Industrial equipment monitoring and control method and system based on internet of things

Abstract

The present application belongs to the technical field of industrial equipment monitoring and control, and particularly relates to an industrial equipment monitoring and control method and system based on the Internet of Things, which comprises the following steps: obtaining continuous fast sequences and slow sequences of industrial equipment, calculating cross-correlation degrees between different physical property data by using sliding cross-gradient correlation logic, and extracting time lag compensation degrees conforming to physical energy conduction rules; subsequently, reconstructing phases of multi-source sensing sequences based on the time lag compensation degrees, and calculating a synchronous degradation modulus representing the overall damage state of the equipment by combining with maximum delay upper limit calculation; when the synchronous degradation modulus is greater than a tolerance threshold, calculating a control response strength, comparing it with a minimum safe idle speed control amount, extracting a maximum value, encoding it as a standard control instruction, and issuing it to reduce driving output. The present application effectively decouples the time lag effect of heterogeneous physical data, and solves the problems of monitoring false negatives and intervention impact caused by physical phase misalignment.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment monitoring and control technology. More specifically, this invention relates to an industrial equipment monitoring and control method and system based on the Internet of Things (IoT). Background Technology

[0002] In the field of IoT industrial equipment monitoring, systems typically deploy various types of sensors on critical rotating equipment, such as vibration sensors for collecting high-frequency vibrations and temperature sensors for collecting surface temperatures. The raw analog signals collected by the underlying sensors are converted from analog to digital by the controller and then uploaded to the cloud system in real time via network nodes for comprehensive analysis. Most existing monitoring and control systems adopt a multi-data alignment mechanism based on absolute timestamps, that is, extracting the readings of each sensor received at the same moment to form a static time-section state vector, and then using a statistical algorithm based on Euclidean distance to evaluate the operational health of the equipment.

[0003] However, due to the inherent propagation delay in energy transfer between different physical fields, the characteristic peak values ​​of the same physical fault exhibited by different physical property sensors are severely misaligned on the time axis. When industrial equipment experiences early mechanical degradation, the energy of physical damage radiates rapidly in the form of mechanical waves, allowing vibration sensors to capture fast response data within milliseconds. In contrast, the heat energy generated by abnormal friction must overcome the thermal inertia of the metal casing and undergo solid-state heat conduction before being collected as slow response data by temperature sensors. Existing absolute timestamp alignment mechanisms forcibly combine high vibration peak values ​​at the same moment with stable, unchanged temperature values, severing the causal coupling of fault characteristics in physical space.

[0004] This physical phase misalignment in the data matrix means that the fused feature vector cannot accurately reflect the overall cumulative degree of equipment degradation, leading to severe system underreporting in the early stages of equipment degradation, or false alarms when a single sensor is subjected to occasional electromagnetic pulse interference. Furthermore, traditional fixed threshold-triggered start-stop control strategies are highly susceptible to mechanical vibrations in the production line under heavy load conditions, causing secondary physical damage to equipment and process failure. Therefore, existing monitoring methods cannot decouple the time lag effects between different physical attribute data, making it difficult to achieve accurate joint assessment and early intervention of the multi-dimensional state of industrial equipment. Summary of the Invention

[0005] To address the technical problem that existing technologies cannot decouple the time delay effect between different physical attribute data, making it difficult to achieve accurate joint evaluation and early intervention of multi-dimensional states of industrial equipment, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an industrial equipment monitoring and control method based on the Internet of Things (IoT), comprising: acquiring high-frequency analog signals and low-frequency analog signals of industrial equipment and performing analog-to-digital conversion to generate digital status data; extracting and constructing a fast sequence containing fast sampled values ​​and a slow sequence containing slow sampled values ​​from the data; extracting the cross-correlation degree under the delay step size based on the first-order difference of the slow sequence and the first-order difference of the fast sequence under the delay step size; extracting the delay step size corresponding to the global maximum value of the cross-correlation degree as the time delay compensation degree within a set time window; and establishing a fast... The system obtains a synchronous degradation modulus based on the ratio of the fast sampled value to the fast reference value (derived using time delay compensation), the ratio of the slow sampled value to the slow reference value, and the penalty based on time delay compensation. When the synchronous degradation modulus exceeds a preset tolerance threshold, the system adjusts the basic control quantity for maintaining the rated speed of the equipment based on the difference between the synchronous degradation modulus and the tolerance threshold, and the difference between the synchronous degradation modulus and the historical degradation modulus of the previous calculation cycle, to obtain the control response strength. The control response strength is encoded into a standard control command and sent to the given register of the field controller to control the industrial equipment to reduce the drive output.

[0007] This invention extracts the time delay compensation degree by acquiring fast and slow sequences and calculating the cross-correlation degree. Then, it combines the maximum delay upper limit and the benchmark value to back-calculate the fast sequence to obtain the synchronous degradation modulus. When the modulus exceeds the limit, it derives the continuous control response intensity based on dynamic feedback and issues commands. This process not only effectively eliminates the propagation delay between multi-source data during the transmission process in the physical medium, but also gathers the dispersed mechanical and thermodynamic degradation characteristics onto a unified evaluation section to achieve a true evaluation of the overall damage state. Furthermore, it transforms the static over-limit modulus into a smooth intervention value, achieving safe and flexible unloading in advance without interrupting the pipeline, thus avoiding mechanical oscillations and secondary damage caused by rigid dynamic cutoff.

[0008] Preferably, the method for acquiring the fast and slow sequences includes: acquiring high-frequency analog signals through a vibration sensor deployed on the surface of the main bearing housing of the industrial equipment, and simultaneously acquiring low-frequency analog signals through a temperature sensor deployed on the surface of the metal casing of the equipment; performing analog-to-digital conversion on the high-frequency and low-frequency analog signals to generate discrete digital state data; transmitting the digital state data to the IoT cloud platform according to the sampling frequency configured by the system; constructing a fixed-length first-in-first-out queue in the system memory as a sliding data buffer to extract continuous data streams within the currently set time window; stripping the data generated by the vibration sensor to construct a fast sequence, and stripping the data generated by the temperature sensor to construct a slow sequence.

[0009] Preferably, based on the first-order difference of the slow sequence and the first-order difference of the fast sequence at the delay step, the cross-correlation degree at the delay step is extracted, including: In the formula, Cross-correlation degree; This represents the total number of sampling points within the time window. This represents the current traversal time. This is the delay step size, and its value range is... , Maximum delay limit; For the current moment Slow sampling values; For the previous moment Slow sampling values; For a historic moment The fast sampled values; For a historic moment The fast sampled values; Indicates taking the absolute value; It is an exponential function with the natural constant as its base; This is the attenuation factor.

[0010] This invention calculates the cross-correlation degree by multiplying the difference of slow sampled values ​​with the difference of fast sampled values ​​after back-calculating the delay step size, and then multiplying the absolute value by an exponential penalty term based on the attenuation factor and the delay step size. This calculation process amplifies the coupling strength when the changing trends of different physical properties are aligned. At the same time, the exponential penalty term constrains the large delay step size, effectively suppressing the irrelevant background noise introduced by excessive time shift, thereby accurately locking the true energy conduction delay that conforms to the objective physical causal relationship.

[0011] Preferably, the step of obtaining the synchronization degradation modulus based on the ratio of the fast sampled value to the fast reference value derived using the time delay compensation degree, the ratio of the slow sampled value to the slow reference value, and the penalty based on the time delay compensation degree includes: In the formula, To synchronously degrade the modulus; To use time delay compensation to quickly retrieve historical moments from the sequence; For rapid baseline values; This is the slow sample value at the current moment; This is the slow reference value; The time delay compensation degree; Maximum delay limit; It is the natural logarithm function.

[0012] This invention calculates the offset by using the sum of the squares of the ratios of the current sampled value and the baseline value, and introduces a natural logarithm penalty term based on the time delay compensation degree and the maximum delay limit to obtain the synchronous degradation modulus. This process synergistically amplifies the magnitude of the deviation from the healthy baseline, while using the time delay compensation degree itself as an internal indicator of mechanical thermal impedance damage, thereby realizing the nonlinear amplification of the early hidden internal damage state of heavy equipment on a unified physical evaluation section.

[0013] Preferably, the method for obtaining the maximum delay limit includes: obtaining the metal material thickness and thermal diffusivity of the industrial equipment; calculating the theoretical maximum physical time required for heat energy to penetrate the equipment casing based on the physical law in thermodynamics that the heat conduction time is proportional to the square of the thickness and inversely proportional to the thermal diffusivity; multiplying the theoretical maximum physical time by the sampling frequency, discretizing it, and determining it as the maximum allowable delay step size, which is the maximum delay limit.

[0014] Preferably, the step of adjusting the basic control quantity for maintaining the rated speed of the equipment based on the difference between the synchronous degradation modulus and a preset tolerance threshold, and the difference between the synchronous degradation modulus and the historical degradation modulus of the previous calculation cycle, to obtain the control response strength, includes: In the formula, To control the response intensity; Basic control quantity; , These are the proportional gain and differential damping of the system, respectively; To synchronously degrade the modulus; This is the tolerance threshold; This is the historical degradation modulus.

[0015] This invention calculates the control response strength by subtracting the difference between the proportional gain and the tolerance threshold from the basic control quantity, and by subtracting the difference between the differential damping and the change in the current and historical degradation modulus. This calculation process transforms static out-of-limit events into continuous and dynamic intervention values ​​by linearly combining proportional countermeasures and differential prediction terms. It can not only quickly suppress current static out-of-limit behaviors, but also intercept and play a game against the rapidly escalating degradation trend, thereby adaptively forming smooth and coherent control values.

[0016] Preferably, the proportional gain and differential damping are preset fixed control parameters; the mechanical load characteristics of the industrial equipment are obtained, and the proportional gain and differential damping are pre-calibrated by writing fixed constants into the register of the field controller based on the mechanical load characteristics.

[0017] Preferably, the total number of sampling points within the set time window is obtained by multiplying the sampling frequency by the observation duration; the observation duration is a set time parameter that covers the complete physical conduction cycle from the sudden change in mechanical kinetic energy to the significant accumulation of thermal energy.

[0018] Preferably, encoding the control response strength into a standard control command and sending it to the given register of the field controller includes: comparing the calculated control response strength with the minimum allowable idling frequency of the motor, encoding the maximum value of the two into a standard control command; and sending the standard control command to the given register of the field controller via a data communication network.

[0019] This invention intervenes with a minimum safe idle speed control quantity before generating the final intervention command to impose a lower limit clamping effect. The maximum value between the calculated control response intensity and the minimum safe idle speed control quantity is extracted and encoded as a standard control command for issuance. This ensures that under extreme deterioration and acceleration conditions, the drive output of the equipment will only smoothly decrease to a slow idle operating state at most. This not only removes the processing load to prevent further damage, but also maintains the basic operation of the transmission system and avoids the huge mechanical impact damage caused by rigid emergency stops, thus providing a safe buffer window for subsequent manual intervention and maintenance.

[0020] In a second aspect, the present invention provides an IoT-based industrial equipment monitoring and control system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned IoT-based industrial equipment monitoring and control method is implemented.

[0021] By adopting the above technical solution, the above-mentioned IoT-based industrial equipment monitoring and control method is generated into a computer program and stored in a memory for loading and execution by a processor. Terminal devices are then manufactured based on the memory and processor for convenient use.

[0022] The beneficial effects of this invention are as follows: This invention extracts the time delay compensation degree by acquiring fast and slow sequences and calculating the cross-correlation degree. Then, it combines the maximum delay upper limit and the benchmark value to back-calculate the fast sequence to obtain the synchronous degradation modulus. When the modulus exceeds the limit, it derives the continuous control response intensity based on dynamic feedback and issues commands. This process not only effectively eliminates the propagation delay between multi-source data during the transmission process in the physical medium, but also gathers the dispersed mechanical and thermodynamic degradation characteristics onto a unified evaluation section to achieve a true evaluation of the overall damage state. Furthermore, it transforms the static over-limit modulus into a smooth intervention value, achieving safe and flexible unloading in advance without interrupting the pipeline, thus avoiding mechanical oscillations and secondary damage caused by rigid dynamic cutoff. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the IoT-based industrial equipment monitoring and control method of the present invention. Figure 2 It is a schematic diagram illustrating the comparison between the original physical sensing sequence and the time delay misalignment phenomenon; Figure 3 This is a schematic diagram illustrating the comparison of synchronous degradation modulus evolution; Figure 4 This is a schematic diagram illustrating the comparison of control response strength. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. 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.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses an industrial equipment monitoring and control method based on the Internet of Things (IoT), referring to... Figure 1 This includes steps S1-S3: S1: Obtain the fast sequence, slow sequence, and maximum delay limit of the industrial equipment, and traverse different delay step sizes to calculate the cross correlation degree to extract the time delay compensation degree.

[0027] It should be noted that, since the existing technology uses absolute timestamp alignment, it will cause physical phase misalignment of multi-dimensional features, which will make the monitoring system prone to false alarms and missed detections under changing operating conditions. Therefore, this invention introduces sliding cross gradient correlation logic to find the real delay step required for physical energy transmission.

[0028] Specifically, a vibration sensor deployed on the surface of the main bearing housing of the industrial equipment continuously collects high-frequency analog signals characterizing changes in mechanical kinetic energy, and simultaneously collects low-frequency analog signals characterizing heat accumulation through a temperature sensor deployed on the surface of the equipment's metal casing; the on-site programmable logic controller receives the high-frequency analog signals and low-frequency analog signals and performs analog-to-digital conversion to generate discrete digital state data.

[0029] Furthermore, the data transmission unit transmits digital status data to the IoT cloud platform in real time via the industrial control protocol according to the sampling frequency configured by the system; the IoT cloud platform constructs a fixed-length first-in-first-out queue in the system memory as a sliding data buffer, capturing the current set time window. The continuous data stream within the system will be stripped from the data generated by the vibration sensor and constructed into a fast sequence. The data generated by the temperature sensor is stripped and constructed into a slow sequence. And record the fast sampled values ​​at each time point. and slow sampling value .

[0030] Among them, time window Total number of sampling points , The sampling frequency; The observation duration is defined as follows: by multiplying the sampling frequency by the observation duration, the data scale covered by the sliding data buffer in physical time is determined; the observation duration is... This time parameter is set to cover the complete physical conduction cycle from the sudden change in mechanical kinetic energy to the significant accumulation of heat energy. If this value is set too small, the time window will not be able to cover the complete waveform of the slow sequence rise, making it impossible for the system to find the complete cross-correlation features in the cache, resulting in failure of time delay optimization. If it is set too large, it will cause the system cache to occupy too much space, increase the memory load and computing resource consumption of the underlying server, and cause system response delay or even the risk of crashing. Therefore, its empirical range is set to 200 seconds to 600 seconds. In this embodiment, it is set to 300 seconds to ensure that the physical time delay characteristics are fully captured while maintaining the lightweight operation of the system. In other embodiments, implementers can obtain this parameter and set its value according to the memory capacity of the underlying hardware.

[0031] Furthermore, calculate the delay step size. Cross-correlation degree It satisfies the expression:

[0032] In the formula, Cross-correlation degree; For time window Total number of sampling points within; This represents the current traversal time. This is the delay step size, and its value range is... , Maximum delay limit; For the current moment Slow sampling values; For the previous moment Slow sampling values; For a historic moment The fast sampled values; For a historic moment The fast sampled values; Indicates taking the absolute value; It is an exponential function with the natural constant as its base; This is the attenuation factor.

[0033] The method for determining the maximum delay upper limit is as follows: obtain the thickness of the metal material of the industrial equipment. With thermal diffusivity Calculate the maximum latency limit This expression utilizes the objective physical law in thermodynamics that the time required for heat conduction is directly proportional to the square of the thickness and inversely proportional to the thermal diffusivity to calculate the theoretically maximum physical time required for heat energy to penetrate the outer shell. and multiplied by the sampling frequency Discretize it into the maximum allowable delay step size.

[0034] The expression calculates the slow sequence. First-order difference and fast sequence Delay step The absolute value of the product of the first-order differences is used to measure the coupling strength of the rate of change between different physical properties, and is obtained by multiplying by an exponential function term. For larger delay steps Apply a penalty; when delaying the step size Cross-correlation degree when deviating from the actual physical delay The cross-correlation degree is low when the trends of the two are aligned. Reaching its peak; as the rapid and slow sampling values ​​fluctuate violently in sync, the cross-correlation degree... The value increases accordingly, thereby measuring the correlation of the evolution of different physical quantities and filtering out noise signals without causal relationship.

[0035] The attenuation factor This is a constraint parameter set to suppress historically irrelevant noise introduced by excessively pursuing timeline shift; if this value is set too small, the penalty for large delay steps will be insufficient, leading to excessive cross-correlation. The system is susceptible to interference from long-standing, non-causally related early fluctuations in equipment, causing it to extract false, excessively long lag times. If the value is set too high, it will excessively compress the allowable time delay calculation range, directly truncating the true solid-state heat conduction delay of heavy, long-term thermal inertia equipment, resulting in misaligned data for different physical properties. Therefore, an empirical range of 0.05 to 0.25 is set; in this embodiment, it is set to 0.15 to ensure that the true physical energy conduction delay is locked while filtering out irrelevant historical noise. In other embodiments, the implementer can set the value according to the specific metal heat capacity characteristics of the equipment under test.

[0036] Furthermore, compare the cross-correlation degrees calculated from all traversals. Extraction improves cross-association. The delay step corresponding to the global maximum value is used as the current time window. Internal time delay compensation .

[0037] It should be noted that this step involves acquiring multi-source sensor sequences containing both fast and slow sequences, and then optimizing the time delay compensation degree of cross-modal physical transmission based on sliding cross-gradient correlation logic. This process not only overcomes the time axis misalignment caused by the natural propagation delay between different physical property data, but also effectively measures the correlation of the evolution of different physical quantities and filters out noise signals without causal relationship by calculating the cross-correlation degree. Thus, the true delay step length required for physical energy transmission is found, providing a time measurement benchmark that conforms to the objective laws of energy transmission for subsequent multidimensional state assessment.

[0038] For example, Figure 2 This diagram illustrates the comparison between the original physical sensing sequence and the time-delay misalignment phenomenon. The curve showing early step-change characteristics corresponds to the fast sequence acquired by the vibration sensor in this invention, while the curve showing a smooth and delayed rise corresponds to the slow sequence acquired by the temperature sensor in this invention. The forward vertical boundary line in the diagram represents the initial moment when the underlying mechanical failure triggers a sudden change in kinetic energy, while the backward vertical boundary line represents the moment when the significant temperature rise occurs after the thermal energy overcomes the thermal inertia of the metal shell. This diagram intuitively reveals that in complex industrial physical environments, due to the inherent differences in the energy conduction rates of different media, the multi-source sensing anomalies cause severe misalignment and fragmentation on the physical time axis.

[0039] S2: Establish fast and slow baseline values, and use the time delay compensation degree to back-calculate the fast sequence to obtain the synchronous degradation modulus.

[0040] It should be noted that setting independent alarm thresholds for misaligned data separately cannot reflect the comprehensive superposition effect of damage to the internal structure of the equipment, and is very likely to cause the system to miss the detection of multi-dimensional data. Therefore, this invention introduces a reconstruction and fusion logic based on time delay compensation benchmark.

[0041] Furthermore, the statistical mean of the fast and slow sampled values ​​recorded during continuous operation of the equipment under ideal health conditions was retrieved to establish a fast baseline value. Compared with slow reference value And based on the calculated time delay compensation degree Combined with the current moment Latest data, calculating the synchronous degradation modulus It satisfies the expression:

[0042] In the formula, To synchronously degrade the modulus; The current moment; To utilize time delay compensation degree The time retrieved by working backward from the sequence The fast sampled values; For rapid baseline values; For the current moment Slow sampling values; This is the slow reference value; It is the natural logarithm function; The time delay compensation degree; This represents the maximum delay limit.

[0043] The left side of the expression uses a ratio to eliminate the dimensional difference between vibration and temperature and amplifies the magnitude of their coordinated deviation from the healthy baseline. The right side of the expression uses a logarithmic penalty term to compensate for the time delay. It itself serves as an additional mechanical thermal resistance damage indicator; with time lag compensation degree or fast sample value and slow sampling values The increase in the synchronous degradation modulus It exhibits a monotonically increasing trend; through this product processing process, the system re-aggregates the dispersed mechanical and thermodynamic degradation characteristics on the time axis onto a unified physical evaluation section, thereby realizing the measurement of the overall irreversible damage state of the equipment.

[0044] It should be noted that this invention utilizes the time delay compensation degree for forward backward calculation and phase reconstruction, and combines this with the maximum delay upper limit to deduce and calculate the synchronization degradation modulus. It successfully eliminated the propagation delay between multiple data sources during the transmission process through the physical medium, solved the problem of fusion judgment failure caused by severe misalignment of physical feature peaks, and gathered the mechanical and thermodynamic deterioration features scattered on the time axis onto a unified physical judgment section, thereby realizing the true measurement of the overall irreversible damage state of the equipment.

[0045] For example, Figure 3 This diagram illustrates the evolution of synchronous degradation modulus. The horizontal straight line running through the time axis represents the system's preset tolerance threshold. The dashed line below the tolerance threshold, showing an extremely slow upward trend, represents the static evaluation modulus calculated using the existing absolute alignment algorithm. The solid line, which rapidly exhibits a significant nonlinear monotonically increasing trend after the physical energy conduction correlation is obtained and pierces the tolerance threshold, represents the synchronous degradation modulus derived by the reconstruction algorithm of this invention. This diagram demonstrates that by introducing time delay compensation for multi-dimensional phase reconstruction in mathematical space, this invention effectively overcomes the problems of modulus misjudgment and alarm failure caused by the dilution of high-frequency vibration peaks due to temperature hysteresis noise in the traditional absolute timestamp mechanism, achieving accurate amplification and early warning of early, hidden internal damage in heavy equipment.

[0046] S3: When the synchronous degradation modulus is greater than the tolerance threshold, obtain the control response strength and issue standard control commands.

[0047] It should be noted that, since traditional fixed threshold-triggered start-stop control strategies are prone to causing mechanical vibrations in the production line under heavy load conditions, resulting in secondary physical damage to the equipment and process scrap, this invention introduces a dynamic feedback computing network to generate continuously issued flexible intervention commands.

[0048] Specifically, obtain the historical degradation modulus of the previous calculation cycle. And obtain the basic control quantities that the system currently uses to maintain the rated speed of the equipment. .

[0049] Furthermore, the system's preset tolerance threshold, generated based on historical steady-state operating data of the equipment, is retrieved. When the system detects synchronous degradation modulus Greater than the tolerance threshold At that time, the dynamic unloading mechanism is triggered to calculate the control response strength at the current moment. It satisfies the expression:

[0050] In the formula, To control the response intensity; Basic control quantity; For proportional gain; To synchronously degrade the modulus; This is the tolerance threshold; For differential damping; This is the historical degradation modulus.

[0051] Wherein, the proportional gain and differential damping The fixed control parameters preset for the system can be pre-calibrated using conventional control parameter tuning techniques in this field, such as the empirical trial-and-error method, the critical proportional method, etc., or the technicians can directly write fixed constants into the register of the field controller based on the mechanical load characteristics of the equipment and expert experience. These will not be elaborated here.

[0052] The expression is derived from the basic control quantity. Subtract proportional gain Multiplied by the tolerance threshold The difference is used to directly counteract static over-limit behavior, and the differential damping is subtracted. Multiply by the difference in modulus changes between two cycles to anticipate and respond to the accelerating deterioration trend; when the modulus deteriorates synchronously... Exceeding the tolerance threshold When the situation shows a rapidly worsening trend, control the response intensity. This results in a rapid and significant decrease in numerical values; through linearly combined operations of the basic, proportional, and differential terms, the system can transform static, single-modulus values ​​into continuous and dynamically smooth control output values.

[0053] For example, Figure 4 The diagram illustrates the control response intensity comparison. The dashed line, which remains rigidly at full load for a considerable period after a low-level fault occurs, represents the control commands issued by existing conventional algorithms. The solid line, which shows a rapid and significant numerical drop after the modulus exceeds the tolerance threshold and adaptively forms a smooth unloading envelope, represents the frequency reduction command issued by this invention based on the dynamic feedback calculation network. This diagram fully demonstrates the servo closed-loop logic of this invention, which derives the dynamic flexible frequency reduction intensity based on the rate of change of the modulus including time delay reconstruction. This effectively avoids the system from falling into an intervention vacuum period of dozens of sampling cycles due to judgment lag, thereby achieving safe unloading and physical blocking of abnormal mechanical loads in advance without interrupting the overall pipeline.

[0054] Furthermore, the system intervenes with the minimum safe idle speed control amount. Apply lower limit clamping and compare the calculated control response strength. With the minimum allowable idling frequency of the motor The size relationship, the maximum value of the two. The code is encoded as a standard control command and sent to the given register of the field controller through the data communication network. The equipment adaptively reduces the drive output according to the command to reduce the underlying mechanical load, so as to ensure that the equipment will slow down to a slow idling state under extreme deterioration. This not only relieves the processing load on the production line, but also maintains the smooth operation of the transmission system, avoids sudden stop impact, and waits for manual intervention for maintenance.

[0055] It should be noted that, after real-time comparison and verification of the synchronous degradation modulus exceeding the limit, this step derives the continuous control response strength based on the dynamic feedback calculation network and issues a flexible intervention command for adaptive frequency reduction and unloading to the industrial equipment. This process dynamically matches the safest static counterforce and smooth buffer intervention strength according to the rated power of equipment of different sizes, transforming the static single modulus value into a continuous and dynamically smooth control value. Thus, without interrupting the overall production line, it achieves safe unloading and physical blocking of abnormal mechanical loads in advance, effectively avoiding secondary damage caused by rigid power cut-off.

[0056] The present invention also discloses an IoT-based industrial equipment monitoring and control system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the IoT-based industrial equipment monitoring and control method according to the present invention is implemented.

[0057] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. An industrial equipment monitoring and control method based on the Internet of Things, characterized in that, include: High-frequency and low-frequency analog signals from industrial equipment are acquired and converted from analog to digital to generate digital status data. From this data, a fast sequence containing fast sampled values ​​and a slow sequence containing slow sampled values ​​are constructed. Based on the first-order difference of the slow sequence and the first-order difference of the fast sequence at a delay step size, the cross-correlation degree at the delay step size is extracted. The delay step size corresponding to the cross-correlation degree reaching the global maximum value is extracted as the time delay compensation degree within a set time window. Based on the statistical mean of the fast and slow sampled values ​​recorded during continuous operation under ideal health conditions, fast and slow baseline values ​​are established; based on the ratio of fast sampled values ​​to fast baseline values ​​and the ratio of slow sampled values ​​to slow baseline values ​​derived by using the time delay compensation degree, and the penalty based on the time delay compensation degree, the synchronous degradation modulus is obtained. When the synchronous degradation modulus is greater than the preset tolerance threshold, the basic control quantity for maintaining the rated speed of the equipment is adjusted based on the difference between the synchronous degradation modulus and the tolerance threshold, and the difference between the synchronous degradation modulus and the historical degradation modulus of the previous calculation cycle, to obtain the control response strength; the control response strength is encoded into a standard control command and sent to the given register of the field controller to control the industrial equipment to reduce the drive output.

2. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, The method for obtaining the fast and slow sequences includes: High-frequency analog signals are collected by a vibration sensor deployed on the surface of the main bearing housing of the industrial equipment, and low-frequency analog signals are collected simultaneously by a temperature sensor deployed on the surface of the equipment's metal casing. The high-frequency and low-frequency analog signals are converted from analog to digital to generate discrete digital status data. The digital status data is transmitted to the IoT cloud platform according to the sampling frequency configured by the system. A fixed-length first-in-first-out queue is constructed in the system memory as a sliding data buffer to capture continuous data streams within the currently set time window. The data generated by the vibration sensor is stripped to construct a fast sequence, and the data generated by the temperature sensor is stripped to construct a slow sequence.

3. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, Based on the first-order difference of the slow sequence and the first-order difference of the fast sequence at the delay step size, the cross-correlation degree at the delay step size is extracted, including: ； In the formula, Cross-correlation degree; This represents the total number of sampling points within the time window. This represents the current traversal time. This is the delay step size, and its value range is... , Maximum delay limit; For the current moment Slow sampling values; For the previous moment Slow sampling values; For a historic moment The fast sampled values; For a historic moment The fast sampled values; Indicates taking the absolute value; It is an exponential function with the natural constant as its base; This is the attenuation factor.

4. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, The method of obtaining the synchronization degradation modulus based on the ratio of the fast sampled value to the fast reference value, the ratio of the slow sampled value to the slow reference value (derived by using the time delay compensation degree), and the penalty based on the time delay compensation degree includes: ； In the formula, To synchronously degrade the modulus; To use time delay compensation to quickly retrieve historical moments from the sequence; For rapid baseline values; This is the slow sample value at the current moment; This is the slow reference value; The time delay compensation degree; Maximum delay limit; It is the natural logarithm function.

5. A method for monitoring and controlling industrial equipment based on the Internet of Things according to claim 3 or 4, characterized in that, The method for obtaining the maximum latency limit includes: Obtain the metal material thickness and thermal diffusivity of the industrial equipment; Based on the physical law in thermodynamics that the heat conduction time is directly proportional to the square of the thickness and inversely proportional to the thermal diffusivity, the theoretical maximum physical time required for heat energy to penetrate the outer shell of the device is calculated. The theoretical maximum physical time is multiplied by the sampling frequency, and after discretization, it is determined as the maximum allowable delay step size, which serves as the upper limit of the maximum delay.

6. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, The adjustment of the basic control quantity for maintaining the rated speed of the equipment by the system based on the difference between the synchronous degradation modulus and the preset tolerance threshold, and the difference between the synchronous degradation modulus and the historical degradation modulus of the previous calculation cycle, to obtain the control response strength, includes: ； In the formula, To control the response intensity; Basic control quantity; , These are the proportional gain and differential damping of the system, respectively; To synchronously degrade the modulus; This is the tolerance threshold; This is the historical degradation modulus.

7. The industrial equipment monitoring and control method based on the Internet of Things according to claim 6, characterized in that, The proportional gain and differential damping are preset fixed control parameters; the mechanical load characteristics of the industrial equipment are obtained, and fixed constants are written into the register of the field controller based on the mechanical load characteristics to pre-calibrate the proportional gain and differential damping.

8. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, The total number of sampling points within the set time window is obtained by multiplying the sampling frequency by the observation duration; the observation duration is a set time parameter that covers the complete physical conduction cycle from the sudden change in mechanical kinetic energy to the significant accumulation of thermal energy.

9. The industrial equipment monitoring and control method based on the Internet of Things according to claim 1, characterized in that, Encoding the control response intensity into standard control commands and issuing them to the given register of the field controller includes: By comparing the calculated control response strength with the minimum allowable idling frequency of the motor, the maximum value of the two is encoded as a standard control command; the standard control command is then sent to the given register of the field controller via a data communication network.

10. An industrial equipment monitoring and control system based on the Internet of Things, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the IoT-based industrial equipment monitoring and control method according to any one of claims 1-9.

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