Quantum random encryption and distributed synchronization information transmission method and device

Abstract

The invention discloses a quantum random encryption and distributed synchronization information transmission method and device, and is applied to the technical field of power systems. Acquiring initial data of the state of the power equipment; generating an unpredictable quantum true random number; encrypting the initial data by using the quantum true random number to obtain target data; synchronizing the clock synchronization precision of the plurality of nodes; generating data slices according to the target data and distributing the data slices to the plurality of nodes; and synchronizing the data slices of the plurality of nodes. According to the invention, the technical problem of safe and reliable information transmission of the power system is solved at least to a certain extent.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power information transmission, and particularly relates to a quantum random encryption and distributed synchronization information transmission method and device. BACKGROUND

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

[0003] Firstly, the current security protection of power information transmission mainly has the following deficiencies: 1. Limitations of traditional encryption technology: The commonly used AES, RSA and other algorithms face the risk of key predictability, long update cycle and fixed encryption strength, and the security will be significantly reduced when facing future quantum computing technology. 2. Reduced network isolation effect: With the increasing close connection between the power system and the external network, traditional physical isolation strategies are difficult to implement effectively. 3. Incompatible security measures: Existing security technologies do not fully consider the special nature of the power system, making it difficult to resist targeted attacks, especially the key management faces great challenges.

[0004] At the same time, the key problem of synchronization accuracy in power system multipoint transmission includes: 1. Insufficient synchronization mechanism: The traditional fixed cycle polling mechanism has a synchronization error of 5-15ms, GPS / Beidou clock synchronization has the risk of signal interference, and IEEE 1588 precise time protocol has unstable accuracy in networks with large load fluctuations. 2. Clock drift impact: Local clocks at each node have drift, especially in environments with large temperature fluctuations, the cumulative error is significant. 3. Network delay fluctuation: Changes in the load of power communication networks result in large uncertainty in delay, and traditional synchronization mechanisms are difficult to effectively compensate. Research shows that insufficient synchronization accuracy directly affects the stability of the power system, and may even lead to the expansion of regional power grid oscillation.

[0005] Traditional power system information architecture mostly adopts a centralized structure, which faces the risk of single point failure, expansion limitation and data consistency problem. Although blockchain technology provides a possibility to solve these problems, the high delay characteristics of public chains are difficult to meet the real-time control requirements of power.

[0006] The quantum random encryption and distributed synchronization information transmission technology can simultaneously solve the above-mentioned security, synchronization accuracy and system reliability problems, providing all-round protection for the power system. With the acceleration of ultra-high voltage power grid construction and the improvement of the intelligentization degree of the power system, innovative technologies are needed to support the safe and efficient operation of the power system. SUMMARY

[0007] The quantum random encryption and distributed synchronization information transmission method and device provided by the embodiment of the present application at least solves the technical problem of insecure power information transmission.

[0008] In a first aspect, the embodiment of the present application provides a quantum random encryption and distributed synchronization information transmission method, applied to a power system, the information transmission method comprising: obtaining initial data of a power equipment state; generating unpredictable quantum true random numbers; encrypting the initial data using the quantum true random numbers to obtain target data; synchronizing clock synchronization accuracy of multiple nodes; generating data slices according to the target data and distributing the data slices to the multiple nodes; and synchronizing the data slices of the multiple nodes, wherein the synchronized data slices adopt distributed ledger synchronization.

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

[0010] In some embodiments, after the generating of the quantum true random numbers, the method further comprises: monitoring an entropy estimation value of the quantum true random numbers, and performing a quantum source health check when the entropy estimation value is lower than a preset threshold.

[0011] In some embodiments, the encrypting of the initial data using the quantum true random numbers to obtain the target data comprises: converting the quantum true random numbers into a fixed-length master random key; generating a sub-key according to the master random key, the sub-key corresponding to the initial data; and encrypting the initial data according to the sub-key to obtain the target data, the sub-key being updated according to a preset time interval and / or a data volume domain.

[0012] In some embodiments, the synchronizing of the clock synchronization accuracy of the multiple nodes comprises: exchanging chaotic state information of the multiple nodes; adjusting the multiple nodes to a synchronized chaotic state according to the chaotic state information; and synchronizing the clock synchronization accuracy of the multiple nodes according to the synchronized chaotic state of the multiple nodes.

[0013] In some embodiments, the generating of the data slices according to the target data and the distributing of the data slices to the multiple nodes comprises: selecting an (n, k) encoding strategy to generate the data slices according to the importance of the target data; judging a node position relationship; and distributing the data slices 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.

[0014] In some embodiments, the synchronizing the data slices of the plurality of nodes comprises: assigning a data priority according to a data type and / or a data importance of the data slice; calculating a first transmission path according to the data priority and a network state, the network state comprising at least one of a network delay, a bandwidth, and a reliability factor; and synchronizing the data slices of the plurality of nodes according to the first transmission path.

[0015] In some embodiments, the synchronizing the data slices of the plurality of nodes further comprises: monitoring a network congestion in real time to obtain a second transmission method, the network congestion comprising at least one of a delay, a packet loss rate, and a throughput; and synchronizing the data slices of the plurality of nodes according to the second transmission method.

[0016] In some embodiments, the synchronizing the data slices of the plurality of nodes further comprises: establishing a synchronization quality scoring system, wherein the synchronization quality scoring system evaluates at least one of a time synchronization accuracy, a data consistency, a network performance, and a system load; and synchronizing the data slices of the plurality of nodes according to the synchronization quality scoring system, wherein a correction operation is triggered when a synchronization quality is lower than a threshold value, the correction operation comprising enhancing a coupling strength or switching a synchronization reference node.

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

[0018] In some embodiments, the processing the detection data by using the digital twin model of the power equipment to obtain the initial data of the power equipment state comprises: constructing a multi-physical quantity model of the power equipment, the multi-physical quantity comprising an electrical parameter, a thermal parameter, and a mechanical parameter; and predicting the detection data according to the multi-physical quantity model to obtain the initial data of the power equipment state.

[0019] In a second aspect, an embodiment of the present application provides an information transmission device for quantum random encryption and distributed synchronization, the information transmission device comprising: a power equipment digital twin module configured to obtain initial data of a power equipment; a quantum true random number generation module configured to generate a true random number by using a quantum physical phenomenon; a dynamic encryption engine connected to the quantum true random number generation module, configured to receive the true random number and generate target data from the initial data by using the true random number; a chaotic synchronization controller configured to synchronize clock synchronization accuracy among a plurality of nodes; a data slice and recovery module connected to the dynamic encryption engine, configured to receive the target data and distribute the target data to the plurality of nodes as data slices; and a distributed ledger management module configured to synchronize the data slices of the plurality of nodes.

[0020] In a third aspect, an electronic device is provided, which comprises a processor and a memory storing a computer program, and the processor implements the steps of the information transmission method according to any one of the above when executing the computer program.

[0021] In a fourth aspect, a computer program product is provided, which comprises a computer program, and the computer program implements the steps of the information transmission method according to any one of the above when executed by a processor.

[0022] The one or more technical solutions provided by the embodiments of the present application have at least the following effects or advantages: first, the information transmission method combines quantum random encryption and distributed information synchronization, quantum random encryption ensures the confidentiality of data during transmission and storage, distributed synchronization ensures the consistency and integrity of data among multiple nodes, and the two work together, when an attacker obtains encrypted data of part of the nodes, because the data is distributed and stored in slices and the nodes need to be synchronized and consistent, the attacker needs to break multiple nodes and crack the quantum random key to obtain complete information, so the security is strong. Second, chaotic synchronization control provides sub-millisecond clock synchronization; the dynamic encryption engine updates the key based on time; the two work together, accurate time synchronization ensures that all nodes can update the key synchronously, avoiding the problem of key mismatch caused by clock asynchronization, and enhancing the timing security cooperation. Third, the data slicing technology provides fault tolerance; the distributed ledger ensures the consistency of data state; the two work together, even in the case of partial node failure, the system can recover data from the remaining slices and verify data integrity through the distributed ledger, enhancing the reliability of data.

[0023] The encryption method and information synchronization method play an important role in stable and secure information transmission, so the combination of 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 only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0026] Figure 1 Structure diagram of an electronic device for information transmission according to an embodiment of the present application; Figure 2A first flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 3 A second flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 4 A third flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 5 A fourth flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 6 A fifth flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 7 A sixth flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 8 A seventh flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application; Figure 9 An eighth flowchart of a quantum random encryption and distributed synchronization information transmission method according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without any creative work under the premise that the embodiments in the present application are within the scope of protection of the present application.

[0029] In addition, the described features, structures or characteristics can be combined in any suitable way in one or more embodiments. In the following description, numerous specific details are provided to give a sufficient understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring the aspects of the present application.

[0030] The block diagrams in the drawings are only functional entities and do not necessarily have to correspond to physically independent entities. That is, the functional entities can be implemented in the form of software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices. The flowcharts in the drawings are only exemplary and do not necessarily include all contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0031] In the description of the present application, it should be understood that the terms "first", "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0032] The following describes an exemplary application of the information transmission device and the electronic device provided by the embodiments of the present application. Referring to Figure 1 , Figure 1 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. The electronic device 500 includes at least one processor 510, at least one network interface 520, and a memory 530. Each component is coupled together through a bus system 540. It can be understood that the bus system 540 is used to realize the connection and communication between the components. In addition to the data bus, the bus system 540 also includes a power bus, a control bus, and a status signal bus. However, for the purpose of clear illustration, only the data bus is shown in the figure. Figure 1Various buses are labeled as a bus system 540. The processor 510 can be an integrated circuit chip that has a processing capability of signals, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., and the general-purpose processor can be a microprocessor or any conventional processor. The memory 530 includes volatile memory or non-volatile memory, and can also include both volatile and non-volatile memory. The non-volatile memory can be a read-only memory (ROM), and the volatile memory can be a random access memory (RAM). The memory 530 described in the embodiments of the application is intended to include any suitable type of memory. The operating system 531 includes system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks; the network communication module 532 is used to reach other computing devices via one or more (wired or wireless) network interfaces 520, and the exemplary network interfaces 520 include Bluetooth, wireless compatibility authentication (WiFi), and universal serial bus (USB), etc.

[0033] In some embodiments, the device provided by the embodiments of the application can be realized in a software manner, Figure 1 The information transmission module 533 stored in the memory 530 is shown, which can be software in the form of programs and plug-ins, etc., including the following software modules: a device state 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 logical, and thus can be combined or further split according to the functions implemented. The functions of each module will be described below.

[0034] The system is implemented on an industrial-grade hardware platform, the main processor is an ARM Cortex-A72 processor (six cores, 1.8 GHz frequency), equipped with 8GB LPDDR4 memory and 128GB industrial-grade SSD storage. The network interface includes three 10G Ethernet interfaces (supporting PTP synchronization) and one SFP+ fiber interface (10Gbps), supporting IEC 61850-9-2 sampled values and GOOSE messages. The system also integrates Xilinx Zynq UltraScale+ MPSoC FPGA for hardware acceleration of encryption algorithms and chaotic synchronization control.

[0035] The system software is implemented based on a real-time Linux operating system (Xenomai kernel), ensuring deterministic latency of critical processing flows.

[0036] An example of information transmission is described below.

[0037] Reference Figure 2 As shown in the figure, the embodiment of the present application provides an information transmission method of quantum random encryption and distributed synchronization, which can be used for information transmission between power systems and the like. The information transmission method provided by the embodiment of the present application includes the following steps 101-106.

[0038] Step 101, obtaining initial data of a power equipment state.

[0039] It can be understood that the power equipment can be any equipment in the power system, such as a power generation, power transmission, power transformation, power distribution, power utilization, and communication network itself, such as an RTU (remote terminal unit), an IED (intelligent terminal), a switch, a smart meter, a protocol converter, and the like.

[0040] The initial data of the state of the power equipment can be various control commands, protection tripping instructions, key equipment states and their alarm information, general measurement data and equipment parameters, historical data and statistical information, and the like according to the purpose; The data acquisition equipment for obtaining the initial data of the power equipment state can be a combination of the following listed multiple, specifically: Optical sensor type equipment: Optical current transformer (OCT) and optical voltage transformer (OVT): based on the principle of Faraday magneto-optical effect and Pockels electro-optic effect. OCT utilizes the characteristic that the rotation angle of polarized light in the magnetic field is proportional to the current, and obtains current information by measuring the change of polarization angle; OVT is based on the principle that the refractive index of an electro-optic crystal changes under the action of an electric field, and measures the voltage by detecting the change of transmitted light intensity. These devices convert the electrical parameters of the power system into optical signals, and then convert them into digital signals through an optical-electric conversion module to achieve electrical isolation and high-precision measurement, with a measurement accuracy of 0.2 level and a dynamic range of more than 120 dB.

[0041] Distributed optical fiber sensing system: adopts backscattering Raman scattering (BOTDR) and Brillouin scattering (BOTDA) technology to realize distributed temperature and strain measurement along a single-mode optical fiber. The system injects pulsed laser into the optical fiber, and obtains the temperature and strain information of each point along the optical fiber by analyzing the frequency shift and intensity change of scattered light. The spatial resolution can reach 1 meter, the temperature measurement accuracy is ±1℃, the strain measurement accuracy is ±20με, and the monitoring distance can reach 50 kilometers.

[0042] Fiber Bragg Grating Sensor (FBG): Based on the wavelength modulation principle of Fiber Bragg Grating, when the external temperature and strain change, the center reflection wavelength of the grating will drift. By high-precision spectrum analyzer to detect the wavelength change, realize the accurate measurement of temperature (resolution 0.1℃) and strain (resolution 1με).

[0043] Electronic Intelligent Acquisition Device: IEC 61850 Standard Intelligent Electronic Device (IED): Integrated with high-performance ARM processor and multi-channel synchronous sampling ADC, supports IEC 61850-9-2 sampled value transmission and GOOSE message processing. The device is equipped with multiple interface modules, including 16-channel differential analog input (precision 0.1%, sampling rate 10kHz), 32-channel digital input / output, and gigabit Ethernet communication interface. It can simultaneously acquire electrical parameters such as voltage, current, power, frequency, and harmonics, as well as switching state, protection action information, etc.

[0044] Remote Terminal Unit (RTU): Uses 24-bit Sigma-Delta ADC to achieve high-precision analog acquisition, supports 4-20mA, 0-5V, thermistor (PT100), etc. Digital input supports 24V / 220V DC and 110V / 220V AC signals, with optical isolation function. Communication interfaces include RS485, Ethernet and optical fiber communication, supporting IEC 104, Modbus, DNP3.0, etc. Multiple protocols.

[0045] Special Physical Quantity Sensing Device: Vibration Sensor Array: Uses MEMS acceleration sensor and piezoelectric vibration sensor, measuring frequency range 0.1Hz-10kHz, sensitivity 100mV / g, dynamic range ±50g. Through multi-point arrangement to realize device vibration modal analysis and fault diagnosis.

[0046] Infrared Thermal Imaging Temperature Measurement Device: Uses uncooled focal plane detector, temperature resolution 0.02℃, temperature measurement range -20℃ to +2000℃, spatial resolution 1.38mrad. Can realize non-contact detection of electrical equipment hot spots and temperature field distribution measurement.

[0047] Partial Discharge Detection Device: Integrates ultra-high frequency (UHF) sensor, ultrasonic sensor and high-frequency current sensor, detection frequency band 30MHz-1.5GHz, sensitivity -110dBm, can detect internal partial discharge signals of transformer, switch cabinet, etc.

[0048] Specific Implementation Method of Multi-source Signal Acquisition: Electrical Parameter Signal Acquisition Link: The acquisition of electrical parameters adopts a full-optical measurement link: the primary side converts the current and voltage signals into modulated optical signals through OCT / OVT, the optical signals are transmitted to the secondary side optical-electric conversion unit through an optical cable, the conversion unit is built-in with a photodiode array and a transimpedance amplifier to convert the optical signals into electrical signals, and then the electrical signals are digitized through a 16-bit ADC (sampling rate 50 kHz) and finally output in a standardized data format through the IEC 61850 protocol.

[0049] Distributed acquisition method of temperature parameters: The temperature monitoring adopts a distributed optical fiber sensing technology: special temperature sensing optical fibers are laid on the transformer winding, cable joint, switch cabinet and other key parts, the optical fibers adopt a polyimide coating and have a temperature resistance level of 200℃. The sensing system emits a pulse light once every 5 seconds, and the time domain analysis technology is used to locate the temperature abnormal points, with a spatial positioning accuracy of ±1 meter and a temperature measurement repeatability of ±0.5℃.

[0050] Multi-parameter monitoring of mechanical state: The mechanical state monitoring is realized through the fusion of multiple sensors: three-axis vibration sensors are arranged on the surface of the transformer oil tank, with a sampling frequency of 25.6 kHz, to realize the vibration signal acquisition in the frequency band of 0.1 Hz-10 kHz; FBG strain sensors are installed at key structural parts to monitor the mechanical stress distribution of the equipment; and acoustic emission sensors (frequency response range 20 kHz-1 MHz) are used to detect the mechanical abnormalities inside the equipment.

[0051] Real-time acquisition method of switch state: The switch state acquisition adopts a redundant detection method: the main detection acquires the auxiliary contact signal of the circuit breaker through an optically isolated digital input module, the auxiliary detection detects the on-off state of the main circuit through a current sensor, and the reliability of the state acquisition is ensured through logic judgment. The acquisition cycle is 10 ms, and the state change response time is less than 5 ms.

[0052] Time synchronization of multi-source data: The data collected by various sensors are time-synchronized at the microsecond level through the IEEE 1588 Precision Time Protocol (PTP), ensuring the time consistency of multi-source data. The main clock server adopts GPS time service, and the slave devices receive the time reference through Ethernet, with a synchronization accuracy better than 10 microseconds.

[0053] Signal preprocessing algorithm: The collected original signals are preprocessed: the analog signals are filtered through an anti-aliasing filter (cutoff frequency is 40% of the Nyquist frequency) to remove high-frequency interference; the digital signals are filtered through a combination of median filtering and Kalman filtering to remove pulse interference and random noise; and the wavelet transform technology is used to extract signal features, realizing multi-scale analysis of the signals.

[0054] State characteristic parameter extraction: Extracting device state features from the preprocessed signal: electrical parameter extraction of effective value, harmonic content, power factor, etc. feature quantity; temperature data extraction of average value, gradient, hot spot temperature, etc. parameter; vibration signal extraction of time domain statistics (mean, variance, skewness, kurtosis) and frequency domain features (power spectral density, characteristic frequency amplitude); realize feature dimension reduction through principal component analysis (PCA), and extract the most representative state parameters as the initial data for subsequent encrypted transmission.

[0055] Through the above detailed technical solutions, a plurality of specific devices, working principles, implementation methods and performance parameters used for obtaining initial data of power equipment state are listed.

[0056] Referring to Figure 2 , in step 102, unpredictable quantum true random numbers are generated.

[0057] Here, the quantum true random number can be a process of obtaining an essentially random original signal based on quantum physical phenomena such as vacuum fluctuation noise, semiconductor PN junction, photon scattering, or quantum tunneling effect. The following is an example of vacuum fluctuation noise.

[0058] In some embodiments, referring to Figure 3 , Figure 3 is a second flowchart of the information transmission method provided by the embodiments of the present application, which is directed to Figure 2 The step 102 shown in the figure can be implemented by the steps 1021 to 1023 of Figure 3 , which will be described in detail below.

[0059] In step 1021, a random electron-hole pair is generated using quantum vacuum fluctuation to generate a random signal.

[0060] Based on the principle of quantum vacuum fluctuation noise, a reverse-biased semiconductor PN junction is used to work at 10mA current, and a random signal is obtained by using the random generation and recombination process of electron-hole pairs. Quantum fluctuation effect generates fluctuations on the nanosecond time scale, which is converted into a weak voltage signal by a preamplifier.

[0061] In step 1022, the random signal is amplified and filtered to obtain a filtered signal.

[0062] Wherein, the amplification of the random signal is to amplify the weak quantum signal to an appropriate level by a three-stage amplification circuit, and the filtering processing is to filter out external interference and system noise by using a band-pass filter (1-200MHz). The amplifier is designed as a low-noise type (noise coefficient <1.2dB) to ensure that no significant deterministic noise is introduced.

[0063] The signal amplification and filtering processing adopts a three-stage amplification circuit, including a low-noise preamplifier (noise figure < 1.2 dB), a band-pass filter (passband 1-200 MHz), and a secondary amplifier. This link amplifies the weak quantum random signal to an appropriate level while retaining its random characteristics and filtering out non-random components.

[0064] In step 1023, the filtered signal is digitized to generate unpredictable quantum true random numbers.

[0065] The digitization is a high-level sampling rate ADC sampling the amplified random signal, and the sampling rate can be 10GS / s, 20GS / s, 50GS / s, 100GS / s, which is not limited here. Each time 1MB data block is collected, the analog random signal is converted into digital form. High sampling rate ensures sufficient capture of quantum source entropy to provide sufficient raw randomness for subsequent processing.

[0066] It can be understood that 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 de-biasing and SHA-256 hash entropy extraction to generate a random number stream with good statistical characteristics, and finally 4Mbps true random number output conforming to NIST SP 800-22 test standard is obtained.

[0067] In some embodiments, after generating quantum true random numbers, it further includes monitoring the entropy estimate value of the quantum true random numbers, and performing quantum source health check when the entropy estimate value is lower than a preset threshold. For example, the system performs online entropy estimation every certain time interval, and the interval time can be 1 second, 2 seconds, 3 seconds, 5 seconds, etc., which is not limited here. When the entropy estimate value is lower than the preset threshold, which can be 0.97 bits / bit or higher, the quantum source health check program is triggered, and if necessary, the backup random source is switched to.

[0068] Here, the quantum vacuum fluctuation noise is used to generate random signals, so that the original signal has unpredictability; after the three-stage amplification circuit processing, the random characteristics are retained and non-random components are filtered out; high-speed ADC is used for digitization of the signal, which fully captures the entropy of the quantum source to provide sufficient raw randomness for subsequent processing; Von Neumann de-biasing, 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 materials for subsequent encryption and fundamentally improving system security.

[0069] Continuing to refer to Figure 2 In step 103, the initial data is encrypted using quantum true random numbers to obtain target data.

[0070] Here, the data encryption is to convert plaintext into ciphertext by an encryption key, and the encryption key is obtained by a key derivation algorithm HKDF-SHA384. Of course, other algorithms can also be used, which are not limited here.

[0071] In some embodiments, referring to Figure 4 , Figure 4 is a third flowchart of the information transmission method provided by the embodiments of the present application, which is for Figure 3 The step 103 shown in the figure can be implemented by the steps 1031 to 1033 of the method shown in the figure, which will be described in detail below. Figure 4

[0072] The steps 1031 to 1033 are for obtaining quantum true random numbers.

[0073] In step 1031, the quantum true random numbers are converted into a fixed-length master random key.

[0074] Wherein, the quantum true random numbers are 102 Mbps true random numbers generated in step 102 that meet the test standards. Here, the master random key is generated by the key derivation algorithm ChaCha20-Poly1305 and the AES (Advanced Encryption Standard) algorithm. ChaCha20 is a stream cipher, and Poly1305 is a message authentication code. ChaCha20-Poly1305 is an AEAD (Authenticated Encryption with Associated Data) algorithm that combines stream cipher and message authentication code, which is highly efficient in software and strong in anti-side channel attack capability. The AES algorithm encrypts the data block through multiple iterations, and the same key is used for encryption and decryption. Of course, it can also be combined with a suitable encryption mode, such as GCM or CBC. Specifically, AES includes AES-256-GCM, AES-256-CBC, AES-128-CBC, and other variants.

[0075] In step 1032, a sub-key is generated according to the master random key, and the sub-key corresponds to initial data.

[0076] Here, one master random key can derive multiple sub-keys to ensure the security of the key material.

[0077] The initial data can be divided into key control instructions, measurement data, and configuration information, or can be divided according to security levels, such as 0-3 levels in descending order, wherein level 0 is the highest level and level 3 is the lowest level.

[0078] For example, the keys used for 0-3 level data are as follows: ​Level 0: ChaCha20-Poly1305 algorithm, 256-bit key; Level 1: AES-256-GCM mode, 256-bit key; Level 2: AES-256-CBC mode, 256-bit key; Level 3: AES-128-CBC mode, 128-bit key.

[0079] Among them, the data of the highest security level can also apply double encryption mechanism, such as first encrypting the level 0 data with ChaCha20-Poly1305, and then encrypting it with AES-256-GCM for the second time, double protecting the core control instruction, which provides additional protection for key data through the series encryption of two different algorithms (such as ChaCha20-Poly1305+AES-256), greatly improves the cracking difficulty, and prevents security risks caused by single algorithm vulnerability.

[0080] In step 1033, the initial data is encrypted to obtain target data according to the sub-key, and the sub-key is updated according to a preset time interval and / or a data volume domain.

[0081] According to the preset time, the sub-key is updated periodically according to the system preset time. When setting the preset time, the policy of differentiated key management can be implemented based on the data sensitivity. The core is to formulate the corresponding key update rule according to the security level of the data. Exemplarily, Level 0: ChaCha20-Poly1305 algorithm, 256-bit key, 90 seconds update; Level 1: AES-256-GCM mode, 256-bit key, 10 minutes update; Level 2: AES-256-CBC mode, 256-bit key, update every session; Level 3: AES-128-CBC mode, 128-bit key, update every day.

[0082] This step realizes the periodic update of the key, realizes the fine security management of "high frequency update of sensitive data key and low frequency update of low sensitive data", and ensures that even if a single key is cracked, its effective period is extremely limited.

[0083] According to the data volume domain, the key is updated, which is a key management strategy with data size as the core trigger condition. By dividing the data volume into different intervals, the key update time is dynamically controlled. When the encrypted data volume reaches a specified threshold (for example, 10MB), the key is updated. The specific implementation includes three stages of key rotation warning (10 seconds before expiration), new and old key smooth transition (two keys are valid for 10 seconds) and key destruction (safe erasure of old key).

[0084] According to the preset time interval and the data volume field update key double trigger mechanism, the security of data encryption is further improved, Ensure that a single key is cracked, and its validity period is not only short in time, but also extremely limited in data volume, while considering the requirements of resource business efficiency and compliance.

[0085] This method solves the problem of traditional fixed key being easy to analyze, and enhances the ability of the system to resist password analysis attack through frequent key update and differential encryption strategy.

[0086] This step ensures that the key of the entire chain of encrypted source data, hierarchical encryption strategy and key update mechanism is unpredictable, even if a single key is cracked, its effectiveness is extremely limited, so it is safe and reliable, and takes into account the resources and efficiency.

[0087] Referring back to Figure 2 , in step 104, the clock synchronization accuracy of multiple nodes is synchronized.

[0088] Clock synchronization accuracy is particularly important in power systems, which may cause data inconsistency of these devices, thereby affecting the normal operation of the system. For example, in relay protection, if the times of different devices are not synchronized, it may cause the protection device to malfunction or refuse to operate, thereby causing a power outage accident. In addition, when analyzing faults, if the recording times of each substation are not consistent, it will be difficult to accurately locate the fault point, affecting the efficiency of fault handling. In terms of electric energy measurement, time asynchronization will lead to inaccurate electric quantity statistics, which may cause electricity fee disputes. However, the local clock of each node drifts, especially in an environment with large temperature fluctuations, the cumulative error is significant, which will seriously affect the stable operation of the power system.

[0089] A chaotic synchronization controller is used to continuously update the synchronization state at a period of 10ms, ensuring that the clock deviation of each node is always controlled within the sub-millisecond range. The update process is independent of the business process of the blockchain, ensuring the continuity and stability of time synchronization. The system dynamically adjusts the chaotic coupling strength according to network conditions and business load. During the peak period of blockchain business, the coupling strength is automatically enhanced to improve synchronization accuracy; when the network condition is poor, a more robust synchronization strategy is used to ensure that synchronization will not fail due to network fluctuations.

[0090] In some embodiments, referring to Figure 5 , Figure 5 is a fourth flow of the information transmission method provided by the embodiment of the application schematic diagram, for Figure 3 the step 104 shown can be implemented by Figure 5 steps 1041 to 1043 of step 104, which will be described in detail below.

[0091] In step 1041, the chaotic state information of multiple nodes is exchanged.

[0092] The system pre-maintains chaotic systems with the same structure at each node. As for the type of chaotic system, the system adopts a Lorenz system or a Lorenz-Rössler hybrid system. The Lorenz system has typical butterfly attractor characteristics and is suitable for basic synchronization control; while the Lorenz-Rössler hybrid system combines the advantages of the two chaotic systems and has stronger adaptability to complex network environments. As for the consistency of system parameters, the system parameters are required to be consistent among nodes to ensure that the chaotic system has the same dynamic characteristics, which is a prerequisite for achieving precise synchronization. As for the synchronization update frequency, the synchronization update frequency is specified to be no less than 10 MHz. This high-speed update ensures that the system can track and compensate for small synchronization deviations in real time, achieving sub-millisecond precise synchronization. These specific parameter settings provide a clear implementation guide for chaotic synchronization control, ensuring that the system can achieve the expected high-precision synchronization effect.

[0093] At the same time, consistent system parameters (such as σ = 10, ρ = 28, β = 8 / 3) are set to establish the basis for synchronization. The coupling relationship between chaotic systems is established through the active-passive coupling mechanism in this sub-step. Usually, one node acts as the active system (driver), and the other nodes act as passive systems (responders), establishing synchronization connections through information exchange. Chaotic state information is exchanged regularly and synchronization errors are calculated. The chaotic state variables (x, y, z) are transmitted between nodes, the local and remote state differences are compared, and the synchronization error vector is calculated to provide a basis for subsequent control. In an embodiment, the chaotic synchronization controller is used to achieve the basic clock synchronization of all nodes, with a synchronization accuracy of sub-millisecond, providing a unified time reference for the entire blockchain network. This layer adopts a master-slave structure, and the master node obtains the standard time through GPS or Beidou satellite, while the slave nodes maintain high-precision synchronization with the master node through the chaotic synchronization algorithm. Before each step is executed, the participating nodes must complete time calibration to ensure that the time error of all nodes during the execution of this process does not exceed the preset threshold.

[0094] In step 1042, the multiple nodes are adjusted to a synchronized chaotic state according to the chaotic state information.

[0095] According to the synchronization error and Lyapunov function, the coupling parameters are adjusted adaptively to ensure that the system stably converges to the synchronization state and adapts to changes in network conditions.

[0096] In step 1043, the clock synchronization accuracy of the multiple nodes is synchronized according to the synchronized chaotic state of the multiple nodes.

[0097] The chaotic state synchronization is used to drive the local clock adjustment, the chaotic system synchronization state is converted into a clock adjustment signal, the local clock frequency and phase are fine-tuned, and accurate time synchronization among multiple nodes is realized.

[0098] The system updates the synchronization state every 10 ms, ensuring sub-millisecond synchronization accuracy. Tests show that the controller can achieve 50-500us synchronization accuracy, which is 10-30 times higher than traditional synchronization schemes. Even in a harsh environment where network delay fluctuates by ±20ms, the system can still maintain a synchronization error of <1ms, providing a reliable timing basis for precise coordinated control of power systems.

[0099] Continuing to refer to Figure 2 In step 105, the data slices are generated according to the target data and distributed to the multiple nodes.

[0100] Data slicing refers to the operation of dividing a large-scale data set into multiple subsets, i.e., "slices", according to certain rules. Here, the data is divided into multiple subsets according to certain rules after encryption, and then distributed to multiple physical nodes.

[0101] In some embodiments, referring to Figure 6 , Figure 6 is a fifth flowchart of the information transmission method provided by the embodiments of the present application, which is for Figure 2 The step 105 shown in the figure can be implemented by the steps 1051 to 1053 of the method shown in Figure 6 The following is a specific description.

[0102] In step 1051, the (n, k) encoding strategy is selected according to the importance of the target data to generate data slices; Here, the (n, k) encoding strategy is an important fault-tolerant mechanism in the field of data storage and transmission, and 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 in the case of loss of any n-k units Different types of data use different (n, k) encoding strategies, for example, Key state data: (5, 3) Reed-Solomon encoding, generating 5 slices, any 3 of which can recover the original data; General configuration data: (4, 2) Reed-Solomon encoding, generating 4 slices, any 2 of which can recover the original data.

[0103] Key control command: (5, 3) encoding, at least 3 pieces are needed to recover, distributed to 5 nodes; State data: (4, 2) encoding, at least 2 pieces are needed to recover, distributed to 4 nodes; General data: (3,2) encoding, at least 2 pieces can be recovered, distributed to 3 nodes.

[0104] This encoding strategy is more efficient in storage than the replication strategy, and by adjusting the values of n and k, the storage cost and fault tolerance level can be flexibly balanced.

[0105] Each slice contains three parts: slice header, metadata, and slice data, which work together to ensure that information is identifiable and organized. The slice header is a fixed-length metadata block (64 bytes) attached to the data slice, which contains key attribute information of the slice, similar to the "identity card" of a file. The metadata is a business-defined field that can be added as needed (such as encryption key ID, compression algorithm identifier), embedded inside the slice, and strongly bound with the data. The slice data reflects the actual load content.

[0106] The structure of the slice header is described below as an example:

[0107] The system constructs a 64-byte slice header for each slice, which contains magic number (0x52534543), version number, slice type, index, timestamp, etc., to ensure that the slice can be independently identified and verified.

[0108] The magic number is a "signature" that identifies the data format, used to quickly determine whether the slice is in a legal format. When the system reads the slice, it first checks the magic number, and if it does not match, it is determined to be an illegal slice, indicating a transmission error or malicious tampering, etc.

[0109] The version number identifies the version of the slice format, ensuring compatibility between new and old systems. When the system upgrades the slice protocol (such as adding new fields), the version number of the old version slice header can trigger compatibility processing logic. The slice type is used to distinguish the business attributes of the slice (such as data slice, index slice, metadata slice, etc.), examples: Type value 0x01: user data slice; Type value 0x02: check code slice (such as RS encoded redundancy block).

[0110] The index is used to identify the order of the slice in the complete data, used for sorting when recombining.

[0111] The timestamp is used to record the time when the slice is generated or last modified, used for time sequence verification or expired slice filtering, such as discarding outdated slices in real-time data streams or tracking multiple versions of data, such as database log slices distinguished by timestamp.

[0112] The slice structure realizes data integrity protection by the checksum field, any slice data modification will cause the checksum to fail, and plays a role in anti-tampering verification; illegally generated slices will be discarded directly by the system due to illegal magic number, index and other fields, and play a role in anti-forgery attack; the timestamp and index can be used to trace the generation source and processing flow of the slice, and play a role in traceability.

[0113] In step 1052, the node position relationship is determined.

[0114] There are many ways to determine the node position relationship, which are not limited here. Exemplarily, a topology discovery-based method: the network topology structure is obtained through a routing protocol, SNMP query, etc., so as to determine the connection relationship between nodes; or a distance vector-based method: such as using hop count, delay, RTT (round trip time) and the like to estimate the distance between nodes, and then deduce the position.

[0115] In step 1053, the data slices are distributed to the plurality of nodes according to the node position relationship, wherein adjacent nodes store different data slices, and any node stores no more than two data slices.

[0116] According to the node position relationship, the data slices are distributed to the plurality of nodes, if two nodes are adjacent, different data slices are stored, and at the same time, any node stores no more than two data slices. The system preferentially distributes the slices to different geographical locations of the transformer substations, to ensure that regional failures will not cause data to be unrecoverable. In a specific implementation, adjacent transformer substations store data of different slice indexes, and any transformer substation stores at most two different indexes of the slices.

[0117] This method significantly improves the fault tolerance and fault recovery capability of the system through slice redundancy and geographical dispersed storage, and even in the case of multi-node failure, the availability of data can still be ensured.

[0118] Continuing to refer to Figure 2 In step 106, the data slices of the plurality of nodes are synchronized.

[0119] In some embodiments, referring to Figure 7 , Figure 7 is a sixth flowchart of the information transmission method provided by the embodiments of the present application, which is directed to Figure 2 The step 106 shown in the figure can realize synchronization of the data slices of the plurality of nodes through the steps 1061 to 1063 of Figure 7 The following will be specifically explained.

[0120] In step 1061, a data priority is assigned according to a data type and / or data importance of the data slice.

[0121] Data priority can be classified by data type and data importance. For example, the system implements a four-level priority mechanism, with priority gradually decreasing from P0 to P4: P0: control commands and protection instructions, maximum end-to-end delay 3ms; P1: status alarm and key measurement, maximum end-to-end delay 10ms; P2: regular status data, maximum end-to-end delay 50ms; P3: historical data and statistical information, best effort transmission; The system uses the DiffServ mechanism to implement priority marking and queue management at the network layer.

[0122] In step 1062, the first transmission path is calculated according to the data priority and the network state, which at least includes one of network delay, bandwidth and reliability factor.

[0123] The system plans the optimal transmission mode based on the improved Dijkstra algorithm. The optimal transmission mode at least includes a transmission path, one of transmission efficiency and reliability. The network state at least includes one of network delay, bandwidth and reliability factor, wherein network delay is the main index considered, available bandwidth is the secondary index considered, and historical reliability is the additional index. The most suitable transmission path.

[0124] In step 1063, the data slices of the multiple nodes are synchronized according to the first transmission path.

[0125] In some embodiments, the synchronization of the data slices of the multiple nodes further includes: monitoring the network congestion in real time to calculate a second transmission mode, wherein the network congestion at least includes one of delay, packet loss rate and throughput; The data slices of the multiple nodes are synchronized according to the second transmission mode.

[0126] The system evaluates network performance by active probing, i.e. sending probe packets every interval, such as 15 seconds; at the same time, the system can also be passively monitored, i.e. analyzing traffic characteristics to continuously evaluate network performance. When network congestion or network delay, or packet loss rate and throughput reach a certain threshold, such as packet loss rate greater than 1% or delay increase greater than 50%, the system automatically adjusts the routing strategy and congestion control parameters to ensure that critical services are not affected.

[0127] In some embodiments, the synchronization of the data slices of the multiple nodes further includes: establishing a synchronization quality scoring system, wherein the synchronization quality scoring system evaluates at least one of time synchronization accuracy, data consistency, network performance, and system load. Synchronize data slices of the plurality of nodes according to a synchronization quality scoring system, wherein a corrective operation is triggered when the synchronization quality is below a threshold, the corrective operation comprising enhancing coupling strength, switching synchronization reference node.

[0128] Sub-millisecond chaotic synchronization clock is used as the time reference for data slice generation and management, realizing the deep integration of timing consistency and data distributed storage.

[0129] Each data slice is assigned a high-precision timestamp (nanosecond level precision) based on the chaotic synchronization clock when generated. This timestamp not only identifies the generation time of the slice, but more importantly, ensures that slices distributed across different nodes have strictly consistent timing relationships. Traditional data slice technology relies on local clocks, which are prone to clock deviation leading to chaotic slice timing. The present invention ensures that all nodes have uniform time references through the chaotic synchronization controller, fundamentally solving the timing consistency problem of distributed data slices. When the chaotic synchronization controller detects that each node reaches the synchronization state, the system will uniformly trigger the update operation of the data slice. The specific process is as follows: the chaotic synchronization controller first confirms that the clock synchronization accuracy of all participating nodes reaches the preset threshold (usually within 100 microseconds), then sends a synchronization trigger signal, and each node starts to execute the generation, distribution or update operation of the data slice at the same time point after receiving the signal, ensuring that the operation timing of the data slice remains strictly consistent even in complex network environments. When data is recovered, the system not only reorganizes based on the index information of the slice, but more importantly, uses the timestamp information to verify the timing consistency of the slice. The algorithm checks the timestamp difference of the slices involved in the reorganization, and if an abnormal deviation (more than 10 times the synchronization accuracy threshold) is found, the system will refuse to use the slice and obtain a replacement slice from other nodes, ensuring the timing correctness of the reorganized data.

[0130] 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 reference of the entire network, ensuring that the chaotic system of all nodes remains in a synchronized state; the regional synchronization layer manages groups of nodes with similar geographical locations, optimizing the synchronization efficiency within the local network; 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 based on the intensity of data slice operations. When the system detects a large number of slice update operations (such as the need to frequently update device status data during power system failures), it automatically enhances the coupling strength between chaotic systems, increasing 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. The entire lifecycle of each data slice, from creation to destruction, is strictly managed by the chaotic synchronization clock. A precise generation timestamp is recorded when the slice is created; during slice transmission, the receiving node verifies that the transmission delay is within a reasonable range; when the slice is updated, the timestamp difference between the new and old versions must meet the preset update interval; and when the slice is destroyed, the system waits for all related nodes to complete synchronization confirmation before executing the deletion operation.

[0131] In one embodiment, the substation fault handling scenario takes the main transformer fault of a 220kV substation in East China Power Grid as an example to illustrate the actual application process of time synchronization and data slice coordination technology, Fault occurrence stage (T=0 time): The digital protection device in the substation detects the main transformer differential protection action 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, adjacent substations is controlled within 50 microseconds. The fault data is marked as the highest priority (P0 level), encrypted using the ChaCha20-Poly1305 algorithm, and then (5, 3) Reed-Solomon encoding is used to generate 5 data slices.

[0132] Slice distribution stage (T=1-3 milliseconds): The 5 data slices are sent to the standby server of the station, the dispatch center, adjacent substation A, adjacent substation B, and the regional standby dispatch center. Due to the precise timing control of the chaotic synchronization controller, all slices carry a unified timestamp (accurate to nanoseconds), ensuring that the receiving party can accurately identify the exact time of fault occurrence. The timestamp information in the slice header is shown as: 2024-03-15 14:32:07.123456789, and the timestamps of all 5 slices are completely consistent.

[0133] Data verification and recovery phase (T=3-5 milliseconds): After receiving the slices, the dispatch center first verifies the validity of the timestamps and finds that one of the slices (from adjacent substation A) has a timestamp deviation exceeding the 200-microsecond threshold due to large network delay. The system automatically recombines data from any three of the other four slices and successfully recovers the complete fault information. At the same time, the chaos synchronization controller detects the synchronization deviation of adjacent substation A and automatically adjusts its chaos coupling parameters to restore its synchronization accuracy to normal level within 500 milliseconds.

[0134] Execution phase (T=5-10 milliseconds): Based on the recovered fault data, the dispatch center generates load transfer instructions, which are also encrypted and processed into (5,3) slices before being distributed to relevant substations. Since all nodes maintain precise time synchronization, each substation can start executing the load transfer operation at the same time point (T=10 milliseconds), avoiding secondary grid failures caused by improper operation timing.

[0135] Daily operation data synchronization: Taking the daily operation data synchronization of a regional power grid as an example, this section demonstrates the application of time synchronization and data slicing technology in routine business.

[0136] The 15 substations in the region generate about 500 MB of operation data per second, including voltage, current, power, switch state, and other information. These data are classified into three categories based on importance: critical measurement data are encoded using (4,2), general monitoring data are encoded using (3,2), and historical statistical data are simply backed up. The chaos synchronization controller ensures that the data collection timestamps of all substations remain consistent, with a time deviation of less than 10 milliseconds.

[0137] Each substation slices its own data and stores it locally, as well as sends slice copies to other nodes dispersed geographically. For example, substation A in the city sends its critical data slices to substation B in the suburbs, substation C in the adjacent city, and the provincial dispatch backup center. The time reference of the chaos synchronization controller ensures that the slice timestamps received by each node remain highly consistent even in the case of network delay fluctuations.

[0138] The system performs a full-network data consistency check every 10 minutes. By comparing the data slices stored by each node at the same time, the system verifies the integrity and timing accuracy of the data. In one check, the system found that the timestamp of a data slice from substation D was abnormal (deviation of 5 seconds), so the chaos synchronization controller immediately recalibrated the node and recovered the correct data from the slice copies of other nodes.

[0139] 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.

[0140] 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: Time synchronization accuracy (weight 40%): measures the clock deviation between nodes; Data consistency (weight 30%): Evaluate the differences in data versions between nodes; Network performance (weight 20%): Monitors the quality of communication between nodes; System load (weight 10%): tracks the utilization of processing and storage resources.

[0141] 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.

[0142] 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.

[0143] See also Figure 2 In step 101, acquiring initial data on the status of the power equipment includes: Obtain testing data from power equipment; 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. 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.

[0144] 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.

[0145] In step 1011, the detection data of the power equipment is acquired.

[0146] Various sensors on the power equipment will transmit various readings such as temperature, pressure, current, voltage, vibration, etc.; there are also information from the control system, protection device, etc., which can be obtained through the acquisition device.

[0147] In step 1012, the initial data of the power equipment state is obtained by processing the detection data using the digital twin model of the power equipment.

[0148] The equipment state digital twin model constructs a virtual mirror for the power equipment and maps the physical equipment state in real time. The specific steps are steps 10121 and 10122 of the equipment state digital twin model. Figure 9

[0149] In step 10121, a multi-physical quantity model of the power equipment is constructed, including electrical parameters, thermal parameters, and mechanical parameters.

[0150] The system constructs a multi-domain coupled model based on physical laws, including electrical parameters, thermal parameters, and mechanical parameters, which are based on the following system acquisition: Electrical model: equivalent circuit model is established based on node voltage method, including resistance, inductance, capacitance and nonlinear elements; Thermal model: simplified thermal network method is used to divide the equipment into multiple temperature nodes to simulate heat conduction and convection processes; Mechanical model: structure vibration characteristics are simulated based on mass-spring system; Model parameters are trained by historical data and configured by expert knowledge to achieve accurate modeling of various power equipment.

[0151] In step 10122, the initial data of the power equipment state is obtained according to the multi-physical quantity model prediction model.

[0152] The system uses an extended Kalman filter algorithm to fuse physical model prediction 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 the measurement data), and adjusts the Kalman gain adaptively to balance the weight of model prediction and measurement correction.

[0153] Here, different parameters can be updated at different frequencies according to their physical characteristics, and the system can implement a multi-level update strategy based on the rate of change of physical quantities: Electrical state (voltage, current): updated every 10ms; Thermal state (temperature distribution): updated every 100ms; Mechanical state (vibration characteristics): updated every 1 second; ​Lifetime parameter (aging indicator): updated every 1 hour; This strategy significantly reduces the computational resource requirements while ensuring the accuracy of the model.

[0154] The system combines the deterministic evolution of the physical model and the statistical analysis of historical data to achieve short-term prediction functions. The prediction algorithm uses a combination of recursive expansion and autoregressive moving average (ARMA) methods to accurately predict future trends in device state changes, providing a foundation for early fault warning.

[0155] The electrical parameter modeling uses an equivalent circuit modeling method based on the node voltage method, which abstracts complex power equipment into an equivalent circuit network composed of resistance, inductance, capacitance, and nonlinear elements.

[0156] 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 (folded value on the primary side 0.125Ω), short-circuit inductance Ls (folded value on the primary side 15.8mH), magnetizing inductance Lm (primary side value 450H), core loss resistance Rm (primary side value 18.5kΩ). The system determines the accurate value of the model parameters through parameter identification algorithms based on transformer nameplate parameters and field test data.

[0157] The electrical behavior of the transformer is described by the following system of differential equations: Primary side voltage equation:

[0158] Secondary side voltage equation:

[0159] Flux linkage equation:

[0160] Where Φsat(im) is a nonlinear flux linkage function considering magnetic saturation effects, which is realized by piecewise linear approximation method: The parameters a1, a2, a3 are obtained by least squares fitting of historical operating data.

[0161] The thermal model uses a lumped parameter thermal network method, dividing the transformer oil 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.

[0162] 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. Each node is connected through thermal resistance Rth and thermal capacity Cth, forming a complete heat conduction network.

[0163] The system adopts the node method based on Fourier heat conduction law to establish the thermal network equation:

[0164] 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 according to the device geometry and material properties: conductive thermal resistance Rcond = L / (k·A), convective thermal resistance Rconv = 1 / (h·A), and radiative thermal resistance .

[0165] Actual parameter determination: convective heat transfer coefficient between copper winding and oil , thermal conductivity of the core k = 45 W / (m·K), and specific heat capacity of transformer oil c = 2.1 kJ / (kg·K). The model accuracy is verified through field thermal imaging testing and historical temperature rise test data, with a temperature prediction error controlled within ±2°C.

[0166] The mechanical model is based on the theory of multi-degree-of-freedom vibration systems, simplifying the transformer structure into a mass-spring-damper system. The system establishes a comprehensive mechanical model that includes winding axial vibration, radial vibration, and oil tank structure vibration.

[0167] The winding structure is discretized into n particles, each with mass mi, spring constant ki, and damping coefficient ci. The vibration equation of the winding under electromagnetic force is:

[0168] 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, calculated by finite element analysis.

[0169] Oil tank structure vibration analysis: The oil tank vibration uses modal analysis method to identify the first 10 natural frequencies and modes. The vibration response at key positions is measured by acceleration sensors, and the vibration transfer function H(ω) = X(ω) / F(ω) is established to realize dynamic modeling from excitation force to structural response. Typical parameters include: first-order natural frequency f1 = 35 Hz, second-order natural frequency f2 = 78 Hz, and structural damping ratio ξ = 0.032.

[0170] The life model is based on the cumulative damage theory, considering the effects of thermal aging, electrical aging, and mechanical aging on the service life of the equipment. The modified Arrhenius equation is used to describe the thermal aging process of insulating materials:

[0171] Where L(T) is the lifetime at temperature T, L0 is the lifetime at reference temperature T0, B is a material constant (B=6000 K for transformer insulation paper). The system calculates the cumulative aging factor in real time, and predicts the remaining life of the equipment.

[0172] The system fuses data from different physical domains such as electricity, heat, and machinery, and uses a multi-sensor data fusion method based on D-S evidence theory. The measurement results of each sensor are used as evidence, and the system calculates the credibility of each evidence to obtain the fusion result. The mathematical expression of the fusion algorithm is:

[0173] K is the normalization constant, and m1, m2 are the basic probability assignment functions of different sensors.

[0174] The system extracts feature parameters reflecting the state of the equipment from the original data. Electrical features include effective value, harmonic distortion rate, power factor, etc.; thermal features include hot spot temperature, temperature gradient, heat flux density, etc.; mechanical features include vibration intensity, frequency spectrum characteristics, phase relationship, etc. Feature extraction uses mathematical tools such as wavelet transform, Fourier transform, and principal component analysis.

[0175] The Extended Kalman Filter (EKF) algorithm is the core algorithm for state estimation of the system, which is used to fuse the prediction of the physical model and the real-time measurement data to achieve the optimal estimation of the internal state of the equipment.

[0176] The state vector of the equipment is defined as X=[T1,T2,...,T15,I1,I2,I3,Φ,ω,θ]T, which contains 15 temperature nodes, 3 phase currents, flux linkage, angular frequency and phase angle, a total of 21 state variables. The state transition equation is:

[0177] 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.

[0178] The observation vector Z contains directly measurable quantities such as some temperature points, current effective value, power, etc. The observation equation is:

[0179] Where h[·] is the nonlinear observation function, and v(k) is the measurement noise.

[0180] The specific implementation steps of the EKF algorithm are: Based on the optimal estimation at the previous time and the state transition model, the state estimation and error covariance at the current time are predicted.

[0181] State prediction:

[0182] Covariance prediction:

[0183] where F(k) is the Jacobian matrix of the state transition function: Q(k) is the process noise covariance matrix.

[0184] The state prediction is corrected by the measurement at the current time to obtain the optimal state estimation.

[0185] Kalman gain calculation:

[0186] State update:

[0187] Covariance update:

[0188] where is the Jacobian matrix of the observation function, and R(k) is the measurement noise covariance matrix.

[0189] 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 load change), the value of Q(k) is appropriately increased to improve the tolerance to model uncertainty. For the measurement noise covariance R(k), it is adjusted according to the real-time performance of the sensor. When the sensor is aging or malfunctioning, the corresponding R(k) elements are increased.

[0190] To prevent filter divergence, the system implements various suppression measures. The performance of the filter is monitored by the innovation sequence algorithm. When the innovation exceeds a reasonable range, the filter is reinitialized. A decaying memory filter is used to assign decaying weights to historical data. Upper and lower boundaries of the covariance matrix are set to prevent numerical abnormalities.

[0191] To address the diversity of equipment operating conditions, the system implements a multi-model adaptive EKF. State transition models for normal operation, heavy load operation, and fault operation are established, and automatic switching between models is achieved through model probability calculation. The model probability update formula is:

[0192] where μi(k) is the probability of the i-th model at time k, and Li(k) is the model likelihood function.

[0193] This method realizes deep perception and prediction of the state of power equipment, surpassing the surface data collection of traditional monitoring systems. In practical applications, the system can significantly advance the early warning of equipment abnormalities, significantly improve the abnormal detection rate, significantly reduce the false alarm rate, significantly enhance the preventive maintenance capability, and significantly reduce unplanned downtime events.

[0194] The following continues to illustrate an exemplary embodiment of the information transmission module 533 provided by the present application as a software module.

[0195] In some embodiments, as shown in Figure 1 The software module of the information transmission module 533 stored in the memory 530 can include: A device state digital twin module 5330 for acquiring initial data of a power device, constructing a virtual mirror for the power device, and mapping the state of the power device in real time; A quantum true random number generation module 5331 for generating a true random number using quantum physical phenomena; A dynamic encryption engine 5332 connected to the quantum true random number generation module, for receiving the true random number and encrypting data using the true random number as a key; A data slicing and recovery module 5333 connected to the dynamic encryption engine, for receiving the encrypted data and distributing it to multiple nodes; A distributed ledger management module 5334 for maintaining a distributed data synchronization architecture for synchronizing and managing sliced data processed by the data slicing and recovery module; A consensus algorithm processor 5335 for providing consistency assurance for the distributed ledger; A chaotic synchronization controller 5336 for achieving sub-millisecond clock synchronization between multiple nodes, which is used to coordinate the data distribution of the data slicing and recovery module and the data synchronization process of the distributed ledger management module.

[0196] An information transmission and synchronization management module for coordinating data transmission and synchronization processes to coordinate the work of each module of the device to achieve a safe and synchronized data transmission and synchronization process.

[0197] In the scheme, the quantum random number generation module is the security foundation of the entire system, generating truly random numbers by utilizing quantum physical phenomena (such as quantum vacuum fluctuation noise, photon scattering, or quantum tunneling effect), rather than predictable sequences generated by traditional pseudo-random algorithms. The quantum random number generation module provides 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, allowing the key to be automatically updated according to a pre-set time or data volume threshold, and implementing a hierarchical encryption strategy according to data importance, thereby optimizing system performance while ensuring security. The data slicing and recovery module receives encrypted data, divides the data into multiple slices through information dispersion technology (such as Reed-Solomon encoding), and distributes these slices to multiple physical nodes. The data slicing and recovery module enables the system to have fault tolerance, allowing complete data to be reconstructed from remaining slices even if some slices are lost or damaged. The distributed ledger management module is based on optimized blockchain technology, building 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 among all nodes, preventing data from being tampered with or lost, and providing a reliable data sharing foundation for the system. The chaos synchronization controller is the key to solving the synchronization accuracy problem of multi-point transmission in the power system, using the characteristics of chaotic systems (sensitive dependence on initial conditions but deterministic evolution) to achieve high-precision clock synchronization among multiple nodes. The chaos synchronization controller can control the synchronization error between nodes to sub-millisecond level, far superior to traditional synchronization schemes. The information transmission and synchronization management module serves as the coordination center of the entire system, responsible for planning and executing data transmission strategies, monitoring synchronization status, and optimizing network resource usage. The information transmission and synchronization management module organically connects all parts of the system, ensuring overall coordinated operation.

[0198] Further comprising: a device state digital twin module and a consensus algorithm processor. The device state digital twin module is connected with the information transmission and synchronization management module and the distributed ledger management module, used to build a virtual mirror of the power equipment, to map the physical device state in real time, and to provide key state information for transmission and synchronization to the dynamic encryption engine for encryption processing; the device state digital twin module creates a high-fidelity digital representation of the equipment through multi-physical quantity modeling, real-time data acquisition and state estimation algorithm. The device state digital twin module enables the system to gain deep insights into the state of the equipment, going beyond the surface data collection of traditional monitoring systems, and achieving state trend prediction and early identification of abnormal behavior. The consensus algorithm processor is connected with the distributed ledger management module, used to provide consistency guarantee for the distributed ledger, and to verify and confirm transactions entering the ledger. The consensus algorithm processor implements an improved practical Byzantine fault tolerance (PBFT) algorithm, in which participating nodes perform time calibration before the start of each stage (preparation, preparation, submission, completion) of the PBFT consensus algorithm. Each receiving node not only verifies the correctness of the block content, but also verifies the consistency of the block timestamp with 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 key stage of the PBFT consensus, the chaotic synchronization controller enters the high-precision mode, shortening the synchronization update period from 10 ms to 1 ms, ensuring that the timing of each node in the consensus process is strictly consistent, and ensuring that even if there are some malicious or faulty nodes in the distributed system, the system can still reach a consistent data view. The consensus algorithm processor closely cooperates with the distributed ledger management module to form the distributed data consistency guarantee mechanism of the system. The above-mentioned device state digital twin module and consensus algorithm processor jointly enhance the state perception ability and distributed data reliability of the system, forming a complete "state perception-data sharing-consistent decision" link.

[0199] The quantum random number generation module comprises a quantum source, a signal amplification link, a digitization processing unit, and a post-processing algorithm unit. The quantum source is used to generate a random signal based on quantum vacuum fluctuation noise; the quantum source utilizes quantum physical phenomena such as semiconductor PN junctions, photon scattering, or quantum tunneling. Specifically, the quantum source is based on the principle of quantum vacuum fluctuation noise, uses a reverse-biased semiconductor PN junction, operates at 10 mA current, and uses the random generation and recombination process of electron-hole pairs to obtain a random signal. This method directly utilizes the principle of quantum uncertainty to generate a truly random signal that is 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 is used to amplify the random signal; the signal amplification link adopts a three-stage amplification design and usually comprises a low-noise preamplifier, a bandpass filter, and a secondary amplification stage, amplifies the weak quantum random signal to an appropriate level, ensures that the quantum random signal is effectively amplified, and at the same time maximizes the retention of its random characteristics and filters out non-random components. The digitization processing unit is connected to the signal amplification link and is used to quantitatively process the amplified random signal and convert the amplified analog random signal into a digital form. The digitization processing unit comprises a high-speed ADC (analog-to-digital converter) and usually has a sampling rate of hundreds of megahertz to several gigahertz, quantizes the continuous random signal into discrete digital values, and a high sampling rate ensures sufficient capture of the entropy of the quantum source to provide sufficient original randomness for subsequent processing. The post-processing algorithm unit is connected to the digitization processing unit and is used to extract entropy and test randomness of the quantitatively processed signal, extract entropy and perform statistical tests on the quantized random data, and output high-quality truly 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 to monitor the quality of random numbers in real time and ensure that the output random numbers meet the high-quality requirements of cryptographic applications. These components form a complete quantum random number generation process, from the generation of random signals at the quantum source, through amplification, digitization, and post-processing, to the final output of high-quality truly random numbers.

[0200] The dynamic encryption engine includes a key derivation framework, a multi-level encryption policy unit, and a real-time key scheduling mechanism. The key derivation framework is used to receive the true random number output by the quantum random number generation module and generate an encryption key from the true random number. The key derivation framework implements HKDF and other key derivation functions to convert the original random number material into a key that meets the encryption algorithm requirements and manages the life cycle of the key. Specifically, the key derivation framework is based on the HKDF-SHA384 algorithm and derives various keys required for encryption from the 32-byte seed material obtained from the quantum random number generation module. The key derivation framework uses a two-stage key derivation process: first, the "extraction" stage converts the input entropy source material into a fixed-length pseudo-random key; then, the "expansion" stage generates sub-keys for specific purposes according to different application contexts. The key derivation framework supports key hierarchy management, allowing multiple sub-keys to be derived from a master key to ensure the secure isolation of key material. The multi-level encryption policy unit is used to select different encryption algorithms and parameters according to the importance of the data and the keys generated by the key derivation framework. The multi-level encryption policy unit classifies data (such as critical control instructions, state monitoring data, and configuration information) and selects appropriate encryption algorithms (such as ChaCha20-Poly1305, AES-256-GCM, etc.) and parameters (such as key length, block mode) for different levels of data. The multi-level encryption policy unit implements differentiated encryption based on data importance classification, with the following specific configurations: Critical control instructions: ChaCha20-Poly1305 algorithm, key length 256 bits, updated every 90 seconds; Measurement data: AES-256-GCM mode, key length 256 bits, updated every 10 minutes; Configuration information: AES-256-CBC mode, key length 256 bits, updated per session.

[0201] The multi-level encryption policy unit optimizes system performance while ensuring security by differentiating encryption resources and adopting different protection strategies for data of different importance levels.

[0202] The real-time key scheduling mechanism is connected to the key derivation framework and is used to automatically update encryption keys based on pre-set time or data volume thresholds. The real-time key scheduling mechanism monitors key usage and automatically triggers a key update process when the key usage time or encrypted data volume reaches the pre-set 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 hierarchical strategies.

[0203] The data slice and recovery module receives encrypted data from the dynamic encryption engine and implements data slicing in the following way: Using (n, k) encoding strategy: the system generates n data slices, any k of which can reconstruct the original data, and n>k; it can be understood that this encoding mechanism (usually based on Reed-Solomon code) provides data redundancy and fault tolerance capability, even if part of the slices are lost or damaged, the complete information can still be recovered.

[0204] Slice structure design: each slice contains three parts of slice header, metadata and slice data, to ensure the self-descriptiveness and manageability of the slice.

[0205] Slice header detailed field: the slice header contains magic number (used to identify the slice), version, slice type, slice index, total slice number, required slice number, original data length, slice data length, original data hash and timestamp, etc. The data recovery process adopts a parallel acquisition strategy, first tries to recover from the local, and when the local data is insufficient, acquires the required slices from the adjacent nodes in parallel. The test shows that under the standard network condition (delay < 50 ms), the average time consumption of data recovery does not exceed 120 ms. These fields ensure that the slice can be correctly identified, verified and recombined, even in complex network environment and storage conditions.

[0206] The slice mechanism of the above scheme also enhances the data security, attackers need to break through multiple nodes and obtain a sufficient number of slices to recover the original data, which greatly improves the attack difficulty, and also improves the resilience of the system in the case of partial node failure.

[0207] The chaotic synchronization controller comprises a chaotic oscillator, an adaptive synchronization algorithm unit, and a clock calibration and compensation unit. The chaotic oscillator is used to generate a reference signal based on a deterministic chaotic system; the chaotic oscillator is usually implemented by an FPGA or an application-specific integrated circuit in hardware to generate a chaotic signal with sensitive dependence on initial conditions but deterministic evolution characteristics. The adaptive synchronization algorithm unit is used to receive the reference signal generated by the chaotic oscillator and chaotic state information from other nodes, to implement an active-passive coupling synchronization mechanism, and to calculate a synchronization error; the adaptive synchronization algorithm unit dynamically adjusts the coupling strength by calculating the difference between the local and remote chaotic states, so that multiple chaotic systems tend to be in a synchronization state. The clock calibration and compensation unit is based on the synchronization error calculated by the adaptive synchronization algorithm unit and refers to the output of the chaotic oscillator, and is used to process temperature drift and phase deviation, and to adjust the local clock to achieve sub-millisecond clock synchronization between multiple nodes; the clock calibration and compensation unit monitors system temperature changes and clock deviation, compensates for synchronization errors caused by these factors in real time, and maintains long-term stable high-precision synchronization. The above three components work together to form a high-precision synchronization control system based on chaos theory, providing a technical basis for millisecond-level or even sub-millisecond-level synchronization between multiple nodes.

[0208] The chaotic oscillator implements a Lorenz chaotic system based on the following differential equation:

[0209] wherein, is a system parameter, and a typical value is = 10, = 28, = Under these parameters, the system exhibits deterministic chaotic behavior, that is, sensitive dependence on initial conditions but long-term evolution following a specific strange attractor. The chaotic system provides a mathematical basis for chaotic synchronization control, and is implemented on a hardware platform such as an FPGA through a high-precision numerical integration method (such as a fourth-order Runge-Kutta method), and the integration process uses four intermediate steps (k1-k4) to calculate state updates, with an error controlled at order, ensuring the accuracy of the chaotic trajectory.

[0210] The adaptive synchronization algorithm unit implements an active-passive coupling (APD) synchronization mechanism, with one node as an active system (driver) and the other nodes as passive systems (responders). The synchronization control uses the following equation:

[0211] where K is the coupling strength parameter, with an initial value of 0.1, dynamically adjusted according to the synchronization error, ranging between 0.1-10. The system uses the error sum of squares as the Lyapunov function, and adaptively adjusts the coupling strength through gradient descent method, ensuring the convergence of synchronization.

[0212] The clock calibration and compensation unit handles temperature drift and phase deviation, including a triple compensation mechanism: Temperature compensation: Adjust the clock frequency based on temperature sensor data ; Frequency calibration: Regularly compare and calibrate the local clock frequency with the master node; Phase locking: Lock the phase relationship using PLL technology.

[0213] The system updates the synchronization state every 10ms, ensuring sub-millisecond synchronization accuracy. Even in a harsh environment where network latency fluctuates by ±20ms, the system can still maintain a synchronization error of <1ms, providing a reliable timing basis for precise coordinated control of the power system.

[0214] In one embodiment, taking the blockchain application of a power system substation group as an example, the implementation process of continuous time synchronization is described in detail: 5 substation nodes form a blockchain network, first establishing basic time synchronization through the chaos synchronization controller, with a synchronization accuracy of within 100μs. When a power equipment state change occurs, the relevant substation generates a state update transaction. Before transaction generation, the node performs fast clock calibration (time consumption <10ms), ensuring the accuracy of the transaction timestamp. Before the PBFT consensus begins, all participating nodes perform accurate time synchronization (target accuracy 50μs). During the consensus process, each stage's message is accompanied by an accurate timestamp, and messages exceeding the preset time window (±100ms) are automatically rejected. After the new block is generated, each node receives and verifies the block while checking the reasonableness of the block timestamp. If abnormalities are found, the node will first perform time resynchronization, then re-verify the block. The chaos synchronization controller continuously updates the time synchronization state of each node at a 10ms cycle, ensuring time consistency in long-term operation.

[0215] The distributed ledger management module includes a lightweight block structure design unit, a hierarchical storage architecture unit, and a ledger sharding and management unit. The lightweight block structure design unit is used to define a block format suitable for the power system; the lightweight block structure design unit designs a lightweight block structure containing a block header (magic number, version number, block height, previous block hash, data root hash, timestamp) and a block body (transaction number, transaction list) for the real-time requirements of the power system, balancing data integrity and processing efficiency. The hierarchical storage architecture unit includes three layers of storage architecture: volatile layer, fast layer, and archive layer; the volatile layer (memory stores recent state data, supports millisecond-level read and write), the fast layer (SSD stores recent blocks, supports second-level query), and the archive layer (compresses and stores historical blocks, supports complete traceability). This hierarchical architecture balances performance and data integrity requirements. The ledger sharding and management unit is used to divide the ledger shards by functional area; the ledger sharding and management unit logically shards the ledger by voltage level, functional area, and other dimensions, reducing the storage and processing burden of individual nodes while maintaining global data consistency. These three components together constitute a distributed ledger management system optimized for the power system, solving the performance and resource consumption problems faced by traditional blockchains in power system applications.

[0216] The distributed ledger management module uses optimized blockchain technology to build a distributed data synchronization architecture suitable for the characteristics of the power system.

[0217] The lightweight block structure design unit defines a block format optimized for the power system: Block header (80 bytes):

[0218] Block body:

[0219] Among them, the block size is limited to within 4KB to ensure fast propagation and processing. A single block contains up to 64 state update transactions, with an average of one block generated every 5 seconds.

[0220] The hierarchical storage architecture unit implements a three-layer storage structure: Volatile layer: stores recent state data in memory, supports millisecond-level read and write; Fast layer: SSD stores recent 24-hour blocks, supports second-level query; Archive layer: compresses and stores historical blocks, supports complete traceability.

[0221] The ledger sharding and management unit divides the ledger shards by functional area: Core shard: stores the status of key devices such as circuit breakers and disconnectors; Measurement shard: stores voltage, current, and other measurement values; Alert shard: stores system alert information.

[0222] The distributed ledger structure overcomes the single point of failure risk of traditional centralized data management, realizes decentralized data storage and synchronization, and greatly improves system reliability.

[0223] The consensus algorithm processor is based on the improved practical Byzantine fault tolerance (PBFT) algorithm, which provides consistency guarantee for the distributed ledger. This algorithm allows the system to reach consensus even if at most f=(n-1) / 3 nodes fail or are malicious, where n is the total number of nodes.

[0224] The consensus process consists of four stages: 1. Pre-preparation stage: the master node (rotated every 10 blocks) broadcasts the proposed block and the serial number.

[0225] 2. Preparation stage: the verification node broadcasts the preparation message after verification.

[0226] 3. Submission stage: the node broadcasts the submission after confirming that more than 2f preparation messages have been received.

[0227] 4. Completion stage: the block is confirmed to be valid after receiving more than 2f submission messages.

[0228] To meet the low-latency requirements of the power system, the embodiment implements a fast-path consensus mechanism: when a node receives more than 3f+1 preparation messages, it can skip the confirmation stage and directly complete the consensus, typically reducing the consensus time from 100ms to 35ms.

[0229] The system also implements an efficient view switching mechanism to handle master node failure: when the timeout parameter is 500ms, the system can complete view switching and resume normal operation within an average of 350ms after the master node fails, significantly improving system reliability.

[0230] The device state digital twin module includes a multi-physical quantity modeling framework, a real-time data integration mechanism, a twin model synchronizer, and a prediction and diagnosis engine. The multi-physical quantity modeling framework is used to build a comprehensive model including electrical characteristics, thermodynamic characteristics, and mechanical characteristics; the multi-physical quantity modeling framework describes device behavior from multiple dimensions based on physical laws and empirical models, covering electrical (such as equivalent circuit model), thermal (temperature distribution model), and mechanical (structural stress model) aspects. The real-time data integration mechanism is used to collect and process multi-source data from physical device sensors and other data sources, and provide the data to the modeling framework and the twin model synchronizer; the real-time data integration mechanism receives real-time data from various sensors, protection devices, and control systems, and provides timely and accurate inputs for the model after filtering, normalization, and other preprocessing. 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; the twin model synchronizer continuously updates model parameters and state variables through state estimation algorithms (such as extended Kalman filter), ensuring that the virtual model accurately reflects the actual state of the physical device. The prediction and diagnosis engine, based on the model established by the multi-physical quantity modeling framework and historical data, is used to analyze device state trends and identify abnormal behavior, and outputs device state information and prediction results for use by the information transmission and synchronization management module and other related modules for encryption, slicing, and synchronization. The prediction and diagnosis engine predicts future device states based on historical data and physical models, identifies potential abnormalities and failure modes, and provides early warning and diagnosis information. The above four components form a complete digital twin system, providing a powerful tool for power device state monitoring and fault prediction.

[0231] The multi-physical quantity modeling framework builds a comprehensive model of the power device: Electrical model: based on equivalent circuit theory, simulates current and voltage characteristics; Thermodynamic model: finite element simplified model, simulates temperature distribution and heat conduction; Mechanical model: describes structural stress and vibration characteristics; Lifetime model: based on cumulative damage theory, predicts the remaining life of key components.

[0232] The real-time data integration mechanism collects and processes multi-source data: Sample value data: obtained through IEC 61850-9-2 protocol GOOSE message: captures switch state changes; Sensor data: collects physical quantities such as temperature and vibration; Protection information: records protection actions and alarms.

[0233] The twin model synchronizer implements state estimation based on extended Kalman filter (EKF), and the core algorithm is as follows: The twin model synchronizer realizes state estimation based on extended Kalman filter (EKF). The algorithm includes two key steps: prediction step and update step. The prediction step is based on the system model to predict the next state: and its error covariance . The update step corrects the predicted state according to the measurement value , calculates the Kalman gain , and updates the state estimation and its error covariance . The algorithm dynamically balances the weights of model prediction and actual measurement, ensuring the accuracy and robustness of state estimation.

[0234] To balance the computing resources and model accuracy, the system implements a differentiated update strategy: electrical state: updated every 10 ms; thermal state: updated every 100 ms; mechanical state: updated every 1 second; life parameters: updated every 1 hour.

[0235] The prediction and diagnosis engine 84 predicts the state changes of the device within the next 5 seconds based on the physical model and historical data, identifies abnormal patterns, and generates early warnings. The engine realizes a dual abnormality detection mechanism based on threshold and trend, significantly improving the abnormal detection rate and reducing the false alarm rate.

[0236] Digital twin technology enables the system to have millisecond-level state awareness and 5-second forward-looking prediction capabilities, allowing for 3-5 seconds of early warning of device abnormalities, providing sufficient response time for maintenance personnel.

[0237] Based on the application of the above software modules, the present application also has another information transmission method with quantum random encryption and distributed synchronization. The method is based on the power system substation group application scenario and is implemented as follows: Step 1. Quantum random number generation method The quantum random number generation step includes: Step 1.1, generate random signal using quantum vacuum fluctuation noise: in a reverse-biased PN junction, quantum vacuum fluctuations produce random electron-hole pairs, forming an original random signal. The specific operation of this step is to reverse bias the PN junction to 10 mA working current, and the quantum fluctuation effect produces fluctuations on the nanosecond time scale, which is converted into a weak voltage signal by the preamplifier circuit.

[0238] Step 1.2, Amplification and filtering of random signals: The weak quantum signal is amplified to an appropriate level through a three-stage amplification circuit, while external interference and system noise are filtered out using a band-pass filter (1-200 MHz). The amplifier is designed as a low-noise type (noise figure <1.2 dB) to ensure that no significant deterministic noise is introduced.

[0239] Step 1.3, Digitization of signals using high-speed ADC: The amplified random signal is sampled by a 20GS / s sampling rate ADC, with each collection of 1MB data blocks converted to digital form.

[0240] Step 1.4, Application of entropy extraction algorithm to obtain high-quality random numbers: The original digitized data is processed by Von Neumann de-biasing and SHA-256 hash entropy extraction to generate a random number stream with excellent statistical properties, ultimately obtaining a 4Mbps true random number output that meets the NIST SP800-22 test standard.

[0241] Step 1.5, Real-time monitoring of random number quality: The system performs online entropy estimation once per second, and when the entropy estimation value is below the preset threshold (0.97 bits / bit), the quantum source health check program is triggered, and if necessary, the backup random source is switched to.

[0242] Compared with 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.

[0243] Step 2. Dynamic encryption method The dynamic encryption steps include: Step 2.1, Divide data into different security levels according to data classification: The system analyzes the input data, identifies the data type, and divides it into four security levels: Level 0 (highest): Control commands and protection trip instructions; Level 1: Critical device status and alarm information; Level 2: General measurement data and device parameters; Level 3: Historical data and statistical information.

[0244] Step 2.2, Select different encryption algorithms and parameters for data of different security levels: Automatically select encryption schemes according to security levels: Level 0: ChaCha20-Poly1305 algorithm, 256-bit key, 90-second update; Level 1: AES-256-GCM mode, 256-bit key, 10-minute update; Level 2: AES-256-CBC mode, 256-bit key, per-session update; Level 3: AES-128-CBC mode, 128-bit key, updated daily. Step 2.3, Apply double encryption mechanism to the highest security level data: encrypt 0-level data first with ChaCha20-Poly1305, then use AES-256-GCM for secondary encryption, double-protect core control instructions.

[0245] Step 2.4, Automatically update encryption keys according to preset time intervals or data volume thresholds: the system implements key update based on a double-trigger mechanism: Time trigger: update the key according to the configured time interval; Data volume trigger: update the key when the encrypted data volume reaches a specified threshold (e.g., 10MB).

[0246] The specific implementation includes three stages: key rotation warning (10 seconds before expiration), smooth transition of new and old keys (two keys are valid simultaneously for 10 seconds), and key destruction (securely erase old keys).

[0247] This method solves the problem of traditional fixed keys being easily analyzed, and enhances the system's ability to resist password analysis attacks through frequent key updates and differentiated encryption strategies.

[0248] Step 3. Data slicing and distribution method Data slicing and distribution steps include: Step 3.1, Select (n, k) encoding strategy according to data importance: the system automatically selects appropriate Reed-Solomon encoding parameters according to data type: Key control commands: (5, 3) encoding, at least 3 pieces needed to recover, distributed to 5 nodes; State data: (4, 2) encoding, at least 2 pieces needed to recover, distributed to 4 nodes; General data: (3, 2) encoding, at least 2 pieces needed to recover, distributed to 3 nodes.

[0249] Step 3.2, Add a slice header containing complete metadata to each slice: the system constructs a 64-byte slice header for each slice, containing magic number (0x52534543), version number, slice type, index, timestamp, etc. information, to ensure that the slice can be independently identified and verified.

[0250] Step 3.3, Calculate the optimal distribution strategy based on network topology and node state: the system updates the network state diagram every 30 seconds, including node availability, link delay, and bandwidth information, and applies an improved Floyd-Warshall algorithm to calculate the optimal distribution path, optimizing transmission efficiency and load balancing.

[0251] Step 3.4, Distribute slices to multiple nodes geographically dispersed: The system preferentially distributes slices to substations with different geographical locations to ensure that regional failures do not result in data unrecoverability. In specific implementations, adjacent substations store data of different slice indexes, and any substation stores at most two different index slices.

[0252] This method significantly improves the fault tolerance and fault recovery capability of the system through slice redundancy and geographically dispersed storage. Even in the case of multiple node failures, the availability of data can still be guaranteed.

[0253] Step 4. Distributed ledger maintenance method The steps for maintaining data consistency include: Step 4.1, Package state changes as transactions: The system monitors device state changes and generates a standard transaction structure containing device ID, state variable, timestamp, and digital signature when a change is detected. The transaction is strictly limited to a size of no more than 1KB to ensure network transmission efficiency.

[0254] Step 4.2, Confirm transaction validity through consensus algorithm: The system uses an improved PBFT consensus process (four stages: pre-preparation, preparation, submission, and completion) to verify transactions among distributed nodes. In each stage of the PBFT consensus algorithm (pre-preparation, preparation, submission, and completion), participating nodes perform time calibration before each stage begins. Each receiving node not only verifies the correctness of the block content but also verifies the consistency of the block timestamp with 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 chaos synchronization controller enters high-precision mode, shortening the synchronization update period from 10ms to 1ms, ensuring that the timing of each node in the consensus process is strictly consistent. When more than 2f nodes confirm (f is the maximum number of malicious nodes tolerated by the system), the transaction is considered to have reached consensus. The system realizes fast-path consensus optimization, which can skip the confirmation stage when more than 3f+1 confirmations are received, reducing the consensus time from the standard 100ms to 35ms.

[0255] Step 4.3, Package confirmed transactions into blocks: The system collects transactions that have reached consensus, constructs a Merkle tree structure, and generates a complete block containing a block header (80 bytes) and a transaction list. A single block is limited to containing a maximum of 64 transactions, with a total size of no more than 4KB, ensuring that the block can be propagated through the network within 100ms.

[0256] 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 transmitting block headers first and then block bodies, allowing nodes to process block header verification and block body transmission in parallel.

[0257] Step 4.5, Verify blockchain integrity: Each node, upon receiving a new block, ensures the ledger's integrity by verifying block hash links, transaction signatures, and Merkle proofs. The system also implements a periodic (every hour) ledger state snapshot mechanism to speed up node recovery and historical data access.

[0258] This method establishes a decentralized data consistency guarantee mechanism, overcoming the single point of failure risk of traditional centralized systems, significantly improving the reliability and data integrity of the system.

[0259] Step 5. Chaos synchronization control method Chaos synchronization control steps include: Step 5.1, Maintain a chaos system with the same structure at each node: The system deploys a Lorenz chaos system with the same structure on each node, with parameters set to , and the initial state synchronized through a secure channel at system startup. The chaos system is implemented on a dedicated FPGA, using a fourth-order Runge-Kutta algorithm for numerical integration, with an update frequency of up to 100MHz to ensure synchronization accuracy.

[0260] Step 5.2, Establish synchronization association between nodes through active-passive coupling mechanism: The system uses a star-type master-slave topology, with one master node (usually the node with the highest computing power and network connection quality) as the driving system, and other nodes as the responding system. The master node periodically (every 10ms) broadcasts its chaos state (x, y, z), and the slave nodes adjust their local chaos systems based on the received state.

[0261] Step 5.3, Periodically exchange chaos state information and calculate synchronization error: Nodes exchange chaos state information through dedicated high-priority channels, using UDP protocol to minimize transmission delay (typical value <2ms). After receiving the master node state, the slave node calculates the Euclidean distance between the local state and the received state as the synchronization error indicator.

[0262] Step 5.4, Dynamically adjust coupling strength based on Lyapunov stability theory: The system adaptively adjusts the coupling strength parameter (range between 0.1-10) based on the synchronization error and network delay. The specific algorithm is based on Lyapunov function , which minimizes V through gradient descent method to ensure the system quickly converges to the synchronization state.

[0263] Step 5.5, Use synchronized chaos state to drive local clock adjustment: The system maps the chaos synchronization state into a clock adjustment signal, which 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).

[0264] 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. Actual measurements show that the system can achieve synchronization accuracy of 50-500 microseconds. Even in a harsh environment where network delay fluctuates by ±20 ms, the system can still maintain a synchronization error of <1 ms.

[0265] Step 6. Digital twin implementation method The digital twin model is implemented as follows: Step 6.1, integrate electrical, thermal, and mechanical multi-physical quantity models: the system constructs a multi-domain coupled model based on physical laws: Electrical model: establish an equivalent circuit model based on node voltage method, including resistance, inductance, capacitance, and nonlinear elements; Thermal model: use the simplified thermal network method to divide the device into multiple temperature nodes to simulate heat conduction and convection processes; Mechanical model: simulate structural vibration characteristics based on the mass-spring system; Model parameters are trained through historical data and configured through expert knowledge to accurately model various power equipment.

[0266] Step 6.2, use extended Kalman filtering for real-time state estimation: the system uses the extended Kalman filtering algorithm to fuse physical model prediction 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), with adaptive adjustment of the Kalman gain to balance the weights of model prediction and measurement correction.

[0267] Step 6.3, use differentiated update frequencies for different parameters based on physical characteristics: the system implements a multi-level update strategy based on the rate of change of physical quantities: Electrical state (voltage, current): update every 10 ms; Thermal state (temperature distribution): update every 100 ms; Mechanical state (vibration characteristics): update every 1 second; Lifetime parameters (aging indicators): update every 1 hour; This strategy ensures model accuracy while significantly reducing computational resource requirements.

[0268] Step 6.4. Predicting the state change of the device in the next 5 seconds based on historical data and physical models: The system combines the deterministic evolution of the physical model and the 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 the future state change trend of the device, providing a basis for early fault warning.

[0269] This method realizes deep perception and prediction of the state of power equipment, surpassing the surface data collection of traditional monitoring systems. In actual application, the system can significantly early warning of equipment anomalies, significantly improve the abnormal detection rate, significantly reduce the false alarm rate, significantly enhance the preventive maintenance capability, and significantly reduce unplanned downtime events.

[0270] In one embodiment, the electrical model is established based on the node voltage method, and the specific implementation process is as follows: First, the power equipment is equivalent to a circuit network composed of resistance R, inductance L, capacitance C and nonlinear elements. For transformer equipment, the T-type equivalent circuit is used, in which the primary side resistance , leakage inductance , secondary side resistance , leakage inductance , and the excitation branch is composed of excitation resistance Rm and excitation inductance Lm in parallel. The mathematical description is as follows: Node voltage equation: [Y][V] = [I] Where: Y is the node admittance matrix, V is the node voltage vector, and I is the injected current vector.

[0271] For switching equipment, a time-varying resistance model is used: when the switch is closed, the resistance Rsw = 0.001 Ω, and when the switch is open, the resistance . The arc model of the circuit breaker uses the Mayr arc model

[0272] Where: g is the arc conductance, τ is the time constant (typical value 50 μs), is the rated power loss.

[0273] The electrical model parameters are determined as follows: the equipment nameplate parameters are used as initial values, and the historical operation data are corrected by least squares fitting. Specifically, for the short-circuit impedance of the transformer, the initial value is the nameplate value Zk%, and then the least squares fitting is used according to the load test data:

[0274] Where: Vmeas is the measured voltage, and Vcalc is the model calculated voltage. The thermal model adopts the lumped parameter thermal network method, which simplifies the complex heat conduction process into a thermal resistance-capacity network. The specific implementation is as follows: For the transformer oil tank, a three-node thermal network model is established: winding node (temperature Tw), oil temperature node (temperature To), and environment node (temperature Ta). The heat transfer equation set is as follows:

[0275]

[0276] Where: Cw is the winding heat capacity (typical value ), Co is the oil temperature heat capacity (typical value ), Rwo is the winding to oil thermal resistance (typical value 0.6 K / kW), Roa is the oil to environment thermal resistance (typical value 1.2 K / kW), and P is the winding loss power.

[0277] For the switch cabinet, a five-node model is adopted: contact node, conductor node, insulation piece node, cabinet body node, and environment node. Each node is connected through thermal resistance, and the thermal resistance values are determined through finite element simulation: contact-conductor thermal resistance 0.1 K / W, conductor-insulation piece thermal resistance 0.5 K / W, insulation piece-cabinet body thermal resistance 0.3 K / W, and cabinet body-environment thermal resistance 2.0 K / W.

[0278] The thermal model parameter calibration uses temperature rise test data: run at rated load to steady state, record the temperature of each measuring point, and adjust the thermal resistance and heat capacity values through parameter optimization algorithm to minimize the mean square error between the model output and the measured value.

[0279] The mechanical model is based on a multi-degree-of-freedom particle-spring-damper system, which describes the vibration characteristics of the equipment. For the transformer, a six-degree-of-freedom model (three translational degrees of freedom X, Y, Z, and three rotational degrees of freedom θx, θy, θz) is established: The motion equation matrix form is as follows:

[0280] Where: [M] is the mass matrix (6x6), [C] is the damping matrix, [K] is the stiffness matrix, {F(t)} is the excitation force vector, and {x} is the displacement vector.

[0281] Typical parameters of the mass matrix: for a 110kV transformer, the main body mass m=15000kg, and the moment of inertia .

[0282] The stiffness matrix is determined through modal testing: the hammering method is used to test the natural frequency and damping ratio of the equipment, the first-order natural frequency is usually in the range of 8-12Hz, and the damping ratio is about 0.02-0.05. The stiffness coefficient is obtained by fitting the frequency response function.

[0283] In one embodiment, the organic integration between each physical quantity model is achieved through the following coupling relationships: Electro-thermal coupling: the loss power calculated by the electrical model is input as a heat source to the thermal model. Specifically, the transformer winding loss where a is the temperature coefficient of resistance (0.004 / K for copper), and AT is the temperature rise. The iron loss Pfe is calculated according to the magnetic flux density and frequency, considering the temperature correction coefficient.

[0284] Thermal-mechanical coupling: temperature changes cause material thermal expansion, resulting in mechanical stress. The formula for calculating thermal stress is: σth = EaAT, where E is the elastic modulus and a is the linear expansion coefficient. For transformer windings, the change in axial force caused by longitudinal expansion will affect the vibration characteristics.

[0285] Electromechanical coupling: electromagnetic force as an excitation source of mechanical vibration. The electromagnetic force Fem experienced by the transformer winding is BIl, where B is the magnetic induction, I is the current, and I is the length of the conductor. This electromagnetic force changes at an even multiple of the power frequency, becoming a vibration excitation source.

[0286] Model parameters are determined by combining the following three methods: Theoretical calculation method: calculate the theoretical value based on the geometric dimensions of the equipment and material properties. For example, the resistance is calculated as R = pI / A, where p is the material resistivity, I is the length, and A is the cross-sectional area. The thermal resistance is calculated as Rth = d / (A), where d is the thickness and A is the thermal conductivity.

[0287] Test calibration method: determine key parameters through special tests. Temperature rise test to determine thermal parameters, short circuit test to determine electrical parameters, and modal test to determine mechanical parameters. Tests are conducted under normal operating conditions and abnormal operating conditions to establish a parameter- operating condition correspondence.

[0288] Data-driven method: use historical operation data to optimize parameters through machine learning methods. Genetic algorithm or particle swarm algorithm is used, and the fitting degree of model output and measured data is used as the objective function to automatically optimize model parameters. The optimization objective function is:

[0289] where wi is the weight coefficient, yi,model is the model output, and yi,meas is the measured value.

[0290] Taking a 110kV transformer as an example, the specific parameter configuration of the multi-physical quantity model is as follows: 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%.

[0291] Thermal parameters: winding thermal capacity , oil thermal capacity , winding-oil thermal resistance Rwo = 0.52 K / kW, oil-ambient thermal resistance Roa = 1.15 K / kW.

[0292] Mechanical parameters: total mass 15.2 t, first natural frequency 9.5 Hz, damping ratio 0.035, main vibration direction stiffness coefficient .

[0293] This model can accurately predict the temperature distribution (error <3K), vibration level (error <10%) and electrical characteristics (error <2%) of the transformer under different loads in actual operation, realizing high-fidelity digital mapping of the device state.

[0294] Through the above detailed modeling method and parameter determination process, the multi-physical quantity modeling framework can construct a high-precision digital twin model for power equipment, providing a reliable technical foundation for state monitoring, fault prediction and optimization control.

[0295] Step 7. Data transmission and synchronization coordination method The steps of coordinating the data transmission and synchronization process include: Step 7.1, priority allocation according to data type and importance: the system implements a four-level priority mechanism: P0 (highest): control commands and protection instructions, maximum end-to-end delay 3ms; P1: state alarm and key measurement, maximum end-to-end delay 10ms; P2: regular state data, maximum end-to-end delay 50ms; P3 (lowest): historical data and statistical information, best effort transmission; The system uses the DiffServ mechanism to implement priority marking and queue management at the network layer.

[0296] Step 7.2, calculate the optimal transmission path considering delay, bandwidth and reliability: the system implements multi-index routing optimization based on the improved Dijkstra algorithm. The routing calculation considers link delay (main index), available bandwidth (secondary index) and historical reliability (additional index) to select the most suitable transmission path for different priority data.

[0297] Step 7.3, real-time monitoring of network state and dynamic adjustment of transmission strategy: the system continuously evaluates network performance through active probing (sending probe packets every 15 seconds) and passive monitoring (analyzing traffic characteristics). When network congestion is detected (such as packet loss rate >1% or delay increase >50%), the system automatically adjusts the routing strategy and congestion control parameters to ensure that critical services are not affected.

[0298] Step 7.4, maintain synchronization quality scoring system: the system establishes a comprehensive evaluation index system to assess the synchronization state in real time: Time synchronization accuracy (weight 40%): measure the clock deviation between nodes; Data consistency (weight 30%): evaluate the version difference of data between nodes; Network performance (weight 20%): monitor the communication quality between nodes; System load (weight 10%): track the utilization rate of processing and storage resources.

[0299] When the score is lower than the preset threshold (85 points), the system triggers a self-recovery process, including enhancing the synchronization signal strength, switching to a backup link, or adjusting the data distribution strategy.

[0300] This method ensures that the system runs efficiently and stably in complex and variable network environments, intelligently adapts to changes in network conditions, and provides an important guarantee for system security and reliability. Actual deployment shows that this method shortens the recovery time of the system from minutes to seconds in the case of local network failure, significantly improving service continuity.

[0301] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A quantum random encryption and distributed synchronization information transmission method applied to a power system, characterized in that, The method comprises: acquiring initial data of power equipment state; generating unpredictable quantum true random numbers; encrypting the initial data with the quantum true random numbers to obtain target data; synchronizing clock synchronization accuracy of multiple nodes; generating data slices from the target data and distributing the data slices to the multiple nodes; synchronizing the data slices of the multiple nodes, wherein the synchronized data slices are synchronized by a distributed ledger.

2. The information transmission method of claim 1, wherein: the generating of the unpredictable quantum true random numbers comprises: generating a random electron-hole random signal using quantum vacuum fluctuations; amplifying and filtering the random signal to obtain a filtered signal; digitizing the filtered signal to generate the unpredictable quantum true random numbers.

3. The information transmission method of claim 1, wherein: after the generating of the unpredictable quantum true random numbers, the method further comprises: monitoring an entropy estimate value of the quantum true random numbers, and performing a quantum source health check when the entropy estimate value is lower than a preset threshold.

4. The information transmission method of claim 1, wherein: the encrypting of the initial data with the quantum true random numbers to obtain the target data comprises: converting the quantum true random numbers into a main random key of a fixed length; generating a sub-key from the main random key, wherein the sub-key corresponds to the initial data; encrypting the initial data with the sub-key to obtain the target data, wherein the sub-key is updated according to a preset time interval and / or a data volume domain.

5. The information transmission method of claim 1, wherein: the synchronizing of the clock synchronization accuracy of the multiple nodes comprises: exchanging chaotic state information of the multiple nodes; adjusting the multiple nodes to a synchronized chaotic state according to the chaotic state information; synchronizing the clock synchronization accuracy of the multiple nodes according to the synchronized chaotic state of the multiple nodes.

6. The information transmission method of claim 1, wherein: the generating of the data slices from the target data and the distributing of the data slices to the multiple nodes comprises: selecting an (n, k) encoding strategy to generate the data slices according to the importance of the target data; judging node position relationships; distributing the data slices to the multiple nodes according to the node position relationships, wherein adjacent nodes store different data slices, and any node stores no more than two data slices.

7. The information transmission method of any one of claims 1-6, wherein: the synchronizing of the data slices of the multiple nodes comprises: assigning data priorities according to the data types and / or the importance of the data slices; calculating a first transmission path according to the data priorities and network states, wherein the network states at least include one of network delay, bandwidth, and reliability factors; synchronizing the data slices of the multiple nodes according to the first transmission path.

8. The information transmission method according to claim 7, characterized by, The synchronizing of the data slices of the multiple nodes further comprises: monitoring network congestion in real time to obtain a second transmission method, wherein the network congestion at least includes one of delay, packet loss rate, and throughput; synchronizing the data slices of the multiple nodes according to the second transmission method.

9. The information transmission method of claim 8, wherein: the synchronizing of the data slices of the multiple nodes further comprises: A synchronization quality scoring system is established, 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; Data slices of the multiple nodes are synchronized according to the synchronization quality scoring system, and when the synchronization quality is lower than a threshold value, a correction operation is triggered, the correction operation including enhancing coupling strength and switching a synchronization reference node.

10. The information transmission method of claim 1, wherein the initial data of the power equipment state is obtained by: obtaining detection data of the power equipment; and processing the detection data using a digital twin model of the power equipment to obtain the initial data of the power equipment state.

11. The information transmission method of claim 10, wherein the initial data of the power equipment state is obtained by: constructing a multi-physical quantity model of the power equipment, the multi-physical quantity including electrical parameters, thermal parameters, and mechanical parameters; and predicting the initial data of the power equipment state from the detection data according to the multi-physical quantity model. The information transmission device comprises: a power equipment digital twin module configured to obtain initial data of the power equipment; 12. A quantum random encryption and distributed synchronization information transmission device applied to a power system, characterized in that, a quantum true random number generation module configured to generate a true random number using a quantum physical phenomenon; a dynamic encryption engine connected to the quantum true random number generation module, configured to receive the true random number and generate target data from the initial data using the true random number; a chaotic synchronization controller configured to synchronize clock synchronization accuracy among the multiple nodes; a data slice and recovery module connected to the dynamic encryption engine, configured to distribute the target data to the multiple nodes as data slices; and a distributed ledger management module configured to synchronize data slices of the multiple nodes.

13. An electronic device comprising a processor and a memory storing a computer program, wherein the processor implements the information transmission method of any one of claims 1 to 11 when executing the computer program.

14. A computer program product comprising a computer program, wherein the computer program implements the information transmission method of any one of claims 1 to 11 when executed by a processor. ​ ​ ​ ​

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