An industrial data full life cycle automatic testing method and system

Abstract

The application discloses an industrial data full life cycle automatic testing method and system, relates to the automatic testing technical field, and is characterized in that: the method is characterized in that: a multi-source disturbance energy efficiency difference formula containing a tension compensation term and a speed disturbance compensation term is adopted to perform unified time sequence fusion calculation on unit length energy consumption parameter E, belt tension measurement parameter T and speed micro-disturbance parameter dV, the interference of normal working condition fluctuation, tension change and speed disturbance on the energy consumption curve can be automatically stripped, and a net energy efficiency offset R used for reflecting the belt wear trend is extracted. The feature enables the system to identify an energy efficiency micro-loss trend with extremely low amplitude but with accumulation in a super-long time window, realizes a low-amplitude and long-period residual feature identification capability that cannot be captured by traditional single-point detection, and thus significantly improves early micro-loss detection precision, and realizes early warning of the implicit degradation state such as slight offset of the belt tension, initial wear of the carrier roller and local resistance change.

Description

Technical Field

[0001] This invention relates to the field of automated testing technology, specifically to an automated testing method and system for the entire lifecycle of industrial data. Background Technology

[0002] In industrial automation systems, ultra-long-distance belt conveyors in large-scale continuous production enterprises are widely used in bulk material transportation scenarios in coal, mining, metallurgy, and ports, serving as key equipment for the continuity and stability of production systems. These belt conveyors typically operate over long distances and have long operating cycles, with complex energy consumption patterns arising from their condition over time. Therefore, it is necessary to conduct refined, full-lifecycle monitoring of energy consumption data, tension variation data, and speed disturbance data during belt conveyor operation.

[0003] Currently, conventional energy consumption monitoring methods in existing belt conveyor monitoring systems mainly rely on single-point detection technologies, such as periodic inspections based solely on changes in drive-end current, voltage, or power. This approach cannot identify extremely subtle energy efficiency deviations in ultra-long-distance belts during long-term operation. Especially when the belt experiences prolonged mild wear, slight tension drift, initial damage to idlers, or increased local resistance, energy consumption changes often exhibit low amplitude, weak trends, and long cycles. These changes are typically far below the threshold perceived by traditional maintenance personnel. Existing technologies lack methods for multi-parameter fusion analysis and effective models for automatically extracting cumulative subtle degradation characteristics from lifecycle data. This results in the inability to identify micro-wear in belts in advance, making it difficult to detect early wear problems through periodic maintenance or manual inspections alone.

[0004] The aforementioned shortcomings mainly stem from the fact that during the long-term operation of ultra-long-distance belt conveyors, energy consumption changes are affected by a combination of factors, including tension micro-drift, load fluctuations, friction differences between idler groups, belt aging, local resistance changes, and speed disturbances. The energy consumption deviations caused by these factors often exhibit extremely small amplitudes, slow trends, and extremely long cycles, making it impossible for traditional single-point measurements to identify such changes within a short time window. When these micro-losses accumulate undetected, they lead to amplified belt tension deviations, accelerated idler wear, decreased energy efficiency over long distances, and increased drive motor load, potentially resulting in serious consequences such as belt breakage risk, significantly increased energy consumption, decreased system efficiency, and even production interruptions. Therefore, there is an urgent need for an automated testing method based on lifecycle data that can automatically extract extremely weak residual energy efficiency characteristics and achieve early warning of micro-losses through multi-source fusion. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an automated testing method and system for the entire lifecycle of industrial data, solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: S1. Collect raw energy consumption data by setting multiple collection points on the belt conveyor, transmit the raw energy consumption data to the life cycle test system, and perform preprocessing to obtain the life cycle dataset; S2. In the life cycle test system, the life cycle dataset is fused and analyzed to obtain the multi-source energy efficiency disturbance residual R, and the residual change rate Rrate is formed based on the multi-source energy efficiency disturbance residual R. A preliminary comparative evaluation is carried out within the preset residual threshold range to obtain the preliminary energy efficiency micro-loss judgment result. S3. When the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, the energy efficiency degradation depth detection is automatically triggered, the comprehensive degradation assessment value F is calculated, and the strategy level is determined based on the output result of the comprehensive degradation assessment value F to obtain the belt energy efficiency degradation depth judgment result. S4. Based on the determination result of the belt energy efficiency degradation depth, start the system operation status control module, execute the strategy level, and compare and analyze the life cycle data before and after the strategy execution is completed.

[0007] Preferably, S1 includes S11; S11. By setting up several collection points on multiple belt conveyors and installing sensing devices at each collection point, the raw energy consumption data of each belt conveyor can be collected in real time. The collection points include a first collection point A1, a second collection point A2, a third collection point A3, and a fourth collection point A4; The sensing devices include an energy monitoring sensor, a torque sensor, a tension measurement sensor, a speed sensor, a speed disturbance monitoring module, and a load measurement sensor; The raw energy consumption data includes the energy consumption per unit length parameter E(t) at time t, the driving torque parameter M(t) at time t, the belt tension measurement parameter T(t) at time t, the belt speed parameter V(t) at time t, the speed micro-disturbance parameter dV(t) at time t, and the end load parameter L(t) at time t.

[0008] Preferably, S1 further includes S12; S12. The raw energy consumption data collected from the collection points is transmitted to the life cycle test system in real time via the fieldbus network. The raw energy consumption data is preprocessed in the life cycle test system to obtain the life cycle dataset. The preprocessing includes time alignment, missing value handling, and normalization. The time alignment is achieved by constructing a main time axis based on the master clock of the life cycle test system, and synchronizing the data from different collection points in the raw energy consumption data using a unified time axis. The missing value processing involves using a time window-based continuity detection algorithm on the time-aligned raw energy consumption data to monitor the continuity of the same parameter in adjacent sampling periods, identify missing values, and fill in the missing values ​​by using the average of the two sampling periods before and after the missing value. The normalization process uses the Z-Score normalization method to normalize the original energy consumption data after handling missing values, thereby eliminating the differences in unit dimensions of all parameters in the original energy consumption data.

[0009] Preferably, S2 includes S21; S21. In the life cycle test system, the life cycle dataset is fused and analyzed. The fusion analysis is performed by using the multi-source disturbance energy efficiency difference formula of tension compensation term and velocity disturbance compensation term to calculate the unified time series time by time to obtain the multi-source energy efficiency disturbance residual R, and to perform quantitative analysis of the net energy efficiency offset of the belt in the current life cycle stage. The multi-source energy efficiency perturbation residual R is calculated and output using the following algorithm formula: ; In the formula, R(t) represents the multi-source energy efficiency disturbance residual at time t, E(t-Δt) represents the energy consumption parameter per unit length corresponding to the sampling period Δt before time t, and T(t-Δt) represents the belt tension measurement parameter of the sampling period Δt before time t. This represents the tension compensation factor, with a value range of 0.01-0.15. This indicates a velocity disturbance compensation factor of 0.5-1.0.

[0010] Preferably, S2 further includes S22; S22. For the multi-source energy efficiency perturbation residuals R(t) at time t of all sampling periods, sort them according to the time sequence of the sampling periods to form a residual time series, and calculate the rate of change to form the residual rate of change Rrate. Then, perform a preliminary comparative evaluation within a preset residual threshold interval to obtain preliminary energy efficiency micro-loss judgment results. The residual threshold interval includes a first residual threshold F1 and a second residual threshold F2. The specific comparison content is as follows: When calculating the first residual threshold F1 of the residual change rate Rrate, it is determined to be a normal micro-perturbation; When the calculated residual change rate Rrate ∈ [first residual threshold F1, second residual threshold F2), it is determined that the belt has entered the initial energy efficiency micro-loss state, and at this time the sampling frequency of the current sampling period is increased by 50%; When the calculated residual change rate Rrate is greater than or equal to the second residual threshold F2, it is determined that the belt has entered an abnormal state of energy efficiency micro-loss, and the energy efficiency degradation depth detection is automatically triggered.

[0011] Preferably, S3 includes S31; S31. After automatically triggering the energy efficiency degradation depth detection, the time accumulation calculation is performed on the multi-source energy efficiency disturbance residual R(t) at multiple sampling periods t in the life cycle dataset to obtain the residual accumulation amount. At the same time, the time accumulation calculation is performed on the belt speed parameter V(t) at multiple sampling periods t to extract the belt running distance. Based on this, the residual accumulation amount and the belt running distance are normalized to obtain the comprehensive degradation evaluation value F, and the energy efficiency degradation intensity of the belt per unit running distance is quantitatively analyzed. The comprehensive degradation assessment value F is calculated and output using the following algorithm formula: In the formula, dt represents the time calculus variable.

[0012] Preferably, S3 further includes S32; S32. Based on the output of the comprehensive degradation assessment value F, determine the strategy level to obtain the belt energy efficiency degradation depth determination result. The determination content is as follows: When the comprehensive degradation assessment value F≤0.3, it indicates that the current conveyor belt has slight degradation, and the first-level control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.3,0.6], it indicates that the current conveyor belt has significant degradation, and the secondary control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.6,0.85], it indicates that the current conveyor belt has degradation analysis, and the three-level control strategy is triggered at this time; When the comprehensive degradation assessment value F > 0.85, it indicates that the current conveyor belt is severely worn, and the machine needs to be stopped and maintained.

[0013] Preferably, S4 includes S41; S41. After obtaining the result of the belt energy efficiency degradation depth determination, the system operation status control module is automatically started, and the control strategy related to the strategy level is executed. The specific content is as follows: When the first-level control strategy is triggered, the following strategies are employed: a tension compensation strategy that lowers the target tension value of the belt tension controller by 2%-5% compared to the current tension value; a speed stabilization strategy that limits the peak value of the belt speed micro-disturbance to the range of 0.02m / s-0.05m / s through the drive controller; and a load balancing strategy that reduces the peak value of the end load by 5%-10% through the load distribution module. When the secondary control strategy is triggered, the segmented speed coordination strategy is to reduce the belt running cycle to 80% of the original belt running cycle and then limit the belt speed to 85%-90% of the original rated speed in the long section, and the active damping strategy of the roller is to trigger the damping execution sequence through the drive controller to reduce the speed micro-disturbance parameter dV(t) by 30%-50%. When the three-level control strategy is triggered, the load suppression strategy reduces the load peak by 10%-20% through the load management module, and the structural vibration reduction strategy reduces the overall operating speed to 70%-80% of the rated speed.

[0014] Preferably, S4 further includes S42; S42. Before implementing the relevant control strategy at the strategy level, record the residual time series and the comprehensive degradation assessment value F as the baseline data before the strategy. After the strategy level is implemented, recalculate the residual time series and the comprehensive degradation assessment value F, and calculate the difference between the residual time series and the baseline data before the strategy in the life test system, and output the difference ΔF of the comprehensive degradation assessment value. If the difference in the comprehensive degradation assessment value ΔF < 0 and the decrease exceeds 10%, the strategy level is deemed to be effectively implemented. Otherwise, if the belt's energy efficiency is deemed to be deteriorating, maintenance recommendations will be pushed to the operation and maintenance team, and the comparative analysis results will be automatically recorded in the system log.

[0015] An automated testing system for the entire lifecycle of industrial data includes a lifecycle extraction module, a residual analysis module, a degradation analysis module, and a control module; The lifecycle extraction module collects raw energy consumption data by setting multiple collection points on the conveyor belt, transmits the raw energy consumption data to the lifecycle testing system, and performs preprocessing to obtain the lifecycle dataset. The residual analysis module obtains the multi-source energy efficiency disturbance residual R by performing fusion analysis on the life cycle dataset in the life cycle test system, and forms the residual change rate Rrate based on the multi-source energy efficiency disturbance residual R. It then performs a preliminary comparative evaluation within a preset residual threshold range to obtain preliminary energy efficiency micro-loss judgment results. The degradation analysis module automatically triggers energy efficiency degradation depth detection when the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, calculates the comprehensive degradation evaluation value F, and determines the strategy level based on the output result of the comprehensive degradation evaluation value F, so as to obtain the belt energy efficiency degradation depth judgment result. The control module activates the system operation status control module based on the belt energy efficiency degradation depth determination result, executes the strategy level, and compares and analyzes the life cycle data before and after the strategy execution is completed.

[0016] This invention provides an automated testing method and system for the entire lifecycle of industrial data. It offers the following advantages: (1) This method employs a multi-source disturbance energy efficiency differential formula, which includes tension compensation and speed disturbance compensation terms, in the life cycle testing system. It performs unified time series fusion calculation on the unit length energy consumption parameter E, belt tension measurement parameter T, and speed micro-disturbance parameter dV. This automatically removes the interference of normal operating condition fluctuations, tension changes, and speed disturbances on the energy consumption curve, and extracts the net energy efficiency offset R, which reflects the belt wear trend. This feature enables the system to identify energy efficiency micro-loss trends with extremely low amplitude but cumulative characteristics within an ultra-long time window. It achieves the ability to identify "low amplitude, long period" residual features that traditional single-point detection cannot capture, thereby significantly improving the accuracy of early micro-loss detection and enabling early warning of latent degradation states such as slight belt tension deviation, initial wear of idlers, and changes in local resistance.

[0017] (2) This method automatically triggers the energy efficiency degradation depth detection module when the preliminary energy efficiency micro-loss judgment result is abnormal. It performs lifecycle accumulation integration on the multi-source energy efficiency disturbance residual R and normalizes the integration result with the belt running distance to obtain the comprehensive degradation evaluation value F, thereby quantifying the energy efficiency degradation intensity of the belt per unit running distance. This feature can uniformly scale the degradation amount under different cycles and different working conditions, making the degradation degree comparable and graded. Based on the dimensionless threshold, it realizes intelligent judgment of mild degradation, obvious degradation, degradation aggravation and severe wear, and further triggers the corresponding graded control strategy to achieve automated and accurate identification and intelligent response of degradation depth.

[0018] (3) This method integrates the identification of micro-loss trends in the multi-source energy efficiency disturbance residual R with the degradation depth classification detection of the comprehensive degradation assessment value F, enabling the system to form a fully automated closed loop throughout its entire lifecycle, from "micro-loss detection, degradation degree measurement, control strategy execution, and strategy effectiveness verification." This fusion mechanism can not only identify trend deviations in a timely manner during the micro-loss stage, but also automatically trigger depth detection and classification strategies before degradation intensifies, achieving continuous monitoring, trend tracking, and adaptive adjustment of operating status based on lifecycle data. Simultaneously, by analyzing the difference between lifecycle data before and after strategy execution, the effectiveness of the control strategy can be verified in real time, enabling rapid recovery and sustainable optimization of the belt's operating status. Compared to traditional periodic inspection methods, this method can significantly reduce the probability of early failure omissions, improve the stability and safety of the belt system's energy efficiency operation, and achieve true predictive maintenance and energy consumption optimization. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of an automated testing method for the entire lifecycle of industrial data according to the present invention. Figure 2 This is a schematic diagram of an automated testing system for the entire lifecycle of industrial data according to the present invention; Figure 3 A schematic diagram showing the layout of data collection points for a belt conveyor system. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 Please see Figure 1 This invention provides an automated testing method for the entire lifecycle of industrial data. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps: S1. Collect raw energy consumption data by setting multiple collection points on the belt conveyor, transmit the raw energy consumption data to the life cycle test system, and perform preprocessing to obtain the life cycle dataset; S2. In the life cycle test system, the life cycle dataset is fused and analyzed to obtain the multi-source energy efficiency disturbance residual R, and the residual change rate Rrate is formed based on the multi-source energy efficiency disturbance residual R. A preliminary comparative evaluation is carried out within the preset residual threshold range to obtain the preliminary energy efficiency micro-loss judgment result. S3. When the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, the energy efficiency degradation depth detection is automatically triggered, the comprehensive degradation assessment value F is calculated, and the strategy level is determined based on the output result of the comprehensive degradation assessment value F to obtain the belt energy efficiency degradation depth judgment result. S4. Based on the determination result of the belt energy efficiency degradation depth, start the system operation status control module, execute the strategy level, and compare and analyze the life cycle data before and after the strategy execution is completed.

[0022] In this embodiment, the method sets up multiple sampling points at key locations on the conveyor belt in S1 and collects raw data such as energy consumption, tension, speed, and disturbance in real time. This is because these parameters in long-distance belt systems undergo extremely slight but continuous changes due to factors such as operating load, idler wear, and tension fluctuations. Without deploying sensors at multiple points, single-point detection cannot capture these long-term accumulated small offsets, making it difficult to identify the belt in the "early stage of light wear" in a timely manner. By uniformly transmitting the data to the life cycle testing system and preprocessing it, the data can be compared on the same time axis, avoiding misjudgments caused by differences in sampling frequencies and time delays of different sensors. In S2, a multi-source energy efficiency disturbance residual R is constructed through multi-source fusion analysis. The core purpose is to remove irrelevant fluctuations caused by tension fluctuations and speed disturbances from energy consumption changes, so that the system only retains the "net energy efficiency offset" that is truly related to belt wear. For example, when the transport load fluctuates greatly or the local vibration of the idler increases, short-term noise will appear in the energy consumption. If this noise is not eliminated by a compensation algorithm, it will be mistakenly identified as a wear signal, leading to false alarms. The purpose of introducing the residual change rate Rrate is to quantify the "current micro-loss trend" into a rate of change, enabling the system to identify low-amplitude but continuously increasing wear trends. These trends are often precursors to hidden faults that traditional inspections cannot detect. In S3, when Rrate exceeds a threshold, the system triggers deep detection and calculates the comprehensive degradation assessment value F. By normalizing the residual accumulation and belt running distance, the system can effectively avoid the misjudgment that "long running time ≠ necessarily greater degradation." For example, if two belts run within the same time period, but one runs a longer distance, its increased energy consumption is not necessarily due to degradation, but rather the amplification of the accumulated amount due to distance. Therefore, the "degradation intensity per unit running distance" F must be used to truly reflect the degree of wear. The F-based grading strategy can intuitively distinguish between mild, significant, and severe degradation, giving the control strategy clear execution boundaries. In S4, the corresponding strategy is automatically executed according to the degradation level to take different levels of intervention at different degradation stages. For example, lightweight tension compensation can eliminate energy efficiency fluctuations during the first stage of degradation, but if speed and vibration are not reduced in advance during the second stage of degradation, the idlers may enter the accelerated wear range; and if load peaks are not suppressed in time during the third stage of degradation, the belt may face the risk of crack propagation due to increased local tension. After the strategy is implemented, the system compares the lifecycle data before and after the strategy to verify the adjustment effect in real time, avoiding the risk of "the strategy being ineffective but continuing to operate". Through the above implementation steps, this method realizes full-link automated management of the belt system from micro-loss identification, trend analysis, and depth judgment to strategy execution and verification, so that long-cycle micro-loss is no longer ignored, avoiding major hidden dangers such as increased energy consumption, belt misalignment, abnormal wear of idlers, and even belt breakage caused by the accumulation of initial wear, and significantly improving the stability, safety, and operating energy efficiency of the belt conveyor system.

[0023] Example 2 Please see Figure 1 and Figure 3 Specifically: S1 includes S11; S11. By setting up several collection points on multiple belt conveyors and installing sensing devices at each collection point, the raw energy consumption data of each belt conveyor can be collected in real time. The data collection points include the first data collection point A1, the second data collection point A2, the third data collection point A3, and the fourth data collection point A4; The sensing devices include power monitoring sensors, torque sensors, tension measurement sensors, speed sensors and speed disturbance monitoring modules, and load measurement sensors; The raw energy consumption data includes the energy consumption per unit length parameter E(t) at time t, the driving torque parameter M(t) at time t, the belt tension measurement parameter T(t) at time t, the belt speed parameter V(t) at time t, the speed micro-disturbance parameter dV(t) at time t, and the end load parameter L(t) at time t. The first acquisition point A1 is set at the input drive end of the belt conveyor system, and an energy monitoring sensor and a torque sensor are configured at the first acquisition point A1 to collect the energy consumption parameter per unit length E(t) and the driving torque parameter M(t) at time t. The second acquisition point A2 is set at the belt tension automatic adjustment mechanism, and a tension measurement sensor is configured at the second acquisition point A2 to acquire the belt tension measurement parameter T(t) at time t; The third acquisition point A3 is set in the middle section of the belt, and a speed sensor and a speed disturbance monitoring module are configured at the third acquisition point A3 to acquire the belt speed parameter V(t) at time t and the speed micro-disturbance parameter dV(t) at time t. The fourth acquisition point A4 is set at the end of the belt, and a load measurement sensor is configured at the fourth acquisition point A4 to acquire the end load parameter L(t) at time t.

[0024] S1 also includes S12; S12. The raw energy consumption data collected from the collection points is transmitted to the life cycle test system in real time via the fieldbus network. The raw energy consumption data is preprocessed in the life cycle test system to obtain the life cycle dataset. Preprocessing includes time alignment, handling of missing values, and normalization. Time alignment is achieved by using the master clock of the lifecycle testing system as a reference to construct a master time axis, and synchronizing the raw energy consumption data from different collection points using a unified time axis. Missing value handling involves using a time window-based continuity detection algorithm on the time-aligned raw energy consumption data to monitor the continuity of the same parameter in adjacent sampling periods, identify missing values, and fill in the missing values ​​by using the average of the two sampling periods before and after the missing value. Normalization is performed on the raw energy consumption data after handling missing values ​​using the Z-Score normalization method, thereby eliminating the differences in unit dimensions of all parameters in the raw energy consumption data.

[0025] In this embodiment, the method first sets up four collection points at the input end, middle section, tensioning mechanism, and end of the belt conveyor according to S11, and configures a corresponding sensing device for each collection point. The purpose is to obtain all the key physical quantities affecting energy consumption changes. If only a single point is detected at the drive end, the energy consumption change will be "averaged out" when encountering slight wear of the idler roller in the middle section of the belt or fluctuation of the end load, resulting in the inability to detect slight early losses. Therefore, by deploying sensors at multiple points from A1 to A4, the "single-point monitoring blind spot" can be avoided, enabling the system to capture the real sources of energy efficiency changes along the line. The collected raw energy consumption data covers energy consumption per unit length E(t), torque M(t), tension T(t), speed V(t), speed disturbance dV(t), and end load L(t). These parameters correspond to typical energy efficiency influencing factors such as drive power change, tension fluctuation, local resistance change, idler roller vibration, and load disturbance. If a parameter is missing, such as dV(t), the system may misinterpret short-cycle disturbances caused by roller vibration as wear trends. Therefore, full data acquisition is the physical basis for ensuring the accuracy of subsequent analysis. According to S12, the acquired multi-source data is transmitted in real-time to the lifecycle testing system via fieldbus and time-aligned to avoid erroneous trends caused by "asynchrony" between different acquisition points. For example, if the mid-stage V(t) occurs at 10:00:00, while the drive-end E(t) is recorded at 10:00:02, this two-second difference will be misjudged as a sudden energy consumption change in micro-loss analysis. By unifying the time axis, it can be ensured that all data are at the same strictly simultaneous moment, avoiding non-physical deviations. Missing value handling addresses the fragmented gaps caused by "instantaneous sensor disconnection" or "communication jitter." If these gaps are not filled, subsequent differential processing will treat missing values ​​as abrupt change points, triggering false signals. Therefore, a continuous detection algorithm based on time windows and a neighboring point mean completion method are used to keep the data stable and continuous, ensuring that no false disturbances are introduced when further calculating the residuals. The Z-score normalization process eliminates the influence of different dimensions. For example, E(t) is expressed in kWh / m³, T(t) in N, and V(t) in m / s. Without normalization, these numerical differences would cause the computational model to incorrectly interpret "larger dimension" as "larger influence." Normalized data allows each parameter to express its true role on the same scale, ensuring that the weights in subsequent residual analysis are derived from physical meaning rather than numerical magnitude. Through the processing steps S1–S11–S12 described above, this method achieves the process of constructing a "computable, alignable, and comparable" lifecycle dataset from raw data acquisition. This enables the system to stably extract weak but continuous energy efficiency change signals under highly complex conveyor operating conditions, avoiding common problems in traditional monitoring methods such as acquisition blind spots, time mismatches, and dimensional errors. This establishes a reliable physical and data foundation for subsequent residual analysis and degradation identification.

[0026] Example 3 Please see Figure 1 Specifically: S2 includes S21; S21. In the life cycle test system, the life cycle dataset is fused and analyzed. The fusion analysis is performed by using the multi-source disturbance energy efficiency difference formula of tension compensation term and velocity disturbance compensation term to calculate the unified time series time by time, obtain the multi-source energy efficiency disturbance residual R, and perform quantitative analysis on the net energy efficiency offset of the belt in the current life cycle stage. The multi-source energy efficiency perturbation residual R is calculated and output using the following algorithm formula: ; In the formula, R(t) represents the multi-source energy efficiency disturbance residual at time t, E(t-Δt) represents the energy consumption parameter per unit length corresponding to the sampling period Δt before time t, and T(t-Δt) represents the belt tension measurement parameter of the sampling period Δt before time t. This represents the tension compensation factor, with a value range of 0.01-0.15. The speed disturbance compensation factor is 0.5-1.0, all based on the user's initial setting based on the initial historical healthy operation data; The multi-source energy efficiency disturbance residual R(t) of this formula is a key technical indicator used to characterize the long-cycle micro-loss trend of belt conveyor systems. Its construction process is based on the comprehensive derivation of differential mathematics principles, belt conveyor energy consumption physical model and belt dynamics disturbance theory. First, starting from the first-order difference method in mathematics, a basic energy consumption change E(t)-E(t-Δt) is constructed for the energy consumption parameter E(t) per unit length, which is used to describe the trend of belt energy consumption over time. This difference term can reflect the local slope of the energy consumption curve and is the basis for exploring long-term trends. Secondly, in the physical model of conveyor energy consumption, belt energy consumption and tension parameter T(t) have a primary coupling relationship. Tension fluctuations directly affect energy consumption. Therefore, this formula starts from the classical energy consumption-tension relationship E=f(T), transforms the tension change term into a deductible compensation energy consumption, and constructs the tension compensation amount. ·(T(t)-T(t-Δt)) / T(t); where, Obtained from historical healthy operating data through a regression model, it reflects the contribution coefficient of tension changes to energy consumption changes; Furthermore, in the belt dynamics model, the instantaneous micro-perturbation dV(t) of the belt speed is often caused by local damage to the idler rollers, load deviation, or mechanical inertia fluctuations. Speed ​​disturbances lead to short-cycle energy consumption fluctuations. Therefore, based on the dynamic energy consumption contribution relationship, this formula extracts a speed disturbance compensation term. ·dV(t), where, The residual R of the multi-source energy efficiency disturbance is obtained by statistical fitting of the relationship between velocity disturbance and energy consumption, which is used to weaken the influence of velocity disturbance on the energy consumption trend signal; the residual R of the multi-source energy efficiency disturbance is constructed by logical superposition of the above three parts. From a physical perspective, the construction process of this formula reflects the hierarchical separation logic of energy consumption changes: E(t)-E(t-Δt) represents the basic energy consumption change, but this change is mixed with energy consumption fluctuations caused by normal operating conditions, tension changes, and short-period disturbances caused by speed disturbances. This formula introduces tension compensation terms and speed disturbance compensation terms to gradually separate these interference components that are "unrelated to belt wear" from the basic energy consumption change E(t)-E(t-Δt), making the final residual R(t) closer to the "net energy" caused by long-term belt wear. "Efficiency offset"; in other words, the basic energy consumption change E(t)-E(t-Δt) is used to capture the total change, the tension compensation term is used to avoid misjudging tension fluctuations as energy efficiency degradation, the speed disturbance term is used to eliminate energy consumption fluctuations caused by roller or load disturbances, and the residual R(t) is the actual wear trend remaining after eliminating disturbances; this combination method not only has a rigorous physical basis, but also can significantly improve the accuracy of micro-loss identification in actual operation, thereby providing reliable core characteristic quantities for subsequent residual change rate assessment, degradation level determination and predictive maintenance strategies.

[0027] S2 also includes S22; S22. For the multi-source energy efficiency perturbation residuals R(t) at time t of all sampling periods, sort them according to the time sequence of the sampling periods to form a residual time series, and calculate the rate of change to form the residual rate of change Rrate. Then, perform a preliminary comparative evaluation within a preset residual threshold range to obtain preliminary energy efficiency micro-loss judgment results. The residual threshold range includes the first residual threshold F1 and the second residual threshold F2. The specific comparison content is as follows: When calculating the first residual threshold F1 of the residual change rate Rrate, it is determined to be a normal micro-perturbation; When the calculated residual change rate Rrate ∈ [first residual threshold F1, second residual threshold F2), it is determined that the belt has entered the initial energy efficiency micro-loss state, and at this time the sampling frequency of the current sampling period is increased by 50%; When the calculated residual change rate Rrate is greater than or equal to the second residual threshold F2, it is determined that the belt has entered an abnormal state of energy efficiency micro-loss, and the energy efficiency degradation depth detection is automatically triggered. Among them, based on the statistical distribution of the residual change rate Rrate calculated in the history of healthy belts over multiple complete life cycles, the upper limit of the residual change rate Rrate calculated in the history of healthy belts is used to calculate the mean and obtain the first residual threshold F1. The calculation under abnormal energy consumption conditions forms the lower limit of the residual change rate Rrate, and the mean is calculated to obtain the second residual threshold F2.

[0028] In this embodiment, method S21 performs multi-source fusion analysis on the lifecycle dataset and calculates the multi-source energy efficiency disturbance residual R(t). Its core purpose is to remove operating condition interference unrelated to belt wear from the energy consumption data. For example, during automatic compensation of the belt tensioning mechanism, the tension will increase briefly, and the energy consumption will increase synchronously. If it is not subtracted by the tension compensation term β·(T(t)-T(t-Δt)) / T(t), the system will misjudge "normal tension fluctuation" as "wear leading to increased energy efficiency", thus generating a false alarm. As another example, when there is a slight eccentricity in the idler roller in the middle section of the belt, it will cause a speed disturbance dV(t), resulting in a jitter peak in energy consumption. The speed disturbance compensation term γ·dV(t) can effectively eliminate this kind of short-cycle noise and avoid mistaking idler roller vibration as a wear trend. By extracting the basic energy consumption change through the difference term E(t)-E(t-Δt), and combining it with tension and speed disturbance compensation, R(t) can more realistically reflect the "energy efficiency degradation trend of the belt body," truly isolating wear-related energy consumption changes from external disturbances. In S22, the obtained R(t) is sorted according to time to form a residual time series, and the residual change rate Rrate is calculated. Its real role is to capture the "micro-loss growth rate." Because belt wear often shows a very low amplitude but continuous growth trend in the early stage, if only the value of R(t) itself is observed, the value may be very small, but its rate of change is constantly accumulating, which is a typical characteristic of initial degradation. Therefore, the change rate Rrate reflects the initial degradation process better than the residual itself. The reason for setting the residual threshold intervals F1 and F2 is that Rrate will fluctuate within a narrow range when the belt is in a healthy state, while when the idler resistance begins to increase or the tension is unbalanced, the change rate will gradually leave the healthy range. For example, a Rrate exceeding F2 usually indicates a "rapidly deteriorating trend." If deep detection isn't triggered immediately, wear will accumulate at an accelerated rate, eventually leading to a sharp increase in energy consumption and even the risk of belt breakage. By allowing a 50% increase in sampling frequency between F1 and F2, monitoring accuracy can be amplified in the early stages of trend changes, enabling the system to capture more subtle degradation changes and avoid delayed identification. Through the engineering implementation of S21 and S22, this method establishes a core analysis link capable of stripping away multi-source interference, accurately reflecting the true wear trend, and quickly identifying micro-loss acceleration. This mechanism fundamentally avoids the double misjudgment of "mistaking noise for wear" and "ignoring early wear" in traditional monitoring, enabling the system to identify trend shifts as soon as wear appears, providing early warning and a clear and reliable triggering basis for subsequent deep degradation detection in S3.

[0029] Example 4 Please see Figure 1 Specifically: S3 includes S31; S31. After automatically triggering the energy efficiency degradation depth detection, the time accumulation calculation is performed on the multi-source energy efficiency disturbance residual R(t) at multiple sampling periods t in the life cycle dataset to obtain the residual accumulation amount. At the same time, the time accumulation calculation is performed on the belt speed parameter V(t) at multiple sampling periods t to extract the belt running distance. Based on this, the residual accumulation amount and the belt running distance are normalized to obtain the comprehensive degradation evaluation value F, and the energy efficiency degradation intensity of the belt per unit running distance is quantitatively analyzed. The overall degradation assessment value F is calculated and output using the following algorithm formula: In the formula, dt represents the time calculus variable; In mathematics, the definite integral of this formula is used to represent the cumulative amount of a physical quantity over time. By integrating the residual R(t), the cumulative value of the "net energy consumption offset" over the entire life cycle of the belt can be obtained. Integrating the belt speed parameter V yields the actual running distance of the belt within the time interval 0 to t. The total distance the belt travels in different cycles is different. If we directly compare the cumulative residuals, it will lead to the following: the longer the distance, the larger the residual; the shorter the distance, the smaller the residual. This will result in a "systematic deviation". Therefore, it is necessary to divide by the running distance to normalize it. This is a result of the joint derivation of mathematical integration, physical quantity normalization and the energy consumption model of the conveyor.

[0030] S3 also includes S32; S32. Based on the output of the comprehensive degradation assessment value F, determine the strategy level to obtain the belt energy efficiency degradation depth determination result. The determination content is as follows: When the comprehensive degradation assessment value F≤0.3, it indicates that the current conveyor belt has slight degradation, and the first-level control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.3,0.6], it indicates that the current conveyor belt has significant degradation, and the secondary control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.6,0.85], it indicates that the current conveyor belt has degradation analysis, and the three-level control strategy is triggered at this time; When the comprehensive degradation assessment value F > 0.85, it indicates that the current conveyor belt is severely worn, and the machine needs to be stopped and maintained.

[0031] In this embodiment, method S31 integrates the residual R of multi-source energy efficiency disturbance after triggering energy efficiency degradation depth detection. Its direct purpose is to obtain the cumulative energy consumption offset of the belt throughout the entire operating range. Time integration is used because micro-losses are often not noticeable in the short term, but accumulate into significant offsets over a long period; if only instantaneous R(t) is observed, these cumulative effects are often undetectable. For example, the energy consumption increase caused by slight wear on the idler roller surface may only result in a 0.3% offset per hour, but after three months of operation, this accumulated offset will significantly affect energy efficiency. Therefore, by... The quantification of the "cumulative wear effect" is crucial information that traditional inspections cannot obtain. Simultaneously, integrating the belt speed parameter V(t) aims to extract the actual belt travel distance. Without a travel distance as a benchmark, belts operating over long distances and in multiple shifts may appear to exhibit a larger energy efficiency deviation due to "more travel time or distance," leading to misjudgments. Extracting the running distance and then normalizing the cumulative residual with the running distance can effectively avoid the systematic bias of "more running ≠ heavier wear," making the degradation assessment more fair and scientific. For example, if two belts have the same residual integral, but belt A runs 200km while belt B only runs 80km, then belt B's wear intensity per unit distance is obviously higher. This is the physical meaning of F. In S32, the comprehensive degradation assessment value F is used as the basis for grading the degradation depth because F has unified the dimensions of the micro-loss accumulation effect and the running distance, making it a comparable and quantifiable dimensionless degradation indicator within the range of 0 to 1. Setting grading thresholds F≤0.3, 0.3~0.6, 0.6~0.85, and >0.85 aims to set different intervention levels for different degradation intensities. For example, when F falls within the 0.3–0.6 range, if the secondary strategy is not implemented in time to reduce speed and suppress vibration, wear will spread more rapidly; when F > 0.85, if operation continues, the tension may quickly become unbalanced, even leading to belt tearing. Therefore, the purpose of multi-level thresholds is to truly achieve "step-by-step defensive maintenance," rather than the traditional "one-size-fits-all shutdown."

[0032] Example 5 Please see Figure 1 Specifically: S4 includes S41; S41. After obtaining the result of the belt energy efficiency degradation depth determination, the system operation status control module is automatically started, and the control strategy related to the strategy level is executed. The specific content is as follows: When the first-level control strategy is triggered, the following strategies are employed: a tension compensation strategy that lowers the target tension value of the belt tension controller by 2%-5% compared to the current tension value; a speed stabilization strategy that limits the peak value of the belt speed micro-disturbance to the range of 0.02m / s-0.05m / s through the drive controller; and a load balancing strategy that reduces the peak value of the end load by 5%-10% through the load distribution module. When the secondary control strategy is triggered, the segmented speed coordination strategy is to reduce the belt running cycle to 80% of the original belt running cycle and then limit the belt speed to 85%-90% of the original rated speed in the long section, and the active damping strategy of the roller is to trigger the damping execution sequence through the drive controller to reduce the speed micro-disturbance parameter dV(t) by 30%-50%. When the three-level control strategy is triggered, the load suppression strategy reduces the load peak by 10%-20% through the load management module, and the structural vibration reduction strategy reduces the overall operating speed to 70%-80% of the rated speed.

[0033] S4 also includes S42; S42. Before implementing the relevant control strategy at the strategy level, record the residual time series and the comprehensive degradation assessment value F as the baseline data before the strategy. After the strategy level is implemented, recalculate the residual time series and the comprehensive degradation assessment value F, and calculate the difference between the residual time series and the baseline data before the strategy in the life test system, and output the difference ΔF of the comprehensive degradation assessment value. If the difference in the comprehensive degradation assessment value ΔF < 0 and the decrease exceeds 10%, the strategy level is deemed to be effectively implemented. Otherwise, if the belt's energy efficiency is deemed to be deteriorating, maintenance recommendations will be pushed to the operation and maintenance team. At the same time, the comparative analysis results will be automatically recorded in the system log to form a traceable recovery record.

[0034] In this embodiment, after obtaining the belt energy efficiency degradation depth determination result, method S41 executes the corresponding level of control strategy. Its core purpose is to apply "intensity-matched intervention" to early degradation to prevent wear from entering the irreversible stage. For example, the reason for reducing the tension by 2%-5% in the case of slight degradation is that the higher the belt tension, the greater the force on the idler, and the more rapidly friction accumulates; if it is not appropriately reduced in the slight stage, it will cause the middle section idler to wear rapidly under high load. The purpose of limiting the speed disturbance peak to 0.02-0.05m / s is to avoid the instantaneous vibration of the idler amplifying the energy consumption fluctuation, which would accelerate the deterioration of slight degradation; if this disturbance is not controlled, micro-loss will change from "reversible wear" to "irreversible wear". The end load balancing strategy reduces the load peak by 5%-10% to suppress the local stress concentration at the tail of the belt and avoid excessive local tension that causes the belt to start to develop micro-cracks. Secondary strategies implemented during the significant degradation stage, such as reducing the operating cycle to 80% of the original cycle and limiting the speed to 85%-90%, are not aimed at reducing production capacity, but rather at suppressing the accelerated wear effect caused by "high-speed + long-frequency operation." This is because idlers or belts are most vulnerable to high-speed impacts during the degradation stage; if the operating frequency is not appropriately reduced, wear will increase exponentially. Triggering active damping via the drive controller to reduce dV(t) by 30%-50% aims to reduce vibration energy input from the dynamic source, thus preventing the residual R(t) from accelerating. Tertiary strategies, such as reducing the load peak by 10%-20% and reducing the operating speed to 70%-80% of the rated speed, physically force the belt into a "low-stress zone" to prevent crack propagation or continued accumulation of wire rope fatigue. If high-load, high-speed operation continues during this stage, the belt will suffer irreparable damage in a short period; therefore, this strategy constitutes a "pre-failure protection mechanism." In S42, by recording the residual time series and comprehensive degradation assessment value F before and after strategy implementation and calculating the difference ΔF, the purpose is to verify whether the control strategy has truly changed the energy efficiency degradation trend. If ΔF < 0 and the decrease exceeds 10%, it indicates that the wear trend has been effectively suppressed; if there is no improvement or even an increase, it indicates that degradation has entered an irreversible stage, and a maintenance warning must be issued to the operation and maintenance department. This process avoids judging the effectiveness of the strategy based solely on experience, enabling the belt adjustment process to have a "quantitative feedback closed loop," ensuring that every intervention can be verified, traced back, and optimized.

[0035] Example 6 Please see Figure 1 and Figure 2 An automated testing system for the entire lifecycle of industrial data, comprising a lifecycle extraction module, a residual analysis module, a degradation analysis module, and a control module; The lifecycle extraction module collects raw energy consumption data by setting up multiple collection points on the belt conveyor, transmits the raw energy consumption data to the lifecycle testing system, and performs preprocessing to obtain the lifecycle dataset; The residual analysis module obtains the multi-source energy efficiency disturbance residual R by performing fusion analysis on the life cycle dataset in the life cycle test system, and forms the residual change rate Rrate based on the multi-source energy efficiency disturbance residual R. It then performs a preliminary comparative evaluation within a preset residual threshold range to obtain preliminary energy efficiency micro-loss judgment results. The degradation analysis module automatically triggers energy efficiency degradation depth detection when the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, calculates the comprehensive degradation assessment value F, and determines the strategy level based on the output result of the comprehensive degradation assessment value F, so as to obtain the belt energy efficiency degradation depth judgment result. The control module activates the system operation status control module based on the belt energy efficiency degradation depth determination result, executes the strategy level, and compares and analyzes the life cycle data before and after the strategy execution is completed.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. An automated testing method for the entire lifecycle of industrial data, characterized in that: Includes the following steps: S1. Collect raw energy consumption data by setting multiple collection points on the belt conveyor, transmit the raw energy consumption data to the life cycle test system, and perform preprocessing to obtain the life cycle dataset; S2. In the life cycle test system, the life cycle dataset is fused and analyzed to obtain the multi-source energy efficiency disturbance residual R, and the residual change rate Rrate is formed based on the multi-source energy efficiency disturbance residual R. A preliminary comparative evaluation is carried out within the preset residual threshold range to obtain the preliminary energy efficiency micro-loss judgment result. S3. When the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, the energy efficiency degradation depth detection is automatically triggered, the comprehensive degradation assessment value F is calculated, and the strategy level is determined based on the output result of the comprehensive degradation assessment value F to obtain the belt energy efficiency degradation depth judgment result. S4. Based on the determination result of the belt energy efficiency degradation depth, start the system operation status control module, execute the strategy level, and compare and analyze the life cycle data before and after the strategy execution is completed.

2. The automated testing method for the entire lifecycle of industrial data according to claim 1, characterized in that: S1 includes S11; S11. By setting up several collection points on multiple belt conveyors and installing sensing devices at each collection point, the raw energy consumption data of each belt conveyor can be collected in real time. The collection points include a first collection point A1, a second collection point A2, a third collection point A3, and a fourth collection point A4; The sensing device includes an energy monitoring sensor, a torque sensor, a tension measurement sensor, a speed sensor, a speed disturbance monitoring module, and a load measurement sensor; The raw energy consumption data includes the energy consumption per unit length parameter E(t) at time t, the driving torque parameter M(t) at time t, the belt tension measurement parameter T(t) at time t, the belt speed parameter V(t) at time t, the speed micro-disturbance parameter dV(t) at time t, and the end load parameter L(t) at time t.

3. The automated testing method for the entire lifecycle of industrial data according to claim 2, characterized in that: S1 further includes S12; S12. The raw energy consumption data collected from the collection points is transmitted to the life cycle test system in real time via the fieldbus network. The raw energy consumption data is preprocessed in the life cycle test system to obtain the life cycle dataset. The preprocessing includes time alignment, missing value handling, and normalization. The time alignment is achieved by constructing a main time axis based on the master clock of the life cycle test system, and synchronizing the data from different collection points in the raw energy consumption data using a unified time axis. The missing value processing involves using a time window-based continuity detection algorithm on the time-aligned raw energy consumption data to monitor the continuity of the same parameter in adjacent sampling periods, identify missing values, and fill in the missing values ​​by using the average of the two sampling periods before and after the missing value. The normalization process uses the Z-Score normalization method to normalize the original energy consumption data after handling missing values, thereby eliminating the differences in unit dimensions of all parameters in the original energy consumption data.

4. The automated testing method for the entire lifecycle of industrial data according to claim 3, characterized in that: S2 includes S21; S21. In the life cycle test system, the life cycle dataset is fused and analyzed. The fusion analysis is performed by using the multi-source disturbance energy efficiency difference formula of tension compensation term and velocity disturbance compensation term to calculate the unified time series time by time to obtain the multi-source energy efficiency disturbance residual R, and to perform quantitative analysis of the net energy efficiency offset of the belt in the current life cycle stage. The multi-source energy efficiency perturbation residual R is calculated and output using the following algorithm formula: ； In the formula, R(t) represents the multi-source energy efficiency disturbance residual at time t, E(t-Δt) represents the energy consumption parameter per unit length corresponding to the sampling period Δt before time t, and T(t-Δt) represents the belt tension measurement parameter of the sampling period Δt before time t. This represents the tension compensation factor, with a value range of 0.01-0.

15. This indicates a velocity disturbance compensation factor of 0.5-1.

0.

5. The automated testing method for the entire lifecycle of industrial data according to claim 4, characterized in that: S2 further includes S22; S22. For the multi-source energy efficiency perturbation residuals R(t) at time t of all sampling periods, sort them according to the time sequence of the sampling periods to form a residual time series, and calculate the rate of change to form the residual rate of change Rrate. Then, perform a preliminary comparative evaluation within a preset residual threshold interval to obtain preliminary energy efficiency micro-loss judgment results. The residual threshold interval includes a first residual threshold F1 and a second residual threshold F2. The specific comparison content is as follows: When calculating the first residual threshold F1 of the residual change rate Rrate, it is determined to be a normal micro-perturbation; When the calculated residual change rate Rrate∈[first residual threshold F1, second residual threshold F2), it is determined that the belt has entered the initial energy efficiency micro-loss state, and at this time the sampling frequency of the current sampling period is increased by 50%; When the calculated residual change rate Rrate is greater than or equal to the second residual threshold F2, it is determined that the belt has entered an abnormal state of energy efficiency micro-loss, and the energy efficiency degradation depth detection is automatically triggered.

6. The automated testing method for the entire lifecycle of industrial data according to claim 5, characterized in that: S3 includes S31; S31. After automatically triggering the energy efficiency degradation depth detection, the time accumulation calculation is performed on the multi-source energy efficiency disturbance residual R(t) at multiple sampling periods t in the life cycle dataset to obtain the residual accumulation amount. At the same time, the time accumulation calculation is performed on the belt speed parameter V(t) at multiple sampling periods t to extract the belt running distance. Based on this, the residual accumulation amount and the belt running distance are normalized to obtain the comprehensive degradation evaluation value F, and the energy efficiency degradation intensity of the belt per unit running distance is quantitatively analyzed. The comprehensive degradation assessment value F is calculated and output using the following algorithm formula: In the formula, dt represents the time calculus variable.

7. The automated testing method for the entire lifecycle of industrial data according to claim 6, characterized in that: S3 further includes S32; S32. Based on the output of the comprehensive degradation assessment value F, determine the strategy level to obtain the belt energy efficiency degradation depth determination result. The determination content is as follows: When the comprehensive degradation assessment value F≤0.3, it indicates that the current conveyor belt has slight degradation, and the first-level control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.3,0.6], it indicates that the current conveyor belt has significant degradation, and the secondary control strategy is triggered at this time; When the comprehensive degradation assessment value F∈(0.6,0.85], it indicates that the current conveyor belt has degradation analysis, and the three-level control strategy is triggered at this time; When the comprehensive degradation assessment value F > 0.85, it indicates that the current conveyor belt is severely worn, and the machine needs to be stopped and maintained.

8. The automated testing method for the entire lifecycle of industrial data according to claim 7, characterized in that: S4 includes S41; S41. After obtaining the result of the belt energy efficiency degradation depth determination, the system operation status control module is automatically started, and the control strategy related to the strategy level is executed. The specific content is as follows: When the first-level control strategy is triggered, the following strategies are employed: a tension compensation strategy that lowers the target tension value of the belt tension controller by 2%-5% compared to the current tension value; a speed stabilization strategy that limits the peak value of the belt speed micro-disturbance to the range of 0.02m / s-0.05m / s through the drive controller; and a load balancing strategy that reduces the peak value of the end load by 5%-10% through the load distribution module. When the secondary control strategy is triggered, the segmented speed coordination strategy is to reduce the belt running cycle to 80% of the original belt running cycle and then limit the belt speed to 85%-90% of the original rated speed in the long section, and the active damping strategy of the roller is to trigger the damping execution sequence through the drive controller to reduce the speed micro-disturbance parameter dV(t) by 30%-50%. When the three-level control strategy is triggered, the load suppression strategy reduces the load peak by 10%-20% through the load management module, and the structural vibration reduction strategy reduces the overall operating speed to 70%-80% of the rated speed.

9. The automated testing method for the entire lifecycle of industrial data according to claim 8, characterized in that: S4 further includes S42; S42. Before implementing the relevant control strategy at the strategy level, record the residual time series and the comprehensive degradation assessment value F as the baseline data before the strategy. After the strategy level is implemented, recalculate the residual time series and the comprehensive degradation assessment value F, and calculate the difference between the residual time series and the baseline data before the strategy in the life test system, and output the difference ΔF of the comprehensive degradation assessment value. If the difference in the comprehensive degradation assessment value ΔF < 0 and the decrease exceeds 10%, the strategy level is deemed to be effectively implemented. Otherwise, if the belt's energy efficiency is deemed to be deteriorating, maintenance recommendations will be pushed to the operation and maintenance team, and the comparative analysis results will be automatically recorded in the system log.

10. An automated testing system for the entire lifecycle of industrial data, applied to the automated testing method for the entire lifecycle of industrial data as described in any one of claims 1-9, characterized in that: It includes a lifecycle extraction module, a residual analysis module, a degradation analysis module, and a control module; The lifecycle extraction module collects raw energy consumption data by setting multiple collection points on the conveyor belt, transmits the raw energy consumption data to the lifecycle testing system, and performs preprocessing to obtain the lifecycle dataset. The residual analysis module obtains the multi-source energy efficiency disturbance residual R by performing fusion analysis on the life cycle dataset in the life cycle test system, and forms the residual change rate Rrate based on the multi-source energy efficiency disturbance residual R. It then performs a preliminary comparative evaluation within a preset residual threshold range to obtain preliminary energy efficiency micro-loss judgment results. The degradation analysis module automatically triggers energy efficiency degradation depth detection when the preliminary energy efficiency micro-loss judgment result is micro-loss abnormal, calculates the comprehensive degradation evaluation value F, and determines the strategy level based on the output result of the comprehensive degradation evaluation value F, so as to obtain the belt energy efficiency degradation depth judgment result. The control module activates the system operation status control module based on the belt energy efficiency degradation depth determination result, executes the strategy level, and compares and analyzes the life cycle data before and after the strategy execution is completed.

Images