Disclosure of Invention
In order to solve the problems, the invention provides a high-power output driving signal exception handling method based on safety control, which solves the problems in the prior art.
In order to achieve the above object, the present invention provides a method for processing abnormality of a high power output driving signal based on safety control, the method comprising a control circuit for controlling high power stable output of an electric forklift, the control circuit comprising the following components:
The device comprises an MCU, a drive conversion circuit, a signal feedback circuit, a drive signal switching circuit, a load operation circuit and a capacitor self-excitation oscillation circuit;
The capacitor self-excitation oscillating circuit comprises a triode U1A, a triode U2A, a capacitor C1, a capacitor C2, a resistor and a power supply;
The collector electrode of the triode U1A is connected with the capacitor C2 and is distributed between the positive electrode of the power supply and the ground wire of the power supply;
The base electrode of the triode U2A is connected with the positive electrode of the power supply through a resistor, and is connected with the ground wire of the power supply through the emitter electrode of the triode U1A;
The collector of the triode U2A is connected with a capacitor C1, and the capacitor C2 is connected with a power ground wire through the emitter of the triode U1A.
Further, the MCU in the control circuit is used as a control center of the circuit, receives an action instruction of the electric forklift to generate a control signal, and sends the control signal to the drive conversion circuit, the drive conversion circuit converts the control signal into a drive signal suitable for driving a high-power load, the state of the drive signal is monitored based on the signal feedback circuit, the feedback signal is generated and returned to the MCU, the MCU judges whether the feedback signal is consistent with the drive signal or not, if yes, the MCU keeps the current switching control signal as a low-level signal, the drive signal switching circuit outputs a normal drive signal, if not, the MCU outputs the switching control signal as a high-level signal, the capacitive self-excitation oscillating circuit is started based on the high-level signal, and the capacitive self-excitation oscillating circuit generates a self-excitation drive signal.
Further, if the feedback signal is inconsistent with the driving signal, the method further comprises the following steps:
S31, judging whether the feedback signal and the driving signal are of the same type, if not, converting the two signals into digital signals, calculating a difference value of the feedback signal and the driving signal, and if the difference value is smaller than or equal to a first threshold value, setting a driving signal self-adaptive adjustment mechanism in the MCU, and adjusting the driving signal based on a self-adaptive algorithm to obtain a new driving signal;
and S32, repeating the step S31 until the new driving signal is consistent with the feedback signal.
Further, if the difference value is greater than the first threshold value, the method includes the following steps:
and establishing a fault diagnosis model based on a Bayesian network, detecting a system of the electric forklift based on the fault diagnosis model, identifying a fault cause, triggering an alarm signal, and transmitting the fault cause to a remote server through wireless communication.
Further, the step of adjusting the driving signal based on the adaptive algorithm to obtain a new driving signal includes the following steps:
The self-adaptive algorithm is a PID control algorithm, initial parameters comprising a proportion term, an integral term and a differential term are set for the PID control algorithm, the difference value is input into the PID control algorithm to adjust the initial parameters, a new driving signal is obtained based on the adjusted initial parameters, a target value of the driving signal is set, and when the new driving signal meets the target value, the new driving signal is output.
Further, the adjusting of the initial parameters comprises the following steps:
Calculating the adjusted proportional term P based on a first formula, wherein p= kpE, E is the difference value, kp is a coefficient of the proportional term, calculating the adjusted integral term I based on a second formula, i=i (t-1) +ki +.e (t) dt, wherein I (t-1) is an integral term value at a previous time point t-1, ki is a coefficient of the integral term, E (t) is a difference value at time t, calculating the adjusted differential term D based on a third formula,Wherein kd is the word number of the differential term,The driving signal U is new based on a fourth formula, where u=p+i+d, as the rate of change of the difference value with time t.
Further, the self-excited drive signal is used for directly driving a high-power output component of the electric forklift.
Further, establishing the fault diagnosis model based on the bayesian network comprises the following steps:
Determining a hardware component of the electric forklift and software for controlling the hardware component, creating nodes for the hardware component and the software in a Bayesian network, defining state information for each node, wherein the state information is used for reflecting the operation condition of the hardware component or the software, determining relation information among the nodes, and integrating all the nodes and the relation information among the nodes into a fault diagnosis model.
Further, identifying the cause of the fault includes the steps of:
Defining fault types, identifying fault reasons for each fault type, grouping the fault reasons, setting a detection period for each group of fault reasons, detecting faults of the electric forklift at intervals of the detection period, obtaining real-time faults, judging the fault types of the real-time faults, and positioning the fault reasons based on the fault types.
Further, the relationship information is represented by a conditional probability table describing probability values that the child node is in a specific state given the parent node state information.
Compared with the prior art, the invention has the following beneficial effects:
The invention designs a control circuit which comprises an MCU, a drive conversion circuit, a signal feedback circuit, a drive signal switching circuit, a load operation circuit and a capacitor self-excitation oscillation circuit, wherein the MCU can judge whether a system works according to expectations by comparing the drive signal with the feedback signal, if the signals are consistent, the system keeps the current state, if the signals are inconsistent, the drive signal sent by the MCU is immediately switched to the self-excitation drive signal, the running state of a vehicle is ensured to be unchanged, the safe operation of operators is ensured, the MCU can calculate the difference value of the drive signal and the feedback signal and adjust the difference value until the output of the system is consistent with the expectations, the stability and the reliability of the system are improved, and when the difference value exceeds a first threshold value, the MCU utilizes a Bayesian network to establish a fault diagnosis model to detect and identify the cause of the fault, the accuracy of the fault diagnosis is improved, and false alarm and missing report are reduced.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, 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, without departing from the scope of this disclosure.
As shown in fig. 1, a method for processing abnormality of a high-power output driving signal based on safety control includes a control circuit for controlling high-power stable output of an electric forklift, the control circuit including the following components:
The device comprises an MCU, a drive conversion circuit, a signal feedback circuit, a drive signal switching circuit, a load operation circuit and a capacitor self-excitation oscillation circuit;
The capacitor self-excitation oscillating circuit comprises a triode U1A, a triode U2A, a capacitor C1, a capacitor C2, a resistor and a power supply;
the collector electrode of the triode U1A is connected with the capacitor C2 and is distributed between the positive electrode of the power supply and the ground wire of the power supply;
The base electrode of the triode U2A is connected with the positive electrode of the power supply through a resistor, and is connected with the ground wire of the power supply through the emitter electrode of the triode U1A;
The collector of the triode U2A is connected with a capacitor C1, and the capacitor C2 is connected with a power ground wire through the emitter of the triode U1A.
Specifically, the invention constructs a control circuit by utilizing the MCU, the drive conversion circuit, the signal feedback circuit, the drive signal switching circuit, the load operation circuit and the capacitance self-excited oscillation circuit together, wherein the control circuit is a circuit diagram of the capacitance self-excited oscillation circuit as shown in figure 2, when the controller is electrified, one triode is conducted preferentially due to component differences, and if U1A is conducted in a leading mode, the collector voltage is pulled down, and the self-excited drive signal 1 outputs a low level. At this time, the left end of the capacitor C2 is close to 0V, the voltage at the two ends of the capacitor cannot be suddenly changed, the U2A base electrode is pulled to be approximately 0V, the U2A is closed, the corresponding U2A collector electrode is in high level, the self-excitation driving signal 2 outputs high level, the voltage of the U2A base electrode is gradually increased along with the charging of the R2 to the C2, when the Vbe threshold is reached, the U2A is conducted, the collector voltage is reduced to 0V, the self-excitation driving signal 2 outputs low level, meanwhile, the voltage value at the two ends of the C1 is reduced along with the reduction of the voltage of the U2A collector electrode, the U1A is closed, the self-excitation driving signal 1 outputs high level, and the self-excitation driving signal 1 and the self-excitation driving signal 2 output driving signals with specific frequencies in such a circulation. The driving signal frequency can be changed by adjusting the capacitance value and the charging time.
As shown in fig. 3, in the circuit diagram of the control circuit, the MCU sends out a driving signal 1, and the driving signal is transferred to the S1 device in the driving signal switching circuit through the conversion of the driving conversion circuit 1, where S1 and S2 in the driving conversion circuit are devices with the same specification. Taking S1 as an example, the pin 6 is a control pin, when the pin 6 is at a low level, the pin 2 and the pin 1 are conducted, when the pin 6 is at a high level, the pin 8 and the pin 1 are conducted, when the pin 1 is at a default state (i.e. when the pin 6 is at a low level) just powered on, the pin 2 and the pin 1 are conducted, at the moment, a driving signal is output to the driving resistor R5 after passing through the S1, and then the MOSFET is driven to conduct after passing through the R5, so that the load circuit operates.
Specifically, sensors such as a GPS positioning sensor, an accelerometer, a pressure sensor and the like are installed at high-power components of the electric forklift, such as a motor, a battery, a hydraulic system and the like, information of the position, the speed, the working state and the surrounding environment of the forklift is collected in real time, the sensor information is transmitted to an MCU (micro control unit), the MCU is used as a brain of the control system of the electric forklift, data from the sensors are received, and corresponding control and adjustment are performed according to the data.
As a preferable technical scheme of the invention, an MCU in a control circuit is used as a control center of the circuit, receives action instructions of the electric forklift to generate control signals, sends the control signals to a drive conversion circuit, the drive conversion circuit converts the signals into drive signals suitable for driving high-power loads, monitors the states of the drive signals based on a signal feedback circuit and generates feedback signals to return to the MCU, and if the feedback signals are consistent with the drive signals, the MCU keeps the current drive signals as low-level signals, if the feedback signals are inconsistent with the drive signals, the MCU outputs switching control signals as high-level signals, a capacitor self-excitation oscillation circuit is started based on the high-level signals, and the self-excitation oscillation circuit generates self-excitation drive signals.
Specifically, referring also to fig. 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 by utilizing a comparator principle, when the voltage of the 11 pins is higher than that of the 10 pins, the high level is output by the 13 pins, and otherwise, the low level is output. The voltage value of the 10 pins is obtained by dividing the voltage of the R8 and the R12 to the VCC1, so that when the input driving signal 1 is at a high level, the feedback signal 13 pin also outputs a high level, and otherwise outputs a low level, the feedback signal 1 is a signal converted by the signal feedback circuit 1, the signal is the same as the driving signal in a normal state, after receiving the feedback signal 1, the MCU compares and judges the feedback signal 1 and the driving signal 1, and confirms whether the sent driving signal is consistent with the fed-back signal. If the switching control signal 1 is consistent, the low level is kept unchanged, the circuit operates normally, and if the switching control signal 1 is inconsistent (when the MCU fails, the driving signal or the driving signal conversion circuit is abnormal), the switching control signal 1 outputs a high level, rapidly switches the driving signal output by the MCU to a corresponding self-excited driving signal, drives the high-power output to operate normally, maintains the state of the contactor or the electromagnetic valve unchanged, and prevents the vehicle from being out of control. Meanwhile, the MCU triggers an alarm to remind a worker of handling the abnormal faults.
If the feedback signal is inconsistent with the driving signal, the method further comprises the following steps:
S31, judging whether the feedback signal and the driving signal are of the same type, if not, converting the two signals into digital signals, calculating the difference value of the feedback signal and the driving signal, and if the difference value is smaller than or equal to a first threshold value, setting a driving signal self-adaptive adjustment mechanism in the MCU, and adjusting the driving signal based on a self-adaptive algorithm to obtain a new driving signal.
And S32, repeating the step S1 until the new driving signal is consistent with the feedback signal.
Specifically, before calculating the difference value, it is first necessary to determine whether the feedback signal and the driving signal are of the same type. This is because different types of signals (analog or digital) require different processing schemes and 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. In general, it is convenient to convert all signals into digital signals, because most modern control systems and MCUs process information in digital form, if the driving signal is inconsistent with the feedback signal, the MCU calculates a difference value between the two, the difference value reflects the magnitude of the systematic deviation, the MCU determines whether the difference value is less than or equal to a preset first threshold, the threshold is an acceptable error range of the system, if the difference value is within the first threshold, the MCU adjusts the driving signal based on an adaptive algorithm to reduce the deviation and obtain a new driving signal, the adaptive algorithm can dynamically adjust the control strategy according to real-time data, improving the adaptability and robustness of the system, and the MCU repeats step S31, i.e. continuously adjusts the driving signal until the new driving signal is consistent with the feedback signal.
If the difference value is larger than the first threshold value, the method comprises the following steps:
And establishing a fault diagnosis model based on the Bayesian network, detecting a system of the electric forklift based on the fault diagnosis model, identifying a fault cause, triggering an alarm signal, and transmitting the fault cause to a remote server through wireless communication.
Specifically, when the difference value (i.e., the inconsistency between the driving signal and the feedback signal) is greater than the first threshold, which indicates that the deviation between the actual output and the expected output of the electric forklift exceeds the allowable error range of the system, the MCU switches the driving signal from the low level to the high level, and also builds a fault diagnosis model by using a Bayesian Network (BN), the model can represent the dependency relationship between random variables, the MCU uses the bayesian network model to detect the electric forklift system, the bayesian network reasoning can calculate the probability of occurrence of various fault causes under the given condition by inputting the observed symptom (such as abnormal sensor data) as an evidence node, and by analyzing the probabilities, the MCU can identify the most likely fault cause, once the fault cause is identified, the MCU triggers an alarm signal to remind an operator or a maintainer to pay attention, and takes corresponding maintenance measures, and the fault cause and related information are transmitted to a remote server through a wireless communication technology (such as Wi-Fi, 4G/5G, bluetooth, etc.). Thus, even under the condition that no field personnel exist, the remote maintenance team can timely know the state of the forklift and provide technical support.
Adjusting the drive signal based on the adaptive algorithm to obtain a new drive signal comprises the steps of:
The self-adaptive algorithm is a PID control algorithm, initial parameters comprising a proportion term, an integral term and a differential term are set for the PID control algorithm, the differential value is input into the PID control algorithm to adjust the initial parameters, a new driving signal is obtained based on the adjusted initial parameters, a target value of the driving signal is set, and when the new driving signal meets the target value, the new driving signal is output.
Specifically, a PID control algorithm is set in the MCU, including a proportional term P, an integral term I, and a derivative term D. These parameters determine the way the controller responds to systematic deviations. The proportional term P directly affects the magnitude of the deviation, the integral term I is used to eliminate steady state errors, the derivative term D predicts future changes in the deviation, and the difference between the actual output (feedback signal) and the expected output (drive signal) of the system is input into the PID control algorithm. The difference value is the basis of the controller adjustment, and is used for calculating how to adjust the driving signal to reduce the deviation, the PID control algorithm dynamically calculates the output signal (new driving signal) according to the input difference value to adjust the response of the system, the algorithm can instantly, cumulatively and take the change rate into consideration according to the P, I, D parameter setting, so as to generate an adjusted driving signal, when the new driving signal meets the target value, the MCU outputs the new driving signal to an actuator, such as a motor or a valve, so as to realize accurate control of the system, and the self-adaptive algorithm can automatically adjust the PID parameter according to the system change, so that the controller can adapt to the system parameter change and external disturbance, and the robustness of the system is enhanced.
The adjusting of the initial parameters comprises the following steps:
Calculating an adjusted proportional term P based on a first formula, wherein p=kp E, where E is a difference value, kp is a coefficient of the proportional term, calculating an adjusted integral term I based on a second formula, i=i (t-1) +ki ≡e (t) dt, where I (t-1) is an integral term value at a previous time point t-1, ki is a coefficient of the integral term, E (t) is a difference value at time t, calculating an adjusted differential term D based on a third formula,Wherein kd is the word number of the differential term,For the rate of change of the difference value with time t, the driving signal U is new based on a fourth formula, which is ωu=p+i+d.
Specifically, the proportion term P is calculated according to the current difference value (error), the integral term I is calculated according to the accumulation of all past difference values, the differential term D is calculated according to the change rate of the difference values, the adjusted proportion term P, the integral term I and the differential term D are added to obtain an adjusted driving signal U, and by combining P, I, D terms, the PID control algorithm can comprehensively consider the current, past and future changes to realize accurate control of the system.
Establishing a fault diagnosis model based on the Bayesian network comprises the following steps:
Determining a hardware component of the electric forklift and software for controlling the hardware component, creating nodes for the hardware component and the software in a Bayesian network, defining state information for each node, wherein the state information is used for reflecting the running condition of the hardware component or the software, determining relation information among the nodes, and integrating all the nodes and the relation information among the nodes into a fault diagnosis model.
Specifically, first, it is necessary to identify all the key hardware components of the electric fork-lift truck (such as the motor, the battery, the sensors, etc.) and the software system for controlling these hardware, create nodes in the bayesian network, such as node a, battery state, node B, motor state, node C, fork-lift truck operation, which represent various parts of the system, define state information for each node, describing the operation condition of the node (hardware component or software), such as normal, warning, failure, etc., determine the relationship between the nodes, for example, assuming that battery state (a) directly affects motor state (B), because if the battery fails, the motor may not obtain enough power to work properly, which in turn directly affects fork-lift truck operation (C), because the motor is the main component driving fork-lift truck operation, and create a failure diagnosis model from the relationship information between the nodes.
Identifying the cause of the fault includes the steps of:
defining fault types, identifying fault reasons for each fault type, grouping the fault reasons, setting a detection period for each group of fault reasons, carrying out fault detection on the electric forklift at intervals of the detection period, obtaining real-time faults, judging the fault types of the real-time faults, and positioning the fault reasons based on the fault types.
Specifically, first, it is necessary to define the types of faults that may occur in an electric forklift, including mechanical faults, electrical faults, hydraulic system faults, and the like, and for each defined type of fault, identify the specific cause that may cause these faults. For example, electrical faults may be caused by short circuits, circuit breaks, or component aging, with periodic detection cycles set for each set of fault causes, ensuring that the system is regularly checked, determining the fault type from real-time fault data, and determining which type of defined fault type the fault belongs to.
The relationship information is represented by a conditional probability table that describes the probability that a child node is in a particular state given the parent node state information.
Specifically, in a bayesian network, the relationship information can be quantified by a Conditional Probability Table (CPT), such as P (b=failure |a=normal) =0.1 (if the battery is normal, the probability of motor failure is 10%), from which we can infer that if the battery state is normal, the probability of motor failure is low, and if the motor state is failed, the forklift operation is likely to be affected as well.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a non-transitory computer readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of 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), among others.
The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, they should be considered as the scope of the disclosure as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.