Fault-tolerant system for low-vacuum operation of biomass generator set and control method
By introducing a fault-tolerant criterion system based on confidence evaluation and multiple cross-verification logic, combined with multi-source data fusion and intelligent adjustment, the error judgment and false alarm problems of the fault-tolerant system of the biomass generator set are solved, and accurate identification and prediction of low vacuum states are achieved, and operating stability and efficiency are improved.
Patent Information
- Application Number
- CN202510826459.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-19
- Publication Date
- 2025-08-15
AI Technical Summary
The fault-tolerant systems of existing biomass generator sets mainly rely on traditional threshold alarms or single sensor data analysis, lacking fault-tolerant processing of data abnormalities and multi-source data fusion analysis, resulting in susceptibility to sensor drift and data loss, and are very prone to identification misjudgment and false alarms.
A fault-tolerant criterion system based on confidence evaluation is introduced, combining multiple cross-verification logics of the data layer, model layer and state layer, data is collected through multiple sensors, integrated learning model and time series prediction algorithm are used to generate an optimal adjustment solution that takes into account power generation efficiency, cooling energy consumption and operation safety, and supports manual intervention through visual presentation.
Accurate identification and prediction of low vacuum states is achieved, ensuring that the system operates normally in the case of incomplete data or distortion, avoiding misjudgment and false alarms, and improving the operating stability and efficiency of the biomass generator set.
Smart Images
Figure CN120491484A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of biomass power generation, and in particular to a fault-tolerant system and a control method for low-vacuum operation of a biomass power generation unit. Background Art
[0002] With the deepening implementation of the "dual carbon" strategy, biomass power generation, as a key component of renewable energy, is playing an increasingly important role in achieving a green and low-carbon transition and promoting the resource utilization of agricultural waste. Biomass generator sets primarily utilize steam turbine power generation systems, whose energy efficiency and stability directly impact power generation costs and economic benefits. In actual operation, the operating status of the steam turbine condenser significantly affects the overall thermal efficiency of the system. In particular, when cooling is not ideal or load fluctuates drastically, the condenser is prone to a decrease in vacuum, a phenomenon known as "low vacuum operation."
[0003] At present, existing fault-tolerant systems mainly rely on traditional threshold alarms or single sensor data analysis to monitor low-vacuum conditions. They lack fault-tolerant processing of data anomalies and multi-source data fusion analysis, making them susceptible to sensor drift and data loss, and prone to misidentification and false alarms. To solve the above problems, we proposed a fault-tolerant system and control method for low-vacuum operation of biomass power generation units. Summary of the Invention
[0004] To this end, the present invention provides a fault-tolerant system and control method for low-vacuum operation of a biomass power generation unit, so as to solve the problem that the existing fault-tolerant system mainly relies on traditional threshold alarms or single sensor data analysis to monitor the low-vacuum state, lacks fault-tolerant processing of data anomalies and multi-source data fusion analysis, is susceptible to sensor drift and data loss, and is prone to identification misjudgment and false alarm problems.
[0005] In order to achieve the above object, the present invention provides the following technical solutions:
[0006] According to a first aspect of the present invention, a fault-tolerant system for low-vacuum operation of a biomass power generation unit includes the following contents:
[0007] Data acquisition module, used to collect various data of the unit;
[0008] The data verification and fault tolerance module performs consistency verification, time series integrity analysis, and anomaly elimination on the collected data, and automatically corrects data missing and deviations using sliding windows, interpolation predictions, or correlation models;
[0009] The low vacuum identification and prediction module identifies whether a low vacuum state currently exists and predicts its development trend based on a multi-source fusion model;
[0010] The fault-tolerant logic judgment module has built-in confidence-based fault-tolerant judgment criteria, which performs multi-dimensional cross-validation on model anomalies, sensor drift, and sudden changes in operating status to avoid false triggering.
[0011] The regulation module generates the state regulation of the generator group based on the optimization algorithm, combining power generation efficiency, cooling energy consumption and safety margin;
[0012] Visual display directly presents the current status, anomaly identification, and recommended strategy information, supporting manual intervention, strategy modification, and switching.
[0013] Furthermore, the data acquisition module collects the operating data of the biomass power generation unit, deploys a variety of sensors, and collects key parameters such as turbine back pressure, extraction volume, cooling water flow and water temperature in real time. Environmental data sensors are deployed near the operation of the biomass power generation unit to collect environmental temperature, atmospheric pressure, humidity, and inlet water quality data.
[0014] Furthermore, the data verification and fault-tolerance module evaluates the accuracy and completeness of the various data collected from the biomass power generation group, and dynamically monitors the data fluctuation range and change rate indicators based on the sliding time window through time integrity detection to determine whether there are packet loss, dislocation or transmission delay problems. After detecting data anomalies, missing data or sensor failure, the fault-tolerant calculation mechanism is automatically started to intelligently correct and reconstruct the target variables to ensure the continuity and rationality of the operating data, and to ensure that the low vacuum identification and adjustment strategy generation can still operate normally even when the data is incomplete or distorted.
[0015] Furthermore, the low vacuum identification and prediction module adopts the integrated learning model random forest, support vector machine or multi-layer perceptron algorithm for training and discrimination. When the data results processed by the data verification and fault tolerance module exceed the set low vacuum threshold and are accompanied by high confidence, the system will trigger an early warning signal and prepare to enter the control process. The time series prediction algorithm is used to perform trend modeling and future state prediction on the key indicators of vacuum degree, back pressure, and cooling water temperature to determine whether a low vacuum trend may occur in the future time window.
[0016] Furthermore, the fault-tolerant logic judgment module introduces a fault-tolerant judgment system based on confidence assessment, performs multiple cross-validation logic on the data processed by the data verification and fault-tolerant module, and realizes safety filtering and redundant judgment of the low vacuum identification and prediction process.
[0017] Furthermore, the adjustment module automatically generates an optimal adjustment plan that takes into account power generation efficiency, cooling energy consumption and operational safety based on the current operating status of the unit, introduces multiple constraints to ensure that the optimization results are within the physical and operational allowable range, and optimizes the strategy results output by the algorithm to ensure that the adjustment process is both efficient and controllable.
[0018] Furthermore, the visual display sets a visual display interface, which can be displayed in real time in the form of charts, curves, and heat maps.
[0019] The present invention also provides a control method for low vacuum operation of a biomass power generation unit, comprising the following contents:
[0020] S1: Multiple sensors are deployed to collect key operating parameters and environmental data of the unit in real time, forming a multi-source operating data set;
[0021] S2: Check the consistency and time series integrity of the collected data, and use sliding windows, interpolation methods, and correlation models to correct abnormal and missing data to ensure data quality;
[0022] S3: Based on the high-quality data output from step S2, a multi-source fusion model is used to determine whether a low vacuum state currently exists, and a time series model is used to predict the low vacuum development trend;
[0023] S4: Conduct confidence analysis and multi-dimensional cross-validation on the recognition and prediction results to eliminate misjudgments, identify abnormal models and sensor drift, and ensure the accuracy of adjustment actions;
[0024] S5: Combining the determination result of step S4 with the current state of the unit, a multi-objective optimization algorithm is used to generate an optimal adjustment plan that comprehensively considers power generation efficiency, energy consumption, and safety;
[0025] S6: Displays operating parameters, identification results, and adjustment strategies through a visual interface, and supports alarm prompts and trend analysis.
[0026] The present invention has the following advantages:
[0027] By deploying a variety of sensors, key parameters such as turbine back pressure, extraction volume, cooling water flow, and water temperature are collected in real time. Turbine back pressure is a core indicator for measuring condenser vacuum and unit expansion end efficiency. The accuracy and completeness of the collected data of the biomass power generation unit are evaluated to ensure that the basic data for subsequent analysis and processing by the system is true and reliable. A multi-sensor cross-comparison method is used to identify drift, mutation, or failure signals. Upon detecting data anomalies, missing data, or sensor failure, the fault-tolerant calculation mechanism is automatically activated to intelligently correct and reconstruct the target variables, ensuring the continuity and rationality of the operating data and ensuring that the low vacuum identification and adjustment strategy generation can still operate normally even in the case of incomplete or distorted data.
[0028] Real-time judgment of whether the current biomass power generation unit is in a low-vacuum operating state is performed. Based on a multi-source fusion modeling method, an integrated learning model, random forest, support vector machine, or multi-layer perceptron algorithm is used for training and discrimination. When the model recognition result exceeds the set low-vacuum threshold with a high confidence level, the system will trigger an early warning signal and prepare to enter the control process. A time series prediction algorithm is used to perform trend modeling and future state prediction for key indicators such as vacuum, back pressure, and cooling water temperature.
[0029] By introducing a fault-tolerant judgment system based on confidence assessment and combining multiple cross-validation logics of the data layer, model layer and state layer, safe filtering and redundant judgment of the low vacuum identification and prediction process are achieved; according to the current operating status of the unit, the optimal adjustment plan that takes into account power generation efficiency, cooling energy consumption and operational safety is automatically generated to achieve dynamic adjustment and intelligent control of the unit. With the goal of maximizing power generation efficiency, minimizing cooling energy consumption and maximizing safety margin, a number of constraints are introduced, including the upper limit of the allowable back pressure of the turbine, the flow regulation capacity of the cooling system, and the exhaust regulation range, to ensure that the optimization results are within the physical and operational allowable ranges. A visual display interface is set up to display key operating parameters and vacuum state change trends in real time in the form of charts, curves, and heat maps, so that operators can quickly understand the current system status. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] To more intuitively illustrate the prior art and the present application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be considered as limiting conditions for implementing the present application. For example, those skilled in the art, based on the technical concepts disclosed in the present application and the exemplary drawings, are capable of easily making routine adjustments or further optimizations to the addition / reduction / attribution division, specific shapes, positional relationships, connection methods, and dimensional ratios of certain units (components).
[0031] Figure 1 A module diagram of a fault-tolerant system and control method for low-vacuum operation of a biomass power generation set provided in some embodiments of the present invention.
[0032] Figure 2 A method diagram of a fault-tolerant system and control method for low-vacuum operation of a biomass power generation unit provided in some embodiments of the present invention. DETAILED DESCRIPTION
[0033] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.
[0034] Example 1
[0035] Figures 1 to 2 As shown, a fault-tolerant system for low-vacuum operation of a biomass power generation unit in an embodiment of the first aspect of the present invention includes the following contents:
[0036] Data acquisition module, used to collect various data of the unit;
[0037] Biomass power generation unit operation data collection, through the deployment of a variety of sensors, real-time collection of key parameters such as turbine back pressure, extraction volume, cooling water flow and water temperature. Turbine back pressure is the core indicator for measuring condenser vacuum and unit expansion end efficiency. Increased back pressure usually indicates the occurrence of low vacuum state, which directly affects power generation efficiency and system stability. Extraction volume reflects the energy distribution of different extraction stages of the turbine. Its changes have a significant impact on exhaust conditions and back pressure, and are an important basis for evaluating thermal system regulation strategies. Cooling water flow determines the heat exchange capacity of the condenser and is a key variable affecting vacuum maintenance and cooling tower load regulation. Water temperature, especially the inlet and outlet water temperatures of the cooling water, can be used to judge the cooling effect and the operating efficiency of the cooling system.
[0038] For data collection of operating environment, environmental data sensors are deployed near the operation of biomass power generation units. Ambient temperature, atmospheric pressure, humidity, and inlet water quality are also crucial. Ambient temperature will directly affect the cooling water temperature and the heat exchange efficiency of the condenser, and is an important external factor for judging the vacuum change trend; atmospheric pressure fluctuations will affect the reference benchmark of the condenser absolute pressure, and thus affect the back pressure judgment and vacuum adjustment strategy; ambient humidity reflects the evaporation capacity and cooling efficiency changes of the cooling tower to a certain extent; and the salt content and turbidity of the water quality parameters of the cooling water source are related to the scaling tendency and heat transfer performance of the heat exchanger, which in turn affects the system's vacuum maintenance ability.
[0039] Data verification and fault tolerance module: performs consistency verification, time series integrity analysis, and anomaly elimination on the data collected by the data acquisition module, and automatically corrects data missing and deviations using sliding windows, interpolation predictions, or association models;
[0040] Data verification is used to perform secondary verification on the data processed by the data verification and fault-tolerant modules to ensure that the basic data for subsequent analysis and processing by the system is authentic and reliable. Through redundant channel consistency verification, physical boundary determination and timing continuity detection methods, a comprehensive review of key parameters such as back pressure, exhaust volume, cooling water flow, water temperature, and ambient temperature is conducted. Consistency verification uses multi-sensor cross-comparison to identify drift, mutation or failure signals; timing integrity detection dynamically monitors the data fluctuation range and change rate indicators based on a sliding time window to determine whether there are packet loss, misalignment or transmission delay problems; boundary verification marks unreasonable values in real time based on the unit's operating experience threshold or physical constraints;
[0041] Through these mechanisms, the system can promptly identify abnormal data and set processing marks to prevent it from being mistakenly transmitted to subsequent analysis modules. When the data fault-tolerance correction module detects data anomalies, missing data, or sensor failures, it automatically activates the fault-tolerant calculation mechanism to intelligently correct and reconstruct the target variables, ensuring the continuity and rationality of the operating data.
[0042] Data fault-tolerance correction automatically activates the fault-tolerant calculation mechanism upon detecting data anomalies, missing data, or sensor failures, intelligently correcting and reconstructing target variables to ensure the continuity and rationality of operating data. It integrates multiple fault-tolerance strategies, including differential prediction, historical regression correction, and multivariate correlation estimation. It uses correlated variables in healthy states to perform reverse prediction and compensation for abnormal variables, effectively improving the system's robustness to input data fluctuations and anomalies, ensuring that low-vacuum identification and adjustment strategy generation can continue to operate normally even with incomplete or distorted data.
[0043] The technical effect achieved by the above scheme is: by deploying a variety of sensors, the key parameters of the turbine back pressure, suction volume, cooling water flow and water temperature are collected in real time. The turbine back pressure is the core indicator for measuring the vacuum degree of the condenser and the efficiency of the expansion end of the unit. The accuracy and completeness of the various data collected from the biomass power generation unit are evaluated to ensure that the basic data for subsequent analysis and processing of the system are true and reliable. The drift, mutation or failure signal is identified by multi-sensor cross-comparison. After detecting data anomalies, missing data or sensor failure, the fault-tolerant computing mechanism is automatically started to intelligently correct and reconstruct the target variables to ensure the continuity and rationality of the operating data and to ensure that the low vacuum identification and adjustment strategy generation can still operate normally even when the data is incomplete or distorted.
[0044] The low vacuum identification and prediction module uses the ensemble learning model random forest, support vector machine or multi-layer perceptron algorithm for training and discrimination based on the trusted data provided by the data verification and fault tolerance module. When the data processed by the data verification and fault tolerance module exceeds the set low vacuum threshold, it predicts its development trend.
[0045] Low vacuum identification is used to determine in real time whether the current biomass power generation unit is in a low vacuum operating state. Based on a multi-source fusion modeling method, it uses back pressure, cooling water flow, water temperature, exhaust volume, ambient temperature, and atmospheric pressure as key operating parameters as input variables. It uses an integrated learning model, random forest, support vector machine, or multi-layer perceptron algorithm for training and discrimination. When the model identification result exceeds the set low vacuum threshold and is accompanied by a high confidence level, the system will trigger a warning signal and prepare to enter the control process;
[0046] Low vacuum prediction: Based on low vacuum identification, the future development trend of the low vacuum state is judged in advance, providing data support for system pre-adjustment. A time series prediction algorithm is used to perform trend modeling and future state prediction on key indicators such as vacuum degree, back pressure, and cooling water temperature. By comparing the predicted values with the safety threshold range, it is determined whether a low vacuum trend is likely to occur within the future time window. If there is a trend deterioration signal, control suggestions are output in advance or fault-tolerant logic is linked to intervene, improving the system's forward-looking and proactive adjustment capabilities.
[0047] Fault-tolerant logic judgment module: Embedded with confidence-based fault-tolerant judgment criteria, it performs multi-dimensional cross-validation on model anomalies, sensor drift, and sudden changes in operating status to avoid false triggering;
[0048] Fault-tolerant logic judgment is used to perform multi-dimensional fault-tolerant judgment and anomaly suppression on the operating status, input data, and model output results of the entire system, preventing misidentification, misprediction, and incorrect execution of adjustment strategies due to local failures or occasional interference. Based on the results of the low-vacuum identification and prediction module, this module introduces a fault-tolerant judgment system based on confidence assessment, combining multiple cross-validation logic at the data layer, model layer, and state layer to achieve safe filtering and redundant judgment of the low-vacuum identification and prediction process;
[0049] The system generates a confidence value for each identification or prediction result, which is determined by the model's own output and input data quality indicators. When the confidence level falls below the set threshold, the system will determine that the result is unreliable and switch to the backup strategy process. The judgment of low vacuum state does not rely solely on the output of a single model, but integrates the results of multiple models, multiple data sources, and historical operating patterns to perform comprehensive scoring and logical consistency verification. Only when the majority of judgment paths reach a consistent conclusion and pass the confidence verification will the system trigger actual adjustment or issue an early warning.
[0050] Adjustment module: Based on the final judgment result of the fault-tolerant logic judgment module and the current unit operating status, the algorithm is optimized, and the power generation efficiency, cooling energy consumption and safety margin are combined to generate the generator unit state adjustment;
[0051] Unit regulation automatically generates an optimal regulation plan that takes into account power generation efficiency, cooling energy consumption, and operational safety based on the current unit operating status. This enables dynamic regulation and intelligent control of the unit, building a multi-objective system with the goals of maximizing power generation efficiency, minimizing cooling energy consumption, and maximizing safety margins. It also introduces multiple constraints, including the upper limit of the turbine back pressure, the cooling system flow regulation capability, and the extraction adjustment range, to ensure that the optimization results are within the physical and operational limits. The strategy output by the optimization algorithm is mapped into actual executable regulation instructions to adjust the cooling water pump frequency, increase the extraction valve opening, and switch the cooling tower operating mode, ensuring that the regulation process is both efficient and controllable.
[0052] Visual display: Intuitively presents current status, anomaly identification, and recommended strategy information, supporting manual intervention, strategy modification, and switching;
[0053] Data shows that setting up a visual display interface can display key operating parameters and vacuum status change trends in real time in the form of charts, curves, and heat maps, making it easier for operators to quickly understand the current system status; the interface can simultaneously display historical data comparison, model recognition results, and predictive warning information, which helps operation and maintenance personnel to promptly discover potential low vacuum risks and make intervention decisions. By integrating the output results and recommended strategies of the optimization algorithm, the interface can display multiple adjustment suggestions to users in the form of decision prompts, thereby improving the efficiency of human-computer collaboration; the display interface can also support authority classification and operation record tracking functions to enhance the standardization and traceability of operation management.
[0054] The technical effect achieved by the above solution is: real-time determination of whether the current biomass power generation unit is in a low-vacuum operating state. Based on a multi-source fusion modeling method, an ensemble learning model, random forest, support vector machine, or multi-layer perceptron algorithm is used for training and discrimination. When the model recognition result exceeds the set low-vacuum threshold with a high confidence level, the system will trigger an early warning signal and prepare to enter the control process. A time series prediction algorithm is used to perform trend modeling and future state prediction for key indicators such as vacuum degree, back pressure, and cooling water temperature.
[0055] By introducing a fault-tolerant judgment system based on confidence assessment and combining multiple cross-validation logics of the data layer, model layer and state layer, safe filtering and redundant judgment of the low vacuum identification and prediction process are achieved; according to the current operating status of the unit, the optimal adjustment plan that takes into account power generation efficiency, cooling energy consumption and operational safety is automatically generated to achieve dynamic adjustment and intelligent control of the unit. With the goal of maximizing power generation efficiency, minimizing cooling energy consumption and maximizing safety margin, a number of constraints are introduced, including the upper limit of the allowable back pressure of the turbine, the flow regulation capacity of the cooling system, and the exhaust regulation range, to ensure that the optimization results are within the physical and operational allowable ranges. A visual display interface is set up to display key operating parameters and vacuum state change trends in real time in the form of charts, curves, and heat maps, so that operators can quickly understand the current system status.
[0056] Example 2
[0057] A control method for low vacuum operation of a biomass power generation unit, comprising the following contents:
[0058] S1: Multiple sensors are deployed to collect key operating parameters and environmental data of the unit in real time, forming a multi-source operating data set;
[0059] S2: Check the consistency and time series integrity of the collected data, and use sliding windows, interpolation methods, and correlation models to correct abnormal and missing data to ensure data quality;
[0060] S3: Based on the high-quality data output from step S2, a multi-source fusion model is used to determine whether a low vacuum state currently exists, and a time series model is used to predict the low vacuum development trend;
[0061] S4: Conduct confidence analysis and multi-dimensional cross-validation on the recognition and prediction results to eliminate misjudgments, identify abnormal models and sensor drift, and ensure the accuracy of adjustment actions;
[0062] S5: Combining the determination result of step S4 with the current state of the unit, a multi-objective optimization algorithm is used to generate an optimal adjustment plan that comprehensively considers power generation efficiency, energy consumption, and safety;
[0063] S6: Displays operating parameters, identification results, and adjustment strategies through a visual interface, and supports alarm prompts and trend analysis.
[0064] The technical features of the above embodiments can be combined arbitrarily (as long as there is no contradiction in the combination of these technical features). In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described; these embodiments that are not explicitly written should also be considered to be within the scope of this specification.
Claims
1. A fault-tolerant system for low vacuum operation of a biomass power generation unit, characterized in that: Includes the following: Data acquisition module, used to collect various data of the unit; The data verification and fault tolerance module performs consistency verification, time series integrity analysis, and anomaly elimination on the collected data, and automatically corrects data missing and deviations using sliding windows, interpolation predictions, or correlation models; The low vacuum identification and prediction module identifies whether a low vacuum state currently exists and predicts its development trend based on a multi-source fusion model; The fault-tolerant logic judgment module has built-in confidence-based fault-tolerant judgment criteria, which performs multi-dimensional cross-validation on model anomalies, sensor drift, and sudden changes in operating status to avoid false triggering. The regulation module generates the state regulation of the generator group based on the optimization algorithm, combining power generation efficiency, cooling energy consumption and safety margin; Visual display directly presents the current status, anomaly identification, and recommended strategy information, supporting manual intervention, strategy modification, and switching.
2. A fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The data acquisition module collects the operating data of the biomass generator set, deploys multiple sensors, and collects key parameters such as turbine back pressure, extraction volume, cooling water flow and water temperature in real time. Environmental data sensors are deployed near the operation of the biomass generator set to collect ambient temperature, atmospheric pressure, humidity, and inlet water quality data.
3. The fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The data verification and fault-tolerant module receives the original data transmitted by the data acquisition module, performs consistency verification, and dynamically monitors the data fluctuation range and change rate indicators based on the sliding time window through time integrity detection to determine whether there are packet loss, dislocation or transmission delay problems. After detecting data anomalies, missing data or sensor failure, the fault-tolerant calculation mechanism is automatically started to intelligently correct and reconstruct the target variables to ensure the continuity and rationality of the operating data and ensure that the low vacuum identification and adjustment strategy generation can still operate normally even when the data is incomplete or distorted.
4. The fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The low vacuum identification and prediction module uses an integrated learning model random forest, support vector machine or multi-layer perceptron algorithm for training and discrimination based on the trusted data provided by the data verification and fault tolerance module. When the data results processed by the data verification and fault tolerance module exceed the set low vacuum threshold and are accompanied by a high confidence level, the system will trigger a warning signal and prepare to enter the control process. It uses a time series prediction algorithm to perform trend modeling and future state prediction on key indicators such as vacuum degree, back pressure, and cooling water temperature to determine whether a low vacuum trend is likely to occur within a future time window.
5. The fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The fault-tolerant logic judgment module introduces a fault-tolerant judgment system based on confidence assessment based on the results of the low vacuum identification and prediction module, performs multi-dimensional cross-validation, identifies model anomalies, sensor drift and sudden changes in operating status, filters false alarms and misjudgments, ensures the reliability of subsequent adjustment decisions, and triggers model retraining or parameter adjustment when necessary.
6. The fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The adjustment module automatically generates an optimal adjustment plan that takes into account power generation efficiency, cooling energy consumption and operational safety based on the final judgment result of the fault-tolerant logic judgment module and the current unit operating status. It introduces multiple constraints to ensure that the optimization result is within the physical and operational allowable range, optimizes the strategy results output by the algorithm, and ensures that the adjustment process is both efficient and controllable.
7. The fault-tolerant system for low vacuum operation of a biomass power generation unit according to claim 1, characterized in that: The visual display sets up a visual display interface, which can display the data of the fault-tolerant logic judgment module and the prediction module in real time in the form of charts, curves, and heat maps.
8. A control method for low vacuum operation of a biomass power generation unit, characterized in that: Includes the following: S1: Multiple sensors are deployed to collect key operating parameters and environmental data of the unit in real time, forming a multi-source operating data set; S2: Check the consistency and time series integrity of the collected data, and use sliding windows, interpolation methods, and correlation models to correct abnormal and missing data to ensure data quality; S3: Based on the high-quality data output from step S2, a multi-source fusion model is used to determine whether a low vacuum state currently exists, and a time series model is used to predict the low vacuum development trend; S4: Conduct confidence analysis and multi-dimensional cross-validation on the recognition and prediction results to eliminate misjudgments, identify abnormal models and sensor drift, and ensure the accuracy of adjustment actions; S5: Combining the determination result of step S4 with the current state of the unit, a multi-objective optimization algorithm is used to generate an optimal adjustment plan that comprehensively considers power generation efficiency, energy consumption, and safety; S6: Displays operating parameters, identification results, and adjustment strategies through a visual interface, and supports alarm prompts and trend analysis.