A Smart Power Plant Data Storage Management Method and System

Abstract

This invention relates to the field of data storage and discloses a data storage management method and system for smart power plants, which integrates quantum physics, electromagnetic field theory, and relativistic effects. It includes calculating the quantum fluctuation threshold of data blocks using the Heisenberg uncertainty principle, constructing a quantum barrier model to achieve intelligent data compression, treating compressed data packets as charged particles, dynamically planning the optimal data migration path to reduce access latency, accurately detecting data anomalies and predicting equipment failures by combining quantum conjugate equivalent physical quantities, and introducing the special relativistic time dilation formula to compensate for data validity period, ensuring data timeliness. This method also possesses fault self-healing capabilities, generating lossless reconstruction paths through local entropy calculation and the Gibbs free energy model to ensure stable system operation. This invention effectively improves the intelligence, efficiency, and reliability of data storage management in smart power plants, providing strong support for the digital transformation of the power industry.

Description

Technical Field

[0001] This invention relates to the field of data storage, and in particular to a data storage management method and system for smart power plants. Background Technology

[0002] With the rapid development of smart power plants, their informatization and intelligence levels are constantly improving, resulting in massive amounts of sensor data being generated at high frequency and density. This data not only contains key information such as equipment operating status and environmental parameters, but also carries time-series characteristics that are crucial to the safe and efficient operation of power plants. However, traditional data storage and management methods are gradually revealing many limitations when faced with such complex and massive data streams.

[0003] First, data compression and storage efficiency become bottlenecks. Traditional compression algorithms, mostly based on statistical regularities or pattern matching, struggle to effectively handle time-series data with quantum fluctuation characteristics, resulting in low compression ratios and inefficient storage resource utilization. Second, data migration and access performance are limited. In distributed storage systems, data migration path planning often lacks dynamic adaptability, failing to intelligently adjust based on real-time network conditions and equipment load, leading to increased access latency and slower system response. Furthermore, anomaly detection and fault prediction capabilities are insufficient. Traditional methods often rely on preset thresholds or simple statistical models, making it difficult to accurately identify abnormal fluctuations and potential faults in complex data streams, impacting the safe and stable operation of power plants.

[0004] Therefore, we propose a smart power plant data storage management method and system to solve the above problems. Summary of the Invention

[0005] This invention provides a smart power plant data storage management method and system that integrates quantum physics, electromagnetic field theory and relativistic effects.

[0006] The first aspect of this invention provides a data storage management method for smart power plants. The method includes: real-time acquisition of sensor data streams and their temporal characteristics from the smart power plant; calculating the quantum fluctuation threshold of data blocks based on the Heisenberg uncertainty principle to determine whether quantum compression should be enabled; when the data bit fluctuation amplitude exceeds the quantum fluctuation threshold, constructing a quantum barrier model, and generating compressed data packets from the bit flow penetrating the barrier; treating the compressed data packets as charged particles based on the physical coordinates of the compressed data packets and the storage node array, and obtaining the electromagnetic field strength distribution map by solving Maxwell's equations; and based on the electromagnetic field strength distribution map and the device access request queue, applying the Lorentz force formula... Calculate the data migration acceleration vector to generate a preloaded path; construct an access flow solitary wave model based on the original network access logs and storage node vibration sensor data, and output an abnormal peak alarm; based on the data migration acceleration vector and node clock synchronization signal, apply the special relativity time dilation formula. The timeliness compensation parameter is calculated and used to calibrate the validity period of the data.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the method includes: extracting the temporal fluctuation amplitude of a data block and the interval between adjacent sampling points based on the real-time data stream from the smart power plant sensors; and mapping the fluctuation amplitude to position uncertainty. The sampling interval is mapped to the momentum uncertainty. Generate quantum conjugate pairs When quantum conjugate pairs When the position uncertainty is high, a compression trigger signal is generated; , The amplitude safety threshold is used to mark abnormal data fluctuations.

[0008] Optionally, in the second implementation of the first aspect of the present invention, the method includes: mapping data bits "0" to low-potential wells and bits "1" to high-potential barriers based on the original data bit flow and quantum fluctuation threshold, constructing a potential flow barrier topology map; solving the site tunneling probability matrix using the Schrödinger equation based on the potential flow barrier topology map and the effective electron mass parameter, for screening penetrable sites; marking sites with a tunneling probability > 0.8 as penetrating sites based on the site tunneling probability matrix and a preset probability threshold, generating a compressed site index sequence; sorting the penetrating sites by quantum energy level based on the compressed site index sequence and the original data bit flow, and reorganizing them into compressed site packets; and marking quantum excited state anomalous segments when the tunneling probabilities of three consecutive sites are < 0.1 based on the site tunneling probability matrix.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the method includes: mapping the compression ratio to charge q and the access frequency to particle velocity v based on the compressed data packet and data access frequency log, generating a charged particle parameter set (q, v) for the data packet; solving the spatial integral form of Maxwell's equations based on the charged particle parameter set and the three-dimensional physical coordinates of the storage node, and outputting a three-dimensional electromagnetic field strength scalar matrix; and calculating the field strength gradient based on the three-dimensional field strength matrix and the node hardware state parameters. The gradient extreme points are marked as the optimal storage node coordinates; based on the three-dimensional field strength matrix and the electromagnetic interference monitoring data of the computer room, when the field strength value is greater than the interference threshold, an electromagnetic avoidance area identifier is generated; based on the three-dimensional field strength matrix, the annular zero field strength area in the field strength distribution is detected, and a hardware topology defect alarm is output.

[0010] Optionally, in the fourth implementation of the first aspect of the present invention, the method includes: mapping the access request frequency to a data particle velocity vector v and the access target node coordinates to the initial displacement direction based on the three-dimensional electromagnetic field strength distribution map and the device access request queue, thereby generating a motion parameter set (v, dir); extracting the electric field strength E and magnetic induction intensity B on the target path based on the motion parameter set, the charged particle parameter q, and the electromagnetic field strength distribution map, and calculating the Lorentz force vector F; and calculating the Lorentz force vector F based on the Lorentz force vector F, the data packet quality parameter, and Newton's second law. Calculate the migration acceleration vector 'a'; based on the acceleration vector 'a' and real-time data from the stored node vibration sensors, detect the node displacement deviation caused by vibration and generate an acceleration correction factor 'δ'; based on the acceleration vector 'a', correction factor 'δ', and the node fault status table, combine the acceleration kinematic equations... Calculate the coordinate sequence of the barrier-free migration path; calculate the Lorentz force power based on the Lorentz force vector F and motion parameter v. ,when High-energy-consuming path segments are marked.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the method includes: mapping network traffic amplitude to wave displacement u and vibration frequency to time step based on the original network access logs and storage node vibration sensor data. A fused wave sequence is generated; based on the fused wave sequence, the KdV equation is solved using Hirota bilinear transform to extract soliton amplitude A and wave velocity c; based on soliton amplitude A, wave velocity c, and a preset normal wave threshold, when... At that time, distorted soliton events are marked; based on the distorted soliton events and the original vibration sensor data, the coherence coefficient γ between the vibration signal and network traffic within the event time window is calculated; based on the distorted soliton events and the coherence coefficient γ, if γ > 0.7, a device fault alarm is generated; if γ < 0.3, a network attack alarm is generated, and a classified anomaly alarm is output; based on the soliton amplitude A and wave velocity c, according to the soliton energy formula... Calculate the abnormal energy value.

[0012] Optionally, in a sixth implementation of the first aspect of the present invention, the method includes: based on the data migration acceleration vector and the initial migration velocity, using kinematic equations... Calculate the real-time migration velocity scalar; based on the real-time migration velocity and the speed of light constant, calculate... Generate the time dilation factor; select the lowest-latency node clock as the reference clock source based on the node clock synchronization signal; calculate the time dilation factor, reference clock source, and original data validity period. Generate calibration validity period; based on real-time migration speed, when A superluminal anomaly marker is generated; the final validity period is calculated a second time based on the calibration validity period, GPS satellite ephemeris data, and satellite orbital velocity.

[0013] Optionally, in a seventh implementation of the first aspect of the invention, a fault prediction and self-healing step is further included: based on quantum conjugate pairs. 3D electromagnetic field strength matrix, used to calculate the local entropy of the storage system. Generate real-time entropy values; based on the real-time entropy values ​​and temperature sensor data from storage nodes, construct a Gibbs free energy model. Output barrier height parameters; based on the barrier height and the activation energy of the equipment materials, according to the Boltzmann distribution... Calculate the failure probability; based on the failure probability and node energy consumption logs, when > 0.7 generates a self-healing instruction; based on the self-healing instruction and the compressed bit index sequence, and adhering to Noether's theorem to maintain system symmetry, a lossless reconstruction path is generated; based on the real-time entropy value, when The liquid cooling system is activated at the specified time, and an entropy reduction completion signal is output.

[0014] The second aspect of this invention provides a smart power plant data storage management system, comprising: an acquisition module for real-time acquisition of smart power plant sensor data streams and their temporal characteristics, and for calculating the quantum fluctuation threshold of data blocks based on the Heisenberg uncertainty principle to determine whether quantum compression should be enabled; a migration module for constructing a quantum barrier model when the data bit fluctuation amplitude exceeds the quantum fluctuation threshold, and generating compressed data packets by the bit flow penetrating the barrier; a detection module for treating the compressed data packets as charged particles based on the compressed data packets and the physical coordinates of the storage node array, and obtaining the electromagnetic field strength distribution map by solving Maxwell's equations; and a compensation module for compensating based on the electromagnetic field strength distribution map and the device access request queue, according to the Lorentz force formula. The system calculates the data migration acceleration vector and generates a preloaded path; a setup module constructs an access flow solitary wave model based on the original network access logs and storage node vibration sensor data, and outputs an abnormal peak alarm; an allocation module uses the data migration acceleration vector and node clock synchronization signal, based on the special relativistic time dilation formula... The timeliness compensation parameter is calculated and used to calibrate the validity period of the data.

[0015] Beneficial effects: By introducing the uncertainty principle and potential barrier model from quantum physics into the field of data compression, the limitations of traditional compression algorithms have been overcome, significantly improving the data compression ratio and reducing storage resource consumption, while ensuring data integrity and recoverability, providing an efficient solution for the massive data storage of smart power plants. By combining electromagnetic field theory with data migration path planning, and solving Maxwell's equations and the Lorentz force formula, dynamic optimization of the data migration path is achieved, which effectively reduces latency and energy consumption during data migration, improves data access speed and system response capability, and enhances the flexibility and adaptability of the distributed storage system. Based on the KdV equation, a solitary wave model is constructed. By combining physical quantities such as quantum conjugate pairs and electromagnetic field strength, anomaly detection and fault prediction are performed. This innovative integration of physical model and data analysis methods can accurately identify abnormal fluctuations and potential faults in complex data streams and issue early warnings, providing strong support for the safe and stable operation of power plants and reducing operation and maintenance costs and risks. By introducing the relativistic time dilation formula for time-related compensation, the influence of complex physical factors such as quantum effects and electromagnetic field distribution on system performance is fully considered, ensuring the accuracy and reliability of data storage management, avoiding errors and failures caused by physical factors, and improving the overall operating efficiency and stability of smart power plants. By calculating the local entropy of the system, constructing the Gibbs free energy model, and combining Noether's theorem to generate a lossless reconstruction path, fault self-healing and system optimization are achieved. When a fault occurs, the system state can be automatically adjusted to restore data integrity, reduce downtime, optimize system resource allocation, and improve overall energy efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of one embodiment of the smart power plant data storage management method according to the present invention; Figure 2 This is a schematic diagram of one embodiment of the intelligent power plant data storage management system of the present invention. Detailed Implementation

[0017] This invention provides a smart power plant data storage management method and system that integrates quantum physics, electromagnetic field theory, and relativistic effects. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the smart power plant data storage management method of the present invention includes: 101. Quantum tunneling compression triggering steps: Input the real-time acquired smart power plant sensor data stream and its timing characteristics; Calculate the quantum fluctuation threshold of the data block (product 1) according to the Heisenberg uncertainty principle; Product 1 is used to determine whether to enable quantum compression (step 2). It is understood that the executing entity of this invention can be a smart power plant data storage management system, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0019] It should be noted that the dynamic parameter extraction steps are as follows: input the real-time data stream (temperature, pressure, vibration waveform) of the smart power plant sensor; extract the temporal fluctuation amplitude of the data block (product A) and the interval between adjacent sampling points (product B); product source: directly from the sensor's raw data stream.

[0020] Steps for calculating quantum conjugate quantities: Input product A (fluctuation amplitude) and product B (sampling interval); map the fluctuation amplitude to position uncertainty. The sampling interval is mapped to the momentum uncertainty. Product generation: quantum conjugate pairs (Product C); Physical basis: Heisenberg uncertainty principle .

[0021] Fluctuation threshold determination steps: Input product C (quantum conjugate pair); when When k is the engineering adjustment coefficient, a compression trigger signal (product D) is generated; Product application: Product D is input to the quantum tunneling compression module.

[0022] Abnormal fluctuation labeling step: Input product C (quantum conjugate pair); if (Γ is the amplitude safety threshold), mark the abnormal data fluctuation (product E); Product application: Product E is input to the topological solitary wave detection module.

[0023] 102. Barrier Penetration Compression Step: Input the quantum fluctuation threshold and original data block bit pattern from Step 1; when the data bit fluctuation amplitude exceeds the quantum fluctuation threshold, construct a quantum barrier model, and generate a compressed data packet (product 2) from the bit flow penetrating the barrier; Product Application: Input product 2 into the hierarchical storage layer (step 3). It should be noted that the potential flow barrier modeling steps are as follows: input the original data potential flow (from the power plant sensor) and the quantum fluctuation threshold; map the data bit "0" to a low potential energy well and the bit "1" to a high potential energy barrier to construct the potential flow barrier topology (product X); the product source is directly from the original data bit mode and the quantum fluctuation threshold.

[0024] Tunneling probability calculation steps: Input product X (potential current barrier topology diagram) and effective electron mass parameters (physical properties of storage medium); Solve the site tunneling probability matrix (product Y) through the Schrödinger equation; Product application: Product Y is used to screen for penetrable sites (step 3).

[0025] Compression site selection steps: Input product Y (site tunneling probability matrix) and preset probability threshold; mark sites with tunneling probability > 0.8 as penetration sites and generate compression site index sequence (product Z); Product application: input product Z to the site recombination module (step 4).

[0026] Energy level transition recombination steps: Input product Z (compressed bit index sequence) and original data bit stream; sort the penetrating bits according to quantum energy level and recombine them into a compressed bit packet (product W); Product application: input product W to the electromagnetic field distribution module (step 3).

[0027] Excited-state anomaly labeling steps: Input product Y (site tunneling probability matrix); when the tunneling probability of 3 consecutive sites is <0.1, label the quantum excited-state anomaly segment (product V); Product application: Input product V into the topological solitary wave detection module.

[0028] 103. Electromagnetic field distribution modeling steps: Input the compressed data packet from step 2 and the physical coordinates of the storage node array; Treat the data packet as charged particles and solve the electromagnetic field strength distribution map using Maxwell's equations (product 3); Product application: Product 3 guides the allocation of high-frequency access data in high field strength regions (step 4). It should be noted that the charge mapping steps are as follows: input compressed data packets and data access frequency logs; map the compression ratio to the charge q (q∝1 / compression ratio), map the access frequency to the particle velocity v, and generate the charged particle parameter set (q,v) (product P) of the data packet; the product source is directly from the compressed data packet attributes and access logs.

[0029] Steps for calculating spatial field strength: Input product P (charged particle parameter set) and store the three-dimensional physical coordinates of the node; solve the spatial integral form of Maxwell's equations and output the three-dimensional electromagnetic field strength scalar matrix (product Q); Product application: Product Q is used to identify high field strength nodes (step 3).

[0030] Gradient optimization assignment steps: Input product Q (3D field strength matrix) and node hardware state parameters; calculate field strength gradient. Mark the gradient extreme points as the optimal storage node coordinates (product R); Product application: Input product R into the data distribution engine for storage (step 4).

[0031] Anti-interference redistribution steps: Input product Q (three-dimensional field strength matrix) and computer room electromagnetic interference monitoring data; When the field strength value > interference threshold, generate electromagnetic avoidance area identifier (product S); Product application: Product S triggers Lorentz force preload path correction.

[0032] Topology defect detection steps: Input product Q (three-dimensional field strength matrix); detect the annular zero field strength region in the field strength distribution, and output hardware topology defect alarm (product T); Product application: input product T to the time delay control module.

[0033] 104. Lorentz force driven preload steps: Input the electromagnetic field strength distribution diagram and device access request queue from step 3; according to the Lorentz force formula... Calculate the data migration acceleration vector (product 4) and generate a preload path; Product application: Product 4 is input to the storage controller to perform data pre-positioning (step 5). It should be noted that the motion parameter conversion steps are as follows: input the three-dimensional electromagnetic field strength distribution map and the device access request queue; map the access request frequency to the data particle velocity vector v, map the access target node coordinates to the initial displacement direction, and generate the motion parameter set (v,dir) (product M); the product source is directly from the access queue and the field strength distribution map.

[0034] Lorentz force synthesis steps: Input product M (motion parameter set), charged particle parameter q, and electromagnetic field strength distribution map; extract the electric field strength E and magnetic induction intensity B on the target path, and calculate the Lorentz force vector F (product N); physical basis: .

[0035] Acceleration path generation steps: Input product N (Lorentz force vector F), data packet quality parameters (physical properties of storage medium); based on Newton's second law Calculate the migration acceleration vector a (product O); Product application: Input product O into the preloaded path planning module (step 4).

[0036] Nodal vibration correction steps: Input product O (acceleration vector a), store real-time data from the nodal vibration sensor; detect the nodal displacement deviation caused by vibration, and generate an acceleration correction factor δ (product P); Product application: Product P is used to calibrate the final migration path (step 5).

[0037] Obstacle avoidance path output steps: Input product O (acceleration vector a), product P (correction factor δ), and node fault status table; combine with acceleration kinematic equations Calculate the coordinate sequence of the barrier-free migration path (product Q); Product application: Product Q is input to the storage controller to perform data pre-positioning.

[0038] Energy loss labeling steps: Input product N (Lorentz force vector F) and product M (motion parameter v); calculate Lorentz force power. ,when High-energy-consuming path segment (product R) is marked by time delay; Product application: Product R is input to the time delay control module.

[0039] 105. Topological solitary wave detection steps: Input raw network access logs and store node vibration sensor data; based on the KdV equation... Construct an access flow isolated wave model and output an abnormal wave peak alarm (product 5); Product application: Product 5 triggers quantum encryption of the target data block (step 6). It should be noted that the dual-source data fusion steps are as follows: input the original network access logs (IP traffic sequence) and store the vibration sensor data of the storage nodes (acceleration time series); map the network traffic amplitude to wave displacement u, and the vibration frequency to time step. Generate a fused wave sequence (product U); product source: directly from access logs and vibration sensors.

[0040] Soliton parameter extraction steps: Input product U (fused wave sequence); Solve the KdV equation through Hirota bilinear transform to extract soliton amplitude A and wave velocity c (product V); Product application: Product V is used for anomaly detection (step 3).

[0041] Distortion peak detection steps: Input product V (soliton parameters A, c), preset normal fluctuation threshold; when When the event occurs, mark the distorted soliton event (product W); Product application: input product W into the alarm generation module (step 4).

[0042] Vibration coherence verification steps: Input product W (distorted soliton event) and raw vibration sensor data; calculate the coherence coefficient γ (product X) between vibration signal and network traffic within the event time window; Product application: Product X is used to distinguish anomaly types (step 5).

[0043] Multi-level alarm generation steps: Input product W (distorted soliton event) and product X (coherence coefficient γ); if γ>0.7, generate equipment failure alarm; if γ<0.3, generate network attack alarm; output classified anomaly alarm (product Y); product application: product Y is the aforementioned abnormal peak alarm, which is input to obstacle avoidance path correction.

[0044] Energy backtracking labeling steps: Input product V (solid parameters A, c); according to the soliton energy formula Calculate the abnormal energy value (product Z); Product application: Product Z is input to the time delay control module.

[0045] 106. Relativistic Time Difference Control Steps: Input data migration acceleration vector and node clock synchronization signal; based on the special relativity time dilation formula... Calculate the time-sensitivity compensation parameter (Product 6); Product application: Input the calibration data validity period of Product 6 into the storage device; It should be noted that the migration velocity calculation steps are as follows: input data migration acceleration vector, initial migration velocity (preset constant); and then calculate using the kinematic equations. Calculate the real-time migration velocity scalar (product A); product source: directly from the acceleration vector and the preset initial velocity.

[0046] Lorentz factor generation steps: Input product A (real-time migration velocity) and speed of light constant (physical constant); calculate Generate time dilation factor (product B); Product application: Product B is used for time compensation calculation (step 3).

[0047] Clock reference alignment steps: Input node clock synchronization signal (NTP / PTP protocol data); Select the lowest latency node clock as the reference clock source (product C); Product source: Directly from clock synchronization protocol data packets.

[0048] Time deviation compensation steps: Input product B (time dilation factor), product C (reference clock source), and original data validity period; calculate... Generate calibration validity period (product D); Product application: Input product D into the storage device metadata update module (step 5).

[0049] Light speed anomaly detection steps: Input product A (real-time migration speed); when A superluminal anomaly marker (product E) is generated; Product application: Product E triggers an emergency correction of the Lorentz force path.

[0050] Satellite drift calibration steps: Input product D (calibration validity period) and GPS satellite ephemeris data; calculate the final validity period (product F) based on the satellite orbital velocity; Product application: Write product F into the storage device firmware calibration data validity period.

[0051] In this embodiment of the invention, data compression is performed using the quantum tunneling principle. By calculating the quantum fluctuation threshold and tunneling probability, compressible data bits are precisely selected, effectively reducing data storage space occupation. Compared with traditional compression methods, it can process large amounts of real-time data generated by smart power plant sensors more efficiently. Based on electromagnetic field distribution modeling, high-frequency access data is allocated to high-field-strength regions, achieving hierarchical optimization of data storage and improving data access speed and storage resource utilization. Through quantum conjugate calculation and fluctuation threshold determination, abnormal fluctuation data is marked, and combined with topological solitary wave detection technology, abnormal events are classified and alarms are generated, triggering quantum encryption of target data blocks in a timely manner, effectively preventing data leakage and malicious attacks. Considering the time dilation effect during data migration, the data validity period is calibrated to ensure the timeliness and accuracy of data during storage and transmission, avoiding data loss due to time dilation. Data security issues caused by inter-process errors are addressed. In electromagnetic field distribution modeling, electromagnetic avoidance zone markers are generated based on electromagnetic interference monitoring data from the computer room, allowing for timely adjustment of data storage locations to prevent electromagnetic interference from affecting data storage and improving system stability. During the Lorentz force-driven preloading process, real-time data from storage node vibration sensors is considered to generate acceleration correction factors. Combined with the node fault status table, the coordinate sequence of the unobstructed migration path is calculated to ensure smooth data migration and reduce system failures caused by node faults and vibrations. By calculating the Lorentz force vector and data migration acceleration vector, a preloading path is generated, achieving efficient data migration and reducing data access latency. During the Lorentz force-driven preloading process, the Lorentz force power is calculated, and high-energy-consumption path segments are marked, providing a basis for subsequent time-delay control and path optimization, thus reducing energy loss during data migration.

[0052] Another embodiment of the smart power plant data storage management method in this invention is basically the same as embodiment one, except that it also includes: 107. Includes fault prediction and self-healing steps: Entropy increase monitoring steps: Input quantum conjugate pairs (Δx, Δp) and a three-dimensional electromagnetic field strength matrix; calculate the local entropy of the storage system. Generate real-time entropy values ​​(product G).

[0053] Thermodynamic barrier modeling steps: Input product G (real-time entropy value), store node temperature sensor data; construct Gibbs free energy model. Output barrier height parameter (product H); Product application: Product H is used for fault probability calculation (step 3).

[0054] Boltzmann fault prediction steps: Input product H (barrier height) and device material activation energy (physical constant); based on Boltzmann distribution... Calculate the failure probability (product I); Product application: Product I triggers the self-healing decision (step 4).

[0055] Maxwell's Demon self-healing decision-making steps: Input product I (failure probability), node energy consumption log; when > Generate self-healing instruction (product J) at 0.7; Product application: Input product J into the data reconstruction engine (step 5).

[0056] Topology conservation reconfiguration steps: Input product J (self-healing instruction) and compressed bit index sequence; maintain system symmetry according to Noether's theorem and generate lossless reconfiguration path (product K); Product application: Product K drives the storage controller to perform data migration.

[0057] Cold trap entropy reduction step: Input product G (real-time entropy value); when The liquid cooling system is activated and an entropy reduction completion signal (product L) is output; Product application: Product L resets the time delay control parameters.

[0058] The above describes the data storage management method for smart power plants in embodiments of the present invention. The following describes the data storage management system for smart power plants in embodiments of the present invention. Please refer to [link / reference]. Figure 2 An embodiment of the intelligent power plant data storage management system of the present invention includes: an acquisition module 201, used to collect real-time sensor data streams and their temporal characteristics from the intelligent power plant, and calculate the quantum fluctuation threshold of the data block according to the Heisenberg uncertainty principle to determine whether quantum compression should be enabled; a migration module 202, used to construct a quantum barrier model when the data bit fluctuation amplitude exceeds the quantum fluctuation threshold, and generate compressed data packets by the bit flow penetrating the barrier; a detection module 203, used to treat the compressed data packets as charged particles based on the compressed data packets and the physical coordinates of the storage node array, and obtain the electromagnetic field strength distribution map by solving Maxwell's equations; and a compensation module 204, used to calculate the electromagnetic field strength distribution map based on the electromagnetic field strength distribution map and the device access request queue, according to the Lorentz force formula. The system calculates the data migration acceleration vector and generates a preloaded path; module 205 is used to construct an access flow solitary wave model based on the original network access logs and storage node vibration sensor data, and outputs an abnormal peak alarm; module 206 is used to calculate the data migration acceleration vector and node clock synchronization signal based on the special relativistic time dilation formula. The timeliness compensation parameter is calculated and used to calibrate the validity period of the data.

[0059] The present invention also provides a smart power plant data storage management device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the smart power plant data storage management method described in the above embodiments.

[0060] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the smart power plant data storage management method.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0062] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart power plant data storage management method, characterized by, The smart power plant data storage management method includes: The real-time data streams and temporal characteristics of smart power plant sensors are collected, and the quantum fluctuation threshold of the data blocks is calculated based on the Heisenberg uncertainty principle to determine whether quantum compression should be enabled. When the fluctuation amplitude of data bits exceeds the quantum fluctuation threshold, a quantum barrier model is constructed, and the bit flow that penetrates the barrier generates compressed data packets. Based on the physical coordinates of the compressed data packet and the storage node array, the compressed data packet is regarded as a charged particle, and the electromagnetic field strength distribution map is obtained by solving Maxwell's equations; According to the electromagnetic field intensity distribution map, the equipment access request queue, and the Lorentz force formula Calculate the data migration acceleration vector and generate a preloading path; Based on the original network access logs and storage node vibration sensor data, an access flow solitary wave model is constructed, and an abnormal wave peak alarm is output. Based on the data migration acceleration vector and the node clock synchronization signal, and based on the special relativity time dilation formula... The timeliness compensation parameter is calculated and used to calibrate the validity period of the data.

2. The intelligent power plant data storage management method according to claim 1, characterized in that, include: Based on the real-time data stream from the smart power plant sensors, extract the temporal fluctuation amplitude of the data block and the interval between adjacent sampling points; Mapping fluctuation amplitude to location uncertainty The sampling interval is mapped to the momentum uncertainty. Generate quantum conjugate pairs ; When quantum conjugate pairs At that time, a compression trigger signal is generated; If the location uncertainty , The amplitude safety threshold is used to mark abnormal data fluctuations.

3. The intelligent power plant data storage management method according to claim 2, characterized in that, include: Based on the original data bit flow and quantum fluctuation threshold, data bit "0" is mapped to a low potential energy well and bit "1" is mapped to a high potential energy barrier, thus constructing a potential flow barrier topology. Based on the potential current barrier topology and the effective electron mass parameter, the site tunneling probability matrix is ​​solved by the Schrödinger equation and used to screen for penetrable sites; Based on the site tunneling probability matrix and a preset probability threshold, sites with a tunneling probability > 0.8 are marked as penetration sites, and a compressed site index sequence is generated. Based on the compressed bit index sequence and the original data bit stream, the penetrating bits are sorted by quantum energy level and reassembled into compressed bit packets; Based on the site tunneling probability matrix, when the tunneling probability of three consecutive sites is <0.1, an abnormal segment of the quantum excited state is marked.

4. The intelligent power plant data storage management method according to claim 3, characterized in that, include: Based on the compressed data packet and data access frequency log, the compression ratio is mapped to the charge q, and the access frequency is mapped to the particle velocity v, generating the charged particle parameter set (q,v) of the data packet; Based on the charged particle parameter set and the three-dimensional physical coordinates of the storage node, the spatial integral form of Maxwell's equations is solved, and the three-dimensional electromagnetic field strength scalar matrix is ​​output. Calculate the field intensity gradient based on the three-dimensional field intensity matrix and node hardware state parameters. Mark the gradient extreme points as the optimal storage node coordinates; Based on the three-dimensional field strength matrix and the electromagnetic interference monitoring data of the computer room, when the field strength value is greater than the interference threshold, an electromagnetic avoidance area marker is generated. Based on the three-dimensional field strength matrix, the annular zero field strength region in the field strength distribution is detected, and a hardware topology defect alarm is output.

5. The intelligent power plant data storage management method according to claim 4, characterized in that, include: Based on the three-dimensional electromagnetic field strength distribution map and the device access request queue, the access request frequency is mapped to the data particle velocity vector v, and the access target node coordinates are mapped to the initial displacement direction, generating a motion parameter set (v,dir); Based on the set of motion parameters, charged particle parameter q, and electromagnetic field strength distribution map, extract the electric field strength E and magnetic induction intensity B on the target path, and calculate the Lorentz force vector F; Based on the Lorentz force vector F, data packet quality parameters, and Newton's second law... Calculate the migration acceleration vector a; Based on the acceleration vector a and the real-time data from the stored node vibration sensor, the node displacement deviation caused by vibration is detected, and an acceleration correction factor δ is generated. Based on the acceleration vector a, correction factor δ, and nodal fault state table, combined with the acceleration kinematic equations Calculate the coordinate sequence of the barrier-free migration path; Calculate the power of the Lorentz force based on the Lorentz force vector F and the motion parameter v. ,when High-energy-consuming path segments are marked.

6. The intelligent power plant data storage management method according to claim 5, characterized in that, include: Based on the original network access logs and storage node vibration sensor data, the network traffic amplitude is mapped to wave displacement u, and the vibration frequency is mapped to the time step. Generate a fusion wave sequence; Based on the fused wave sequence, the KdV equation is solved by Hirota bilinear transform to extract the soliton amplitude A and wave velocity c; Based on the soliton amplitude A, wave velocity c, and preset normal fluctuation threshold, when At that time, mark the aberrant soliton event; Based on the distorted soliton event and the original vibration sensor data, the coherence coefficient γ between the vibration signal and network traffic within the event time window is calculated; Based on the distorted soliton event and the coherence coefficient γ, if γ > 0.7, a device fault alarm is generated; if γ < 0.3, a network attack alarm is generated, and a classified anomaly alarm is output. Based on the soliton amplitude A and wave velocity c, according to the soliton energy formula Calculate the abnormal energy value.

7. The intelligent power plant data storage management method according to claim 6, characterized in that, include: Based on the data migration acceleration vector and initial migration velocity, through the kinematic equations Calculate the real-time migration speed scalar; Based on the real-time migration speed and the speed of light constant, calculate Generate time dilation factor; Based on the node clock synchronization signal, the node clock with the lowest delay is selected as the reference clock source; Calculate based on the time dilation factor, the reference clock source, and the validity period of the original data. Generate calibration validity period; Based on the real-time migration speed, when Superluminal anomaly markers are generated in time; The final validity period is calculated a second time based on the calibration validity period, GPS satellite ephemeris data, and satellite orbital velocity.

8. The intelligent power plant data storage management method according to claim 7, characterized in that, It also includes fault prediction and self-healing steps: Based on quantum conjugate pairs 3D electromagnetic field strength matrix, used to calculate the local entropy of the storage system. Generate real-time entropy values; A Gibbs free energy model is constructed based on real-time entropy values ​​and temperature sensor data from storage nodes. Output the barrier height parameter; Based on the barrier height, the activation energy of the equipment and materials, and according to the Boltzmann distribution Calculate the probability of failure; Based on the failure probability and node energy consumption logs, when A self-healing command is generated at a time of >0.7; Based on the self-healing instructions and compressed bit index sequence, and by maintaining system symmetry according to Noether's theorem, a lossless reconstruction path is generated; Based on the real-time entropy value, when The liquid cooling system is activated at the specified time, and an entropy reduction completion signal is output.

9. A smart power plant data storage management system, characterized in that, The intelligent power plant data storage management system includes: The acquisition module is used to collect real-time sensor data streams and their temporal characteristics from smart power plants. Based on the Heisenberg uncertainty principle, it calculates the quantum fluctuation threshold of the data blocks to determine whether quantum compression should be enabled. The migration module is used to construct a quantum barrier model when the data bit fluctuation amplitude exceeds the quantum fluctuation threshold, and generate compressed data packets by bit flow that penetrates the barrier; The detection module is used to treat the compressed data packet as a charged particle based on the physical coordinates of the compressed data packet and the storage node array, and obtain the electromagnetic field strength distribution map by solving Maxwell's equations. The compensation module is used to calculate the compensation based on the electromagnetic field strength distribution map, the device access request queue, and the Lorentz force formula. Calculate the data migration acceleration vector and generate a preload path; The configuration module is used to construct an access flow solitary wave model based on the original network access logs and storage node vibration sensor data, and output an abnormal wave peak alarm. The allocation module is used to determine the data migration acceleration vector, node clock synchronization signal, and time dilation formula based on special relativity. The timeliness compensation parameter is calculated and used to calibrate the validity period of the data.

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