A method for abnormal handling of high-power output drive signals based on security control

Abstract

The present invention discloses a method for abnormal processing of high-power output drive signals based on safety control, belonging to the technical field of safety control, including: the MCU in the control circuit serves as the control center of the circuit, receives the action instructions of the electric forklift to generate control signals, and sends them to the drive conversion circuit. The drive conversion circuit converts the control signals into drive signals suitable for driving high-power loads, monitors the state of the drive signals based on the signal feedback circuit, and generates feedback signals to return to the MCU. The MCU determines whether the feedback signals are consistent with the drive signals. If they are consistent, the MCU maintains the current switching control signal as a low-level signal. If they are inconsistent, the switching control signal is a high-level signal, and based on the high-level signal, the capacitor self-excited oscillation circuit is started to generate self-excited drive signals. When the MCU fails, the present invention can quickly switch to the capacitor self-excited oscillation circuit to maintain the state of high-power components unchanged and achieve safety control.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety control, and particularly relates to an abnormal processing method for high-power output drive signals based on safety control. Background Art

[0002] In the modern industrial and logistics fields, as a highly efficient handling device, the safety and reliability of electric forklifts are of crucial importance. However, since electric forklifts involve complex electronic control and high-power output systems, the abnormal processing of drive signals has become one of the key technologies to ensure their safe operation.

[0003] For the existing processing methods when an abnormality occurs in a processor, for example, the Chinese patent application with the publication number CN105446851A proposes a processor monitoring method, which includes the following steps: The MCU starts after obtaining power supply, and the MCU is communicatively connected to the processor; the MCU controls each circuit in the processor to be powered on and started in sequence according to a preset power-on timing sequence. During the process of controlling each circuit to be powered on and started, it monitors whether the previous circuit is powered on and started normally. After monitoring that the previous circuit is powered on and started normally, it then controls the next circuit to be powered on and started. If it monitors that any one circuit is abnormally powered on and started, it sends an abnormal power-on signal corresponding to this circuit to the alarm circuit; the alarm circuit alarms according to the abnormal power-on signal. The above method can quickly locate the circuit in the processor where an abnormality occurs during the startup process of the processor, so as to find out the cause of the fault.

[0004] However, the above existing technology alarms based on the abnormal power-on signals of specific circuits. This mechanism may not consider the severity and impact of different types of abnormalities, resulting in non-refined alarm information and low efficiency in processing abnormal signals. Therefore, an abnormal processing method for high-power output drive signals is needed to ensure the safe output of high-power components. Summary of the Invention

[0005] To solve the above problems, the present invention provides an abnormal processing method for high-power output drive signals based on safety control to solve the problems in the prior art.

[0006] To achieve the above invention purpose, the present invention proposes an abnormal processing method for high-power output drive signals based on safety control. The method includes a control circuit for controlling the stable high-power output of an electric forklift. The control circuit includes the following components:

[0007] MCU, drive conversion circuit, signal feedback circuit, drive signal switching circuit, load operation circuit, and capacitor self-excited oscillation circuit;

[0008] Among them, the capacitive self-excited oscillation circuit includes a triode U1A, a triode U2A, a capacitor C1, a capacitor C2, a resistor, and a power supply;

[0009] The collector of the triode U1A is connected to the capacitor C2 and is distributed between the positive power supply and the power supply ground wire;

[0010] The base of the triode U2A is connected to the positive power supply through a resistor and is connected to the power supply ground wire through the emitter of the triode U1A;

[0011] The collector of the triode U2A is connected to the capacitor C1, and the capacitor C2 is connected to the power supply ground wire through the emitter of the triode U1A.

[0012] Further, the MCU in the control circuit serves as the control center of the circuit, receives the action instructions of the electric forklift to generate control signals, and sends them to the drive conversion circuit. The drive conversion circuit converts the control signals into drive signals suitable for driving high-power loads, monitors the status of the drive signals based on the signal feedback circuit, and generates feedback signals to return to the MCU. The MCU determines whether the feedback signals are consistent with the drive signals. If so, the MCU keeps the current switching control signal as a low-level signal, and the drive signal switching circuit outputs a normal drive signal; if not, the MCU outputs a switching control signal as a high-level signal, starts the capacitive self-excited oscillation circuit based on the high-level signal, and the capacitive self-excited oscillation circuit generates a self-excited drive signal.

[0013] Further, if the feedback signal is inconsistent with the drive signal, the following steps are also included:

[0014] S31: Determine whether the feedback signal and the drive signal are of the same type. If not, convert both signals into digital signals, calculate the difference value between the feedback signal and the drive signal. If the difference value is less than or equal to the first threshold, set a drive signal adaptive adjustment mechanism in the MCU, and adjust the drive signal based on the adaptive algorithm to obtain a new drive signal;

[0015] S32: Repeat step S31 until the new drive signal is consistent with the feedback signal.

[0016] Further, when the difference value is greater than the first threshold, the following steps are included:

[0017] Establish a fault diagnosis model based on the Bayesian network, detect the system of the electric forklift based on the fault diagnosis model, identify the fault cause, trigger an alarm signal, and transmit the fault cause to the remote server through wireless communication.

[0018] Further, adjusting the drive signal based on the adaptive algorithm to obtain a new drive signal includes the following steps:

[0019] The adaptive algorithm is a PID control algorithm. Set initial parameters for the PID control algorithm, including a proportional term, an integral term, and a derivative term. Input the difference value into the PID control algorithm to adjust the initial parameters, obtain a new drive signal based on the adjusted initial parameters, set the target value of the drive signal, and when the new drive signal meets the target value, output the new drive signal.

[0020] Further, adjusting the initial parameters includes the following steps:

[0021] Calculate the adjusted proportional term P based on the first formula. The first formula is: P = kpE, where E is the difference value and k p is the coefficient of the proportional term. Calculate the adjusted integral term I based on the second formula. The second formula is: I = I(t - 1) + k i ∫E(t)dt, where I(t - 1) is the value of the integral term at the previous time point t - 1, k i is the coefficient of the integral term, and E(t) is the difference value at time t. Calculate the adjusted derivative term D based on the third formula, where, where k d is the coefficient of the derivative term, is the rate of change of the difference value with respect to time t, and calculate the new drive signal U based on the fourth formula. The fourth formula is: U = P + I + D.

[0022] Further, the self-excited drive signal is used to directly drive the high-power output components of the electric forklift.

[0023] Further, establishing a fault diagnosis model based on a Bayesian network includes the following steps:

[0024] Determine the hardware components of the electric forklift and the software used to control the hardware components. Create nodes for the hardware components and software in the Bayesian network, define status information for each node, where the status information is used to reflect the operating conditions of the hardware components or software, determine the relationship information between the nodes, and integrate all the nodes and the relationship information between the nodes into the fault diagnosis model.

[0025] Further, identifying the cause of a fault includes the following steps:

[0026] Define the types of faults. For each type of fault, identify the cause of the fault, group the causes of the fault, set a detection period for each group of causes of the fault, perform a fault detection on the electric forklift every detection period, obtain a real-time fault, determine the type of the real-time fault, and locate the cause of the fault based on the type of the fault.

[0027] Further, the relationship information is represented by a conditional probability table, which is used to describe the probability value of a child node being in a specific state given the state information of a parent node.

[0028] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0029] The present invention designs a control circuit, including an MCU, a drive conversion circuit, a signal feedback circuit, a drive signal switching circuit, a load operation circuit, and a capacitor self-excited oscillation circuit; by comparing the drive signal and the feedback signal, the MCU can determine whether the system is working as expected. If the signals are consistent, the system maintains its current state; if not, the drive signal sent by the MCU is immediately switched to the self-excited drive signal to ensure that the vehicle running state remains unchanged and ensure that the operators can operate safely; the MCU will also calculate the difference value between the drive signal and the feedback signal and make adjustments until the system output is consistent with the expectation, improving the stability and reliability of the system; when the difference value exceeds the first threshold, the MCU uses a Bayesian network to establish a fault diagnosis model to detect the system and identify the cause of the fault, improving the accuracy of fault diagnosis and reducing false alarms and missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of a method for processing abnormal drive signals with high-power output based on safety control according to the present invention;

[0031] Figure 2 is a circuit diagram of the capacitor self-excited oscillation circuit according to the present invention;

[0032] Figure 3 is a circuit diagram of the control circuit according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] It can be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of the present application, the first xx script may be referred to as the second xx script, and similarly, the second xx script may be referred to as the first xx script.

[0035] Such as Figure 1As shown in the figure, a method for abnormal processing of high-power output drive signals based on security control includes a control circuit for controlling the stable high-power output of an electric forklift. The control circuit comprises the following components:

[0036] MCU, drive conversion circuit, signal feedback circuit, drive signal switching circuit, load operation circuit, and capacitor self-excited oscillation circuit;

[0037] Among them, the capacitor self-excited oscillation circuit includes triode U1A, triode U2A, capacitor C1, capacitor C2, resistor, and power supply;

[0038] The collector of triode U1A is connected to capacitor C2 and is distributed between the positive power supply and the power supply ground wire;

[0039] The base of triode U2A is connected to the positive power supply through a resistor and is connected to the power supply ground wire through the emitter of triode U1A;

[0040] The collector of triode U2A is connected to capacitor C1, and capacitor C2 is connected to the power supply ground wire through the emitter of triode U1A.

[0041] Specifically, the present invention uses MCU, drive conversion circuit, signal feedback circuit, drive signal switching circuit, load operation circuit, and capacitor self-excited oscillation circuit to jointly construct a control circuit. Among them, as Figure 2 shown in the figure, it is the circuit diagram of the capacitor self-excited oscillation circuit; when the controller is powered on, due to component differences, one of the triodes will conduct preferentially. Assuming U1A conducts first, the collector voltage is pulled low, and the self-excited drive signal 1 outputs a low level. At this time, the left end of capacitor C2 is close to 0V. Since the voltage across the capacitor cannot change suddenly, the base of U2A is also pulled to approximately 0V, and U2A is turned off. The corresponding collector of U2A is at a high level, and the self-excited drive signal 2 outputs a high level; as R2 charges C2, the base voltage of U2A gradually increases. When it reaches the Vbe threshold, U2A conducts, and the collector voltage drops to 0V, and the self-excited drive signal 2 outputs a low level; at the same time, along with the drop of the collector voltage of U2A, the voltage value across C1 decreases, turning off U1A, and the self-excited drive signal 1 outputs a high level; this cycle repeats, causing the self-excited drive signal 1 and the self-excited drive signal 2 to output drive signals with a specific frequency. The drive signal frequency can be adjusted by adjusting the capacitance value of the capacitor to change the charging time.

[0042] As Figure 3As shown in the figure, it is the circuit diagram of the control circuit. The MCU issues drive signal 1, which, through drive conversion circuit 1, after its conversion, transmits the drive signal to device S1 in the drive signal switching circuit. S1 and S2 in the drive conversion circuit are devices of the same specification. Taking S1 as an example, pin 6 is the control pin. When pin 6 is at a low level, pin 2 and pin 1 are conducting; when pin 6 is at a high level, pin 8 and pin 1 are conducting. When just powered on, when S1 is in the default state (i.e., 6 is at a low level), pin 2 and pin 1 are conducting; at this time, the drive signal is output to drive resistor R5 after passing through S1, and after passing through R5, it drives the MOSFET to conduct, and the load circuit operates.

[0043] Specifically, sensors such as GPS positioning sensors, accelerometers, pressure sensors, etc. are installed on high-power components of the electric forklift, such as motors, batteries, hydraulic systems, etc., to collect information on the position, speed, working status of the forklift and the surrounding environment in real time, and transmit the sensor information to the MCU. The MCU (micro control unit), as the brain of the electric forklift control system, receives data from the sensors and performs corresponding control and adjustment based on these data.

[0044] As a preferred technical solution of the present invention, the MCU in the control circuit serves as the control center of the circuit, receives the action instructions of the electric forklift to generate control signals, and sends them to the drive conversion circuit. The drive conversion circuit converts these signals into drive signals suitable for driving high-power loads, monitors the status of the drive signals based on the signal feedback circuit, and generates feedback signals to return to the MCU. The MCU determines whether the feedback signal is consistent with the drive signal. If so, the MCU keeps the current drive signal as a low-level signal. If not, the MCU outputs a switching control signal as a high-level signal, and based on the high-level signal, starts the capacitor self-excited oscillation circuit, and the self-excited oscillation circuit generates a self-excited drive signal.

[0045] Specifically, also referring to Figure 3, the drive signal 1 is also output to the signal feedback circuit 1 after passing through the drive conversion circuit 1. The working principle of the signal feedback circuit 1 is to build a circuit using the comparator principle. When the voltage of pin 11 is higher than the voltage of pin 10, pin 13 outputs a high level, otherwise it outputs a low level. The voltage value of pin 10 is obtained by dividing the voltage of VCC1 by R8 and R12. Therefore, when the input drive signal 1 is at a high level, the feedback signal at pin 13 also outputs a high level, and vice versa; the feedback signal 1 is the signal after being converted by the signal feedback circuit 1. This signal is the same as the drive signal in the normal state. After the MCU receives the feedback signal 1, it compares and judges the feedback signal 1 and the drive signal 1 to confirm whether the issued drive signal is consistent with the feedback signal. If they are consistent, the switching control signal 1 remains low and the circuit operates normally; at this time, if they are inconsistent (when the MCU fails, the drive signal or the drive signal conversion circuit is abnormal), the switching control signal 1 will output a high level, quickly switch the drive signal output by the MCU to the corresponding self-excited drive signal, drive the high-power output to operate normally, maintain the state of the contactor or solenoid valve unchanged, and prevent the vehicle from getting out of control. At the same time, an alarm is triggered inside the MCU to remind the staff to handle the abnormal fault.

[0046] If the feedback signal and the drive signal are inconsistent, it also includes the following steps:

[0047] S31: Judge whether the feedback signal and the drive signal are of the same type. If not, convert both signals into digital signals, calculate the difference value between the feedback signal and the drive signal. If the difference value is less than or equal to the first threshold, set a drive signal adaptive adjustment mechanism in the MCU, and adjust the drive signal based on the adaptive algorithm to obtain a new drive signal.

[0048] S32: Repeat step S1 until the new drive signal is consistent with the feedback signal.

[0049] Specifically, before calculating the difference value, it is first necessary to determine whether the feedback signal and the drive signal are of the same type. This is because different types of signals (analog or digital) require different processing methods. If the feedback signal and the drive signal are not of the same type, they need to be converted to the same type for comparison. Usually, it is a convenient practice to convert all signals to digital signals because most modern control systems and MCUs process information in digital form. If the drive signal is inconsistent with the feedback signal, the MCU calculates the difference value between the two, which reflects the magnitude of the system deviation. The MCU determines whether the difference value is less than or equal to a preset first threshold, which is the acceptable error range of the system. If the difference value is within the first threshold, the MCU adjusts the drive signal based on an adaptive algorithm to reduce the deviation and obtain a new drive signal. The adaptive algorithm can dynamically adjust the control strategy according to real-time data, improving the adaptability and robustness of the system. The MCU repeats step S31, that is, continuously adjusts the drive signal until the new drive signal is consistent with the feedback signal.

[0050] When the difference value is greater than the first threshold, the following steps are included:

[0051] Based on the Bayesian network, a fault diagnosis model is established. The system of the electric forklift is detected based on the fault diagnosis model to identify the cause of the fault, trigger an alarm signal, and transmit the cause of the fault to the remote server through wireless communication.

[0052] Specifically, when the difference value (i.e., the inconsistency between the drive signal and the feedback signal) is greater than the first threshold, it indicates that the deviation between the actual output and the expected output of the electric forklift exceeds the error range allowed by the system. While switching the drive signal from low level to high level, the MCU also uses the Bayesian network (BN) to construct a fault diagnosis model, which can represent the dependence relationship between random variables. The MCU uses the Bayesian network model to detect the electric forklift system. By inputting the observed symptoms (such as abnormal sensor data) as evidence nodes, Bayesian network inference can calculate the probabilities of various fault causes occurring under the given symptoms. By analyzing these probabilities, the MCU can identify the most likely cause of the fault. Once the cause of the fault is identified, the MCU will trigger an alarm signal to alert the operator or maintenance personnel and take corresponding maintenance measures. The cause of the fault and relevant information are transmitted to the remote server through wireless communication technologies (such as Wi-Fi, 4G / 5G, Bluetooth, etc.). In this way, even in the absence of on-site personnel, the remote maintenance team can timely understand the status of the forklift and provide technical support.

[0053] Adjusting the drive signal based on the adaptive algorithm to obtain a new drive signal includes the following steps:

[0054] The adaptive algorithm is the PID control algorithm. Set the initial parameters for the PID control algorithm, including the proportional term, integral term, and derivative term. Input the difference value into the PID control algorithm to adjust the initial parameters. Obtain a new drive signal based on the adjusted initial parameters. Set the target value of the drive signal. When the new drive signal meets the target value, output the new drive signal.

[0055] Specifically, set the PID control algorithm in the MCU, including the proportional term P, integral term I, and derivative term D. These parameters determine the response mode of the controller to the system deviation. The proportional term P directly affects the magnitude of the deviation. The integral term I is used to eliminate the steady-state error. The derivative term D predicts the future change of the deviation. Input the difference value between the actual output (feedback signal) and the expected output (drive signal) of the system into the PID control algorithm. This difference value is the basis for the controller to adjust and is used to calculate how to adjust the drive signal to reduce the deviation. The PID control algorithm dynamically calculates the output signal (new drive signal) according to the input difference value to adjust the system response. The algorithm will consider the deviation instantaneously, cumulatively, and in terms of the rate of change according to the settings of the P, I, and D parameters, thus generating an adjusted drive signal. When the new drive signal meets the target value, the MCU will output this new drive signal to the actuator, such as a motor or valve, to achieve precise control of the system. The adaptive algorithm can automatically adjust the PID parameters according to the changes of the system, enabling the controller to adapt to the changes of system parameters and external disturbances and enhancing the robustness of the system.

[0056] Adjusting the initial parameters includes the following steps:

[0057] Calculate the adjusted proportional term P based on the first formula. The first formula is: P = k p E, where E is the difference value and k p is the coefficient of the proportional term. Calculate the adjusted integral term I based on the second formula. The second formula is: I = I(t - 1)+k i ∫E(t)dt, where I(t - 1) is the value of the integral term at the previous time point t - 1, k i is the coefficient of the integral term, and E(t) is the difference value at time t. Calculate the adjusted derivative term D based on the third formula, where where k d is the coefficient of the derivative term, is the rate of change of the difference value with respect to time t. Calculate the new drive signal U based on the fourth formula. The fourth formula is ωU = P + I + D.

[0058] Specifically, the proportional term P is calculated based on the current difference value (error), the integral term I is calculated based on the accumulation of all past difference values, the derivative term D is calculated based on the change rate of the difference value. The adjusted proportional term P, integral term I, and derivative term D are added together to obtain the adjusted drive signal U. By combining the three terms of P, I, and D, the PID control algorithm can comprehensively consider the current, past, and future changes to achieve precise control of the system.

[0059] Establishing a fault diagnosis model based on a Bayesian network includes the following steps:

[0060] Determine the hardware components of the electric forklift and the software for controlling the hardware components. Create nodes in the Bayesian network for the hardware components and the software, and define status information for each node. The status information is used to reflect the operating conditions of the hardware components or the software. Determine the relationship information between the nodes, and integrate all the nodes and the relationship information between the nodes into the fault diagnosis model.

[0061] Specifically, first, it is necessary to identify all the key hardware components of the electric forklift (such as motors, batteries, sensors, etc.) and the software system for controlling these hardware. Create nodes in the Bayesian network. For example, node A: battery status, node B: motor status, node C: forklift operation. These nodes represent the various parts of the system. Define status information for each node. The status information describes the operating conditions of the node (hardware component or software), such as normal, warning, fault, etc. Determine the relationships between the nodes. For example, assume that the battery status (A) directly affects the motor status (B) because if the battery is faulty, the motor may not receive enough power to operate properly. The motor status (B) directly affects the forklift operation (C) because the motor is the main component driving the forklift operation. Establish a fault diagnosis model based on the node and the relationship information between the nodes.

[0062] Identifying the causes of faults includes the following steps:

[0063] Define the types of faults. For each type of fault, identify the causes of the fault, group the causes of the fault, set a detection period for each group of causes of the fault, perform fault detection on the electric forklift every detection period, obtain real-time faults, determine the types of the real-time faults, and locate the causes of the faults based on the types of the faults.

[0064] Specifically, first, it is necessary to define the types of faults that may occur in the electric forklift, including mechanical faults, electrical faults, and hydraulic system faults, etc. For each defined type of fault, identify the specific causes that may lead to these faults. For example, electrical faults may be caused by short circuits, open circuits, or component aging. Set a regular detection period for each group of causes of the fault to ensure that the system is regularly inspected. Determine the type of the fault based on the real-time fault data, and determine which type of the defined types of faults the fault belongs to.

[0065] The relationship information is represented by a conditional probability table, which describes the probability that a child node is in a specific state given the state information of the parent node.

[0066] Specifically, in a Bayesian network, the relationship information can be quantified by a conditional probability table (CPT). For example, P(B = failure | A = normal) = 0.1 (if the battery is normal, the probability of the motor failing is 10%). Through probability, we can infer that if the battery state is normal, the probability of the motor failing is low; if the motor state is a failure, then the forklift operation is likely to be affected as well.

[0067] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0068] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The above program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0069] Each technical feature of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0070] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

[0071] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for abnormal processing of high-power output drive signals based on security control, characterized in that, The method includes a control circuit for controlling the stable high-power output of an electric forklift. The control circuit includes the following components: MCU, drive conversion circuit, signal feedback circuit, drive signal switching circuit, load operation circuit, and capacitor self-excited oscillation circuit; Among them, the capacitor self-excited oscillation circuit includes triode U1A, triode U2A, capacitor C1, capacitor C2, resistor, and power supply; The collector of the triode U1A is connected to the capacitor C2 and is distributed between the positive power supply and the power supply ground wire; The base of the triode U2A is connected to the positive power supply through a resistor and is connected to the power supply ground wire through the emitter of the triode U1A; The collector of the triode U2A is connected to the capacitor C1, and the capacitor C2 is connected to the power supply ground wire through the emitter of the triode U1A; Among them, the MCU in the control circuit serves as the control center of the circuit, receives the action instruction of the electric forklift to generate a control signal, and sends it to the drive conversion circuit. The drive conversion circuit converts the control signal into a drive signal suitable for driving a high-power load, monitors the state of the drive signal based on the signal feedback circuit, and generates a feedback signal to return to the MCU. The MCU judges whether the feedback signal is consistent with the drive signal. If so, the MCU keeps the current switching control signal as a low-level signal, and the drive signal switching circuit outputs a normal drive signal; if not, the MCU outputs the switching control signal as a high-level signal, starts the capacitor self-excited oscillation circuit based on the high-level signal, and the capacitor self-excited oscillation circuit generates a self-excited drive signal; Among them, if the feedback signal is inconsistent with the drive signal, it further includes the following steps: S31: Judge whether the feedback signal and the drive signal are of the same type. If not, convert both signals into digital signals, calculate the difference value between the feedback signal and the drive signal. If the difference value is less than or equal to the first threshold, set a drive signal adaptive adjustment mechanism in the MCU, and adjust the drive signal based on the adaptive algorithm to obtain a new drive signal; S32: Repeat step S31 until the new drive signal is consistent with the feedback signal.

2. The method according to claim 1, characterized in that, When the difference value is greater than the first threshold, it includes the following steps: Establish a fault diagnosis model based on the Bayesian network, detect the system of the electric forklift based on the fault diagnosis model, identify the fault cause, trigger an alarm signal, and transmit the fault cause to a remote server through wireless communication.

3. The method according to claim 1, characterized in that, Adjusting the drive signal based on the adaptive algorithm to obtain a new drive signal includes the following steps: The adaptive algorithm is a PID control algorithm. Set initial parameters for the PID control algorithm, including a proportional term, an integral term, and a differential term. Input the difference value into the PID control algorithm to adjust the initial parameters, obtain a new drive signal based on the adjusted initial parameters, set the target value of the drive signal, and when the new drive signal meets the target value, output the new drive signal.

4. The method according to claim 3, characterized in that, Adjusting the initial parameters includes the following steps: Calculate the adjusted proportional term P based on the first formula, where the first formula is: P = k p E, where E is the difference value and k p is the coefficient of the proportional term. Calculate the adjusted integral term I based on the second formula, where the second formula is: I = I(t - 1) + k i ∫E(t)dt, where I(t - 1) is the value of the integral term at the previous time point t - 1 and k i is the coefficient of the integral term, and E(t) is the difference value at time t. Calculate the adjusted differential term D based on the third formula, where where k d is the number of words of the differential term, is the rate of change of the difference value with respect to time t. Calculate the new drive signal U based on the fourth formula, where the fourth formula is: U = P + I + D.

5. The method according to claim 1, characterized in that, The self-excited drive signal is used to directly drive the high-power output components of the electric forklift.

6. The method according to claim 2, wherein Establishing a fault diagnosis model based on the Bayesian network includes the following steps: Determine the hardware components of the electric forklift and the software for controlling the hardware components. Create nodes for the hardware components and software in the Bayesian network, define status information for each node, where the status information is used to reflect the operating conditions of the hardware components or software. Determine the relationship information between the nodes, and integrate all the nodes and the relationship information between the nodes into the fault diagnosis model.

7. The method according to claim 2, characterized in that, Identifying the cause of the fault includes the following steps: Define the fault types, identify the cause of the fault for each fault type, group the cause of the fault, set a detection period for each group of the cause of the fault, perform fault detection on the electric forklift every the detection period, obtain the real-time fault, judge the fault type of the real-time fault, and locate the cause of the fault based on the fault type.

8. The method according to claim 6, wherein The relationship information is represented by a conditional probability table, and the conditional probability table is used to describe the probability value of the child node being in a specific state given the status information of the parent node.

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