A real-time diagnosis system of a professional stage lighting control system

Abstract

This invention discloses a real-time diagnostic system for a professional stage lighting control system, belonging to the field of stage lighting technology. It includes a health monitoring module for assessing the health status of the equipment; a behavior verification module for verifying the equipment's execution behavior; an output verification module for verifying the actual output results of the equipment; a fault tracing module for determining the fault type and identifying upstream anomalies; and a feedback repair module for generating feedback based on the fault type and sending repair action commands to equipment that meets repairability conditions. This invention achieves stable operation and rapid response of the lighting control system in a stage performance environment through full-process variable tracking, dimensional normalization, and the introduction of explanatory factors.

Description

Technical Field

[0001] This invention belongs to the field of stage lighting technology, and in particular relates to a real-time diagnostic system for a professional stage lighting control system. Background Technology

[0002] Current professional stage lighting systems generally rely on digital communication protocols (such as DMX512, Art-Net, and sACN) to achieve unified scheduling and control of various lighting fixtures, lifting devices, and optical components. As the scale of lighting systems in large-scale performances continues to expand, the complexity of equipment distribution and network topology levels increase significantly, placing higher demands on the integrity of command transmission, the consistency of equipment execution, and the accuracy of light effect output. In practical applications, common problems include: signals failing to be delivered due to link aging or connection abnormalities; equipment receiving commands but exhibiting significant physical deviations; and optical output deviating from preset effects. These problems are often interconnected and manifest in various ways, making it difficult for traditional systems to determine the root cause of the failure through a single link or variable.

[0003] Current diagnostic methods largely focus on whether the equipment is "online" or whether the echo signal is "responsive," lacking dynamic verification of the equipment's execution process and perception of the luminous efficacy output. This makes it difficult for the system to accurately determine whether the equipment has truly completed the expected action, and also makes it difficult to distinguish whether the luminous efficacy anomaly is caused by a problem in the front-end link or by damage to the light source hardware itself. In addition, most current systems fail to build an effective data closed loop, lacking a mechanism to support the process from anomaly detection to strategy execution and effect feedback, and are unable to provide feasible operation and maintenance responses or automated repair methods in the immediate aftermath of a problem.

[0004] To address these issues, we propose a real-time diagnostic system for professional stage lighting control systems. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of the inability to determine the cause of abnormalities in the prior art, and to propose a real-time diagnostic system for a professional stage lighting control system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A real-time diagnostic system for a professional stage lighting control system, comprising:

[0008] A health monitoring module is used to assess the health status of the device. The health status assessment is completed through the device's stability index and effective response rate. The stability index is obtained by calculating the average time interval between sending a probe signal to the device and receiving an echo signal. The effective response rate is obtained by calculating the ratio of the echo signal to the probe signal.

[0009] The behavior verification module is used to verify the execution behavior of the device. The verification is accomplished by calculating the device's behavior execution error and drift error. The behavior execution error is obtained by comparing the preset execution trajectory with the actual execution trajectory. The actual execution trajectory is obtained by performing attitude calculation on the actual execution data collected by the sensor using Kalman filtering. The drift error is obtained by comparing the device's attitude angle within a preset time window.

[0010] The output verification module is used to verify the actual output results of the device. The verification is completed by calculating the luminous efficacy deviation score of the device. The luminous efficacy deviation score is obtained by weighted calculation of the deviation between the actual data of the device and the preset data. At the same time, a judgment factor is introduced to judge the cause of error. When the judgment factor value is the largest, it indicates that the deviation is most likely to be caused by abnormal operation rather than the light source itself.

[0011] The fault tracing module is used to determine the fault type and upstream anomaly. The determination of upstream anomaly is obtained by calculating an upstream anomaly score, which is obtained by weighting the behavior execution error, drift error, equipment stability index, effective response rate, and judgment factor. The fault type is determined based on the upstream anomaly score and the light efficiency deviation score, and the fault type label is output.

[0012] The feedback repair module is used to generate feedback based on the fault type and send repair action instructions to devices that meet the repairable conditions. After the repair action is completed, other modules will perform the detection again and output the repair result status.

[0013] Preferably, when the stability index and effective response rate of the output device in the health monitoring module are both higher than the preset threshold, the process proceeds to the behavior verification module to execute the next action; otherwise, it ends directly and is marked as not receiving instructions.

[0014] Preferably, the behavior execution error in the behavior verification module includes a dynamic delay function. The dynamic delay function is defined as the time lag between the actual execution trajectory and the preset execution trajectory being less than a threshold. This indicates that although the device has reached the target angle, the time taken exceeds the preset time.

[0015] Preferably, when the behavior execution error in the behavior verification module is higher than the threshold and / or the drift error is higher than the threshold, the execution is determined to be unsuccessful or unstable, and the module enters the output verification module for cross-verification.

[0016] Preferably, the actual data of the device includes light intensity, color vector, dominant wavelength, and flicker frequency.

[0017] Preferably, a spatial matching weight term is also introduced in the light effect deviation score. The spatial matching weight term is calculated based on the luminaire posture and the sensor projection area by the field of view matching degree of the light beam in the corresponding viewing angle area, and is used to suppress erroneous judgments caused by viewing angle deviation.

[0018] Preferably, the fault type label is determined according to the following rules:

[0019] When the stability index is less than the first threshold or the effective response rate is lower than the first threshold, it is determined to be a signal fault;

[0020] When the execution error of the action is greater than the second threshold or the drift error is greater than the second threshold, it is determined to be an action failure;

[0021] When the light efficiency deviation score is greater than the third threshold and the upstream anomaly score is less than the fourth threshold, it is determined to be an optical fault;

[0022] When the light efficiency deviation score is less than the third threshold and the upstream anomaly score is less than the fourth threshold, it is judged to be in normal operation;

[0023] When a situation other than the above occurs, it is determined to be a mixed fault.

[0024] Preferably, the repairable condition in the feedback repair module is that the fault type label is signal fault and / or action fault and the upstream anomaly score is greater than the repair threshold.

[0025] In summary, the technical effects and advantages of this invention are as follows: This invention achieves link health status assessment based on a protocol-compatible detection mechanism, ensuring signal delivery; subsequently, it models and calculates errors in the device response process using attitude sensors, quantifying the quality of action completion; then, it combines this with an optical acquisition module to perform actual measurement comparisons of the output beam, determining light efficiency deviations; furthermore, it classifies and interprets anomaly sources by constructing a causal consistency scoring function, achieving precise fault type localization; the system automatically triggers repair commands based on fault type and reliability score, and judges repair effectiveness through indicator recovery, completing a closed-loop execution process from diagnosis to control. The entire system, through full-process variable tracking, dimensional normalization, and the introduction of explanatory factors, constructs a highly reproducible, real-time, and engineering-deployable diagnostic and recovery mechanism, achieving stable operation and rapid response of the lighting control system in a stage performance environment. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the system structure in this invention. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] like Figure 1 As shown, a real-time diagnostic system for a professional stage lighting control system includes:

[0029] A health monitoring module is used to assess the health status of the device. The health status assessment is completed through the device's stability index and effective response rate. The stability index is obtained by calculating the average time interval between sending a probe signal to the device and receiving an echo signal. The effective response rate is obtained by calculating the ratio of the echo signal to the probe signal.

[0030] The behavior verification module is used to verify the execution behavior of the device. The verification is accomplished by calculating the device's behavior execution error and drift error. The behavior execution error is obtained by comparing the preset execution trajectory with the actual execution trajectory. The actual execution trajectory is obtained by performing attitude calculation on the actual execution data collected by the sensor using Kalman filtering. The drift error is obtained by comparing the device's attitude angle within a preset time window.

[0031] The output verification module is used to verify the actual output results of the device. The verification is completed by calculating the luminous efficacy deviation score of the device. The luminous efficacy deviation score is obtained by weighted calculation of the deviation between the actual data of the device and the preset data. At the same time, a judgment factor is introduced to judge the cause of error. When the judgment factor value is the largest, it indicates that the deviation is most likely to be caused by abnormal operation rather than the light source itself.

[0032] The fault tracing module is used to determine the fault type and upstream anomaly. The determination of upstream anomaly is obtained by calculating an upstream anomaly score, which is obtained by weighting the behavior execution error, drift error, equipment stability index, effective response rate, and judgment factor. The fault type is determined based on the upstream anomaly score and the light efficiency deviation score, and the fault type label is output.

[0033] The feedback repair module is used to generate feedback based on the fault type and send repair action instructions to devices that meet the repairable conditions. After the repair action is completed, other modules will perform the detection again and output the repair result status.

[0034] The specific execution steps of this system are as follows:

[0035] Step 1: Signal Link Health Monitoring and Echo Detection

[0036] In professional stage lighting control systems, most lighting fixtures are connected to the main control platform via wired DMX512, Art-Net, or sACN protocols, forming complex star or serial cascade topologies. Since performance venues are often temporary setups with varying wiring lengths and numerous relay nodes, problems such as poor connector contact, signal delay, and timing drift are prone to occur in the links. This step aims to use an echo response mechanism-based technique to perform real-time health status assessments of each signal link, outputting a "link stability index" that can be used to judge subsequent equipment behavior. This mechanism can operate continuously without interrupting the performance.

[0037] The core logic of this step consists of the echo communication mechanism between the main control platform and the luminaires. The main control platform periodically sends signals to each registered luminaire device via DMX channel or UDP broadcast (using Art-Net as an example). i A diagnostic probe frame is broadcast. This frame conforms to the existing communication protocol standard and includes a unique diagnostic identifier and timestamp in its data field. All devices receiving this diagnostic probe frame must, within a fixed response delay window (set during system initialization, e.g., 5ms), package the original frame content along with the reception time and send it back to the master controller via the original path. The system uses the master controller's clock as a global reference and records the round-trip transmission and reception time difference of the frame using a high-precision timer (e.g., a hardware-level 100μs time slice), denoted as the single echo delay.

[0038] To enhance robustness to device response jitter, the main controller continuously sends N probe frames (typically 5-10 times) to each device, recording the round-trip time for each frame, and calculating the following stability index:

[0039]

[0040] in:

[0041] R i Indicates device d i The echo stability index;

[0042] The time of the kth signal transmission (recorded by the main control system);

[0043] This corresponds to the echo signal reception time (recorded by the main control system);

[0044] N is the number of detection signals sent within the detection period, typically 5 to 10.

[0045] Simultaneously, the system also counts the response frequency of each device within this period, defined as the effective reception rate P. i :

[0046]

[0047] in:

[0048] P i Indicates device d i The effective response ratio;

[0049] M i This indicates the number of response frames successfully returned by the device within the current period;

[0050] Q represents the total number of probe frames set by the system, which is usually equal to N.

[0051] For example, if the main controller sends 10 diagnostic signals to the lamp d3 within a detection cycle and only successfully receives 7 echoes, then its P3 = 0.7, which is lower than the set threshold of 0.85, and will be marked as "unstable signal reception".

[0052] The above data collection was successful:

[0053] The main control platform's signal scheduling module automatically broadcasts probe frames (implemented based on the protocol stack);

[0054] Network control chips embedded in each device (such as WIZnet modules, ESP32, and STM32 built-in MAC);

[0055] All timing data are uniformly calibrated through a high-precision clock module within the main control system (usually using a crystal oscillator stabilized below ±50ppm);

[0056] The data is cached in the main controller's circular buffer via a dedicated communication thread for subsequent analysis modules to access.

[0057] All data acquisition is done through non-intrusive communication, which does not affect the device's regular command channels and does not require additional hardware burden, making it suitable for mass deployment in large-scale stage engineering scenarios.

[0058] This step outputs two variables for use in the next step:

[0059] R i Device d i Signal echo stability index within the current period;

[0060] P i Device d i The effective signal response ratio.

[0061] Step 2: Device Behavior Execution Verification

[0062] This step is used to perform real-time verification of the performance of professional stage lighting equipment. In complex performance environments, lighting equipment typically performs physical actions such as rotation, zoom, and lifting in response to commands issued by the control system. However, even if the equipment has received a signal (via R in step one), i and P i Despite the existing system's robustness, issues such as equipment malfunctions, motion delays, and incomplete movements persist. While these anomalies may not be immediately apparent visually, they can severely disrupt stage performance and timing. Traditional systems lack feedback mechanisms for equipment execution, failing to provide accurate warnings when actions are incomplete or malfunction. This paper proposes a motion execution deviation evaluation mechanism that combines motion trajectory modeling, dynamic filtering, and residual constraints to address these challenges and establish an objective and calculable criterion for determining whether an instruction has been completed.

[0063] enter:

[0064] R i Device d i The signal link stability index;

[0065] P i Device d i Effective response rate.

[0066] Only when R is satisfied simultaneously i >0.85 and P i Only devices with a signal strength greater than 0.85 are considered valid signal recipients by the system and thus enter the behavior verification process. Otherwise, this step will be skipped, and the device will be directly marked as "no instruction received".

[0067] When device d i Receive action commands from the control system (such as rotating the lamp head to a specified angle) Afterward, the system continuously collects actual motion data from the device within the action window T. The data source is the IMU module installed inside each lamp or adjacent to the bracket. Commonly used sensors include MPU6050, ICM-42688, LSM6DS3, etc., equipped with a three-axis gyroscope and a three-axis accelerometer. This data is uploaded to the main control system at a fixed sampling frequency (usually 100Hz), and attitude calculation is performed through Kalman filtering to form a complete attitude trajectory θ. i (t).

[0068] To quantify whether the equipment's action has been completed, the system calculates its trajectory deviation index E. i Furthermore, a dynamic delay penalty is introduced to strengthen the distinction between "action delayed but eventually completed" and "action not completed." The definition is as follows:

[0069]

[0070] in:

[0071] E i Device d i Overall behavioral execution deviations;

[0072] θ i (t): The actual angular trajectory of the device at time t, calculated from the IMU;

[0073] The target motion trajectory is set in the system control logic;

[0074] D i (t): Represents the dynamic delay function of the device in achieving the target angle, defined as θ i (t) and Time lag with a difference less than the threshold δ;

[0075] λ: Delay penalty weight, reflecting the sensitivity of action timing (usually set to 2-5 in stage performances).

[0076] Specifically, D i A large (t) value indicates that although the device has reached the target angle, it has taken too long; therefore, the system increases the penalty, thereby improving the accuracy of time-sensitive motion detection. For example, in fast-paced beat light motions, a motion delay of 300ms will be judged as unsuccessful.

[0077] Furthermore, considering the potential jitter errors caused by hardware aging and load interference in stage equipment, the system introduces a "steady-state drift penalty term" into the sensor data, which is based on the drift residual S when t>T after the action is completed. i Express:

[0078]

[0079] in:

[0080] S i Device d i The steady-state attitude drift index reflects whether the behavior remains unstable after the action is completed;

[0081] Δt: The drift observation window after the action is completed, typically set to 0.5 seconds;

[0082] dθ i (t) / dt: Approximately obtained from the derivative of the filtered attitude angle to avoid noise amplification.

[0083] After the main control system collects IMU data, it compares and analyzes it with the target trajectory to finally obtain E. i With S iTwo quantitative indicators. They respectively reflect whether the equipment completes the action according to the expected trajectory and whether it enters a stable state after completion. The system judges the execution quality by combining these two indicators:

[0084] If E i <0.1 and S i If the value is less than 0.05, the device is deemed to have successfully completed the instruction.

[0085] If E i Larger or S i If the light effect does not decrease over a long period, it is recorded as an execution failure or instability, and will be handed over to subsequent steps for cross-validation of light effect anomalies.

[0086] IMU data is transmitted via I 2 The C interface connects to the local microcontroller, which then uploads data via RS485 or CAN interface. All trajectory comparisons and integration calculations are performed on the main control platform, supporting GPU acceleration or edge computing acceleration chip deployment to enhance the processing capabilities of concurrent devices.

[0087] This step outputs two variables for use in the next step:

[0088] E i Device d i Error in the execution of actions;

[0089] S i Device d i The steady-state drift error after execution.

[0090] Step 3: Verify Light Effect Output

[0091] This step is used to accurately verify the actual light output effect of the stage lighting fixtures. It is the final step in the entire diagnostic system to determine whether the "perceived results meet the expected instructions." Unlike the previous two steps of "signal reception" and "action completion," light output is directly related to the stage visual presentation effect. It is the stage information that the audience ultimately receives, and it is also the most difficult step to judge as normal using traditional methods. Many abnormal light output problems, such as color deviation, insufficient brightness, color temperature drift, and flicker interference, do not originate from problems with the execution of control commands or equipment posture, but rather from aging of light source components, abnormal power supply drive, or environmental interference. Therefore, this step uses an optical sensor array as the core to construct a comprehensive light output diagnostic mechanism that combines spectral analysis, temporal drift, and posture execution for collaborative judgment. The aim is to systematically quantify hidden problems such as "light not shining as planned" and establish a deep causal relationship with the previous two steps.

[0092] The input for this step is the output for step two:

[0093] E i Device d i Posture and movement errors;

[0094] S i Device d i Steady-state attitude drift index.

[0095] These two variables determine whether the equipment's operation is completed and stable. Only when E is satisfied... i <0.2 and S i Only devices with a value <0.1 are allowed to proceed to this step for independent light effect verification; if any device exceeds the threshold, the system will mark the light effect as "abnormal action leading to inability to judge" to avoid misjudgment due to uncertainty of the source.

[0096] Device d i After completing the gesture execution, the emitted beam of light will create a specific visual effect in the performance area. To measure this effect, the system deploys directional optical acquisition devices (including miniature spectrometers, color sensors, fast photosensitive arrays, etc.) on the stage, each device d i Each has a corresponding monitoring viewing area A i The main control system schedules the corresponding acquisition viewpoint and performs synchronous sampling in real time based on the device number and current attitude trajectory.

[0097] The physical quantities collected first include:

[0098] Light intensity L i Unit illuminance sensor data collection;

[0099] Color vector C i Reconstructing XYZ or RGB representations based on a three-channel color sensor;

[0100] dominant wavelength λ i The main emission frequency was analyzed using a small spectrometer.

[0101] Strobe frequency F i : Obtained by Fourier transform after sampling by a fast ADC and photodiode.

[0102] To address issues such as equipment viewpoint shift, projection overlap, and ambient light interference in real-world stage environments, the system introduces a spatial matching weight term w based on the target reference model. i (t), dynamically adjusting the proportion of reference luminous efficacy in the comparison process. The final definition of the fused luminous efficacy deviation index V. i for:

[0103]

[0104] in:

[0105] V i : Light output deviation score, the larger the score, the more serious the deviation;

[0106] Li C i , λ i F i Device d i The actual measured light intensity, color vector, dominant wavelength, and flicker frequency;

[0107] The system uses a preset lighting effect model based on the lamp model, current angle, and scene.

[0108] w i (t): The reference beam in the current viewing area A i The field-of-view matching degree is calculated based on the lamp posture and the sensor projection area, with a value range of [0.5, 1.0], and is used to suppress erroneous judgments caused by viewing angle deviation;

[0109] The coefficients α, β, γ, and δ represent the weighting coefficients of the deviation terms. The system automatically configures these coefficients based on the type of lighting fixture, such as δ for strobe lights and β for colored lights.

[0110] In addition, to determine the cause of light efficiency deviation by combining the equipment's operating status, the system introduces an action-light efficiency coupling factor.

[0111]

[0112] The innovative aspect of this factor design lies in: when E i With S i When it approaches 0, When E approaches 0, it indicates that the luminous efficacy deviation should be attributed to the light source or the optical system itself; while when E... i or S i When it is large, The system rapidly escalates, attributing the lighting effect problem to incomplete action or unstable posture, which is a downstream response to the abnormalities in the first two steps.

[0113] All collected data is timestamped at the millisecond level. The main control platform establishes a three-dimensional data index of device-action-light effect in a distributed database, supporting retrospective analysis and fault reproduction. The sampling module uploads data via wired RS485 or wireless 2.4GHz Zigbee protocols and is precisely synchronized with the main control clock (error controlled within ±2ms).

[0114] After processing all the sampled data, the system outputs V. i and The results will be stored in the diagnostic log. If the device’s light efficiency is abnormal for more than 3 consecutive testing cycles, the subsequent fault attribution and maintenance suggestion module will be triggered.

[0115] This step outputs two variables:

[0116] Vi Device d i The light efficiency deviation score characterizes the overall difference between the output beam and the expected beam.

[0117] The coupling factor between equipment execution abnormalities and light effect abnormalities is used to determine whether the deviation is caused by the action or an independent light effect problem.

[0118] Step 4: Root Cause Analysis and Source Tracing

[0119] The task of this step is to systematically model and analyze the light effect deviation results output from the previous step, achieving "root cause tracing" of equipment failures. It occupies a central position in the diagnostic chain within the entire patent architecture, responsible for integrating and attributing all quantitative results between the signal link status (step 1), equipment behavior execution status (step 2), and light effect output (step 3), ultimately outputting a fault type label with operational guidance significance. By constructing a causal consistency scoring mechanism oriented towards the dynamic characteristics of stage performances, the system can automatically determine whether the current equipment anomaly is caused by upstream factors or by the optical system itself, providing a reliable basis for subsequent response and repair.

[0120] To standardize the dimensions, the system first normalizes the following two indices that have physical dimensions:

[0121] in The maximum execution error threshold is preset for the device category;

[0122] in This is the preset maximum allowable steady-state drift.

[0123] Normalized and All values ​​are dimensionless values ​​within the interval [0,1], ensuring dimensional consistency in subsequent model calculations.

[0124] The input for this step includes:

[0125] V i Device d i Luminous efficacy deviation score;

[0126] The coupling factor between light efficiency deviation and device operating status (output from step 3);

[0127] Read-only reference variables include:

[0128] The device's motion execution error and attitude stability (output from step 2, after normalization processing);

[0129] The signal link stability of the device (output from step 1);

[0130] P i Effective response rate of the device (output from step 1).

[0131] The core innovation of this step lies in defining a multi-source causal consistency scoring function Φ. i Used to measure luminous efficacy deviation V i Can the above upstream variables be "reasonably explained"?

[0132]

[0133] in:

[0134] Φ i : Upstream explainability score for abnormal light effect; the higher the value, the more likely the light effect deviation is caused by an upstream failure.

[0135] θ1~θ5: Configurable weight parameters. The system default value is [0.25,0.15,0.25,0.15,0.20], which can be dynamically adjusted according to equipment type or performance task.

[0136] From step 3, the statistical coupling between light effect deviation and equipment operation status is measured. The larger the value, the more likely the deviation is caused by abnormal operation rather than the light source itself.

[0137] In obtaining Φ i Subsequently, the system further incorporates the light effect deviation itself V i Output fault type label G i Used for subsequent response strategy generation:

[0138]

[0139] illustrate:

[0140] If Φ i A higher value indicates that the optical efficiency deviation is "explainable" by the preceding fault chain and does not require tracing back to the optical components;

[0141] If Φ i Lower and V i If the problem is large, it is determined to be a problem with the optical system itself, such as LED aging or filter damage.

[0142] All threshold values ​​(such as 0.85, 0.2, 0.3) can be optimized and adjusted using historical data during system deployment.

[0143] The system will V i Φ i and G iThe data is stored in the diagnostic database for use by the next step, the strategy scheduling module, and a structured log is generated, including the device number, fault type, and recommended actions (such as resending instructions, scheduling maintenance, skipping execution, etc.).

[0144] System implementation method:

[0145] All variables are retrieved from the cached results of steps 1 to 3, and there is no need to collect them repeatedly;

[0146] Rating and classification rules can be managed through configuration files and support hot reloading;

[0147] The computational logic is implemented using a rule tree approach to ensure response speed and stability.

[0148] This step outputs two variables:

[0149] Φ i The rating for whether the light effect deviation is "explainable" by the upstream anomaly;

[0150] G i Fault classification tags are used by the subsequent repair execution module.

[0151] Step 5: Real-time visual feedback and automated repair execution

[0152] This step, based on the fault diagnosis results, completes the visual presentation and executable automated repair operations.

[0153] Step details:

[0154] The system is based on G i Categorize and generate visual feedback (color markings, abnormal trajectories, suggested strategies, etc.);

[0155] If G i Belongs to the "repairable" category and Φ i If the value is greater than 0.5, an automatic repair operation will be triggered;

[0156] Repair actions are performed by resending commands via the control protocol (e.g., reset, reconfigure parameters, resend action commands, etc.).

[0157] After repair, relevant indicators will be re-collected (E) i ,S i V i To determine whether the repair was successful;

[0158] Automatic repair will only attempt twice; if it fails, it will be switched to manual processing.

[0159] The triggering conditions are as follows:

[0160]

[0161] Repair result status:

[0162]

[0163] Output:

[0164] A i : Whether to trigger automatic repair;

[0165] F i The status of the repair results is displayed and recorded by the system.

[0166] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: This invention achieves link health status assessment based on a protocol-compatible detection mechanism, ensuring signal delivery; subsequently, it models and calculates errors in the device response process using attitude sensors, quantifying the quality of action completion; then, it combines an optical acquisition module to perform actual measurement comparisons of the output beam, determining light efficiency deviations; furthermore, it classifies and interprets anomaly sources by constructing a causal consistency scoring function, achieving precise fault type localization; the system automatically triggers repair instructions based on fault type and credibility score, and judges repair effectiveness through indicator recovery, completing a closed-loop execution process from diagnosis to control. The entire system, through full-process variable tracking, dimensional normalization, and the introduction of explanatory factors, constructs a highly reproducible, real-time, and engineering-deployable diagnostic and recovery mechanism, achieving stable operation and rapid response of the lighting control system in a stage performance environment.

[0167] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A real-time diagnostic system for a professional stage lighting control system, characterized in that, include: A health monitoring module is used to assess the health status of the device. The health status assessment is completed through the device's stability index and effective response rate. The stability index is obtained by calculating the average time interval between sending a probe signal to the device and receiving an echo signal. The effective response rate is obtained by calculating the ratio of the echo signal to the probe signal. The behavior verification module is used to verify the execution behavior of the device. The verification is accomplished by calculating the behavior execution error and drift error of the device. The behavior execution error is obtained by comparing the preset execution trajectory with the actual execution trajectory. A dynamic delay function is set in the behavior execution error. The dynamic delay function is defined as the time lag when the difference between the actual execution trajectory and the preset execution trajectory is less than a threshold. It is used to indicate that although the device has reached the target angle, the time taken exceeds the preset time. The actual execution trajectory is obtained by performing attitude calculation on the actual execution data collected by the sensor using Kalman filtering; the drift error is obtained by comparing the device's attitude angle within a preset time window. The output verification module is used to verify the actual output results of the device. The verification is completed by calculating the luminous efficacy deviation score of the device. The luminous efficacy deviation score is obtained by weighted calculation of the deviation between the actual data of the device and the preset data. The luminous efficacy deviation score also introduces a spatial matching weight term. The spatial matching weight term is calculated based on the field of view matching degree of the light beam in the corresponding viewing angle area, according to the lamp posture and the sensor projection area, and is used to suppress erroneous judgments caused by viewing angle deviation. Simultaneously, a judgment factor is introduced for error attribution judgment. The judgment factor is calculated through light effect deviation score, device behavior execution error and drift error. When the judgment factor value is the largest, it indicates that the deviation is most likely to be caused by abnormal action rather than the light source itself. The fault tracing module is used to determine the fault type and upstream anomaly. The determination of upstream anomaly is obtained by calculating an upstream anomaly score, which is obtained by weighting the behavior execution error, drift error, equipment stability index, effective response rate and judgment factor. The fault type is determined based on the upstream anomaly score and the light efficiency deviation score, and a fault type label is output. The feedback repair module is used to generate feedback based on the fault type and send repair action instructions to devices that meet the repairable conditions. After the repair action is completed, other modules will perform the detection again and output the repair result status.

2. The real-time diagnostic system for the professional stage lighting control system according to claim 1, characterized in that, If the stability index and effective response rate of the device in the health monitoring module are both higher than the preset threshold, proceed to the behavior verification module to execute the next action; otherwise, end directly and mark as no instruction received.

3. The real-time diagnostic system for the professional stage lighting control system according to claim 1, characterized in that, If the behavior execution error in the behavior verification module is higher than the threshold and / or the drift error is higher than the threshold, the execution is determined to be unsuccessful or unstable, and the module will proceed to the output verification module for cross-verification.

4. The real-time diagnostic system for the professional stage lighting control system according to claim 1, characterized in that, The actual data of the device includes light intensity, color vector, dominant wavelength, and flicker frequency.

5. The real-time diagnostic system for the professional stage lighting control system according to claim 1, characterized in that, The fault type label is determined according to the following rules: When the stability index is less than the first threshold or the effective response rate is lower than the first threshold, it is determined to be a signal fault; When the execution error of the action is greater than the second threshold or the drift error is greater than the second threshold, it is determined to be an action failure; When the light efficiency deviation score is greater than the third threshold and the upstream anomaly score is less than the fourth threshold, it is determined to be an optical fault; When the light efficiency deviation score is less than the third threshold and the upstream anomaly score is less than the fourth threshold, it is judged to be in normal operation; When a situation other than the above occurs, it is determined to be a mixed fault.

6. The real-time diagnostic system for the professional stage lighting control system according to claim 1, characterized in that, The repairable condition described in the feedback repair module is that the fault type label is signal fault and / or action fault and the upstream anomaly score is greater than the repair threshold.

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