High-power output driving signal abnormity processing method based on safety control

Abstract

The invention discloses a high-power output driving signal exception handling method based on safety control, which belongs to the technical field of safety control, and comprises the following steps: an MCU (Microprogrammed Control Unit) in a control circuit is used as a control center of the circuit, receives an action instruction of an electric forklift, generates a control signal, and sends the control signal to a driving conversion circuit; the driving conversion circuit converts the control signal into a driving signal suitable for driving a high-power load, monitors the state of the driving signal based on the signal feedback circuit, generates a feedback signal and returns the feedback signal to the MCU, the MCU judges whether the feedback signal is consistent with the driving signal or not, if yes, the MCU keeps the current switching control signal as a low-level signal, and if not, the MCU stops switching. The switching control signal is a high-level signal, and the capacitor self-excitation oscillation circuit is started based on the high-level signal to generate a self-excitation driving signal. When the MCU fails, the capacitor self-excitation oscillation circuit can be quickly switched to, the state of a high-power component is kept unchanged, and safety control is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety control, and in particular relates to a method for handling abnormalities of high-power output drive signals based on safety control. Background Art

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

[0003] The existing processing method when the processor is abnormal, such as the Chinese patent application with publication number CN105446851A, proposes a processor monitoring method, including the following steps: the MCU starts after obtaining power supply, and the MCU communicates with the processor; the MCU controls each circuit in the processor to power on and start in sequence according to the preset power-on sequence, and in the process of controlling the power-on of each circuit, monitors whether the power-on of the previous circuit is normal, and after monitoring that the power-on of the previous circuit is normal, the next circuit is controlled to power on and start, and if any circuit is monitored to be abnormal, a power-on abnormality signal corresponding to the circuit is sent to the alarm circuit; the alarm circuit alarms according to the power-on abnormality signal. The above method can quickly locate the abnormal circuit in the processor during the processor startup process, so as to find out the cause of the fault.

[0004] However, the above-mentioned prior art is based on the power-on abnormal signal of a specific circuit for alarm. This mechanism may not take into account the severity and impact of different types of abnormalities, resulting in insufficient refinement of the alarm information and low efficiency in abnormal signal processing. Therefore, a high-power output drive signal abnormality processing method 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 a method for handling abnormalities of high-power output drive signals based on safety control to solve the problems in the prior art.

[0006] In order to achieve the above-mentioned invention object, the present invention proposes a method for handling abnormal high-power output drive signals based on safety control, the method comprising a control circuit, the control circuit is used to control the high-power stable output of the electric forklift, the control circuit comprises 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] The capacitor self-excited oscillation circuit includes a transistor U1A, a transistor U2A, a capacitor C1, a capacitor C2, a resistor and a power supply;

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

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

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

[0012] Furthermore, the MCU in the control circuit serves as the control center of the circuit, receives the action instructions of the electric forklift, generates 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 and returns it to the MCU. The MCU determines 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, and 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.

[0013] Furthermore, if the feedback signal is inconsistent with the driving signal, the method further includes the following steps:

[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 between the feedback signal and the drive signal, and if the difference is less than or equal to a first threshold, set a drive signal adaptive adjustment mechanism in the MCU to adjust the drive signal based on an adaptive algorithm to obtain a new drive signal;

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

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

[0017] A fault diagnosis model is established based on the Bayesian network, and 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 a remote server through wireless communication.

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

[0019] The adaptive algorithm is a PID control algorithm, and initial parameters are set for the PID control algorithm, including proportional terms, integral terms and differential terms. The difference value is input into the PID control algorithm to adjust the initial parameters, and a new drive signal is obtained based on the adjusted initial parameters. The target value of the drive signal is set, and when the new drive signal meets the target value, the new drive signal is output.

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

[0021] The adjusted proportional term P is calculated based on a first formula, wherein the first formula is: P=kpE, where E is the difference value, k p is the coefficient of the proportional term, and the adjusted integral term I is calculated 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 integral value at the last time point t-1, k i is the coefficient of the integral term, E(t) is the difference value at time t, and the adjusted differential term D is calculated based on the third formula, where: Among them, k d is the number of words in the differential term, is the rate of change of the difference value with time t, and a new driving signal U is obtained based on the fourth formula, and the fourth formula is: U=P+I+D.

[0022] Furthermore, the self-excited driving signal is used to directly drive a high-power output component of the electric forklift.

[0023] Furthermore, establishing a fault diagnosis model based on the 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 a Bayesian network, define status information for each node, the status information is used to reflect the operating status of the hardware components or software, determine the relationship information between nodes, and integrate the relationship information between all nodes into a fault diagnosis model.

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

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

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

[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 a drive signal and a feedback signal, the MCU can determine whether the system works as expected, if the signals are consistent, the system maintains the current state; if they are inconsistent, the drive signal sent by the MCU is immediately switched to a self-excited drive signal, so as to ensure that the vehicle operation state remains unchanged and that operators can operate safely; the MCU also calculates the difference value between the drive signal and the feedback signal and adjusts it until the system output is consistent with expectations, thereby improving the stability and reliability of the system; when the difference value exceeds a first threshold value, the MCU uses a Bayesian network to establish a fault diagnosis model, detects the system and identifies the cause of the fault, improves the accuracy of fault diagnosis, and reduces false alarms and missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a method for handling abnormalities of high-power output drive signals based on safety control according to the present invention;

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

[0032] Figure 3 4 is a circuit diagram of the control circuit of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0034] It is understood that the terms "first", "second", etc. used in this 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 this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0035] like Figure 1As shown, a method for handling abnormal high-power output drive signals based on safety control includes a control circuit, which is used to control the high-power stable output of an electric forklift. The control circuit includes 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] The capacitor self-excited oscillation circuit includes transistor U1A, transistor U2A, capacitor C1, capacitor C2, resistor and power supply;

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

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

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

[0041] Specifically, the present invention uses 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 to jointly construct a control circuit, wherein, Figure 2 As shown in the figure, it is the circuit diagram of the capacitor self-excited oscillation circuit; when the controller is powered on, due to the difference in components, one of the transistors will be turned on first. If U1A is turned on first, the collector voltage is pulled down, 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, U2A is turned off, and the corresponding collector of U2A is high, and the self-excited drive signal 2 outputs a high level; as R2 charges C2, the base voltage of U2A gradually increases. When the Vbe threshold is reached, U2A is turned on, the collector voltage drops to 0V, and the self-excited drive signal 2 outputs a low level; at the same time, with the decrease in the collector voltage of U2A, the voltage value across C1 decreases, U1A is turned off, and the self-excited drive signal 1 outputs a high level; in this cycle, the self-excited drive signal 1 and the self-excited drive signal 2 output drive signals of a specific frequency. The drive signal frequency can be changed by adjusting the capacitance value of the capacitor and adjusting the charging time.

[0042] like Figure 3As shown in the figure, it is the circuit diagram of the control circuit. The MCU sends a driving signal 1, which is converted by the driving conversion circuit 1 and then transmitted to the S1 device in the driving signal switching circuit. The S1 and S2 in the driving 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 connected; when pin 6 is at a high level, pin 8 and pin 1 are connected. When the power is just turned on, when S1 is in the default state (i.e., 6 is at a low level), pin 2 and pin 1 are connected; at this time, the driving signal is output to the driving resistor R5 after passing through S1, and the MOSFET is driven to turn on after passing through R5, and the load circuit runs.

[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 real-time information on the forklift's position, speed, working status and surrounding environment, and transmit the sensor information to the MCU. The MCU (microcontroller unit) serves as the brain of the electric forklift control system, receives data from the sensors, and performs corresponding control and adjustment based on the 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 state of the drive signal based on the signal feedback circuit, and generates a feedback signal and returns it 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 starts the capacitor self-excited oscillation circuit based on the high-level signal, and the self-excited oscillation circuit generates a self-excited drive signal.

[0045] Specifically, refer 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. Among them, the voltage value of pin 10 is obtained by the voltage division of VCC1 by R8 and R12, so when the input drive signal 1 is high, the feedback signal pin 13 also outputs a high level, otherwise it also outputs a low level; the feedback signal 1 is the signal converted by the signal feedback circuit 1. The 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 sent drive signal is consistent with the feedback signal. If they are consistent, the switching control signal 1 remains at a low level and the circuit operates normally; if they are inconsistent at this time (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 losing control. At the same time, the MCU triggers an alarm to remind the staff to deal with the abnormal fault.

[0046] If the feedback signal is inconsistent with the drive signal, the following steps are also included:

[0047] 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 between the feedback signal and the drive signal, and if the difference is less than or equal to a first threshold, set a drive signal adaptive adjustment mechanism in the MCU, and adjust the drive signal based on an 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 signals or digital signals) 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. Generally, it is convenient to convert all signals into 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 between the two, and this difference reflects the size of the system deviation. The MCU determines whether the difference is less than or equal to a preset first threshold value, which is an acceptable error range for the system. If the difference is within the first threshold value, 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 to improve 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] If the difference value is greater than the first threshold, the following steps are included:

[0051] A fault diagnosis model is established based on the Bayesian network. The electric forklift system is detected based on the fault diagnosis model, the cause of the fault is identified, an alarm signal is triggered, and the cause of the fault is transmitted 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 means that the deviation between the actual output and the expected output of the electric forklift exceeds the error range allowed by the system. When the MCU switches the drive signal from a low level to a high level, it also uses the Bayesian network (BN) to build a fault diagnosis model, which can represent the dependency 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, the Bayesian network reasoning can calculate the probability of various fault causes under 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 remind the operator or maintenance personnel to pay attention and take corresponding maintenance measures. The cause of the fault and related information are transmitted to the remote server through wireless communication technology (such as Wi-Fi, 4G / 5G, Bluetooth, etc.). In this way, even in the absence of on-site personnel, the remote maintenance team can understand the status of the forklift in a timely manner and provide technical support.

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

[0054] The adaptive algorithm is a PID control algorithm. Initial parameters are set for the PID control algorithm, including proportional terms, integral terms and differential terms. The difference value is input into the PID control algorithm to adjust the initial parameters. A new drive signal is obtained based on the adjusted initial parameters. The target value of the drive signal is set. When the new drive signal meets the target value, the new drive signal is output.

[0055] Specifically, the PID control algorithm is set in the MCU, including the proportional term P, the integral term I and the differential term D. These parameters determine how the controller responds to system deviations. The proportional term P directly affects the size of the deviation, the integral term I is used to eliminate steady-state errors, and the differential term D predicts future changes in the deviation. The difference between the actual output (feedback signal) and the expected output (drive signal) of the system is input into the PID control algorithm. This difference value is the basis for controller adjustment 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) based on the input difference value to adjust the system response. The algorithm will consider the deviation in real time, cumulatively and at a rate of change based on the settings of the P, I, and D parameters, thereby 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 changes in the system, so that the controller can adapt to changes in system parameters and external disturbances, and enhance the robustness of the system.

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

[0057] The adjusted proportional term P is calculated based on the first formula, which is: P = k p E, where E is the difference value, k p is the coefficient of the proportional term, and the adjusted integral term I is calculated based on the second formula, which is: I = I (t-1) + k i ∫E(t)dt, where I(t-1) is the integral value at the last time point t-1, k i is the coefficient of the integral term, E(t) is the difference value at time t, and the adjusted differential term D is calculated based on the third formula, where: Among them, k d is the number of words in the differential term, is the rate of change of the difference value with time t, and the new driving signal U is obtained based on the fourth formula, and 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, and the differential term D is calculated based on the rate of change of the difference value. The adjusted proportional term P, integral term I and differential term D are added to obtain the adjusted drive signal U. By combining P, I and D, the PID control algorithm can comprehensively consider current, past and future changes to achieve precise control of the system.

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

[0060] Identify 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, the status information is used to reflect the operating status of the hardware components or software, determine the relationship information between the nodes, and integrate all 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 systems used to control these hardware, and create nodes in the Bayesian network, such as node A: battery status, node B: motor status, node C: forklift operation. These nodes represent the various parts of the system, and define status information for each node. The status information describes the operating status of the node (hardware component or software), such as normal, warning, fault and other states. Determine the relationship 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 be able to obtain enough power to work properly. The motor status (B) directly affects the forklift operation (C), because the motor is the main component that drives the forklift operation. A fault diagnosis model is established based on the relationship information between the nodes.

[0062] Identifying the cause of the failure involves the following steps:

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

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

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

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

[0067] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0068] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0069] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The above embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0071] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for handling abnormal high-power output drive signals based on safety control, characterized in that: The method includes a control circuit, which is used to control the high-power stable output of the electric forklift, and 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; The capacitor self-excited oscillation circuit includes a transistor U1A, a transistor U2A, a capacitor C1, a capacitor C2, a resistor and a power supply; The collector of the transistor U1A is connected to the capacitor C2 and is distributed between the positive electrode of the power supply and the power ground line; The base of the transistor U2A is connected to the positive electrode of the power supply through a resistor, and is connected to the power ground through the emitter of the transistor U1A; The collector of transistor U2A is connected to capacitor C1, and capacitor C2 is connected to the power ground through the emitter of transistor U1A.

2. The method according to claim 1, characterized in that The MCU in the control circuit serves as the control center of the circuit, receives the action instructions of the electric forklift, generates 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 and returns it to the MCU. The MCU determines 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, and 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.

3. The method according to claim 2, characterized in that If the feedback signal is inconsistent with the drive signal, the following steps are also included: 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 between the feedback signal and the drive signal, and if the difference is less than or equal to a first threshold, set a drive signal adaptive adjustment mechanism in the MCU to adjust the drive signal based on an adaptive algorithm to obtain a new drive signal; S32: Repeat step S31 until the new driving signal is consistent with the feedback signal.

4. The method according to claim 3, characterized in that If the difference value is greater than the first threshold, the following steps are included: A fault diagnosis model is established based on the Bayesian network, and 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 a remote server through wireless communication.

5. The method according to claim 3, characterized in that: Adjusting the driving signal based on the adaptive algorithm to obtain a new driving signal comprises the following steps: The adaptive algorithm is a PID control algorithm, and initial parameters are set for the PID control algorithm, including proportional terms, integral terms and differential terms. The difference value is input into the PID control algorithm to adjust the initial parameters, and a new drive signal is obtained based on the adjusted initial parameters. The target value of the drive signal is set, and when the new drive signal meets the target value, the new drive signal is output.

6. The method according to claim 5, characterized in that Adjusting the initial parameters comprises the following steps: The adjusted proportional term P is calculated based on a first formula, where the first formula is: P=k p E, where E is the difference value, k p is the coefficient of the proportional term, and the adjusted integral term I is calculated 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 integral value at the last time point t-1, k i is the coefficient of the integral term, E(t) is the difference value at time t, and the adjusted differential term D is calculated based on the third formula, where: Among them, k d is the number of words in the differential term, is the rate of change of the difference value with time t, and a new driving signal U is obtained based on the fourth formula, and the fourth formula is: U=P+I+D.

7. The method according to claim 2, characterized in that The self-excited driving signal is used to directly drive the high-power output components of the electric forklift.

8. The method according to claim 4, characterized in that Establishing a fault diagnosis model based on Bayesian network includes the following steps: 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 a Bayesian network, define status information for each node, the status information is used to reflect the operating status of the hardware components or software, determine the relationship information between nodes, and integrate the relationship information between all nodes into a fault diagnosis model.

9. The method according to claim 4, characterized in that Identifying the cause of the failure involves the following steps: Define fault types, identify fault causes for each fault type, group the fault causes, set a detection period for each group of fault causes, perform fault detection on the electric forklift every detection period, obtain real-time faults, determine the fault type of the real-time faults, and locate the fault cause based on the fault type.

10. The method according to claim 8, characterized in that 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 under the condition of given parent node state information.

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