Hydropower station data compression and encryption integrated method, system, equipment and medium

Abstract

The invention relates to the technical field of power equipment remote monitoring and information security, in particular to a hydropower station data compression and encryption integration method, system, equipment and medium, and the method comprises the steps: obtaining various types of original data related to the operation of hydropower station equipment, and carrying out the primary processing operation of the obtained data; performing format conversion and feature extraction on the primarily processed data to obtain an intermediate processing result; performing compression processing on the intermediate processing result to generate compressed data; performing encryption processing on the compressed data to obtain encrypted data output for communication; according to the communication state and the equipment operation condition, the execution mode of the compression and encryption process is coordinated, the scheduling logic of the processing strategy is controlled, and the data processing capability, the transmission efficiency and the system reliability of the hydropower station monitoring terminal under the low power consumption constraint are improved.

Description

Technical Field

[0001] This invention relates to the field of remote monitoring and information security technology for power equipment, and in particular to an integrated method, system, equipment and medium for data compression and encryption in hydropower stations. Background Technology

[0002] With the widespread application of remote monitoring and intelligent operation and maintenance in the hydropower industry, the demand for online monitoring of equipment operating status in remote hydropower stations is increasing. In areas lacking terrestrial communication infrastructure, satellite communication is gradually becoming an important means of achieving data backhaul. Current systems typically collect key operating parameters such as vibration and temperature by deploying embedded terminals, and then transmit the raw or preliminarily processed data back to the cloud platform via low-speed satellite links to support equipment status assessment and fault diagnosis.

[0003] However, existing technologies still have significant shortcomings in such application scenarios. First, traditional compression algorithms are not optimized for the timing characteristics of hydropower equipment, resulting in low compression efficiency and a high risk of losing critical fault characteristics. Second, encryption algorithms generally employ universal symmetric encryption methods, which are computationally burdensome, consume a lot of power, and are difficult to adapt to battery-powered terminal devices. Furthermore, compression and encryption are often sequential and independent processing steps, lacking a fusion mechanism, which increases processing latency and reduces overall energy efficiency and security levels. These problems collectively restrict the ability of remote hydropower stations to achieve efficient and secure data transmission and long-term autonomous operation under low-power satellite communication conditions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an integrated method and system for data compression and encryption in hydropower stations, addressing issues such as low data compression rate, high encryption energy consumption, and easy loss of features in low-power satellite communication scenarios. By constructing a processing flow that integrates a lightweight compression model with national cryptographic algorithms, and combining it with a hardware-level power management mechanism, it achieves high-fidelity extraction of fault features, secure data transmission, and ultra-low power operation of terminal equipment, meeting the needs of long-term online monitoring.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an integrated method for data compression and encryption in hydropower stations, comprising: Acquire various types of raw data related to the operation of hydropower station equipment, and perform preliminary processing on the acquired data; The data after preliminary processing is converted in format and its features are extracted to obtain intermediate processing results; The intermediate processing results are compressed to generate compressed data; The compressed data is encrypted to produce encrypted data output for communication. Based on the communication status and equipment operating conditions, coordinate the execution mode of the compression and encryption process, and control the scheduling logic of the processing strategy.

[0007] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the step of acquiring various types of raw data related to the operation of hydropower station equipment and performing preliminary processing operations on the acquired data includes: Collect raw data from multiple sources related to operational status; Perform a normalization operation on the collected raw data and output the pre-processed data.

[0008] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the step of performing format conversion and feature extraction on the pre-processed data to obtain intermediate processing results includes: The pre-processed data is standardized and converted into a unified data format. Extract effective features from the transformed data and output the inter-processing results for subsequent processing.

[0009] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the step of performing compression processing on intermediate processing results to generate compressed data includes: Structural adjustments and data organization are performed on the intermediate processing results; The processed data is then encoded and converted to obtain compressed data.

[0010] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the compressed data includes: Feature-compressed bitstream generated by a function; The feature-compressed bitstream has a fixed-dimensional data representation format; The symbol encoding result generated after performing encoding processing on the feature code stream; A compressed output structure containing the symbol encoding results, used for encryption processing calls.

[0011] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the step of encrypting the compressed data to obtain encrypted data output for communication includes: The compressed data is encrypted using the SM4 encryption key integrated in the FPGA; The block encryption operation is performed after the compressed feature bitstream is received; The SM4 encryption key is generated from the device fingerprint and timestamp; Among them, the equipment fingerprint includes the unit number and the physical characteristics of the sensor array; The encryption process uses a unique set of key parameters to implement a one-time pad encryption method.

[0012] The advantages of this preferred technical solution are as follows: by dynamically combining the device fingerprint and timestamp to generate a unique key, a "one-time key" encryption method can be achieved, effectively avoiding the data security risks caused by the reuse of fixed keys; by using the SM4 encryption engine integrated in the FPGA to perform block encryption on the compressed feature stream, the processing efficiency can be improved and the energy consumption reduced, which is suitable for the low power consumption and high security transmission requirements in the satellite communication environment.

[0013] As a preferred embodiment of the integrated data compression and encryption method for hydropower stations described in this invention, the step of coordinating the execution mode of the compression and encryption processes and controlling the scheduling logic of the processing strategy based on the communication status and equipment operating conditions includes: When the link is in good condition, enable high-speed compression mode to improve processing speed; When the link status deteriorates, switch to power saving mode and adjust the processing voltage; When the link is interrupted, activate the local cache and suspend data transmission operations; By monitoring the signal strength of the communication link in real time, dynamic switching and control of the compression and encryption processes can be achieved.

[0014] The beneficial effects of this preferred technical solution are as follows: it realizes dynamic control of the compression and encryption process under different communication states, effectively avoiding resource waste and data backlog caused by link instability; through the switching between high-speed compression and energy-saving modes, as well as the local caching mechanism when the link is interrupted, it improves the system's communication adaptability and task continuity under low power constraints, and enhances the stability and communication robustness of terminal devices in remote areas for long periods of time.

[0015] Secondly, the present invention provides an integrated data compression and encryption system for hydropower stations, comprising: The data acquisition and preprocessing module is used to collect vibration, temperature and operating condition data related to equipment operation, and to perform signal preprocessing and format conversion operations on the data; A lightweight compression and encryption processing module is used to extract features, compress and encrypt preprocessed data, and generate data results for transmission. The satellite communication and power management unit is used to realize remote data transmission and control the operation status and energy usage strategy of various parts of the system according to the communication status and power consumption conditions.

[0016] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of an integrated method for data compression and encryption in hydropower stations.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the integrated method for data compression and encryption in a hydropower station.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: Addressing the engineering requirements of low-power and high-security transmission in remote monitoring of hydropower stations, this invention constructs a compression and encryption method integrating data preprocessing, compression coding, encryption protection, and communication scheduling. By collecting and pre-processing various types of raw data, a unified expression of multi-source operational information is achieved, providing standardized input for subsequent transmission. Format conversion and feature extraction mechanisms solve the problems of inconsistent heterogeneous signal formats and difficulties in feature extraction. Compressed data is constructed through structural adjustments and encoding methods, reducing data volume and mitigating the risk of transmission congestion under limited satellite link bandwidth. At the security level, SM4 packet encryption and one-time pad mechanisms are used to ensure encryption compliance and tamper-proof capabilities during compressed data link transmission. Combined with a communication status awareness and adaptive scheduling mechanism for processing strategies, dynamic switching between high-speed compression and energy-saving modes is achieved, ensuring low-power operation and communication continuity of equipment in remote scenarios. The overall solution has good engineering deployment adaptability, effectively improving the data processing capabilities, transmission efficiency, and system reliability of hydropower station monitoring terminals. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall process of an integrated data compression and encryption method for hydropower stations according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the software algorithm flow of an integrated data compression and encryption method for hydropower stations according to an embodiment of the present invention.

[0022] Figure 3This is a hardware layer functional diagram of an integrated data compression and encryption system for hydropower stations, as described in one embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for integrated data compression and encryption in hydropower stations is provided, comprising: S1: Acquire various types of raw data related to the operation of hydropower station equipment, and perform preliminary processing on the acquired data; S2: Perform format conversion and feature extraction on the pre-processed data to obtain intermediate processing results; S3: Perform compression processing on the intermediate processing results to generate compressed data; S4: Encrypt the compressed data to obtain encrypted data output for communication; S5: Based on the communication status and device operating conditions, coordinate the execution mode of the compression and encryption process, and control the scheduling logic of the processing strategy.

[0025] It should be noted that traditional remote monitoring of hydropower stations generally suffers from the following problems when facing scenarios such as remote areas, limited communication bandwidth, and limited equipment energy consumption: First, the original data types are complex and the formats are inconsistent, resulting in low processing efficiency and chaotic structure. Second, the compression algorithms are highly general but poorly targeted, and cannot balance compression ratio and feature preservation under low computing power conditions. Third, the data encryption mechanism is executed independently, which cannot achieve efficient coordination with the compression process and has a heavy energy burden. Fourth, the system response is sluggish when the communication status changes, and it is prone to operational bottlenecks such as data blocking, buffer overflow, or excessive power consumption.

[0026] Therefore, to address the aforementioned issues of unstable communication links, complex data formats, and inefficient processing due to the separation of compression and encryption, steps S1-S5 were implemented to gradually achieve collaborative processing across each stage: S1 resolved the issue of multi-type data acquisition and preprocessing in the field, ensuring the consistency of data sources; S2 completed format conversion and feature extraction, providing a structurally standardized data foundation for subsequent processing; S3 compressed intermediate results, reducing data volume and alleviating transmission pressure under low-bandwidth satellite links; S4 encrypted the compressed results through an integrated encryption mechanism, meeting the data security requirements of remote communication; and S5 adjusted the compression and encryption strategies based on link status, ensuring energy consumption control and continuous data transmission under different communication conditions.

[0027] Example 2, refer to Figure 2 As an embodiment of the present invention, based on the above embodiment, a method for integrated data compression and encryption of hydropower stations is provided.

[0028] In this embodiment of the application, step S1 involves acquiring various types of raw data related to the operation of hydropower station equipment and performing preliminary processing operations on the acquired data, including: A1: Collect raw data from multiple sources related to operational status; A2: Performs a normalization operation on the collected raw data and outputs the pre-processed data.

[0029] It should be noted that the preliminary processing operation acquires various types of raw signals through on-site integrated sensors, and combines filtering and analog-to-digital conversion to form a unified format of digital data input. This effectively solves the problems of complex data sources, diverse signal forms, and difficulty in directly processing analog information at hydropower station sites, providing a standardized and directly calculable data foundation for subsequent compression and encryption, and improving the overall data flow efficiency and processing stability of the system.

[0030] Specifically, by integrating vibration, temperature, and operating condition parameter acquisition devices, multiple types of data on the operating status are acquired, and the raw signals are filtered and converted from analog to digital to output standardized digital data as the input basis for subsequent steps. That is, A1 integrates a triaxial MEMS vibration sensor, a PT100 temperature sensor, and an operating condition parameter acquisition module to acquire operating parameters such as equipment vibration acceleration, temperature of key parts, rotational speed, and load in real time. In A2, the raw signal is preprocessed by an anti-aliasing filter and then input to a low-power analog-to-digital converter to be converted into a digital signal.

[0031] In an optional implementation, the preliminary processing operation in step S1 can also be implemented by introducing a data acquisition terminal with edge computing capabilities to achieve preprocessing functions, integrating normalization, filtering and analog-to-digital conversion into the same embedded device, completing preprocessing calculations at the data acquisition end and outputting digital results.

[0032] In another alternative implementation, the preliminary processing operation in step S1 can also introduce a time synchronization mechanism on the sensor side, so that multiple types of raw data are collected and processed under a unified time reference. This helps to improve the accuracy of feature association and reduce cross-source data alignment errors, providing a more stable data foundation for subsequent feature extraction and compression analysis.

[0033] In this embodiment of the application, step S2 involves format conversion and feature extraction of the pre-processed data to obtain intermediate processing results, including: B1: Standardize the format of the pre-processed data and convert it into a unified data format; B2: Extract effective features from the transformed data and output the inter-processing results for subsequent processing.

[0034] It should be noted that the format conversion and feature extraction operations, by standardizing the pre-processed multi-source data from the time domain to a unified frequency domain form and combining it with a lightweight convolutional neural network for feature analysis and vectorized output, effectively solve the problems of inconsistent data formats, large differences in feature distribution, and difficulty in unifying input models for hydropower station field monitoring data. This processing flow can improve the input consistency and feature concentration of subsequent compression processing, avoid the decrease in compression accuracy caused by redundancy or missing features in the original data, and at the same time, enable the rapid extraction and representation of key features under low computing power conditions in scenarios with limited terminal computing resources, thereby enhancing the overall adaptability and processing efficiency of the system.

[0035] Specifically, format conversion can be achieved through Fast Fourier Transform (FFT) to standardize the signal from the time domain to the frequency domain; feature extraction can be completed by a customized convolutional neural network, which extracts abnormal features from the frequency domain data through convolutional layers and activation functions, and finally outputs a structured feature vector as an intermediate processing result for subsequent compression and encryption processing; that is, B1 includes input preprocessing using 16-point Fast Fourier Transform (FFT) to convert the time domain vibration signal into a frequency domain feature vector (dimension 128); B2 highlights key frequency bands through a learnable frequency domain mask, which is a parameterized weight matrix based on the equipment's operating characteristics, and its parameters are obtained through training with historical fault data; Taking mixed-flow turbine units as an example, typical faults such as guide vane wear and impeller cracks usually manifest as abnormal vibration energy at 1-5 times the rotational frequency. During mask parameter training, 1X, 2X, 3X, 4X, and 5X will be forcibly set. The weighting coefficients for frequency switching are increased to ≥0.8, while the coefficients for other frequency bands are suppressed to ≤0.3, achieving targeted enhancement of the sensitive frequency bands for key faults of this model and improving the accuracy of feature extraction. A grouped convolution technique is employed, dividing the input feature map into several subgroups according to the channel dimension (e.g., dividing the three typical faults common in hydropower station equipment—bearing wear, blade cracks, and shaft misalignment—into three groups), with each group independently focusing on feature extraction for one type of fault. Through a channel attention mechanism, the allocation of computational resources is dynamically adjusted based on the importance of features in each group. For groups containing significant fault features (e.g., groups detecting bearing wear features), the update frequency and computational priority of their convolutional kernels are increased; for groups dominated by normal operating condition features, the proportion of computational resources is reduced, thereby reducing redundant computational power consumption while ensuring the accuracy of fault feature extraction. A 32-dimensional compressed feature vector is output, and arithmetic coding is used to symbolically encode high-frequency feature patterns (e.g., low-amplitude stable signals under normal operating conditions), generating a compressed bitstream with an average code length of <200 bits.

[0036] In an optional implementation, the format conversion and feature extraction in step S2 can also be achieved by wavelet packet decomposition combined with parametric filtering: Specifically, multi-layer wavelet packet decomposition is first performed on the pre-processed vibration signal to extract the energy distribution features of sub-signals in different frequency bands, and then the passband range of each frequency band filter is dynamically adjusted according to the operating conditions of the equipment so that the sensitive response to the fault characteristic frequency band can still be maintained when the operating conditions change.

[0037] In another optional implementation, the format conversion and feature extraction in step S2 can also be achieved through a feature alignment mechanism based on a prior model: that is, after the format conversion, an embedding model pre-trained with historical fault samples is introduced to measure the similarity between the current data feature vector and the known abnormal pattern, and the input feature dimension is adjusted according to the matching result so that the output vector is automatically aligned with the dimension required by the compression module.

[0038] In this embodiment of the application, step S3 involves performing compression processing on the intermediate processing results to generate compressed data, including: C1: Perform structural adjustments and data organization on intermediate processing results; C2: Performs encoding conversion on the processed data and obtains compressed data.

[0039] Specifically, the compressed data includes a feature-compressed bitstream generated by a function; a symbol encoding result generated after encoding processing based on the feature bitstream; and a compressed output structure containing the symbol encoding result for use in encryption processing calls; wherein the feature-compressed bitstream has a fixed-dimensional data representation.

[0040] Structural adjustments and data organization can be achieved by constructing fixed-dimensional data structures using feature vectors output from convolutional networks; encoding transformation can be completed by an arithmetic coding module, which models and symbols the frequency of feature patterns to generate compressed data with a shorter average code length for subsequent encryption processing.

[0041] In an optional implementation, the compression process in step S3 can also be achieved by introducing a dictionary matching compression mechanism; a local compressed dictionary of finite length is established for the structured feature vectors, the dictionary entries are dynamically updated according to the frequency and repetition of the features, and the compression process is performed by replacing the original vector segments with dictionary indexes, thereby further reducing redundancy and shortening the average encoding length.

[0042] In another optional implementation, the compression process in step S3 can also be achieved by adding a data packaging mechanism after entropy coding: after completing arithmetic coding or Huffman coding, a packaging strategy is set according to the characteristic distribution of different data blocks, and the coding result is encapsulated into a variable length segment and additional segment header information (such as offset, check value, etc.) is added to improve the identification and synchronization capabilities in the subsequent link transmission process.

[0043] In this embodiment of the application, step S3 involves structural adjustment and data organization of the intermediate processing results, including: integrating the FPGA with the national cryptographic SM4 encryption engine and arithmetic coding module, receiving the compressed feature code stream output by the MCU, and further compressing the code stream through arithmetic coding.

[0044] In an optional implementation, the structural adjustment and data processing in step S3 can also be achieved by aligning the feature data according to the sensor timestamp, eliminating the failed intervals and trimming the static redundant segments, thereby improving the information density in the feature compression code stream. This further reduces the overall average code length of the data while ensuring the complete transmission of the main dynamic features, and enhances the adaptability to low-bandwidth links of remote hydropower stations.

[0045] In another optional implementation, the structural adjustment and data sorting in step S3 can also be carried out by introducing a sliding window caching mechanism to perform batch mode merging processing on continuous data, pre-judging the changing trend of adjacent feature segments on the MCU side, merging and encoding segments with high similarity, reducing the number of symbol changes, and reducing the entropy value during subsequent arithmetic encoding. This further compresses the data volume and improves real-time processing efficiency while ensuring the complete expression of core fault symptom features.

[0046] In this embodiment of the application, step S3 involves performing an encoding conversion operation on the processed data to obtain compressed data, including rearranging the feature vector order according to node and sensor type to reduce duplicate fields and zero-fill rate, thereby improving compression efficiency.

[0047] In an optional implementation, the encoding conversion operation in step S3 can also introduce a variable-length encoding mechanism, which allocates shorter encoding bit widths to frequently occurring feature bits based on the frequency and distribution characteristics of feature values ​​corresponding to different sensor types, thereby reducing the overall encoding length and further reducing the compressed data volume.

[0048] In another optional implementation, the encoding conversion operation in step S3 can also be combined with a dynamic update mechanism for the compression dictionary to dynamically adjust the dictionary encoding structure based on the feature templates of historical compression results in the cache. This enables adaptive reconstruction of feature patterns generated under different operating conditions, improves data compression consistency and algorithm convergence performance under varying operating conditions, and enhances the stability of compression processing in periodic and non-periodic scenarios.

[0049] In this embodiment of the application, step S4 involves encrypting the compressed data to obtain encrypted data output for communication, including: D1: The compressed data is encrypted using the SM4 encryption key integrated in the FPGA; D2: The encryption process uses a unique set of key parameters to implement a one-time pad encryption method.

[0050] It should be noted that by constructing a "compression-encryption" fusion mechanism, the feature compression process and the key generation process are bound together; the SM3 hash algorithm generates the initial key seed for CNN weights based on the device's operating parameters, ensuring that the feature compression rules are unique to each device; arithmetic coding introduces a dynamic confusion factor based on the operating state, making the encoding result change over time, thus enhancing the security of the compression stage; the SM4 encryption key is generated by the device fingerprint and timestamp, achieving "one-time key"; the FPGA integrates the SM4 encryption engine to perform block encryption on the compressed bitstream, improving processing efficiency, reducing energy consumption, and adapting to the requirements of low-power, high-security communication.

[0051] Specifically, D1~D4 include the construction of a "compression is encryption" fusion mechanism: In the feature compression stage, the CNN weight matrix generates an initial key seed using the national cryptographic SM3 hash algorithm. The specific process is as follows: First, collect the device-specific operating parameters (such as unit model, sensor deployment location code, etc.) as hash input, and generate a unique key seed through the SM3 algorithm (outputting a 256-bit hash value). This seed serves as the baseline parameter for initializing the CNN model weights and is deeply bound to the feature compression process, ensuring that the feature compression rules of different devices are irreplaceable and blocking the possibility of reverse-engineering the original data through a general model from the source. During the arithmetic coding process, the symbol probability table is dynamically obfuscated (the obfuscation factor is updated every 5 minutes), so that the coding results of the same features change over time, which is equivalent to introducing a dynamic encryption key. Among them, the arithmetic coding dynamic obfuscation factor is a security enhancement parameter in the feature compression stage. Every 5 minutes, a random number is generated based on the equipment operating status (such as real-time speed, load, etc.) as an obfuscation factor, which is used to dynamically update the symbol probability table, so that the encoding result of the same feature (such as a low-amplitude signal under normal operating conditions) changes randomly at different times, achieving the effect of "compressed bitstream obfuscation", which is essentially a security hardening mechanism in the compression process. The SM4 encryption key is generated by combining the device fingerprint (unit number + sensor array physical characteristics) and the timestamp. An independent key is generated for each transmission to achieve a "one-time key" security mechanism. Among them, the SM4 encryption key is a dedicated encryption key for the data transmission stage. It follows the national standard SM4 algorithm specification (key length 128 bits) and is generated and updated through an independent key management module. It is used to encrypt the compressed and obfuscated bitstream in groups to ensure the security of satellite link transmission.

[0052] In an optional implementation, the encryption process in step S4 can also be achieved by introducing a perturbation enhancement mechanism to add perturbation chips to the feature compression result; that is, after feature compression is completed, a perturbation factor is generated according to the current operating state of the device (such as the root mean square value of vibration and the amplitude of temperature fluctuation), the output feature vector is subjected to displacement perturbation processing, and the perturbation information is encapsulated into the verification field, which is then decrypted by the receiving end using symmetric logic; this perturbation acts as a secure buffer layer between the compressed output and the final encryption, which can improve the data's resistance to tampering in the satellite link, and even if the key is leaked for a short time, it is difficult to restore the original feature structure, thus forming a physical state-driven encryption enhancement.

[0053] In another optional implementation, the encryption process in step S4 can also be implemented by constructing a context-dependent key update mechanism. Before each data transmission, this mechanism dynamically adjusts the timestamp step value and fingerprint information weight in the SM4 key generation logic according to the device fault warning level and communication success rate index of the previous period. For example, when the device experiences a high-frequency abnormal state or the number of consecutive link failures exceeds a set threshold, the system increases the timestamp resolution and fingerprint encoding depth to enhance key granularity and uniqueness, thereby ensuring communication security and key unpredictability under high-risk conditions.

[0054] In this embodiment of the application, step S5 coordinates the execution mode of the compression and encryption process according to the communication status and device operating conditions, and controls the scheduling logic of the processing strategy, including: E1: When the link is in good condition, enable high-speed compression mode to improve processing speed; E2: When the link status deteriorates, switch to power saving mode and adjust the processing voltage; E3: When the link is interrupted, activate the local buffer and suspend data transmission operations; E4: By monitoring the signal strength of the communication link in real time, dynamic switching and control of the compression and encryption process can be achieved.

[0055] It should be noted that by combining the communication link status and device operating conditions, dynamic control and strategy scheduling of the compression and encryption process are realized, effectively solving the problems of transmission interruption, increased energy consumption and data backlog caused by link instability. When the link status is good, the system automatically activates the high-speed compression mode to speed up data processing and uploading, and alleviate cache pressure. When the link status deteriorates, it switches to energy-saving mode and reduces the FPGA operating voltage to extend device uptime. When the link is interrupted, the local cache is activated and data transmission is suspended to ensure data integrity while reducing system power consumption to the microampere level.

[0056] Specifically, E1 to E4 include: Based on the satellite communication link status (monitored in real time via RSSI signal strength), the core processing mode is automatically adjusted. When the link quality is good (SNR > 15dB), enable high-speed compression mode (compression ratio 8:1, processing rate 500 frames / second) to quickly upload accumulated data; When the link quality degrades (SNR 10-15dB), switch to power saving mode (compression ratio 12:1, processing rate 200 frames / second), and reduce the FPGA operating voltage to 1.0V. When the link is interrupted, the local cache is activated and the overall power consumption of the device is reduced to <20μA. After the link is restored, the unencrypted compressed features are transmitted first.

[0057] After the device is powered on and enters the initialization phase, it collects steady-state operating signals of the unit through sensors, generates a device fingerprint, and registers it with the cloud. During normal operation, the sensors collect vibration signals at a frequency of 12.8kHz, packaging them into a frame every second (containing 12,800 sampling points). The data frame is input to the MCU after being filtered by anti-aliasing, and 32-dimensional frequency domain features are extracted by a lightweight CNN. After the feature vector enters the FPGA, it is first arithmetic encoded and then encrypted by the SM4 engine. The encrypted data packet is appended with the device fingerprint, timestamp, and CRC check code, and then sent to the cloud via a satellite module. During non-communication periods, it enters deep sleep mode, retaining only the sensor timed wake-up function; when a satellite link window is detected (predicted by the built-in GPS module), the compression and encryption module is woken up 1 minute in advance to preload model parameters into the cache (reducing startup delay). Throughout the entire process, key fault characteristics trigger the real-time transmission mechanism, which can wake up and complete data transmission within 10ms even when in a dormant state, ensuring the timeliness of early warning in emergency situations.

[0058] In an optional implementation, the execution method of coordinating the compression and encryption process in step S5 can also be achieved through a fault level-based processing mode switching mechanism. That is, based on link status monitoring, the execution strategy of compression and encryption is dynamically adjusted in combination with the fault level index output by the feature extraction module. When a potential high-risk fault is identified (such as an abnormal amplitude in the frequency domain exceeding a set threshold), even if the link signal is weak, the high compression rate and low latency encryption path is prioritized to ensure that fault data is uploaded in real time. Conversely, low-level events can be delayed until the link is restored before processing, thereby realizing the control strategy of "driving communication priority with event level" and ensuring the timeliness of early warning response.

[0059] In another optional implementation, the execution method of coordinating the compression and encryption process in step S5 can also be achieved by combining the compression power consumption adjustment mechanism of the device's working cycle. Specifically, the compression processing frequency is dynamically reduced according to the current working cycle of the device (such as the non-peak operation stage at night or the low load range), and the start-stop rhythm of relevant modules in the FPGA is adjusted synchronously. For example, during the night stage, intermittent compression is used for slow processing, and data is cached and sent uniformly before the satellite window opens. During the high load period, the data processing density is increased and upload bandwidth space is reserved, thereby matching the different requirements of the operating state for data timeliness and power consumption, and realizing the two-way integration of time control and communication control.

[0060] In summary, this invention proposes a data compression and encryption method for hydropower station operating equipment. By standardizing the initial data acquisition format, constructing a frequency domain feature extraction model, and integrating compression coding and national cryptographic algorithm encryption modules, a highly adaptable and resource-efficient processing flow is formed. Furthermore, by dynamically adjusting the compression ratio and encryption strategy based on communication link status and equipment fault levels, the continuity and security of data processing are effectively ensured under conditions of unstable communication or limited power consumption. In addition, the overall power consumption management is optimized through rhythm control of equipment operation cycles and a sleep mechanism, improving the reliability and early warning response capabilities of the system for long-term deployment in remote areas.

[0061] Example 3 is an embodiment of the present invention, which provides an integrated method for data compression and encryption in hydropower stations. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0062] Taking the real-time status monitoring of the No. 32 mixed-flow turbine generator unit (single unit capacity 700MW) of the Three Gorges Hydropower Station as an example, the application process and technical implementation of this patented device are as follows: Accurate acquisition of multi-dimensional data: The device captures the vibration acceleration signals of the runner and bearing housing in real time through an integrated triaxial MEMS vibration sensor (sampling rate 12.8kHz), and simultaneously monitors the temperature of the thrust bearing and guide bearing using a PT100 temperature sensor (sampling interval 1 second). It also acquires operating data such as unit speed (rated 75r / min) and active load through the operating condition parameter acquisition module. After the original analog signal is preprocessed by a 5kHz anti-aliasing filter, it is converted into a digital signal by a low-power ADC (power consumption <1mW), forming a vibration data frame containing 12,800 sampling points / second and a multi-dimensional operating condition dataset.

[0063] Integrated Feature-Aware Compression and Encryption Processing: Digital signals are input to the ARM Cortex-M7+FPGA heterogeneous processing core, and feature compression is performed by a customized CNN inference engine on the MCU. The time-domain vibration signal is converted into a 128-dimensional frequency-domain feature vector through 16-point FFT, and key frequency bands are highlighted by the device-specific frequency-domain mask. Early fault features such as bearing wear and blade cracks are extracted through 32 3×3 separable convolution kernels, and a 32-dimensional compressed feature code stream is output. The FPGA module first performs arithmetic encoding on the compressed code stream, and uses the SM4 encryption engine (combined with the device fingerprint key seed generated by SM3 hash to achieve dual protection of "compression is encryption" to ensure that the data cannot be reversed and restored) to achieve this.

[0064] Satellite link adaptive transmission and power consumption management: The device connects to the satellite through the L-band satellite communication module and monitors the link signal-to-noise ratio (SNR) in real time; when SNR > 15dB, it enables high-speed compression mode (processing rate 500 frames / second) to quickly upload data; when SNR drops to 10-15dB, it switches to power-saving mode; when the link is interrupted, the data is temporarily stored in the local Flash and waits for reconnection.

[0065] Example 4 illustrates a schematic scheme for an integrated data compression and encryption method for hydropower stations. It should be noted that the technical solution of this integrated data compression and encryption system for hydropower stations is based on the same concept as the aforementioned integrated data compression and encryption method for hydropower stations. Details not described in detail in this embodiment can be found in the description of the aforementioned integrated data compression and encryption method for hydropower stations.

[0066] like Figure 3 As shown, this embodiment also provides an integrated data compression and encryption system for hydropower stations, including: The data acquisition and preprocessing module is used to acquire vibration, temperature and operating condition data related to equipment operation, and to perform signal preprocessing and format conversion operations on the data; Specifically, it integrates a triaxial MEMS vibration sensor, a PT100 temperature sensor, and a working condition parameter acquisition module to acquire equipment vibration acceleration (sampling rate 12.8kHz), temperature of key components (sampling interval 1s), and operating parameters such as rotational speed and load in real time. The raw signal is preprocessed by an anti-aliasing filter (cutoff frequency 5kHz) and then input to a low-power analog-to-digital converter (ADC, power consumption <1mW) to convert it into a digital signal. A lightweight compression and encryption processing module is used to extract features, compress and encrypt preprocessed data, and generate data results for transmission. Specifically, a heterogeneous architecture of "ARM Cortex-M7 microcontroller (MCU) + field-programmable gate array (FPGA)" is adopted: the MCU is equipped with a customized lightweight convolutional neural network (CNN) inference engine, which is responsible for executing the feature compression model; the network structure is optimized by model pruning and quantization, with a total number of parameters <1MB and a single frame inference power consumption <5mW; the input layer uses 16-point fast Fourier transform (FFT) preprocessing to convert the time-domain vibration signal into a frequency-domain feature vector (dimension 128); the convolutional layer uses 3×3 separable convolution kernels (number 32) to extract frequency-domain anomalous features; the output layer generates a feature-compressed bitstream (dimension 32) through the Softmax function. The FPGA integrates the national standard SM4 encryption engine and arithmetic coding module. It receives the compressed feature code stream output by the MCU, further compresses the code stream through arithmetic coding, and performs SM4 block encryption on the compressed data (key length 128 bits, encryption rate 20Mbps). The FPGA adopts dynamic voltage frequency adjustment (DVFS) technology to automatically adjust the operating frequency according to the data processing load.

[0067] The satellite communication and power management unit is used to realize remote data transmission and control the operation status and energy usage strategy of various parts of the system according to the communication status and power consumption conditions. Specifically, it is equipped with an L-band satellite communication module (supporting the Inmarsat BGAN protocol), integrating a directional antenna and a low-noise amplifier (30dB gain); the power management module adopts energy harvesting technology, is equipped with a solar panel, and has a built-in 3.7V / 5Ah lithium battery; a three-level sleep mechanism is designed. Idle state: During non-communication windows, the MCU and FPGA enter deep sleep (power consumption <10μA), with only the sensor timer wake-up circuit remaining (wake-up interval 5s). Acquisition state: After the sensor is woken up, the MCU starts preprocessing and feature compression (duration < 100ms), and immediately enters the waiting state after completion; Transmission state: When the satellite link is connected, the FPGA activates the encryption and encoding module, and returns to the idle state within 5 seconds after the data transmission is completed.

[0068] This embodiment also provides an electronic device applicable to a hydropower station data compression and encryption integration scenario, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the hydropower station data compression and encryption integration method proposed in the above embodiment.

[0069] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the integrated method for data compression and encryption of a hydropower station as proposed in the above embodiments.

[0070] The storage medium proposed in this embodiment belongs to the same inventive concept as the integrated method for data compression and encryption of a hydropower station proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0071] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A water power station data compression and encryption integrated method, characterized in that, The method comprises the following steps: acquiring multiple types of original data related to the operation of a hydropower station device and performing preliminary processing operations on the acquired data; performing format conversion and feature extraction on the preliminarily processed data to obtain intermediate processing results; performing compression processing on the intermediate processing results to generate compressed data; performing encryption processing on the compressed data to obtain encrypted data output for communication; coordinating the execution mode of the compression and encryption processes and controlling the scheduling logic of the processing strategy according to the communication state and the device operation condition.

2. The method for data compression and encryption integration of a hydropower station according to claim 1, characterized in that, The method of acquiring multiple types of original data related to the operation of a hydropower station device and performing preliminary processing operations on the acquired data comprises the following steps: collecting original data corresponding to multiple data sources related to the operation state; performing normalization data preliminary processing operations on the collected original data and outputting the preliminarily processed data.

3. The integrated data compression and encryption method for hydropower stations as described in claim 2, characterized in that, The method of performing format conversion and feature extraction on the preliminarily processed data to obtain intermediate processing results comprises the following steps: performing format standardization on the preliminarily processed data to convert it into a unified data format; extracting effective features from the converted data and outputting intermediate processing results for subsequent processing.

4. The method of claim 3, wherein the method further comprises: The method of performing compression processing on the intermediate processing results to generate compressed data comprises the following steps: performing structure adjustment and data arrangement on the intermediate processing results; performing encoding conversion operations on the arranged data and obtaining compressed data.

5. The integrated data compression and encryption method for a hydroelectric power station according to claim 4, wherein, The compressed data comprises: a feature compression code stream generated by a function; the feature compression code stream has a fixed-dimensional data representation form; a symbol encoding result generated after encoding processing based on the feature code stream; a compressed output structure containing the symbol encoding result for encryption processing call.

6. The method for data compression and encryption integration of a hydropower station according to claim 5, characterized in that, The method of performing encryption processing on the compressed data to obtain encrypted data output for communication comprises the following steps: performing a group encryption operation on the compressed data by an SM4 encryption key integrated in an FPGA; the group encryption operation is performed after receiving the compressed feature code stream; the SM4 encryption key is generated by a device fingerprint and a timestamp; wherein the device fingerprint comprises a unit number and physical characteristic information of a sensor array; the encryption processing uses a unique set of key parameters to achieve a one-time pad encryption mode.

7. The integrated data compression and encryption method for a hydroelectric power station according to claim 6, wherein, The method of coordinating the execution mode of the compression and encryption processes and controlling the scheduling logic of the processing strategy according to the communication state and the device operation condition comprises the following steps: when the link state is good, enable the high-speed compression mode to improve the processing rate; when the link state decreases, switch to the energy-saving mode and adjust the processing voltage; when the link is interrupted, activate the local cache and suspend the data transmission operation; by monitoring the communication link signal strength in real time, the dynamic switching and control of the compression and encryption processes are realized.

8. A hydropower station data compression and encryption integrated system, applying the method of any one of claims 1-7, characterized in that, The method comprises the following steps: a data acquisition and preprocessing module for acquiring device operation related vibration, temperature and working condition data and performing signal preprocessing and format conversion operations on the data; a lightweight compression and encryption processing module for performing feature extraction, compression processing and encryption processing on the preprocessed data and forming data results for transmission; a satellite communication and power consumption management unit for realizing remote transmission of data and controlling the operation state and energy use strategy of each part of the system according to the communication state and power consumption condition. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The computer program is executed by the processor to implement the steps of the water power station data compression and encryption integrated method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the water power station data compression and encryption integrated method in any one of claims 1 to 7.

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