A hardware and software combined anomaly protection method for startup control circuits

Abstract

This application relates to the field of startup control circuit technology, and discloses a hardware-software combined anomaly protection method for startup control circuits. It uses an electrical metering chip to detect the effective current value in real time, and the MCU control unit reads the effective current value from the electrical metering chip and compares it with a dynamic current threshold determined based on ambient temperature, humidity, and equipment operating time. Once the detected effective current value exceeds the set dynamic current threshold, the MCU control unit sends a control signal through software logic to shut down the main thyristor to cut off the current path, thereby preventing damage caused by excessive current. This method can more accurately reflect the safe range under current operating conditions, thus providing a more refined and personalized protection strategy.

Description

Technical Field

[0001] This application relates to the field of startup control circuit technology, and more specifically, to an anomaly protection method combining hardware and software for startup control circuits. Background Technology

[0002] In the operation of modern electrical equipment, ensuring the safety and reliability of its starting control circuits is crucial. Traditionally, many devices rely on simple mechanical or basic electronic component-based protection mechanisms (such as PTC thermistors) to prevent overcurrent. However, with technological advancements and increasingly stringent safety standards, these traditional methods have gradually revealed their limitations.

[0003] First, traditional overcurrent protection measures are often based on fixed threshold settings, meaning they cannot adapt to different operating environmental conditions, such as changes in temperature and humidity, as well as the aging or wear of the equipment itself. In practical applications, this static protection method may lead to insufficient protection in some cases and oversensitivity in others, resulting in unnecessary downtime. Furthermore, these protection mechanisms typically lack consideration for the critical factor of equipment operating time, making it difficult to provide precise and personalized protection strategies, especially in applications with long-term continuous operation or frequent start-stop cycles. Moreover, most previous current detection methods only focus on whether the effective value of the current exceeds a certain fixed threshold, ignoring other variables that may affect equipment safety. For example, ambient temperature and humidity can not only directly affect the operating performance of electrical equipment but may also change the properties of materials in the circuit, thus indirectly affecting the actual current performance. Simultaneously, since the operating state of equipment may vary significantly at different stages, relying solely on a preset current threshold for protection is clearly insufficient to cope with complex real-world operating conditions. Based on this, this application proposes a hardware-software combined anomaly protection method for the start-up control circuit. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a hardware-software combined anomaly protection method for a startup control circuit. This method uses an electricity metering chip as its core to monitor the effective current value in the circuit in real time and transmits this data to the MCU control unit for analysis and processing. Unlike previous fixed threshold settings, this method dynamically calculates a more reasonable current threshold based on factors such as ambient temperature, humidity, and the cumulative operating time of the equipment. This design allows the system to more accurately reflect the safe range under current operating conditions, thereby providing a more refined and personalized protection strategy.

[0005] According to one aspect of this application, a hardware-software combined anomaly protection method for starting a control circuit is provided, comprising: real-time detection of the effective value of current via an electricity metering chip; an MCU control unit reading the effective value of current from the current register of the electricity metering chip; the MCU control unit comparing the effective value of current with a dynamic current threshold to obtain a comparison result, wherein the dynamic current threshold is determined based on ambient temperature, ambient humidity, and equipment operating time; in response to the comparison result that the effective value of current is greater than or equal to the dynamic current threshold, the MCU control unit sending a control signal via software logic, the control signal being used to turn off the main thyristor to cut off the current path.

[0006] In one possible implementation, the method further includes: detecting the effective voltage value in real time through an electricity metering chip; the MCU control unit reading the effective voltage value from the voltage register of the electricity metering chip; the MCU control unit determining whether the effective voltage value is within a preset range, and if the effective voltage value is not within the preset range, the MCU control unit sending a control signal through software logic, the control signal being used to turn off the main thyristor to cut off the current path.

[0007] In one possible implementation, determining the dynamic current threshold includes: the MCU control unit acquiring real-time temperature, real-time humidity, and cumulative device operating time; inputting the real-time temperature, real-time humidity, and cumulative device operating time into a regression model to obtain a current threshold adjustment coefficient; the MCU control unit acquiring the recent effective current average value and the recent current fluctuation standard deviation; inputting the recent effective current average value and the recent current fluctuation standard deviation into the regression model to obtain a current threshold modulation secondary control coefficient; and adjusting the initial current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain the dynamic current threshold.

[0008] In one possible implementation, adjusting the initial current threshold to obtain the dynamic current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient includes: multiplying the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain a current threshold dynamic adjustment coefficient; multiplying the current threshold dynamic adjustment coefficient by the initial current threshold to obtain a current threshold floating portion; and adding the current threshold floating portion to the initial current threshold to obtain the dynamic current threshold.

[0009] In one possible implementation, adjusting the initial current threshold to obtain the dynamic current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient includes: performing nonlinear cross-correlation probabilistic coupling correction on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain optimized current threshold adjustment coefficient and optimized current threshold modulation secondary control coefficient; multiplying the optimized current threshold adjustment coefficient and the optimized current threshold modulation secondary control coefficient to obtain a dynamic current threshold adjustment coefficient; multiplying the dynamic current threshold adjustment coefficient by the initial current threshold to obtain a floating current threshold portion; and adding the floating current threshold portion to the initial current threshold to obtain the dynamic current threshold.

[0010] In one possible implementation, nonlinear cross-correlation probabilistic coupling correction is performed on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain optimized current threshold adjustment coefficient and optimized current threshold modulation secondary control coefficient. This includes: calculating a dynamic probabilistic coupling response value for the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient; calculating a cross-correlation perturbation factor for the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient; applying a nonlinear perturbation constraint of the cross-correlation probability distribution to the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient based on the dynamic probabilistic coupling response value and the cross-correlation perturbation factor to obtain current threshold adjustment perturbation constraint coefficient and current threshold modulation secondary control perturbation constraint coefficient; calculating the derivative of the dynamic probabilistic coupling response value with respect to the cross-correlation perturbation factor, and optimizing the current threshold adjustment perturbation constraint coefficient and the current threshold modulation secondary control perturbation constraint coefficient based on the derivative to obtain the optimized current threshold adjustment coefficient and the optimized current threshold modulation secondary control coefficient.

[0011] In one possible implementation, the electricity metering chip is electrically connected to the MCU control unit, and the main thyristor is electrically connected to the MCU control unit.

[0012] In one possible implementation, the start control circuit further includes a secondary thyristor electrically connected to the MCU control unit for controlling the on / off state of the start winding.

[0013] Compared to existing technologies, the hardware-software combined anomaly protection method for starting control circuits provided in this application detects the effective current value in real time through an electricity metering chip. The MCU control unit reads the effective current value from the electricity metering chip and compares it with a dynamic current threshold determined based on ambient temperature, humidity, and equipment operating time. Once the effective current value is detected to exceed the set dynamic current threshold, the MCU control unit sends a control signal through software logic to turn off the main thyristor to cut off the current path, thereby preventing damage caused by excessive current. This method can more accurately reflect the safe range under current operating conditions, thus providing a more refined and personalized protection strategy. Attached Figure Description

[0014] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 The illustration shows a schematic flowchart of an anomaly protection method combining hardware and software for starting a control circuit according to an embodiment of this application.

[0016] Figure 2 The illustration shows a schematic flowchart of the determination of the dynamic current threshold in an abnormal protection method combining hardware and software for starting a control circuit according to an embodiment of this application.

[0017] Figure 3 The illustration shows a schematic flowchart of an abnormal protection method combining hardware and software for starting a control circuit according to an embodiment of this application, in which an initial current threshold is adjusted to obtain the dynamic current threshold.

[0018] Figure 4 The illustration shows a schematic flowchart of another embodiment of an abnormal protection method combining hardware and software for starting a control circuit according to an embodiment of this application, in which an initial current threshold is adjusted to obtain the dynamic current threshold.

[0019] Figure 5 The figure shows a schematic block diagram of a start-up control circuit according to an embodiment of the present application.

[0020] Figure 6 The illustration shows a schematic flowchart of another embodiment of an anomaly protection method combining hardware and software for starting a control circuit according to an embodiment of this application. Detailed Implementation

[0021] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0022] Figure 1 The illustration shows a schematic flowchart of an anomaly protection method combining hardware and software for starting a control circuit according to an embodiment of this application. Figure 1 As shown, this application provides a hardware-software combined anomaly protection method for starting a control circuit, including: S1, real-time detection of the effective current value via an electricity metering chip; S2, the MCU control unit reading the effective current value from the current register of the electricity metering chip; S3, the MCU control unit comparing the effective current value with a dynamic current threshold to obtain a comparison result, wherein the dynamic current threshold is determined based on ambient temperature, ambient humidity, and equipment operating time; S4, in response to the comparison result that the effective current value is greater than or equal to the dynamic current threshold, the MCU control unit sending a control signal via software logic, the control signal being used to turn off the main thyristor to cut off the current path.

[0023] Specifically, the first step involves real-time monitoring of the effective current value using an electrical metering chip. This step is fundamental to the entire protection mechanism. As a high-precision measuring tool, the electrical metering chip accurately captures current changes in the circuit, which is crucial for timely detection of potential anomalies. Next, the MCU control unit reads the effective current value from the current register of the electrical metering chip. Specifically, the MCU control unit establishes a communication connection with the electrical metering chip and periodically reads the current data stored in the current register. This design not only ensures the real-time nature and accuracy of the data but also allows the MCU to react quickly based on this information.

[0024] Then, the MCU control unit compares the read RMS current value with the dynamic current threshold. This dynamic current threshold is not fixed but determined based on factors such as ambient temperature, humidity, and equipment operating time. This characteristic makes the protection method more intelligent and adaptable. For example, in high-temperature environments, increased resistance may lead to an increase in current, thus requiring adjustment of the current threshold to avoid false alarms. The MCU control unit uses a preset algorithm model to calculate a suitable dynamic current threshold based on the current operating conditions and compares it with the actually measured current value.

[0025] If the comparison result shows that the effective current value is greater than or equal to the dynamic current threshold, the MCU control unit will send a control signal through software logic to turn off the main thyristor, thereby cutting off the current path. This is to take emergency measures to protect the circuit from damage when a potential hazard (such as overcurrent) is detected. In this process, the MCU control unit plays a crucial role; it must not only accurately determine when to trigger the protection mechanism, but also ensure that the control signal can reach the target component quickly and effectively.

[0026] In one embodiment, such as Figure 2 As shown, the determination of the dynamic current threshold includes: S31, the MCU control unit acquires the real-time temperature value, the real-time humidity value, and the cumulative operating time of the equipment; S32, the real-time temperature value, the real-time humidity value, and the cumulative operating time of the equipment are input into a regression model to obtain a current threshold adjustment coefficient; S33, the MCU control unit acquires the recent effective current average value and the recent current fluctuation standard deviation; S34, the recent effective current average value and the recent current fluctuation standard deviation are input into a regression model to obtain a current threshold modulation secondary control coefficient; S35, based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient, the initial current threshold is adjusted to obtain the dynamic current threshold.

[0027] Specifically, the MCU control unit collects temperature and humidity data in real time by connecting to a sensor network and records the cumulative operating time of the device since its last startup. This information helps the system understand potential risk factors under current operating conditions, such as high temperatures potentially causing motor overheating or high humidity potentially increasing the risk of short circuits.

[0028] Next, the MCU control unit inputs real-time temperature, real-time humidity, and cumulative equipment operating time into a pre-trained regression model to calculate the current threshold adjustment coefficient. This regression model is built based on extensive historical data analysis and aims to capture the impact of different environmental conditions on the current threshold. For example, if the current ambient temperature is high, the regression model may output a higher current threshold adjustment coefficient, indicating that higher current flow is allowed in this environment without triggering the protection mechanism, because the motor may exhibit different electrical characteristics due to insufficient cooling. This step ensures that the current threshold can be flexibly adjusted according to actual operating conditions, improving the system's adaptability and reliability.

[0029] Simultaneously, the MCU control unit also acquires the recent effective current average value and the recent current fluctuation standard deviation. These two indicators reflect the motor's operating status over a recent period and help identify any abnormal behavior or trends. For example, a significant increase in the recent current fluctuation standard deviation could be an early sign of impending motor failure. The MCU control unit then inputs these current-related statistics into another regression model specifically designed to generate secondary control coefficients for current threshold modulation. These coefficients are primarily used to fine-tune the initial current threshold, considering the short-term trend of the current itself, rather than external environmental factors.

[0030] In one embodiment, a multiple linear regression model is used as the base model to process the real-time temperature value, the real-time humidity value, and the cumulative operating time of the device to estimate the current threshold adjustment coefficient. This multiple linear regression model can be expressed as: ;in, This represents the current threshold adjustment coefficient. This indicates the real-time temperature value. This indicates the real-time humidity value. Indicates the cumulative operating time of the equipment. These are model parameters, which need to be obtained through training with historical data.

[0031] Similarly, a multiple linear regression model was used as the basic model to analyze the recent effective current average value. and the standard deviation of the current fluctuation The process is performed to calculate the secondary control coefficient of the current threshold modulation. This multiple linear regression model can be expressed as: ;in, This represents the second-level control coefficient of current threshold modulation. These are model parameters, which need to be trained based on actual data.

[0032] In another embodiment, the regression model is a multilayer perceptron based on a neural network. Specifically, taking the input of the real-time temperature value, the real-time humidity value, and the cumulative device operating time into a multilayer perceptron based on a neural network to obtain a current threshold adjustment coefficient as an example, this neural network will learn the mapping between input features (such as real-time temperature, humidity, and cumulative device operating time) and the output target (i.e., the current threshold adjustment coefficient). First, the input layer is defined to contain three nodes, corresponding to the real-time temperature value, the real-time humidity value, and the cumulative device operating time, respectively. These input data are preprocessed and then fed into the first layer of the neural network, i.e., the hidden layer. The hidden layer can be set to have multiple layers, each containing multiple neurons. In a specific embodiment, the first hidden layer is set to have 64 neurons. The main purpose of this layer is to initially process the input data and introduce nonlinear factors through the activation function. Choosing 64 neurons is based on experience; it can usually provide sufficient capacity to capture the complex relationships between input features without excessively increasing the computational burden. The second hidden layer is reduced to 32 neurons. Reducing the number of neurons helps to refine information, remove unnecessary details, while maintaining the ability to learn the main patterns. Such an architecture design can simplify the model without losing important information. The third hidden layer further reduces the number of neurons to 16. This layer serves to further condense the information learned from the previous layers, preparing for the final output. A smaller number of neurons helps avoid overfitting, ensuring the model performs well even on unseen data. Each neuron performs a weighted summation operation and applies an activation function (e.g., ReLU or tanh) to introduce nonlinearity, which is crucial for capturing complex interactions between input variables. The outputs of these hidden layers are then passed to the output layer, which has only one neuron and generates the predicted current threshold adjustment coefficients. Training such a neural network model requires a large historical dataset containing the true values ​​of the current threshold adjustment coefficients under different environmental conditions. Using backpropagation combined with gradient descent optimization, the connection weights between layers in the network can be gradually adjusted to make the model output as close as possible to the actual observations. Those skilled in the art will understand that inputting the recent effective current average and the recent current fluctuation standard deviation into a multilayer perceptron based on a neural network to obtain the second-order current threshold modulation control coefficients can be found in the model setup and training methods described above, and will not be repeated here.

[0033] Finally, based on the current threshold adjustment coefficient and the secondary control coefficient of current threshold modulation obtained in the above two steps, the MCU control unit performs a final adjustment to the initial current threshold, thereby determining the dynamic current threshold. Here, the initial current threshold can be set and adjusted according to actual conditions. In one embodiment, such as... Figure 3As shown, the dynamic current threshold is obtained by adjusting the initial current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient, including: S351, multiplying the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain the current threshold dynamic adjustment coefficient; S352, multiplying the current threshold dynamic adjustment coefficient by the initial current threshold to obtain the current threshold floating portion; S353, adding the current threshold floating portion to the initial current threshold to obtain the dynamic current threshold.

[0034] As can be seen, the current threshold adjustment coefficient is mainly based on regression of macroscopic environmental factors (the cumulative operating time of the equipment also belongs to the operating environmental factors in the time-series dimension), while the current threshold modulation secondary control coefficient is based on regression of microscopic current factors. Therefore, when the two are directly multiplied, there will be a dynamic balance deviation. That is, the nonlinear cross-correlation probability between the two needs to be coupled and corrected. In other words, before multiplying the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient, nonlinear cross-correlation probability coupling correction needs to be performed on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient.

[0035] Based on this, such as Figure 4 As shown, in another embodiment, the initial current threshold is adjusted based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain the dynamic current threshold, including: S354, performing nonlinear cross-correlation probability coupling correction on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain an optimized current threshold adjustment coefficient and an optimized current threshold modulation secondary control coefficient; S355, multiplying the optimized current threshold adjustment coefficient and the optimized current threshold modulation secondary control coefficient to obtain a dynamic adjustment coefficient for the current threshold; S356, multiplying the dynamic adjustment coefficient for the current threshold by the initial current threshold to obtain a floating portion of the current threshold; S357, adding the floating portion of the current threshold to the initial current threshold to obtain the dynamic current threshold.

[0036] In one embodiment, performing nonlinear cross-correlation probabilistic coupling correction on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain optimized current threshold adjustment coefficient and optimized current threshold modulation secondary control coefficient includes: first, calculating the dynamic probabilistic coupling response value for the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient, where the current threshold adjustment coefficient is set to... And the current threshold modulation secondary control coefficient is The dynamic probabilistic coupled response is expressed as: ;in, Represents the natural constant. This represents the dynamic probabilistic coupled response value.

[0037] In other words, while ensuring the convergence of dynamic coupling error through exponential decay characteristics, the current threshold adjustment coefficient is effectively fitted through the coupling deconstruction of the threshold. and the current threshold modulation secondary control coefficient The probability response phase coupling effect.

[0038] Then, a cross-correlation perturbation factor is introduced, that is, the cross-correlation perturbation factor for the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient is calculated, and expressed as: ;in, This represents the cross-correlation perturbation factor.

[0039] Next, based on the dynamic probabilistic coupling response value and the cross-correlation perturbation factor, a nonlinear perturbation constraint of the cross-correlation probability distribution is applied to the current threshold adjustment coefficient and the current threshold modulation second-level control coefficient to obtain the current threshold adjustment perturbation constraint coefficient and the current threshold modulation second-level control perturbation constraint coefficient, expressed as: ;in, This represents the current threshold adjustment disturbance constraint coefficient. This represents the disturbance constraint coefficient for the second-level control of current threshold modulation. This represents the natural index.

[0040] This enables the nonlinear disturbance blocking of the error relative to the dynamic probabilistic coupled response under the action of cross-correlation perturbation.

[0041] In this way, the dynamic probability can be coupled to the response value. Relative to the cross-correlation perturbation factor Based on the derivative, gradient-truncation-based reverse perturbation suppression is performed, thereby optimizing the current threshold adjustment perturbation constraint coefficient and the current threshold modulation secondary control perturbation constraint coefficient.

[0042] That is, the derivative of the dynamic probabilistic coupling response value with respect to the cross-correlation perturbation factor is calculated, and the current threshold adjustment perturbation constraint coefficient and the current threshold modulation secondary control perturbation constraint coefficient are optimized based on the derivative to obtain the optimized current threshold adjustment coefficient and the optimized current threshold modulation secondary control coefficient, which are expressed as follows: ;in, This represents the optimized current threshold adjustment coefficient. This represents the optimized second-level control coefficient of the current threshold modulation. This represents the derivative of the dynamic probabilistic coupling response value with respect to the cross-correlation perturbation factor.

[0043] in, based on and Represented as: .

[0044] Therefore, by suppressing the propagation of nonlinear disturbances on the coupled link based on gradient truncation, the current threshold adjustment coefficient with dynamic equilibrium deviation is realized. and the current threshold modulation secondary control coefficient The nonlinear cross-correlation probability coupling between them improves the accuracy of the obtained dynamic adjustment coefficient of the current threshold when the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient are directly multiplied.

[0045] Here, considering that the voltage of a power system is generally kept relatively stable to ensure the normal operation of various electrical devices, the real-time voltage is considered as a default value in the above embodiment. However, in some application scenarios, the real-time voltage may change. Therefore, in another embodiment, the determination factor of the dynamic current threshold also includes the device's real-time voltage. It should be understood that the operating state of electrical equipment is not only affected by ambient temperature, humidity, and operating time, but also directly depends on the state of its supply voltage. Voltage changes directly affect the operating conditions and power consumption of internal components, thereby affecting the effective value of the current. For example, at lower voltages, the device may need to increase current to compensate for maintaining the same power output, and vice versa. Therefore, taking the device's real-time voltage into consideration allows for more precise setting of the current threshold, ensuring accurate judgment of overcurrent risk even under voltage fluctuations.

[0046] In one embodiment, the electricity metering chip is electrically connected to the MCU control unit, the main thyristor is electrically connected to the MCU control unit, and the main thyristor is electrically connected to the main winding. The start-up control circuit further includes a secondary thyristor electrically connected to the MCU control unit for controlling the on / off state of the start-up winding. Here, for ease of understanding, a schematic block diagram of the start-up control circuit is also provided for the above embodiments, as shown below. Figure 5 As shown.

[0047] Furthermore, considering that voltage is one of the key factors affecting the normal operation of motors and other electrical equipment, if the mains voltage exceeds the rated range specified in the equipment's design, it may cause serious damage to the equipment. Figure 6As shown, in another embodiment, the hardware and software combined abnormal protection method for starting the control circuit further includes: S5, detecting the effective voltage value in real time through the electricity metering chip; S6, the MCU control unit reading the effective voltage value from the voltage register of the electricity metering chip; S7, the MCU control unit determining whether the effective voltage value is within a preset range, and if the effective voltage value is not within the preset range, the MCU control unit sending a control signal through software logic, the control signal being used to turn off the main thyristor to cut off the current path.

[0048] Specifically, firstly, the effective voltage value is detected in real time by an electricity metering chip. This is to promptly capture changes in the grid voltage, as voltage fluctuations can have a serious impact on motors and other electrical equipment.

[0049] Then, the MCU control unit reads the effective voltage value from the voltage register of the electricity metering chip, determines whether the effective voltage value is within the preset range, and takes action based on the determination result. If the effective voltage value exceeds the preset range, it means that the current operating conditions do not meet the safety standards. At this time, the MCU needs to respond quickly to avoid potential damage. Specifically, the MCU will send a control signal through software logic to turn off the main thyristor, thereby cutting off the current path. In a specific embodiment, the effective voltage value is 200V to 240V. If the mains voltage suddenly rises to 260V, exceeding the maximum voltage limit that the start-up control circuit can withstand, the MCU control unit will immediately issue a command to disconnect the main thyristor circuit after recognizing this abnormality, preventing the high voltage from causing irreversible damage to the motor. This rapid response mechanism can not only protect electrical equipment from the effects of overvoltage or undervoltage, but also isolate the fault source in time when abnormal conditions occur, reducing the possibility of accidents.

[0050] In summary, the hardware-software combined anomaly protection method for the start-up control circuit provided in this application detects the effective current value in real time through an electrical metering chip. The MCU control unit reads the effective current value from the electrical metering chip and compares it with a dynamic current threshold determined based on ambient temperature, humidity, and equipment operating time. Once the effective current value is detected to exceed the set dynamic current threshold, the MCU control unit sends a control signal through software logic to turn off the main thyristor to cut off the current path, thereby preventing damage caused by excessive current. This method can more accurately reflect the safe range under current operating conditions, thus providing a more refined and personalized protection strategy.

[0051] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0052] The block diagrams of devices, apparatuses, devices, 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 those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0053] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0054] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this 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.

[0055] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary 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. A hardware-software combined anomaly protection method for a startup control circuit, characterized in that, include: The effective value of the current is detected in real time using an electrical metering chip; The MCU control unit reads the effective value of the current from the current register of the electricity metering chip; The MCU control unit compares the effective value of the current with a dynamic current threshold to obtain a comparison result, wherein the dynamic current threshold is determined based on ambient temperature, ambient humidity and device operating time; in response to the comparison result that the effective value of the current is greater than or equal to the dynamic current threshold, the MCU control unit sends a control signal through software logic, the control signal being used to turn off the main thyristor to cut off the current path; The determination of the dynamic current threshold includes: the MCU control unit acquiring real-time temperature, real-time humidity, and cumulative device operating time; inputting the real-time temperature, real-time humidity, and cumulative device operating time into a regression model to obtain a current threshold adjustment coefficient; the MCU control unit acquiring the recent effective current average value and recent current fluctuation standard deviation; inputting the recent effective current average value and recent current fluctuation standard deviation into the regression model to obtain a current threshold modulation secondary control coefficient; and adjusting the initial current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain the dynamic current threshold.

2. The hardware and software combined anomaly protection method for starting control circuits according to claim 1, characterized in that, Also includes: The effective voltage value is detected in real time by an electricity metering chip; the MCU control unit reads the effective voltage value from the voltage register of the electricity metering chip. The MCU control unit determines whether the effective voltage value is within a preset range. If the effective voltage value is not within the preset range, the MCU control unit sends a control signal through software logic. The control signal is used to turn off the main thyristor to cut off the current path.

3. The hardware and software combined anomaly protection method for starting control circuits according to claim 1, characterized in that, The adjustment of the initial current threshold to obtain the dynamic current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient includes: multiplying the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain the current threshold dynamic adjustment coefficient; multiplying the current threshold dynamic adjustment coefficient by the initial current threshold to obtain the current threshold floating portion; and adding the current threshold floating portion to the initial current threshold to obtain the dynamic current threshold.

4. The hardware and software combined anomaly protection method for starting control circuits according to claim 1, characterized in that, The adjustment of the initial current threshold to obtain the dynamic current threshold based on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient includes: performing nonlinear cross-correlation probability coupling correction on the current threshold adjustment coefficient and the current threshold modulation secondary control coefficient to obtain optimized current threshold adjustment coefficient and optimized current threshold modulation secondary control coefficient; multiplying the optimized current threshold adjustment coefficient and the optimized current threshold modulation secondary control coefficient to obtain a dynamic current threshold adjustment coefficient; multiplying the dynamic current threshold adjustment coefficient by the initial current threshold to obtain a current threshold fluctuation portion; and adding the current threshold fluctuation portion to the initial current threshold to obtain the dynamic current threshold.

5. The hardware and software combined anomaly protection method for starting control circuits according to claim 4, characterized in that, To obtain optimized current threshold adjustment coefficients and optimized current threshold modulation secondary control coefficients, nonlinear cross-correlation probabilistic coupling correction is performed on the current threshold adjustment coefficients and the current threshold modulation secondary control coefficients. This includes: calculating the dynamic probabilistic coupling response value for the current threshold adjustment coefficients and the current threshold modulation secondary control coefficients; calculating the cross-correlation perturbation factor for the current threshold adjustment coefficients and the current threshold modulation secondary control coefficients; based on the dynamic probabilistic coupling response value and the cross-correlation perturbation factor, applying nonlinear perturbation constraints of the cross-correlation probability distribution to the current threshold adjustment coefficients and the current threshold modulation secondary control coefficients to obtain current threshold adjustment perturbation constraint coefficients and current threshold modulation secondary control perturbation constraint coefficients; calculating the derivative of the dynamic probabilistic coupling response value with respect to the cross-correlation perturbation factor, and optimizing the current threshold adjustment perturbation constraint coefficients and the current threshold modulation secondary control perturbation constraint coefficients based on the derivative to obtain the optimized current threshold adjustment coefficients and the optimized current threshold modulation secondary control coefficients.

6. The hardware and software combined anomaly protection method for starting control circuits according to claim 1, characterized in that, The electricity metering chip is electrically connected to the MCU control unit, and the main thyristor is electrically connected to the MCU control unit.

7. The hardware and software combined anomaly protection method for starting control circuits according to claim 6, characterized in that, The start-up control circuit also includes a secondary thyristor electrically connected to the MCU control unit for controlling the on / off state of the start-up winding.

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