include CPU load rate (0%-100%), computing throughput (10 6 -10 9 (Times / second), storage resource parameters include storage utilization (0%-100%), read / write speed (10... 6 -10 9 Based on the collected data (bytes / second), a resource status matrix is ​​established. Subsequently, considering the size of the encryption matrix (1024-16384 elements) and transmission priority (levels 1-5, with level 1 being the highest), a resource allocation objective function is constructed, aiming to maximize resource utilization and minimize transmission latency. A gradient descent optimization algorithm is used to solve the objective function, with the number of iterations set to 100-1000 and the convergence threshold set to 10. -4 The algorithm obtains the number of fragments in the encryption matrix (2-32 fragments), the transmission path for each fragment (1-8 selectable paths), and the transmission timing arrangement (hierarchical scheduling based on time scale). During fragmentation, the matrix elements are split according to their logical relationships to ensure that each fragment includes complete semantic units and signature verification information, avoiding data integrity corruption caused by fragmentation. Finally, based on the solution results, corresponding channel resources, computing resources, and storage resources are allocated to each fragment. Simultaneously, the fragment identifier, resource allocation parameters, and transmission timing information are recorded to generate a resource allocation table for fragment reassembly and resource verification at the receiving end. The entire process achieves dynamic optimization of resource allocation, adapting to resource status changes at different time scales.

[0030] S6, the receiving end performs inverse mapping decryption and signature verification through a multi-dimensional matrix homomorphic signature encryption and decoding model, and restores the original data fields and frame structure information of the power grid communication protocol by combining the semantic parsing rules of the integrated power semantic short packet communication model, thus completing the entire encoding and decoding process.

[0031] Specifically, step S6, as the final stage of the entire encoding and decoding process, involves the receiving end collaborating with a multi-dimensional matrix homomorphic signature encryption and decoding model and a sensor-integrated power semantic short packet communication model to decrypt, verify, and restore the original data. First, the receiving end receives the fragmented encryption matrix. Based on the fragment identifier and resource allocation table recorded in step S5, it reassembles the fragmented data to restore the complete encryption matrix. During the reassembly process, the integrity of the fragments is verified using a fragment checksum. If any fragments are lost or erroneous, a retransmission mechanism is triggered. Subsequently, the multi-dimensional matrix homomorphic signature encryption and decoding model is invoked, loading the public key parameters corresponding to the sending end. An inverse mapping decryption operation is performed on the encryption matrix, restoring the signature and hash matrices according to the inverse operation rules of homomorphic encryption. Then, the signature identifier is verified using the private key to check for security risks such as tampering or forgery during data transmission. If the verification pass rate is less than 99.9%, data reception is rejected. After successful verification, the signature matrix is ​​transmitted to the semantic parsing module of the integrated sensing power semantic short packet communication model. Based on the model's built-in semantic parsing rules, the semantic identifier information in the matrix is ​​decoded to restore the original semantic meaning of power data such as voltage, current, and power. Simultaneously, combined with the adaptation information of communication sensing parameters, core information such as the frame structure parameters and data field identifier parameters of the power grid communication protocol are parsed out. Finally, according to the frame format requirements of the power grid communication protocol, the parsed parameters are recombined into a standard protocol frame, restoring the complete structure of the frame header, data payload, check field, and frame trailer. This ensures that the restored original data fields are consistent with the sending end, and that field errors are controlled within acceptable limits, completing the entire process from encrypted data to original protocol data and achieving a closed-loop completion of the encoding and decoding task.

[0032] Preferably, the expression of the integrated sensing and sensing power semantic short packet communication model is: ,in, This is a short packet matrix for electricity semantics. The number of fields in the power grid communication protocol. For each field, the semantic feature dimension, For the first The semantic weight coefficient of each field, It is a homomorphic mapping operator. The third element in the three-dimensional original matrix Line number Column elements, For the homomorphic mapping parameter set, For the first Perceptual adaptation coefficient of each semantic feature For synesthetic fusion operators, For the first Line number Channel-aware data of column elements, For the sensing parameter set, For matrix tensor product operations, For time-scale adaptation functions, For the first Each timescale parameter For the first Channel resource parameters.

[0033] Specifically, the integrated sensing and perceptual power semantic short packet communication model achieves deep fusion and encapsulation of semantic features and perceptual information in power grid communication protocol data. When implementing this model, the number of power grid communication protocol fields is first defined as 16-64, and the semantic feature dimension for each field is set to 8-32 dimensions to ensure comprehensive semantic features. Semantic weight coefficients are allocated according to the importance of power data, ranging from 0.1 to 0.9, with coefficients higher than 0.7 for core data fields such as voltage and current, and lower than 0.3 for secondary state fields. The perception adaptation coefficient is adjusted based on the reliability of channel perception data, ranging from 0.2 to 0.8, with coefficients higher than 0.6 when channel quality is good and lower than 0.4 when channel interference is strong. The homomorphic mapping parameter set includes configuration information such as mapping function type and mapping domain range, while the perception parameter set includes real-time acquired data such as channel bandwidth, interference intensity, and transmission delay. Time scale parameters are divided into three levels: 1ms, 10ms, and 100ms, and channel resource parameters include indicators such as available bandwidth and channel bit error rate. The model integrates the semantic features of each field with the corresponding perceived data through a double summation operation, and adapts the time scale and channel resource status through tensor product operation. The final generated power semantic short packet matrix not only retains the complete semantic information of power data, but also adapts to the channel characteristics of communication transmission, realizing the collaborative optimization of semantic parsing and communication perception, and improving the adaptability and semantic integrity of short packet transmission.

[0034] Preferably, the expression for the multi-dimensional matrix homomorphic signaturecryption encoding / decoding model is: ,in, For signature encryption matrix, It is a homomorphic encryption function. For the number of signature dimensions, The number of columns in the matrix. To generate operators for signature encryption, For private key parameters, For hash functions, For the public key parameter set, It is a finite cyclic group. For group generators, For the dimension fusion operator, Configure functions for dimensions. For signature dimension parameters, For semantic parsing dimension parameters, To transmit adapted dimension parameters.

[0035] Specifically, a multi-dimensional matrix homomorphic signature encryption and decoding model integrates encryption and signature encryption of short packet matrices to ensure data transmission security and integrity. During implementation, the signature encryption dimension is set to 4-12 dimensions, including multiple security dimensions such as key verification and data integrity verification. The number of matrix columns is consistent with the column dimension of the short packet matrix, ranging from 8-32 columns. The private key parameters use a 256-1024 bit asymmetric key, generated and strictly stored using a key generation algorithm. The hash function used is SHA-256 or SHA-512 to ensure the uniqueness and irreversibility of the hash value. The public key parameter set includes key information such as the order of the finite cyclic group, and the generators g and h. The order of the finite cyclic group is a large prime number, and the generators g and h are selected from the group using a specific algorithm. The dimension fusion operator uses matrix dot product operations to fuse multi-dimensional information. The dimension configuration function specifies the values ​​of parameters for the signature dimension, semantic parsing dimension, and transmission adaptation dimension. Signature dimension parameters include encryption level and number of verifications; semantic parsing dimension parameters involve semantic rule version and matching threshold; and transmission adaptation dimension parameters include transmission rate adaptation range and fragment size limit. The model first performs hash operations on the elements of the short packet matrix and signs them with the private key. Then, it maps the signed matrix to a finite cyclic group using a homomorphic encryption function. The dimension fusion operator integrates the multi-dimensional information to generate a signature-encryption matrix, achieving deep binding between encryption and signature, ensuring that data is not tampered with or forged during transmission, while also supporting some computational operations in the encrypted state.

[0036] Preferably, the expression for the multi-timescale heterogeneous resource collaborative optimization algorithm is: ,in, This is the optimal resource allocation scheme. The total number of time scales. The number of heterogeneous resource types. For the first Weight coefficients of resource classes For the first The first time scale Available capacity of the resource class For the first Size of the short package matrix at each time scale For the number of transmission tasks, For the first The first time scale The transmission load of each task For resource coordination operators, For the first Channel state parameters at various time scales For the first Computational resource load parameters at each time scale.

[0037] Specifically, a multi-timescale heterogeneous resource collaborative optimization algorithm achieves efficient resource adaptation and scheduling during the transmission of the encrypted matrix. In the algorithm implementation, the total number of timescales is set to three, corresponding to short-term (1-10ms), medium-term (10-100ms), and long-term (100-1000ms) levels. Heterogeneous resources are classified into three categories: channel resources, computing resources, and storage resources. Weighting coefficients are allocated according to resource importance, with channel resource coefficients ranging from 0.4 to 0.6, and computing and storage resource coefficients each ranging from 0.2 to 0.3. At each timescale, the available capacity of each type of resource is collected in real time through a resource awareness module. The available capacity of channel resources is represented by bandwidth percentage (range 0%-100%), the available capacity of computing resources is represented by CPU idle rate (range 0%-100%), and the available capacity of storage resources is represented by the percentage of remaining storage space (range 0%-100%). The size of the short packet matrix is ​​measured by the total number of matrix elements, ranging from 1024 to 16384. The number of transmission tasks is dynamically adjusted according to the current power grid communication load, ranging from 2 to 16. The transmission load of each task is represented by a relative value of the data volume, ranging from 10 to 100. The resource coordination operator integrates channel state parameters and computational resource load parameters through weighted summation operations. Channel state parameters include channel utilization and bit error rate, while computational resource load parameters are represented by CPU utilization and task queue length. The algorithm traverses all time scales to solve for the maximum value of the resource allocation objective function, determines the optimal resource allocation scheme, clarifies the transmission path, priority, and resource occupancy ratio of the encryption matrix, and achieves dynamic balanced scheduling of heterogeneous resources across multiple time scales, improving resource utilization and transmission efficiency.

[0038] Preferably, the transmission adaptation model expression of the smart grid homomorphic signature data transmission platform is: ,in, For transmission adaptation matrix, For the first A matrix of protocol frame structure parameters, For transmission adaptation functions, For platform parameter set, For bandwidth parameters, For frame synchronization parameters, For transmission delay parameters, This is a matrix addition operation. For link monitoring operators, These are link state parameters. This is the bit error rate parameter.

[0039] Specifically, the transmission adaptation model of the smart grid homomorphic signature encryption data transmission platform achieves precise adaptation between the signature encryption matrix and transmission resources, ensuring the stability of data transmission. During implementation, the protocol frame structure parameter matrix corresponds to the structural configuration of each protocol frame, including information such as frame header length, data field position, and check field type. The matrix dimension matches the number of protocol frames and fields. The transmission adaptation function adjusts transmission parameters based on the size of the signature encryption matrix, transmission priority, and resource status. The bandwidth parameter in the platform parameter set ranges from 10Mbps to 10Gbps. Frame synchronization parameters include synchronization signal frequency and synchronization code length. The synchronization signal frequency is 1kHz-10kHz, and the synchronization code length is 16-64 bits. The transmission delay parameter sets a maximum allowable delay threshold of 1ms-100ms. The link monitoring operator collects link status parameters and bit error rate parameters in real time. Link status parameters include link connection status and signal strength, while the bit error rate parameter ranges from 10... -6 -10 -2 The model first integrates all protocol frame structure parameter matrices through tensor product operations. Then, it combines the signature encryption matrix with the platform parameter set to execute a transmission adaptation function, obtaining a basic transmission adaptation matrix. Finally, it superimposes the results of the link monitoring operator to dynamically correct the transmission adaptation matrix, generating the final transmission adaptation matrix. This matrix includes specific transmission parameters such as transmission rate, frame transmission interval, and verification method, ensuring stable transmission of the signature encryption matrix under different link states and resource conditions, reducing transmission error rate and latency.

[0040] Preferably, the frame structure reconstruction model expression for the power grid communication protocol encoding and decoding is: ,in, The restored frame structure data, This is a homomorphic decryption function. For frame parsing functions, For the set of field parameters, Identify parameters for data fields. For field length parameters, For field offset parameters, To receive the adaptation operator, For receiver channel parameters, These are synchronization parameters.

[0041] Specifically, the frame structure restoration model for power grid communication protocol encoding and decoding achieves accurate restoration of the original protocol frame structure by decrypting the signature encryption matrix at the receiving end. During implementation, the decryption function uses the inverse operation corresponding to the encryption function, decrypting the signature encryption matrix based on the private key parameter. The private key parameter is consistent with that of the sending end, ensuring the validity of the decryption. The frame parsing function extracts key information such as data field identifiers, lengths, and offsets based on the structural characteristics of the power semantic short packet matrix. The data field identifier parameter in the field parameter set is a unique identifier with a length of 8-32 bits. The field length parameter ranges from 8-1024 bits, and the field offset parameter is determined according to the frame structure, ranging from 0-4096 bits. The receiver adaptation operator adjusts the parsing strategy by combining the receiver channel parameters and synchronization parameters. The receiver channel parameters include receiving bandwidth and signal attenuation, while the synchronization parameters include the synchronization signal detection threshold and synchronization timeout. The synchronization signal detection threshold is set to 30%-50% of the signal peak value, and the synchronization timeout is 1ms-10ms. The model first reconstructs the signature matrix using a decryption function, then extracts key protocol parameters using a frame parsing function, and finally adapts the receiver's channel and synchronization state using a receive adaptation operator. Tensor product operations are then used to integrate the results of each step, generating the reconstructed frame structure data. This data fully preserves the original power grid communication protocol's frame header, data payload, checksum field, and frame trailer structures. The field order and values ​​precisely match the original data from the sending end, achieving a closed loop in the encoding and decoding process.

[0042] Preferably, step S3 includes the following sub-steps: S31, calling the semantic feature extraction module of the integrated sensing power semantic short packet communication model, performing semantic correlation analysis on each element in the three-dimensional original matrix, and extracting semantic identification information including voltage, current, and power data features through mapping and matching of matrix elements with the power data semantic library; S32, based on the correlation between communication sensing parameters and semantic identification information, performing dimensional compression processing on the row and column vectors of the three-dimensional original matrix, retaining the core feature dimensions adapted to short packet transmission, and eliminating redundant dimension information; S33, according to the integrated sensing short packet encapsulation rules, rearranging the compressed matrix elements according to the transmission timing requirements of the power grid communication protocol to generate fixed-length short packet data blocks, each short packet data block including a semantic verification identifier and a sensing adaptation field; S34, combining multiple short packet data blocks into a short packet matrix through a short packet fusion algorithm, where the row dimension of the short packet matrix corresponds to the short packet number, and the column dimension corresponds to the data fields within the short packet, performing fusion encapsulation of semantic features and communication sensing features.

[0043] Specifically, step S3 performs the entire process of semantic extraction and short packet encapsulation for the integrated power semantic short packet communication model, with each sub-step progressing progressively to ensure the data fusion effect. After starting the model semantic feature extraction module in S31, a semantic library containing 1000-5000 power professional semantic rules is loaded. For each element of the (8-32)×(16-64) scale in the three-dimensional original matrix, core semantic identifier information such as voltage level, current change trend, and power balance status is accurately extracted through parameter value and semantic rule generation matching operation, ensuring the correlation between semantic features and power data. Based on the weight correlation between channel bandwidth, interference intensity and semantic identifier information in the communication sensing parameters, principal component analysis is used to perform dimensional compression on the three-dimensional original matrix, retaining core feature dimensions with a variance contribution rate higher than 85%, compressing the matrix size to (4-16)×(8-32), and efficiently eliminating duplicate and irrelevant redundant dimensions. S33 strictly adheres to the integrated short packet encapsulation rules for communication and sensing. It reorders the compressed matrix elements according to the power grid communication protocol's transmission timing requirements, generating fixed-length short packet data blocks containing 256-1024 elements. Each data block embeds a 16-bit semantic verification identifier and an 8-bit perception adaptation field, used for semantic integrity verification and channel scenario adaptation, respectively. S34 combines 1-64 short packet data blocks into a short packet matrix using a short packet fusion algorithm. The matrix's row dimensions correspond to the short packet number, and the column dimensions correspond to the data fields within the short packet, achieving deep fusion and encapsulation of semantic features and communication perception features, providing structured adaptation data for subsequent signature encryption operations.

[0044] Preferably, step S4 includes the following sub-steps: S41, based on the private key parameters of the multi-dimensional matrix homomorphic signature encryption and decoding model, perform a hash operation on each element of the short packet matrix to generate element-level hash values ​​and construct a hash matrix; S42, use the private key to perform signature encryption processing on the hash matrix, embedding the signature information into the element bits of the short packet matrix through a homomorphic mapping algorithm to form a signature encryption matrix; S43, call the homomorphic encryption algorithm to perform encryption transformation on the signature encryption matrix, mapping the matrix elements to a specified finite cyclic group to generate an encryption matrix, the encryption process maintaining the dimensional structure and element correlation of the matrix; S44, add dimension identifiers and signature verification identifiers to the encryption matrix, the dimension identifiers are used to label the signature encryption dimension, semantic parsing dimension and transmission adaptation dimension information of the matrix, and the signature verification identifiers are used for signature validity verification at the receiving end.

[0045] Specifically, step S4 involves the step-by-step implementation of encryption and signature binding using a multi-dimensional matrix homomorphic signature encoding / decoding model to ensure data transmission security and operational feasibility. S41 loads the model's preset security configuration parameters, where the private key length is set to 256-1024 bits, and the hash function is selected from the SHA-256 to SHA-512 series. A hash operation is performed on each element in the short packet matrix to generate a fixed-length element-level hash value. A one-to-one corresponding hash matrix is ​​constructed according to the dimensions of the short packet matrix, ensuring the uniqueness and relevance of each element's hash value. S42 uses the preset private key to perform signature processing on the hash matrix. The private key information is embedded into the hash matrix elements using a digital signature algorithm. The embedding process uses bitwise operations to achieve the fusion and storage of signature information and hash values, forming a signature matrix that includes signature verification information, ensuring the binding relationship between signature information and data. S43 invokes a homomorphic encryption algorithm to perform an encryption transformation on the signature matrix, mapping matrix elements to a predefined finite cyclic group. The encryption process strictly follows the homomorphic mapping rules, maintaining the homomorphic properties of matrix addition and multiplication operations, ensuring that the encrypted matrix can perform specified operations without decryption, while maintaining the original dimensional structure and element association of the matrix. S44 adds dual identification information to the encrypted matrix. The dimension identifier clarifies the specific parameter configurations of the signature dimension, semantic parsing dimension, and transmission adaptation dimension of the matrix. The signature verification identifier includes the core verification information of the private key signature. The entire process uses a matrix parallel processing mechanism to complete encryption and signature synchronously, controlling the processing efficiency to 10-100ms / matrix, achieving a balance between security and efficiency.

[0046] Preferably, step S5 includes the following sub-steps: S51, using the resource awareness module of the multi-timescale heterogeneous resource collaborative optimization algorithm, collecting parameters such as channel resource bandwidth, computing resource processing capability, and storage resource capacity at different time scales, and establishing a resource state matrix; S52, based on the scale of the resource state matrix and the encryption matrix, and the transmission priority requirements, constructing a resource allocation objective function, which is guided by maximizing resource utilization and transmission efficiency; S53, using an optimization algorithm to solve the objective function, obtaining the number of fragments of the encryption matrix, the transmission path of each fragment, and the transmission timing arrangement, while maintaining the logical correlation and integrity of the matrix elements during the fragmentation process; S54, performing fragmentation processing on the encryption matrix according to the solution results, allocating corresponding transmission resources and storage resources to each fragment, and recording the fragment identifier and resource allocation information for fragment reassembly at the receiving end.

[0047] Specifically, step S5 is implemented in stages based on a multi-timescale heterogeneous resource collaborative optimization algorithm to achieve dynamic resource adaptation and efficient scheduling. S51 activates the algorithm's resource awareness module, dividing the timescale into three phases: 1-10ms (short-term), 10-100ms (medium-term), and 100-1000ms (long-term). It collects heterogeneous resource parameters in real time at each scale, including channel resources such as bandwidth utilization (0%-100%) and bit error rate (10...). -6 -10 -2 ), transmission latency (1-100ms), computing resources including CPU load (0%-100%), computing throughput (10 6 -10 9 (times / second), storage resources include storage utilization (0%-100%), read / write speed (10... 6 -10 9 (Bytes / second), a resource status matrix is ​​constructed based on the collected data. S52, considering the requirements of the encryption matrix size (1024-16384 elements) and transmission priority (levels 1-5), constructs an objective function oriented towards maximizing resource utilization and minimizing transmission latency, clarifying the weight allocation rules for each resource parameter. S53 uses a gradient descent optimization algorithm to solve the objective function, with the number of iterations set to 100-1000 and a convergence threshold of 10. -4 The solution process yields the number of fragments in the encryption matrix (2-32 fragments), the transmission paths for each fragment (1-8 selectable paths), and the transmission timing arrangement. The fragmentation process strictly maintains the logical correlation and semantic integrity of the matrix elements. S54 performs fragmentation processing on the encryption matrix based on the solution results, allocating corresponding channels, computational and storage resources to each fragment. Simultaneously, it records the fragment identifier, resource allocation parameters, and transmission timing information, generating a resource allocation table. This ensures that the receiving end can complete fragment reassembly and resource verification based on this table, achieving dynamic balanced scheduling of heterogeneous resources across multiple time scales.

[0048] The integrated power semantic short packet communication model is a specialized processing model that integrates power data semantic parsing and communication perception adaptation functions. It is designed specifically for power grid communication protocol encoding and decoding scenarios, and its core is to achieve deep fusion of semantic features and communication perception information. Its implementation process is based on a three-dimensional original matrix. First, it loads a built-in library of 1,000-5,000 power professional semantic rules. Then, it performs semantic correlation analysis on each element of the (8-32)×(16-64) scale in the matrix to extract core semantic identifiers such as voltage level and current change trend. At the same time, it collects perception parameters such as channel bandwidth and interference intensity and establishes correlation mapping. Then, it retains the core dimensions with a variance contribution rate higher than 85% through principal component analysis, compresses the matrix to the (4-16)×(8-32) scale, and removes redundant information. Subsequently, it generates short packet data blocks containing 256-1024 elements according to the transmission time sequence. Each data block embeds a 16-bit semantic verification identifier and an 8-bit perception adaptation field. Finally, it merges 1-64 data blocks into a short packet matrix, with the row dimension corresponding to the short packet number and the column dimension corresponding to the data field. This model provides a structured and highly adaptable data carrier for subsequent signature encryption, solves the problem of semantic and perceptual information separation in traditional encoding and decoding, breaks through the bottleneck of semantic integrity and channel adaptability in power data transmission, ensures that semantics are not lost and redundancy is not accumulated in short packet transmission, and lays a solid foundation for the efficient transmission of massive heterogeneous data in smart grids.

[0049] The multi-dimensional matrix homomorphic signature encryption and decoding model is a core security model for integrating encryption and signature encryption of power grid communication data. It achieves deep binding of encryption and signature encryption through matrix operations, balancing security and computational feasibility. Its implementation revolves around a short packet matrix. First, it loads a 256-1024 bit private key and a series of hash functions from SHA-256 to SHA-512, performing hash operations on each element of the short packet matrix to generate element-level hash values ​​and construct a corresponding hash matrix. Then, it embeds the private key information into the hash matrix elements using a digital signature algorithm, forming a signature encryption matrix. Next, it calls a homomorphic encryption algorithm to map the signature encryption matrix to a preset finite cyclic group. The encryption process maintains the homomorphic properties of matrix addition and multiplication operations, preserving the original dimensional structure. Finally, it adds dual identifiers to the encryption matrix: a dimension identifier clearly defines three types of dimensional parameters—signature encryption, semantic parsing, and transmission adaptation; and a signature verification identifier includes core verification information for the private key signature. The entire process achieves high efficiency of 10-100ms per matrix through parallel processing. This model ensures that data transmission is tamper-proof and forgery-proof, while supporting specified operations in encrypted mode, achieving "encryption does not affect operation and operation does not leak data". It solves the security risks and inefficiencies caused by the separation of encryption and signature in traditional methods, provides high-strength security for sensitive data transmission in smart grids, and meets the encrypted data operation needs in edge computing scenarios.

[0050] The multi-timescale heterogeneous resource collaborative optimization algorithm is a core resource scheduling algorithm adapted to encrypted matrix transmission, achieving dynamic balanced allocation of heterogeneous resources at different time scales. Its essence is a scheduling mechanism based on multi-timescale resource state awareness and optimization solution. Specifically, it first divides the time scale into three segments: 1-10ms (short-term), 10-100ms (medium-term), and 100-1000ms (long-term). A resource awareness module collects three types of resource parameters in real time at each scale: channel, computation, and storage. Channel resources include bandwidth utilization (0%-100%), bit error rate (BER)... -6 -10 -2 Computing resources include CPU load (0%-100%), computing throughput (10), etc. 6 -10 9 Storage resources include storage utilization (0%-100%), read / write speed (10 times / second), etc. 6 -10 9 We construct a resource state matrix based on metrics such as bytes per second (Bytes / second); then, we construct an objective function based on the size of the encryption matrix (1024-16384 elements) and transmission priority (levels 1-5), aiming to maximize resource utilization and minimize transmission latency; subsequently, we use a gradient descent algorithm to iterate 100-1000 times to solve the problem, with a convergence threshold set at 10. -4 The algorithm obtains the number of fragments (2-32 fragments), transmission paths (1-8 selectable paths), and timing arrangements. Finally, it fragments and allocates resources according to the solution results, recording relevant identification information. This algorithm dynamically adapts to resource status and transmission requirements, optimizes resource allocation schemes, breaks through the limitations of rigid traditional resource scheduling, improves the utilization rate and transmission efficiency of heterogeneous resources, and alleviates resource conflicts and delays during peak power grid communication periods.

[0051] The smart grid homomorphic signature encryption data transmission platform is an integrated hardware and software system supporting the entire encoding and decoding process. As the core hub for the collaborative operation of various modules, it integrates six functional units: parameter acquisition, short packet generation, signature encryption and decoding, resource scheduling, transmission adaptation, and data restoration. This is achieved through orderly connections and data flow between units: the multi-dimensional acquisition unit of power grid communication protocol parameters acquires frame structure, data field identifiers, and other parameters at a frequency of 10ms / time, and transmits them to the integrated power semantic short packet generation unit; the short packet matrix output by the short packet generation unit is sent to the multi-dimensional matrix homomorphic signature encryption and decoding unit for encryption and signature encryption; the encrypted matrix is ​​transmitted to the multi-timescale heterogeneous resource collaborative scheduling unit, which generates an allocation scheme based on resource status; the encrypted data transmission adaptation unit adjusts the transmission parameters according to the scheme and sends the data to the original data frame structure restoration unit; the restoration unit and the signature encryption and decoding unit interact bidirectionally to obtain verification information and complete the data restoration. The platform's transmission adaptation model dynamically corrects transmission parameters by integrating protocol frame structure parameter matrices, transmission adaptation functions, and link monitoring operators. It supports bandwidth ranging from 10Mbps to 10Gbps and synchronization signal frequencies from 1kHz to 10kHz. This platform provides hardware support and data flow channels for the entire encoding and decoding process, enabling collaborative operation of various models. It breaks down the barriers of fragmented operation among functional modules, constructing an end-to-end integrated transmission architecture. This provides a reliable platform for the implementation of the entire encoding and decoding method and adapts to the communication needs of multiple devices and scenarios in the smart grid.

[0052] like Figure 2As shown, a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices is implemented through different units, including: a multi-dimensional acquisition unit for power grid communication protocol parameters, a sensor-integrated power semantic short packet generation unit, a multi-dimensional matrix homomorphic signature encryption and decoding unit, a multi-timescale heterogeneous resource collaborative scheduling unit, an encrypted data transmission adaptation unit, and an original data frame structure restoration unit. The output of the multi-dimensional acquisition unit for power grid communication protocol parameters is connected to the input of the sensor-integrated power semantic short packet generation unit to transmit the acquired protocol parameters to the short packet generation unit. The output of the sensor-integrated power semantic short packet generation unit is connected to the input of the multi-dimensional matrix homomorphic signature encryption and decoding unit for... The short packet matrix is ​​transmitted to the signature decoding unit; the output of the multi-dimensional matrix homomorphic signature encoding and decoding unit is connected to the input of the multi-timescale heterogeneous resource collaborative scheduling unit, transmitting the encrypted matrix to the resource scheduling unit; the output of the multi-timescale heterogeneous resource collaborative scheduling unit is connected to the input of the encrypted data transmission adaptation unit, providing a resource allocation scheme for the transmission adaptation unit; the output of the encrypted data transmission adaptation unit is connected to the input of the original data frame structure restoration unit, transmitting the adapted encrypted data to the restoration unit; the original data frame structure restoration unit, through a bidirectional connection with the multi-dimensional matrix homomorphic signature encoding and decoding unit, obtains the signature verification information required for decryption, completing the restoration output of the original data.

[0053] This paper presents a power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices. Using a smart grid homomorphic signature encryption data transmission platform as its core, it integrates a sensing-integrated power semantic short-packet communication model, a multi-dimensional matrix homomorphic signature encryption encoding and decoding model, and a multi-timescale heterogeneous resource collaborative optimization algorithm. This constructs a closed-loop process from parameter acquisition to data reconstruction, achieving deep collaboration in protocol parameter processing, semantic encapsulation, signature encryption, and resource scheduling. The method utilizes a three-dimensional matrix to achieve structured integration of multi-dimensional data, short-packet encapsulation technology to enhance the fusion of semantic features and communication perception information, a homomorphic signature encryption mechanism to ensure the binding of encryption and signature encryption, and a resource collaborative optimization algorithm to dynamically adapt to heterogeneous resource states. The seamless integration of these technical modules significantly improves the adaptability, security, and efficiency of the encoding and decoding process.

[0054] This method addresses the issues of insufficient semantic feature integration, redundancy in short packet transmission, or information loss in traditional methods. It employs a sensor-integrated power semantic short packet communication model to deeply extract and precisely encapsulate semantic features from the three-dimensional original matrix. By combining multi-dimensional matrix construction, key information is retained and redundant data is eliminated, achieving efficient fusion of semantic features and communication perception information. Furthermore, it addresses the security risks and transmission delays caused by the separation of encoding / decoding and encryption processes and rigid resource allocation in traditional methods. A multi-dimensional matrix homomorphic signature encryption / decoding model is used to bind encryption and signature operations together. Homomorphic mapping operations of matrix elements ensure data transmission security. Simultaneously, a multi-timescale heterogeneous resource collaborative optimization algorithm is used to perceive resource status in real time and dynamically adjust transmission priorities and fragmentation strategies, achieving precise resource scheduling and efficient utilization. This comprehensively solves the technical pain point of the imbalance between security and efficiency.

[0055] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices, characterized in that, Includes the following steps: S1, collect frame structure parameters, data field identifier parameters, transmission timing parameters and channel transmission characteristic parameters of the power grid communication protocol through the smart grid homomorphic signature data transmission platform, and establish an original data feature set adapted to the integrated power semantic short packet communication model; S2, based on the dimension configuration rules of the multi-dimensional matrix homomorphic signature encryption and decoding model, maps the original data feature set into row vectors and column vectors of a multi-dimensional matrix according to the communication protocol field type, and constructs a three-dimensional original matrix including signature encryption dimension, semantic parsing dimension, and transmission adaptation dimension. S3, invoke the integrated sensing power semantic short packet communication model to extract semantic features and encapsulate short packets into the three-dimensional original matrix, and generate a short packet matrix that integrates communication sensing information and power data semantics; S4 employs a multi-dimensional matrix homomorphic signature encryption and decoding model to perform homomorphic encryption transformation and signature verification operations on the short packet matrix, and performs signature binding of encrypted data through homomorphic mapping operations of matrix elements; S5 adapts the transmission resources of the encrypted matrix after signing based on the multi-timescale heterogeneous resource collaborative optimization algorithm. It adjusts the matrix transmission priority and fragmentation strategy according to the channel resource status, computational resource load and storage resource capacity under different time scales. S6, the receiving end performs inverse mapping decryption and signature verification through the multi-dimensional matrix homomorphic signature encryption and decoding model, and restores the original data fields and frame structure information of the power grid communication protocol by combining the semantic parsing rules of the integrated power semantic short packet communication model, thus completing the entire encoding and decoding process; The expression for the integrated sensing power semantic short packet communication model is as follows: ， in, This is a short packet matrix for electricity semantics. The number of fields in the power grid communication protocol. For each field, the semantic feature dimension, For the first The semantic weight coefficient of each field, It is a homomorphic mapping operator. The third element in the three-dimensional original matrix Line number Column elements, For the homomorphic mapping parameter set, For the first Perceptual adaptation coefficient of each semantic feature For synesthetic fusion operators, For the first Line number Channel-aware data of column elements, For the sensing parameter set, For matrix tensor product operations, For time-scale adaptation functions, For the first Each timescale parameter For the first Channel resource parameters; The expression for the multi-dimensional matrix homomorphic signcryption encoding and decoding model is: ， in, For signature encryption matrix, It is a homomorphic encryption function. For the number of signature dimensions, The number of columns in the matrix. To generate operators for signature encryption, For private key parameters, For hash functions, For the public key parameter set, It is a finite cyclic group. For group generators, For the dimension fusion operator, Configure functions for dimensions. For signature dimension parameters, For semantic parsing dimension parameters, To adapt dimensional parameters for transmission; The expression for the multi-timescale heterogeneous resource collaborative optimization algorithm is: ， in, This is the optimal resource allocation scheme. The total number of time scales. The number of heterogeneous resource types. For the first Weight coefficients of resource classes For the first The first time scale Available capacity of the resource class For the first Size of the short package matrix at each time scale For the number of transmission tasks, For the first The first time scale The transmission load of each task For resource coordination operators, For the first Channel state parameters at various time scales For the first Computational resource load parameters at each time scale; The transmission adaptation model expression of the smart grid homomorphic signature encryption data transmission platform is as follows: ， in, For transmission adaptation matrix, For the first A matrix of protocol frame structure parameters, For transmission adaptation functions, For platform parameter set, For bandwidth parameters, For frame synchronization parameters, For transmission delay parameters, This is a matrix addition operation. For link monitoring operators, These are link state parameters. For bit error rate parameters; The frame structure reconstruction model expression for the power grid communication protocol encoding and decoding is as follows: ， in, The restored frame structure data, This is a homomorphic decryption function. For frame parsing functions, For the set of field parameters, Identify parameters for data fields. For field length parameters, For field offset parameters, To receive the adaptation operator, For receiver channel parameters, These are synchronization parameters.

2. The power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices according to claim 1, characterized in that, S3 includes the following steps: S31, call the semantic feature extraction module of the integrated power semantic short packet communication model, perform semantic correlation analysis on each element in the three-dimensional original matrix, and extract semantic identification information including voltage, current and power data features by mapping and matching matrix elements with the power data semantic library; S32, based on the correlation between communication sensing parameters and semantic identification information, performs dimensional compression processing on the row and column vectors of the original three-dimensional matrix, retaining the core feature dimensions adapted to short packet transmission and eliminating redundant dimensional information; S33, in accordance with the short packet encapsulation rules of the integrated sensing, the compressed matrix elements are rearranged according to the transmission timing requirements of the power grid communication protocol to generate short packet data blocks of fixed length. Each short packet data block includes a semantic verification identifier and a sensing adaptation field. S34. Multiple short packet data blocks are combined into a short packet matrix using a short packet fusion algorithm. The row dimension of the short packet matrix corresponds to the short packet number, and the column dimension corresponds to the data fields within the short packet. This allows for the fusion and encapsulation of semantic features and communication-aware features.

3. The power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices according to claim 1, characterized in that, S4 includes the following steps: S41, based on the private key parameters of the multi-dimensional matrix homomorphic signature encryption and decoding model, performs a hash operation on each element of the short packet matrix to generate element-level hash values ​​and construct a hash matrix; S42, the hash matrix is ​​processed using a private key, and the signature information is embedded into the element bits of the short packet matrix through a homomorphic mapping algorithm to form a signature matrix; S43, call the homomorphic encryption algorithm to perform encryption transformation on the signcrypt matrix, map the matrix elements to the specified finite cyclic group, generate the encryption matrix, and the encryption process maintains the dimensional structure and element correlation of the matrix; S44. Add dimension identifiers and signature verification identifiers to the encryption matrix. The dimension identifiers are used to mark the signature verification dimension, semantic parsing dimension and transmission adaptation dimension information of the matrix. The signature verification identifiers are used to verify the signature validity at the receiving end.

4. The power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices according to claim 1, characterized in that, S5 includes the following steps: S51, through the resource perception module of the multi-time-scale heterogeneous resource collaborative optimization algorithm, collects channel resource bandwidth, computing resource processing capability and storage resource capacity parameters at different time scales, and establishes a resource status matrix. S52, based on the size of the resource state matrix and encryption matrix and the transmission priority requirements, constructs a resource allocation objective function, which is guided by maximizing resource utilization and transmission efficiency; S53, an optimization algorithm is used to solve the objective function to obtain the number of fragments of the encryption matrix, the transmission path of each fragment and the transmission timing arrangement. The fragmentation process maintains the logical correlation and integrity of the matrix elements. S54. Based on the solution results, the encryption matrix is ​​fragmented, and corresponding transmission and storage resources are allocated to each fragment. At the same time, the fragment identifier and resource allocation information are recorded for fragment reassembly at the receiving end.

5. The power grid communication protocol encoding and decoding method based on homomorphic mapping and multi-dimensional matrices according to any one of claims 1-4, characterized in that, This method is implemented through different units, including: The system comprises a multi-dimensional power grid communication protocol parameter acquisition unit, a sensing-integrated power semantic short packet generation unit, a multi-dimensional matrix homomorphic signature decoding unit, a multi-timescale heterogeneous resource collaborative scheduling unit, an encrypted data transmission adaptation unit, and an original data frame structure restoration unit. The output of the multi-dimensional power grid communication protocol parameter acquisition unit is connected to the input of the sensing-integrated power semantic short packet generation unit, used to transmit the acquired protocol parameters to the short packet generation unit. The output of the sensing-integrated power semantic short packet generation unit is connected to the input of the multi-dimensional matrix homomorphic signature decoding unit, used to transmit the short packet matrix to the signature decoding unit. The output of the homomorphic signature encoding / decoding unit is connected to the input of the multi-timescale heterogeneous resource collaborative scheduling unit, transmitting the encryption matrix to the resource scheduling unit. The output of the multi-timescale heterogeneous resource collaborative scheduling unit is connected to the input of the encrypted data transmission adaptation unit, providing a resource allocation scheme for the transmission adaptation unit. The output of the encrypted data transmission adaptation unit is connected to the input of the original data frame structure restoration unit, transmitting the adapted encrypted data to the restoration unit. The original data frame structure restoration unit, through a bidirectional connection with the multi-dimensional matrix homomorphic signature encoding / decoding unit, obtains the signature verification information required for decryption and completes the restoration output of the original data.

Images