Self-adaptive starting method and system based on voltage and current monitoring

Abstract

The invention relates to the technical field of self-adaptive starting, and discloses a self-adaptive starting method and system based on voltage and current monitoring, and the method comprises the steps that an MCU (Microprogrammed Control Unit) triggers an auxiliary silicon controlled rectifier and a main silicon controlled rectifier through preset starting logic to start a motor, and carries out the high-frequency sampling of current and voltage data in real time; once starting failure is detected, short-time voltage sequences and short-time current sequences before and after a failure occurrence point can be automatically captured and stored. Then, key features are extracted from the sequences, and failure causes are determined through decision tree model analysis. And based on the analysis result and the current retry times, the MCU outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safety mode. Thus, the reliability of motor starting is improved, subsequent fault diagnosis is supported through detailed data recording, and the maintainability and safety of the system are greatly improved.

Description

Technical Field

[0001] The present application relates to the field of adaptive startup technology, and more specifically, to an adaptive startup method and system based on voltage and current monitoring. Background Art

[0002] Motors, as key components in modern industrial and household appliances, play a vital role. However, traditional motor starting methods, such as PTC starters and deadweight starters, have exposed numerous limitations and flaws in practical applications. These shortcomings directly impact motor reliability and safety, while also increasing maintenance costs and technical complexity.

[0003] The design philosophy of traditional mechanical starters is primarily based on simple physical mechanisms, resulting in limited protection functions and passive responses. For example, a hammer starter relies on the electromagnetic force generated by the high current at the start-up moment to overcome gravity to attract the contacts, and gravity opens them after the current drops. PTC starters, on the other hand, utilize the material's characteristic of a sharp increase in resistance at a specific temperature to disconnect the starting winding. Neither method actively monitors the compressor's real-time operating status and only provides indirect, situation-specific protection. This design approach means they lack real-time monitoring and comprehensive protection for multiple critical parameters, such as voltage, power, and temperature, limiting their ability to address complex electrical faults. Furthermore, due to their lack of intelligent analysis capabilities, traditional starters often fail to respond effectively to conditions such as undervoltage, overvoltage, or power anomalies, and may even cause further damage due to erroneous actions. Summary of the Invention

[0004] To address the deficiencies of traditional motor starters in protection mechanisms and fault diagnosis, this application proposes an adaptive starting method and system based on voltage and current monitoring, which utilizes an MCU control unit combined with an electricity metering chip to achieve precise control and intelligent monitoring of the motor starting process.

[0005] According to one aspect of the present application, an adaptive starting method and system based on voltage and current monitoring are provided, including: an MCU control unit triggers a secondary thyristor and a main thyristor through a preset starting logic to start a motor, and simultaneously samples current and voltage at high frequency through an electricity metering chip; the MCU control unit starts a failure determination logic, and once a startup failure is determined, captures and stores a short voltage sequence and current sequence before and after the failure occurs through the electricity metering chip; the MCU control unit extracts current key features and voltage key features from the voltage sequence and current sequence, and determines the cause of failure based on the current key features and voltage key features; the MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries; and the MCU control unit executes the retry strategy.

[0006] In one possible implementation, the MCU control unit triggers the auxiliary thyristor and the main thyristor to start the motor through a preset starting logic, including: the MCU control unit first triggers the auxiliary thyristor to turn on to connect the starting winding of the motor; the MCU control unit then triggers the main thyristor to turn on to connect the main winding of the motor.

[0007] In a possible implementation, the failure determination logic includes the current not reaching an expected threshold within a specified time, the current not falling back after rising, and the overcurrent / overvoltage protection being triggered.

[0008] In one possible implementation, the key current characteristics include peak current, current rise rate, time to peak current, current before failure, and current fluctuation characteristics; the key voltage characteristics include average voltage before failure, minimum voltage during startup, and voltage drop amplitude.

[0009] In a possible implementation, determining the failure cause based on the current key feature and the voltage key feature includes: inputting the current key feature and the voltage key feature into a decision tree model to obtain the failure cause.

[0010] In one possible implementation, the MCU control unit outputs a retry strategy based on the failure cause and the current number of retries, including: the MCU control unit queries a preset multi-level retry strategy table based on the failure cause and the current number of retries to obtain the retry strategy, the retry strategy including a strategy type, a waiting number of seconds, and a parameter configuration identifier for the next startup, the strategy type including retry and lock.

[0011] In one possible implementation, inputting the current key feature and the voltage key feature into a decision tree model to obtain the failure cause includes: correlating the peak current time, the pre-failure current and the current fluctuation feature through a current fluctuation function to obtain a current correlation expression, wherein the current correlation expression includes a fluctuation function adjustment factor; correlating the current fluctuation feature, the pre-failure average voltage, the minimum voltage during startup and the voltage drop amplitude through a system voltage stability function to obtain a voltage correlation expression, wherein the voltage correlation expression includes a stability risk adjustment factor; minimizing the current correlation expression while maximizing the voltage correlation expression by adjusting the fluctuation function adjustment factor and the stability risk adjustment factor; substituting the determined fluctuation function adjustment factor and the stability risk adjustment factor into the current correlation expression and the voltage correlation expression, and inputting the current correlation expression, the voltage correlation expression, the current key feature and the voltage key feature into a decision tree model to obtain the failure cause.

[0012] According to another aspect of the present application, an adaptive starting system based on voltage and current monitoring is also provided. The adaptive starting system based on voltage and current monitoring includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The system is characterized in that when the processor executes the computer program, the steps of the adaptive starting method based on voltage and current monitoring as described above are implemented.

[0013] Compared with the prior art, the adaptive starting method and system based on voltage and current monitoring provided by the present application include: the MCU control unit triggers the auxiliary thyristor and the main thyristor through a preset starting logic to start the motor, and samples the current and voltage data in real time at high frequency. Once a startup failure is detected, the short-term voltage sequence and current sequence before and after the failure point are automatically captured and stored. Then, key features are extracted from these sequences, and the cause of the failure is determined through decision tree model analysis. Based on this analysis result and the current number of retries, the MCU control unit outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safe mode. In this way, not only the reliability of motor starting is improved, but also detailed data records are used to support subsequent fault diagnosis, greatly improving the maintainability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 The figure illustrates a schematic flow chart of an adaptive startup method based on voltage and current monitoring according to an embodiment of the present application.

[0016] Figure 2 The figure shows a schematic principle block diagram of a startup control circuit according to an embodiment of the present application.

[0017] Figure 3 The figure shows a schematic flow chart of an adaptive starting method and system based on voltage and current monitoring according to an embodiment of the present application, in which the MCU control unit triggers the auxiliary thyristor and the main thyristor to start the motor through a preset starting logic.

[0018] Figure 4 The figure shows a schematic structural diagram of an adaptive starting system based on voltage and current monitoring according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0020] Figure 1 The figure illustrates a schematic flow chart of an adaptive startup method based on voltage and current monitoring according to an embodiment of the present application. Figure 2 FIG2 shows a schematic block diagram of a startup control circuit according to an embodiment of the present application. Figure 1 and Figure 2 As shown, the present application provides an adaptive starting method based on voltage and current monitoring, including: S1: the MCU control unit triggers the auxiliary thyristor and the main thyristor through a preset starting logic to start the motor, and at the same time samples the current and voltage at high frequency through the electricity metering chip; S2: the MCU control unit starts the failure judgment logic, and once the startup failure is determined, the electricity metering chip captures and stores the short voltage sequence and current sequence before and after the failure point; S3: the MCU control unit extracts the current key features and the voltage key features from the voltage sequence and the current sequence, and determines the cause of the failure based on the current key features and the voltage key features; S4: the MCU control unit outputs a retry strategy based on the failure cause and the current number of retries; S5: the MCU control unit executes the retry strategy.

[0021] For example, in step S1, the MCU control unit triggers the auxiliary thyristor and the main thyristor through the preset starting logic to start the motor, and at the same time samples the current and voltage at high frequency through the electric metering chip. It should be understood that one of the main challenges in the motor starting process is to ensure sufficient starting torque and avoid unnecessary burden on the motor and the power grid. Traditionally, the direct starting method may cause a momentary large current shock, which may not only damage the motor itself, but also affect the stability of the entire power grid. Therefore, by using thyristor technology, the starting process of the motor can be controlled more accurately, thereby reducing this shock. In one embodiment, as Figure 3 As shown, the MCU control unit triggers the auxiliary thyristor and the main thyristor to start the motor through a preset starting logic, including: S11: the MCU control unit first triggers the auxiliary thyristor to turn on to connect the starting winding of the motor; S12: the MCU control unit then triggers the main thyristor to turn on to connect the main winding of the motor.

[0022] Specifically, at the beginning of the startup process, the MCU control unit triggers the secondary thyristor to conduct, connecting the motor's starter winding. This step is crucial for providing initial torque, as the starter winding is designed to generate a large electromagnetic torque at low speeds, helping the motor overcome static friction and other resistance, allowing it to rotate from a standstill. During this process, the power metering chip samples current and voltage at high frequency, monitoring the motor's status in real time and feeding this data back to the MCU control unit.

[0023] As the motor speed gradually increases and the starting conditions are met, for example, the starting conditions include whether the starting current of the motor has dropped to a level close to or within the expected operating current range, the MCU control unit triggers the main thyristor to connect the main circuit of the compressor and enter normal operating state. During this process, the MCU control unit is not only responsible for controlling the triggering timing of the thyristor, but also continuously monitors the operating parameters of the motor through the electrical metering chip.

[0024] For example, in step S2, the MCU control unit activates the failure determination logic. Once a startup failure is determined, the power metering chip captures and stores the short voltage and current sequences before and after the failure point. It should be understood that motor startup is highly susceptible to multiple factors, such as power supply quality, load fluctuations, and mechanical structure, leading to common startup failures. Therefore, during the startup process, the MCU control unit activates the failure determination logic, enabling the device to not only immediately detect and determine startup failures but also fully preserve the electrical signals at critical moments, providing a data foundation for subsequent analysis and maintenance.

[0025] Specifically, startup failure determination involves the MCU control unit, integrated within the control system, leveraging high-speed communication with the power metering chip to receive and analyze the current and voltage waveforms generated during the compressor startup process in real time. The MCU control unit performs algorithmic analysis on the data stream throughout the startup window, promptly issuing a startup success or failure determination based on predefined criteria, and executing corresponding data storage and protection actions. In one embodiment, the failure determination logic includes determining if the current has not reached the expected threshold within a specified timeframe, if the current has not fallen back after rising, and if overcurrent / overvoltage protection has been triggered.

[0026] Specifically, at the initial stage of motor startup, as the motor accelerates from a stationary state, it needs to overcome a large inertial resistance, resulting in a relatively high starting current. This current is often referred to as the stall current or peak current. If the actual measured current fails to reach or exceed the set expected threshold within a preset time window (e.g., 1 second), the startup is judged to have failed. The expected threshold can be determined based on the motor's rated power, design parameters, and startup characteristics. In a specific embodiment, for a household refrigerant compressor with a rated operating current of 3.5A and an actual startup current ranging from 8A to 12A, the expected threshold can be set to 7A. Once the 1-second period has passed, if the highest current value recorded by the sampling point analysis is consistently less than the predetermined threshold, the startup is judged to have failed. Common causes of such failures include excessive external load, capacitor degradation or disconnection, insufficient power supply (undervoltage), etc.

[0027] Next, consider the physical process of motor startup, which dictates that the current curve undergoes several stages: rising at startup, reaching a peak, and then falling back and stabilizing. Under normal circumstances, the current is initially limited only by the winding short-circuit impedance. After power is applied, the current rapidly increases. As the speed increases and the back EMF strengthens, the current inevitably falls back and stabilizes. If the motor rotor is stalled or the load is stuck, the peak current can remain high for a long time, showing no clear trend of falling back. This not only increases the risk of equipment damage but also often indicates mechanical failure, a coil short circuit, or severe stalling. Therefore, after detecting the peak current, the MCU control unit continuously monitors the subsequent current waveform. For example, within 0.7 to 1.2 seconds after the peak current occurs, the current must drop by at least 25%—that is, the current must drop to 0.75 times the peak value. Otherwise, it is considered to have not fallen back. Of course, this is just an example and can be adjusted according to specific circumstances.

[0028] Furthermore, considering that during actual operation, under certain circumstances, the compressor's input voltage may suddenly increase abnormally, or the current may momentarily exceed the permitted limit, potentially causing high-risk component damage or even circuit fires, the MCU control unit continuously reads samples from the power metering chip, closely monitoring the peak current and voltage extremes. If the current exceeds the safe limit (e.g., 12A) or the voltage exceeds the design allowable range (e.g., 265V), overcurrent / overvoltage protection is activated regardless of the operating state, shutting down all main and auxiliary thyristors and declaring startup a complete failure.

[0029] For example, in step S3, the MCU control unit extracts key current and voltage features from the voltage and current sequences and uses these features to determine the cause of the failure. It should be understood that motor start-up failure can be caused by a variety of factors, such as power supply issues, mechanical resistance, and electrical faults. Relying solely on simple threshold comparisons (e.g., whether the current reaches the expected level) does not provide sufficient information to accurately identify the specific issue. Therefore, by extracting and analyzing key current and voltage features, the cause of the failure can be accurately determined based on these features.

[0030] In one embodiment, the key current characteristics include peak current, current rise rate, time to peak current, current before failure, and current fluctuation characteristics; the key voltage characteristics include average voltage before failure, minimum voltage during startup, and voltage drop amplitude.

[0031] Specifically, peak current extraction is achieved by the MCU control unit sampling real-time current values ​​at a high frequency throughout the entire startup process (typically within the startup task determination window). The maximum value in the time series sampled data is taken as the peak current. For example, during the initial power-up of the compressor, the current rises sharply due to the low stator back EMF. As the speed increases and the back EMF strengthens, the current decreases and eventually stabilizes to the operating value. Within the MCU sampling window, the program continuously records the maximum current sampling point, which is defined as the peak current. The current rise rate is determined by calculating the difference between the current sampled value at each moment and the previous moment, dividing it by the sampling interval (which can be approximately equal to the differential), and extracting the maximum slope, reflecting the startup impact. The time to peak current is determined by simply using the sampling start time as the zero point and finding the time corresponding to the peak current sampling. This time is the peak current time, providing intuitive feedback on whether the equipment startup efficiency is abnormal. The pre-failure current is generally determined by taking the average current for the N milliseconds (e.g., 100 ms) preceding the startup failure determination, or by taking the current at the last sampling point as the pre-failure current to reflect the dynamic changes in the system before the failure. Current fluctuation characteristics focus on describing the amplitude and pattern of the current curve during startup. This can be accomplished by taking a segment within the determination period and calculating statistical characteristics such as slope, first-order and second-order differences, variance, and standard deviation. This can reveal abnormalities such as unstable startup, violent oscillation, or current jitter. For example, if the current sampled 200 milliseconds before failure is I(t), the mean square error (var(I(t))) is calculated to characterize current activity and abnormal fluctuations.

[0032] The average voltage before failure is the arithmetic mean of all voltage samples within a short window (e.g., 100ms) before failure is detected. This feature accurately reflects whether the voltage at the power supply or line end drops significantly when the load suddenly increases during startup. The minimum voltage during startup represents the lowest point in the entire startup interval and is often used to detect hidden circuit issues such as power quality anomalies and transient voltage drops. The voltage drop amplitude refers to the difference between the voltage before startup and the lowest voltage during startup. This metric intuitively depicts the impact of the motor starting and loading on the entire power grid, and indirectly indicates the quality of the line, power supply, or overall electrical matching.

[0033] Next, considering that diagnosing the cause of a fault solely based on independent parameters is far from sufficient, consider this. In complex electrical systems, faults such as poor starting, stalled rotor damage, and undervoltage failure often manifest themselves through cross-coupled and highly variable current and voltage characteristics. While a single characteristic can partially reflect a phenomenon, accurate differentiation based solely on one characteristic is difficult. For this reason, in one embodiment, the cause of the failure is determined based on key current and voltage characteristics, including inputting these key current and voltage characteristics into a decision tree model to determine the cause of the failure. The decision tree model is preferred for this scenario primarily due to its inherent advantage of hierarchical conditional flow, weaving multiple characteristic variables into a judgment path that combines traditional empirical criteria with data-driven analysis, rather than passively superimposing individual boundary thresholds. Higher-order decision trees can also better reflect nonlinear mapping relationships between parameters, are interpretable, and are easy to maintain, facilitating subsequent optimization and expansion based on actual engineering operations and maintenance experience.

[0034] In a specific embodiment, all of the aforementioned characteristic parameters are first extracted in parallel within the MCU control unit. These parameters are then input into the decision tree algorithm module in a predetermined format, such as a vector of {peak current, current rise rate, time to peak current, current before failure, current fluctuation characteristics, average voltage before failure, minimum voltage during startup, and voltage drop amplitude}. The decision tree modeling and training process can be completed offline during the experimental phase using large amounts of fault data, and the resulting criteria and paths are embedded within the MCU control unit. For example, a compressor stall typically manifests as a high peak current with no significant drop for a long period, and a significant drop in minimum voltage during startup, even below the threshold. Failures caused by poor power quality are characterized by peak currents far below the nominal value and a sharp instantaneous voltage drop during startup. The decision tree model prioritizes root node classification based on the most sensitive and common abnormal characteristics, then branches to fine-grained parameter comparisons, achieving multi-level, progressive failure type screening. For example, the first-level criteria may determine whether the peak current exceeds the limit. If so, it is considered to be stall protection or motor jam. If not, the analysis turns to the ratio of the current rise rate to the time to reach the peak current to determine whether the load is abnormal or the starting capacitor has failed. The next step is to determine the power supply side fault based on the voltage drop amplitude and the average voltage drop before failure. This layered nesting ultimately outputs a clear and semantically clear failure cause code to facilitate subsequent maintenance, analysis, and optimization.

[0035] When inputting the current key feature and the voltage key feature into the decision tree model, if the multidimensional coupling characteristics between the current key feature and the voltage key feature, that is, the coupling characteristics between current parameters, between voltage parameters, and between current / voltage parameters, are not considered, it may cause the failure of multiple joint decisions of the decision tree model, affecting the training / inference efficiency of the decision tree model. Therefore, it is preferred to first perform decision stability correction on the current key feature and the voltage key feature based on multidimensional coupling modeling.

[0036] Based on this, in another embodiment, inputting the current key feature and the voltage key feature into a decision tree model to obtain the failure cause includes: first, correlating the peak current time, the pre-failure current, and the current fluctuation feature through a current fluctuation function to obtain a current correlation expression, wherein the current correlation expression includes a fluctuation function adjustment factor, which is expressed as: ;in, is the fluctuation function adjustment factor, Indicates the current before failure, Indicates the time to reach peak current, Indicates the current fluctuation characteristics, represents a natural constant, Represents the current-related expression.

[0037] Then, the current fluctuation characteristics, the average voltage before failure, the minimum voltage during startup and the voltage drop amplitude are associated through the system voltage stability function to obtain a voltage correlation expression, which includes a stability risk adjustment factor, that is, taking into account the current fluctuation characteristics and voltage drop The random stability fluctuation diffusion effect of the overall stability of the system voltage, that is, the stability diffusion characteristics with inconsistent fluctuation directions, is used to model the system voltage stability function, which is expressed as: ;in, is the stability risk adjustment factor, Indicates the average voltage before failure, Indicates the minimum voltage during startup. Indicates the voltage drop amplitude, Indicates the current fluctuation characteristics, Represents voltage-dependent expression.

[0038] Next, a joint decision constraint is performed, that is, by adjusting the fluctuation function adjustment factor and the stability risk adjustment factor Minimize the current correlation expression While maximizing the voltage-dependent expression , that is, maximize , to determine the factors and .

[0039] Then, the determined fluctuation function adjustment factor and the stability risk adjustment factor Substituting the current-related expression and the voltage-related expression into the decision tree model, the current-related expression, the voltage-related expression, the current key feature, and the voltage key feature are input into the decision tree model to obtain the failure cause. This mechanism improves the decision stability of the current key feature and the voltage key feature based on multi-dimensional coupled modeling, thereby increasing the training and inference efficiency of the decision tree model.

[0040] For example, in step S4, the MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries. It should be understood that ordinary startup failures are most likely due to occasional anomalies, such as temporary fluctuations in the power grid, changes in the operating environment, occasional minor faults in the equipment itself, etc. If subsequent operations are completely prohibited based on a single failure, not only will some recoverable on-site disturbances be intolerable, but unnecessary service risks may also be buried. On the contrary, if all failure causes are retried without limit, it may cause repeated shocks and excessive wear and tear on the equipment, or even run repeatedly under essentially unrepairable problems, posing a serious safety hazard. Therefore, the MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries. The retry strategy is not a simple restart upon failure, but should depend on the type of startup failure, the environmental context in which it occurs, and the historical number of retries, and make customized decisions after systematic analysis.

[0041] In one embodiment, the MCU control unit outputs a retry strategy based on the failure cause and the current number of retries, including: the MCU control unit queries a preset multi-level retry strategy table based on the failure cause and the current number of retries to obtain the retry strategy, wherein the retry strategy includes a strategy type, a wait time in seconds, and a parameter configuration identifier for the next startup, and the strategy types include retry and lock. It should be understood that the multi-level retry strategy table is a pre-set strategy rule database that arranges and combines different response methods based on various potential failure causes and plans the combined actions to be taken for each next step based on the current number of completed retries. The table not only records the corresponding retry category tags for common failures such as undervoltage, overvoltage, stalled rotor, damaged start capacitor, abnormal initial startup data, failure of the main and auxiliary thyristors to properly conduct, and sensor signal loss, but also provides a hierarchical and progressive retry number - for example, after the first failure, the most gentle wait and retry is adopted, the second failure is moderately extended or the parameters are slightly adjusted, and the third failure is completely locked and manually reset. This prevents the device from falling into a vicious cycle of frequent self-resets.

[0042] In one embodiment, the MCU control unit's program structure constructs a two-dimensional lookup table indexed by the failure cause code and the number of retries. In this embodiment, the table is divided into the following fields: cause code (e.g., code 001 for undervoltage, 002 for overvoltage, 003 for stall, 004 for short-term overcurrent, 005 for signal interference), current retry round (1st, 2nd, 3rd, etc.), retry strategy type (retry / lock), wait time (in seconds, e.g., 10s, 30s, 60s, etc.), and parameter configuration flag (used to indicate whether to modify the parameter plan or enhance protection measures for the next startup, such as amplitude compensation or prematurely disconnecting the starting winding). If a startup fails, the MCU judgment module outputs the cause code and the accumulated number of retries, and the strategy query module then indexes the table to retrieve the optimal strategy.

[0043] In one specific embodiment, the corresponding policy table is populated as follows: For undervoltage (code 001), after the first failure, the MCU control unit automatically waits 15 seconds before retrying. If the second failure continues, it waits 30 seconds. Upon the subsequent failure, the system is immediately locked and cannot be reset until manual on-site inspection is performed. For overvoltage (code 002), after the first failure, the MCU control unit automatically enters a protective shutdown state and simultaneously activates the timer module, delaying the first restart attempt for 30 seconds. If the fault is still determined to be an overvoltage fault, the system enters a protective shutdown state again, with the waiting time extended to 60 seconds. If the overvoltage fault is still detected after the third retry, the MCU control unit immediately locks the system and does not automatically restart. The fault indicator is illuminated and the event is stored in non-volatile storage. Resilience is not possible until the source of the fault is manually eliminated or a manual reset is performed on-site. For stall (code 003), due to the dual electrical and mechanical risks, only one retry is allowed. If the failure continues, the system is immediately locked. Even if a restart request is made later, the device remains in the protective lock state unless a manual reset is performed. This protects the windings and semiconductor devices from damage, and a severe event flag is written to the storage area. For example, short-term overcurrent (code 004) can be attributed to intermittent grid fluctuations, allowing up to three retries. The retry interval can be gradually increased from the initial 10 seconds to 60 seconds to reduce the impact on the system. For suspected soft faults such as signal interference (code 005) or sensor loss, temporary downgrade startup parameters can be added, extending the disturbance observation period to provide an additional opportunity. If two or three consecutive failures occur, the system will be immediately locked to prevent accidental continuous operation.

[0044] Parameter configuration flags, a crucial component of the retry strategy, provide enhanced adaptability. For example, if repeated retry attempts fail due to excessive load, the parameter configuration flags can prompt fine-tuning of the secondary thyristor on-off timing, appropriately advancing the primary thyristor conduction, appropriately lowering the starting criteria, or switching to soft protection current limiting mode upon restart to address compatibility issues in specialized operating environments. Conversely, for pure line voltage anomalies, local parameter modifications are unnecessary; instead, the original configuration should be maintained, focusing on either extending the wait time or completely locking the circuit.

[0045] This strictly layered, multi-level retry strategy not only ensures the device's resilience to occasional environmental disturbances during actual use, but also ensures that essential systemic failures are completely blocked, preventing automation from potentially posing a significant safety hazard. This effectively achieves a multi-objective system balance (high availability, automation, long life, safety protection, and traceability) for a wide range of household, commercial, and industrial products. Finally, in step S5, the MCU control unit executes the retry strategy output from step S4.

[0046] In summary, the adaptive starting method and system based on voltage and current monitoring provided by the present application include: the MCU control unit triggers the auxiliary thyristor and the main thyristor through a preset starting logic to start the motor, and samples the current and voltage data in real time at high frequency. Once a startup failure is detected, the short-term voltage sequence and current sequence before and after the failure point are automatically captured and stored. Then, key features are extracted from these sequences, and the cause of the failure is determined through decision tree model analysis. Based on this analysis result and the current number of retries, the MCU control unit outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safe mode. In this way, not only the reliability of motor starting is improved, but also detailed data records are used to support subsequent fault diagnosis, greatly improving the maintainability and safety of the system.

[0047] This application also provides an adaptive starting system based on voltage and current monitoring, such as Figure 4 As shown, the adaptive starting system 400 based on voltage and current monitoring includes a memory 401, a processor 402, and a computer program 403 stored in the memory and executable on the processor, wherein the processor 402 implements the steps of the aforementioned adaptive starting method based on voltage and current monitoring when executing the computer program 403.

[0048] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0049] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0050] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0051] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0052] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An adaptive startup method based on voltage and current monitoring, characterized in that: include: The MCU control unit triggers the auxiliary thyristor and the main thyristor to start the motor through a preset startup logic, and simultaneously samples the current and voltage at high frequency through the electricity metering chip; the MCU control unit starts the failure judgment logic, and once it determines that the startup has failed, it captures and stores the short-term voltage and current sequences before and after the failure occurs through the electricity metering chip; the MCU control unit extracts the current key features and the voltage key features from the voltage sequence and the current sequence, and determines the cause of the failure based on the current key features and the voltage key features; the MCU control unit outputs a retry strategy based on the failure cause and the current number of retries; and the MCU control unit executes the retry strategy.

2. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that: The MCU control unit triggers the auxiliary thyristor and the main thyristor to start the motor through a preset starting logic, including: the MCU control unit first triggers the auxiliary thyristor to turn on to connect the starting winding of the motor; the MCU control unit then triggers the main thyristor to turn on to connect the main winding of the motor.

3. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that: The failure determination logic includes the current not reaching the expected threshold within a specified time, the current not falling back after rising, and the overcurrent / overvoltage protection being triggered.

4. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that: The key current characteristics include peak current, current rise rate, time to peak current, current before failure and current fluctuation characteristics. The key voltage characteristics include average voltage before failure, minimum voltage during startup and voltage drop amplitude.

5. The adaptive startup method based on voltage and current monitoring according to claim 4, characterized in that: Determining the failure cause based on the current key feature and the voltage key feature includes: inputting the current key feature and the voltage key feature into a decision tree model to obtain the failure cause.

6. The adaptive startup method based on voltage and current monitoring according to claim 5, characterized in that: The MCU control unit outputs a retry strategy based on the failure cause and the current number of retries, including: the MCU control unit queries a preset multi-level retry strategy table based on the failure cause and the current number of retries to obtain the retry strategy, the retry strategy including a strategy type, a number of waiting seconds, and a parameter configuration identifier for the next startup, the strategy type including retry and lock.

7. The adaptive startup method based on voltage and current monitoring according to claim 6, characterized in that: Inputting the current key feature and the voltage key feature into a decision tree model to obtain the failure cause includes: correlating the peak current time, the current before failure and the current fluctuation feature through a current fluctuation function to obtain a current correlation expression, wherein the current correlation expression includes a fluctuation function adjustment factor; correlating the current fluctuation feature, the average voltage before failure, the minimum voltage during startup and the voltage drop amplitude through a system voltage stability function to obtain a voltage correlation expression, wherein the voltage correlation expression includes a stability risk adjustment factor; minimizing the current correlation expression while maximizing the voltage correlation expression by adjusting the fluctuation function adjustment factor and the stability risk adjustment factor; substituting the determined fluctuation function adjustment factor and the stability risk adjustment factor into the current correlation expression and the voltage correlation expression, and inputting the current correlation expression, the voltage correlation expression, the current key feature and the voltage key feature into a decision tree model to obtain the failure cause.

8. An adaptive starting system based on voltage and current monitoring, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the adaptive startup method based on voltage and current monitoring are implemented as described in any one of claims 1 to 7.

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