Modular integrated system for frequency converter control cabinets

Abstract

The present application relates to a kind of modular integrated system of frequency converter control cabinet, belong to power electronics field, wherein, the system includes: energy distribution module, signal processing module, operation control module and protection module;Pre-judge the energy demand trend of electrical components when working condition switches, dynamically allocate redundant energy supply reserve, establish energy supply parameter self-calibration closed loop;Extract signal exclusive feature code to distinguish effective signal and interference signal, introduce signal transmission delay dynamic compensation logic, real-time calibration signal output rhythm;Build abnormal working condition prediction model, obtain operating state data, identify potential deviation risk, establish dynamic control threshold self-adaptation mechanism and start preventive control measures;Protection module senses environmental interference factors, dynamically matches protection strategy, according to environmental change and protection feedback continuously optimizes protection strategy.The system realizes the collaborative integration of multiple modules, and can improve the operation stability and environmental adaptability of frequency converter control cabinet.

Description

Technical Field

[0001] This invention belongs to the field of power electronics technology, and specifically relates to a modular integrated system for a frequency converter control cabinet. Background Technology

[0002] As a core power electronic device in industrial automation, frequency converters are widely used in various automated production scenarios such as textiles, papermaking, machine tools, packaging, fans, and water pumps. They achieve energy saving, emission reduction, and refined control of the production process by precisely adjusting the speed and torque of three-phase AC asynchronous motors, making them indispensable key equipment in modern industrial production. With the increasing level of industrial intelligence, production systems place higher demands on the integration, adaptability, stability, and control accuracy of frequency converter control cabinets. However, the technical shortcomings of traditional frequency converter control cabinets are becoming increasingly apparent, making it difficult to meet the actual needs under complex operating conditions. In terms of energy supply, traditional frequency converter control cabinets rely heavily on fixed parameter configurations, lacking the ability to predict changes in operating conditions. When electrical components switch between operating conditions such as starting and stopping, or sudden load changes, energy demand fluctuates drastically. Fixed energy supply parameters cannot dynamically match this change, leading to an imbalance in the allocation of redundant energy reserves—excessive energy supply can easily result in energy waste, while insufficient energy supply may cause problems such as difficulty in starting motors and insufficient torque output. At the same time, traditional systems do not have a self-calibration mechanism for energy supply parameters, and cannot adjust the energy output in real time according to the actual energy consumption fluctuations of components. Over the long term, this can easily lead to a mismatch between energy supply and load demand, affecting equipment operating efficiency and stability.

[0003] In terms of signal processing, industrial sites are subject to complex interference factors such as electromagnetic interference and harmonic pollution. Traditional signal identification methods often use simple filtering or fixed feature comparison, which makes it difficult to accurately distinguish between valid signals and interference signals, and can easily lead to false triggering of control commands or signal loss. In addition, signal transmission is affected by line loss, changes in ambient temperature, etc., which will cause transmission delays. Traditional systems lack dynamic compensation logic, and the accumulation of delays will cause the signal output rhythm to become out of sync with the timing requirements of the working conditions. This will significantly reduce control accuracy, especially in scenarios with strict timing synchronization requirements, such as high-precision machine tools and high-speed transmissions.

[0004] In terms of operation and control, traditional frequency converter control cabinets mostly adopt a passive control mode of "fault response" and lack the ability to predict abnormal operating conditions. They rely on preset fixed thresholds to trigger protection actions and cannot identify potential deviation risks based on real-time operating status data; moreover, fixed thresholds are difficult to adapt to the status changes brought about by operating condition switching, which easily leads to false alarms, missed alarms, or delayed protection actions, resulting in insufficient equipment operation reliability and even production interruption.

[0005] In terms of protection performance, traditional frequency converter control cabinets employ fixed protection strategies, designed only for specific environmental interferences (such as dust prevention and basic electromagnetic shielding), lacking the ability to perceive environmental changes in real time. When the on-site environment experiences fluctuations in temperature and humidity, changes in electromagnetic interference intensity, or excessive dust concentration, the fixed protection strategy cannot dynamically adjust the protection intensity and method. This can lead to either over-protection affecting equipment heat dissipation or insufficient protection causing damage to internal components, thus limiting the applicability of frequency converters in complex environments.

[0006] In addition, the energy supply, signal processing, operation control, and protection modules of traditional frequency converter control cabinets are mostly designed independently and lack a collaborative linkage mechanism. This not only leads to low system integration and complex debugging and maintenance, but also makes it difficult to flexibly expand and customize according to different application scenarios, thus restricting the flexible development of industrial production systems.

[0007] Therefore, there is an urgent need to develop a modular integrated frequency converter control cabinet system. By optimizing energy distribution, signal processing, operation control and protection mechanisms, this system can solve the pain points of traditional technologies in terms of dynamic adaptation, precise control, active protection and integrated collaboration, and meet the application requirements of industrial production for intelligent and highly reliable frequency converter control cabinets. Summary of the Invention

[0008] To address the aforementioned problems in the existing technology, this invention provides a modular integrated system for a frequency converter control cabinet.

[0009] The objective of this invention can be achieved through the following technical solution: a modular integrated system for a frequency converter control cabinet, comprising: an energy distribution module, a signal processing module, an operation control module, and a protection module; The energy distribution module predicts the energy demand change trend of electrical components when switching operating conditions, dynamically allocates redundant energy reserves, and establishes a self-calibration closed loop of energy supply parameters based on the energy consumption fluctuation characteristics of the components to balance energy supply. The signal processing module distinguishes between valid and interference signals by extracting the unique feature codes of the signals, and introduces dynamic compensation logic for signal transmission delay to calibrate the signal output rhythm in real time according to the signal timing requirements. The operation control module constructs an abnormal operating condition prediction model, acquires operating status data, identifies potential deviation risks, establishes a dynamic control threshold self-adaptation mechanism, and initiates preventive control measures. The protection module senses interference factors in the environment, dynamically matches protection strategies based on the module's environmental tolerance characteristics, and continuously optimizes the protection strategies based on environmental changes and protection feedback.

[0010] Specifically, the process for predicting the energy demand change trend of electrical components during operating condition switching is as follows: The system acquires the status information associated with the switching of operating conditions of electrical components, identifies triggering characteristics, and infers the switching trend. By combining the energy consumption data corresponding to historical operating condition switching, it infers the energy consumption change trend corresponding to the current operating condition switching and generates the energy demand change trend of the electrical components during operating condition switching based on the energy consumption change trend.

[0011] Specifically, the process of dynamically allocating redundant energy reserves is as follows: The system acquires the real-time inventory and availability of redundant energy reserves, combines the energy demand change trends corresponding to the switching of operating conditions, matches the real-time energy consumption characteristics of electrical components, plans the supply timing and allocation of redundant energy reserves, executes supply allocation, synchronously adapts to the energy consumption fluctuations of real-time operating conditions, and performs dynamic allocation of redundant energy reserves.

[0012] Specifically, the process of simultaneously establishing a self-calibration closed loop for energy supply parameters based on the energy consumption fluctuation characteristics of the components to balance energy supply is as follows: Based on the energy consumption fluctuation characteristics of the component, the real-time parameter status of the power supply output is correlated, the degree of adaptation between the energy consumption fluctuation and the power supply output is compared, the parameter configuration of the power supply output is adjusted, the adjusted power supply output is fed back to the energy consumption fluctuation sensing link, the configuration status of the power supply parameters is iteratively optimized, and the self-calibration closed loop of the power supply parameters is generated.

[0013] Specifically, the process of distinguishing between valid signals and interference signals by extracting the unique feature codes of the signals is as follows: Based on the inherent characteristics of the transmitted signal, a unique feature code representing the signal source and transmission characteristics is extracted. The unique feature code is then compared and fitted with a preset feature benchmark of valid signals. Based on the fitting result, the signal is classified into categories. Signals belonging to the valid signal category are retained for transmission, while signals belonging to the interference signal category are blocked and eliminated.

[0014] Specifically, the dynamic compensation logic for signal transmission delay includes: Based on the inherent characteristics of the signal transmission path and the influence of environmental interference, the delay fluctuation factor in the signal transmission process is obtained, the changing trend of signal transmission delay is fitted, and a differentiated compensation benchmark is preset in combination with the signal timing priority and transmission requirements. The actual delay of signal transmission is tracked in real time, the deviation between the actual delay and the trend delay is compared, and a dynamic compensation adjustment is generated to adjust the triggering timing and transmission rate of the signal output. Iterative optimization of compensation parameters corrects delay deviations; simultaneously, it adapts to the dynamic changes in signal transmission characteristics during operating condition switching and dynamically adjusts adaptation parameters.

[0015] Specifically, the process of real-time calibration of the signal output rhythm according to signal timing requirements is as follows: Based on the compensation results of the dynamic compensation logic for signal transmission delay, the timing requirements corresponding to the signal are matched, the real-time rhythm state of the signal output is perceived, the adaptation deviation between the real-time rhythm state and the timing requirements is compared, the triggering node and transmission interval of the signal output are adjusted based on the compensation results, the output rhythm is iteratively optimized in combination with the real-time state of the transmission link, the timing requirements are adapted to the changes in operating conditions, and the matching between the signal output rhythm and the timing requirements is maintained.

[0016] Specifically, the process of constructing the abnormal operating condition prediction model is as follows: Collect the effective signal data and the operating status data, sort out the range and characteristics of operating parameters, and obtain the pre-conditions and evolution patterns of abnormal operating conditions. Extract the core feature indicators associated with abnormal operating conditions and build the corresponding matching logic between features and abnormal types. Combined with the control cabinet operation adaptation requirements, solidify the judgment rules and execution process of the model and generate a basic prediction model. Based on real-time operational data, the feature weights and judgment criteria are dynamically adjusted to adapt to the state changes brought about by the switching of operating conditions, optimize the recognition accuracy of the model, and generate the abnormal operating condition prediction model.

[0017] Specifically, the process for identifying potential deviation risks is as follows: Based on the abnormal operating condition prediction model, and combined with the operating status data and the effective signals, the deviation between the operating status data and the preset benchmark parameters is compared to capture any signs of abnormality; the inherent relationship between deviation fluctuations and operating condition switching and power supply fluctuations is analyzed to determine the development trend of deviations and the possible system operation anomalies that may be caused, and the severity level of deviation risks is distinguished; the hidden deviations generated are investigated simultaneously, and potential deviation risks are summarized and a risk identification list is generated.

[0018] Specifically, the process of establishing the dynamic adjustment threshold self-adaptation mechanism is as follows: Based on the output of the abnormal operating condition prediction model and the identified potential deviation risks, combined with real-time operating parameters and the effective signals, and referring to the operating benchmark under normal operating conditions, combined with the operating tolerance characteristics and energy supply adaptation requirements, the initial control threshold and threshold fluctuation range are preset. The system acquires real-time dynamic changes in operating condition switching, power supply fluctuations, and deviation risks. It compares the actual operating status with the current control threshold, and dynamically adjusts the threshold parameters and fluctuation range based on the deviation risk level. It iteratively optimizes the threshold setting logic to adapt to the operating requirements under different operating conditions, thereby generating the dynamic control threshold self-adaptation mechanism.

[0019] Specifically, the process of the dynamic matching protection strategy is as follows: Based on the potential deviation risks and the output results of the abnormal operating condition prediction model, and combined with the dynamic adjustment threshold self-adaptation mechanism, corresponding protection limits and protection actions are configured for the deviation risks; based on the operating condition switching and environmental fluctuation status, a protection execution mode adapted to the current operating scenario is matched.

[0020] Specifically, the process of continuously optimizing the protection strategy based on environmental changes and protection feedback is as follows: Real-time acquisition of environmental interference change information, combined with the execution feedback results of the protection strategy, and correlation of potential deviation risks and abnormal operating condition prediction status, to analyze the impact of environmental fluctuations on the protection effect; based on the degree of environmental change and the protection execution effect, the response threshold, protection intensity and adaptation conditions of the protection strategy are adjusted, and the optimization parameters are synchronously linked to the dynamic control threshold self-adaptation mechanism.

[0021] The beneficial effects of this invention are as follows: Precise and efficient energy supply: The energy distribution module dynamically allocates redundant energy reserves by predicting the changing trend of energy demand during operating condition switching. Combined with the self-calibration closed loop of energy supply parameters, it achieves real-time matching between energy supply and load demand. This avoids energy waste caused by oversupply and prevents equipment malfunctions caused by insufficient energy supply, thereby improving the system's energy utilization efficiency and operational stability.

[0022] Precise and reliable signal processing: The signal processing module accurately distinguishes between valid and interference signals through exclusive feature codes, reducing the impact of factors such as electromagnetic interference in industrial environments. At the same time, it ensures that the signal output rhythm is synchronized with the timing requirements of the operating conditions by dynamically compensating for transmission delays, thereby improving the accuracy and response speed of control commands and adapting to the control requirements of high-precision production scenarios.

[0023] Active and intelligent operation control: The abnormal operating condition prediction model and dynamic control threshold self-adaptation mechanism of the operation control module realize the transformation from "responding after failure" to "predicting and preventing in advance". It can accurately identify potential deviation risks and initiate preventive control, reduce false alarms, missed alarms and production interruptions, and improve the reliability and continuity of equipment operation.

[0024] Flexible and adaptable protection performance: The protection module can sense changes in environmental interference in real time, dynamically match the protection strategy with the module's environmental tolerance characteristics, and ensure the protection effect through continuous optimization. This avoids the problem of over- or under-protection caused by fixed protection strategies, and also broadens the application range of the frequency converter control cabinet in complex environments, extending the service life of the equipment.

[0025] Highly efficient system integration and collaboration: The modular integrated architecture enables the energy distribution, signal processing, operation control, and protection modules to be both relatively independent and collaborative. This not only improves the system integration and structural flexibility, facilitating debugging, maintenance, and functional expansion, but also adapts to the customized needs of different industrial scenarios, enhancing the system's flexible application capabilities. Attached Figure Description

[0026] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0027] Figure 1 This is a flowchart of a modular integrated system for a frequency converter control cabinet according to the present invention; Figure 2 This is a structural block diagram of a modular integrated system for a frequency converter control cabinet according to the present invention. Detailed Implementation

[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0029] Please see Figure 1-2 A modular integrated system for a frequency converter control cabinet includes: an energy distribution module, a signal processing module, an operation control module, and a protection module; The energy distribution module predicts the energy demand change trend of electrical components when switching operating conditions, dynamically allocates redundant energy reserves, and establishes a self-calibration closed loop of energy supply parameters based on the energy consumption fluctuation characteristics of the components to balance energy supply. The signal processing module distinguishes between valid and interference signals by extracting the unique feature codes of the signals, and introduces dynamic compensation logic for signal transmission delay to calibrate the signal output rhythm in real time according to the signal timing requirements. The operation control module constructs an abnormal operating condition prediction model, acquires operating status data, identifies potential deviation risks, establishes a dynamic control threshold self-adaptation mechanism, and initiates preventive control measures. The protection module senses interference factors in the environment, dynamically matches protection strategies based on the module's environmental tolerance characteristics, and continuously optimizes the protection strategies based on environmental changes and protection feedback.

[0030] Specifically, the process for predicting the energy demand change trend of electrical components during operating condition switching is as follows: The system acquires the status information associated with the switching of operating conditions of electrical components, identifies triggering characteristics, and infers the switching trend. By combining the energy consumption data corresponding to historical operating condition switching, it infers the energy consumption change trend corresponding to the current operating condition switching and generates the energy demand change trend of the electrical components during operating condition switching based on the energy consumption change trend.

[0031] Specifically, the process of dynamically allocating redundant energy reserves is as follows: The system acquires the real-time inventory and availability of redundant energy reserves, combines the energy demand change trends corresponding to the switching of operating conditions, matches the real-time energy consumption characteristics of electrical components, plans the supply timing and allocation of redundant energy reserves, executes supply allocation, synchronously adapts to the energy consumption fluctuations of real-time operating conditions, and performs dynamic allocation of redundant energy reserves.

[0032] Specifically, the process of simultaneously establishing a self-calibration closed loop for energy supply parameters based on the energy consumption fluctuation characteristics of the components to balance energy supply is as follows: Based on the energy consumption fluctuation characteristics of the component, the real-time parameter status of the power supply output is correlated, the degree of adaptation between the energy consumption fluctuation and the power supply output is compared, the parameter configuration of the power supply output is adjusted, the adjusted power supply output is fed back to the energy consumption fluctuation sensing link, the configuration status of the power supply parameters is iteratively optimized, and the self-calibration closed loop of the power supply parameters is generated.

[0033] In this embodiment, a commonly used industrial water pump (i.e., an electrical component) is taken as an example, and the specific implementation process is as follows: Predicting the energy demand trend during operating condition switching: Collect the operating status information of the water pump, including the current speed a1, load b1, and control command c1.

[0034] The collected information is analyzed. When the load b1 reaches the preset trigger threshold b2 and the control command c1 is switched to "increase flow", the trigger feature is identified. At the same time, the switching trend is deduced to be that the speed gradually increases from a1 to the target speed a2 and the load increases from b1 to the target load b2.

[0035] By retrieving historical data stored in the module and extracting energy consumption data corresponding to the switching of similar operating conditions, the historical pattern can be identified: the energy consumption rises rapidly to the peak Epm in the initial stage of switching, remains stable for a period of time t1, and then stabilizes at the rated energy consumption Ert.

[0036] By combining the speed difference and load difference during the current operating condition switch, the historical energy consumption pattern is corrected, the energy consumption change trend during the current operating condition switch is deduced, and finally the energy demand change trend is generated.

[0037] Dynamically allocate redundant energy reserves: The real-time inventory Es and availability Ss of redundant energy reserves are obtained in real time. In this embodiment, the availability status is fully available.

[0038] Based on the energy demand change trend generated above, the real-time energy consumption characteristics of the matching water pump are as follows: the real-time energy consumption E1 rises rapidly in the initial stage of switching, the real-time energy consumption E2 stabilizes at around Epm in the peak stage, and the real-time energy consumption E3 drops to Ert in the stable stage.

[0039] The supply sequence and allocation of redundant energy reserves are planned according to demand: In the initial stage (t2 period), a continuous and rapid supply mode is adopted, and the allocation is determined by combining the adaptation coefficient with the difference between peak energy consumption and real-time energy consumption; in the peak stage (t3 period), a continuous and stable supply mode is adopted, and the allocation is set according to the difference between peak energy consumption and real-time energy consumption; in the stable stage (t4 period), an on-demand intermittent supply mode is adopted, and the allocation is configured by combining the energy saving coefficient with the difference between rated energy consumption and real-time energy consumption.

[0040] Supply allocation is carried out in accordance with the plan, while changes in operating conditions are monitored in real time. If changes in pipeline resistance cause the real-time energy consumption E4 to exceed Epm, the supply sequence is immediately adjusted to dynamic response supply, the allocation quota is updated, and the dynamic allocation of redundant energy reserves is completed.

[0041] Establish a self-calibration closed loop for energy supply parameters to balance energy supply: Record the energy consumption fluctuation characteristics of the entire process of water pump operation switching, that is, the energy consumption fluctuation amplitude of each of the three stages, and at the same time associate the real-time parameter status of the power supply output, including output voltage U, output current I, and output frequency f.

[0042] By comparing the degree of adaptation between energy consumption fluctuations and energy output, it can be determined whether the current energy supply parameter configuration can match the energy consumption fluctuation requirements.

[0043] If the adaptation is insufficient, adjust the power output parameter configuration accordingly: if the initial energy consumption fluctuation is too large due to the low output voltage U, increase the output voltage appropriately; if the peak energy consumption fluctuation exceeds the standard due to the lag in the output frequency f response, speed up the response rate of the output frequency.

[0044] The adjusted energy output is fed back to the energy consumption fluctuation sensing link. Based on the new energy consumption fluctuation data, the configuration status of the energy supply parameters is iteratively optimized. The above process is continuously repeated to generate a self-calibration closed loop of energy supply parameters and achieve dynamic balance of energy supply.

[0045] Specifically, the process of distinguishing between valid signals and interference signals by extracting the unique feature codes of the signals is as follows: Based on the inherent characteristics of the transmitted signal, a unique feature code representing the signal source and transmission characteristics is extracted. The unique feature code is then compared and fitted with a preset feature benchmark of valid signals. Based on the fitting result, the signal is classified into categories. Signals belonging to the valid signal category are retained for transmission, while signals belonging to the interference signal category are blocked and eliminated.

[0046] Specifically, the dynamic compensation logic for signal transmission delay includes: Based on the inherent characteristics of the signal transmission path and the influence of environmental interference, the delay fluctuation factor in the signal transmission process is obtained, the changing trend of signal transmission delay is fitted, and a differentiated compensation benchmark is preset in combination with the signal timing priority and transmission requirements. The actual delay of signal transmission is tracked in real time, the deviation between the actual delay and the trend delay is compared, and a dynamic compensation adjustment is generated to adjust the triggering timing and transmission rate of the signal output. Iterative optimization of compensation parameters corrects delay deviations; simultaneously, it adapts to the dynamic changes in signal transmission characteristics during operating condition switching and dynamically adjusts adaptation parameters.

[0047] Specifically, the process of real-time calibration of the signal output rhythm according to signal timing requirements is as follows: Based on the compensation results of the dynamic compensation logic for signal transmission delay, the timing requirements corresponding to the signal are matched, the real-time rhythm state of the signal output is perceived, the adaptation deviation between the real-time rhythm state and the timing requirements is compared, the triggering node and transmission interval of the signal output are adjusted based on the compensation results, the output rhythm is iteratively optimized in combination with the real-time state of the transmission link, the timing requirements are adapted to the changes in operating conditions, and the matching between the signal output rhythm and the timing requirements is maintained.

[0048] In this embodiment, continuing the application scenario of commonly used industrial water pumps, the signal processing module processes the control signals and feedback signals of the water pump. The specific implementation process is as follows: Extract unique feature codes to distinguish valid signals from interference signals: The system collects transmission signals during the operation of the water pump, including speed control signal S, inlet and outlet water pressure feedback signal P, and motor temperature monitoring signal T. These signals all carry their own attribute characteristics (such as signal amplitude range, frequency range, and transmission period).

[0049] Based on the inherent characteristics of the above signals, exclusive feature codes are extracted: For the speed control signal S, feature code F1 is extracted to characterize its source (controller output) and transmission characteristics (digital pulse form); for the pressure feedback signal P, feature code F2 is extracted to characterize its source (pressure sensor) and transmission characteristics (analog voltage form); for the temperature monitoring signal T, feature code F3 is extracted to characterize its source (temperature sensor) and transmission characteristics (low-frequency analog signal form).

[0050] The preset valid signal feature benchmark F in the signal processing module is retrieved. This benchmark includes the standard feature range of commonly used control and feedback signals for industrial water pumps. The extracted unique feature codes F1, F2, and F3 are then compared and fitted with F, respectively.

[0051] Based on the fitting results, the signals are classified into categories: F1, F2, and F3 all fit F very well and are determined to be valid signals, which are retained for transmission; at the same time, the electromagnetic interference generated by the noise signal I has extremely low fitting degree between its extracted feature code Fᵢ and F, and is determined to be an interference signal, which is blocked and eliminated.

[0052] Implementation of dynamic compensation logic for signal transmission delay: Based on the inherent characteristics of the signal transmission path (such as cable lines and interface modules) of the water pump control system, and combined with the environmental influences such as electromagnetic interference and temperature fluctuations in the industrial field, the delay fluctuation factor in the signal transmission process is obtained, including the fixed delay caused by line loss and the dynamic fluctuation delay caused by environmental interference.

[0053] Based on the aforementioned delay fluctuation factors, the trend of signal transmission delay is fitted: during the stable operating phase, the delay exhibits small, stable fluctuations; during the operating phase transition (e.g., normal operation → high-flow operation), the delay fluctuation amplitude increases. Combining signal timing priority and transmission requirements, differentiated compensation benchmarks are preset: the speed control signal S is of high priority, and the compensation benchmark is set to "delay deviation controlled within a small range"; the pressure feedback signal P and temperature monitoring signal T are of medium priority, and the compensation benchmark is set to "delay deviation controlled within a reasonable range".

[0054] Real-time tracking of the actual delay time of each signal transmission: actual delay T of speed control signal S s The actual delay T of the pressure feedback signal P p The actual delay T of the temperature monitoring signal T t The actual delay is compared with the fitted trend delay to generate a dynamic compensation adjustment: when T... s When the trend lag exceeds a small range, a corresponding adjustment amount is generated; when T p When the trend lag exceeds a reasonable range, a corresponding adjustment amount is generated.

[0055] The trigger timing and transmission rate of the signal output are adjusted according to the dynamic compensation adjustment: the trigger timing of S is advanced to correspond to the adjustment amount, and its transmission rate is appropriately increased; the trigger timing of P is advanced to correspond to the adjustment amount to maintain a stable transmission rate. The compensation parameters are iteratively optimized to continuously correct the delay deviation; when the water pump switches to a high-flow operation condition, the signal transmission characteristics change, and the adaptation parameters are dynamically adjusted to ensure that the compensation logic matches the new operating condition.

[0056] Real-time calibration signal output rhythm: Based on the results of the above-mentioned dynamic compensation for signal transmission delay, the timing requirements of each signal are matched: the speed control signal S needs to be transmitted at high frequency to ensure the timeliness of speed adjustment; the pressure feedback signal P needs to be transmitted at medium frequency to ensure the accuracy of pressure monitoring; and the temperature monitoring signal T needs to be transmitted at low frequency to meet the basic requirements of temperature monitoring.

[0057] Real-time rhythm status of the perceived signal output: S's current output interval is slightly longer than required; P's current output interval has a small deviation; T's current output interval basically meets the requirements. Comparison of the adaptation deviation between the real-time rhythm status and the timing requirements: S has a small adaptation deviation; P has a moderate adaptation deviation; T has a small deviation.

[0058] Based on the delay compensation results, adjust the trigger node and transmission interval of the signal output: advance the compensation amount of the trigger node of S to shorten the transmission interval to the required range; advance the compensation amount of the trigger node of P to adjust the transmission interval to the required standard; if the deviation of T is small, there is no need to adjust the trigger node and maintain the original transmission interval.

[0059] By iteratively optimizing the output rhythm based on the real-time status of the transmission link (such as link connectivity and signal attenuation), the system can adapt to new timing requirements in a timely manner when the water pump experiences changes in operating conditions due to load variations, thus maintaining a precise match between the signal output rhythm and timing requirements.

[0060] Specifically, the process of constructing the abnormal operating condition prediction model is as follows: Collect the effective signal data and the operating status data, sort out the range and characteristics of operating parameters, and obtain the pre-conditions and evolution patterns of abnormal operating conditions. Extract the core feature indicators associated with abnormal operating conditions and build the corresponding matching logic between features and abnormal types. Combined with the control cabinet operation adaptation requirements, solidify the judgment rules and execution process of the model and generate a basic prediction model. Based on real-time operational data, the feature weights and judgment criteria are dynamically adjusted to adapt to the state changes brought about by the switching of operating conditions, optimize the recognition accuracy of the model, and generate the abnormal operating condition prediction model.

[0061] Specifically, the process for identifying potential deviation risks is as follows: Based on the abnormal operating condition prediction model, and combined with the operating status data and the effective signals, the deviation between the operating status data and the preset benchmark parameters is compared to capture any signs of abnormality; the inherent relationship between deviation fluctuations and operating condition switching and power supply fluctuations is analyzed to determine the development trend of deviations and the possible system operation anomalies that may be caused, and the severity level of deviation risks is distinguished; the hidden deviations generated are investigated simultaneously, and potential deviation risks are summarized and a risk identification list is generated.

[0062] Specifically, the process of establishing the dynamic adjustment threshold self-adaptation mechanism is as follows: Based on the output of the abnormal operating condition prediction model and the identified potential deviation risks, combined with real-time operating parameters and the effective signals, and referring to the operating benchmark under normal operating conditions, combined with the operating tolerance characteristics and energy supply adaptation requirements, the initial control threshold and threshold fluctuation range are preset. The system acquires real-time dynamic changes in operating condition switching, power supply fluctuations, and deviation risks. It compares the actual operating status with the current control threshold, and dynamically adjusts the threshold parameters and fluctuation range based on the deviation risk level. It iteratively optimizes the threshold setting logic to adapt to the operating requirements under different operating conditions, thereby generating the dynamic control threshold self-adaptation mechanism.

[0063] This embodiment continues the application scenario of commonly used industrial water pumps. The operation control module works based on the valid signals output by the signal processing module and the real-time operation data of the water pump. The specific implementation process is as follows: Construct an abnormal operating condition prediction model: Collect valid signal data and operating status data: Collect valid signals (speed control signal S, pressure feedback signal P, temperature monitoring signal T) after being filtered by the signal processing module, and at the same time collect water pump operating status data (power supply voltage U, operating current I, output torque M) to form a comprehensive data set.

[0064] Analysis of parameter ranges and abnormal patterns: Analysis of the normal operating parameter ranges and characteristics of the above data, clarifying the precursors of common abnormal operating conditions of water pumps (such as motor overload, undervoltage, abnormal pressure fluctuations, and excessive temperature) -- before overload, the current continuously rises and the torque increases; before undervoltage, the voltage fluctuates slightly and the speed response lags; at the same time, summarize the evolution of each abnormal operating condition (such as the current continuously exceeding the normal range and gradually triggering an overload alarm).

[0065] Extract core feature indicators and build matching logic: Extract core feature indicators that are strongly correlated with abnormal operating conditions from the dataset, including current fluctuation amplitude, voltage stability, temperature change rate, and pressure deviation value, and build corresponding matching logic between features and abnormal types (e.g., current fluctuation amplitude exceeding the normal range → associated with "motor overload"; voltage stability below the standard → associated with "undervoltage power supply").

[0066] Generate a basic prediction model: Based on the operational adaptation requirements of the frequency converter control cabinet, solidify the judgment rules of the model (such as triggering anomaly prediction if the core feature indicators meet a certain type of abnormal correlation logic for a certain period of time) and the execution process (data acquisition → feature extraction → logic matching → result output) to generate a basic prediction model.

[0067] Optimize and generate the final model: Based on the real-time operation data of the water pump, dynamically adjust the weight of each core feature indicator (such as increasing the weight of current and speed features during the operation condition switching stage) and judgment criteria, adapt to the state changes brought about by the operation condition switching such as "normal operation → high flow operation", continuously optimize the model recognition accuracy, and form an abnormal operation condition prediction model.

[0068] Identify potential deviation risks: Detecting early signs of anomalies: Based on the above-mentioned abnormal operating condition prediction model, combined with the real-time operating status data and effective signals of the water pump, the current operating parameters (current I, voltage U, temperature T, pressure P) are compared with preset benchmark parameters. When the current I is detected to be slightly higher than the benchmark range and the temperature T shows a slow upward trend, the early signs of anomalies are detected.

[0069] Analysis of Deviation Correlation and Development Trend: The inherent relationship between the above deviations and operating conditions and power supply was analyzed—the current and temperature deviations occurred after the water pump switched to high-flow operation and changed synchronously with the power supply fluctuations; further analysis of the deviation development trend: if the current state continues, it may cause abnormal motor overload in the short term; at the same time, the risk level was distinguished and the deviation risk was determined to be moderate risk.

[0070] Investigate and compile a list of latent deviations: Simultaneously investigate latent deviations—if the response sensitivity of the pressure feedback signal is slightly reduced but does not reach the explicit abnormality threshold, it is determined to be a latent deviation (which may lead to pressure regulation lag); summarize the above-mentioned moderate risk deviations and latent deviations to generate a risk identification list, and clarify the deviation type, related factors and risk level.

[0071] Establish a dynamic adjustment threshold self-adaptation mechanism: Preset initial thresholds and fluctuation ranges: Based on the output results of the abnormal working condition prediction model and the identified potential deviation risks, combined with the real-time operating parameters of the water pump (current I, voltage U) and effective signals (speed S, pressure P), referring to the operating benchmark under normal working conditions, and combining the water pump's operating tolerance characteristics (such as maximum tolerance current, voltage fluctuation tolerance range) and power supply adaptation requirements, preset initial control thresholds (such as current upper limit threshold, temperature warning threshold) and corresponding threshold fluctuation ranges.

[0072] Dynamically adjust threshold parameters: Real-time acquisition of dynamic changes in pump operating conditions (such as high flow rate operation → normal operation), power supply fluctuations (small voltage fluctuations), and deviation risks; comparison of the actual operating status with the current control threshold; when the operating condition is switched to normal operation, the original current threshold adaptability decreases; combined with the deviation risk level (moderate risk has been mitigated), the fluctuation range of the current threshold is appropriately narrowed, and the threshold parameters are adjusted to a range that is compatible with normal operating conditions.

[0073] Iterative optimization of threshold setting logic: The threshold setting logic is continuously optimized through iteration. When the frequency of power supply fluctuations increases, the fluctuation range of the voltage threshold is widened to avoid false triggering. When the risk of temperature deviation increases, the fluctuation range of the temperature warning threshold is tightened to improve the warning sensitivity. This ensures that the threshold mechanism can adapt to the operating requirements under different working conditions, forming a dynamic control threshold self-adaptation mechanism.

[0074] Specifically, the process of the dynamic matching protection strategy is as follows: Based on the potential deviation risks and the output results of the abnormal operating condition prediction model, and combined with the dynamic adjustment threshold self-adaptation mechanism, corresponding protection limits and protection actions are configured for the deviation risks; based on the operating condition switching and environmental fluctuation status, a protection execution mode adapted to the current operating scenario is matched.

[0075] Specifically, the process of continuously optimizing the protection strategy based on environmental changes and protection feedback is as follows: Real-time acquisition of environmental interference change information, combined with the execution feedback results of the protection strategy, and correlation of potential deviation risks and abnormal operating condition prediction status, to analyze the impact of environmental fluctuations on the protection effect; based on the degree of environmental change and the protection execution effect, the response threshold, protection intensity and adaptation conditions of the protection strategy are adjusted, and the optimization parameters are synchronously linked to the dynamic control threshold self-adaptation mechanism.

[0076] In this embodiment, continuing the application scenario of commonly used industrial water pumps, the protection module operates based on the potential deviation risks, abnormal operating condition prediction results, and dynamic adjustment thresholds output by the operation control module. The specific process is as follows: Dynamic matching protection strategy: Link core information: obtain potential deviation risks q identified by the operation control module, as well as abnormal operating condition prediction results z and dynamic adjustment thresholds y, clarify the core protection objectives as suppressing the expansion of q, avoiding the occurrence of z, and resisting interference from the industrial site environment, while linking y to ensure the system's operational adaptability.

[0077] Configure protection limits and actions: For potential deviation risks q, configure protection limits v (covering current withstand peak, temperature safety range, and signal interference adaptation range); for abnormal operating condition prediction results z, supplement protection limits v (matching torque safety threshold); the corresponding protection actions are to start the heat dissipation module, strengthen signal shielding, and link the power supply module to adjust the output, forming a precise correspondence between "risk-limit-action".

[0078] Matching execution mode: Combining the current operating conditions of the water pump (high flow rate operation, high load, increased module heating) and the environmental fluctuations (slightly increased electromagnetic interference, higher ambient temperature), the appropriate protection execution mode c is matched to ensure that the actions such as heat dissipation, shielding, and power supply linkage are fully consistent with the current operating scenario and interference level.

[0079] Continuously optimize protection strategies: Collect environmental and feedback data: acquire real-time information on changes in environmental interference (including electromagnetic interference intensity, ambient temperature, and air humidity), collect feedback results after the implementation of protection strategies; synchronously correlate potential deviation risks (q) with abnormal operating condition prediction status (z) to clarify the real-time changes in current risks and warnings.

[0080] Analyze the impact patterns: Analyze the impact patterns of environmental fluctuations on the protection effect, clarify the direct correlation between changes in u (such as increased electromagnetic interference, higher temperature) and the effectiveness of protection, the expansion of q, and the persistence of z, so as to provide a basis for parameter adjustment.

[0081] Adjust protection parameters: Based on the degree of change of u and the effect of protection execution, adjust the response threshold, protection strength and adaptation conditions of the protection strategy to generate optimized protection parameters d, so as to ensure the adaptability of the strategy to the environment and working conditions.

[0082] Linkage threshold mechanism: The protection parameter d is synchronized with the dynamic control threshold y, so that y and the protection strategy are coordinated and adapted, avoiding false triggering of control due to the adjustment of the protection strategy, and ensuring the consistency of protection and control of the entire system.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A modular integrated system for a frequency converter control cabinet, characterized in that, include: Energy distribution module, signal processing module, operation control module, protection module; The energy distribution module predicts the energy demand change trend of electrical components when switching operating conditions, dynamically allocates redundant energy reserves, and establishes a self-calibration closed loop of energy supply parameters based on the energy consumption fluctuation characteristics of the components to balance energy supply. The signal processing module distinguishes between valid and interference signals by extracting the unique feature codes of the signals, and introduces dynamic compensation logic for signal transmission delay to calibrate the signal output rhythm in real time according to the signal timing requirements. The operation control module constructs an abnormal operating condition prediction model, acquires operating status data, identifies potential deviation risks, establishes a dynamic control threshold self-adaptation mechanism, and initiates preventive control measures. The protection module senses interference factors in the environment, dynamically matches protection strategies based on the module's environmental tolerance characteristics, and continuously optimizes the protection strategies based on environmental changes and protection feedback.

2. The system according to claim 1, characterized in that, The specific process for predicting the energy demand change trend of electrical components during operating condition switching is as follows: The system acquires the status information associated with the switching of operating conditions of electrical components, identifies triggering characteristics, and infers the switching trend. By combining the energy consumption data corresponding to historical operating condition switching, it infers the energy consumption change trend corresponding to the current operating condition switching and generates the energy demand change trend of the electrical components during operating condition switching based on the energy consumption change trend.

3. The system according to claim 1, characterized in that, The specific process of dynamically allocating redundant energy reserves is as follows: The system acquires the real-time inventory and availability of redundant energy reserves, combines the energy demand change trends corresponding to the switching of operating conditions, matches the real-time energy consumption characteristics of electrical components, plans the supply timing and allocation of redundant energy reserves, executes supply allocation, synchronously adapts to the energy consumption fluctuations of real-time operating conditions, and performs dynamic allocation of redundant energy reserves.

4. The system according to claim 1, characterized in that, The specific process of establishing a self-calibration closed loop for energy supply parameters based on the energy consumption fluctuation characteristics of the components and balancing energy supply is as follows: Based on the energy consumption fluctuation characteristics of the component, the real-time parameter status of the power supply output is correlated, the degree of adaptation between the energy consumption fluctuation and the power supply output is compared, the parameter configuration of the power supply output is adjusted, the adjusted power supply output is fed back to the energy consumption fluctuation sensing link, the configuration status of the power supply parameters is iteratively optimized, and the self-calibration closed loop of the power supply parameters is generated.

5. The system according to claim 1, characterized in that, The specific process of distinguishing between valid signals and interference signals by extracting the unique feature codes of the signals is as follows: Based on the inherent characteristics of the transmitted signal, a unique feature code representing the signal source and transmission characteristics is extracted. The unique feature code is then compared and fitted with a preset feature benchmark of valid signals. Based on the fitting result, the signal is classified into categories. Signals belonging to the valid signal category are retained for transmission, while signals belonging to the interference signal category are blocked and eliminated.

6. The system according to claim 1, characterized in that, The dynamic compensation logic for signal transmission delay specifically includes: Based on the inherent characteristics of the signal transmission path and the influence of environmental interference, the delay fluctuation factor in the signal transmission process is obtained, the changing trend of signal transmission delay is fitted, and a differentiated compensation benchmark is preset in combination with the signal timing priority and transmission requirements. The actual delay of signal transmission is tracked in real time, the deviation between the actual delay and the trend delay is compared, and a dynamic compensation adjustment is generated to adjust the triggering timing and transmission rate of the signal output. Iterative optimization of compensation parameters corrects delay deviations; simultaneously, it adapts to the dynamic changes in signal transmission characteristics during operating condition switching and dynamically adjusts adaptation parameters.

7. The system according to claim 1, characterized in that, The specific process of real-time calibration of the signal output rhythm according to signal timing requirements is as follows: Based on the compensation results of the dynamic compensation logic for signal transmission delay, the timing requirements corresponding to the signal are matched, the real-time rhythm state of the signal output is perceived, the adaptation deviation between the real-time rhythm state and the timing requirements is compared, the triggering node and transmission interval of the signal output are adjusted based on the compensation results, the output rhythm is iteratively optimized in combination with the real-time state of the transmission link, the timing requirements are adapted to the changes in operating conditions, and the matching between the signal output rhythm and the timing requirements is maintained.

8. The system according to claim 1, characterized in that, The specific process for constructing the abnormal operating condition prediction model is as follows: Collect the effective signal data and the operating status data, sort out the range and characteristics of operating parameters, and obtain the pre-conditions and evolution patterns of abnormal operating conditions. Extract the core feature indicators associated with abnormal operating conditions and build the corresponding matching logic between features and abnormal types. Combined with the control cabinet operation adaptation requirements, solidify the judgment rules and execution process of the model and generate a basic prediction model. Based on real-time operational data, the feature weights and judgment criteria are dynamically adjusted to adapt to the state changes brought about by the switching of operating conditions, optimize the recognition accuracy of the model, and generate the abnormal operating condition prediction model.

9. The system according to claim 1, characterized in that, The specific process for identifying potential deviation risks is as follows: Based on the abnormal operating condition prediction model, combined with the operating status data and the effective signals, the deviation between the operating status data and the preset benchmark parameters is compared to capture the signs of abnormality; the inherent relationship between deviation fluctuations and operating condition switching and power supply fluctuations is analyzed to determine the development trend of deviations and the possible system operation anomalies, and to distinguish the severity level of deviation risks. Simultaneously investigate any hidden deviations that may arise, summarize potential deviation risks, and generate a risk identification list.

10. The system according to claim 1, characterized in that, The specific process for establishing the dynamic adjustment threshold self-adaptation mechanism is as follows: Based on the output of the abnormal operating condition prediction model and the identified potential deviation risks, combined with real-time operating parameters and the effective signals, and referring to the operating benchmark under normal operating conditions, combined with the operating tolerance characteristics and energy supply adaptation requirements, the initial control threshold and threshold fluctuation range are preset. The system acquires real-time dynamic changes in operating condition switching, power supply fluctuations, and deviation risks. It compares the actual operating status with the current control threshold, and dynamically adjusts the threshold parameters and fluctuation range based on the deviation risk level. It iteratively optimizes the threshold setting logic to adapt to the operating requirements under different operating conditions, thereby generating the dynamic control threshold self-adaptation mechanism.

11. The system according to claim 1, characterized in that, The specific process of the dynamic matching protection strategy is as follows: Based on the potential deviation risks and the output results of the abnormal operating condition prediction model, and combined with the dynamic adjustment threshold self-adaptation mechanism, corresponding protection limits and protection actions are configured for the deviation risks; based on the operating condition switching and environmental fluctuation status, a protection execution mode adapted to the current operating scenario is matched.

12. The system according to claim 1, characterized in that, The specific process of continuously optimizing the protection strategy based on environmental changes and protection feedback is as follows: Real-time acquisition of environmental interference change information, combined with the execution feedback results of the protection strategy, and correlation of potential deviation risks and abnormal operating condition prediction status, to analyze the impact of environmental fluctuations on the protection effect; based on the degree of environmental change and the protection execution effect, the response threshold, protection intensity and adaptation conditions of the protection strategy are adjusted, and the optimization parameters are synchronously linked to the dynamic control threshold self-adaptation mechanism.

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