Control system of stage mechanical electronic screen

Abstract

The invention discloses a control system of a stage mechanical electronic screen, and relates to the field of stage mechanical control systems. Comprising a data acquisition module used for acquiring load and motion data during operation of the stage mechanical electronic screen and generating a time domain load history; the load spectrum analysis module is used for performing cycle counting and statistical analysis on the time domain load history to generate a standard load spectrum; the damage calculation module is used for calculating damage increment according to the standard load spectrum and a pre-stored component material fatigue characteristic curve and accumulating and updating the accumulated damage degree of the component; the service life prediction module is used for predicting the remaining service life of the part based on the accumulated damage degree and the expected damage increment of future predetermined performance task simulation; and the decision output module is used for generating a maintenance decision report according to a comparison result of the accumulated damage degree and a preset threshold value. Through real-time data acquisition and dynamic life prediction, the maintenance decision is optimized, the equipment safety and the operation efficiency are improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of stage machinery control systems, specifically to the control system of electronic screens for stage machinery. Background Technology

[0002] The reliability of stage machinery's electronic screen drive system directly impacts performance safety and operating costs. Under frequent start-stop, speed-changing, and load-varying conditions, the primary failure mode for its key mechanical components is performance degradation and fracture due to material fatigue accumulation. The industry generally employs a planned maintenance strategy based on fixed cycles, setting uniform maintenance or replacement times based on experience. However, this strategy fails to adequately consider the randomness and dynamic changes in the actual load on the equipment. Different productions have vastly different requirements for the speed, load, and start-stop frequency of the machinery, causing the actual rate of fatigue damage accumulation to deviate significantly from the fixed cycle based on the assumption of uniform use. This creates a dilemma: insufficient maintenance may lead to malfunctions under high-intensity use scenarios, while over-maintenance and resource waste may occur under low-frequency use scenarios.

[0003] While the concept of predictive maintenance is gradually being promoted, directly applying a general framework to stage machinery presents significant challenges. The core difficulty lies in how to reconstruct the true load history of mechanical components with high fidelity from readily available motor operating signals, and how to establish a dedicated fatigue damage accumulation model that conforms to the intermittent and impact load characteristics of stage machinery, thereby achieving accurate dynamic prediction of remaining service life. Currently, research on publicly available technical solutions in this interdisciplinary field is insufficient, especially lacking a complete and engineerable systematic method encompassing data acquisition, load inversion, damage calculation, life prediction, and maintenance decision generation. Summary of the Invention

[0004] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a control system for electronic screens of stage machinery to solve the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a control system for a stage machinery electronic screen, comprising: The data acquisition module is used to collect load and motion data of the electronic screen of the stage machinery during operation and generate time-domain load history. The load spectrum analysis module is used to perform cyclic counting and statistical analysis on the time-domain load history and generate a standard load spectrum. The damage calculation module is used to calculate the damage increment and accumulate and update the cumulative damage degree of the component based on the standard load spectrum and the pre-stored fatigue characteristic curve of the component material. The life prediction module is used to predict the remaining life of a component based on the cumulative damage level and the expected damage increment simulated from future scheduled performance tasks. The decision output module is used to generate a maintenance decision report based on the comparison between the cumulative damage level and a preset threshold.

[0006] The present invention is further configured such that the data acquisition module includes: The current signal of the drive motor and the angular displacement signal fed back by the encoder are synchronously acquired at a preset sampling frequency. Based on the known motor torque constant, the current signal is converted into a motor output shaft torque sequence, and the drum shaft torque sequence is calculated based on the reducer transmission ratio and mechanical efficiency. Based on the changing characteristics of the angular displacement signal, a single complete mechanical motion cycle is identified. The angular displacement signal within the cycle is differentially processed to obtain the rotational speed sequence. The drum shaft torque sequence and the rotational speed sequence are time-synchronized, aligned, and combined to generate a time-domain load history characterizing a single operating cycle.

[0007] The present invention is further configured such that the load spectrum analysis module includes: The rainflow counting algorithm is used to identify all load peak points and valley points from the drum shaft torque sequence of the time-domain load history, and the peak points and valley points are paired to form independent load cycles. For each load cycle, the mean torque and torque amplitude of that load cycle are calculated based on its peak torque value and valley torque value. Based on multiple preset continuous torque amplitude level ranges, each load cycle is assigned to the corresponding amplitude level according to its torque amplitude. The total number of load cycles within each frame level is counted as the cycle number for that level, and the arithmetic mean of the torque of all load cycles within that level is calculated as the representative value of the torque mean for that level. A standard load spectrum is generated based on the median value of the torque amplitude level interval, the representative value of the mean torque, and the number of cycles for each amplitude level.

[0008] The present invention is further configured such that the damage calculation module includes: Based on the component type and geometric parameters, the median value of the torque amplitude level range corresponding to each amplitude level in the standard load spectrum is converted into the equivalent alternating stress amplitude acting on the component material. Based on the pre-stored stress-life relationship of component materials, determine the theoretical fatigue failure cycle number corresponding to each equivalent alternating stress amplitude; Divide the actual number of cycles for each amplitude level recorded in the standard load spectrum by the corresponding theoretical fatigue failure cycle number to obtain the damage score of the component caused by that amplitude level. The damage scores of all amplitude levels are summed to obtain the damage increment caused to the component in the current operating cycle. The damage increment is added to the component's historical cumulative damage to update the component's cumulative damage.

[0009] The present invention is further configured such that the lifetime prediction module includes: Obtain the standard operating procedures for stage machinery corresponding to future scheduled performances; Run the standard program in the simulation environment to generate the predicted load spectrum corresponding to one execution of the program, and calculate the predicted damage increment for a single task execution based on the predicted load spectrum. Based on the preset component failure damage threshold, cumulative damage degree and predicted damage increment, the remaining service life of the component that can safely perform future scheduled performance tasks before fatigue failure is calculated.

[0010] The present invention is further configured such that the process of updating the predicted remaining useful life includes: In response to the completion of the actual operation task of the component and the resulting update of the cumulative damage level, the remaining service life prediction is recalculated based on the updated cumulative damage level. In response to the modification or replacement of the standard operating procedures for future scheduled performances, the corresponding predicted load spectrum and predicted damage increment are regenerated based on the new operating procedures, and the remaining service life prediction is recalculated using the current cumulative damage level.

[0011] The present invention is further configured such that the decision output module includes: The cumulative damage of the component is compared with a preset first damage threshold and a preset second damage threshold, respectively. When the cumulative damage is greater than or equal to the first damage threshold, a first maintenance instruction is generated to instruct immediate maintenance and restrict equipment operation. When the cumulative damage level is greater than or equal to the second damage threshold and less than the first damage threshold, a second maintenance instruction is generated to instruct the execution of planned maintenance. The output includes a maintenance decision report containing component identification information, current cumulative damage level, predicted remaining service life, and corresponding maintenance instructions.

[0012] The present invention is further configured to perform low-pass filtering on the current signal and the angular displacement signal before generating the time-domain load history.

[0013] The present invention is further configured such that the first damage threshold is greater than the second damage threshold.

[0014] The present invention is further configured such that the system also includes a visualization module for generating a visual maintenance monitoring interface, the maintenance monitoring interface including a visualization indicator for displaying the current cumulative damage status of the component, an area for displaying the predicted value of the remaining service life of the component, and a chart for presenting the historical cumulative damage trend of the component.

[0015] This invention provides a control system for a stage machinery electronic screen. It includes a data acquisition module for collecting load and motion data during screen operation and generating a time-domain load history; a load spectrum analysis module for performing cyclic counting and statistical analysis on the time-domain load history to generate a standard load spectrum; a damage calculation module for calculating damage increments and accumulating and updating the cumulative damage level of the component based on the standard load spectrum and pre-stored component material fatigue characteristic curves; a lifespan prediction module for predicting the remaining service life of the component based on the cumulative damage level and the expected damage increments simulated for future scheduled performances; and a decision output module for generating a maintenance decision report based on the comparison results of the cumulative damage level and a preset threshold. The beneficial effects include: 1. By collecting load data in real time and dynamically calculating the cumulative damage and remaining life of components based on the fatigue damage accumulation theory, maintenance decisions are made entirely based on the actual health status of the components. Compared with traditional fixed-cycle maintenance methods, this effectively avoids the risks of over-maintenance or under-maintenance, ensuring that the equipment is always in the best working condition and improving the operational safety of the equipment. In addition, accurate predictive maintenance can effectively prevent sudden failures caused by fatigue accumulation and ensure the safe operation of the equipment during the performance.

[0016] 2. By introducing simulations of future scheduled performances as input for lifespan prediction, the system innovatively achieves dynamic prediction of the remaining lifespan of equipment. This makes lifespan prediction no longer a static value, but automatically updated according to changes in the performance season, reflecting in real time the impact of different performance intensity on equipment lifespan. Such personalized and flexible prediction capabilities provide venue operators with forward-looking decision support, optimize maintenance plans, and ensure that performance tasks and equipment maintenance are carried out simultaneously, thereby improving operational efficiency and reducing maintenance costs.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a structural diagram of a control system for a stage machinery electronic screen, illustrating an exemplary embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0021] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0022] The control system of stage machinery electronic screens, such as Figure 1 As shown, it includes: The data acquisition module is used to collect load and motion data of the electronic screen of the stage machinery during operation and generate time-domain load history. The load spectrum analysis module is used to perform cyclic counting and statistical analysis on the time-domain load history and generate a standard load spectrum. The damage calculation module is used to calculate the damage increment and accumulate and update the cumulative damage degree of the component based on the standard load spectrum and the pre-stored fatigue characteristic curve of the component material. The life prediction module is used to predict the remaining life of a component based on the cumulative damage level and the expected damage increment simulated from future scheduled performance tasks. The decision output module is used to generate a maintenance decision report based on the comparison between the cumulative damage level and a preset threshold.

[0023] The present invention is further configured such that the data acquisition module includes: The current signal of the drive motor and the angular displacement signal fed back by the encoder are synchronously acquired at a preset sampling frequency. Based on the known motor torque constant, the current signal is converted into a motor output shaft torque sequence, and the drum shaft torque sequence is calculated based on the reducer transmission ratio and mechanical efficiency. Based on the changing characteristics of angular displacement signals, a single complete mechanical motion cycle is identified. Differential operations are performed on the angular displacement signals within this cycle to obtain a rotational speed sequence. The drum shaft torque sequence and the rotational speed sequence are then time-synchronized, aligned, and combined to generate a time-domain load history characterizing a single operating cycle. The invention further includes low-pass filtering of the current signal and angular displacement signal before generating the time-domain load history. Specifically, this embodiment describes the high-frequency synchronous acquisition and preprocessing process of operating data in the predictive maintenance method for key components of stage machinery. This process aims to extract components with loads that accurately reflect the operating state from the raw electrical signals that directly characterize the mechanical system's operating status. The process involves time-series data acquisition. First, high-precision synchronous acquisition of multi-source physical signals from the servo motor system driving the electronic screen's lifting and lowering is performed: one acquisition channel continuously acquires the real-time instantaneous phase current values ​​of the motor's three-phase windings via the analog output port of the servo driver or a real-time industrial Ethernet communication bus; the other acquisition channel is connected to a high-precision multi-turn absolute encoder installed at the motor shaft extension end or the reducer input shaft end to continuously read its feedback shaft angular displacement digital pulse signal. To ensure strict time-series correspondence between load and motion state data, the acquisition of both signals is uniformly triggered and controlled by the same high-stability hardware clock source, achieving strict synchronization throughout the entire process from sampling start to stop. Step 1: Due to the complexity of industrial environments, the original current and angular displacement signals often contain high-frequency interference components introduced by electromagnetic interference, switching noise, and signal transmission, as well as the sensor's own background measurement noise. This embodiment uses a digital low-pass filter to perform low-pass filtering on the two synchronized original signals. The cutoff frequency of the applied low-pass filter is preset based on the highest operating frequency of the stage machinery during normal operation and the main inherent vibration mode frequencies of its mechanical structure. The setting principle is to retain the low-frequency effective signal components that can truly reflect the mechanical motion and load characteristics, while suppressing frequencies significantly higher than the system's main operating frequency to the greatest extent possible. The high-frequency noise in the operating frequency band is filtered and purified by digital filtering to obtain a smooth current signal sequence and angular displacement signal sequence, thus laying the foundation for subsequent accurate physical quantity conversion calculations. The purified current signal sequence is converted into a torque physical quantity with direct mechanical meaning. This conversion is based on the inherent torque constant of the servo motor, which is provided by the motor manufacturer. Its physical meaning is the torque of the motor output shaft that can be generated by a unit current. The specific operation is as follows: the instantaneous current value corresponding to each sampling point in the filtered current signal sequence is multiplied by the torque constant provided by the motor manufacturer to obtain the instantaneous torque value of the motor output shaft at the same time point, and a continuous motor output shaft torque sequence is constructed.Since stage machinery transmission systems generally achieve torque amplification and speed regulation through reduction mechanisms, the conversion relationship of the transmission chain also needs to be considered. The instantaneous torque values ​​in the motor output shaft torque sequence are multiplied by the fixed transmission ratio of the reduction mechanism, and then multiplied by a preset mechanical efficiency coefficient. The drum shaft torque sequence is obtained through point-by-point calculation. The mechanical efficiency coefficient is used to quantify the energy loss caused by factors such as friction during transmission. The angular displacement signal fed back by the encoder plays two key roles in this implementation: First, it accurately defines a single complete mechanical motion cycle, i.e., it identifies a complete lifting operation including start-up, acceleration, constant speed, deceleration, stop, and reset. Its identification logic involves continuously monitoring the trajectory of the angular displacement signal amplitude over time. When a trend change in the angular displacement value is detected starting from a relatively stable initial state, and after experiencing one or more local extreme points, the trend reverses and eventually returns to near the initial state, the start and end timestamps of the complete waveform corresponding to the operation are automatically determined. The second core function of the angular displacement signal is to deduce the real-time speed of the mechanical system. Specifically, this is achieved by analyzing the angular displacement within the identified complete operating cycle. The numerical difference operation is performed on the inter-sequence, that is, the difference between the angular displacement readings at two adjacent sampling times is calculated sequentially, and this difference is divided by the fixed time interval between the two sampling points to obtain the average angular velocity within that time interval. By performing this operation sequentially on the entire cycle sequence, a rotational speed sequence describing the evolution of instantaneous angular velocity is generated. This difference calculation can effectively reveal the dynamic details of the motion states experienced by the system during operation, such as acceleration, uniform speed, deceleration, and stillness. Finally, the drum shaft torque sequence representing the load state of the components and the rotational speed sequence representing the kinematic state of the system are aligned and structured point by point according to the precise timestamps assigned to them during acquisition. Since the two sequences originate from synchronous acquisition triggered by the same master clock and have undergone all preprocessing stages based on the same time base, the load value and motion velocity value at each sampling time can be accurately matched. This perfectly aligned dual sequence in the time dimension is combined into a multidimensional dataset, which constitutes a complete time-domain load history representing a single operating cycle. This history records the entire process of the load intensity and motion state of the mechanical components evolving over time during a specific operating task.

[0024] The present invention is further configured such that the load spectrum analysis module includes: The rainflow counting algorithm is used to identify all load peak points and valley points from the drum shaft torque sequence of the time-domain load history, and the peak points and valley points are paired to form independent load cycles. For each load cycle, the mean torque and torque amplitude of that load cycle are calculated based on its peak torque value and valley torque value. Based on multiple preset continuous torque amplitude level ranges, each load cycle is assigned to the corresponding amplitude level according to its torque amplitude. The total number of load cycles within each frame level is counted as the cycle number for that level, and the arithmetic mean of the torque of all load cycles within that level is calculated as the representative value of the torque mean for that level. Based on the median value of the torque amplitude level interval, the representative value of the torque mean, and the number of cycles for each amplitude level, a standard load spectrum is generated. Specifically, this embodiment describes the specific implementation of the load spectrum statistical quantification process based on the rainflow counting method. This process is used to deconstruct and transform the time-domain history characterizing the continuous change of component load into a standardized statistical spectrum suitable for quantitative assessment of fatigue damage. Its core lies in using the rainflow counting algorithm to systematically process and statistically analyze the load time series to extract and quantify all the load fluctuation cycle information contained therein, and finally generate a structured standard load spectrum. The load feature points of the drum shaft torque sequence characterizing a single operating cycle are accurately identified. By performing sequential scanning analysis on the sequence, each data point is compared with its... The numerical relationship between adjacent data points is used to identify and mark all local maxima and minima in the sequence, defining them as load peaks and valleys, respectively. To improve the engineering effectiveness of the analysis, a load fluctuation tolerance threshold is preset during the identification process. Only when the absolute value of the torque difference between the identified peak and the adjacent valley exceeds this preset load fluctuation tolerance threshold is the peak-valley pair determined as a valid load inflection point and retained. This filters out spurious fluctuations caused by measurement noise or minor disturbances that have no actual fatigue damage significance. After obtaining the filtered valid peak and valley sequence, the classic rainflow counting algorithm is used to pair and combine these discrete peaks and valleys to form a series of complete independent loads. The algorithm strictly follows the basic principles of the rainflow method, decomposing arbitrarily complex continuous time-domain load histories into several independent closed load loops, either starting from a peak and ending at a trough, or starting from a trough and ending at a peak, through a programmed implementation of its core pairing rules. Each loop is defined as a load cycle with a definite start and end point. This step deconstructs the continuous load fluctuation history into a discrete set of load events that can be independently evaluated for fatigue. For each extracted independent load cycle, two core characteristic parameters are calculated to complete the quantitative characterization: the first is the torque amplitude, which is equal to half the absolute value of the difference between the peak torque and the trough torque of the cycle, representing the intensity range of the load fluctuation; the second is the torque mean, which is equal to the value of the average torque of the cycle. The arithmetic mean of peak torque and valley torque represents the average load level at which load fluctuations occur. Through the above calculation, each load cycle is quantified into a data unit represented by two scalars: torque amplitude and torque mean. To effectively statistically summarize a large number of load cycles, they need to be classified and frequency-counted according to their fluctuation intensity, i.e., torque amplitude. First, a set of continuous torque amplitude level intervals covering the entire expected load range is preset. Each interval is defined by a definite lower limit and upper limit value. Adjacent intervals are connected sequentially to completely cover the entire numerical domain from zero to the maximum possible torque amplitude. The interval width is determined comprehensively based on the required analytical precision and statistical efficiency. Then, all quantified load cycles are traversed, and their torque amplitudes are assigned to the corresponding amplitude level intervals.After classification, for each amplitude level interval, the total number of load cycles it contains is counted and recorded as the cycle number for that level. Simultaneously, the arithmetic mean of the torque values ​​of all load cycles within that interval is calculated as the representative torque mean value for that level. The statistical results of all amplitude levels are integrated to generate a structured standard load spectrum. This standard load spectrum is presented in matrix form, where each row corresponds to a torque amplitude level interval. Each row contains three core data fields: the median of the torque amplitude level interval, the representative torque mean value, and the cycle number. This standard load spectrum statistically comprehensively characterizes the frequency distribution and average load level of load fluctuation events of various strength levels experienced by mechanical components within a single operating cycle.

[0025] The present invention is further configured such that the damage calculation module includes: Based on the component type and geometric parameters, the median value of the torque amplitude level range corresponding to each amplitude level in the standard load spectrum is converted into the equivalent alternating stress amplitude acting on the component material. Based on the pre-stored stress-life relationship of component materials, determine the theoretical fatigue failure cycle number corresponding to each equivalent alternating stress amplitude; Divide the actual number of cycles for each amplitude level recorded in the standard load spectrum by the corresponding theoretical fatigue failure cycle number to obtain the damage score of the component caused by that amplitude level. The damage scores of all amplitude levels are summed to obtain the damage increment caused to the component in the current operating cycle. The incremental damage is added to the historical cumulative damage of the component to update the cumulative damage of the component. Specifically, this embodiment describes the component damage calculation process based on material fatigue characteristics and linear cumulative damage criteria. Its purpose is to quantify the load cycle information from the standard load spectrum statistics into material fatigue damage, and update the component's health status through cumulative calculation. The core is to perform a step-by-step damage assessment and summary of the load spectrum based on the material stress-life characteristics and linear cumulative damage theory: First, the load spectrum statistical parameters are converted into material mechanical parameters. Based on the target component type and its geometric parameters, the damage is assessed step-by-step. A defined mechanical model converts the median of the torque amplitude range corresponding to each amplitude level in the load spectrum into the equivalent alternating stress amplitude acting on the critical point of the material. Subsequently, a pre-established database of component material fatigue characteristics is queried. This database stores stress-life relationship data for specific materials obtained from material handbooks or experiments. This data characterizes the quantitative mapping relationship between the number of cycles leading to material fatigue failure under constant alternating stress amplitude and the stress amplitude. This relationship is linear in a double logarithmic coordinate system, and its mathematical expression is: a power of the stress amplitude as the base and the material-specific stress exponent as the exponent, multiplied by the number of failure cycles. The product is always equal to another characteristic constant representing the fatigue strength of the material. Based on the stress-life relationship of the material, for each amplitude level in the standard load spectrum corresponding to the equivalent alternating stress amplitude, the theoretical fatigue failure cycle count of the component under that constant stress amplitude level is determined. The actual cycle count recorded for each amplitude level in the standard load spectrum is divided by its corresponding theoretical fatigue failure cycle count; the quotient is the damage fraction caused to the component by that load level, representing the proportion of the component's theoretical life consumed by that load at the corresponding stress level. After completing the calculations for all amplitude levels, the Pamgren algorithm is applied. The Mainner linear cumulative damage criterion algebraically sums the damage scores calculated from each amplitude level within the current operating cycle to obtain the fatigue damage increment caused in this operating cycle. A global variable, namely the historical cumulative damage degree, representing the total historical damage of the component is maintained in non-volatile memory. After each operating cycle is completed, the obtained damage increment value is added to the current value of the global variable, and the result is used to overwrite and update the global variable, thereby realizing the iterative accumulation and real-time update of the overall fatigue damage state of the component. The updated value is the latest cumulative damage degree of the component.

[0026] The present invention is further configured such that the lifetime prediction module includes: Obtain the standard operating procedures for stage machinery corresponding to future scheduled performances; Run the standard program in the simulation environment to generate the predicted load spectrum corresponding to one execution of the program, and calculate the predicted damage increment for a single task execution based on the predicted load spectrum. Based on a preset component failure damage threshold, cumulative damage degree, and predicted damage increment, the remaining service life prediction value of the component that can safely perform future scheduled performance tasks before fatigue failure is calculated; the present invention is further configured such that the process of updating the remaining service life prediction value includes: In response to the completion of the actual operation task of the component and the resulting update of the cumulative damage level, the remaining service life prediction is recalculated based on the updated cumulative damage level. In response to modifications or replacements to the standard operating procedures for future scheduled performances, the corresponding predicted load spectrum and predicted damage increment are regenerated based on the new operating procedures, and the remaining service life prediction value is recalculated using the current cumulative damage level. Specifically, this embodiment describes the specific implementation method of the dynamic remaining service life prediction process for integrated task rehearsal. Its core lies in using simulation technology to pre-assess the theoretical damage impact of future predetermined workloads on components, and coupling the current real-time health status of the components to calculate their safe service life indicators under a specific task planning framework, thereby achieving forward-looking and customizable service life management. The standard operating procedures for future scheduled performances are deterministically obtained from the integrated performance management unit or the preset program database. This program, as a control file containing a complete sequence of motion instructions, clearly defines the target position, motion curve, and temporal relationship between actions of the machinery throughout the entire performance. To quantify the theoretical damage generated by performing this planned future performance once, the program is loaded into an offline simulation environment to drive a refined mechanical model with the same dynamic parameters as the actual object to run through its entire cycle. During this process, the calculation flow from data acquisition to load spectrum generation is reproduced, thereby deriving a predicted load spectrum reflecting the frequency and average load levels of various intensities of load fluctuations that the components will endure during a single performance of this specific show. Based on this predicted load spectrum, theoretical damage increment calculation is performed using a damage calculation process completely consistent with actual data processing, based on the same component materials. The predicted load spectrum is converted into equivalent alternating stress amplitudes at material critical points using geometric structural parameters. The theoretical fatigue failure cycle count at each stress level is determined by querying pre-stored material stress-life curves. The sum of theoretical damage fractions caused by all load cycles in the predicted load spectrum is then calculated. This sum represents the predicted damage increment generated by executing this future standard operating procedure once, quantifying the proportion of component fatigue life theoretically consumed in a single performance of this specific program. The latest cumulative damage level of the component is obtained, and the difference between it and the preset component failure damage threshold is calculated to obtain the remaining damage tolerance. This remaining damage tolerance is divided by the predicted damage increment for a single task, and the quotient is the safe operating value of the component before fatigue failure. The maximum number of times a specific future performance task is predicted is used, and this maximum number of predictions constitutes the remaining service life prediction value based on the task plan. If the future task plan includes a definite performance schedule, it can be further converted into the remaining safe operating cycle in calendar time units according to the maximum number of predictions and the schedule pattern. The remaining service life prediction value has dynamic adaptive update capability, and its update is triggered by two types of events to ensure the real-time performance and accuracy of the prediction: the first type of triggering event is the actual operation of the component, that is, whenever the component completes the actual operation task and its cumulative damage degree is updated, the remaining damage tolerance and the maximum number of predictions are recalculated based on the latest cumulative damage degree to achieve synchronous update of the remaining service life prediction value and the actual damage consumption.The second type of triggering event is a change in future mission planning. If the planned performance program or its standard operating procedure is modified, the simulation process will be automatically re-executed according to the new procedure to generate an updated predicted load spectrum and predicted damage increment. The remaining service life prediction value will then be recalculated based on the current cumulative damage status of the component. Through this dual triggering and response mechanism, the service life prediction results are always based on the latest component health status and the latest work mission plan, thus achieving dynamic and adaptive service life prediction.

[0027] The present invention is further configured such that the decision output module includes: The cumulative damage of the component is compared with a preset first damage threshold and a preset second damage threshold, respectively; the present invention is further configured such that the first damage threshold is greater than the second damage threshold. When the cumulative damage is greater than or equal to the first damage threshold, a first maintenance instruction is generated to instruct immediate maintenance and restrict equipment operation. When the cumulative damage level is greater than or equal to the second damage threshold and less than the first damage threshold, a second maintenance instruction is generated to instruct the execution of planned maintenance. The output includes a maintenance decision report containing component identification information, current cumulative damage level, predicted remaining service life, and corresponding maintenance instructions. Specifically, it continuously monitors the component's cumulative damage level, updated in real time by the damage calculation module, and compares and logically judges this current cumulative damage level with preset first and second damage thresholds. If the current cumulative damage level is greater than or equal to the first damage threshold, the component is determined to be in a high-risk state approaching fatigue failure, and a first-class maintenance instruction with the highest priority is immediately generated. This instruction is mandatory, and its core requirement is to immediately shut down the equipment for maintenance and automatically trigger the safety interlock logic with the equipment control system to restrict or prohibit the component from continuing to perform any high-intensity or high-load operating tasks at the control level, thereby forcing maintenance. If the cumulative damage level is greater than or equal to the second damage threshold but less than the first damage threshold, the component is determined to have entered a wear acceleration stage requiring planned intervention, and a second-class maintenance instruction is generated. The maintenance instruction, a suggestion, recommends that the equipment maintenance team arrange preventative maintenance or replacement work on the component during a predetermined production downtime window that will not affect the normal performance schedule. This proactively integrates necessary maintenance activities into the established production schedule to mitigate the risk of unplanned performance interruptions. Based on the judgment results of the aforementioned decision-making logic and combined with the status data obtained from real-time monitoring, a structured maintenance decision report is automatically generated. This report integrates the unique identification information of the target component, quantitative details of its current health status, remaining service life prediction information based on task simulation, and the triggered maintenance instruction level and its specific content. This report is visualized through a human-computer interaction interface and simultaneously pushed to the relevant workflow management system or sent through a designated communication interface. This transforms the quantitative evaluation results obtained from algorithmic analysis into specific and clear action instructions that maintenance personnel can directly understand and execute.

[0028] The invention is further configured such that the system also includes a visualization module for generating a visual maintenance monitoring interface. The maintenance monitoring interface includes a visual indicator displaying the current cumulative damage status of a component, an area displaying the predicted remaining service life of the component, and a chart presenting the historical cumulative damage trend of the component. Specifically, the maintenance monitoring interface includes the following core visualization components: a dashboard component displaying the current cumulative damage of the component, which visually presents the degree of cumulative damage and its corresponding risk level through pointer position and color changes associated with a preset damage threshold; a data dashboard displaying the predicted remaining service life of the component calculated based on a simulation of a future scheduled performance task; and a chart plotting the historical cumulative damage trend of the component over time or operating cycle in the form of a line chart or bar chart. The display data of each of the above visualization components is synchronized in real time with the corresponding modules in the system, jointly constituting an integrated monitoring view of the component's health status evolution and remaining service life trend.

[0029] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control system for a stage mechanical electronic screen, characterized in that, include: The data acquisition module is used to collect load and motion data of the electronic screen of the stage machinery during operation and generate time-domain load history. The load spectrum analysis module is used to perform cyclic counting and statistical analysis on the time-domain load history and generate a standard load spectrum. The damage calculation module is used to calculate the damage increment and accumulate and update the cumulative damage degree of the component based on the standard load spectrum and the pre-stored fatigue characteristic curve of the component material. The life prediction module is used to predict the remaining life of a component based on the cumulative damage level and the expected damage increment simulated from future scheduled performance tasks. The decision output module is used to generate a maintenance decision report based on the comparison between the cumulative damage level and a preset threshold.

2. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The data acquisition module includes: The current signal of the drive motor and the angular displacement signal fed back by the encoder are synchronously acquired at a preset sampling frequency. Based on the known motor torque constant, the current signal is converted into a motor output shaft torque sequence, and the drum shaft torque sequence is calculated based on the reducer transmission ratio and mechanical efficiency. Based on the changing characteristics of the angular displacement signal, a single complete mechanical motion cycle is identified. The angular displacement signal within the cycle is differentially processed to obtain the rotational speed sequence. The drum shaft torque sequence and the rotational speed sequence are time-synchronized, aligned, and combined to generate a time-domain load history characterizing a single operating cycle.

3. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The load spectrum analysis module includes: The rainflow counting algorithm is used to identify all load peak points and valley points from the drum shaft torque sequence of the time-domain load history, and the peak points and valley points are paired to form independent load cycles. For each load cycle, the mean torque and torque amplitude of that load cycle are calculated based on its peak torque value and valley torque value. Based on multiple preset continuous torque amplitude level ranges, each load cycle is assigned to the corresponding amplitude level according to its torque amplitude. The total number of load cycles within each frame level is counted as the cycle number for that level, and the arithmetic mean of the torque of all load cycles within that level is calculated as the representative value of the torque mean for that level. A standard load spectrum is generated based on the median value of the torque amplitude level interval, the representative value of the mean torque, and the number of cycles for each amplitude level.

4. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The damage calculation module includes: Based on the component type and geometric parameters, the median value of the torque amplitude level range corresponding to each amplitude level in the standard load spectrum is converted into the equivalent alternating stress amplitude acting on the component material. Based on the pre-stored stress-life relationship of component materials, determine the theoretical fatigue failure cycle number corresponding to each equivalent alternating stress amplitude; Divide the actual number of cycles for each amplitude level recorded in the standard load spectrum by the corresponding theoretical fatigue failure cycle number to obtain the damage score caused to the component by that amplitude level. The damage scores of all amplitude levels are summed to obtain the damage increment caused to the component in the current operating cycle. The damage increment is added to the component's historical cumulative damage to update the component's cumulative damage.

5. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The lifetime prediction module includes: Obtain the standard operating procedures for stage machinery corresponding to future scheduled performances; Run the standard program in the simulation environment to generate the predicted load spectrum corresponding to one execution of the program, and calculate the predicted damage increment for a single task execution based on the predicted load spectrum. Based on the preset component failure damage threshold, cumulative damage degree and predicted damage increment, the remaining service life of the component that can safely perform future scheduled performance tasks before fatigue failure is calculated.

6. The control system for the stage mechanical electronic screen according to claim 5, characterized in that, The process of updating the remaining useful life prediction includes: In response to the completion of the actual operation task of the component and the resulting update of the cumulative damage level, the remaining service life prediction is recalculated based on the updated cumulative damage level. In response to the modification or replacement of the standard operating procedures for future scheduled performances, the corresponding predicted load spectrum and predicted damage increment are regenerated based on the new operating procedures, and the remaining service life prediction is recalculated using the current cumulative damage level.

7. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The decision output module includes: The cumulative damage of the component is compared with a preset first damage threshold and a preset second damage threshold, respectively. When the cumulative damage is greater than or equal to the first damage threshold, a first maintenance instruction is generated to instruct immediate maintenance and restrict equipment operation. When the cumulative damage level is greater than or equal to the second damage threshold and less than the first damage threshold, a second maintenance instruction is generated to instruct the execution of planned maintenance. The output includes a maintenance decision report containing component identification information, current cumulative damage level, predicted remaining service life, and corresponding maintenance instructions.

8. The control system for the stage mechanical electronic screen according to claim 2, characterized in that, Before generating the time-domain load history, the current signal and angular displacement signal are subjected to low-pass filtering.

9. The control system for the stage mechanical electronic screen according to claim 7, characterized in that, The first damage threshold is greater than the second damage threshold.

10. The control system for the stage mechanical electronic screen according to claim 1, characterized in that, The system also includes a visualization module for generating a visual maintenance monitoring interface. The maintenance monitoring interface includes a visual indicator for displaying the current cumulative damage status of the component, an area for displaying the predicted value of the component's remaining service life, and a chart for presenting the historical cumulative damage trend of the component.

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