Lamp code system for power station secondary system

Abstract

The present application relates to the technical field of lamp language monitoring management, especially to the lamp language system of the secondary system of power station, comprising a data acquisition module, a state analysis module, a lamp language mapping and generating module and a lamp language presentation device; the data acquisition module acquires the data of protection, measurement and control, communication and power supply subsystems in parallel, forms a feature vector through preprocessing and feature fusion; the state analysis module divides the system state into four levels of emergency, abnormality, attention and normality through a fuzzy logic model, identifies the abnormality in combination with a diagnostic rule base and generates an abnormal event code with a time stamp; the lamp language mapping and generating module maps the state level into a basic lamp language mode and maps the abnormal code into an enhanced lamp language element, and generates a composite control instruction through space-time superposition; the lamp language presentation device drives an RGB-LED array and a light beam deflection mechanism to present a dynamic light signal with spatial directivity on an analog screen. The present application improves the comprehensiveness of monitoring and the accuracy of state recognition, speeds up the operation and maintenance response, and ensures the reliable operation of the secondary system.

Description

Technical Field

[0001] This invention relates to the field of light signal monitoring and management technology, specifically to a light signal system for a power plant's secondary system. Background Technology

[0002] The secondary system of a power plant is a core supporting link to ensure the safe and stable operation of the primary system. It includes four key subsystems: protection, measurement and control, communication, and power supply. The operating status of this system is directly related to the overall operation and maintenance efficiency and safety performance of the power plant. Therefore, real-time monitoring, accurate analysis, and intuitive presentation of the operating status of the secondary system have become important requirements for power plant operation and maintenance.

[0003] Current monitoring solutions for power plant secondary systems suffer from several technical deficiencies. At the data acquisition level, existing monitoring systems mostly employ a single subsystem independent acquisition model, failing to achieve parallel and synchronous acquisition of operational status data from the four subsystems: protection, measurement and control, communication, and power supply. This results in a lack of consistency in the data over time. Furthermore, there is a lack of effective preprocessing methods for the raw data collected, including protection device action sequence signals, analog signal over-limit signals from measurement and control devices, communication network delay jitter signals, and power system ripple anomaly signals. Operations such as action count statistics and timing alignment, signal validity verification, time-domain filtering, and frequency-domain analysis are not performed. The raw data is easily affected by environmental noise, resulting in insufficient accuracy and validity, making it difficult to form a comprehensive and effective data foundation that fully characterizes the overall operational status of the secondary system.

[0004] At the status analysis level, existing system status assessment methods mostly rely on single feature indicators for judgment, without introducing multi-source information fusion technology and fuzzy logic analysis models. This makes it impossible to accurately classify and assess the system's operating status into four levels: "urgent, abnormal, attention, and normal," and status judgments are prone to bias or misjudgment. In addition, anomaly pattern recognition relies heavily on the manual experience of operation and maintenance personnel and lacks a standardized diagnostic rule base built on historical fault data of power plant secondary systems. It is difficult to automatically extract anomaly feature indicators and match them with rule conditions, resulting in difficulties in anomaly localization. It is impossible to quickly determine the subsystem to which the fault belongs and the specific fault type, and it is also impossible to record the precise time stamp information of the anomaly occurrence, which hinders subsequent fault tracing and analysis.

[0005] At the status presentation level, existing systems mostly convey status information through text reports, numerical display interfaces, or simple indicator lights, resulting in a single form of information transmission and poor intuitiveness. In scenarios where multiple systems operate simultaneously in the main control room and various monitoring information is dense, maintenance personnel need to spend a lot of time sifting through complex text or numerical data to sift through key status information, making it difficult to quickly capture abnormal changes in the system. Some presentation methods lack spatial directional functionality, making it impossible to accurately point to the physical area corresponding to the faulty subsystem through visual signals, thus prolonging troubleshooting time. At the same time, existing display devices are prone to displaying blurry information and low recognizability in harsh operating environments such as strong light or electromagnetic interference, further affecting the effective transmission of status information. Therefore, a light signal system for the power plant's secondary system is proposed to address the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a signaling system for a power plant's secondary system to solve the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The power plant's secondary system signaling system includes: Data acquisition module: used to collect real-time operating status data of four subsystems in the secondary system of the power plant: protection, measurement and control, communication and power supply. The operating status data includes the action sequence signals of protection devices, the analog quantity over-limit signals of measurement and control devices, the time delay jitter signals of communication networks and the ripple abnormal signals of the power supply system. Status Analysis Module: Communicates with the data acquisition module to perform multi-source information fusion on the operating status data to establish a fuzzy logic-based status assessment model. Based on a pre-set diagnostic rule base, it identifies abnormal patterns in the operating status data and outputs a comprehensive system status level and at least one abnormal event code with time stamp information. The system status level is divided into four levels: emergency, abnormal, attention, and normal. The abnormal event code includes a fault location identifier and a fault type identifier. The light language mapping and generation module communicates with the status analysis module and has a pre-stored light language encoding rule library. It is used to receive system status levels and abnormal event codes, query the light language encoding rule library to map the system status level to a basic light language mode containing primary colors and basic flashing frequencies, and to map abnormal event codes to enhanced light language elements containing color halo parameters, additional flashing sequence modes and beam scanning directions. Then, it spatiotemporally superimposes the basic light language mode and enhanced light language elements to generate composite light language control commands. Light signal presentation device: connected to the light signal mapping and generation module, used to receive composite light signal control commands and drive multiple light sources including RGB-LED arrays and beam deflection mechanisms. On the large simulation screen in the main control room of the power plant, the composite light signal control commands control each light source to present a dynamic light signal with spatial directionality; wherein, spatial directionality is achieved by beam deflection mechanism to control the beam scanning direction.

[0008] As a preferred option, the data acquisition module is used to collect real-time operational status data of four subsystems in the secondary system of the power plant: protection, measurement and control, communication, and power supply. Parallel acquisition of protection device action sequence signals of protection subsystem, analog quantity over-limit signals of measurement and control subsystem, network delay jitter signals of communication subsystem and DC power supply ripple abnormal signals of power supply subsystem. The collected protection device action sequence signals are processed by counting the number of actions and aligning the timing; the analog quantity over-limit signals are processed by verifying the signal validity and classifying the over-limit amplitude; the network delay jitter signals are processed by time domain filtering and jitter intensity calculation; and the DC power supply ripple abnormal signals are processed by frequency domain analysis and ripple coefficient extraction. Based on the preprocessed action sequence signal, the protection action frequency characteristics and action logic relationship characteristics are extracted; based on the preprocessed analog quantity over-limit signal, the over-limit duration characteristics and over-limit change trend characteristics are extracted; based on the preprocessed network delay jitter signal, the communication quality degradation characteristics are extracted; based on the preprocessed DC power supply ripple anomaly signal, the power supply stability characteristics are extracted. The extracted protection action frequency features, action logic relationship features, over-limit duration features, over-limit change trend features, communication quality degradation features, and power supply stability features are fused at the feature level to form a feature vector characterizing the overall operating status of the power plant's secondary system. The feature vectors are fed into the state analysis module as input data for the fuzzy logic-based state evaluation model.

[0009] As a preferred option, the state analysis module is used to fuse multi-source information from operational state data to establish a state assessment model based on fuzzy logic, including: Receive the feature vector transmitted by the data acquisition module; The feature vectors are standardized preprocessed to eliminate differences in the units and numerical ranges of different features, and a standardized feature vector is generated. The standardized feature vector is input into a predefined fuzzy logic state evaluation model, which includes a fuzzy set and membership function defined for each feature, used to convert each feature value into the membership degree of the corresponding fuzzy set; Based on a pre-set fuzzy rule base, fuzzy reasoning is performed on the membership degree to calculate the fuzzy membership degree of the power station's secondary system corresponding to the four state levels of emergency, abnormal, attention, and normal. The fuzzy membership degree is defuzzified, and the fuzzy output is converted into an accurate preliminary system state level by using the centroid method or the maximum membership degree method. The system outputs a preliminary system state level, which serves as the basis for anomaly pattern recognition and comprehensive state decision-making.

[0010] As a preferred approach, the status analysis module identifies abnormal patterns in the operational status data based on a pre-built diagnostic rule base, outputting a comprehensive system status level and at least one abnormal event code with time-stamped information, including: Receive the initial system status level; Extract abnormal feature indicators corresponding to the pre-set diagnostic rule base from the feature vector. The abnormal feature indicators include abnormal protection action frequency indicators, abnormal analog quantity over-limit duration indicators, abnormal network latency jitter intensity indicators, and abnormal power supply ripple coefficient indicators. The abnormal feature indicators are matched one by one with the rule conditions in the diagnostic rule base, which contains predefined rules based on historical fault data of the power plant's secondary system. Each rule is associated with an abnormal feature indicator threshold and the corresponding abnormal mode type. When an anomaly indicator exceeds the threshold defined in the rule conditions, the corresponding anomaly mode is triggered, and the precise timestamp of the anomaly is recorded. Based on the triggered exception pattern, an exception event code is generated, where the fault location identifier is automatically assigned based on the subsystem to which the exception feature index belongs, and the fault type identifier is automatically mapped based on the triggered exception pattern type. All triggered abnormal modes are sorted according to preset severity classification rules, and combined with the preliminary system status level for fusion calculation. The final comprehensive system status level is determined through weighted evaluation. Output the comprehensive system status level and at least one exception event code with time stamp information to the light signal mapping and generation module.

[0011] As a preferred embodiment, the light signal mapping and generation module is used to receive system status levels and exception event codes, and generate composite light signal control instructions, including: Receive the system status level and at least one exception event code with time stamp information from the status analysis module, and initialize a light control data structure; The system queries the pre-stored light language encoding rule library, maps the corresponding primary color and basic flashing frequency according to the system status level, and sets the primary color and basic flashing frequency into the light language control data structure to form the basic light language mode. Based on the processed light signal control data structure, the light signal coding rule base is queried, and the corresponding color halo parameters, additional flashing sequence mode and beam scanning direction are mapped according to the fault location identifier and fault type identifier in the abnormal event code. These enhanced light signal elements are then added to the light signal control data structure. The basic light language patterns and enhanced light language elements in the processed light language control data structure are spatiotemporally integrated. The temporal integration includes synchronizing the basic flashing frequency with the additional flashing sequence pattern, and the spatial integration includes fusing the primary color with the color halo parameters and determining the spatial directivity by the beam scanning direction, thereby generating composite light language control commands.

[0012] As a preferred embodiment, the light signal presentation device performs the following processing steps to generate and present dynamic light signals: Receive composite light language control commands from the light language mapping and generation module, parse the commands, and extract the basic light language mode parameters and enhanced light language element parameters contained therein; The obtained basic lighting mode parameters are converted into driving signals for the RGB-LED array, where the primary color parameters correspond to the ratio values ​​of the three primary colors of RGB, and the basic flashing frequency parameters correspond to the periodic on / off timing of the LED light source. Based on the color halo parameters in the enhanced light language elements, additional color effects are superimposed on the base color of the basic light language mode to generate a halo control signal with color gradient levels. The additional flashing sequence pattern is time-sequentially synthesized with the basic flashing frequency to form an integrated flashing control signal containing the composite flashing law; The beam scanning direction parameters are converted into control commands for the beam deflection mechanism to determine the dynamic scanning path of the beam emitted by the RGB-LED array on the large analog screen. The RGB-LED array and beam deflection mechanism are synchronously driven, so that the RGB-LED array emits a beam with a specific color, flashing pattern and halo effect according to the integrated flashing control signal and halo control signal. At the same time, the beam deflection mechanism guides the beam to deflect along the specified scanning path according to the control command. By working together with multiple light sources, a dynamic light signal with spatial directionality is formed on a large simulation screen. The spatial directionality is characterized by the scanning trajectory and dwell position of the light beam on the simulation screen.

[0013] As can be seen from the technical solution provided by the present invention above, the light signal system for the secondary system of the power plant provided by the present invention has the following beneficial effects: The data acquisition module can collect the operating status data of four subsystems in the secondary system of the power plant, namely protection, measurement and control, communication and power supply, in parallel. It eliminates data interference through preprocessing operations such as action count statistics, timing alignment, signal validity verification, time domain filtering and frequency domain analysis. Then, it forms a feature vector that represents the overall operating status of the system through feature-level fusion, ensuring that the data delivered to the subsequent modules covers the key subsystems and is accurate and reliable, providing a solid data foundation for system status assessment. The status analysis module constructs a status assessment model based on fuzzy logic, classifying the system status into four levels: emergency, abnormal, attention, and normal. It generates preliminary status levels through standardized preprocessing, fuzzy inference, and defuzzification. Simultaneously, it extracts abnormal feature indicators by combining a pre-set diagnostic rule base, matches them with rule conditions to trigger abnormal modes, and generates abnormal event codes containing fault location identifiers, fault type identifiers, and time markers. This not only accurately judges the overall operating status of the system but also accurately identifies the subsystem to which the abnormality belongs and the specific type, providing clear guidance for fault location. The light language mapping and generation module maps the system status level to a basic light language mode containing primary colors and basic flashing frequencies, and maps abnormal event codes to enhanced light language elements containing color halo parameters, additional flashing sequence modes, and beam scanning directions. Through spatiotemporal overlay, it generates composite light language control instructions, enabling the light language to simultaneously convey the overall system status and abnormal details, breaking through the information limitations of a single light language mode and meeting the needs of personnel to obtain status information at different levels. The light signal display device uses an RGB-LED array to achieve color fusion of primary colors and halo, and temporal synthesis of basic and additional flashing. Combined with a beam deflection mechanism to control the beam scanning direction, it forms a spatially directional dynamic light signal on a large simulation screen. This light signal is intuitive and easy to understand. Personnel can quickly identify the system status and abnormalities by scanning the color flashing pattern. Moreover, the high brightness and high contrast characteristics of RGB-LEDs ensure that the information remains clearly readable even in harsh environments such as strong light in the main control room or in the presence of electromagnetic interference. The system operates fully automatically from data acquisition and status analysis to light signal generation, reducing manual intervention. Personnel can quickly grasp the system status and abnormal situations through light signals without having to check the data of each subsystem one by one, which greatly shortens the time for status identification and fault location. At the same time, the time stamp information and fault identifier of the abnormal event code make it easy for maintenance personnel to trace the abnormal process, reduce the workload of fault investigation, and reduce maintenance time and labor costs. Support the long-term reliable operation of the power plant's secondary system; the system can monitor and record the operating status and abnormal events of the secondary system in real time. The historical data generated can help maintenance personnel summarize fault patterns, such as the high incidence of common abnormal types in specific subsystems, and optimize the operation and maintenance plan accordingly. Targeted hidden danger investigation can be carried out in advance to reduce the probability of failure and improve the long-term reliability and stability of the power plant's secondary system. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of the lighting system of the secondary system of the power plant according to the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0017] like Figure 1 As shown, this embodiment of the invention provides a signaling system for a power plant's secondary system, comprising: Data acquisition module: used to collect real-time operating status data of four subsystems in the secondary system of the power plant: protection, measurement and control, communication and power supply. The operating status data includes the action sequence signals of protection devices, the analog quantity over-limit signals of measurement and control devices, the time delay jitter signals of communication networks and the ripple abnormal signals of the power supply system. Status Analysis Module: Communicates with the data acquisition module to perform multi-source information fusion on the operating status data to establish a fuzzy logic-based status assessment model. Based on a pre-set diagnostic rule base, it identifies abnormal patterns in the operating status data and outputs a comprehensive system status level and at least one abnormal event code with time stamp information. The system status level is divided into four levels: emergency, abnormal, attention, and normal. The abnormal event code includes a fault location identifier and a fault type identifier. The light language mapping and generation module communicates with the status analysis module and has a pre-stored light language encoding rule library. It is used to receive system status levels and abnormal event codes, query the light language encoding rule library to map the system status level to a basic light language mode containing primary colors and basic flashing frequencies, and to map abnormal event codes to enhanced light language elements containing color halo parameters, additional flashing sequence modes and beam scanning directions. Then, it spatiotemporally superimposes the basic light language mode and enhanced light language elements to generate composite light language control commands. Light signal presentation device: connected to the light signal mapping and generation module, used to receive composite light signal control commands and drive multiple light sources including RGB-LED arrays and beam deflection mechanisms. On the large simulation screen in the main control room of the power plant, the composite light signal control commands control each light source to present a dynamic light signal with spatial directionality; wherein, spatial directionality is achieved by beam deflection mechanism to control the beam scanning direction.

[0018] In this embodiment, the data acquisition module is the "data foundation" of the power plant's secondary system light signal system. It collects, preprocesses, extracts and fuses the operating status data of the four core subsystems of protection, measurement and control, communication and power supply in real time, providing accurate and comprehensive input data for the subsequent status analysis module, ensuring that the entire light signal system can generate effective light signals based on the real operating status. The data acquisition module is primarily responsible for acquiring real-time operational status data from four subsystems within the power plant's secondary system: protection, measurement and control, communication, and power supply. This data specifically includes action sequence signals from protection devices, analog limit-breaking signals from measurement and control devices, time delay jitter signals from the communication network, and ripple anomaly signals from the power supply system. The module performs targeted preprocessing on the acquired raw data, removing interference and extracting key features. It then fuses the features from each subsystem to form a feature vector characterizing the overall operational status of the secondary system. This feature vector is ultimately fed into the status analysis module as input data for the subsequent fuzzy logic status assessment model, providing data support for system status level judgment and anomaly pattern recognition, including: Parallel acquisition unit: Multi-channel synchronous acquisition technology is adopted to simultaneously acquire the operating status data of four subsystems; specifically, the acquisition includes the protection device action sequence signal of the protection subsystem, the analog quantity over-limit signal of the measurement and control subsystem, the network delay jitter signal of the communication subsystem, and the DC power supply ripple abnormal signal of the power supply subsystem. By using a parallel acquisition design, we ensure that the data from the four subsystems remain synchronized over time, avoiding deviations in subsequent analysis due to differences in acquisition timing, and ensuring the temporal consistency and integrity of the data. Parallel acquisition technology is based on a multi-channel synchronous acquisition architecture, which configures independent signal acquisition channels for the four subsystems of protection, measurement and control, communication and power supply. Each channel uses the same sampling clock to ensure that the operating status signals of different subsystems are acquired at the same time, eliminating data time deviation caused by different sampling timing. This technology can acquire multi-source data at the same time, ensuring the time synchronization of data, laying the foundation for subsequent multi-subsystem collaborative analysis, and avoiding the inefficiency and timing disorder caused by single-channel serial acquisition. Data preprocessing unit: Differentiated preprocessing operations are performed on different types of signals acquired by the parallel acquisition unit; the action sequence signals of the protection device are processed by counting the number of actions and aligning the timing to ensure that the timing sequence of the action signals is accurate and quantifiable; the analog quantity over-limit signals are processed by validating the signal validity and classifying the over-limit amplitude, eliminating invalid signals and distinguishing the severity of over-limit; the network delay jitter signals are processed by time-domain filtering and jitter intensity calculation to filter high-frequency noise and quantify the jitter degree; the DC power supply ripple anomaly signals are processed by frequency-domain analysis and ripple coefficient extraction to clarify the frequency characteristics and intensity indicators of the ripple; The core objective of preprocessing is to eliminate noise, interference, and invalid information in the raw data, transform the data into a standardized format that meets the requirements of subsequent feature extraction, and improve data quality. Data preprocessing techniques employ differentiated processing methods tailored to the characteristics of different signal types. For protection device action sequence signals, a time-series alignment algorithm corrects the timestamp deviation of the signal, while simultaneously counting the number of actions per unit time to achieve signal quantification and standardization. For analog signal exceeding limits, anomalies caused by sensor failures or interference are eliminated using a validity verification algorithm, and the exceedance amplitude is then graded according to a preset threshold to clarify the severity of the exceedance. For network delay jitter signals, a time-domain filtering algorithm (such as moving average filtering) is used to filter high-frequency noise, and the standard deviation of the delay is calculated as a jitter intensity index. For DC power supply ripple anomaly signals, frequency domain analysis methods such as Fourier transform are used to decompose the frequency components of the ripple and extract ripple coefficients to quantify the ripple intensity. This technology effectively improves data quality through targeted processing, providing a reliable foundation for feature extraction. Feature extraction unit: Based on the preprocessed signals, key features reflecting the operating status of the corresponding subsystems are extracted. From the preprocessed protection device action sequence signals, protection action frequency features and action logic relationship features are extracted; the former reflects the frequency of protection device actions, and the latter reflects the correlation between actions. From the preprocessed analog quantity over-limit signals, over-limit duration features and over-limit change trend features are extracted; the former characterizes the duration of the over-limit state, and the latter shows the direction and rate of change of the over-limit amplitude. From the preprocessed network delay jitter signals, communication quality degradation features are extracted to quantify the transmission stability of the communication network. From the preprocessed DC power supply ripple anomaly signals, power supply stability features are extracted to reflect the power supply stability of the power system. The extracted features must be representative and able to accurately map the operating status of the subsystem, providing a detailed basis for subsequent overall system status assessment. Feature extraction technology extracts key information from preprocessed signals based on the correlation between signals and subsystem operating states. For protection device action sequence signals, the frequency of actions directly reflects the activity level of the protection system, while the logical relationship of actions reflects the coordinated working state of the protection devices. The combination of the two can map the operational stability of the protection subsystem. For analog signal exceeding limits, the longer the duration of the exceedance and the steeper the trend of the exceedance, the more severe the abnormality of the measurement and control subsystem. For network delay jitter signals, the communication quality degradation characteristics are reflected by the deviation of the delay jitter intensity from the preset normal range, directly reflecting the transmission quality of the communication subsystem. For DC power supply ripple anomaly signals, the power supply stability characteristics are characterized by the magnitude of the ripple coefficient; the larger the ripple coefficient, the worse the power supply stability. This technology transforms raw data into feature indicators that reflect the state of the subsystem by mining key information in the signals. Feature fusion unit: Collect all features output by the feature extraction unit, including protection action frequency features, action logic relationship features, over-limit duration features, over-limit change trend features, communication quality degradation features, and power stability features; By adopting a feature-level fusion approach, the aforementioned scattered subsystem features are integrated into a unified feature vector. This feature vector can comprehensively reflect the overall operating status of the power plant's secondary system, avoiding the limitations of single subsystem features, and providing comprehensive input data for the status analysis module to construct a fuzzy logic status assessment model. The fused feature vector is sent to the state analysis module to complete the core data output task of the data acquisition module; Feature fusion technology employs a feature-level fusion strategy to integrate scattered features from four subsystems into a unified feature vector. This technology first normalizes each feature to eliminate differences in scale and numerical range (e.g., mapping features of different magnitudes, such as action frequency and over-limit duration, to the [0,1] interval). Then, through feature concatenation or weighted fusion, all features are integrated into a single vector. This fusion method can comprehensively integrate the operational status information of each subsystem, avoiding the limitations of single subsystem features, and forming a feature vector that fully reflects the overall operational status of the secondary system, providing complete and coordinated input data for subsequent status analysis modules. Module workflow: Initialization phase: After the data acquisition module is started, it first completes a hardware self-test, including circuit testing of each acquisition channel, functional verification of the signal conditioning module, and connectivity testing of the data transmission interface, to ensure that the hardware device is running normally without faults or abnormalities. Load preset acquisition parameters, including the sampling frequency of each subsystem signal, the judgment threshold for analog quantity exceeding the limit, the calculation period of network delay jitter, and the extraction standard of power supply ripple coefficient, etc. Establish a communication connection with the status analysis module, confirm that the data transmission link is unobstructed, prepare to receive subsequent data output instructions, and complete all preparations before the module starts. Parallel acquisition phase: The module collects the action sequence signals of the protection device of the protection subsystem, the analog over-limit signals of the measurement and control subsystem, the network delay jitter signals of the communication subsystem, and the DC power supply ripple abnormal signals of the power supply subsystem in real time according to the preset sampling frequency and through the multi-channel synchronous acquisition architecture. During the acquisition process, the timestamps of each signal are recorded in real time to ensure that the acquired data from different subsystems remain synchronized in the time dimension and avoid timing deviations. The acquired raw signals are temporarily stored in the module's internal buffer, awaiting the next processing stage; Data preprocessing stage: The raw signals collected from the buffer are read and assigned to the corresponding preprocessing channels according to the signal type. Differentiated preprocessing operations are performed on the protection device action sequence signal, analog quantity over-limit signal, network delay jitter signal and DC power supply ripple abnormal signal respectively. The action sequence signals of the protection device are statistically analyzed and time-aligned; the analog quantity over-limit signals are validated for signal validity and graded for over-limit amplitude; the network delay jitter signals are filtered in the time domain and jitter intensity is calculated; and the DC power supply ripple abnormal signals are analyzed in the frequency domain and ripple coefficient is extracted. The preprocessed signal is stored in the designated data area to prepare for the feature extraction stage. Feature extraction stage: Read the preprocessed signal from the data area and start the feature extraction program for the signals of different subsystems respectively; Extract protection action frequency characteristics and action logic relationship characteristics from protection device action sequence signals; extract over-limit duration characteristics and over-limit change trend characteristics from analog over-limit signals; extract communication quality degradation characteristics from network delay jitter signals; and extract power supply stability characteristics from DC power supply ripple abnormal signals. All extracted features are temporarily stored in the feature cache area, awaiting feature fusion operation; Feature fusion stage: Read all features from the feature buffer, normalize each feature, and eliminate the influence of differences in units and numerical ranges; By using a feature splicing method, the normalized protection action frequency features, action logic relationship features, over-limit duration features, over-limit change trend features, communication quality degradation features, and power supply stability features are integrated into a unified feature vector; The integrity of the fused feature vector is checked. After confirming that there are no missing or incorrect features, it is ready to be output to the state analysis module. Data output stage: The module transmits the feature vector to the state analysis module through the established communication link, and records the timestamp of the data output and key information of the feature vector to facilitate subsequent data traceability. After the output is completed, the module returns to the parallel acquisition phase to continue to collect the operating status data of the four subsystems in real time, and enters the next round of data processing loop to ensure the continuity and real-time nature of data acquisition.

[0019] In this embodiment, the status analysis module is the "decision core" of the power plant's secondary system light signal system. It receives the feature vectors output by the data acquisition module, constructs a fuzzy logic status evaluation model through multi-source information fusion, identifies abnormal patterns based on a pre-set diagnostic rule base, and finally outputs a comprehensive system status level and anomaly event codes with time stamp information, providing accurate decision-making basis for the light signal mapping and generation module. The system integrates multi-source information to construct a fuzzy logic-based state assessment model. Through standardized preprocessing, fuzzy inference, and defuzzification, it generates a preliminary system state level (divided into four levels: emergency, abnormal, caution, and normal). The second part, based on a pre-built diagnostic rule base, extracts abnormal feature indicators from feature vectors and performs rule matching. Upon triggering an abnormal mode, it generates an abnormal event code containing fault location and fault type identifiers. This code is then weighted and fused with the preliminary system state level to determine the final comprehensive system state level. Finally, the module outputs the comprehensive system state level and at least one abnormal event code with time stamp information to the light signal mapping and generation module, providing decision support for the generation of light signal control commands. This includes: Fuzzy logic state evaluation unit: Feature vector reception and standardization: Receive feature vectors representing the overall operating state of the secondary system from the data acquisition module, and perform standardization preprocessing on the feature vectors; by eliminating the differences in units (such as "times / minute" for action frequency and "milliseconds" for time delay jitter) and numerical range of different features, the feature vectors are converted into standardized feature vectors to ensure the accuracy of subsequent fuzzy inference. Fuzzy set and membership degree calculation: The standardized feature vector is input into a predefined fuzzy logic state evaluation model. This model defines a fuzzy set (such as "high", "medium", "low") and a corresponding membership degree function for each feature (such as the frequency of protection actions and the duration of exceeding limits). The standardized value of each feature is converted into the membership degree of the corresponding fuzzy set through the membership degree function (such as the membership degree of a feature value corresponding to the "high" fuzzy set is 0.8, and the membership degree corresponding to "medium" is 0.2). Fuzzy reasoning and defuzzification: Based on a pre-set fuzzy rule base (e.g., "if the membership degree of protection action frequency is 'high' and the membership degree of over-limit duration is 'high', then the membership degree of system state is 'emergency'"), fuzzy reasoning is performed on the membership degree of each feature to calculate the fuzzy membership degree of the secondary system corresponding to the four state levels of emergency, abnormal, attention, and normal; then, the fuzzy membership degree is defuzzified by the centroid method or the maximum membership degree method to convert the fuzzy output into a precise preliminary system state level; Preliminary level output: The preliminary system state level output obtained by defuzzification is used as the basis for subsequent abnormal pattern recognition and comprehensive state decision-making; Anomaly pattern recognition and integrated decision-making unit: Anomaly feature index extraction: Receive the preliminary system state level output by the fuzzy logic state evaluation unit, and extract anomaly feature indices corresponding to the preset diagnostic rule base from the raw feature vector transmitted by the data acquisition module. These indices include anomaly indicators of protection action frequency (such as the number of actions per unit time exceeding the normal range), anomaly indicators of analog quantity over-limit duration (such as the duration of over-limit state exceeding the threshold), anomaly indicators of network latency jitter intensity (such as the amplitude of jitter value exceeding the normal range), and anomaly indicators of power supply ripple coefficient (such as the proportion of ripple coefficient exceeding the standard value). Diagnostic rule matching and anomaly triggering: The extracted abnormal feature indicators are matched one by one with the rule conditions in the diagnostic rule base; the diagnostic rule base is predefined based on historical fault data of the power plant's secondary system, and each rule is associated with an abnormal feature indicator threshold and a corresponding abnormal mode type (e.g., "If the abnormal indicator of protection action frequency > 5 times / minute, then trigger the 'frequent action of protection device' abnormal mode"); when an abnormal feature indicator exceeds the threshold defined in the rule condition, the corresponding abnormal mode is triggered, and the precise timestamp of the anomaly occurrence (i.e., the timestamp information of the abnormal event) is recorded. Abnormal event code generation: Abnormal event codes are generated based on the triggered abnormal mode; among them, the fault location identifier is automatically assigned based on the subsystem to which the abnormal feature index belongs (e.g., the abnormal index of protection action frequency corresponds to "protection subsystem", and the abnormal index of power supply ripple coefficient corresponds to "power supply subsystem"); the fault type identifier is automatically mapped based on the triggered abnormal mode type (e.g., the "frequent operation of protection device" mode corresponds to the "abnormal protection action" type, and the "excessive delay jitter" mode corresponds to the "degraded communication quality" type). Determining the overall system status level: All triggered abnormal modes are sorted according to a preset severity classification rule (e.g., "emergency abnormal mode weight 0.6, general abnormal mode weight 0.3"), and combined with the preliminary system status level output by the fuzzy logic status assessment unit for fusion calculation; the final comprehensive system status level is determined through weighted evaluation (e.g., "the preliminary level is 'abnormal', and after adding 2 emergency abnormal modes, the comprehensive level is upgraded to 'emergency'"). Output: The final comprehensive system status level and at least one abnormal event code with time stamp information (including timestamp, fault location identifier, and fault type identifier) ​​will be output to the light signal mapping and generation module; The workflow of the status analysis module is as follows: Initialization phase: After the state analysis module starts, it completes the initialization of internal parameters, including loading the fuzzy set definition, membership function parameters, fuzzy rule base content, and preset parameters such as the threshold of abnormal feature indicators, abnormal pattern type, and severity weight of the diagnostic rule base. Establish communication connections with the data acquisition module and the light signal mapping and generation module, confirm that the data transmission link is smooth, and prepare to receive the feature vector output by the data acquisition module; Feature vector reception and preprocessing stage Receive the feature vector representing the overall operating state of the secondary system transmitted by the data acquisition module; The feature vectors are preprocessed by standardization to eliminate the differences in the units and numerical ranges of different features and generate standardized feature vectors. Preliminary System Status Level Generation Phase The standardized feature vector is input into the fuzzy logic state evaluation model, and the membership degree of each feature to each fuzzy set is calculated through the membership function; Based on the fuzzy rule base, fuzzy reasoning is performed on the membership degree of each feature to obtain the fuzzy membership degree of the system corresponding to four levels: emergency, abnormal, attention, and normal. The fuzzy membership degree is defuzzified by the centroid method or the maximum membership degree method to generate an accurate preliminary system state level; Anomaly pattern recognition stage Extract the following indicators from the original feature vector: abnormal protection action frequency, abnormal analog quantity over-limit duration, abnormal network latency jitter intensity, and abnormal power supply ripple coefficient. Each abnormal feature indicator is matched against the rule conditions in the diagnostic rule base to determine whether any indicator exceeds the corresponding threshold. If the indicator exceeds the threshold, the corresponding abnormal mode is triggered, the precise timestamp of the abnormality is recorded, and an abnormal event code containing the fault location identifier and the fault type identifier is generated according to the abnormal mode. Comprehensive System State Level Determination Phase All triggered anomaly patterns are sorted according to a preset severity grading rule, and the weight of each anomaly pattern is determined. The final comprehensive system status level is determined by weighted fusion calculation combining the preliminary system status level and the weights of each abnormal mode. Result output stage Package the comprehensive system status level and at least one exception event code with time stamp information (including timestamp, fault location identifier, and fault type identifier); The packaged results are output to the light signal mapping and generation module through the communication link to complete a state analysis process. The module returns to the "Feature Vector Reception and Preprocessing Stage," waiting for the data acquisition module to output the next round of feature vectors, thus enabling continuous state analysis.

[0020] In this embodiment, the light signal mapping and generation module is the "command converter" of the light signal system of the secondary system of the power plant. It receives the system status level and abnormal event code output by the status analysis module. Relying on the pre-stored light signal coding rule library, it transforms the abstract status information into concrete composite light signal control commands through mapping and integration operations, providing accurate control basis for the light signal presentation device. It is the core link connecting "status decision-making" and "light signal presentation". The light signal mapping and generation module communicates with the status analysis module and has a pre-stored light signal encoding rule library. Its core function consists of three steps: The first step is to receive the comprehensive system status level (emergency, abnormal, alert, and normal) and at least one abnormal event code with time stamp information (including fault location and fault type identifiers) output from the status analysis module. The second step, based on the light signal encoding rule library, maps the system status level to a basic light signal pattern containing primary colors and basic flashing frequencies, and maps the abnormal event code to enhanced light signal elements containing color halo parameters, additional flashing sequence patterns, and beam scanning direction. The third step is to perform spatiotemporal overlay of the basic light signal pattern and enhanced light signal elements (synchronizing flashing patterns in time and fusing color and scanning direction in space) to generate a composite light signal control command that accurately represents the system status, and finally outputs the command to the light signal presentation device. This includes: Data receiving and initialization unit: Data reception: Through a preset communication link, receive two types of core data from the status analysis module: comprehensive system status level (such as "emergency" or "normal") and at least one abnormal event code with time stamp information (such as "protection subsystem - protection action abnormal - 202510161000", including fault location, fault type, and time stamp). Data verification: Perform integrity verification on the received data to confirm that the system status level belongs to one of the four categories: "urgent, abnormal, attention, normal". Abnormal event codes include complete fault location identifier, fault type identifier, and time information. If data is missing or formatted incorrectly, trigger a data retransmission request to ensure that the input data is valid. Control data structure initialization: Create and initialize a light signal control data structure, which includes a "basic light signal parameter area" (reserved primary color and basic flashing frequency fields) and an "enhanced light signal parameter area" (reserved color halo parameters, additional flashing sequence mode, and beam scanning direction fields), providing a standardized data container for subsequent parameter filling; Basic light signal pattern mapping unit: Rule base query: Call the internally stored light code encoding rule base and match the corresponding mapping rule according to the received system status level; the light code encoding rule base predefines a one-to-one correspondence between "system status level - basic light code parameters" (e.g., "emergency" level corresponds to "red primary color + 2Hz basic flashing frequency", "normal" level corresponds to "green primary color + 0.5Hz basic flashing frequency"); Parameter extraction and filling: Extract the primary colors (such as the ratio values ​​of the three primary colors of RGB) and the basic flashing frequency (such as the number of times it lights up and turns off per unit time) from the matching rules, and fill these two types of parameters into the "basic light language parameter area" of the light language control data structure to form a complete basic light language mode. This mode is used to intuitively reflect the severity of the overall operating status of the system. Enhanced light language element mapping unit: Abnormal event code parsing: The received abnormal event code is disassembled, and the fault location identifier (such as "protection subsystem" "power supply subsystem") and fault type identifier (such as "abnormal protection action" "ripple exceeds standard") are extracted. The time stamp information is ignored (the time stamp is only used for event tracing and does not participate in light signal mapping). Multi-dimensional rule matching: The light code rule library is queried again, and the matching is performed based on the predefined rules of "fault location identifier - beam scanning direction" and "fault type identifier - color halo parameter / additional flashing sequence mode". For example, "protection subsystem" corresponds to "beam scanning direction of the left area of ​​the analog screen", and "ripple exceeds the standard" corresponds to "yellow halo parameter + 1.5Hz additional flashing sequence mode". Enhanced parameter filling: The matched color halo parameters (such as halo color and gradient amplitude), additional flashing sequence patterns (such as flashing interval and duration), and beam scanning direction (such as area pointing and deflection angle on the simulation screen) are filled into the "enhanced light language parameter area" of the light language control data structure to form enhanced light language elements. These elements are used to accurately supplement the location and type information of anomalies. Spatiotemporal integration and instruction generation unit: Time-dimensional integration: The basic flashing frequency and additional flashing sequence patterns in the light signal control data structure are synchronized and arranged; according to predefined timing rules, the additional flashing sequence patterns are superimposed on the basic flashing frequency to ensure that the two are coordinated in time (e.g., when the basic flashing frequency is 2Hz, the additional flashing sequence patterns are embedded according to the rule of "on for 0.2 seconds, off for 0.3 seconds", forming a composite flashing timing sequence of "basic frequency + additional rule"), avoiding timing conflicts; Spatial dimension integration: The primary color and colored halo parameters are fused to generate a composite color that combines the primary color background and halo effect according to the color superposition rules (such as red primary color superimposed with yellow halo to form the visual effect of "red background and yellow halo"); at the same time, combined with the beam scanning direction, the spatial pointing path of the composite color beam on the simulation screen is determined (such as the "red background and yellow halo" beam scanning along the left side of the simulation screen), and the spatial directionality is clarified. Composite instruction generation: The composite blinking sequence after time integration, the composite color after spatial integration, and the scanning path are integrated into a unified composite light language control instruction. The instruction includes the driving parameters of the RGB-LED array (color ratio, blinking sequence) and the control parameters of the beam deflection mechanism (scanning direction, deflection angle), ensuring that the light language presentation device can directly parse and execute it. Command output verification: The generated composite light signal control command is format verified. After confirming that the parameters are complete and the logic is correct, it is output to the light signal display device through the communication link. The spatiotemporal integration and instruction generation unit addresses the coordination issue between basic light signal patterns and enhanced light signal elements, divided into two dimensions: time and space. Temporal integration is based on timing synchronization rules. By analyzing the period and duty cycle of the basic flashing frequency and the additional flashing sequence patterns, a superposition strategy is formulated to ensure that the additional sequence does not disrupt the core rhythm of the basic frequency, while simultaneously highlighting abnormal indications. Spatial integration is based on color fusion and spatial pointing rules. An RGB color mixing algorithm is used to achieve the natural superposition of the primary color and the halo. Combined with the area division of the simulated screen, the scanning direction is determined, ensuring that the light signal can both reflect the "overall state" and locate the "abnormal position" in space, forming a composite light signal logic of "spatiotemporal coordination." The workflow of the light signal mapping and generation module is as follows: Initialization phase: After the light signal mapping and generation module is started, it loads the internally stored light signal encoding rule library and confirms that the mapping rules such as "status level - basic parameters" and "fault information - enhanced parameters" in the rule library are complete and without any missing parts; Establish communication connections with the status analysis module and the light signal display device, test the smoothness of the data transmission link, and set the format standard for command output (such as parameter encoding method and transmission baud rate). Initialize the internal data processing buffer, clear historical data, and ensure that the reception and processing of new data are not interfered with; Data reception and verification stage: Receive the comprehensive system status level and at least one exception event code with time stamp information output by the status analysis module; Perform integrity and validity checks on the received data: check whether the system status level belongs to the preset four categories, and whether the abnormal event code contains the fault location, fault type and time label; if the check fails, send a data retransmission request to the status analysis module until valid data is received. Initialize the light signal control data structure and create the "basic light signal parameter area" and the "enhanced light signal parameter area"; Basic light signal pattern mapping phase: Query the light signal encoding rule library and match the corresponding basic light signal mapping rule according to the system status level; Extract the primary color (e.g., RGB value of 255,0,0 corresponds to red) and the basic blink frequency (e.g., 2Hz corresponds to 2 times on and off per second) from the matching rules. Fill the primary color and basic flashing frequency into the "basic light language parameter area" of the light language control data structure to complete the construction of the basic light language mode; Enhanced light language element mapping phase: Parse the abnormal event code and extract the fault location identifier (such as "communication subsystem") and fault type identifier (such as "latency jitter exceeds the standard"). Query the light code rule base, match the corresponding beam scanning direction according to the fault location identifier (e.g., "communication subsystem" corresponds to "horizontal scanning of the middle area of ​​the analog screen"), match the corresponding color halo parameters according to the fault type identifier (e.g., "excessive delay jitter" corresponds to "blue halo, gradient amplitude 50%)) and additional flashing sequence mode (e.g., "1.5Hz, on for 0.2 seconds and off for 0.3 seconds"). Fill the color halo parameters, additional flashing sequence mode, and beam scanning direction into the "enhanced light language parameter area" of the light language control data structure to complete the construction of enhanced light language elements; Spatiotemporal integration and instruction generation stage: Timing integration: Synchronize the basic flashing frequency with the additional flashing sequence pattern to generate a composite flashing timing (e.g., in the basic 2Hz flashing, an additional 1.5Hz flashing is embedded every 2 cycles). Spatial integration: The primary color and color halo parameters are fused through a color mixing algorithm to generate a composite color; the spatial scanning path of the composite color beam is determined by combining the beam scanning direction. The composite blinking timing, composite color parameters, and beam scanning path are integrated into a composite LED control command, which includes complete control parameters for the RGB-LED array and beam deflection mechanism. Instruction output stage: Perform format verification on the composite light control commands to confirm that there are no missing parameters and no logical conflicts. The command is output to the light signal display device through the communication link, and the command generation time and corresponding system status level and abnormal event code are recorded for easy traceability later. The module returns to the "data reception and verification stage" and waits for the status analysis module to output the next round of data, thus realizing the continuous generation of light control commands.

[0021] In this embodiment, the light signal presentation device is the "optical signal output terminal" of the secondary system light signal system of the power plant. It receives the composite light signal control commands output by the light signal mapping and generation module. By driving multiple light sources including RGB-LED arrays and beam deflection mechanisms, it presents spatially directional dynamic light signals on a large analog screen in the main control room of the power plant. It transforms abstract system status and abnormal information into intuitive and recognizable visual signals, and is the core hardware carrier for realizing "status visualization". The light signal presentation device is connected to the light signal mapping and generation module. Its core function consists of four steps: The first step is receiving composite light signal control commands, which include basic light signal mode parameters (primary color, basic flashing frequency) and enhanced light signal element parameters (color halo parameters, additional flashing sequence mode, beam scanning direction). The second step is parsing the commands and extracting various control parameters. The third step is converting these parameters into drive signals for the RGB-LED array and control commands for the beam deflection mechanism, achieving color fusion of the primary color and halo, temporal synthesis of the basic and additional flashing, and precise control of the beam scanning direction. The fourth step is synchronously driving the RGB-LED array and the beam deflection mechanism. Through the coordinated operation of multiple light sources, a dynamic light signal with spatial directionality (characterized by the beam scanning trajectory and dwell position) is formed on a large simulation screen, allowing control room personnel to intuitively grasp the operating status and abnormal information of the power plant's secondary system. The light signal presentation device executes the following processing flow to achieve the generation and presentation of the dynamic light signal: Receive composite light language control commands from the light language mapping and generation module, parse the commands, and extract the basic light language mode parameters and enhanced light language element parameters contained therein; The obtained basic lighting mode parameters are converted into driving signals for the RGB-LED array, where the primary color parameters correspond to the ratio values ​​of the three primary colors of RGB, and the basic flashing frequency parameters correspond to the periodic on / off timing of the LED light source. Based on the color halo parameters in the enhanced light language elements, additional color effects are superimposed on the base color of the basic light language mode to generate a halo control signal with color gradient levels. The additional flashing sequence pattern is time-sequentially synthesized with the basic flashing frequency to form an integrated flashing control signal containing the composite flashing law; The beam scanning direction parameters are converted into control commands for the beam deflection mechanism to determine the dynamic scanning path of the beam emitted by the RGB-LED array on the large analog screen. The RGB-LED array and beam deflection mechanism are synchronously driven, so that the RGB-LED array emits a beam with a specific color, flashing pattern and halo effect according to the integrated flashing control signal and halo control signal. At the same time, the beam deflection mechanism guides the beam to deflect along the specified scanning path according to the control command. By working together with multiple light sources, a dynamic light signal with spatial directionality is formed on a large simulation screen. The spatial directionality is characterized by the scanning trajectory and dwell position of the light beam on the simulation screen. Furthermore, the light language presentation device includes: Instruction parsing unit: Command reception: Receives composite light control commands from the light signal mapping and generation module through a preset communication interface to ensure the stability and integrity of command transmission; Parameter Extraction: The received composite light signal control commands are parsed, and the basic light signal mode parameters and enhanced light signal element parameters are separated and extracted. The basic light signal mode parameters include the RGB three primary color ratio values ​​corresponding to the primary colors and the periodic on / off timing parameters corresponding to the basic flashing frequency. The enhanced light signal element parameters include color halo parameters (halo color, gradient amplitude), additional flashing sequence mode parameters (flash interval, duration), and beam scanning direction parameters (area pointing on the simulation screen, deflection angle). Parameter verification: The extracted parameters are validated to confirm that the RGB ratio of the primary color is within the range of 0-255, the flashing frequency meets the hardware driving capability, and the beam scanning direction is within the effective display area of ​​the simulated screen. If the parameters exceed the reasonable range, the abnormal parameter information is fed back to the light language mapping and generation module, requesting the resend of the command to ensure the safety and accuracy of the subsequent hardware driver. RGB-LED array driver unit: Basic drive signal conversion: The parsed basic lighting mode parameters are converted into basic drive signals for the RGB-LED array; among them, the RGB three primary color ratio values ​​are converted into current control signals for the LED chips. By adjusting the current ratio of the red, green, and blue LEDs, the precise presentation of the preset primary colors is achieved; the basic flashing frequency parameters are converted into periodic on / off timing signals for the LED light source, controlling the RGB-LED array to cycle on and off at a set frequency; Halo effect generation: Based on the color halo parameters in the enhanced light language elements, additional color effects are superimposed on the base color of the basic light language mode; through the color mixing algorithm, the RGB values ​​corresponding to the halo color are fused with the RGB values ​​of the base color to generate a halo control signal with color gradient layers, and then the signal is superimposed on the basic driving signal to make the RGB-LED array emit composite light that has both base color and halo effect; Flashing timing synthesis: The additional flashing sequence pattern in the enhanced light signal element is synthesized with the basic flashing frequency in a timing sequence; according to the flashing interval and duration parameters of the additional flashing sequence pattern, additional flashing logic is embedded in the period of the basic flashing timing to form an integrated flashing control signal containing "basic frequency + additional pattern" to ensure that the two flashing modes work together without conflict, while highlighting the abnormal indication effect. Array drive: The color drive signal that integrates the halo effect with the synthesized integrated flashing control signal is integrated and output to the drive circuit of the RGB-LED array to control multiple RGB-LED light sources to emit light according to the set color and flashing pattern; The RGB-LED array driver unit is based on the theory of three primary colors mixing. It achieves color presentation by precisely controlling the luminous intensity of RGB LEDs. The RGB-LED array consists of three monochromatic LED chips: red, green, and blue. The current of each color LED chip is linearly related to its luminous intensity. The core technology is to convert the RGB ratio values ​​of the primary color and the halo into current control signals for the corresponding color LEDs. By adjusting the current, the intensity of the monochromatic light is changed. Then, by utilizing the visual color mixing effect of the human eye, the three colors are mixed to present a preset primary color or a composite color of "primary color + halo". For example, when the primary color is red (RGB: 255,0,0) and the halo is yellow (RGB: 255,255,0), by adjusting the red LED to full current and the green LED to half current, a visual effect of red background and yellow halo is formed. Beam deflection mechanism control unit: Scanning direction conversion: The analyzed and extracted beam scanning direction parameters (simulation screen area pointing, deflection angle) are converted into mechanical control commands for the beam deflection mechanism; the beam deflection mechanism has a built-in stepper motor or servo motor, and the control commands determine the dynamic scanning path of the beam emitted by the RGB-LED array on the large simulation screen by adjusting the rotation angle and speed of the motor. Path planning: Based on the beam scanning direction parameters and the size and partition information of the large simulation screen, the specific scanning trajectory of the beam is planned; for example, if the beam scanning direction parameters point to the "left area of ​​the simulation screen corresponding to the protection subsystem", then a linear scanning trajectory from the left edge of the simulation screen to the center of the area is planned, and the dwell time at the center of the area is set to enhance spatial directivity. Mechanism drive: The planned scanning path is converted into a pulse control signal for the motor, which drives the motor of the beam deflection mechanism to rotate along the set trajectory, guiding the beam emitted by the RGB-LED array to deflect on the simulation screen along the specified scanning path, thereby achieving precise control of the spatial directionality of the beam. The beam deflection mechanism control unit, based on a motor drive and position feedback mechanism, achieves precise control of the beam scanning direction. The core of the beam deflection mechanism is a stepper motor or servo motor. The rotation angle of the motor is proportional to the number of pulses in the pulse control signal, and the rotation speed is proportional to the pulse frequency. The technical process is as follows: First, the deflection angle of the beam scanning direction is converted into the number of pulses required by the motor, and the scanning speed is converted into the pulse frequency. Then, the motor driver sends a pulse control signal to the motor, driving the motor to rotate the optical deflection component (such as a reflector). At the same time, the position sensor built into the motor provides real-time feedback of the actual rotation angle, which is compared with the preset angle. The deviation is corrected through closed-loop control to ensure that the beam can be accurately deflected on the simulation screen along the set scanning path, achieving accurate spatial directionality. Cooperative control unit: Timing synchronization: Receives the integrated flashing control signal from the RGB-LED array drive unit and the motor pulse control signal from the beam deflection mechanism control unit, and performs timing synchronization calibration on both; ensures that the flashing changes of the RGB-LED array are coordinated with the scanning action of the beam deflection mechanism. For example, when the beam scans to a specific abnormal area of ​​the analog screen, an additional flashing sequence is triggered synchronously to enhance the visual cueing effect of the abnormal location. Multi-light source coordinated scheduling: If the device contains multiple RGB-LED arrays and corresponding beam deflection mechanisms, the coordinated control unit will schedule different groups of light sources to work according to preset logic based on the beam scanning direction parameters in the instruction; for example, when multiple subsystems malfunction simultaneously, the RGB-LED arrays in the corresponding areas will be controlled to present light signals that match the malfunction, avoiding mutual interference between the light signals of multiple light sources and ensuring the clarity of information transmission; Status feedback: Real-time monitoring of the RGB-LED array's light emission status (such as whether it is normally on or off, and whether the color is deviated) and the beam deflection mechanism's operating status (such as whether the motor is stuck, and whether the scanning position is accurate) feeds the status information back to the main control unit, so that personnel can promptly detect hardware faults and ensure the device's continuous and stable operation. The blinking timing synchronization technology is based on timing logic control to achieve the coordinated synthesis of basic blinking and additional blinking. The core of the technology is to build a timing control module, which takes the period corresponding to the basic blinking frequency as the main timing period. Within the main period, secondary timing segments are divided according to the parameters (blink interval, duration) of the additional blinking sequence mode. For example, if the basic blinking period is 0.5 seconds (frequency 2Hz) and the additional blinking sequence is "on for 0.2 seconds, off for 0.3 seconds", then within each basic blinking period, an additional blinking secondary timing sequence is embedded, so that the RGB-LED array can achieve a more complex blinking effect according to the additional pattern while the basic blinking is on and off. At the same time, the timing control module is linked with the scanning timing of the beam deflection mechanism to ensure that the blinking changes are synchronized with the beam scanning action and avoid visual information misalignment. The working process of the light language presentation device is as follows: Initialization phase: After the light language presentation device is started, it completes a hardware self-test, including testing the light emission of each LED chip in the RGB-LED array, testing the no-load rotation of the motor of the beam deflection mechanism, and testing the communication interface of the instruction parsing unit, to confirm that each hardware module is fault-free and functions normally. Load hardware parameter configuration, set the maximum driving current of the RGB-LED array, the maximum rotation angle and speed limit of the beam deflection mechanism, and the coordinate range of the effective display area of ​​the simulated screen, so as to provide hardware boundary basis for subsequent parameter analysis and driving; Establish a communication connection with the light signal mapping and generation module, confirm that the communication protocol matches and the data transmission rate is consistent, and enter the instruction receiving ready state; Command reception and parsing stage: The instruction parsing unit receives composite light control instructions through the communication interface and stores them in a temporary buffer. The instructions in the buffer are parsed to extract the basic light signal mode parameters (RGB ratio, basic flashing frequency) and enhanced light signal element parameters (halo parameters, additional flashing parameters, scanning direction parameters). The extracted parameters are validated to ensure they meet the hardware configuration requirements. If the validation passes, the process proceeds to the next stage. If the validation fails, an error message is sent and the process waits for a new instruction. Drive signal and control command generation stage: The RGB-LED array driver unit converts the RGB color ratio into a current control signal and the basic flashing frequency into a brightness-off timing signal. It then combines the color halo parameters to generate a halo control signal, which is then merged with the basic current control signal. Simultaneously, it combines additional flashing parameters to generate an additional timing signal, which is then combined with the basic brightness-off timing signal to form an integrated drive signal. The beam deflection mechanism control unit converts the beam scanning direction parameters into motor pulse control signals, combines the information of the simulation screen area to plan the scanning path, determines the motor rotation angle and speed, and generates complete mechanism control commands. Synchronous drive and optical signal presentation stage: The collaborative control unit receives the integrated drive signal from the RGB-LED array and the control command from the beam deflection mechanism, and performs synchronous calibration of the timing of the two to ensure that the blinking and scanning actions are coordinated. The integrated drive signal is synchronously output to the RGB-LED array drive circuit to control multiple RGB-LED light sources to emit light according to the set color and flashing pattern; at the same time, the control command is output to the beam deflection mechanism, the drive motor drives the optical components to rotate, and guides the beam to scan on the simulation screen along the planned path; Through the coordinated operation of multiple light sources, a dynamic light signal with spatial directionality is formed on a large simulation screen. The spatial directionality is characterized by the scanning trajectory of the light beam (such as pointing to the area corresponding to the protection subsystem) and the dwell position (such as briefly dwelling at an abnormal position). Status monitoring and feedback phase: The collaborative control unit monitors the light emission status of the RGB-LED array and the operating status of the beam deflection mechanism in real time, and records abnormal information (such as LEDs not lighting up or motor jamming). The device's operating status information is periodically fed back to the light signal mapping and generation module. If a hardware fault is detected, a fault alarm is immediately sent to facilitate timely troubleshooting and repair by maintenance personnel, ensuring the device's continuous and stable operation.

[0022] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A signaling system for a power plant's secondary system, characterized in that: include: Data acquisition module: used to collect real-time operating status data of four subsystems in the secondary system of the power plant: protection, measurement and control, communication and power supply. The operating status data includes the action sequence signals of protection devices, the analog quantity over-limit signals of measurement and control devices, the time delay jitter signals of communication networks and the ripple abnormal signals of the power supply system. Status Analysis Module: Communicates with the data acquisition module to perform multi-source information fusion on the operating status data to establish a fuzzy logic-based status assessment model. Based on a pre-set diagnostic rule base, it identifies abnormal patterns in the operating status data and outputs a comprehensive system status level and at least one abnormal event code with time stamp information. The system status level is divided into four levels: emergency, abnormal, attention, and normal. The abnormal event code includes a fault location identifier and a fault type identifier. The light signal mapping and generation module communicates with the status analysis module and has a pre-stored light signal encoding rule library. It receives system status levels and abnormal event codes, queries the light signal encoding rule library to map the system status level to a basic light signal pattern containing primary colors and basic flashing frequencies, and maps abnormal event codes to enhanced light signal elements containing color halo parameters, additional flashing sequence patterns, and beam scanning directions. It then spatiotemporally superimposes the basic light signal pattern and enhanced light signal elements to generate composite light signal control instructions. The light signal mapping and generation module receives system status levels and abnormal event codes and generates composite light signal control instructions, including: Receive the system status level and at least one exception event code with time stamp information from the status analysis module, and initialize a light control data structure; The system queries the pre-stored light language encoding rule library, maps the corresponding primary color and basic flashing frequency according to the system status level, and sets the primary color and basic flashing frequency into the light language control data structure to form the basic light language mode. Based on the processed light signal control data structure, the light signal coding rule base is queried, and the corresponding color halo parameters, additional flashing sequence mode and beam scanning direction are mapped according to the fault location identifier and fault type identifier in the abnormal event code. These enhanced light signal elements are then added to the light signal control data structure. The basic light language patterns and enhanced light language elements in the processed light language control data structure are spatiotemporally integrated. The spatiotemporal integration includes synchronizing the basic flashing frequency with the additional flashing sequence pattern, and the spatial integration includes fusing the primary color with the color halo parameters and determining the spatial directivity through the beam scanning direction, thereby generating composite light language control commands. Light signal presentation device: connected to the light signal mapping and generation module, used to receive composite light signal control commands and drive multiple light sources including RGB-LED arrays and beam deflection mechanisms. On the large simulation screen in the main control room of the power plant, the composite light signal control commands control each light source to present a dynamic light signal with spatial directionality; wherein, spatial directionality is achieved by beam deflection mechanism to control the beam scanning direction.

2. The signal lighting system for the secondary system of a power plant according to claim 1, characterized in that: The data acquisition module is used to collect real-time operational status data of four subsystems in the secondary system of the power plant: protection, measurement and control, communication, and power supply. Parallel acquisition of protection device action sequence signals of protection subsystem, analog quantity over-limit signals of measurement and control subsystem, network delay jitter signals of communication subsystem and DC power supply ripple abnormal signals of power supply subsystem. The collected protection device action sequence signals are processed by counting the number of actions and aligning the timing; the analog quantity over-limit signals are processed by verifying the signal validity and classifying the over-limit amplitude; the network delay jitter signals are processed by time domain filtering and jitter intensity calculation; and the DC power supply ripple abnormal signals are processed by frequency domain analysis and ripple coefficient extraction. Based on the preprocessed action sequence signal, the protection action frequency characteristics and action logic relationship characteristics are extracted; based on the preprocessed analog quantity over-limit signal, the over-limit duration characteristics and over-limit change trend characteristics are extracted; based on the preprocessed network delay jitter signal, the communication quality degradation characteristics are extracted; based on the preprocessed DC power supply ripple anomaly signal, the power supply stability characteristics are extracted. The extracted protection action frequency features, action logic relationship features, over-limit duration features, over-limit change trend features, communication quality degradation features, and power supply stability features are fused at the feature level to form a feature vector characterizing the overall operating status of the power plant's secondary system. The feature vectors are fed into the state analysis module as input data for the fuzzy logic-based state evaluation model.

3. The signal lighting system for the secondary system of a power plant according to claim 1, characterized in that: The state analysis module is used to fuse multi-source information from operational state data to establish a state assessment model based on fuzzy logic, including: Receive the feature vector transmitted by the data acquisition module; The feature vectors are standardized preprocessed to eliminate differences in the units and numerical ranges of different features, and a standardized feature vector is generated. The standardized feature vector is input into a predefined fuzzy logic state evaluation model, which includes a fuzzy set and membership function defined for each feature, used to convert each feature value into the membership degree of the corresponding fuzzy set; Based on a pre-set fuzzy rule base, fuzzy reasoning is performed on the membership degree to calculate the fuzzy membership degree of the power station's secondary system corresponding to the four state levels of emergency, abnormal, attention, and normal. The fuzzy membership degree is defuzzified, and the fuzzy output is converted into an accurate preliminary system state level by using the centroid method or the maximum membership degree method. The system outputs a preliminary system state level, which serves as the basis for anomaly pattern recognition and comprehensive state decision-making.

4. The signal lighting system for the secondary system of a power plant according to claim 1, characterized in that: The status analysis module identifies abnormal patterns in the operational status data based on a pre-set diagnostic rule base, and outputs a comprehensive system status level and at least one abnormal event code with time stamp information, including: Receive the initial system status level; Extract abnormal feature indicators corresponding to the pre-set diagnostic rule base from the feature vector. The abnormal feature indicators include abnormal protection action frequency indicators, abnormal analog quantity over-limit duration indicators, abnormal network latency jitter intensity indicators, and abnormal power supply ripple coefficient indicators. The abnormal feature indicators are matched one by one with the rule conditions in the diagnostic rule base, which contains predefined rules based on historical fault data of the power plant's secondary system. Each rule is associated with an abnormal feature indicator threshold and the corresponding abnormal mode type. When an anomaly indicator exceeds the threshold defined in the rule conditions, the corresponding anomaly mode is triggered, and the precise timestamp of the anomaly is recorded. Based on the triggered exception pattern, an exception event code is generated, where the fault location identifier is automatically assigned based on the subsystem to which the exception feature index belongs, and the fault type identifier is automatically mapped based on the triggered exception pattern type. All triggered abnormal modes are sorted according to preset severity classification rules, and combined with the preliminary system status level for fusion calculation. The final comprehensive system status level is determined through weighted evaluation. Output the comprehensive system status level and at least one exception event code with time stamp information to the light signal mapping and generation module.

5. The signal lighting system for the secondary system of a power plant according to claim 1, characterized in that: The light signal presentation device performs the following processing steps to generate and present dynamic light signals: Receive composite light language control commands from the light language mapping and generation module, parse the commands, and extract the basic light language mode parameters and enhanced light language element parameters contained therein; The obtained basic lighting mode parameters are converted into driving signals for the RGB-LED array, where the primary color parameters correspond to the ratio values ​​of the three primary colors of RGB, and the basic flashing frequency parameters correspond to the periodic on / off timing of the LED light source. Based on the color halo parameters in the enhanced light language elements, additional color effects are superimposed on the base color of the basic light language mode to generate a halo control signal with color gradient levels. The additional flashing sequence pattern is time-sequentially synthesized with the basic flashing frequency to form an integrated flashing control signal containing the composite flashing law; The beam scanning direction parameters are converted into control commands for the beam deflection mechanism to determine the dynamic scanning path of the beam emitted by the RGB-LED array on the large analog screen. The RGB-LED array and beam deflection mechanism are synchronously driven, so that the RGB-LED array emits a beam with a specific color, flashing pattern and halo effect according to the integrated flashing control signal and halo control signal. At the same time, the beam deflection mechanism guides the beam to deflect along the specified scanning path according to the control command. By working together with multiple light sources, a dynamic light signal with spatial directionality is formed on a large simulation screen. The spatial directionality is characterized by the scanning trajectory and dwell position of the light beam on the simulation screen.

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