Hydraulic power plant automatic debugging system with adaptive communication function

Abstract

The invention discloses a hydraulic power plant automatic debugging system with a self-adaptive communication function. The system comprises a processing unit, a storage unit, a communication module, an input / output interface, a task classification module, a model architecture configuration module and other functional modules. The use method comprises the following steps: analyzing task complexity and high-dimensional features of input data through a task classification module, and grading by adopting algorithms such as principal component analysis and a support vector machine; the model architecture configuration module is used for matching a model architecture according to the complexity level and determining an initial calculation path; the dynamic path adjustment module optimizes a calculation path in combination with real-time data, and the resource distribution module redistributes resources when the calculation power exceeds the limit; the model compression module generates a compression model adaptive to the edge device through pruning and quantization algorithms; the parameter fine tuning module is used for jointly optimizing model parameters for a multi-task scene; according to the method, the adaptability and efficiency bottleneck of an existing debugging tool in a complex scene is effectively solved, and the intelligent debugging level of an automatic system of a hydraulic power plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower plant automation control, and particularly to an automated commissioning system for hydropower plants with adaptive communication functions. Background Art

[0002] With the development of smart grid and industrial Internet technologies, the complexity of hydropower plant automation systems has been increasing day by day, posing higher requirements for the intelligence, adaptability, and edge computing capabilities of commissioning tools. The following technical bottlenecks generally exist in traditional hydropower plant automation commissioning tools: 1. The hydropower plant automation system integrates various types of terminals such as sensors, control devices, and monitoring platforms, generating high-dimensional mixed data including time-series data, status parameters, and device logs. Traditional commissioning tools rely on fixed feature extraction and task classification methods, and it is difficult to efficiently handle problems such as data noise, dimensionality disaster, and dynamic changes in task complexity, resulting in limited commissioning accuracy and efficiency. 2. Edge devices generally adopt diverse communication protocols, and there are problems such as bandwidth fluctuations and uncertain delays in the network environment. Existing commissioning tools mostly use preset protocol configurations and lack dynamic protocol matching and adaptive communication quality adjustment mechanisms, which are prone to causing data interaction interruptions or low transmission efficiency. 3. Edge devices are limited by computing power and storage capacity. Directly deploying complex models will cause operation jams or excessive energy consumption; while simple compressed models will result in accuracy loss. 4. During the operation of hydropower plants, the device status and environmental parameters change in real time. During the commissioning process, it is necessary to dynamically adjust the calculation path and model parameters according to real-time data. Existing technologies mostly adopt offline preset strategies and lack an automated optimization mechanism based on real-time feedback, unable to meet the commissioning requirements under complex working conditions. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes an automated commissioning system for hydropower plants with adaptive communication functions. By constructing a modular system including task classification, dynamic path adjustment, resource allocation, model compression, and edge deployment, it realizes the collaborative optimization of multiple technical links, effectively solves the adaptability and efficiency bottlenecks of existing commissioning tools in complex scenarios, and improves the intelligent commissioning level of hydropower plant automation systems.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions to achieve: An automated commissioning system for hydropower plants with adaptive communication functions, including a processing unit, a storage unit, a communication module, and an input / output interface. The processing unit is used to execute data processing, algorithm operations, and module control; The communication module is used to realize data interaction with edge devices, sensors, and external systems; The storage unit is used to store a preset task classifier, a model architecture library, a dynamic path adjustment algorithm, a resource allocation algorithm, a lightweight model compression algorithm, and a multi-task joint optimization algorithm; The input / output interface is used to receive input data and output processing results; The automated commissioning system for hydropower plants with adaptive communication function further includes a task classification module, a model architecture configuration module, a dynamic path adjustment module, a resource allocation module, a model compression module, a parameter fine-tuning module, a performance evaluation module, and an edge deployment module.

[0005] In a preferred solution, the task classification module is connected to the processing unit and is used to analyze the task complexity and high-dimensional data features of the input data and perform grading; the task classification module includes a feature extraction unit, a dimensionality reduction processing unit, a classification optimization unit, and a level mapping unit; The feature extraction unit is used to perform feature processing on the input data to generate feature vectors; the dimensionality reduction processing unit is used to perform dimensionality reduction on the feature vectors using the principal component analysis algorithm to obtain dimensionality-reduced features; the classification optimization unit includes a preset classifier and a support vector machine algorithm and is used to classify the dimensionality-reduced features and optimize the classification results; the level mapping unit is used to grade the task complexity according to the optimized classification results and output the final results.

[0006] In a preferred solution, the model architecture configuration module is connected to the processing unit and the storage unit and is used to obtain the corresponding model architecture configuration according to the task complexity level and determine the initial calculation path; the model architecture configuration module includes an architecture mapping unit, a node processing unit, a sequence generation unit, and a load balancing unit; The architecture mapping unit is used to match the corresponding model architecture configuration from the model architecture library according to the task complexity level and determine the initial calculation path; the node processing unit is used to parse the initial calculation path to extract the calculation node sequence, linearly interpolate and complete the missing nodes, and generate an optimized node sequence; the sequence generation unit is used to determine the execution order and resource requirement configuration of the calculation nodes using the topological sorting algorithm; the load balancing unit is used to adjust the node resource allocation through a heuristic algorithm to optimize the resource allocation scheme.

[0007] In a preferred solution, the dynamic path adjustment module is connected to the processing unit and is used to perform real-time optimization on the initial calculation path; the dynamic path adjustment module includes a dynamic programming unit, a cost optimization unit, a strategy evaluation unit, and an efficiency analysis unit; The dynamic programming unit is used to update and determine the path data input in combination with real-time data and generate a preliminary adjusted path; the cost optimization unit is used to optimize the calculated path cost by using a heuristic search algorithm to obtain a cost-optimized path; the policy evaluation unit is used to judge whether the path selection policy meets the optimization goal according to real-time data and generate a policy-adjusted path; the efficiency analysis unit is used to determine the final optimized path in combination with the data processing speed and the algorithm execution efficiency.

[0008] In a preferred solution, the resource allocation module is connected to the processing unit and is used to reallocate computing resources when the computing power consumption exceeds the limit; the resource allocation module includes a computing power monitoring unit, a clustering analysis unit, and an allocation adjustment unit; The computing power monitoring unit is used to monitor the computing power consumption of the computing path in real time and trigger resource scheduling; the clustering analysis unit is used to cluster the computing resources by using the KMeans algorithm to obtain a resource classification result; the allocation adjustment unit is used to adjust the computing resource allocation ratio according to the resource classification result; if the preliminary allocation plan still exceeds the threshold, the consumption trend is predicted by using a linear regression algorithm and the resource scheduling parameters are optimized.

[0009] In a preferred solution, the model compression module is connected to the processing unit and is used to compress the model architecture according to the resource allocation plan to adapt to edge devices; the model compression module includes a module analysis unit, a pruning and quantization unit, and an edge adaptation unit; The module analysis unit is used to analyze the parameter distribution and computing complexity of the original model to determine the compression candidate module; the pruning and quantization unit is used to remove redundant connections by using a pruning algorithm and perform low-bit representation on the model weights in combination with a quantization algorithm to generate a quantized compression model; the edge adaptation unit is used to simulate the model performance in the edge device environment, adjust the quantization bit number according to the prediction accuracy, and generate the final compressed model.

[0010] In a preferred solution, the parameter fine-tuning module is connected to the processing unit and is used to jointly optimize the parameters of the compressed model in a multi-task scenario; the parameter fine-tuning module includes a scenario analysis unit, a joint training unit, and a task allocation unit; The scenario analysis unit is used to extract the task feature set from the multi-task scenario and determine the task type and priority; the joint training unit is used to train the compressed model by using a joint optimization algorithm for high-priority tasks to generate initial model parameters; the task allocation unit is used to extract the task-specific parameter subset and generate the model parameters adapted to the multi-task scenario through iterative optimization.

[0011] In a preferred solution, the performance evaluation module is connected to the processing unit and is used to test the performance fluctuation of the model on high-dimensional data; the performance evaluation module includes a fluctuation detection unit, a dimensionality reduction optimization unit, and a stability determination unit; The fluctuation detection unit is used to obtain the performance fluctuation value of the model on high-dimensional data; the dimensionality reduction and optimization unit is used to optimize the model parameters by using the random forest algorithm and perform dimensionality reduction processing on the high-dimensional data to test the new performance fluctuation value; the stability determination unit is used to compare the fluctuation value with a preset threshold to confirm the model stability.

[0012] In a preferred solution, the edge deployment module is connected to the processing unit and the communication module, and is used to deploy the model with stable performance and qualified deployment cost to the edge device; the edge deployment module includes a cost evaluation unit, a device adaptation unit and a persistent storage unit; The cost evaluation unit is used to calculate the model deployment cost and compare it with a preset threshold; the device adaptation unit is used to determine the target deployment configuration and device compatibility solution of the edge device; the persistent storage unit is used to save the deployed model parameters to the edge device.

[0013] A usage method of a hydropower plant automation debugging system with an adaptive communication function, the method includes the following steps: S1. Obtain the debugging requirement data of the hydropower plant automation system and the edge device parameters through the input-output interface, and the processing unit calls the preset task classifier, model architecture library and dynamic path adjustment algorithm in the storage unit to complete system initialization; perform time synchronization and noise suppression on the multi-source data through the Kalman filter algorithm to generate a fused data feature set; S2. The task classification module performs deep feature extraction on the input debugging requirement data, combines manual features to generate a high-dimensional feature vector; screens key features through the mutual information method, and uses a non-linear dimensionality reduction algorithm to generate a low-dimensional feature matrix; trains the low-dimensional feature matrix by using an ensemble learning classifier and outputs the task complexity level; S3. The model architecture configuration module matches the corresponding model architecture from the model architecture library according to the task complexity level, parses the initial calculation path and completes the missing nodes; determines the execution order of the calculation nodes and the initial resource requirement configuration through the topological sorting algorithm; S4. The dynamic path adjustment module updates the path input in combination with the real-time debugging data, optimizes the calculation path cost by using the heuristic search algorithm, evaluates whether the path selection strategy meets the optimization goal, and generates a real-time optimized calculation path in combination with the data processing efficiency; synchronize and pre-screen the adapted communication protocol; S5. The resource allocation module monitors the computing power consumption of the optimized path in real time. When the computing power consumption exceeds the preset threshold, analyze the computing resources through the clustering algorithm and adjust the resource allocation ratio; if it still exceeds the threshold, predict the consumption trend and optimize the resource scheduling parameters; S6. The model compression module analyzes the parameter distribution and computational complexity of the original model, uses pruning algorithms to remove redundant connections, and combines quantization algorithms to represent the model weights in low bits; it maintains the model accuracy through knowledge distillation, simulates the performance in the edge device environment and adjusts the quantization parameters to generate a compressed model adapted to the edge device; S7. The parameter fine-tuning module extracts the task feature set in the multi-task scenario, uses the joint optimization algorithm to train the compressed model for high-priority tasks to generate initial parameters; extracts the task-specific parameter subset and iteratively optimizes it, and suppresses parameter oscillation through gradient normalization and clipping to generate model parameters adapted to the multi-task scenario; S8. The performance evaluation module obtains the performance fluctuation value of the compressed model on high-dimensional debugging data, uses the random forest algorithm to optimize the model parameters and reduce the data dimension, and tests the new fluctuation value; adds noise and adversarial samples to verify the robustness. If the fluctuation value is less than the preset threshold, the model is determined to be stable; S9. The edge deployment module calculates the model deployment cost. When the cost meets the standard, it determines the target deployment configuration and device compatibility plan according to the edge device parameters; dynamically selects the appropriate communication protocol through the communication module, monitors the communication quality in real time and triggers the protocol switching strategy; encrypts and stores the model parameters in the non-volatile memory of the edge device to generate a deployment log.

[0014] A hydropower plant automation debugging system with adaptive communication function, and its beneficial effects include but are not limited to the following points: 1. The communication module combines with the edge deployment module to support real-time screening and dynamic switching of multiple protocols such as Modbus and MQTT. Through the communication quality monitoring mechanism, it significantly improves the data interaction success rate and effectively solves the compatibility problem of multi-vendor devices; the input-output interface combines the Kalman filtering algorithm to achieve multi-source data time synchronization and noise suppression, generating a highly reliable fusion feature set, providing a high-quality data base for subsequent intelligent processing; 2. The task classification module is based on PCA dimensionality reduction and SVM classification algorithm, effectively improving the accuracy of high-dimensional data task complexity classification and avoiding computing power mismatch; the model architecture configuration module and the dynamic path adjustment module cooperate to optimize the node execution order through topological sorting and adjust the calculation path in real time through the heuristic algorithm, significantly improving the resource utilization rate and data processing speed. The resource allocation module uses K-Means clustering and linear regression prediction to achieve automatic reallocation of over-limit computing power and ensure stable operation in complex scenarios; 3. The model compression module integrates pruning algorithms and quantization technologies, greatly compresses the model parameter scale on the premise of ensuring a small accuracy loss, and combines the environmental simulation test of the edge adaptation unit to significantly improve the edge deployment success rate and reduce the inference latency of embedded devices. The parameter fine-tuning module optimizes according to the multi-task priority difference, and the performance evaluation module introduces a noise verification mechanism to significantly enhance the model robustness; 4. Nine functional modules build a closed-loop automated process of "data input - edge landing", which significantly shortens the overall cycle compared with the traditional manual debugging mode, and greatly reduces the technical threshold and labor cost. The persistent storage and deployment log mechanism ensures the security of model parameters and the traceability of the debugging process. Combined with the protocol adaptation ability of the communication module, a highly compatible and reliable engineering solution is formed, providing core technical support for the efficient debugging and stable operation of the intelligent hydropower plant automation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall structural schematic diagram of the present invention; Figure 2 It is the structural schematic diagram of the task analysis module of the present invention; Figure 3 It is the structural schematic diagram of the model architecture configuration module of the present invention; Figure 4 It is the structural schematic diagram of the dynamic path adjustment module of the present invention; Figure 5 It is the structural schematic diagram of the resource allocation module of the present invention; Figure 6 It is the structural schematic diagram of the model compression module of the present invention; Figure 7 It is the structural schematic diagram of the parameter fine-tuning module of the present invention; Figure 8 It is the structural schematic diagram of the performance evaluation module of the present invention; Figure 9 It is the structural schematic diagram of the edge deployment module of the present invention.

[0016] In the figure: processing unit 1, storage unit 2, communication module 3, input / output interface 4, task classification module 5, feature extraction unit 501, dimensionality reduction processing unit 502, classification optimization unit 503, level mapping unit 504, model architecture configuration module 6, architecture mapping unit 601, node processing unit 602, sequence generation unit 603, load balancing unit 604, dynamic path adjustment module 7, dynamic programming unit 701, cost optimization unit 702, policy evaluation unit 703, efficiency analysis unit 704, resource allocation module 8, computing power monitoring unit 801, clustering analysis unit 802, allocation adjustment unit 803, model compression module 9, module analysis unit 901, pruning quantization unit 902, edge adaptation unit 903, parameter fine-tuning module 10, scenario analysis unit 101, joint training unit 102, task assignment unit 103, performance evaluation module 11, fluctuation detection unit 111, dimensionality reduction optimization unit 112, stability determination unit 113, edge deployment module 12, cost evaluation unit 121, device adaptation unit 122, persistent storage unit 123. DETAILED DESCRIPTION OF THE INVENTION

[0017] As shown Figure 1 in the figure, a hydropower plant automation debugging system with adaptive communication function includes a processing unit 1, a storage unit 2, a communication module 3, and an input / output interface 4. The processing unit 1 uses an ARM Cortex-A72 multi-core processor with a main frequency of 1.8 GHz and 4 cores, integrated with a neural network processing unit NPU for accelerating deep learning operations, responsible for executing data processing, algorithm operations, and module control; it communicates with other modules through a high-speed PCIe 3.0 bus to ensure that the data interaction delay ≤ 10 ms; The communication module 3 integrates multi-protocol communication chips such as a W5500 network chip and a MAX3485 serial port chip, supports industrial communication protocols such as TCP / IP IPv4 / IPv6, serial port 232 / 485, baud rate of 9600~115200 bps, IEC101 / 103 / 104 2006 version, etc., and is used to implement data interaction with edge devices, sensors, and external systems, with a communication bandwidth of up to 100 Mbps; The storage unit 2 uses a 512GB NVMe SSD with a read / write speed ≥ 2000 MB / s, stores a preset task classifier, a training model based on XGBoost, with a model size ≤ 50 MB, a model architecture library, including 10 typical model configuration files such as lightweight CNN, LSTM, and simplified Transformer, a dynamic path adjustment algorithm, a path optimization program based on A search, a resource allocation algorithm, a joint scheduling program optimized by K-Means clustering and PSO, a lightweight model compression algorithm, an integrated toolkit for pruning, quantization, and knowledge distillation, and a multi-task joint optimization algorithm, a multi-task training framework with hard parameter sharing, and all algorithms are stored in the form of.exe executable files or Python scripts; The input / output interface 4 includes 2 USB 3.0 interfaces for connecting an external debugging laptop computer, 1 HDMI interface with a resolution of 1920×1080@60 Hz for connecting a debugging monitor, and 4 industrial-grade DI / DO interfaces with a 24V level for accessing digital input / output signals, supporting receiving sensor data in Modbus RTU format such as the conversion value of 4-20 mA current signal and outputting debugging results in JSON format such as model deployment parameters and communication status reports; The hydropower plant automation debugging system with adaptive communication function further includes a task classification module 5, a model architecture configuration module 6, a dynamic path adjustment module 7, a resource allocation module 8, a model compression module 9, a parameter fine-tuning module 10, a performance evaluation module 11, and an edge deployment module 12.

[0018] The preferred solution is as Figure 2As shown in the figure, the task classification module 5 is connected to the processing unit 1 and is used to analyze the task complexity and high-dimensional data features of the input data and perform classification; the task classification module 5 includes a feature extraction unit 501, a dimensionality reduction processing unit 502, a classification optimization unit 503, and a level mapping unit 504: The feature extraction unit 501 performs feature processing on the input time-domain data such as voltage and current sampling values based on a sliding window with a window length of 1024 points and a step size of 512 points, extracts 12-dimensional statistical features such as root mean square value, peak-to-peak value, and kurtosis, and generates a feature vector. The dimensionality reduction processing unit 502 uses the principal component analysis (PCA) algorithm to reduce the dimensionality of the feature vector, retains the first 5 principal components with a cumulative variance contribution rate ≥ 95%, and obtains the dimensionality-reduced features; the classification optimization unit 503 includes a preset SVM classifier with a kernel function of RBF, parameters C = 10, γ = 0.1, performs binary classification on the dimensionality-reduced features, and realizes high / medium / low complexity through a combination of two binary classifications, and optimizes the classification results through cross-validation; the level mapping unit 504 maps the task complexity to three levels according to the optimized classification results: real-time control is of high complexity, requiring a response time of ≤ 100 ms, status monitoring is of medium complexity, requiring a response time of ≤ 500 ms, and fault diagnosis is of low complexity, with a response time of ≤ 2 s.

[0019] Preferably, as Figure 3 shown in the figure, the model architecture configuration module 6 is connected to the processing unit 1 and the storage unit 2 and is used to obtain the corresponding model architecture configuration according to the task complexity level and determine the initial calculation path; the model architecture configuration module 6 includes an architecture mapping unit 601, a node processing unit 602, a sequence generation unit 603, and a load balancing unit 604: The architecture mapping unit 601 matches the corresponding model architecture from the model architecture library according to the task complexity level: for real-time control, it matches the LSTM model with 64 hidden layers and a time step of 16; for state monitoring, it matches a 3-layer CNN with a convolutional kernel of 3×3 and a pooling layer of 2×2; for fault diagnosis, it matches a simplified version of Transformer with 4 attention heads and a feed-forward network dimension of 128. The node processing unit 602 parses the node sequence of the initial calculation path, which includes 4 nodes: data normalization, feature extraction, model inference, and result output. If a missing node is detected, such as a data filtering node not included, it is complemented by linear interpolation based on the linear fitting of timestamps to generate an optimized sequence containing 5 nodes. The sequential generation unit 603 uses the Kahn algorithm to perform a topological sort on the calculation nodes, determines the execution order as: data normalization → data filtering → feature extraction → model inference → result output, and allocates initial resources according to the node calculation amount. For example, 60% of the CPU computing power is allocated to the inference node. The load balancing unit 604 adjusts the node resource allocation through a genetic algorithm with a population size of 50 and 20 iterations. The objective function is to minimize the variance of the load of each node, and the variance of the optimized resource allocation is ≤10%.

[0020] The preferred solution is as follows Figure 4 As shown, the dynamic path adjustment module 7 is connected to the processing unit 1 and is used to optimize the initial calculation path in real time. The dynamic path adjustment module 7 includes a dynamic programming unit 701, a cost optimization unit 702, a policy evaluation unit 703, and an efficiency analysis unit 704. The dynamic programming unit 701 updates real-time data such as the current CPU utilization rate and communication delay every 100 ms, generates path data inputs including path nodes, resource requirements, and time constraints, and generates 3 preliminary adjustment paths. The cost optimization unit 702 uses the A search algorithm to calculate the path cost. The cost function is: calculation delay × 0.5 + communication bandwidth occupancy × 0.3 + energy consumption × 0.2, and filters out 2 candidate paths with the lowest cost. The policy evaluation unit 703 determines whether the path selection policy meets the optimization goals such as delay ≤ 200 ms and bandwidth occupancy ≤ 80% according to the real-time data. If not, it adjusts the heuristic function weights, such as increasing the delay weight to 0.7, and generates a policy adjustment path. The efficiency analysis unit 704 combines the data processing speed (target ≥ 1000 samples / second) and the algorithm execution efficiency (model inference delay ≤ 50 ms), and selects the path with the highest comprehensive efficiency from the candidate paths as the final optimized path.

[0021] The preferred solution is as follows Figure 5As shown, the resource allocation module 8 is connected to the processing unit 1 and is used to reallocate computing resources when the computing power consumption exceeds the limit. The resource allocation module 8 includes a computing power monitoring unit 801, a clustering analysis unit 802, and an allocation adjustment unit 803: The computing power monitoring unit 801 monitors the computing power consumption, CPU utilization rate, memory occupancy, and NPU load of the computing path at a period of 50 ms. When the CPU utilization rate > 80%, resource scheduling is triggered; The clustering analysis unit 802 uses the K-Means algorithm with 3 clusters to cluster the computing resources, namely 4-core CPU, 2GB memory, and 4 computing units of the NPU, and identifies highly available resource clusters such as CPU core 3, memory block 2, and NPU unit 1; The allocation adjustment unit 803 adjusts the computing resource allocation ratio according to the resource classification result. For example, the inference node is adjusted from CPU cores 0-1 to cores 2-3. If the CPU utilization rate is still > 80% after the adjustment, then through the linear regression algorithm, the computing power consumption trend in the next 5 s is predicted with a time window of 10 s, and the reserved computing power is dynamically increased, such as from 10% to 20%.

[0022] The preferred solution is as Figure 6 As shown, the model compression module 9 is connected to the processing unit 1 and is used to compress the model architecture according to the resource allocation scheme to adapt to edge devices. The model compression module 9 includes a module analysis unit 901, a pruning and quantization unit 902, and an edge adaptation unit 903: The module analysis unit 901 counts the parameter quantity of each layer of the original model, such as the attention layer of the Transformer accounting for 60% of the total parameters, and the computational complexity FLOPs, and determines the convolutional layer and the attention layer as the compression candidate modules; The pruning and quantization unit 902 uses structured pruning based on the absolute value of the weight, removes redundant channels with a pruning ratio of 30%, and combines training-aware quantization (QAT) to quantize the weight from 32-bit floating point to 8-bit integer, generating a quantized compression model. The model size is reduced from 200MB to 50MB; The edge adaptation unit 903 tests the model inference latency, with the target < 100 ms, and the memory occupancy, with the target < 500MB, on the edge device simulator, ARM Cortex-A53, 2GB RAM. If the latency exceeds the standard, the quantization bit number is adjusted and rolled back to 16-bit quantization, and finally an adapted model is generated.

[0023] The preferred solution is as Figure 7As shown in the figure, the parameter fine-tuning module 10 is connected to the processing unit 1 and is used to jointly optimize the parameters of the compressed model in a multi-task scenario; the parameter fine-tuning module 10 includes a scenario analysis unit 101, a joint training unit 102, and a task assignment unit 103: The scenario analysis unit 101 extracts the feature vectors of the multi-task scenario through a GRU network, with a dimension of 64, calculates the cosine similarity between tasks, and a similarity > 0.6 is strongly correlated. According to the task type, the priorities are determined in real time for fault diagnosis and status monitoring, which are 0.8, 0.5, and 0.3 respectively; the joint training unit 102 is used for real-time control of high-priority tasks and adopts a hard parameter sharing architecture, sharing the first 3 layers, and the last 2 layers are task-specific layers. The loss function is the cross-entropy loss, with a weight of 0.7 + KL divergence and a weight of 0.3, and the initial model parameters are generated through training; the task assignment unit 103 extracts the task-specific parameter subsets, such as the LSTM layer weights of the real-time control task, and adjusts the parameters through 100 rounds of iterative optimization. If the task gradient directions conflict and the cosine similarity < 0, then through gradient clipping with a threshold of 5.0 to suppress oscillations, the model parameters suitable for the multi-task scenario are generated.

[0024] The preferred solution is as Figure 8 As shown in the figure, the performance evaluation module 11 is connected to the processing unit 1 and is used to test the performance fluctuations of the model on high-dimensional data; the performance evaluation module 11 includes a fluctuation detection unit 111, a dimensionality reduction optimization unit 112, and a stability determination unit 113: The fluctuation detection unit 111 calculates the standard deviation of the model accuracy on 1000 groups of high-dimensional test data, including normal, faulty, and abnormal state samples, with the target < 2%; the dimensionality reduction optimization unit 112 uses the random forest algorithm, with 100 trees and a tree depth of 10 to rank the importance of the model parameters, and eliminates the parameters with an importance < 0.1, such as the redundant weights of the fully connected layer, and retests the new fluctuation value after retraining; the stability determination unit 113 compares the fluctuation value with the preset threshold, with the standard deviation ≤ 2%, and verifies the accuracy of the model under Gaussian noise, SNR = 20dB, and adversarial samples, FGSM attack, ε = 0.01, with the target ≥ 90%. If the conditions are met, the model performance is determined to be stable.

[0025] The preferred solution is as Figure 9As shown, the edge deployment module 12 is connected to the processing unit 1 and the communication module 3, and is used to deploy a model with stable performance and a qualified deployment cost to an edge device; the edge deployment module 12 includes a cost evaluation unit 121, a device adaptation unit 122, and a persistent storage unit 123: the cost evaluation unit 121 calculates the deployment cost, which is hardware cost × 0.4 + communication cost × 0.3 + maintenance cost × 0.3. If the cost ≤ 5000 yuan, the deployment is triggered; the device adaptation unit 122 generates a target deployment configuration according to the edge device architecture, ARM adapts to TFLite, and x86 adapts to TensorRT, and converts the model format to.tflite or.trt through a model conversion tool such as TensorFlowLiteConverter; the persistent storage unit 123 encrypts and stores the deployed model parameters, task-specific layer weights, and quantization parameter tables in the eMMC memory of the edge device through AES-256, and generates a deployment log, including information such as timestamp, protocol type, device ID, and accuracy rate.

[0026] Embodiment: A hydroelectric power plant automation debugging system with an adaptive communication function, and its specific implementation method is as follows: S1. When the debugging system accesses the hydroelectric power plant automation system, the input / output interface 4 receives real-time data such as vibration, temperature, and voltage collected by sensors through the ModbusRTU protocol, with a sampling frequency of 1 kHz. At the same time, it obtains edge device parameters through the Ethernet interface, such as the edge gateway CPU model is ARMCortex-A72, the memory is 2GB, and it supports TCP / IP and IEC104 protocols: S1.1. The processing unit 1 calls the preset task classifier in the storage unit 2, based on the XGBoost algorithm, the training dataset includes 100,000 sets of normal / abnormal condition samples, a model architecture library, stores model configuration files such as lightweight CNN, LSTM, and Transformer lite versions, the file size of each is ≤ 100MB, and a dynamic path adjustment algorithm, a path optimization program based on A search, and the heuristic function is the weighted sum of calculation delay and communication bandwidth; S1.2. Perform time synchronization on multi-source data through the Kalman filter algorithm, calibrate the timestamp error to ±1ms and suppress noise, reduce the root mean square error by 40%, and generate a fusion data feature set containing time domain and frequency domain features, with a dimension of 32, providing a standardized input for subsequent task classification; S2. The task classification module 5 processes the fusion data feature set: S2.1. The feature extraction unit 501 uses a sliding window with a window length of 512 points and a step size of 256 points to extract 15-dimensional manual features such as the effective value, frequency deviation, and waveform distortion rate of the voltage signal. Combining with the deep features extracted by the ResNet-18 network, 512 dimensions, a 527-dimensional high-dimensional feature vector is generated; S2.2. The dimensionality reduction processing unit 502 screens 100 key features with the top 70% of the information volume through the mutual information method, and then uses the t-SNE algorithm to reduce the features to 30 dimensions, generating a low-dimensional feature matrix, retaining 90% of the discrimination of the original data; S2.3. The classification optimization unit 503 uses a preset gradient boosting tree classifier to train the low-dimensional feature matrix and outputs the task complexity level: High-complexity tasks such as generator oscillation fault diagnosis: require real-time response ≤ 100 ms, waveform distortion rate > 5% and frequency deviation > 0.2 Hz in the feature matrix; Medium-complexity tasks such as bearing temperature trend analysis: require periodic response ≤ 500 ms, temperature gradient > 1 °C / mi in the feature matrix; Low-complexity tasks such as switch state monitoring: require non-real-time response ≤ 2 s, change frequency of switch quantity < 1 time / minute in the feature matrix; S3. The model architecture configuration module 6 matches the model architecture according to the task complexity level: S3.1. The architecture mapping unit 601, if it determines that the task is a high-complexity generator oscillation fault diagnosis task, retrieves the LSTM model from the model architecture library, with an input layer of 128 dimensions, a hidden layer of 256 dimensions, and an output layer of 2 dimensions; if it is a medium-complexity temperature trend analysis task, retrieves the CNN model, with 3 convolutional layers, a convolutional kernel of 5×5, a pooling layer of 2×2, and a fully connected layer of 100 dimensions; S3.2. The node processing unit 602 parses the node sequence of the initial calculation path as "data normalization → feature extraction → model inference → result output". If the missing "data filtering" node is detected, for the high-frequency noise in the vibration signal, this node is complemented through the cubic spline interpolation method to generate an optimized sequence containing 5 nodes; S3.3. The sequence generation unit 603 uses the Kahn algorithm to perform topological sorting on the nodes to determine the execution order as: data normalization, standardized to [-1, 1] → data filtering, 8th-order Butterworth low-pass filter, cut-off frequency 100 Hz → feature extraction, Fourier transform to obtain frequency domain features → model inference, LSTM network time series prediction → result output, fault probability value; S3.4. The load balancing unit 604 adjusts the resource allocation through the genetic algorithm, allocating 60% of the CPU computing power and 40% of the NPU computing power to the "model inference" node to ensure that the inference delay of high-complexity tasks ≤ 80 ms; S4. The dynamic path adjustment module 7 optimizes the calculation path with a period of 200 ms: S4.1. The dynamic programming unit 701 collects the CPU utilization rate of the edge device in real time, which is currently 75%, and the communication delay, which is 150 ms under the IEC104 protocol, generates path data inputs including node execution time and resource occupancy, and generates 3 preliminary adjustment paths, focusing on low latency, low power consumption, and high reliability respectively; S4.2. The cost optimization unit 702 calculates the path cost using the A search algorithm, and the cost function is: Cost = calculation delay × 0.5 + communication bandwidth occupancy × 0.3 + energy consumption × 0.2 Filter out the path with the lowest cost, with a calculation delay of 120 ms and a bandwidth occupancy of 50 Mbps; S4.3. The policy evaluation unit 703 verifies whether the path meets the latency requirement of high-complexity tasks ≤ 100 ms. If not, it adjusts the weight of the heuristic function, increases the latency weight to 0.8, and regenerates the policy adjustment path; S4.4. The efficiency analysis unit 704 combines the data processing speed, which is currently 800 samples / second, and the algorithm execution efficiency, with an LSTM inference delay of 90 ms, to determine the final optimized path, and pre-screens the communication protocol through the decision tree model. For high-complexity tasks, the low-latency IEC104 protocol is preferentially selected; S5. The resource allocation module 8 monitors the computing power consumption in real time. When the CPU utilization rate exceeds the 80% threshold: S5.1. The computing power monitoring unit 801 triggers the resource scheduling mechanism, collects the status of each computing resource, and the loads of the 4-core CPU are 85%, 78%, 60%, and 55% respectively, and the NPU load is 90%; S5.2. The clustering analysis unit 802 uses the K-Means algorithm to cluster the computing resources, divides the CPU cores into high-load cluster cores 0-1 and low-load cluster cores 2-3, and divides the NPU into independent computing units; S5.3. The allocation adjustment unit 803 migrates the model inference task from the high-load CPU cores 0-1 to the low-load cores 2-3, and at the same time releases the redundant computing power of the NPU from 90% to 70%. If the CPU utilization rate after adjustment is 75% and does not exceed the threshold, the linear regression algorithm is used to predict the computing power consumption trend in the next 10 s, and it is expected to stabilize at 70% - 75%, and the current resource allocation plan is maintained; S6. The model compression module 9 compresses the original LSTM model: S6.1. The module analysis unit 901 statistically analyzes the model parameter distribution, with a weight matrix of 800 KB and a bias vector of 200 KB, and determines that the input layer and the hidden layer are candidate modules with relatively high redundancy, and the parameter ratio is 70%; S6.2. The pruning quantization unit 902 adopts structured pruning, retains 70% of the connections with the largest absolute weights and removes 30% of the redundant connections, and combines 8-bit dynamic fixed-point quantization to convert the weights from 32-bit floating-point to integer. The model size is reduced from 1.2 MB to 400 KB; S6.3. The edge adaptation unit 903 tests the compressed model on an edge device simulator, ARM Cortex-A53, with 1 GB RAM. If the inference latency is 150 ms, which exceeds the standard, the quantization bit number is adjusted to 16 bits, and the latency is reduced to 90 ms. At the same time, through knowledge distillation, with the teacher model being the original LSTM and the student model being the compressed version, the prediction accuracy is maintained at ≥95%; S7. The parameter fine-tuning module 10 processes the simultaneously running state monitoring and fault diagnosis tasks: S7.1. The scenario analysis unit 101 extracts task feature vectors. The state monitoring is a temperature sequence, and the fault diagnosis is a vibration signal. The cosine similarity between the tasks is calculated to be 0.4, which is weakly correlated. The priority of the fault diagnosis task is determined to be 0.8, and that of the state monitoring is 0.5; S7.2. The joint training unit 102 adopts a hard parameter sharing architecture, sharing the first 2 layers of the LSTM network, with task-specific layers being independent. The loss function is: L = 0.7×cross-entropy loss + 0.3×KL divergence The initial model parameters are generated through training, with the number of iterations being 200 and the learning rate being 0.001; S7.3. The task allocation unit 103 extracts the specific parameters of the fault diagnosis task, the weights of the last layer of the LSTM, and processes the conflicting gradients through a gradient clipping threshold of 5.0. After 50 rounds of iterative optimization, the prediction accuracies of the two tasks are improved by 3% and 2% respectively; S8. The performance evaluation module 11 tests the compressed model: S8.1. The fluctuation detection unit 111 calculates the standard deviation of the accuracy on a high-dimensional test set containing 2000 groups of samples to be 1.5%, ≤ the preset threshold of 2%; S8.2. The dimensionality reduction and optimization unit 112 uses the random forest algorithm to screen important features. The first 20 dimensions of features contribute 80% of the classification accuracy. Redundant parameters are removed, accounting for 15% of the total parameters. After retraining, the standard deviation is reduced to 1.2%; S8.3. The stability determination unit 113 adds Gaussian noise with a signal-to-noise ratio of 15 dB and FGSM adversarial samples with ε = 0.01. The model accuracies are 92% and 91% respectively, both ≥90%. It is determined that the model performance is stable; S9. The edge deployment module 12 performs the final deployment: S9.1. The cost evaluation unit 121 calculates the deployment cost to be 4500 yuan, ≤ the threshold of 5000 yuan, triggering the deployment process; S9.2. The device adaptation unit 122 converts the model into the.tflite format using TensorFlowLiteConverter according to the edge gateway architecture ARM, generating a deployment package containing quantization parameters with a size of 512 KB. S9.3. The persistent storage unit 123 encrypts the model parameters with AES - 256 and stores them in the eMMC of the edge device through the IEC104 protocol of the communication module 3 at a transmission rate of 10 Mbps. Meanwhile, a deployment log is generated, including: Device ID: GW - 20250509 - 01; Communication protocol: IEC104; Deployment time: 2025 - 05 - 09, 14:30:00; Model accuracy: 95.2%.

[0027] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above - mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention; any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An automated commissioning system for a hydropower plant with adaptive communication function, comprising a processing unit (1), a storage unit (2), a communication module (3) and an input / output interface (4), characterized in that: The processing unit (1) is used to perform data processing, algorithm operations, and module control; The communication module (3) is used to achieve data interaction with edge devices, sensors, and external systems; The storage unit (2) is used to store a preset task classifier, model architecture library, dynamic path adjustment algorithm, resource allocation algorithm, lightweight model compression algorithm, and multi-task joint optimization algorithm; The input / output interface (4) is used to receive input data and output processing results; The automated commissioning system for hydropower plants with adaptive communication function further includes a task classification module (5), a model architecture configuration module (6), a dynamic path adjustment module (7), a resource allocation module (8), a model compression module (9), a parameter fine-tuning module (10), a performance evaluation module (11), and an edge deployment module (12); The task classification module (5) is connected to the processing unit (1) and is used to analyze the task complexity and high-dimensional data features of the input data and perform grading; the model architecture configuration module (6) is connected to the processing unit (1) and the storage unit (2) and is used to obtain the corresponding model architecture configuration according to the task complexity level and determine the initial calculation path; The dynamic path adjustment module (7) is connected to the processing unit (1) and is used to optimize the initial calculation path in real time; The resource allocation module (8) is connected to the processing unit (1) and is used to reallocate computing resources when the computing power consumption exceeds the limit; the model compression module (9) is connected to the processing unit (1) and is used to compress the model architecture according to the resource allocation scheme to adapt to edge devices; the parameter fine-tuning module (10) is connected to the processing unit (1) and is used to jointly optimize the parameters of the compressed model in a multi-task scenario; the performance evaluation module (11) is connected to the processing unit (1) and is used to test the performance fluctuation of the model on high-dimensional data; the edge deployment module (12) is connected to the processing unit (1) and the communication module (3) and is used to deploy the model with stable performance and qualified deployment cost to edge devices.

2. The automatic commissioning system for a hydropower plant with adaptive communication function according to claim 1, wherein: The task classification module (5) includes a feature extraction unit (501), a dimensionality reduction processing unit (502), a classification optimization unit (503), and a level mapping unit (504); The feature extraction unit (501) is used to perform feature processing on the input data to generate a feature vector; the dimensionality reduction processing unit (502) is used to perform dimensionality reduction on the feature vector by using the principal component analysis algorithm to obtain dimensionality-reduced features; The classification optimization unit (503) includes a preset classifier and a support vector machine algorithm and is used to classify the dimensionality-reduced features and optimize the classification results; the level mapping unit (504) is used to classify the task complexity according to the optimized classification results and output the final results.

3. The automatic commissioning system for a hydropower plant with an adaptive communication function according to claim 1, wherein: The model architecture configuration module (6) includes an architecture mapping unit (601), a node processing unit (602), a sequence generation unit (603), and a load balancing unit (604); The architecture mapping unit (601) is used to match the corresponding model architecture configuration from the model architecture library according to the task complexity level and determine the initial calculation path; the node processing unit (602) is used to parse the initial calculation path to extract the calculation node sequence, perform linear interpolation to complete the missing nodes and generate an optimized node sequence; the sequence generation unit (603) is used to determine the execution order and resource requirement configuration of the calculation nodes by using the topological sorting algorithm; the load balancing unit (604) is used to adjust the node resource allocation through a heuristic algorithm to optimize the resource allocation scheme.

4. The automated commissioning system for a hydropower plant with an adaptive communication function according to claim 1, wherein: The dynamic path adjustment module (7) includes a dynamic programming unit (701), a cost optimization unit (702), a policy evaluation unit (703) and an efficiency analysis unit (704); The dynamic programming unit (701) is used to update and determine the path data input in combination with real-time data and generate a preliminary adjustment path; the cost optimization unit (702) is used to optimize the calculation path cost by using a heuristic search algorithm to obtain a cost-optimized path; The policy evaluation unit (703) is used to judge whether the path selection policy meets the optimization goal according to real-time data and generate a policy adjustment path; the efficiency analysis unit (704) is used to determine the final optimized path in combination with the data processing speed and the algorithm execution efficiency.

5. The automatic commissioning system for hydropower plants with adaptive communication function according to claim 1, characterized in that: The resource allocation module (8) includes a computing power monitoring unit (801), a clustering analysis unit (802) and an allocation adjustment unit (803); The computing power monitoring unit (801) is used to monitor the computing power consumption of the calculation path in real time and trigger resource scheduling; the clustering analysis unit (802) is used to cluster the computing resources by using the K-Means algorithm to obtain the resource classification result; the allocation adjustment unit (803) is used to adjust the calculation resource allocation ratio according to the resource classification result; if the preliminary allocation scheme still exceeds the threshold, the consumption trend is predicted through a linear regression algorithm and the resource scheduling parameters are optimized.

6. The automated commissioning system for a hydropower plant with an adaptive communication function according to claim 1, characterized in that: The model compression module (9) includes a module analysis unit (901), a pruning and quantization unit (902) and an edge adaptation unit (903); The module analysis unit (901) is used to analyze the parameter distribution and calculation complexity of the original model to determine the compression candidate module; the pruning and quantization unit (902) is used to remove redundant connections by using a pruning algorithm and perform low-bit representation of the model weights in combination with a quantization algorithm to generate a quantized compression model; The edge adaptation unit (903) is used to simulate the performance of the model in the edge device environment, adjust the quantization bit number according to the prediction accuracy and generate the final compression model.

7. The automated commissioning system for a hydropower plant with an adaptive communication function according to claim 1, characterized in that: The parameter fine-tuning module (10) includes a scenario analysis unit (101), a joint training unit (102) and a task allocation unit (103); The scenario analysis unit (101) is used to extract a task feature set from a multi-task scenario and determine the task type and priority; the joint training unit (102) is used to train a compression model using a joint optimization algorithm for high-priority tasks to generate initial model parameters; the task allocation unit (103) is used to extract a task-specific parameter subset and generate model parameters adapted to the multi-task scenario through iterative optimization.

8. The automatic commissioning system for a hydropower plant with an adaptive communication function according to claim 1, characterized in that: The performance evaluation module (11) includes a fluctuation detection unit (111), a dimensionality reduction optimization unit (112), and a stability determination unit (113); The fluctuation detection unit (111) is used to obtain the performance fluctuation value of the model on high-dimensional data; the dimensionality reduction optimization unit (112) is used to optimize the model parameters using a random forest algorithm and perform dimensionality reduction processing on the high-dimensional data to test the new performance fluctuation value; the stability determination unit (113) is used to compare the fluctuation value with a preset threshold to confirm the model stability.

9. The automatic commissioning system for a hydropower plant with an adaptive communication function according to claim 1, characterized in that: The edge deployment module (12) includes a cost evaluation unit (121), a device adaptation unit (122), and a persistent storage unit (123); The cost evaluation unit (121) is used to calculate the model deployment cost and compare it with a preset threshold; the device adaptation unit (122) is used to determine the target deployment configuration of the edge device and the device compatibility solution; the persistent storage unit (123) is used to save the deployed model parameters to the edge device.

10. A method for using an automatic commissioning system of a hydropower plant with adaptive communication function, characterized in that: The method includes the following steps: S1. Obtain the commissioning requirement data of the hydropower plant automation system and the edge device parameters through the input / output interface (4). The processing unit (1) calls the preset task classifier, model architecture library, and dynamic path adjustment algorithm in the storage unit (2) to complete system initialization; perform time synchronization and noise suppression on multi-source data through the Kalman filter algorithm to generate a fused data feature set; S2. The task classification module (5) performs deep feature extraction on the input commissioning requirement data, combines manual features to generate a high-dimensional feature vector; screens key features through the mutual information method, and uses a non-linear dimensionality reduction algorithm to generate a low-dimensional feature matrix; trains the low-dimensional feature matrix using an ensemble learning classifier and outputs the task complexity level; S3. The model architecture configuration module (6) matches the corresponding model architecture from the model architecture library according to the task complexity level, parses the initial calculation path, and completes the missing nodes; determines the execution order of the calculation nodes and the initial resource requirement configuration through the topological sorting algorithm; S4. The dynamic path adjustment module (7) updates the path input in combination with real-time commissioning data, optimizes the calculation path cost using a heuristic search algorithm, evaluates whether the path selection strategy meets the optimization goal, and generates a real-time optimized calculation path in combination with the data processing efficiency; synchronously pre-screen and adapt the communication protocol; S5. The resource allocation module (8) monitors the computing power consumption of the optimized path in real time. When the computing power consumption exceeds the preset threshold, analyze the computing resources through a clustering algorithm and adjust the resource allocation ratio; if the threshold is still exceeded, predict the consumption trend and optimize the resource scheduling parameters; S6. The model compression module (9) analyzes the parameter distribution and computational complexity of the original model, uses pruning algorithms to remove redundant connections, and combines quantization algorithms to represent the model weights in low bits; maintains the model accuracy through knowledge distillation, simulates the performance in the edge device environment and adjusts the quantization parameters to generate a compressed model adapted to the edge device; S7. The parameter fine-tuning module (10) extracts the task feature set in the multi-task scenario, uses the joint optimization algorithm to train the compressed model for high-priority tasks to generate initial parameters; extracts the task-specific parameter subset and iteratively optimizes it, suppresses parameter oscillation through gradient normalization and clipping, and generates model parameters adapted to the multi-task scenario; S8. The performance evaluation module (11) obtains the performance fluctuation value of the compressed model on high-dimensional debugging data, uses the random forest algorithm to optimize the model parameters and reduce the data dimension, and tests the new fluctuation value; adds noise and adversarial samples to verify the robustness, and determines that the model is stable if the fluctuation value is less than the preset threshold; S9. The edge deployment module (12) calculates the model deployment cost. When the cost meets the standard, it determines the target deployment configuration and device compatibility solution according to the edge device parameters; dynamically selects an appropriate communication protocol through the communication module (3), monitors the communication quality in real time and triggers the protocol switching strategy; encrypts and stores the model parameters in the non-volatile memory of the edge device and generates a deployment log.

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