Side perception analysis method considering characteristics of speed regulator

Abstract

The invention provides a side sensing analysis method considering the characteristics of a speed regulator, and the method comprises the following steps: S1, deploying side sensing nodes near the speed regulator, and collecting the operation data of the speed regulator in real time; s2, the side sensing node performs local analysis on the operation data based on a dynamic characteristic model to obtain a speed regulator state evaluation result; s3, transmitting the state evaluation result to a central node; and S4, the central node receives the data of each side node and carries out global optimization decision making. Through dynamic characteristic modeling and a self-adaptive analysis algorithm, real-time and accurate monitoring of the state of the speed regulator is realized, and the overall performance of an industrial system is improved.

Description

Technical Field

[0001] This invention relates to the field of side-sensing analysis technology, specifically to a side-sensing analysis method that takes into account the characteristics of a speed governor. Background Technology

[0002] Intelligent manufacturing and the Industrial Internet have become important forces driving industrial upgrading. As a key component of industrial equipment, the speed control system is used to control the speed of equipment in industrial systems, and its performance directly affects the operating efficiency and safety of the entire production line. Traditional analysis methods mostly rely on centralized data processing, which has the following defects: (1) Insufficient real-time performance: data transmission delay leads to analysis lag; (2) Low reliability: failure of the central node may cause system collapse; (3) Poor adaptability: it is difficult to dynamically adapt to changes in the characteristics of the speed controller. Therefore, there is an urgent need to provide a side-sensing analysis method to achieve real-time and accurate monitoring of the speed controller status and improve the overall performance of the industrial system. Summary of the Invention

[0003] The purpose of this invention is to solve the technical problems mentioned above, and to propose a side-sensing analysis method that considers the characteristics of a speed governor, comprising the following steps: S1. Deploy side-side sensing nodes near the speed controller to collect speed controller operation data in real time; S2. The side-side sensing node performs local analysis on the operating data based on the dynamic characteristic model to obtain the governor status evaluation result. S3. Transmit the status assessment results to the central node; S4. The central node receives data from each side node and makes global optimization decisions.

[0004] In a preferred embodiment, the side-sensing node in step S1 includes a data acquisition module, a local processing module, and a communication module. The data acquisition module is used to acquire at least one of the following: rotational speed, current, temperature, and vibration signals. The local processing module performs the dynamic characteristic model analysis, and the communication module performs the data transmission in step C.

[0005] In the preferred embodiment, the data transmission in step S3 adopts a partitioned architecture: in Zone I, data from the speed control system PLC and the hydraulic press PLC are collected via the Modbus TCP protocol and time-stamped at the source end; in Zone II, the data is classified by operating condition and re-time-stamped via the data collection server; and in Zone III, the data is transmitted to the industrial internet platform via the plant-side big data center and the company's backbone network.

[0006] In the preferred embodiment, the classification of operating conditions in step S3 divides the data into two main categories: transient process data and steady-state data. The transient process data includes at least one of the following: start-up process, shutdown process, shutdown state, no-load state, load adjustment process, load shedding process, and primary frequency regulation process.

[0007] In the preferred embodiment, step S3 identifies the transition process data through preset logic judgment: the start-up process satisfies start-up command = 1 and lock pin is pulled out and guide vane opening > 3%; the load shedding process satisfies circuit breaker closing signal changing from 1 to 0 and load > 50%; the primary frequency regulation process satisfies circuit breaker closing signal = 1, unit frequency is stable at rated value error < 0.15Hz and active power is basically stable error < 2% of rated load.

[0008] In the preferred embodiment, step S2 involves preserving all transient process data and performing baseline correction and filtering preprocessing; the steady-state process data is compressed using digital run-length encoding and analog differential encoding.

[0009] In the preferred embodiment, step S3 uses the Zstandard compression algorithm to compress the analysis results or processed data. The compression level is adjustable, and the compressed data is uploaded to the industrial internet platform via the FTP protocol.

[0010] In the preferred embodiment, the global optimization decision in step S4 includes: extracting key features from the governor's operating data, dynamically adjusting the weight parameters of the analysis algorithm based on the governor's characteristic prediction results, increasing the weight of energy consumption-related features when a decrease in efficiency is predicted, and enhancing the weight of vibration signal monitoring when a stability risk is predicted.

[0011] In the preferred scheme, the edge sensing nodes and the central node form an edge-cloud collaborative architecture. The edge nodes are responsible for real-time data processing and rapid response, while the central node is responsible for global optimization and decision-making, thereby realizing elastic allocation and dynamic collaboration of computing resources.

[0012] In the preferred embodiment, the dynamic characteristic model in step S2 automatically adjusts the data sampling frequency, feature extraction method, and analysis algorithm parameters based on the performance of the speed governor at different times and under different operating conditions.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention pioneers an edge-side intelligent sensing computing architecture paradigm. This architecture innovatively and deeply couples real-time edge processing capabilities with a global optimization mechanism in the cloud, achieving elastic allocation and dynamic collaboration of computing resources. This technical approach significantly improves the system's response speed to time-varying conditions and enhances its operational reliability in complex environments through multi-level fault-tolerance mechanisms, providing a groundbreaking technical solution for scenarios with stringent real-time requirements, such as the Industrial Internet. Attached Figure Description

[0014] Figure 1 To upload the technology research roadmap.

[0015] Figure 2This is a flowchart of the adaptive analysis algorithm.

[0016] Figure 3 Flowchart of a side-sensing analysis method that takes into account governor characteristics. Detailed Implementation

[0017] This embodiment provides a side-sensing analysis method that considers the characteristics of the speed governor, such as Figures 1-3 As shown, the specific steps include: 1) Data Collection The side nodes collect real-time data on the speed controller's operation.

[0018] Side-side sensing node: Deployed near the speed controller, it includes a data acquisition module, a local processing module, and a communication module.

[0019] Data acquisition module: Real-time acquisition of governor operating data (such as speed, current, temperature, vibration signals); Local processing module: performs data analysis based on dynamic characteristic model and outputs governor status evaluation results; Communication module: Transmits the analysis results to the central node.

[0020] Central node: Receives data from each side node and performs global optimization and decision-making.

[0021] 2) Data transmission Main channel: On the acquisition end side of Zone I, a data acquisition device is used to acquire data from the speed control system PLC and the hydraulic press PLC via Modbus TCP, and hard contact signals are acquired through the AI ​​module; each system performs time synchronization at the source end, and the data in the corresponding area is time-stamped when the data is sent up; the data acquisition device communicates with each system via Modbus TCP.

[0022] After entering Zone II, the data is sent to the data collection server, where it is categorized by operating condition into transient and steady-state data, and time-stamped accordingly. The data is then uploaded via the Modbus TCP protocol.

[0023] After entering Zone III, the data is transferred to the plant-side big data center for storage and then transmitted to the company-side big data center via the company's backbone network. At this point, the data has entered the cloud in Zone III. The data eventually reaches the industrial internet platform data center through the intranet, and the speed control system's digital twin advanced application platform can directly retrieve the collected data from the industrial internet data center.

[0024] 3) Data working condition classification (1) The raw data is packaged every 1 minute and cached locally. In the cache, the datasets in the file package are classified according to the working conditions. The collected data are divided into two categories: "transition process data" and "steady-state data", and caches are set up for storage in separate categories. The transition process working conditions include 7 categories: power-on process, power-off process, power-off state, no-load state, load adjustment process, load shedding process, and primary frequency regulation process.

[0025] The following table summarizes the dynamic process of the speed control system, the identification of monitoring points, the basis for judgment, and process references.

[0026] Table 1 Dynamic Process Identification of the Turbine Speed ​​Regulation System

[0027] (2) All data is retained during the transition process. During the data processing stage, modern algorithms are used to preprocess and deeply analyze the collected raw data (various dynamic parameters of the turbine unit during the transition process, including speed, flow rate, pressure, etc.), which can automatically perform baseline correction, filter out clutter interference, and thus restore the original pattern of the dynamic parameters.

[0028] During the data caching phase, a robust data access mechanism is established, employing an efficient serialization file storage method to ensure that data and related settings are saved in a fixed format. To ensure the security and integrity of steady-state process data, necessary backup and recovery measures are implemented to prevent data loss or corruption.

[0029] (3) Steady-state process data encoding and processing Run-length encoding for digital signals: Only values ​​that appear more than twice consecutively in the original data are encoded, while values ​​that do not appear consecutively or only appear twice consecutively are not encoded.

[0030] Analog differential coding: Converts analog signals into digital signals and performs differential coding.

[0031] 4) Data storage By preprocessing and encoding to optimize the data, and then utilizing Zstandard's high-efficiency compression capabilities, the compression ratio and speed are balanced by adjusting parameters, thus achieving efficient storage and transmission of speed regulation system parameter data.

[0032] After data preprocessing and encoding are completed, the data is input into the Zstandard compression algorithm for compression.

[0033] (1) Configure compression parameters Adjust the Zstandard compression level according to actual needs to find the optimal balance between compression ratio and compression speed.

[0034] (2) Compress data The Zstandard compression function is called, taking the preprocessed data as input for compression. During the compression process, Zstandard utilizes advanced compression techniques and algorithm optimizations to achieve efficient data compression.

[0035] (3) Data Upload The compressed data is uploaded to the industrial internet platform data center via FTP protocol for decompression and storage.

[0036] 5) Data operating condition analysis Based on the above steps, localized data analysis is performed, and the analysis results are transmitted to the central node.

[0037] 6) Global optimization The central node integrates data from all side nodes to make global optimization decisions. It extracts key features from the governor's operating data and adjusts the weight parameters of the analysis algorithm based on the predicted values ​​of the governor's characteristics. For example, when a decrease in governor efficiency is predicted, the weight of energy consumption-related features is increased; when a stability risk is predicted, the monitoring of vibration signals is enhanced.

[0038] The key technical point of this invention is: By precisely deploying edge sensing nodes around the speed governor, an efficient data acquisition system is constructed. During operation, the edge sensing nodes fully leverage their real-time advantages, utilizing high-precision data acquisition modules to continuously and comprehensively monitor and collect various operational data of the speed governor. This data covers key parameters across multiple dimensions, including the speed, temperature, pressure, and vibration of the speed governor. Subsequently, the edge sensing nodes rapidly and accurately transmit the collected raw data to the local data processing unit or cloud platform.

[0039] An adaptive parameter adjustment mechanism driven by prediction results is constructed, and a dynamic weight allocation strategy is designed. The performance evaluation index of the speed governor system is used as a feedback signal. The weight parameter matrix of the underlying analysis algorithm is adjusted in real time through preset parameter optimization logic, so as to realize the adaptive matching of the analysis algorithm to the changes in system operating conditions, thereby significantly improving the prediction accuracy and robustness of the analysis model under complex operating conditions.

[0040] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A side-sensing analysis method considering governor characteristics, characterized in that, Includes the following steps: S1. Deploy side-side sensing nodes near the speed controller to collect speed controller operation data in real time; S2. The side-side sensing node performs local analysis on the operating data based on the dynamic characteristic model to obtain the governor status evaluation result. S3. Transmit the status assessment results to the central node; S4. The central node receives data from each side node and makes global optimization decisions.

2. The side-sensing analysis method considering governor characteristics according to claim 1, characterized in that, The side sensing node in step S1 includes a data acquisition module, a local processing module, and a communication module. The data acquisition module is used to acquire at least one of the following: rotational speed, current, temperature, and vibration signals. The local processing module performs the dynamic characteristic model analysis, and the communication module performs the data transmission in step C.

3. The side-sensing analysis method considering governor characteristics according to claim 1, characterized in that, The data transmission in step S3 adopts a partitioned architecture: in zone I, data from the speed control system PLC and the hydraulic press PLC are collected via the Modbus TCP protocol and time-stamped at the source end; in zone II, the data is classified by operating condition and re-time-stamped via the data collection server; and in zone III, the data is transmitted to the industrial internet platform via the plant-side big data center and the company's backbone network.

4. The side-side sensing analysis method considering governor characteristics according to claim 3, characterized in that, The classification of operating conditions in step S3 divides the data into two main categories: transient process data and steady-state data. The transient process data includes at least one of the following: start-up process, shutdown process, shutdown state, no-load state, load adjustment process, load shedding process, and primary frequency regulation process.

5. The side-sensing analysis method considering governor characteristics according to claim 4, characterized in that, In step S3, the transition process data is identified by preset logic judgment: the start-up process meets the following conditions: start-up command = 1, lock pin is pulled out and guide vane opening > 3%; the load shedding process meets the following conditions: circuit breaker closing signal changes from 1 to 0 and load > 50%; the primary frequency regulation process meets the following conditions: circuit breaker closing signal = 1, unit frequency is stable at rated value with error < 0.15 Hz and active power is basically stable with error < 2% of rated load.

6. The side-sensing analysis method considering governor characteristics according to claim 4, characterized in that, In step S2, the transient process data is fully preserved, and baseline correction and filtering preprocessing are performed; the steady-state process data is compressed using digital run-length encoding and analog differential encoding.

7. The side-side sensing analysis method considering governor characteristics according to claim 1, characterized in that, In step S3, the Zstandard compression algorithm is used to compress the analysis results or processed data. The compression level is adjustable, and the compressed data is uploaded to the industrial internet platform via FTP protocol.

8. The side-side sensing analysis method considering governor characteristics according to claim 1, characterized in that, The global optimization decision in step S4 includes: extracting key features from the governor's operating data, dynamically adjusting the weight parameters of the analysis algorithm based on the governor's characteristic prediction results, increasing the weight of energy consumption-related features when a decrease in efficiency is predicted, and enhancing the weight of vibration signal monitoring when a stability risk is predicted.

9. The side-sensing analysis method considering governor characteristics according to claim 1, characterized in that, The edge sensing nodes and the central node form an edge-cloud collaborative architecture. The edge nodes are responsible for real-time data processing and rapid response, while the central node is responsible for global optimization and decision-making, realizing elastic allocation and dynamic collaboration of computing resources.

10. The side-sensing analysis method considering governor characteristics according to claim 1, characterized in that, In step S2, the dynamic characteristic model automatically adjusts the data sampling frequency, feature extraction method, and analysis algorithm parameters based on the performance of the speed governor at different times and under different operating conditions.

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