Quantum random encryption and distributed synchronization information transmission method and device

Abstract

The application discloses a kind of quantum random encryption and distributed synchronous information transmission method and device, applied to electric power system technical field.Initial data of power equipment state is obtained;Unpredictable quantum true random number is generated;Quantum true random number is used to encrypt initial data to obtain target data;The clock synchronization accuracy of multiple nodes is synchronized;According to target data, data slice is generated and data slice is distributed to the multiple nodes;The data slice of the multiple nodes is synchronized.The technical problem of safe and reliable electric power system information transmission is at least solved to some extent by the application.

Description

Technical Field

[0001] This invention belongs to the field of power information transmission technology, and particularly relates to a quantum random encryption and distributed synchronization information transmission method and device. Background Technology

[0002] Power information transmission technology has evolved from early point-to-point analog communication to current networked digital communication based on the IEC 61850 standard. However, this evolution has also brought new security and synchronization challenges.

[0003] First, current security measures for power information transmission suffer from the following shortcomings: 1. Limitations of traditional encryption technologies: Commonly used algorithms such as AES and RSA face issues such as key predictability risks, long update cycles, and fixed encryption strength, which will significantly reduce security, especially in the face of future quantum computing technologies. 2. Reduced network isolation effectiveness: As power systems become increasingly interconnected with external networks, traditional physical isolation strategies are difficult to implement effectively. 3. Mismatched security measures: Existing security technologies do not fully consider the unique characteristics of power systems, making them vulnerable to targeted attacks, especially in key management, which faces significant challenges.

[0004] Meanwhile, key issues regarding synchronization accuracy in multi-point power system transmission include: 1. Insufficient synchronization mechanisms: Traditional fixed-period polling mechanisms have a synchronization error of 5-15ms; GPS / BeiDou clock synchronization is susceptible to signal interference; and the IEEE 1588 precise time protocol is unstable in networks with large load fluctuations. 2. Clock drift impact: Local clocks at each node drift, especially in environments with large temperature fluctuations where the accumulated error is significant. 3. Network delay fluctuations: Load changes in power communication networks lead to large delay uncertainties, which traditional synchronization mechanisms cannot effectively compensate for. Research shows that insufficient synchronization accuracy directly affects the stability of the power system and may even lead to amplified regional power grid oscillations.

[0005] Traditional power system information architectures often employ centralized structures, facing risks of single points of failure, scalability limitations, and data consistency challenges. While blockchain technology offers a potential solution to these problems, the high latency of public blockchains makes it difficult to meet the demands of real-time power control.

[0006] Quantum random encryption and distributed synchronous information transmission technologies can simultaneously solve the aforementioned security, synchronization accuracy, and system reliability issues, providing comprehensive protection for power systems. With the accelerated construction of ultra-high-voltage power grids and the increasing intelligence of power systems, this innovative technology is urgently needed to support the safe and efficient operation of power systems. Summary of the Invention

[0007] The quantum random encryption and distributed synchronous information transmission method and apparatus provided in this invention at least partially solve the technical problem of insecure power information transmission.

[0008] In a first aspect, embodiments of the present invention provide a quantum random encryption and distributed synchronization information transmission method applied to a power system. The information transmission method includes: acquiring initial data on the status of power equipment; generating unpredictable quantum true random numbers; encrypting the initial data using the quantum true random numbers to obtain target data; synchronizing the clock synchronization accuracy of multiple nodes; generating data slices based on the target data and distributing the data slices to the multiple nodes; synchronizing the data slices of the multiple nodes, wherein the synchronization of the data slices adopts distributed ledger synchronization.

[0009] In some implementations, generating unpredictable quantum true random numbers includes: generating random electron-hole pairs using quantum vacuum fluctuations to generate a random signal; amplifying and filtering the random signal to obtain a filtered signal; and digitizing the filtered signal to generate unpredictable quantum true random numbers.

[0010] In some implementations, after generating the quantum true random number, the method further includes: monitoring the entropy estimate of the quantum true random number, and performing a health check on the quantum source when the entropy estimate is lower than a preset threshold.

[0011] In some implementations, the step of encrypting initial data with quantum true random numbers to obtain target data includes: converting quantum true random numbers into a fixed-length master random key; generating a subkey based on the master random key, wherein the subkey corresponds to the initial data; and encrypting the initial data with the subkey to obtain target data, wherein the subkey is updated according to a preset time interval and / or data size domain.

[0012] In some implementations, synchronizing the clock synchronization accuracy of multiple nodes includes: exchanging chaotic state information of multiple nodes; adjusting multiple nodes to a synchronized chaotic state based on the chaotic state information; and synchronizing the clock synchronization accuracy of multiple nodes based on the synchronized chaotic state of multiple nodes.

[0013] In some implementations, generating data slices based on target data and distributing the data slices to the plurality of nodes includes: selecting an (n,k) encoding strategy to generate data slices based on the importance of the target data; determining the node positional relationship; and distributing the data slices to the plurality of nodes based on the node positional relationship, wherein adjacent nodes store different data slices, and any node stores no more than two data slices.

[0014] In some implementations, synchronizing the data slices of the multiple nodes includes: assigning data priorities based on the data type and / or data importance of the data slices; calculating a first transmission path based on the data priorities and network status, wherein the network status includes at least one of network latency, bandwidth, and reliability factors; and synchronizing the data slices of the multiple nodes based on the first transmission path.

[0015] In some implementations, synchronizing the data slices of the multiple nodes further includes: real-time monitoring of network congestion to obtain a second transmission mode, wherein the network congestion includes at least one of latency, packet loss rate, and throughput; and synchronizing the data slices of the multiple nodes according to the second transmission mode.

[0016] In some implementations, synchronizing the data slices of the multiple nodes further includes: establishing a synchronization quality scoring system, wherein the evaluation dimensions of the synchronization quality scoring system include at least one of time synchronization accuracy, data consistency, network performance, and system load; synchronizing the data slices of the multiple nodes according to the synchronization quality scoring system, wherein when the synchronization quality is lower than a threshold, a correction operation is triggered, the correction operation including strengthening coupling strength and switching the synchronization reference node.

[0017] In some implementations, obtaining initial data on the state of the power equipment includes: obtaining detection data of the power equipment; and processing the detection data using a digital twin model of the power equipment to obtain initial data on the state of the power equipment.

[0018] In some implementations, the process of using a digital twin model of the power equipment to process detection data and obtain initial data on the state of the power equipment includes: constructing a multi-physical quantity model of the power equipment, wherein the multi-physical quantities include electrical parameters, thermal parameters, and mechanical parameters; and predicting the detection data based on the multi-physical quantity model to obtain initial data on the state of the power equipment.

[0019] Secondly, embodiments of the present invention provide an information transmission device for quantum random encryption and distributed synchronization. The information transmission device includes: a power equipment digital twin module for acquiring initial data of the power equipment; a quantum true random number generation module for generating true random numbers using quantum physical phenomena; a dynamic encryption engine connected to the quantum true random number generation module for receiving the true random numbers and using them to generate target data from the initial data; a chaotic synchronization controller for synchronizing the clock synchronization accuracy among the multiple nodes; a data slicing and recovery module connected to the dynamic encryption engine for receiving the target data and generating data slices from the target data and distributing them to the multiple nodes; and a distributed ledger management module for synchronizing the data slices of the multiple nodes.

[0020] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the steps of any of the above-described information transmission methods.

[0021] Fourthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the information transmission methods described above.

[0022] The technical solutions provided in this invention have at least the following effects or advantages: First, by combining quantum random encryption and distributed information synchronization in the information transmission method, quantum random encryption ensures the confidentiality of data during transmission and storage, while distributed synchronization ensures the consistency and integrity of data across multiple nodes. The two work together so that when an attacker obtains encrypted data from some nodes, because the data is stored in slices and each node needs to maintain synchronization, the attacker needs to simultaneously compromise multiple nodes and crack the quantum random key to obtain the complete information, thus ensuring strong security. Second, chaotic synchronization control provides sub-millisecond clock synchronization; the dynamic encryption engine triggers key updates based on time; the two work together, and precise time synchronization ensures that all nodes can update keys synchronously, avoiding key mismatch problems caused by clock asynchrony, and enhancing time-series security collaboration. Third, data slicing technology provides fault tolerance; the distributed ledger ensures data state consistency; the two work together so that even when some nodes fail, the system can recover data from the remaining slices and verify data integrity through the distributed ledger, enhancing data reliability.

[0023] Encryption and information synchronization methods play a crucial role in the stable and secure transmission of information. Therefore, combining quantum random encryption and distributed information synchronization can more stably and securely transmit information between different devices, thereby improving the reliability of power information transmission.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the structure of an electronic device for information transmission according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the first process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the second process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the third process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of the fourth process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the fifth step of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the sixth process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0033] Figure 8 This is a schematic diagram of the seventh process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention;

[0034] Figure 9 This is a schematic diagram of the eighth process of an information transmission method for quantum random encryption and distributed synchronization according to an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0038] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0039] The following describes exemplary applications of the information transmission apparatus and electronic device provided in the embodiments of this application. See also Figure 1 , Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 500 includes: at least one processor 510, at least one network interface 520, and a memory 530. The various components are coupled together via a bus system 540. It is understood that the bus system 540 is used to implement communication between these components. In addition to a data bus, the bus system 540 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 1All buses are labeled as bus system 540. Processor 510 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. Memory 530 includes volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), and volatile memory can be random access memory (RAM). The memory 530 described in this application embodiment is intended to include any suitable type of memory. Operating system 531 includes system programs for handling various basic system services and performing hardware-related tasks, such as framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks; network communication module 532 is used to reach other computing devices via one or more (wired or wireless) network interfaces 520, exemplary network interfaces 520 include: Bluetooth, WiFi, and Universal Serial Bus (USB), etc.

[0040] In some embodiments, the apparatus provided in this application can be implemented in software. Figure 1 An information transmission module 533 stored in memory 530 is shown. This module can be software in the form of programs and plug-ins, and includes the following software modules: a device status digital twin module 5330, a quantum true random number generation module 5331, a dynamic encryption engine 5332, a data slicing and recovery module 5333, a distributed ledger management module 5334, a consensus algorithm processor 5335, and a chaotic synchronization controller 5336. These modules are logically connected and can therefore be arbitrarily combined or further divided according to their implemented functions. The functions of each module will be described below.

[0041] The system is implemented using an industrial-grade hardware platform, with a main processor being an ARM Cortex-A72 processor (six cores, 1.8GHz), equipped with 8GB of LPDDR4 memory and 128GB of industrial-grade SSD storage. Network interfaces include three Gigabit Ethernet ports (supporting PTP synchronization) and one SFP+ fiber optic port (10Gbps), supporting IEC 61850-9-2 sampling values ​​and GOOSE messages. The system also integrates a Xilinx Zynq UltraScale+ MPSoC FPGA for hardware acceleration of encryption algorithms and chaotic synchronization control.

[0042] The system software is implemented based on a real-time Linux operating system (Xenomai kernel), ensuring deterministic latency for critical processing flows.

[0043] The following is an example of information transmission.

[0044] refer to Figure 2 As shown, this embodiment of the invention provides a quantum random encryption and distributed synchronization information transmission method, which can be used for information transmission between power systems and other systems. The information transmission method provided by this embodiment of the invention includes the following steps 101-106.

[0045] Step 101: Obtain initial data on the status of power equipment.

[0046] It is understandable that power equipment can be any equipment in the power system, including power generation, transmission, transformation, distribution, and consumption, as well as the communication network itself, such as RTU (Remote Terminal Unit), IED (Intelligent Terminal), switch, smart meter, protocol converter, etc.

[0047] The initial data on the status of power equipment can be categorized by purpose into various control commands, protection trip instructions, critical equipment status and alarm information, general measurement data and equipment parameters, historical data and statistical information, etc.

[0048] The data acquisition equipment for obtaining initial data on the status of power equipment can be a combination of the following:

[0049] Optical sensor devices:

[0050] Optical current transformers (OCTs) and optical voltage transformers (OVTs) operate based on the Faraday magneto-optical effect and the Paulck electro-optical effect. OCTs utilize the property that the rotation angle of polarized light in a magnetic field is proportional to the current, obtaining current information by measuring changes in the polarization angle. OVTs are based on the principle of refractive index change of an electro-optic crystal under an electric field, measuring voltage by detecting changes in transmitted light intensity. These devices convert the electrical parameters of the power system into optical signals, which are then converted into digital signals by a photoelectric conversion module, achieving electrical isolation and high-precision measurement. The measurement accuracy can reach 0.2%, and the dynamic range exceeds 120 dB.

[0051] Distributed fiber optic sensing system: Employing backscattering Raman scattering (BOTDR) and Brillouin scattering (BOTDA) techniques, this system achieves distributed temperature and strain measurements along a single-mode fiber. Pulsed laser light is injected into the fiber, and temperature and strain information at various points along the fiber is obtained by analyzing the frequency shift and intensity changes of the scattered light. Spatial resolution can reach 1 meter, temperature measurement accuracy is ±1℃, strain measurement accuracy is ±20με, and monitoring distance can reach 50 kilometers.

[0052] Fiber Bragg grating (FBG) sensor: Based on the wavelength modulation principle of fiber Bragg gratings, the central reflected wavelength of the grating shifts as external temperature and strain change. High-precision spectrometers detect these wavelength changes, enabling accurate measurement of temperature (resolution 0.1℃) and strain (resolution 1με).

[0053] Electronic intelligent data acquisition device:

[0054] IEC 61850 Standard Intelligent Electronic Device (IED): Integrates a high-performance ARM processor and a multi-channel synchronous sampling ADC, supporting IEC 61850-9-2 sampled value transmission and GOOSE message processing. The device is equipped with various interface modules, including 16 differential analog inputs (0.1% accuracy, 10kHz sampling rate), 32 digital inputs / outputs, and a Gigabit Ethernet communication interface. It can simultaneously acquire electrical parameters such as voltage, current, power, frequency, and harmonics, as well as switch status and protection action information.

[0055] Remote Terminal Unit (RTU): Employs a 24-bit Σ-Δ ADC for high-precision analog signal acquisition, supporting multiple signal types including 4-20mA, 0-5V, and PT100 resistance temperature detectors (RTDs). Digital inputs support 24V / 220V DC and 110V / 220V AC signals and feature opto-isolation. Communication interfaces include RS485, Ethernet, and fiber optic communication, supporting multiple protocols such as IEC 104, Modbus, and DNP3.0.

[0056] Specialized physical quantity sensing equipment:

[0057] Vibration sensor array: Employing MEMS accelerometers and piezoelectric vibration sensors, the measurement frequency range is 0.1Hz-10kHz, sensitivity is 100mV / g, and dynamic range is ±50g. Multi-point arrangement enables equipment vibration modal analysis and fault diagnosis.

[0058] Infrared thermal imaging temperature measurement equipment: Employs an uncooled focal plane detector, with a temperature resolution of 0.02℃, a temperature measurement range of -20℃ to +2000℃, and a spatial resolution of 1.38mrad. It enables non-contact detection of hot spots and measurement of temperature field distribution in electrical equipment.

[0059] Partial discharge detection device: integrates ultra-high frequency (UHF) sensor, ultrasonic sensor and high frequency current sensor, with a detection frequency band of 30MHz-1.5GHz and a sensitivity of -110dBm, capable of detecting partial discharge signals inside equipment such as transformers and switch cabinets.

[0060] Specific implementation methods for multi-source signal acquisition:

[0061] Electrical parameter signal acquisition link:

[0062] Electrical parameters are acquired using a fully optical measurement link: on the primary side, current and voltage signals are converted into modulated optical signals via OCT / OVT. The optical signals are transmitted to the photoelectric conversion unit on the secondary side via optical fiber. The conversion unit has a built-in photodiode array and transimpedance amplifier to convert the optical signals into electrical signals. The signals are then digitized by a 16-bit ADC (sampling rate 50kHz) and finally output in a standardized data format according to the IEC 61850 protocol.

[0063] Distributed acquisition method for temperature parameters:

[0064] Temperature monitoring employs distributed fiber optic sensing technology: dedicated temperature-sensing optical fibers are laid in key areas such as transformer windings, cable joints, and inside switchgear. The optical fibers are coated with polyimide and have a temperature resistance rating of 200℃. The sensing system emits a pulse of light every 5 seconds and uses time-domain analysis technology to locate temperature anomalies, achieving a spatial positioning accuracy of ±1 meter and a temperature measurement repeatability of ±0.5℃.

[0065] Multi-parameter monitoring of mechanical condition:

[0066] Mechanical condition monitoring is achieved through the fusion of multiple sensors: a triaxial vibration sensor is placed on the surface of the transformer tank with a sampling frequency of 25.6kHz to acquire vibration signals in the 0.1Hz-10kHz frequency band; FBG strain sensors are installed in key structural parts to monitor the mechanical stress distribution of the equipment; and acoustic emission sensors (frequency response range 20kHz-1MHz) are used to detect mechanical anomalies inside the equipment.

[0067] Real-time acquisition method of switch status:

[0068] The switch status acquisition employs a redundant detection method: the main detection uses an opto-isolated digital input module to acquire the circuit breaker's auxiliary contact signals, while the auxiliary detection uses a current sensor to detect the on / off status of the main circuit. The two signals are logically evaluated to ensure the reliability of the status acquisition. The acquisition period is 10ms, and the status change response time is less than 5ms.

[0069] Multi-source data time synchronization:

[0070] Data collected by various sensors is synchronized at the microsecond level via the IEEE 1588 Precision Time Protocol (PTP), ensuring time consistency across multiple data sources. The master clock server uses GPS for time synchronization, while slave devices receive time references via Ethernet, achieving a synchronization accuracy better than 10 microseconds.

[0071] Signal preprocessing algorithm:

[0072] The acquired raw signals are preprocessed: analog signals are filtered by an anti-aliasing filter (cutoff frequency of 40% of the Nyquist frequency) to remove high-frequency interference; digital signals are processed by a combination of median filtering and Kalman filtering to remove impulse interference and random noise; wavelet transform is used to extract signal features to achieve multi-scale analysis of the signals.

[0073] State feature parameter extraction:

[0074] The equipment status features are extracted from the preprocessed signals: electrical parameters are extracted to include RMS values, harmonic content, power factor, and other features; temperature data is extracted to include average values, gradients, hot spot temperatures, and other parameters; vibration signals are extracted to include time-domain statistics (mean, variance, skewness, kurtosis) and frequency-domain features (power spectral density, characteristic frequency amplitude); feature dimensionality reduction is achieved through principal component analysis (PCA) to extract the most representative status parameters as the initial data for subsequent encrypted transmission.

[0075] The above detailed technical solutions list the specific equipment, working principles, implementation methods, and performance parameters used to acquire initial status data for multiple power equipment.

[0076] See also Figure 2 In step 102, unpredictable quantum true random numbers are generated.

[0077] Here, quantum true random number generation can be the process of obtaining an inherently random original signal based on quantum physical phenomena such as vacuum fluctuation noise, semiconductor PN junctions, photon scattering, or quantum tunneling effects. The following explanation uses vacuum fluctuation noise as an example.

[0078] In some embodiments, see Figure 3 , Figure 3 This is a second flowchart illustrating the information transmission method provided in the embodiments of this application, specifically for... Figure 2 Step 102 shown can be achieved through... Figure 3 Steps 1021 to 1023 are implemented, and will be explained in detail below.

[0079] In step 1021, random signals are generated by using quantum vacuum fluctuations to produce random electron-hole pairs.

[0080] Based on the principle of quantum vacuum fluctuation noise, a reverse-biased semiconductor PN junction is used to operate at a current of 10mA. Random signals are obtained by utilizing the random generation and recombination process of electron-hole pairs. The quantum fluctuation effect generates fluctuations on a nanosecond time scale, and these fluctuations are converted into weak voltage signals by a pre-amplifier circuit.

[0081] In step 1022, the random signal is amplified and filtered to obtain the filtered signal.

[0082] The random signal is amplified by a three-stage amplifier circuit to bring the weak quantum signal to a suitable level. Filtering is performed using a bandpass filter (1-200MHz) to remove external interference and system noise. The amplifier is designed for low noise (noise figure <1.2dB) to ensure no significant deterministic noise is introduced.

[0083] The signal amplification and filtering process employs a three-stage amplification circuit, including a low-noise preamplifier (noise figure < 1.2 dB), a bandpass filter (passband 1-200 MHz), and a secondary amplifier. This circuit amplifies the weak quantum random signal to a suitable level while preserving its random characteristics and filtering out non-random components.

[0084] In step 1023, the filtered signal is digitized to generate unpredictable quantum true random numbers.

[0085] In this process, digitization involves a high-level sampling rate ADC sampling the amplified random signal. The sampling rate can be 10GS / s, 20GS / s, 50GS / s, or 100GS / s, without any limitation. Each sampling acquires a 1MB data block, converting the analog random signal into digital form. The high sampling rate ensures that the entropy of the quantum source is fully captured, providing sufficient original randomness for subsequent processing.

[0086] Understandably, after generating quantum true random numbers, in order to extract high-quality random numbers, an entropy extraction algorithm can be applied to obtain high-quality random numbers. For example, the original digitized data is first processed by Von Neumann debiasing correction and SHA-256 hash entropy extraction to generate a random number stream with excellent statistical properties, and finally obtains a 4Mbps true random number output that meets the NIST SP 800-22 test standard.

[0087] In some embodiments, after generating quantum true random numbers, the method further includes: monitoring the entropy estimate of the quantum true random numbers; and performing a quantum source health check when the entropy estimate is lower than a preset threshold. For example, the system performs an online entropy estimation at regular intervals, such as 1 second, 2 seconds, 3 seconds, 5 seconds, etc., without limitation. When the entropy estimate is lower than a preset threshold, which may be 0.97 bits / bit or higher, a quantum source health check procedure is triggered, and if necessary, the system switches to a backup random source.

[0088] Here, quantum vacuum fluctuation noise is used to generate random signals, making the original signals unpredictable. After processing by a three-stage amplification circuit, its random characteristics are preserved while non-random components are filtered out. A high-speed ADC is used to digitize the signal, fully capturing the entropy of the quantum source and providing sufficient original randomness for subsequent processing. Von Neumann debiasing correction, SHA-256 hash entropy extraction processing, and subsequent online real-time monitoring are applied to ensure the true randomness and unpredictability of the quantum true random numbers, providing high-quality key material for subsequent encryption and fundamentally improving system security.

[0089] See also Figure 2 In step 103, the initial data is encrypted using quantum true random numbers to obtain the target data.

[0090] Here, data encryption transforms plaintext into ciphertext using an encryption key, which is obtained through the key derivation algorithm HKDF-SHA384. Of course, other algorithms can also be used; this is not a limitation.

[0091] In some embodiments, see Figure 4 , Figure 4 This is a third flowchart illustrating the information transmission method provided in the embodiments of this application, specifically for... Figure 3 Step 103 shown can be achieved through... Figure 4 Steps 1031 to 1033 are implemented, and will be explained in detail below.

[0092] Steps 1031 to 1033 are for obtaining quantum true random numbers.

[0093] In step 1031, the quantum true random number is converted into a fixed-length master random key.

[0094] The quantum true random number is a 4Mbps true random number generated in 102 steps that meets the test standard. The master random key is generated using the key derivation algorithm ChaCha20-Poly1305 and the AES (Advanced Encryption Standard) algorithm. ChaCha20 is a stream cipher, while Poly1305 is a message authentication code. ChaCha20-Poly1305 is an AEAD (Authenticated Encryption with Associated Data) algorithm that combines stream ciphers and message authentication codes, offering strong software efficiency and resistance to side-channel attacks. The AES algorithm encrypts data blocks through multiple rounds of iterative operations, using the same key for encryption and decryption. It can also be combined with appropriate encryption modes, such as GCM or CBC, specifically AES (including variations like AES-256-GCM, AES-256-CBC, and AES-128-CBC).

[0095] In step 1032, a subkey is generated based on the master random key, and the subkey corresponds to the initial data.

[0096] Here, a master random key can derive multiple subkeys to ensure the security of key materials.

[0097] Initial data can be categorized into key control commands, measurement data, and configuration information; alternatively, it can be divided according to security levels, such as security levels ranging from 0 to 3, with level 0 being the highest and level 3 being the lowest.

[0098] For example, the keys used for data at levels 0-3 are as follows:

[0099] Level 0: ChaCha20-Poly1305 algorithm, 256-bit key;

[0100] Level 1: AES-256-GCM mode, 256-bit key;

[0101] Level 2: AES-256-CBC mode, 256-bit key;

[0102] Level 3: AES-128-CBC mode, 128-bit key.

[0103] Among them, the highest security level data can also apply a dual encryption mechanism. For example, level 0 data is first encrypted with ChaCha20-Poly1305 and then encrypted a second time with AES-256-GCM. This dual protection of core control commands provides additional protection for critical data. By cascading encryption of two different algorithms (such as ChaCha20-Poly1305+AES-256), the difficulty of cracking is greatly increased, preventing security risks caused by vulnerabilities in a single algorithm.

[0104] In step 1033, the initial data is encrypted using a subkey to obtain the target data, wherein the subkey is updated according to a preset time interval and / or data volume field.

[0105] Updating subkeys according to a preset time means periodically updating subkeys according to a system-preset schedule. When setting the preset time, differentiated key management strategies can be implemented based on data sensitivity. The core principle is to formulate corresponding key update rules based on the data's security level. For example,

[0106] Level 0: ChaCha20-Poly1305 algorithm, 256-bit key, updated every 90 seconds;

[0107] Level 1: AES-256-GCM mode, 256-bit key, updated every 10 minutes;

[0108] Level 2: AES-256-CBC mode, 256-bit key, updated with each session;

[0109] Level 3: AES-128-CBC mode, 128-bit key, updated daily.

[0110] This step enables regular key updates, achieving refined security management with "high-frequency key updates for sensitive data and low-frequency key updates for low-sensitivity data," ensuring that even if a single key is cracked, its validity period is extremely limited.

[0111] The key update strategy based on data volume domain is a key management strategy that uses data size as the core trigger condition. By dividing the data volume into different ranges, the timing of key updates is dynamically controlled. When the amount of encrypted data reaches a specified threshold (e.g., 10MB), the key is updated. The specific implementation includes three stages: key rotation warning (10 seconds before expiration), smooth transition between old and new keys (both keys are valid for 10 seconds at the same time), and key destruction (securely erasing the old key).

[0112] The dual-trigger mechanism of updating the key based on a preset time interval and a data volume domain further enhances the security of data encryption.

[0113] To ensure that a single key is cracked, its validity period is not only short in terms of time, but also extremely limited in terms of the amount of data it can handle, while also taking into account the requirements of resource efficiency and compliance.

[0114] This method solves the problem of traditional fixed keys being easily analyzed. By frequently updating keys and using differentiated encryption strategies, it enhances the system's ability to resist cryptanalysis attacks.

[0115] This step ensures that the keys throughout the entire chain—from encrypted source data and hierarchical encryption strategies to key update mechanisms—are unpredictable. Even if a single key is cracked, its effectiveness is extremely limited, thus ensuring security and reliability while balancing resources and efficiency.

[0116] See also Figure 2 In step 104, the clock synchronization accuracy of multiple nodes is synchronized.

[0117] Clock synchronization accuracy is particularly crucial in power systems. Inconsistencies in data across devices can disrupt normal system operation. For example, in relay protection, time discrepancies between different devices can lead to malfunctions or failures to operate, resulting in power outages. Furthermore, during fault analysis, inconsistent recording times across substations make accurate fault location difficult, impacting fault handling efficiency. In electricity metering, time discrepancies can lead to inaccurate electricity consumption statistics, potentially causing billing disputes. Moreover, local clock drift at each node is a concern, especially in environments with large temperature fluctuations where accumulated errors are significant, severely impacting the stable operation of the power system.

[0118] A chaotic synchronization controller continuously updates the synchronization state at 10ms intervals, ensuring that the clock deviation of each node is always controlled within the sub-millisecond range. This update process is independent of the blockchain's business processes, guaranteeing the continuity and stability of time synchronization. The system dynamically adjusts the chaotic coupling strength based on network conditions and business load. During peak blockchain business periods, the coupling strength is automatically increased to improve synchronization accuracy; under poor network conditions, a more robust synchronization strategy is adopted to ensure that synchronization does not fail due to network fluctuations.

[0119] In some embodiments, see Figure 5 , Figure 5 This is the fourth step in the information transmission method provided in the embodiments of this application.

[0120] Schematic diagram, for Figure 3 Step 104 shown can be achieved through... Figure 5 Steps 1041 to 1043 are implemented, and will be explained in detail below.

[0121] In step 1041, the chaotic state information of multiple nodes is exchanged.

[0122] The system pre-maintains a chaotic system with identical structure at each node. Regarding the type of chaotic system, the system employs either a Lorenz system or a Lorenz-Rössler hybrid system. The Lorenz system exhibits typical butterfly attractor characteristics, making it suitable for basic synchronization control; while the Lorenz-Rössler hybrid system combines the advantages of both chaotic systems, offering greater adaptability to complex network environments. Regarding system parameter consistency, it is required that system parameters remain consistent across all nodes to ensure that the chaotic systems possess identical dynamic characteristics, a prerequisite for achieving precise synchronization. Regarding the synchronization update frequency, a minimum of 10MHz is specified. This high-speed update ensures that the system can track and compensate for minute synchronization deviations in real time, achieving sub-millisecond precision synchronization. These specific parameter settings provide clear implementation guidelines for chaotic synchronization control, ensuring that the system achieves the expected high-precision synchronization effect.

[0123] Simultaneously, consistent system parameters (e.g., σ=10, ρ=28, β=8 / 3) are set to establish a foundation for synchronization. The sub-step of establishing synchronization associations between nodes through an active-passive coupling mechanism realizes the coupling relationship between chaotic systems. Typically, one node acts as the active system (driver), and other nodes act as passive systems (responders), establishing synchronization connections through information exchange. Chaotic state information is periodically exchanged, and synchronization errors are calculated. Chaotic state variables (x, y, z) are transmitted between nodes, and the differences between local and remote states are compared to calculate the synchronization error vector, providing a basis for subsequent control. In one implementation, a chaotic synchronization controller is used to achieve basic clock synchronization for all nodes, achieving sub-millisecond synchronization accuracy and providing a unified time reference for the entire blockchain network. This layer adopts a master-slave structure. The master node obtains standard time via GPS or BeiDou satellite, and slave nodes maintain high-precision synchronization with the master node through a chaotic synchronization algorithm. Before each step, participating nodes must complete time calibration to ensure that the time error of all nodes does not exceed a preset threshold during the execution of the process.

[0124] In step 1042, multiple nodes are adjusted to a synchronized chaotic state based on the chaotic state information.

[0125] Based on the synchronization error and the Lyapunov function, the coupling parameters are adaptively adjusted to ensure that the system converges stably to the synchronization state and adapts to changes in network conditions.

[0126] In step 1043, the clock synchronization accuracy of multiple nodes is synchronized according to the synchronous chaotic state of multiple nodes.

[0127] By using the synchronized chaotic state to drive the local clock adjustment, the synchronized state of the chaotic system is transformed into a clock adjustment signal, which finely adjusts the local clock frequency and phase to achieve precise time synchronization among multiple nodes.

[0128] The system performs a synchronization status update every 10ms to ensure sub-millisecond synchronization accuracy. Real-world testing shows that the controller can achieve a synchronization accuracy of 50μs-500μs, which is 10-30 times higher than traditional synchronization schemes. Even in harsh environments with network latency fluctuations of ±20ms, the system can still maintain a synchronization error of <1ms, providing a reliable timing foundation for precise coordinated control of power systems.

[0129] See also Figure 2 In step 105, data slices are generated based on the target data and distributed to the multiple nodes.

[0130] Data slicing refers to the operation of dividing a large dataset into multiple subsets according to specific rules. Here, the data is encrypted, divided into multiple subsets according to certain rules, and then distributed to multiple physical nodes.

[0131] In some embodiments, see Figure 6 , Figure 6 This is a fifth flowchart illustrating the information transmission method provided in this application embodiment, specifically for... Figure 2 Step 105 shown can be achieved through... Figure 6 Steps 1051 to 1053 are implemented, and will be explained in detail below.

[0132] In step 1051, a (n,k) encoding strategy is selected to generate data slices based on the importance of the target data;

[0133] Here, the (n,k) encoding strategy refers to an important fault-tolerance mechanism in the field of data storage and transmission. The core idea is to encode k original data units into n redundant units (n>k) through mathematical algorithms, so that the system can recover the original data even if any nk units are lost.

[0134] Different types of data employ differentiated (n,k) encoding strategies, for example,

[0135] Key state data: (5,3) Reed-Solomon encoding, generating 5 slices, any 3 of which can recover the original data;

[0136] Typical configuration data: (4,2) Reed-Solomon encoding, generating 4 slices, any 2 of which can be used to recover the original data.

[0137] Critical control command: (5,3) encoding, requires at least 3 fragments to recover, distributed to 5 nodes;

[0138] State data: (4,2) encoding, requires at least 2 pieces to recover, distributed to 4 nodes;

[0139] General data: (3,2) encoding, at least 2 pieces are needed to recover, and it is distributed to 3 nodes.

[0140] This encoding strategy has better storage efficiency than the replication strategy, and by adjusting the values ​​of n and k, it can flexibly balance storage costs and fault tolerance levels.

[0141] Each slice consists of three parts: a slice header, metadata, and slice data. These three parts work together to ensure that information is identifiable and organizeable. The slice header is a fixed-length metadata block (64 bytes) appended to the data slice, containing key attribute information of the slice, similar to a file's "identity card." Metadata consists of business-defined fields that can be added as needed (such as encryption key IDs and compression algorithm identifiers), embedded within the slice, and strongly bound to the data. Slice data reflects the actual payload content.

[0142] The structure of the slice header is illustrated below as an example:

[0143]

[0144] The system constructs a 64-byte slice header for each slice, which includes information such as the magic number (0x52534543), version number, slice type, index, and timestamp, ensuring that the slice can be independently identified and verified.

[0145] The magic number is a "signature" that identifies the data format. It is used to quickly determine whether a slice is in a valid format. When the system reads a slice, it first checks the magic number. If the magic number does not match, the slice is determined to be invalid, which can indicate transmission errors or malicious tampering.

[0146] The version number identifies the version of the slice format, ensuring compatibility between new and old systems. When the system upgrades the slice protocol (e.g., by adding a field), the version number in the old slice header can trigger compatibility processing logic. The slice type is used to distinguish the business attributes of the slice (e.g., data slice, index slice, metadata slice, etc.), for example:

[0147] Type value 0x01: User data slice;

[0148] Type value 0x02: Check sheet slice (such as RS-encoded redundant block).

[0149] Indexes are used to identify the position and order of slices within the complete data, and are used for sorting during reorganization.

[0150] Timestamps are used to record the time when a slice was generated or last modified. They are used for time-series verification or filtering of expired slices, such as discarding outdated slices in real-time data streams or tracking multiple versions of data, such as distinguishing versions of database log slices by timestamps.

[0151] This slice structure protects data integrity and serves as an anti-tampering verification mechanism by using a checksum field, ensuring that any modification to the slice data will cause the checksum to fail. Illegally generated slices, due to invalid magic number, index, and other fields, will be directly discarded by the system, thus preventing forgery attacks. Timestamps and indexes can be used to trace the source of slice generation and processing flow, enabling traceability.

[0152] In step 1052, the positional relationship of the nodes is determined.

[0153] There are many ways to determine the location relationships between nodes, and we will not limit them here. For example, topology discovery methods can be used to obtain the network topology structure through routing protocols, SNMP queries, etc., and thus determine the connection relationship between nodes. Alternatively, distance vector methods can be used to estimate the distance between nodes by using metrics such as hop count, latency, and RTT (round-trip time), and then infer their location.

[0154] In step 1053, data slices are distributed to the multiple nodes according to the node position relationship, wherein adjacent nodes store different data slices, and any node stores no more than two data slices.

[0155] Data slices are distributed to the multiple nodes based on their location relationships. If two nodes are adjacent, they store different data slices. Furthermore, no single node can store more than two data slices. The system prioritizes distributing slices to substations with different geographical locations to ensure that regional faults do not lead to unrecoverable data. In specific implementation, adjacent substations store data with different slice indices, and any substation can store at most two slices with different indices.

[0156] This method significantly improves the system's fault tolerance and recovery capabilities through slicing redundancy and geographically distributed storage, ensuring data availability even in the event of multi-node failures.

[0157] See also Figure 2 In step 106, the data slices of the multiple nodes are synchronized.

[0158] In some embodiments, see Figure 7 , Figure 7 This is a sixth flowchart illustrating the information transmission method provided in this application embodiment, specifically for... Figure 2 Step 106 shown can be achieved through... Figure 7 Steps 1061 to 1063 synchronize the data slices of multiple nodes, which will be explained in detail below.

[0159] In step 1061, data priorities are assigned based on the data type and / or data importance of the data slice.

[0160] Data priority can be categorized according to data type and importance. For example, the system implements a four-level priority mechanism, with priority decreasing from P0 to P4:

[0161] P0: Control commands and protection instructions, with a maximum end-to-end delay of 3ms;

[0162] P1: Status alarms and critical measurements, with a maximum end-to-end delay of 10ms;

[0163] P2: Normal status data, maximum end-to-end delay 50ms;

[0164] P3: Historical data and statistical information will be transmitted as much as possible;

[0165] The system uses the DiffServ mechanism to implement priority marking and queue management at the network layer.

[0166] In step 1062, a first transmission path is calculated based on data priority and network status, where network status includes at least one of network latency, bandwidth, and reliability factors.

[0167] The system plans the optimal transmission method based on an improved Dijkstra's algorithm. The optimal transmission method includes at least one of the following: transmission path, transmission efficiency, and reliability. Network status includes at least one of the following factors: network latency, bandwidth, and reliability. Network latency is the primary consideration, available bandwidth is a secondary consideration, and historical reliability is an additional consideration. The most suitable transmission path is determined.

[0168] In step 1063, data slices of multiple nodes are synchronized according to the first transmission path.

[0169] In some embodiments, synchronizing the data slices of the plurality of nodes further includes: real-time monitoring of network congestion to calculate a second transmission method, wherein the network congestion includes at least one of latency, packet loss rate, and throughput;

[0170] The data slices of the multiple nodes are synchronized according to the second transmission method.

[0171] The system assesses network performance through proactive probing, sending probe packets at regular intervals, such as every 15 seconds. It also performs passive monitoring, continuously evaluating network performance by analyzing service traffic characteristics. When network congestion or latency is detected, or when packet loss rate or throughput reaches certain thresholds (e.g., packet loss rate greater than 1% or latency increase greater than 50%), the system automatically adjusts routing strategies and congestion control parameters to ensure critical services are not affected.

[0172] In some embodiments, synchronizing the data slices of the plurality of nodes further includes: establishing a synchronization quality scoring system, wherein the evaluation dimensions of the synchronization quality scoring system include at least one of time synchronization accuracy, data consistency, network performance, and system load;

[0173] The data slices of the multiple nodes are synchronized according to the synchronization quality scoring system. When the synchronization quality is lower than the threshold, a correction operation is triggered, which includes strengthening the coupling strength and switching the synchronization reference node.

[0174] By using a sub-millisecond chaotic synchronization clock as the time base for data slice generation and management, a deep integration of timing consistency and distributed data storage is achieved.

[0175] Each data slice is assigned a high-precision timestamp (nanosecond-level accuracy) based on a chaotic synchronization clock upon generation. This timestamp not only identifies the slice's generation time but, more importantly, ensures that slices distributed across different nodes maintain a strictly consistent timing relationship. Traditional data slicing technologies rely on local clocks, which are prone to timing discrepancies due to clock skew. This invention, however, uses a chaotic synchronization controller to ensure a unified time base for all nodes, fundamentally solving the timing consistency problem of distributed data slicing. When the chaotic synchronization controller detects that all nodes have reached a synchronization state, the system triggers a unified update operation for the data slices. The specific process is as follows: the chaotic synchronization controller first confirms that the clock synchronization accuracy of all participating nodes reaches a preset threshold (usually within 100 microseconds), then sends a synchronization trigger signal. Each node begins executing the generation, distribution, or update operation of the data slice at a unified time point after receiving the signal, ensuring that the timing of data slice operations remains strictly consistent even in complex network environments. During data recovery, the system not only reassembles the slices based on their index information but, more importantly, uses timestamp information to verify the timing consistency of the slices. The algorithm checks the timestamp differences of the slices involved in the reassembly. If an abnormal deviation is found (more than 10 times the synchronization accuracy threshold), the system will refuse to use the slice and obtain a replacement slice from other nodes to ensure the temporal correctness of the reassembled data.

[0176] On the other hand, the system establishes a three-layer synchronization management architecture to support the distributed operation of data slices. The global synchronization layer is responsible for maintaining the time base of the entire network, ensuring that the chaotic systems of all nodes remain synchronized; the regional synchronization layer manages geographically proximate node groups, optimizing synchronization efficiency within the local network; and the node synchronization layer handles the timing coordination of different slice operations within a single node. This hierarchical architecture not only guarantees the synchronization performance of large-scale networks but also provides flexible local optimization capabilities. The chaotic synchronization controller dynamically adjusts the coupling strength parameter according to the density of data slice operations. When the system detects a large number of slice update operations (such as the need for frequent updates of equipment status data during power system faults), it automatically strengthens the coupling strength between chaotic systems, improving the synchronization accuracy from the standard 500 microseconds to within 100 microseconds, ensuring the timing accuracy of high-frequency data operations. When the system load is low, the coupling strength is appropriately reduced to save computing resources. Each data slice's entire lifecycle, from creation to destruction, is strictly managed by a chaotic synchronization clock. When a slice is created, a precise generation timestamp is recorded. During slice transmission, the receiving node verifies whether the transmission delay is within a reasonable range. When a slice is updated, the timestamp difference between the old and new versions must meet the preset update interval. When a slice is destroyed, the system waits for all relevant nodes to complete synchronization confirmation before uniformly executing the deletion operation.

[0177] In one implementation, taking a main transformer failure at a 220kV substation in the East China Power Grid as an example, the practical application process of time synchronization and data slicing collaboration technology is explained in detail.

[0178] During the fault occurrence phase (T=0): The digital protection device in the substation detects the differential protection action of the main transformer and immediately generates fault alarm data. At this time, the chaotic synchronization controller ensures that the clock deviation between the substation and the dispatch center, as well as adjacent substations, is controlled within 50 microseconds. The fault data is marked with the highest priority (P0 level), encrypted using the ChaCha20-Poly1305 algorithm, and then encoded using (5,3) Reed-Solomon encoding to generate 5 data slices.

[0179] Slice Distribution Phase (T=1-3 milliseconds): Five data slices are sent to the local backup server, dispatch center, adjacent substation A, adjacent substation B, and regional backup center, respectively. Due to the precise timing control of the chaotic synchronization controller, all slices carry a unified timestamp (accurate to the nanosecond level), ensuring that the receiver can accurately identify the exact time of the fault. The timestamp information in the slice header is displayed as: 2024-03-15 14:32:07.123456789, and the timestamps of all five slices are completely identical.

[0180] Data verification and recovery phase (T=3-5 milliseconds): After receiving the data slices, the dispatch center first verifies the validity of the timestamps. It finds that one slice (from adjacent substation A) has a timestamp deviation exceeding the 200-microsecond threshold due to significant network latency. The system automatically selects three of the other four slices for data reassembly, successfully recovering the complete fault information. Simultaneously, the chaotic synchronization controller detects the synchronization deviation of adjacent substation A and automatically adjusts its chaotic coupling parameters, restoring its synchronization accuracy to normal levels within 500 milliseconds.

[0181] Execution Phase (T=5-10 milliseconds): Based on the recovered fault data, the dispatch center generates a load transfer instruction, which is also encrypted and sliced ​​(5,3) before being distributed to the relevant substations. Because all nodes maintain precise time synchronization, each substation can start executing the load transfer operation at a unified time point (T=10 milliseconds), avoiding secondary grid faults caused by improper operation timing.

[0182] Daily operation data synchronization: Taking the daily operation data synchronization of a power grid in a certain region as an example, this paper demonstrates the application of time synchronization and data slicing technology in routine business.

[0183] The 15 substations in the area generate approximately 500MB of operational data per second, including information on voltage, current, power, and switch status. This data is categorized into three types based on importance: critical measurement data uses (4,2) encoding, general monitoring data uses (3,2) encoding, and historical statistical data uses simple backups. A chaotic synchronization controller ensures that the data acquisition timestamps of all substations remain consistent, with time deviations controlled within 10 milliseconds.

[0184] Each substation slices its data and stores it locally, while also sending copies of the slices to other geographically dispersed nodes. For example, substation A, located in the city center, sends its critical data slices to substation B in the suburbs, substation C in a neighboring city, and the provincial dispatch backup center. The time base of the chaotic synchronization controller ensures that the timestamps of the slices received by each node remain highly consistent even under network latency fluctuations.

[0185] The system performs a network-wide data consistency check every 10 minutes. It verifies data integrity and timing correctness by comparing data slices stored at the same time point on each node. During one check, the system detected an anomaly in the timestamp of a data slice at substation D (a deviation of 5 seconds). The chaotic synchronization controller immediately recalibrated the node and restored the correct data from slice copies from other nodes.

[0186] To verify the system's reliability, fault simulation tests were conducted periodically. In the extreme case of simultaneous failure of three substations, thanks to the (4,2) encoding mechanism of the data slices and the geographically distributed storage strategy, the system was still able to recover over 98% of the critical data from the slices of the remaining nodes. The chaotic synchronization controller ensured the temporal consistency of the recovered data, with the timestamp error of the recovered data not exceeding 100 microseconds.

[0187] The system establishes a synchronization quality scoring system, whose synchronization score is composed of several weights: time synchronization accuracy, data consistency, network performance, and system load. An example is as follows:

[0188] Time synchronization accuracy (weight 40%): measures the clock deviation between nodes;

[0189] Data consistency (weight 30%): Evaluate the differences in data versions between nodes;

[0190] Network performance (weight 20%): Monitors the quality of communication between nodes;

[0191] System load (weight 10%): Tracks the utilization of processing and storage resources.

[0192] When the score is lower than the preset threshold, it can generally be determined based on the synchronization quality requirements. The higher the synchronization quality requirements, the higher the preset threshold for the score. For example, the preset threshold can be 85 points. When the score is lower than 85 points, the system triggers a self-recovery process, including strengthening the synchronization signal strength, switching to a backup link, or adjusting the data distribution strategy.

[0193] This method ensures the system maintains efficient and stable operation in complex and ever-changing network environments, intelligently adapting to changes in network conditions and providing crucial guarantees for system security and reliability. Actual deployments demonstrate that this method reduces system recovery time from minutes to seconds in the event of partial network failures, significantly improving service continuity.

[0194] See also Figure 2 In step 101, acquiring initial data on the status of the power equipment includes:

[0195] Obtain testing data from power equipment;

[0196] The initial data on the status of the power equipment are obtained by processing the detection data using a digital twin model of the power equipment.

[0197] In some embodiments, see Figure 8 , Figure 8 This is a schematic diagram of the seventh process of the information transmission method provided in the embodiments of this application.

[0198] against Figure 2 Step 101 shown can be achieved through... Figure 8 Steps 1011 to 1012 are used to acquire the detection data of the power equipment, which will be explained in detail below.

[0199] In step 1011, the detection data of the power equipment is acquired.

[0200] Various sensors on power equipment transmit a wide range of readings, including temperature, pressure, current, voltage, and vibration; information from control systems and protection devices is also available, and all of this data can be acquired through data acquisition devices.

[0201] In step 1012, the initial data of the power equipment status are obtained by processing the detection data using the digital twin model of the power equipment.

[0202] A digital twin model of equipment status constructs a virtual mirror of power equipment, mapping the physical equipment status in real time. Specific steps are detailed in [reference needed]. Figure 9 Steps 10121 and 10122.

[0203] In step 10121, a multi-physical quantity model of the power equipment is constructed, including electrical parameters, thermal parameters, and mechanical parameters.

[0204] The system constructs a multi-domain coupled model based on physical laws. The multiple physical quantities include electrical parameters, thermal parameters, and mechanical parameters. These parameters are collected by the following system:

[0205] Electrical Model: An equivalent circuit model is established based on the nodal voltage method, including resistors, inductors, capacitors, and nonlinear components;

[0206] Thermal model: A simplified thermal network method is used to divide the equipment into multiple temperature nodes to simulate heat conduction and convection processes;

[0207] Mechanical model: Simulation of structural vibration characteristics based on a mass-spring system;

[0208] The model parameters are trained using historical data and configured with expert knowledge to achieve accurate modeling of various types of power equipment.

[0209] In step 10122, initial data on the state of the power equipment are obtained based on the multi-physical quantity model prediction model.

[0210] The system employs an extended Kalman filter algorithm, fusing physical model predictions and real-time measurement data to estimate internal state variables that cannot be directly measured. The algorithm includes a prediction step (based on the physical model) and an update step (based on measurement data), adaptively adjusting the Kalman gain to balance the weights of model predictions and measurement corrections.

[0211] Here, different update frequencies can be applied to different parameters based on their physical characteristics, and the system can implement a multi-level update strategy based on the rate of change of physical quantities.

[0212] Electrical status (voltage, current): updated every 10ms;

[0213] Thermal state (temperature distribution): Updated every 100ms;

[0214] Mechanical condition (vibration characteristics): Updated every 1 second;

[0215] Lifespan parameters (aging indicators): updated every hour;

[0216] This strategy significantly reduces the computational resource requirements while ensuring model accuracy.

[0217] The system combines the deterministic evolution of the physical model with statistical analysis of historical data to achieve short-term prediction capabilities. The prediction algorithm uses a combination of recursive expansion and autoregressive moving average (ARMA) to accurately predict future trends in equipment status, providing a foundation for early fault warning.

[0218] The electrical parameter modeling adopts the equivalent circuit modeling method based on the nodal voltage method, which abstracts the complex power equipment into an equivalent circuit network composed of resistors, inductors, capacitors and nonlinear components.

[0219] Taking a 220kV power transformer as an example, a detailed electrical equivalent model is established. The model includes the following core parameters: short-circuit resistance Rs (primary side equivalent value 0.125Ω), short-circuit inductance Ls (primary side equivalent value 15.8mH), magnetizing inductance Lm (primary side value 450H), and core loss resistance Rm (primary side value 18.5kΩ). Based on the transformer nameplate parameters and field test data, the system determines the accurate values ​​of the model parameters using a parameter identification algorithm.

[0220] The electrical behavior of a transformer is described by the following set of differential equations:

[0221] Primary voltage equation:

[0222] Secondary voltage equation:

[0223] Magnetic flux linkage equation:

[0224] Wherein, Φsat(im) is a nonlinear flux linkage function considering magnetic saturation effects, which is realized through a piecewise linear approximation method: The parameters a1, a2, and a3 were obtained by least squares fitting of historical running data.

[0225] The thermal model employs a lumped-parameter thermal network method, dividing the interior of the transformer tank into multiple temperature nodes, each representing the average temperature of a specific region. The system establishes a thermal network model containing 15 key temperature nodes.

[0226] The temperature nodes are: top oil temperature node T1, upper winding temperature nodes T2 to T5, core temperature nodes T6 to T9, lower winding temperature nodes T10 to T13, bottom oil temperature node T14, and radiator temperature node T15. These nodes are connected by thermal resistance Rth and thermal capacity Cth, forming a complete heat conduction network.

[0227] The system uses a nodal method based on Fourier's law of heat conduction to establish the heat network equations:

[0228]

[0229] Where Ti is the temperature of node i, Qi is the internal heat source of node i (including copper loss and iron loss), and Rth,ij is the thermal resistance from node i to node j. The thermal resistance parameters are calculated based on the equipment geometry and material properties: conduction thermal resistance Rcond = L / (k·A), convection thermal resistance Rconv = 1 / (h·A), and radiation thermal resistance... .

[0230] Actual parameters determined: Convective heat transfer coefficient between copper windings and oil The thermal conductivity of the iron core is k=45W / (m·K), and the specific heat capacity of the transformer oil is c=2.1kJ / (kg·K). The accuracy of the model was verified by on-site thermal imaging tests and historical temperature rise test data, and the temperature prediction error was controlled within ±2°C.

[0231] The mechanical model is based on the theory of multi-degree-of-freedom vibration systems, simplifying the transformer structure into a mass-spring-damping system. A comprehensive mechanical model is established, incorporating the axial and radial vibrations of the windings and the vibration of the tank structure.

[0232] The winding structure is discretized into n point masses, each with mass mi, spring constant ki, and damping coefficient ci. The vibration equation of the winding under electromagnetic force is:

[0233]

[0234] Where [M], [C], and [K] are the mass matrix, damping matrix, and stiffness matrix, respectively, and {F(t)} is the time-varying electromagnetic force vector. The electromagnetic force calculation considers the leakage magnetic field distribution. , where Li is the inductance coefficient of the i-th turn, which is calculated through finite element analysis.

[0235] Vibration analysis of the fuel tank structure: Modal analysis was used to identify the first 10 natural frequencies and mode shapes. Vibration response at key locations was measured using accelerometers, and a vibration transfer function H(ω) = X(ω) / F(ω) was established to achieve dynamic modeling from excitation force to structural response. Typical parameters include: first natural frequency f1 = 35Hz, second natural frequency f2 = 78Hz, and structural damping ratio ξ = 0.032.

[0236] The lifespan model is based on the cumulative damage theory, comprehensively considering the impact of thermal aging, electrical aging, and mechanical aging on equipment lifespan. A modified Arrhenius equation is used to describe the thermal aging process of insulating materials:

[0237]

[0238] Where L(T) is the lifespan at temperature T, L0 is the lifespan at a reference temperature T0, and B is a material constant (B=6000K for transformer insulation paper). The system calculates the cumulative aging factor in real time to predict the remaining lifespan of the equipment.

[0239] The system fuses data from different physical domains, including electrical, thermal, and mechanical data, using a multi-sensor data fusion method based on DS evidence theory. The measurement results of each sensor serve as evidence, and the system calculates the credibility of each piece of evidence to arrive at the final fusion result. The mathematical expression of the fusion algorithm is as follows:

[0240]

[0241] K is a normalization constant, and m1 and m2 are the basic probability allocation functions for different sensors.

[0242] The system extracts characteristic parameters reflecting the equipment status from the raw data. Electrical characteristics include RMS values, harmonic distortion rate, and power factor; thermal characteristics include hot spot temperature, temperature gradient, and heat flux density; and mechanical characteristics include vibration intensity, spectral characteristics, and phase relationships. Feature extraction employs mathematical tools such as wavelet transform, Fourier transform, and principal component analysis.

[0243] The Extended Kalman Filter (EKF) algorithm is the core algorithm for state estimation in this system. It is used to fuse physical model predictions and real-time measurement data to achieve the optimal estimation of the internal state of the equipment.

[0244] The state vector of the device is defined as X = [T1, T2, ..., T15, I1, I2, I3, Φ, ω, θ]T, containing 15 temperature nodes, 3 phase currents, flux linkage, angular frequency, and phase angle, totaling 21 state variables. The state transition equation is:

[0245]

[0246] Where f[·] is the nonlinear state transition function, U(k) is the control input vector (including applied voltage, ambient temperature, etc.), and w(k) is the process noise.

[0247] The observation vector Z contains directly measurable quantities, such as partial temperature points, RMS current values, and power. The observation equation is:

[0248]

[0249] Where h[·] is the nonlinear observation function and v(k) is the measurement noise.

[0250] The specific implementation steps of the EKF algorithm are as follows:

[0251] Based on the optimal estimate and state transition model of the previous time step, predict the state estimate and error covariance of the current time step.

[0252] State prediction:

[0253] Covariance prediction:

[0254] Where F(k) is the Jacobian matrix of the state transition function: Q(k) is the process noise covariance matrix.

[0255] The state prediction is corrected using the current measurement value to obtain the optimal state estimate.

[0256] Kalman gain calculation:

[0257] Status Update:

[0258] Covariance update:

[0259] in, R(k) is the Jacobian matrix of the observation function, and R(k) is the measurement noise covariance matrix.

[0260] The system dynamically adjusts the noise covariance matrix according to the actual operating conditions. For the process noise covariance Q(k), when a large external disturbance is detected (such as a sudden change in load), the value of Q(k) is appropriately increased to improve the tolerance for model uncertainty. For the measurement noise covariance R(k), it is adjusted according to the real-time performance of the sensor. When the sensor ages or fails, the corresponding R(k) element is increased.

[0261] To prevent filter divergence, the system implements multiple suppression measures: monitoring algorithm performance through the innovation sequence and reinitializing the filter when the innovation exceeds a reasonable range; employing a decay memory filter to assign decay weights to historical data; and setting upper and lower boundaries for the covariance matrix to prevent numerical anomalies.

[0262] To address the diverse operating conditions of equipment, the system implements a multi-model adaptive Early Key Function (EKF). State transition models are established for various operating modes, including normal operation, heavy load operation, and fault operation. Automatic switching between modes is achieved through model probability calculation. The model probability update formula is:

[0263]

[0264] Where μi(k) is the probability of the i-th model at time k, and Li(k) is the model likelihood function.

[0265] This method achieves deep perception and prediction of the status of power equipment, surpassing the superficial data acquisition of traditional monitoring systems. In practical applications, the system can significantly provide early warnings of equipment anomalies, significantly improve the anomaly detection rate, significantly reduce the false alarm rate, significantly enhance preventive maintenance capabilities, and significantly reduce unplanned downtime events.

[0266] The following description further illustrates that the implementation of the information transmission module 533 provided in the embodiments of this application is an example of a software module.

[0267] In some embodiments, such as Figure 1 As shown, the software module of the information transmission module 533 stored in the memory 530 may include:

[0268] The 5330 device status digital twin module is used to acquire initial data of power equipment, build a virtual image of the power equipment, and map the status of the power equipment in real time.

[0269] The quantum true random number generation module 5331 is used to generate true random numbers using quantum physical phenomena.

[0270] The dynamic encryption engine 5332 is connected to the quantum true random number generation module and is used to receive the true random number and use the true random number as a key to encrypt the data.

[0271] The data slicing and recovery module 5333 is connected to the dynamic encryption engine and is used to receive the encrypted data and distribute it to multiple nodes;

[0272] The distributed ledger management module 5334 is used to maintain a distributed data synchronization architecture, which is used to synchronize and manage slice data processed by the data slicing and recovery module.

[0273] The consensus algorithm processor 5335 is used to provide consistency guarantees for distributed ledgers;

[0274] The chaotic synchronization controller 5336 is used to realize sub-millisecond clock synchronization among multiple nodes. The clock synchronization is used to coordinate the data distribution of the data slice and recovery module and the data synchronization process of the distributed ledger management module.

[0275] The information transmission and synchronization management module is used to coordinate the data transmission and synchronization process, so as to coordinate the work of each module of the device and achieve secure and synchronized data transmission and synchronization.

[0276] In this scheme, the quantum random number generation module is the security foundation of the entire system. It generates truly random numbers by utilizing quantum physical phenomena (such as quantum vacuum fluctuation noise, photon scattering, or quantum tunneling), rather than predictable sequences generated by traditional pseudo-random algorithms. The quantum random number generation module provides the system with a high-entropy true random source required for encryption, fundamentally enhancing encryption strength. The dynamic encryption engine is directly connected to the quantum random number generation module, receiving true random numbers and using them as encryption keys. The dynamic encryption engine can implement a dynamic key update mechanism, automatically updating the key according to a preset time or data volume threshold. It also implements a hierarchical encryption strategy based on data importance, thereby optimizing system performance while ensuring security. The data slicing and recovery module receives the encrypted data, divides it into multiple slices using information discretization techniques (such as Reed-Solomon encoding), and distributes these slices to multiple physical nodes. The data slicing and recovery module enables the system to be fault-tolerant; even if some slices are lost or damaged, the complete data can still be reconstructed from the remaining slices. The distributed ledger management module, based on optimized blockchain technology, constructs a distributed data synchronization architecture suitable for the characteristics of the power system. The distributed ledger management module ensures the consistency and integrity of data across all nodes, preventing data tampering or loss and providing a reliable foundation for data sharing. The chaotic synchronization controller is key to solving the synchronization accuracy problem of multi-point transmission in power systems. Utilizing the characteristics of chaotic systems (sensitive to initial conditions but deterministic evolution), it achieves high-precision clock synchronization among multiple nodes. The chaotic synchronization controller can control synchronization errors between nodes to sub-millisecond levels, far superior to traditional synchronization schemes. The information transmission and synchronization management module, as the coordination center of the entire system, is responsible for planning and executing data transmission strategies, monitoring synchronization status, and optimizing network resource utilization. This module organically connects all parts of the system, ensuring overall coordinated operation.

[0277] It also includes: a device status digital twin module and a consensus algorithm processor. The device status digital twin module is connected to the information transmission and synchronization management module and the distributed ledger management module. It is used to construct a virtual image of the power equipment, map the physical equipment status in real time, and provide key status information for transmission and synchronization to the dynamic encryption engine for encryption processing. The device status digital twin module creates a high-fidelity digital representation of the equipment through multi-physical quantity modeling, real-time data acquisition, and status estimation algorithms. The device status digital twin module enables the system to gain a deep understanding of the equipment status, surpassing the superficial data acquisition of traditional monitoring systems, and realizing status trend prediction and early identification of abnormal behavior. The consensus algorithm processor is connected to the distributed ledger management module and is used to provide consistency guarantees for the distributed ledger, verifying and confirming transactions entering the ledger. The consensus algorithm processor implements an improved Practical Byzantine Fault Tolerance (PBFT) algorithm. At each stage of the PBFT consensus algorithm (pre-preparation, preparation, commit, and completion), participating nodes perform time calibration before each stage. Each receiving node not only verifies the correctness of the block content but also verifies the consistency between the block timestamp and its local time. If the time deviation is too large, the node will first perform time synchronization adjustment and then re-verify the block. In the critical stage of PBFT consensus, the chaotic synchronization controller enters a high-precision mode, shortening the synchronization update cycle from 10ms to 1ms, ensuring strict consistency in the timing of each node during the consensus process. This ensures that even with some malicious or faulty nodes in the distributed system, the system can still achieve a consistent data view. The consensus algorithm processor works closely with the distributed ledger management module to form the system's distributed data consistency guarantee mechanism. The aforementioned device state digital twin module and consensus algorithm processor together enhance the system's state awareness capability and distributed data reliability, forming a complete "state awareness-data sharing-consistent decision-making" chain.

[0278] The quantum random number generation module includes a quantum source, a signal amplification link, a digitization processing unit, and a post-processing algorithm unit. The quantum source generates random signals based on quantum vacuum fluctuation noise. It utilizes quantum physics phenomena such as semiconductor PN junctions, photon scattering, or quantum tunneling. Specifically, based on the principle of quantum vacuum fluctuation noise, the quantum source employs a reverse-biased semiconductor PN junction, operates at a 10mA current, and obtains random signals through the random generation and recombination of electron-hole pairs. This method directly utilizes the quantum uncertainty principle to generate truly random signals that are unpredictable in information theory, fundamentally improving the quality of random numbers and significantly enhancing key security. The signal amplification link is connected to the quantum source and amplifies the random signal. The signal amplification link employs a three-stage amplification design, typically including a low-noise preamplifier, a bandpass filter, and a secondary amplification stage. This amplifies the weak quantum random signal to a suitable level, ensuring effective amplification while preserving its random characteristics to the maximum extent and filtering out non-random components. The digitization processing unit is connected to the signal amplification link and quantizes the amplified random signal, converting the amplified analog random signal into digital form. The digitization unit includes a high-speed ADC (analog-to-digital converter), typically with sampling rates ranging from hundreds of megahertz to several gigahertz, quantizing continuous random signals into discrete digital values. The high sampling rate ensures sufficient capture of the quantum source's entropy, providing adequate raw randomness for subsequent processing. The post-processing algorithm unit, connected to the digitization unit, performs entropy extraction and randomness testing on the quantized signal. It performs entropy extraction and statistical verification on the quantized random data and outputs high-quality true random numbers for use by the dynamic encryption engine. The post-processing algorithm unit implements Von Neumann debiasing correction and the entropy extraction algorithm recommended by NIST SP 800-90B, and performs NIST SP 800-22 statistical tests, monitoring the quality of random numbers in real time to ensure that the output random numbers meet the high-quality requirements of cryptographic applications. These components form a complete quantum random number generation process, generating random signals from a quantum source, amplifying, digitizing, and post-processing them to ultimately output high-quality true random numbers.

[0279] The dynamic encryption engine includes a key derivation framework, multi-level encryption strategy units, and a real-time key scheduling mechanism. The key derivation framework receives truly random numbers output by the quantum random number generation module and generates encryption keys from these numbers. It implements key derivation functions such as HKDF to convert the raw random number material into keys that meet the requirements of the encryption algorithm and manages the key's lifecycle. Specifically, the key derivation framework is based on the HKDF-SHA384 algorithm, obtaining 32 bytes of seed material from the quantum random number generation module to derive various keys required for encryption. The key derivation framework employs a two-stage key derivation process: first, an "extraction" stage converts the input entropy source material into a fixed-length pseudo-random key; then, an "expansion" stage generates sub-keys for specific purposes based on different application contexts. The key derivation framework supports hierarchical key management, allowing multiple sub-keys to be derived from the master key, ensuring the secure isolation of key materials. The multi-level encryption strategy unit, based on data importance and the key generated by the key derivation framework, selects different encryption algorithms and parameters according to data importance. The multi-level encryption strategy unit classifies data (e.g., critical control commands, status monitoring data, configuration information, etc.) and selects appropriate encryption algorithms (e.g., ChaCha20-Poly1305, AES-256-GCM, etc.) and parameters (e.g., key length, block mode) for different levels of data. The multi-level encryption strategy unit implements differentiated encryption based on data importance classification, with the specific configuration as follows:

[0280] Key control instructions: ChaCha20-Poly1305 algorithm, 256-bit key length, updated every 90 seconds;

[0281] Measurement data: AES-256-GCM mode, key length 256 bits, updated every 10 minutes;

[0282] Configuration information: AES-256-CBC mode, key length 256 bits, updated with each session.

[0283] The multi-level encryption strategy unit optimizes system performance while ensuring security by allocating encryption resources in a differentiated manner, and adopts protection strategies of different strengths for data of different importance.

[0284] A real-time key scheduling mechanism, connected to the key derivation framework, is used to automatically update the encryption key according to a preset time or data volume threshold. This mechanism monitors key usage and automatically triggers a key update process when the key usage time or the amount of encrypted data reaches a preset threshold, ensuring the timeliness of the key. These three components work together to form a dynamic encryption system based on quantum randomness, ensuring security while optimizing system performance through a hierarchical strategy.

[0285] The data slicing and recovery module receives encrypted data from the dynamic encryption engine and performs data slicing in the following ways:

[0286] Using the (n,k) encoding strategy: The system generates n data slices, where any k slices can reconstruct the original data, and n > k; it can be understood that this encoding mechanism (usually based on Reed-Solomon codes) provides data redundancy and fault tolerance, and can still recover complete information even if some slices are lost or damaged.

[0287] Slice structure design: Each slice consists of three parts: slice header, metadata, and slice data, which are used to ensure the self-descriptiveness and manageability of the slice.

[0288] Detailed fields in the slice header: The slice header contains information such as the magic number (used to identify the slice), version, slice type, slice index, total number of slices, required number of slices, original data length, slice data length, original data hash, and timestamp. The data recovery process employs a parallel acquisition strategy, first attempting recovery from local storage; if local data is insufficient, it acquires the required slices from neighboring nodes in parallel. Real-world testing shows that under standard network conditions (latency < 50ms), the average data recovery time does not exceed 120ms. These fields ensure that slices can be correctly identified, verified, and reassembled, even in complex network environments and storage conditions.

[0289] The slicing mechanism described above also enhances data security. Attackers need to compromise multiple nodes and obtain a sufficient number of slices to recover the original data, which greatly increases the difficulty of the attack and also improves the resilience of the system in the event of partial node failure.

[0290] The chaotic synchronization controller includes a chaotic oscillator, an adaptive synchronization algorithm unit, and a clock calibration and compensation unit. The chaotic oscillator generates a reference signal based on a deterministic chaotic system; it is typically implemented in hardware using an FPGA or application-specific integrated circuit (ASIC), generating a chaotic signal that is sensitive to initial conditions but exhibits deterministic evolution characteristics. The adaptive synchronization algorithm unit receives the reference signal generated by the chaotic oscillator and chaotic state information from other nodes, implementing an active-passive coupling synchronization mechanism and calculating the synchronization error. By calculating the difference between local and remote chaotic states, the adaptive synchronization algorithm unit dynamically adjusts the coupling strength, bringing multiple chaotic systems towards synchronization. The clock calibration and compensation unit, based on the synchronization error calculated by the adaptive synchronization algorithm unit and referencing the output of the chaotic oscillator, handles temperature drift and phase deviation, adjusting the local clock to achieve sub-millisecond clock synchronization among the multiple nodes. The clock calibration and compensation unit monitors system temperature changes and clock deviations, compensating for synchronization errors caused by these factors in real time, maintaining long-term stable high-precision synchronization. The three components work together to form a high-precision synchronization control system based on chaos theory, providing a technical foundation for millisecond-level or even sub-millisecond-level synchronization between multiple nodes.

[0291] The chaotic oscillator realizes the Lorenz chaotic system based on the following differential equation:

[0292]

[0293] in, These are system parameters, with typical values... =10, =28, = Under these parameters, the system exhibits deterministic chaotic behavior, meaning it is sensitive to initial conditions but its long-term evolution follows a specific strange attractor. This chaotic system provides a mathematical foundation for chaotic synchronization control, which is implemented on hardware platforms such as FPGAs using high-precision numerical integration methods (such as fourth-order Runge-Kutta). The integration process employs four intermediate steps (k1-k4) to calculate state updates, with error controlled within a certain range. The magnitude ensures the accuracy of the chaotic trajectory.

[0294] The adaptive synchronization algorithm unit implements an active-passive coupling (APD) synchronization mechanism, where one node acts as the active system (driver) and the other nodes act as passive systems (responders). Synchronization control employs the following equations:

[0295]

[0296] Where K is the coupling strength parameter, initially set to 0.1, and dynamically adjusted based on the synchronization error, ranging from 0.1 to 10. The system uses the sum of squared errors as the Lyapunov function, and adaptively adjusts the coupling strength through gradient descent to ensure synchronous convergence.

[0297] The clock calibration and compensation unit handles temperature drift and phase deviation, including a triple compensation mechanism:

[0298] Temperature compensation: Adjusting the clock frequency based on temperature sensor data ;

[0299] Frequency calibration: Periodically compare and calibrate the local clock frequency with the master node;

[0300] Phase locking: Using PLL technology to lock the phase relationship.

[0301] The system performs a synchronization status update every 10ms to ensure sub-millisecond synchronization accuracy. Even in harsh environments with network latency fluctuations of ±20ms, the system can still maintain a synchronization error of <1ms, providing a reliable timing basis for precise coordinated control of power systems.

[0302] In one embodiment, taking the blockchain application of a power system substation group as an example, the implementation process of duration synchronization is described in detail:

[0303] A blockchain network is composed of five substation nodes. Basic time synchronization is first established through a chaotic synchronization controller, achieving a synchronization accuracy within 100μs. When a change in the state of power equipment occurs, the relevant substation generates a state update transaction. Before transaction generation, nodes perform fast clock calibration (time <10ms) to ensure the accuracy of the transaction timestamp. Before PBFT consensus begins, all participating nodes perform precise time synchronization (target accuracy 50μs). During consensus, messages at each stage carry precise timestamps; messages exceeding a preset time window (±100ms) are automatically rejected. After a new block is generated, each node checks the reasonableness of the block's timestamp while receiving and verifying the block. If an anomaly is found, the node first performs time resynchronization and then re-verifies the block. The chaotic synchronization controller continuously updates the time synchronization status of each node at 10ms intervals to ensure time consistency during long-term operation.

[0304] The distributed ledger management module comprises a lightweight block structure design unit, a hierarchical storage architecture unit, and a ledger sharding and management unit. The lightweight block structure design unit defines a block format suitable for power systems. Addressing the real-time requirements of power systems, it designs a lightweight block structure including a block header (magic number, version number, block height, previous block hash, data root hash, timestamp) and a block body (number of transactions, transaction list), balancing data integrity and processing efficiency. The hierarchical storage architecture unit comprises a three-tiered storage architecture: a volatile layer, a fast layer, and an archive layer. The volatile layer stores the most recent state data in memory, supporting millisecond-level read / write operations; the fast layer stores the most recent blocks on SSDs, supporting second-level queries; and the archive layer stores compressed historical blocks, supporting complete traceability. This hierarchical architecture balances performance and data integrity requirements. Ledger sharding and management units are used to divide the ledger into shards based on functional areas. These units logically shard the ledger according to dimensions such as voltage level and functional area, reducing the storage and processing burden on individual nodes while maintaining global data consistency. These three components together constitute a distributed ledger management system optimized for power systems, solving the performance and resource consumption problems faced by traditional blockchain applications in power systems.

[0305] The distributed ledger management module employs optimized blockchain technology to construct a distributed data synchronization architecture suitable for the characteristics of power systems.

[0306] The lightweight block structure design unit defines a block format optimized for power systems:

[0307] Block header (80 bytes):

[0308]

[0309] Block body:

[0310]

[0311] The block size is limited to 4KB to ensure rapid propagation and processing. A single block can contain a maximum of 64 state update transactions, and a block is generated on average every 5 seconds.

[0312] The tiered storage architecture unit implements a three-tier storage structure:

[0313] Volatile layer: Stores the most recent state data in memory, supporting millisecond-level read and write operations;

[0314] Fast tier: SSDs store the most recent 24 hours of blocks, supporting second-level queries;

[0315] Archive layer: Compressed storage of historical blocks, supporting complete traceability.

[0316] Ledger Segmentation and Management Units: Ledger segments are divided according to functional areas.

[0317] Core segment: Stores the status of critical equipment such as circuit breakers and disconnect switches;

[0318] Measurement segmentation: Stores measured values ​​such as voltage and current;

[0319] Alarm fragmentation: Saves system alarm information.

[0320] Distributed ledger structures overcome the single point of failure risk of traditional centralized data management, realize decentralized data storage and synchronization, and greatly improve system reliability.

[0321] The consensus algorithm processor is based on the Improved Practical Byzantine Fault Tolerance (PBFT) algorithm, providing consistency guarantees for distributed ledgers. This algorithm allows the system to reach consensus even if a maximum of f = (n-1) / 3 nodes fail or act maliciously, where n is the total number of nodes.

[0322] The consensus process consists of four phases:

[0323] 1. Preparatory phase: The master node (rotates every 10 blocks generated) broadcasts the proposed block and its sequence number.

[0324] 2. Preparation phase: After verification, the verification node broadcasts a preparation message.

[0325] 3. Commit Phase: After a node confirms that it has received more than 2f preparation messages, it broadcasts the commit.

[0326] 4. Completion Phase: After receiving more than 2f commit messages, the block is confirmed to be valid.

[0327] To meet the low latency requirements of power systems, this embodiment implements a fast path consensus mechanism: when a node receives more than 3f+1 preparation messages, it can skip the confirmation phase and directly complete the consensus, which typically reduces the consensus time from 100ms to 35ms.

[0328] The system also implements an efficient view switching mechanism to handle master node failures: when the timeout parameter is 500ms, the system can complete the view switching and resume normal operation within an average of 350ms after a master node failure, which significantly improves system reliability.

[0329] The equipment status digital twin module includes a multi-physical quantity modeling framework, a real-time data integration mechanism, a twin model synchronizer, and a prediction and diagnostic engine. The multi-physical quantity modeling framework is used to construct a comprehensive model including electrical, thermodynamic, and mechanical characteristics. Based on physical laws and empirical models, it describes equipment behavior from multiple dimensions, covering electrical (e.g., equivalent circuit model), thermal (temperature distribution model), and mechanical (structural stress model) aspects. The real-time data integration mechanism collects and processes multi-source data from physical equipment sensors and other data sources, and provides this data to the modeling framework and the twin model synchronizer. It receives real-time data from various sensors, protection devices, and control systems, performs filtering and normalization preprocessing, and provides timely and accurate input to the model. The twin model synchronizer updates the model state generated by the modeling framework based on the data provided by the real-time data integration mechanism. Through state estimation algorithms (e.g., extended Kalman filtering), the twin model synchronizer continuously updates model parameters and state variables to ensure that the virtual model accurately reflects the actual state of the physical equipment. The prediction and diagnosis engine, based on the model and historical data built upon the multi-physical quantity modeling framework, analyzes equipment status trends and identifies abnormal behaviors. It outputs equipment status information and prediction results for use by the information transmission and synchronization management module and other related modules for encryption, slicing, and synchronization. The engine predicts future equipment states based on historical data and physical models, identifies potential anomalies and fault modes, and provides early warnings and diagnostic information. These four components form a complete digital twin system, providing a powerful tool for power equipment status monitoring and fault prediction.

[0330] Multi-physical modeling framework for constructing comprehensive models of power equipment:

[0331] Electrical model: Based on equivalent circuit theory, it simulates current and voltage characteristics;

[0332] Thermodynamic model: A simplified finite element model that simulates temperature distribution and heat conduction;

[0333] Mechanical model: describes the stress and vibration characteristics of the structure;

[0334] Life model: Based on the cumulative damage theory, predict the remaining life of critical components.

[0335] Real-time data integration mechanisms collect and process multi-source data:

[0336] Sampled data: obtained via IEC 61850-9-2 protocol.

[0337] GOOSE message: Captures switch state changes;

[0338] Sensor data: collects physical quantities such as temperature and vibration;

[0339] Protection information: Record protection actions and alarms.

[0340] The twin model synchronizer uses extended Kalman filtering (EKF) for state estimation, and the core algorithm is as follows:

[0341] The twin model synchronizer uses an extended Kalman filter (EKF) for state estimation. The algorithm comprises two key steps: a prediction step and an update step. The prediction step is based on the system model. Predict the next state: and its error covariance The update steps are based on the measured values. Correct the predicted state and calculate the Kalman gain. Update state estimation and its error covariance The algorithm dynamically balances the weights of model predictions and actual measurements to ensure the accuracy and robustness of state estimation.

[0342] To balance computational resources and model accuracy, the system implements a differentiated update strategy:

[0343] Electrical status: Updated every 10ms;

[0344] Thermal status: Updated every 100ms;

[0345] Mechanical status: Updated every 1 second;

[0346] Lifespan parameters: updated every hour.

[0347] The Prediction and Diagnostics Engine 84, based on physical models and historical data, predicts device status changes within the next 5 seconds, identifies abnormal patterns, and generates warnings. This engine implements a dual anomaly detection mechanism based on thresholds and trends, significantly improving the anomaly detection rate and reducing the false alarm rate.

[0348] Digital twin technology enables the system to have millisecond-level state awareness and 5-second forward prediction capabilities, providing early warning of equipment anomalies 3-5 seconds in advance and giving maintenance personnel sufficient response time.

[0349] Based on the application of the aforementioned software modules, this application also provides another information transmission method with quantum random encryption and distributed synchronization. This method is based on the application scenario of power system substation groups, and its specific implementation is as follows:

[0350] Step 1. Quantum Random Number Generation Method

[0351] The steps involved in generating quantum random numbers include:

[0352] Step 1.1: Generating random signals using quantum vacuum fluctuation noise: In a reverse-biased PN junction, quantum vacuum fluctuations generate random electron-hole pairs, forming a primitive random signal. Specifically, this step involves reverse-biasing the PN junction to a 10mA operating current. The quantum fluctuation effect causes fluctuations on a nanosecond timescale, which are then converted into a weak voltage signal by a preamplifier circuit.

[0353] Step 1.2: Amplify and filter the random signal: The weak quantum signal is amplified to a suitable level using a three-stage amplifier circuit, while a bandpass filter (1-200MHz) is used to filter out external interference and system noise. The amplifier is designed to be low-noise (noise figure <1.2dB) to ensure that no significant deterministic noise is introduced.

[0354] Step 1.3: Digitize the signal using a high-speed ADC: Sample the amplified random signal using a 20GS / s sampling rate ADC, acquiring 1MB data blocks each time, and converting them into digital form.

[0355] Step 1.4: Applying the entropy extraction algorithm to obtain high-quality random numbers: The original digitized data is processed by Von Neumann debiasing correction and SHA-256 hash entropy extraction to generate a random number stream with excellent statistical properties, and finally obtains a 4Mbps true random number output that meets the NIST SP800-22 test standard.

[0356] Step 1.5: Real-time monitoring of random number quality: The system performs an online entropy estimation once per second. When the entropy estimation value is lower than the preset threshold (0.97 bits / bit), the quantum source health check program is triggered, and the system switches to the backup random source if necessary.

[0357] Compared to traditional pseudo-random number generators, this method ensures the true randomness and unpredictability of random numbers, providing high-quality key material for subsequent encryption and fundamentally improving system security.

[0358] Step 2. Dynamic Encryption Method

[0359] The dynamic encryption steps include:

[0360] Step 2.1: Classify data into different security levels based on data type: The system analyzes the input data, identifies the data type, and classifies it into four security levels:

[0361] Level 0 (highest): Control commands and protection trip instructions;

[0362] Level 1: Status and alarm information of critical equipment;

[0363] Level 2: General measurement data and equipment parameters;

[0364] Level 3: Historical data and statistical information.

[0365] Step 2.2: Select different encryption algorithms and parameters for data with different security levels: Automatically select the encryption scheme based on the security level.

[0366] Level 0: ChaCha20-Poly1305 algorithm, 256-bit key, updated every 90 seconds;

[0367] Level 1: AES-256-GCM mode, 256-bit key, updated every 10 minutes;

[0368] Level 2: AES-256-CBC mode, 256-bit key, updated with each session;

[0369] Level 3: AES-128-CBC mode, 128-bit key, updated daily.

[0370] Step 2.3: Apply a dual encryption mechanism to the highest security level data: First encrypt the level 0 data with ChaCha20-Poly1305, and then perform a second encryption with AES-256-GCM to provide dual protection for the core control commands.

[0371] Step 2.4: Automatically update the encryption key according to a preset time interval or data volume threshold: The system implements key updates based on a dual-trigger mechanism.

[0372] Time-triggered: Updates the key according to the configured time interval;

[0373] Data volume trigger: The key is updated when the amount of encrypted data reaches a specified threshold (e.g., 10MB).

[0374] The implementation includes three stages: key rotation warning (10 seconds before expiration), smooth transition between old and new keys (both keys are valid for 10 seconds simultaneously), and key destruction (securely erasing the old key).

[0375] This method solves the problem of traditional fixed keys being easily analyzed. By frequently updating keys and using differentiated encryption strategies, it enhances the system's ability to resist cryptanalysis attacks.

[0376] Step 3. Data Slicing and Distribution Methods

[0377] The data slicing and distribution steps include:

[0378] Step 3.1: Select an (n,k) encoding strategy based on data importance: The system automatically selects appropriate Reed-Solomon encoding parameters based on the data type.

[0379] Critical control command: (5,3) encoding, requires at least 3 fragments to recover, distributed to 5 nodes;

[0380] State data: (4,2) encoding, requires at least 2 pieces to recover, distributed to 4 nodes;

[0381] General data: (3,2) encoding, at least 2 pieces are needed to recover, and it is distributed to 3 nodes.

[0382] Step 3.2: Add a slice header containing complete metadata to each slice: The system builds a 64-byte slice header for each slice, which includes information such as the magic number (0x52534543), version number, slice type, index, and timestamp, to ensure that the slice can be independently identified and verified.

[0383] Step 3.3: Calculate the optimal distribution strategy based on network topology and node status: The system updates the network status graph every 30 seconds, including node availability, link latency and bandwidth information. The improved Floyd-Warshall algorithm is applied to calculate the optimal distribution path to optimize transmission efficiency and load balancing.

[0384] Step 3.4: Distribute slices to multiple geographically dispersed nodes: The system prioritizes distributing slices to substations with different geographical locations to ensure that regional failures do not lead to unrecoverable data. In specific implementation, adjacent substations store data with different slice indices, and any substation stores at most two slices with different indices.

[0385] This method significantly improves the system's fault tolerance and recovery capabilities through slicing redundancy and geographically distributed storage, ensuring data availability even in the event of multi-node failures.

[0386] Step 4. Distributed Ledger Maintenance Method

[0387] The steps to maintain data consistency include:

[0388] Step 4.1: Package state changes into transactions: The system monitors device state changes. When a change is detected, a standard transaction structure containing the device ID, state variables, timestamp, and digital signature is generated. Transaction size is strictly limited to no more than 1KB to ensure network transmission efficiency.

[0389] Step 4.2: Confirm Transaction Validity via Consensus Algorithm: The system uses an improved PBFT consensus process (four phases: pre-preparation, preparation, commit, and completion) to verify transactions among distributed nodes. In each phase of the PBFT consensus algorithm (pre-preparation, preparation, commit, and completion), participating nodes must perform time calibration before each phase begins. Each receiving node must not only verify the correctness of the block content but also verify the consistency between the block timestamp and its local time. If the time deviation is too large, the node will first perform time synchronization adjustment and then re-verify the block. In the critical phase of the PBFT consensus, the chaotic synchronization controller enters high-precision mode, shortening the synchronization update cycle from 10ms to 1ms, ensuring strict consistency in the timing of each node during the consensus process. When more than 2f nodes have confirmed the transaction (f being the maximum number of malicious nodes the system can tolerate), the transaction is considered to have reached consensus. The system implements fast path consensus optimization; when more than 3f+1 confirmations are received, the confirmation phase can be skipped, reducing the consensus time from the standard 100ms to 35ms.

[0390] Step 4.3: Package the confirmed transactions into a block: The system collects the consensus transactions, constructs a Merkle tree structure, and generates a complete block containing a block header (80 bytes) and a list of transactions. A single block is limited to a maximum of 64 transactions, with a total size not exceeding 4KB, ensuring that the block can be propagated across the network within 100ms.

[0391] Step 4.4: Synchronize blocks among distributed nodes: New blocks are broadcast to other nodes in the network through an optimized block propagation protocol. The protocol reduces latency by sending the block header first and then transmitting the block body, allowing nodes to process block header verification and block body transmission in parallel.

[0392] Step 4.5: Verify Blockchain Integrity: After receiving a new block, each node ensures the integrity of the ledger by verifying the block hash link, transaction signature, and Merkle proof. The system also implements a periodic (hourly) ledger state snapshot mechanism to accelerate node recovery and access to historical data.

[0393] This method establishes a decentralized data consistency guarantee mechanism, overcomes the single point of failure risk of traditional centralized systems, and significantly improves the reliability and data integrity of the system.

[0394] Step 5. Chaotic Synchronization Control Method

[0395] Chaotic synchronization control steps include:

[0396] Step 5.1: Maintain a chaotic system with the same structure on each node: The system deploys a Lorenz chaotic system with identical structure on each node, with parameters set to... The initial state is synchronized via a secure channel upon system startup. The chaotic system is implemented on a dedicated FPGA, using a fourth-order Runge-Kutta algorithm for numerical integration, with an update frequency of 100MHz to ensure synchronization accuracy.

[0397] Step 5.2: Establish synchronization relationships between nodes through an active-passive coupling mechanism: The system adopts a star master-slave topology, with one master node (usually the node with the highest computing power and network connection quality) acting as the driving system, and other nodes acting as the responding system. The master node periodically (every 10ms) broadcasts its chaotic state (x, y, z), and the slave nodes adjust their local chaotic systems according to the received state.

[0398] Step 5.3: Periodically exchange chaotic state information and calculate synchronization error: Each node exchanges chaotic state information through a dedicated high-priority channel, using the UDP protocol to minimize transmission latency (typically <2ms). After receiving the master node's state, the slave node calculates the Euclidean distance between its local state and the received state as a synchronization error indicator.

[0399] Step 5.4: Dynamically adjust coupling strength based on Lyapunov stability theory: The system adaptively adjusts the coupling strength parameter (ranging from 0.1 to 10) according to synchronization error and network delay. The specific algorithm is based on the Lyapunov function. By minimizing V using gradient descent, the system is ensured to converge quickly to a synchronized state.

[0400] Step 5.5: Utilizing the synchronized chaotic state to drive local clock adjustment: The system maps the chaotic synchronization state to a clock adjustment signal, and fine-tunes the local clock frequency and phase through a dedicated PLL circuit. The adjustment algorithm includes two stages: coarse phase adjustment (initial synchronization, adjustment range ±1ms) and fine frequency adjustment (maintaining synchronization, adjustment range ±0.1ppm).

[0401] This method overcomes the limitations of traditional synchronization techniques in complex network environments, achieving sub-millisecond high-precision synchronization and providing a timing basis for precise coordinated control of power systems. Experimental results show that the system can achieve synchronization accuracy of 50μs-500μs, and even in harsh environments with network latency fluctuations of ±20ms, it can maintain a synchronization error of <1ms.

[0402] Step 6. Digital Twin Implementation Methods

[0403] Digital twin models are implemented in the following ways:

[0404] Step 6.1, Integrating Electrical, Thermal, and Mechanical Multi-Physical Quantity Models: The system constructs a multi-domain coupled model based on physical laws.

[0405] Electrical Model: An equivalent circuit model is established based on the nodal voltage method, including resistors, inductors, capacitors, and nonlinear components;

[0406] Thermal model: A simplified thermal network method is used to divide the equipment into multiple temperature nodes to simulate heat conduction and convection processes;

[0407] Mechanical model: Simulation of structural vibration characteristics based on a mass-spring system;

[0408] The model parameters are trained using historical data and configured with expert knowledge to achieve accurate modeling of various types of power equipment.

[0409] Step 6.2: Real-time state estimation using extended Kalman filter: The system employs the extended Kalman filter algorithm, fusing physical model predictions and real-time measurement data to estimate internal state variables that cannot be directly measured. The algorithm includes a prediction step (based on the physical model) and an update step (based on measurement data), adaptively adjusting the Kalman gain and balancing the weights of model predictions and measurement corrections.

[0410] Step 6.3: Based on different physical characteristics, adopt differentiated update frequencies for different parameters: The system implements a multi-level update strategy based on the rate of change of physical quantities.

[0411] Electrical status (voltage, current): updated every 10ms;

[0412] Thermal state (temperature distribution): Updated every 100ms;

[0413] Mechanical condition (vibration characteristics): Updated every 1 second;

[0414] Lifespan parameters (aging indicators): updated every hour;

[0415] This strategy significantly reduces the computational resource requirements while ensuring model accuracy.

[0416] Step 6.4: Predict equipment state changes within the next 5 seconds based on historical data and physical models: The system combines the deterministic evolution of the physical model with statistical analysis of historical data to achieve short-term prediction. The prediction algorithm uses a combination of recursive expansion and autoregressive moving average (ARMA) to accurately predict future equipment state change trends, providing a foundation for early fault warning.

[0417] This method achieves deep perception and prediction of the status of power equipment, surpassing the superficial data acquisition of traditional monitoring systems. In practical applications, the system can significantly provide early warnings of equipment anomalies, significantly improve the anomaly detection rate, significantly reduce the false alarm rate, significantly enhance preventive maintenance capabilities, and significantly reduce unplanned downtime events.

[0418] In one embodiment, the electrical model is established based on the nodal voltage method, and the specific implementation process is as follows:

[0419] First, the power equipment is equivalently represented as a circuit network consisting of resistors R, inductors L, capacitors C, and nonlinear elements. For transformer equipment, the electrical model adopts a T-type equivalent circuit, where the primary side resistance... Leakage Secondary side resistance Leakage The excitation branch is composed of the excitation resistor Rm and the excitation inductance Lm connected in parallel. Mathematically, it is described as follows:

[0420] Node voltage equation: [Y][V] = [I]

[0421] Where: Y is the node admittance matrix, V is the node voltage vector, and I is the injected current vector.

[0422] For switching devices, a time-varying resistance model is used: when the switch is closed, the resistance Rsw = 0.001Ω, and when the switch is open, the resistance... The arc model of the circuit breaker adopts the Mayr arc model.

[0423]

[0424] Where: g is the arc conductance, and τ is the time constant (typical value 50 μs). This is the rated power loss.

[0425] The electrical model parameters are determined as follows: Equipment nameplate parameters are used as initial values, and then corrected by fitting historical operating data using the least squares method. Specifically, for the transformer short-circuit impedance, the initial value is the nameplate value Zk%, and then least squares fitting is used based on load test data.

[0426]

[0427] Where: Vmeas is the measured voltage, and Vcalc is the voltage calculated by the model;

[0428] The thermal model employs the lumped-parameter thermal network method, simplifying the complex heat conduction process into a thermal resistance-thermal capacity network. Specifically, it is implemented as follows:

[0429] For the transformer tank, a three-node thermal network model is established: winding node (temperature Tw), oil temperature node (temperature To), and ambient node (temperature Ta). The heat transfer equations are as follows:

[0430]

[0431]

[0432] Where: Cw is the winding heat capacity (typical value) Co is the oil temperature heat capacity (typical value). Rwo is the thermal resistance from the winding to the oil (typical value 0.6 K / kW), Roa is the thermal resistance from the oil to the environment (typical value 1.2 K / kW), and P is the winding power loss.

[0433] For the switchgear, a five-node model is adopted: contact node, conductor node, insulating component node, cabinet node, and environment node. The nodes are connected by thermal resistance, the values ​​of which are determined through finite element simulation: contact-conductor thermal resistance 0.1 K / W, conductor-insulator thermal resistance 0.5 K / W, insulating component-cabinet thermal resistance 0.3 K / W, and cabinet-environment thermal resistance 2.0 K / W.

[0434] The thermal model parameters were calibrated using temperature rise test data: the model was run under rated load until steady state, the temperature at each measuring point was recorded, and the thermal resistance and heat capacity were adjusted through parameter optimization algorithms to minimize the mean square error between the model output and the measured values.

[0435] The mechanical model is based on a multi-degree-of-freedom mass-spring-damped system to describe the vibration characteristics of the equipment. For the transformer, a six-degree-of-freedom model is established (three translational degrees of freedom X, Y, Z, and three rotational degrees of freedom θx, θy, θz):

[0436] The equations of motion are in matrix form:

[0437]

[0438] Where: [M] is the mass matrix (6×6), [C] is the damping matrix, [K] is the stiffness matrix, {F(t)} is the excitation force vector, and {x} is the displacement vector.

[0439] Typical parameters of the mass matrix: For a 110kV transformer, the main body mass m = 15000kg, and the moment of inertia... .

[0440] The stiffness matrix is ​​determined through modal testing: the natural frequency and damping ratio of the equipment are tested using the impact test method. The first-order natural frequency is typically in the range of 8-12Hz, and the damping ratio is approximately 0.02-0.05. The stiffness coefficients are obtained by fitting the frequency response function.

[0441] In one embodiment, the physical quantity models are organically combined through the following coupling relationship:

[0442] Electrothermal coupling: The power loss calculated in the electrical model serves as the heat source input for the thermal model. Specifically, transformer winding losses... Where α is the temperature coefficient of resistance (0.004 K for copper), and ΔT is the temperature rise. Iron loss Pfe is calculated based on magnetic flux density and frequency, taking into account a temperature correction factor.

[0443] Thermomechanical coupling: Temperature changes cause thermal expansion of materials, leading to mechanical stress. The formula for calculating thermal stress is: σth = EαΔT, where E is the elastic modulus and α is the coefficient of linear expansion. For transformer windings, the change in axial force caused by longitudinal expansion affects vibration characteristics.

[0444] Motor coupling: Electromagnetic force serves as the excitation source for mechanical vibration. The electromagnetic force on the transformer winding is Fem = BIl, where B is the magnetic induction intensity, I is the current, and l is the conductor length. This electromagnetic force varies at an even multiple of the power frequency, thus becoming the vibration excitation source.

[0445] Model parameters are determined by combining the following three methods:

[0446] Theoretical calculation method: Calculates theoretical values ​​based on the equipment's geometry and material properties. For example, resistance is calculated as R = ρl / A, where ρ is the material resistivity, l is the length, and A is the cross-sectional area. Thermal resistance is calculated as Rth = δ / (λA), where δ is the thickness and λ is the thermal conductivity.

[0447] Test calibration method: Key parameters are determined through specialized tests. Temperature rise tests determine thermal parameters, short-circuit tests determine electrical parameters, and modal tests determine mechanical parameters. Tests are conducted under rated and abnormal operating conditions to establish the parameter-operating condition correspondence.

[0448] Data-driven approach: Utilizing historical operational data to optimize parameters through machine learning methods. Employing genetic algorithms or particle swarm optimization, the model parameters are automatically optimized using the fit between the model output and the measured data as the objective function. Optimization objective function:

[0449]

[0450] Where: wi is the weight coefficient, yi,model is the model output, and yi,meas is the measured value.

[0451] Taking a 110kV transformer as an example, the specific parameter configuration of the multi-physical quantity model is as follows:

[0452] Electrical parameters: Rated capacity 63MVA, high voltage side resistance R1=0.15Ω, low voltage side resistance R2=0.008Ω, short circuit reactance Xk=8.5%, excitation current Im=0.8%.

[0453] Thermal parameters: winding heat capacity Oil temperature heat capacity The winding-oil thermal resistance Rwo = 0.52 K / kW, and the oil-ambient thermal resistance Roa = 1.15 K / kW.

[0454] Mechanical parameters: Total mass 15.2t, first natural frequency 9.5Hz, damping ratio 0.035, stiffness coefficient in the main vibration direction. .

[0455] In actual operation, this model can accurately predict the temperature distribution (error <3K), vibration level (error <10%), and electrical characteristics (error <2%) of transformers under different loads, achieving high-fidelity digital mapping of equipment status.

[0456] Through the detailed modeling methods and parameter determination process described above, the multi-physical quantity modeling framework can build high-precision digital twin models for power equipment, providing a reliable technical foundation for condition monitoring, fault prediction, and optimized control.

[0457] Step 7. Data Transmission and Synchronization Coordination Methods

[0458] The steps involved in coordinating data transmission and synchronization include:

[0459] Step 7.1: Prioritize data based on data type and importance: The system implements a four-level priority mechanism.

[0460] P0 (highest): Control commands and protection instructions, maximum end-to-end delay of 3ms;

[0461] P1: Status alarms and critical measurements, with a maximum end-to-end delay of 10ms;

[0462] P2: Normal status data, maximum end-to-end delay 50ms;

[0463] P3 (Lowest): Historical data and statistical information, transmitted with the best possible effort;

[0464] The system uses the DiffServ mechanism to implement priority marking and queue management at the network layer.

[0465] Step 7.2: Calculate the optimal transmission path considering delay, bandwidth, and reliability: The system implements multi-metric route optimization based on an improved Dijkstra algorithm. The route calculation considers link delay (primary metric), available bandwidth (secondary metric), and historical reliability (additional metric) to select the most suitable transmission path for data of different priorities.

[0466] Step 7.3: Real-time monitoring of network status and dynamic adjustment of transmission strategies: The system continuously evaluates network performance through active probing (sending probe packets every 15 seconds) and passive monitoring (analyzing service traffic characteristics). When network congestion is detected (such as packet loss rate > 1% or latency increase > 50%), the system automatically adjusts routing strategies and congestion control parameters to ensure that critical services are not affected.

[0467] Step 7.4, Maintain the synchronization quality scoring system: The system establishes a comprehensive evaluation index system to assess the synchronization status in real time.

[0468] Time synchronization accuracy (weight 40%): measures the clock deviation between nodes;

[0469] Data consistency (weight 30%): Evaluate the differences in data versions between nodes;

[0470] Network performance (weight 20%): Monitors the quality of communication between nodes;

[0471] System load (weight 10%): Tracks the utilization of processing and storage resources.

[0472] When the score is lower than the preset threshold (85 points), the system triggers a self-recovery process, including strengthening the synchronization signal strength, switching to a backup link, or adjusting the data distribution strategy.

[0473] This method ensures the system maintains efficient and stable operation in complex and ever-changing network environments, intelligently adapting to changes in network conditions and providing crucial guarantees for system security and reliability. Actual deployments demonstrate that this method reduces system recovery time from minutes to seconds in the event of partial network failures, significantly improving service continuity.

[0474] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A quantum random encryption and distributed synchronization information transmission method, applied to a power system, wherein... Features are ,include: Obtain initial data on the status of power equipment; Generate unpredictable quantum true random numbers; The target data is obtained by encrypting the initial data using quantum true random numbers, including: Convert quantum true random numbers into a fixed-length master random key; A subkey is generated based on the master random key, and the subkey corresponds to the initial data. The target data is obtained by encrypting the initial data using a subkey, wherein the subkey is used according to a preset time interval. and / or data volume domain update; The clock synchronization accuracy of multiple nodes includes: Exchange chaotic state information among multiple nodes; Adjust multiple nodes to a synchronized chaotic state based on chaotic state information; The clock synchronization accuracy of multiple nodes is synchronized based on their chaotic synchronization states. Using a sub-millisecond chaotic synchronization clock as the time base for data slice generation and management, a deep integration of timing consistency and distributed data storage is achieved. Generating data slices based on target data and distributing the data slices to the multiple nodes includes: Data slices are generated by selecting an (n,k) encoding strategy based on the importance of the target data; Determine the positional relationship between nodes; Data slices are distributed to the multiple nodes based on their location relationships, where adjacent nodes store different data. Each node stores no more than two data slices, and no single node stores more than one data slice. The data slices of the multiple nodes are synchronized, and the data slice synchronization adopts distributed ledger synchronization. In each stage of the PBFT consensus algorithm, participating nodes must perform time calibration before the start of each stage. Each receiving node must not only verify the correctness of the block content, but also verify the consistency between the block timestamp and the local time. If the time deviation is too large, the node will first perform time synchronization adjustment, and then re-verify the block. In the critical stage of PBFT consensus, the chaotic synchronization controller enters high-precision mode, shortening the synchronization update cycle from 10ms to 1ms, ensuring that the timing of each node is strictly consistent during the consensus process. The process of synchronizing data slices from the multiple nodes further includes: establishing a synchronization quality scoring system, wherein the evaluation dimensions of the synchronization quality scoring system include at least one of time synchronization accuracy, data consistency, network performance, and system load; synchronizing data slices from the multiple nodes according to the synchronization quality scoring system, wherein when the synchronization quality is lower than a threshold, a correction operation is triggered, the correction operation includes strengthening coupling strength and switching the synchronization reference node; each data slice is assigned a high-precision timestamp based on a chaotic synchronization clock when it is generated; during data recovery, it is reassembled based on the index information of the slice, and the temporal consistency of the slice is verified using the timestamp information; if an abnormal deviation is found, the slice is rejected and a replacement slice is obtained from other nodes.

2. The information transmission method according to claim 1, characterized in that, The generation of unpredictable quantum true random numbers includes: Random electron-hole signals are generated using quantum vacuum fluctuations; A filtered signal is obtained by amplifying and filtering a random signal; The filtered signal is digitally processed to generate unpredictable quantum true random numbers.

3. The information transmission method according to claim 1, characterized in that, Following the generation of unpredictable quantum true random numbers, the process also includes: Monitor the entropy estimate of quantum true random numbers, and perform a health check on the quantum source when the entropy estimate is lower than a preset threshold.

4. The information transmission method according to any one of claims 1-3, characterized in that, The synchronization of data slices across the multiple nodes includes: Data priority is assigned based on the data slice's data type and / or data importance; The first transmission path is calculated based on data priority and network status, wherein the network status includes at least one of network latency, bandwidth and reliability factors. Data slices of the multiple nodes are synchronized according to the first transmission path.

5. The information transmission method according to claim 4, characterized in that, The synchronization of data slices from the multiple nodes also includes: A second transmission method is obtained by real-time monitoring of network congestion, wherein the network congestion includes at least one of latency, packet loss rate, and throughput; The data slices of the multiple nodes are synchronized according to the second transmission method.

6. The information transmission method according to claim 1, characterized in that, The initial data for obtaining the status of the power equipment includes: Obtain testing data from power equipment; The initial data on the status of power equipment are obtained by processing the detection data using a digital twin model of the power equipment.

7. The information transmission method according to claim 6, characterized in that, The process of using a digital twin model of the power equipment to process detection data and obtain initial data on the state of the power equipment includes: Construct a multi-physical quantity model for power equipment, wherein the multi-physical quantities include electrical parameters, thermal parameters, and mechanical parameters; Initial data on the status of power equipment are obtained by predicting detection data based on multi-physical quantity models.

8. A quantum random encryption and distributed synchronization information transmission device, comprising the information transmission method as described in any one of claims 1-7, applied to a power system, characterized in that, include: A digital twin module for power equipment is used to acquire initial data of the power equipment; The quantum true random number generation module is used to generate true random numbers using quantum physics phenomena. A dynamic encryption engine, connected to the quantum true random number generation module, is used to receive the true random numbers and generate target data from initial data using the true random numbers; including: Convert quantum true random numbers into a fixed-length master random key; A subkey is generated based on the master random key, and the subkey corresponds to the initial data. The target data is obtained by encrypting the initial data with a subkey, and the subkey is updated according to a preset time interval and / or data volume field. A chaotic synchronization controller is used to synchronize the clock synchronization accuracy between multiple nodes; it includes: Exchange chaotic state information among multiple nodes; Adjust multiple nodes to synchronize chaotic state based on chaotic state information; The clock synchronization accuracy of multiple nodes is synchronized based on the chaotic state of the synchronization of multiple nodes; The data slicing and recovery module, connected to the dynamic encryption engine, is used to generate data slices from the target data and distribute them to multiple nodes; it includes: Data slices are generated by selecting an (n,k) encoding strategy based on the importance of the target data; Determine the positional relationship between nodes; Data slices are distributed to the multiple nodes according to their positional relationships, wherein adjacent nodes store different data slices, and any node stores no more than two data slices. The distributed ledger management module is used to synchronize data slices from the multiple nodes.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the information transmission method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the information transmission method according to any one of claims 1 to 7.

Images