Integrated intelligent detection and automatic identification method and system for photoelectric tracking device

Abstract

The application relates to an integrated intelligent detection and automatic identification method and system of an optoelectronic tracking device, and belongs to the technical field of automatic identification. The optoelectronic tracking system comprises a configuration screen, a serial port to IO module, a solid-state relay, a servo control module, an optoelectronic tracking device and a power supply. The method comprises the following steps: the configuration screen inputs an equal-amplitude variable-frequency current of a chirp function waveform to a current loop q-axis open loop of the servo control module, and a zero value is given to a current loop d-axis open loop; the servo control module collects motor phase current values and encoder values of the optoelectronic tracking device, and calculates feedback current values in real time through a FOC control algorithm; the configuration screen calculates the amplitude-frequency characteristic of the optoelectronic tracking device, and calculates a first-order inertia model through a recursive least square algorithm; the servo control module collects motor current values, motor bus voltages and encoder state information of the optoelectronic tracking device in real time, and the configuration screen judges whether a fault exists. The application is convenient for single-soldier carrying and operation, short in identification time, and easy to realize convergence quickly.

Description

Technical Field

[0001] This invention relates to the field of automatic identification technology, and in particular to an integrated intelligent detection and automatic identification method and system for photoelectric tracking devices. Background Technology

[0002] In existing photoelectric tracking systems, the following problems exist in the automatic identification of the controlled object model:

[0003] I. Currently, remotely controlled photoelectric tracking equipment is generally deployed in harsh environments such as islands and reefs, with the equipment located far from the control room. When testing the status of photoelectric tracking equipment and identifying faults in the field, it is necessary to connect peripherals such as industrial computers, displays, mice, and keyboards to the equipment, which is inconvenient for individual soldiers to carry and operate.

[0004] Second, the identification of photoelectric tracking devices employs complex algorithms such as the step response method, frequency sweep method, and their derivatives, including Markov algorithms. These methods utilize input / output data and prior knowledge to obtain the controlled object model in MATLAB through fitting or other fitting methods. However, they are all time-consuming and cannot automatically identify the controlled object model within the frequency range of 0.1Hz to 1259Hz in a single step. For example, Chinese patent CN119396012A, "A Hammerstein Nonlinear Dynamic System Identification Method and System Based on Industrial Processes," while improving computational efficiency and enhancing the algorithm's identification effect and stability, still cannot automatically identify the system model in one step, and the model identification time still needs further improvement. Moreover, it has certain limitations in practical use: it frequently encounters problems of non-convergence or slow convergence speed, affecting the efficiency and accuracy of photoelectric tracking device model determination, and thus impacting the application of the identification method.

[0005] Therefore, there is an urgent need to design a method and system for identifying photoelectric tracking devices in a photoelectric tracking system to solve the above problems. Summary of the Invention

[0006] Therefore, it is necessary to provide an integrated intelligent detection and automatic identification method and system for photoelectric tracking devices to address the above problems.

[0007] The present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides an integrated intelligent detection and automatic identification method for a photoelectric tracking device. The method is based on a photoelectric tracking system, which includes a configuration panel, a serial-to-IO module, a solid-state relay, a servo control module, and a photoelectric tracking device connected sequentially. It also includes a power supply connected to the configuration panel, the solid-state relay, and the photoelectric tracking device. The servo control module is communicatively connected to the configuration panel. The method includes the following steps:

[0009] Intelligent detection: The servo control module collects the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device in real time; the configuration screen obtains and displays the motor current value, motor bus voltage, and encoder status information in real time; the configuration screen determines whether the photoelectric tracking device is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information, and sends a signal to the photoelectric tracking device in real time to trigger the alarm mechanism of the photoelectric tracking device when a fault is detected.

[0010] Automatic calculation model: The configuration panel inputs a constant amplitude frequency-converted current with a chirp function waveform to the servo control module via an open-loop current loop on the q-axis. The servo control module provides a zero value to the open-loop current loop d-axis; the servo control module acquires data in real time. The corresponding A-phase current value of the motor in the photoelectric tracking device, The corresponding B-phase current value of the motor in the photoelectric tracking device, The corresponding encoder value is used to calculate the FOC control algorithm in real time. Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device; the configuration screen obtains the rotation angle in real time. ,according to and The amplitude-frequency characteristics of the photoelectric tracking device are calculated in real time, and the first-order inertial model of the photoelectric tracking device is calculated in real time using the recursive least squares algorithm.

[0011] In a preferred embodiment, the amplitude-frequency characteristic of the photoelectric tracking device is: .

[0012] In a preferred embodiment, the satisfy:

[0013] ;

[0014] in, Indicates time, Indicates the current amplitude. , This represents the minimum recognition frequency. This represents the maximum value of the recognition frequency. The configuration panel provides Total duration.

[0015] In a preferred embodiment, the first-order inertial model is:

[0016] ;

[0017] in, Indicates the servo control module number Second pair Sampling time, express The time servo control module sampled , express The corresponding feedback current value, , These are all parameters of a first-order inertial model.

[0018] In a preferred embodiment, the step of calculating the first-order inertial model of the photoelectric tracking device using the recursive least squares algorithm specifically involves: calculating the... and stated .

[0019] In a preferred embodiment, the step of automatically calculating the model further includes:

[0020] The configuration screen is fitted with an amplitude-frequency response curve;

[0021] The configuration screen displays the amplitude-frequency characteristic curve using the historical curve control API interface function;

[0022] The configuration screen displays a first-order inertial model of the photoelectric tracking device.

[0023] In a preferred embodiment, the step of automatically calculating the model further includes: the configuration screen calculating the transfer function of the photoelectric tracking device based on the first-order inertial model of the photoelectric tracking device.

[0024] In a preferred embodiment, the steps of intelligent detection and automatic calculation model can be performed simultaneously.

[0025] Secondly, the present invention provides an optoelectronic tracking system capable of integrated intelligent detection and automatic identification of optoelectronic tracking devices, comprising: a configuration screen, a serial port to I / O module, a solid-state relay, a servo control module, an optoelectronic tracking device, and a power supply. The configuration screen, the serial port to I / O module, the solid-state relay, the servo control module, and the optoelectronic tracking device are connected sequentially. The power supply is connected to the configuration screen, the solid-state relay, and the optoelectronic tracking device. The servo control module is communicatively connected to the configuration screen.

[0026] The servo control module is used to collect the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device in real time.

[0027] The configuration panel is used to obtain and display the motor current value, the motor bus voltage, and the encoder status information in real time. It is used to determine in real time whether the photoelectric tracking device is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information. When a fault is detected in the photoelectric tracking device, it sends a signal to the device in real time to trigger its alarm mechanism. It is also used to input a constant-amplitude frequency-converted current with a chirp function waveform into the open-loop current loop of the q-axis of the servo control module. This is used to provide a zero value to the open-loop current loop d-axis of the servo control module.

[0028] The servo control module is also used for real-time data acquisition. The corresponding A-phase current value of the motor in the photoelectric tracking device, The corresponding B-phase current value of the motor in the photoelectric tracking device, The corresponding encoder value is used to calculate the FOC control algorithm in real time. Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device;

[0029] The configuration panel is also used to obtain real-time data. , used according to and The amplitude-frequency characteristics of the photoelectric tracking device are calculated in real time, and the first-order inertial model of the photoelectric tracking device is calculated in real time using the recursive least squares algorithm.

[0030] In a preferred embodiment, the configuration screen is further used to fit the amplitude-frequency characteristic curve, display the amplitude-frequency characteristic curve using the historical curve control API interface function, and display the first-order inertial model of the photoelectric tracking device.

[0031] This invention discloses an integrated intelligent detection and automatic identification method and system for photoelectric tracking devices. It involves inputting a constant-amplitude frequency-converting current with a Chirp function waveform into the servo control module via an open-loop current loop on the q-axis of the current loop through a configuration panel. A zero value is given to the open-loop current loop d-axis of the servo control module, and the servo control module calculates the value in real time. Corresponding feedback current value The configuration screen is based on and This invention utilizes real-time calculation of the amplitude-frequency characteristics of photoelectric tracking devices and a recursive least squares algorithm to calculate the first-order inertial model of the device. By employing the recursive least squares algorithm, convergence is easily and quickly achieved, ensuring model accuracy and improving model identification efficiency. Model identification is quick, and photoelectric tracking device models within the frequency range of 0.1Hz to 1259Hz can be automatically identified in a single operation. The invention also employs a servo control module to collect relevant information from the photoelectric tracking device in real-time. The configuration screen displays and judges whether the photoelectric tracking device is faulty. When a fault is detected, a signal is sent to the photoelectric tracking device in real-time to trigger its alarm mechanism. This design facilitates single-soldier operation and status detection during photoelectric tracking device status monitoring and fault identification. It enables single-soldier operation of the configuration screen in island or harsh environments, eliminating the need for large peripherals such as industrial computers. Attached Figure Description

[0032] Figure 1 This is a flowchart of an integrated intelligent detection and automatic identification method for a photoelectric tracking device according to the present invention;

[0033] Figure 2 This is a schematic diagram showing the structure and connection relationship of the photoelectric tracking system of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and preferred embodiments.

[0035] In the field of photoelectric tracking, the photoelectric tracking device 5 needs to undergo first-order inertial model identification, especially when precise control is required, necessitating the acquisition of a relatively accurate photoelectric tracking device model. It is understood that the photoelectric tracking device 5 includes a motor and an encoder, etc. The photoelectric tracking device 5 achieves movement through its motor; the photoelectric tracking device 5 itself can rotate; the encoder is an angle encoder used to encode the rotation angle of the photoelectric tracking device 5.

[0036] In existing technologies, there are several problems with model identification for photoelectric tracking devices 5, including: 1. In harsh environments such as islands, reefs, and rainforests, the status detection and fault identification of photoelectric tracking devices 5 are inconvenient for individual soldiers to carry and operate, and cannot be performed simultaneously; 2. Model identification of photoelectric tracking devices 5 is time-consuming and cannot automatically identify the controlled object model in the frequency range of 0.1Hz to 1259Hz in one go. Furthermore, the currently used model identification algorithms are complex and often suffer from non-convergence or slow convergence speed, affecting the efficiency and accuracy of model determination, resulting in low efficiency and unstable accuracy. Therefore, this invention provides an integrated intelligent detection and automatic identification method for photoelectric tracking devices.

[0037] See Figure 1 This invention provides an integrated intelligent detection and automatic identification method for photoelectric tracking devices. The method is based on a photoelectric tracking system, which includes a configuration panel 1, a serial port to I / O module 2, a solid-state relay 3, a servo control module 4, a photoelectric tracking device 5, and a power supply 6. The configuration panel 1, the serial port to I / O module 2, the solid-state relay 3, the servo control module 4, and the photoelectric tracking device 5 are connected sequentially. The power supply 6 is connected to the configuration panel 1, the solid-state relay 3, and the photoelectric tracking device 5. The servo control module 4 is communicatively connected to the configuration panel 1.

[0038] The method includes the following steps:

[0039] Intelligent detection:

[0040] The servo control module 4 collects the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device 5 in real time.

[0041] The configuration screen 1 obtains and displays the motor current value, the motor bus voltage, and the encoder status information in real time.

[0042] The configuration panel 1 determines in real time whether the photoelectric tracking device 5 is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information. When it determines that the photoelectric tracking device 5 is faulty, it sends a signal to the photoelectric tracking device 5 in real time to trigger the alarm mechanism of the photoelectric tracking device 5.

[0043] Automatic calculation model:

[0044] The configuration panel 1 inputs a constant-amplitude frequency-converting current satisfying the Chirp function waveform into the servo control module 4 via an open-loop current loop on the q-axis. The current loop d-axis of the servo control module 4 is given a zero value in the open loop.

[0045] The servo control module 4 collects data in real time. The corresponding photoelectric tracking device 5 collects the motor A-phase current value in real time. The corresponding photoelectric tracking device 5 collects the motor B-phase current value in real time. The corresponding encoder value is calculated in real time by the servo control module 4 based on the motor A-phase current value, B-phase current value, and encoder value using the FOC control algorithm (FOC stands for field-oriented control, also known as vector control). Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device 5;

[0046] The configuration panel 1 obtains real-time data. ;

[0047] The configuration panel 1 is based on and Real-time calculation of the amplitude-frequency characteristics of photoelectric tracking device 5;

[0048] The configuration panel 1 is based on and The first-order inertial model of the photoelectric tracking device 5 is calculated in real time using a recursive least squares algorithm.

[0049] It should be understood that the above-mentioned intelligent detection steps can be performed before or after the automatic calculation model. In a preferred embodiment, the intelligent detection and automatic calculation model steps can be performed synchronously. Therefore, the above method does not imply that all steps must be performed in this order. Those skilled in the art can modify or change the execution order of the above steps based on this invention. Some embodiments of the above method are illustrated below.

[0050] Understandably, the solid-state relay 3 serves to achieve automatic control and isolation of the circuit, that is, to enable the power supply module to control the power on and off of the servo control module 4. The servo control module 4 is used to drive the photoelectric tracking device 5. The serial port to I / O (Input / Output) module is used to convert the serial communication interface (such as RS232, RS485, etc.) into a general-purpose input / output interface.

[0051] Understandably, the configuration panel 1 and the serial-to-IO module 2 are communicatively connected, and the servo control module 4 and the configuration panel 1 are also communicatively connected. In this embodiment, the configuration panel 1 is connected to the serial-to-IO module 2 and the servo control module 4 via RS422. Specifically, the configuration panel 1 inputs a current setpoint to the servo control module 4 through serial port one, and the servo control module 4 sends information to the configuration panel 1 through serial port two.

[0052] Understandably, the photoelectric tracking device 5 is controlled by the servo control module 4, and the power supply 6 supplies power to the photoelectric tracking device 5. The power supply 6 supplies power to the servo control module 4 through the solid-state relay 3. The motor of the photoelectric tracking device 5 includes phase A and phase B. Preferably, the motor is a brushless DC motor.

[0053] In this embodiment, the configuration panel 1 carries a Lua compiler. The Lua compiler calculates the amplitude-frequency characteristics of the photoelectric tracking device 5, obtains the amplitude-frequency characteristic curve based on the amplitude-frequency characteristics, and uses the recursive least squares algorithm to determine the first-order inertial model of the photoelectric tracking device 5.

[0054] In this embodiment, the method (the step of automatically calculating the model) further includes:

[0055] The configuration screen 1 is fitted with an amplitude-frequency response curve;

[0056] The configuration screen 1 displays the amplitude-frequency characteristic curve using the historical curve control API interface function;

[0057] The configuration screen 1 displays a first-order inertial model of the photoelectric tracking device 5;

[0058] Furthermore, the method (the step of automatically calculating the model) also includes the configuration screen 1 calculating the transfer function of the photoelectric tracking device 5 based on the first-order inertial model of the photoelectric tracking device 5.

[0059] Specifically, the amplitude-frequency characteristic of the photoelectric tracking device 5 is as follows: Configuration screen 1 according to The amplitude point is determined, and the amplitude-frequency characteristic curve is fitted based on the amplitude point. The amplitude-frequency characteristic curve is displayed in real time on the screen of the configuration screen 1 through the API interface function set_history_graph_value(screen,control,channel1) set in the configuration screen 1. The configuration screen 1 displays the first-order inertial model of the photoelectric tracking device 5 and displays the calculated transfer function.

[0060] In one embodiment, the method (the step of automatically calculating the model) further includes: the configuration screen 1 displays the feedback current value on the configuration screen 1. Specifically, the feedback current value is displayed on the screen through the record_add(screen,control,record) recording control in the configuration screen 1. This also includes displaying the current value of the configuration screen input to the servo control module corresponding to the displayed feedback current value.

[0061] In this embodiment, the satisfy:

[0062]

[0063] in, The given current value output for configuration panel 1 (i.e., the open-loop current value of the q-axis current loop in servo control module 4) is a constant amplitude frequency conversion chirp function. Indicates time, Indicates the current amplitude. , This represents the minimum recognition frequency. This represents the maximum value of the recognition frequency. Indicates the total duration, specifically provided by configuration panel 1. Total duration It is π.

[0064] For 4 pairs of servo control modules Regarding data acquisition, the acquired current is:

[0065]

[0066] in, Indicates the servo control module 4 Second pair Sampling time, express The time servo control module 4 sampled .

[0067] In a preferred embodiment, the current amplitude =0.5 amperes, , , , In this paper, the time unit is seconds, and data is updated at a frequency of 1 kHz, i.e., the sampling frequency is 0.001 s, and the next sampling time value is the previous sampling time value plus 0.001. The photoelectric tracking device performs a frequency sweep from 0.1 Hz to 1259 Hz, and data is updated at a frequency of 1 kHz.

[0068] In one embodiment, the photoelectric tracking device 5 model is a first-order inertial model, that is, the photoelectric tracking device 5 is a first-order inertial element, and the first-order discrete system model corresponding to the first-order inertial element is:

[0069]

[0070] The model is a difference equation. and Both represent the number of samples, which is understandable; each time... Each sample corresponds to one value, express Time corresponding ,Right now corresponding , express Time corresponding , , The coefficients of the difference equation are... , It is a value that needs to be calculated using a recursive least squares algorithm. , The value can then be used to obtain the first-order inertial model of the photoelectric tracking device 5. Therefore, , The parameters of the first-order inertial model are given, and the parameter matrix of the model of the photoelectric tracking device 5 to be identified is given. .

[0071] Specifically, in the S-domain, the first-order inertial model of the photoelectric tracking device 5 is... ,in This represents the magnification factor of a first-order inertial model. Represents the electromechanical time constant, where , representing the complex frequency domain, This indicates the frequency of the 5-current loop in the photoelectric tracking device. Representing the imaginary unit, we get and This yields the first-order inertial model of the photoelectric tracking device 5. The transfer function is in the S-domain, meaning the model is... and The amplitude-to-frequency ratio, by transforming the first-order inertial model of the photoelectric tracking device 5 with the transfer function in the S-domain into a difference equation, yields the following: Discrete equations in the form of [formula missing].

[0072] In this specific implementation, 10,000 samples were measured within a total time of 10 seconds. ,part and corresponding Numerical value.

[0073] In configuration panel 1, the parameters of the first-order inertial model are calculated using a recursive least squares algorithm. When the Lua compiler in configuration panel 1 calculates the parameters of the first-order inertial model using the recursive least squares algorithm, it has preset initial values, which are: , ,

[0074] The iterative calculation process is as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] renew , and The value of, that is, let , ,

[0082] judge If the condition is true, then the calculation ends. The final calculated model parameter matrix is ​​based on the current... If a first-order inertial model can be obtained, then continue iterating and execute the above calculation process again.

[0083] in, and All are integers in the range [2, 10000]. It represents a data matrix. Represents the gain matrix. This represents a one-dimensional weighted coefficient matrix. and Both represent model parameter matrices. and Both represent estimated matrices. Indicates the convergence error. , Indicates the convergence factor. .

[0084] In one specific embodiment, convergence is achieved after 25 iterations, ending the calculation of model parameters, and the final parameters are obtained. That is, the model of a first-order inertial system is Since the sampling period is 0.001s, according to the first-order inertial model of photoelectric tracking device 5... The transfer function is obtained by transforming from the Z-domain to the S-domain. .

[0085] In one embodiment, specifically, the configuration panel 1 is equipped with a Lua compiler. Information such as the motor current value, the motor bus voltage, and the encoder status of the photoelectric tracking device 5 are transmitted back via serial port 2. By setting fault boundary values ​​through the Lua compiler in configuration panel 1, automatic system alarms can be achieved, improving the speed and intelligence of fault diagnosis for the photoelectric tracking device 5. In this embodiment, the motor current value includes: the motor current value, specifically the phase current value of all phases of the motor under the power supply 6. It can be understood that if the status detection and fault identification steps of the photoelectric tracking device 5 are synchronized with the model identification, then the motor current value is... The motor current value under action.

[0086] For configuration panel 1, the current value of the open-loop current loop on the q-axis of the servo control module 4 is a constant amplitude frequency conversion Chirp function waveform, and the open-loop current value on the d-axis is set to zero. The current sensor in the servo control module 4 collects the current values ​​of the A and B phases of the brushless DC motor, and the servo control module 4 collects the encoder values. The feedback current value is calculated through the FOC control algorithm. The feedback current value serves as the output current value of the servo control module 4. The output current value is transmitted back to the configuration screen 1 in real time via serial port 2. The amplitude-frequency characteristic is written using the Lua compiler in the configuration screen 1 based on the input current value and output current value of the servo control module 4. The amplitude-frequency characteristic curve is obtained based on the amplitude-frequency characteristic. The Lua compiler uses the recursive least squares algorithm to determine the first-order inertial model of the photoelectric tracking device 5. The configuration screen 1 will display the relevant data.

[0087] For the servo control module 4: the servo control module 4 receives a given current as the q-axis input of the inner current loop in the FOC control, and at the same time, the open-loop given of the d-axis of the inner current loop in the FOC control is 0. By acquiring the value of the angle encoder of the photoelectric tracking device 5 and the A-phase and B-phase current of the motor, the feedback current value is calculated by the FOC control algorithm. At the same time, the servo control module 4 assigns the three duty cycles calculated by the FOC control algorithm based on the feedback current value to the three comparison registers of the DSP of the servo control module 4 in a one-to-one correspondence. The comparison registers output a six-phase PWM wave, and the six-phase PWM wave controls the six IGBTs (semiconductor power devices) in the servo control module 4 in a one-to-one correspondence to realize the movement of the photoelectric tracking device 5.

[0088] The following example illustrates in detail an integrated intelligent detection and automatic identification method for photoelectric tracking devices.

[0089] In this embodiment, the configuration screen 1 is a 7-inch configuration screen 1, which is obviously only an example and not a limitation. The 7-inch configuration screen 1 is equipped with a chirp constant amplitude frequency conversion function, a 32-bit floating-point to hexadecimal conversion function, and a serial port table input / output function. The 7-inch configuration screen 1 includes: a 7-inch display screen, a microcontroller, a serial port chip, an IO interface chip, and its control circuit. The power supply 6 is an AC 220V to DC 5V and DC 48V power supply, which powers the entire photoelectric tracking system; the serial port to IO module 2 includes a microcontroller, a serial port chip, an IO interface chip, and a transistor control circuit; the solid-state relay is a low-voltage 5V input open-collector NAND gate switch circuit; the servo control module 4 includes a DSP chip, an IO interface chip, a serial port chip, a current acquisition chip, an integrated operational amplifier, and a high-power MOSFET circuit. The servo control module 4 adopts a three-closed-loop control method of position, speed, and current, and uses the DSP to implement the FOC control algorithm to drive the photoelectric tracking device 5 to move. The encoder in the photoelectric tracking device 5 is a 26-bit circular grating, and the motor is a 48V brushless DC motor.

[0090] The 7-inch configuration screen 1 is matched with a 32-bit high-level output serial-to-IO module 2, which has 32 switch-type button controls to control the solid-state relay to power the servo module. Secondly, the given current is set as a chirp function with constant amplitude and changing frequency, and the given current value is sent to the servo control module 4 via serial port 1. The timer within the configuration screen 1 controls the transmission period to be 1kHz. Simultaneously, the servo control module 4 collects the encoder angle position information from the photoelectric tracking device 5 and the motor A-phase and B-phase current values ​​collected by the current chip. The feedback current value is calculated using the FOC control algorithm and fed back to the configuration screen 1 via serial port 2. Finally, the amplitude-frequency characteristics of the photoelectric tracking device 5 are calculated in real time from the feedback current value and the given current value. The amplitude-frequency characteristics and their fitted curve are displayed in real time on the configuration screen 1 through a historical curve control, and the controlled object model is automatically identified using a recursive least squares algorithm. Simultaneously, the servo control module 4 can output the motor current value, bus voltage, and encoder status information to the configuration screen 1 via serial port 2, displaying them in the text control of the configuration screen 1, facilitating fault location and diagnosis of the photoelectric tracking device 5.

[0091] The specific implementation process is as follows:

[0092] (1) When the photoelectric tracking system is powered on, the LOGO animation and timer are started in the API interface function of the configuration screen 1. At the same time, the period of the timer is set to 2s and the number of starts is set to 1. The timer timing time is judged in the timer timeout callback function. After 2s, the LOGO animation stops playing and the screen is switched. The configuration screen 1 enters the start switch switching interface of each smallest replaceable unit.

[0093] (2) Click the power-on switch control of the solid-state relay 3 in the switch switching interface, and the screen switches to the power-on control interface. The configuration screen 1 matches the 32-bit level control module of the serial port to IO module 2, so the power-on control interface is set with 32 switch button controls. In the LUA compiler in the configuration screen 1, the button notification control function on_control_notify(screen,control,value) is used to poll and collect the 32 button states using a for loop statement, and store the button states in a 32-element one-dimensional array. In the LUA of the configuration screen 1, the uart_send_data() function is used to send the button states to the serial port to IO control module to realize the conduction of the solid-state relay, complete the power supply 6 to power on and off the servo control module 4, and finally realize the servo control module 4 to drive the photoelectric tracking device 5 to move.

[0094] (3) After power-on, click the "Return to Homepage" button in the power-on control interface to switch back to the switch switching interface. In the switch switching interface, click the "Azimuth Power Level" button to trigger the button notification function in configuration panel 1. In the button notification function, set the switch to the azimuth power level test interface. Click the "Stop Switch" button in the azimuth power level test interface to start the timer. Send the constant amplitude frequency conversion chirp excitation to the servo control module 4 at a frequency of 1kHz through the serial port. That is, convert the sweep frequency float real-time current value into 32-bit hexadecimal data. Set the frequency to 1kHz through the timer in configuration panel 1 and send it to the current loop in the servo control module 4 through serial port 1.

[0095] (4) The chirp excitation is directly fed into the q-axis of the current loop in the servo control module 4, while the open-loop setpoint of the d-axis of the current loop is 0. The servo control module 4 collects the angle encoder value of the photoelectric tracking device 5 corresponding to the chirp excitation and the A-phase and B-phase current values ​​of the DC brushless motor in the photoelectric tracking device 5. The duty cycle of the three PWM waves is calculated by the FOC control algorithm in the servo control module 4 and assigned to the three comparators in the DSP of the servo control module 4 to control the turn-off of the six IGBTs in the servo control module 4, thereby driving the photoelectric tracking device 5. At the same time, the feedback current value is calculated in real time using the FOC control algorithm in the servo control module 4. The status information of the photoelectric tracking device 5 and the feedback current value are fed back to the configuration screen 1 in real time through serial port 2. Using the amplitude-frequency formula of the controlled object, the error is calculated through a recursive least squares algorithm, and a first-order inertial model is fitted. The amplitude-frequency characteristic curve is obtained in real time. The fitted amplitude-frequency characteristic is output to the display screen in real time using the `set_history_graph_direction(screen, control, direction)` API function in configuration screen 1. Within 10 seconds, the inner-loop controlled object model of the photoelectric tracking device 5's control system within the frequency range of 0.1Hz to 1259Hz can be automatically identified. The identification parameters can be automatically obtained from the azimuth power level test interface. , .

[0096] Among them, and The data is sent to the record control `record_add(screen, control, record)` in configuration screen 1 via serial port 2, and the data can be displayed in real time. Recording is done in a record table format, with the final sequence number in the table being 10000. Since the sampling frequency is 0.001s, the recording time is 10000 * 0.001s = 10s. Therefore, this method can automatically identify the first-order inertial model in one go within 10 seconds.

[0097] (5) The configuration panel 1 receives various information fed back by the servo control module 4 through serial port 2. Through the serial port callback function on_uart_recv_data(packet), the received feedback information, such as motor current value, motor bus voltage, encoder status information, etc., is displayed in real time in the text control of the configuration panel 1. If the configuration panel 1 receives an encoder fault bit, or the encoder angle exceeds the maximum and minimum boundary values, or other fault conditions, the text control displays the encoder fault and triggers the alarm mechanism of the photoelectric tracking device 5. The photoelectric tracking device 5 issues an alarm, thus realizing intelligent fault diagnosis.

[0098] This invention provides an integrated intelligent detection and automatic identification photoelectric tracking system capable of realizing photoelectric tracking devices, including a configuration panel 1, a serial port to I / O module 2, a solid-state relay 3, a servo control module 4, a photoelectric tracking device 5, and a power supply 6. The configuration panel 1, the serial port to I / O module 2, the solid-state relay 3, the servo control module 4, and the photoelectric tracking device 5 are connected sequentially. The power supply 6 is connected to the configuration panel 1, the solid-state relay 3, and the photoelectric tracking device 5. The servo control module 4 is communicatively connected to the configuration panel 1.

[0099] The servo control module 4 is used to collect the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device 5 in real time.

[0100] The configuration panel 1 is used to obtain and display the motor current value, the motor bus voltage, and the encoder status information in real time. It is used to determine in real time whether the photoelectric tracking device 5 is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information. When a fault is detected in the photoelectric tracking device 5, it sends a signal to the photoelectric tracking device 5 in real time to trigger its alarm mechanism. It is also used to input a constant-amplitude frequency-converting current with a chirp function waveform into the open-loop current loop of the servo control module 4. This is used to provide a zero value to the open-loop current loop d-axis of the servo control module 4.

[0101] The servo control module 4 is also used for real-time data acquisition. The corresponding photoelectric tracking device 5 collects the motor A-phase current value. The corresponding photoelectric tracking device 5's motor B-phase current value, acquisition The corresponding encoder value is calculated in real time by the servo control module 4 based on the motor A-phase current value, B-phase current value, and encoder value using the FOC control algorithm. Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device 5;

[0102] The configuration panel 1 is used to obtain real-time data. , used according to and Real-time calculation of the amplitude-frequency characteristics of the photoelectric tracking device 5, used for... and The first-order inertial model of the photoelectric tracking device 5 is calculated in real time using a recursive least squares algorithm.

[0103] In one embodiment, the configuration screen 1 is also used to fit the amplitude-frequency characteristic curve, display the amplitude-frequency characteristic curve using the historical curve control API interface function, and display the first-order inertial model of the photoelectric tracking device 5.

[0104] In one embodiment, the configuration panel 1 is also used to calculate the transfer function of the photoelectric tracking device 5 based on the first-order inertial model of the photoelectric tracking device 5.

[0105] This invention discloses an integrated intelligent detection and automatic identification method and system for photoelectric tracking devices. The method involves inputting a constant-amplitude frequency-converting current with a Chirp function waveform into the servo control module 4 via an open-loop current loop input to the q-axis of the current loop through a configuration panel 1. The servo control module 4 provides a zero value to the open-loop d-axis of the current loop; it also collects the motor A-phase current value, motor B-phase current value, and encoder value from the photoelectric tracking device 5. Based on these values, the servo control module 4 calculates the current in real-time using the FOC control algorithm. Corresponding feedback current value The configuration screen 1 is based on and The amplitude-frequency characteristics of the photoelectric tracking device 5 are calculated, and the first-order inertial model of the photoelectric tracking device 5 is calculated using a recursive least squares algorithm. This design simplifies the identification method, shortens the identification time, and enables automatic identification of the photoelectric tracking device 5 model within the frequency range of 0.1Hz to 1259Hz in one go. It can automatically identify the photoelectric tracking device 5 model in one go within a set 10s frequency sweep range of 0.1Hz to 1259Hz. Specifically, the method uses a recursive least squares algorithm to calculate the first-order inertial model of the photoelectric tracking device 5, which is easy to converge and converges quickly, thus ensuring the accuracy and efficiency of model determination. In this invention, the servo control module 4 collects the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device 5 in real time; the configuration screen 1 obtains and displays the motor current value, motor bus voltage, and encoder status information in real time; the configuration screen 1 determines whether the photoelectric tracking device 5 has a fault based on at least one of the motor current value, motor bus voltage, and encoder status information, and sends a signal to the photoelectric tracking device 5 in real time to trigger the alarm mechanism of the photoelectric tracking device 5 when a fault is detected; this design is convenient for individual soldiers to carry and operate, and enables individual soldiers to carry the configuration screen 1 in islands or harsh environments to independently complete the status detection and fault identification of the photoelectric tracking device 5 without carrying large peripherals such as industrial computers.

[0106] Specifically, based on this invention, a small-sized configuration screen 1 can be carried by a single soldier in an island or harsh environment, connected to a remote serial port, and independently complete the model identification of the photoelectric tracking device 5 and the status detection and fault identification of the photoelectric tracking device 5, without having to carry large peripherals such as industrial computers to carry out field maintenance testing and fault diagnosis.

[0107] Specifically, this invention integrates the display of status information of photoelectric tracking device 5 and automatic system identification, achieving a high degree of integration.

[0108] Specifically, in this invention, the chirp function is used as input to apply excitation to the servo control module 4 with constant amplitude and variable frequency, so as to identify the photoelectric tracking device 5 model in a one-time, fast and automatic manner. Based on this design, the design cycle of related designs in the photoelectric tracking system can be improved to a certain extent.

[0109] Specifically, in this invention, the intelligent detection step and the automatic calculation model step can be performed simultaneously, which improves the efficiency of the method.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0111] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An integrated intelligent detection and automatic identification method for photoelectric tracking equipment, characterized in that, The method is based on a photoelectric tracking system, which includes a configuration panel, a serial-to-IO module, a solid-state relay, a servo control module, and a photoelectric tracking device connected in sequence. It also includes a power supply connected to the configuration panel, the solid-state relay, and the photoelectric tracking device. The servo control module is communicatively connected to the configuration panel. The method includes the following steps: Intelligent detection: The servo control module collects the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device in real time; the configuration screen obtains and displays the motor current value, motor bus voltage, and encoder status information in real time. The configuration panel determines in real time whether the photoelectric tracking device is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information. When a fault is detected in the photoelectric tracking device, a signal is sent to the photoelectric tracking device in real time to trigger the alarm mechanism of the photoelectric tracking device. Automatic calculation model: The configuration panel inputs a constant amplitude frequency-converted current with a chirp function waveform to the servo control module via an open-loop current loop on the q-axis. The servo control module provides a zero value to the open-loop current loop d-axis; the servo control module acquires data in real time. The corresponding A-phase current value of the motor in the photoelectric tracking device, The corresponding B-phase current value of the motor in the photoelectric tracking device, The corresponding encoder value is used to calculate the FOC control algorithm in real time. Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device; The configuration screen obtains real-time data. ,according to and The amplitude-frequency characteristics of the photoelectric tracking device are calculated in real time, and the first-order inertial model of the photoelectric tracking device is calculated in real time using the recursive least squares algorithm. Indicates time.

2. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 1, characterized in that, The amplitude-frequency characteristic of the photoelectric tracking device is: .

3. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 1, characterized in that, The satisfy: ； in, Indicates the current amplitude. , This represents the minimum recognition frequency. This represents the maximum value of the recognition frequency. The configuration panel provides Total duration.

4. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 3, characterized in that, The first-order inertial model is as follows: ； in, Indicates the servo control module number Second pair Sampling time, express The time servo control module sampled , express The corresponding feedback current value, , These are all parameters of a first-order inertial model.

5. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 4, characterized in that, The specific method for calculating the first-order inertial model of the photoelectric tracking device using the recursive least squares algorithm is as follows: The recursive least squares algorithm is used to calculate... and stated .

6. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 1, characterized in that, The steps of the automatic calculation model also include: The configuration screen is fitted with an amplitude-frequency response curve; The configuration screen displays the amplitude-frequency characteristic curve using the historical curve control API interface function; The configuration screen displays a first-order inertial model of the photoelectric tracking device.

7. The integrated intelligent detection and automatic identification method for a photoelectric tracking device according to claim 6, characterized in that, The automatic calculation model step further includes: the configuration screen calculates the transfer function of the photoelectric tracking device based on the first-order inertial model of the photoelectric tracking device.

8. The integrated intelligent detection and automatic identification method for photoelectric tracking equipment according to claim 1, characterized in that, The steps of the intelligent detection and the automatic calculation model can be performed simultaneously.

9. A photoelectric tracking system capable of integrating intelligent detection and automatic identification of photoelectric tracking devices, characterized in that, include: The system includes a configuration panel, a serial-to-IO module, a solid-state relay, a servo control module, a photoelectric tracking device, and a power supply. The configuration panel, serial-to-IO module, solid-state relay, servo control module, and photoelectric tracking device are connected in sequence. The power supply is connected to the configuration panel, solid-state relay, and photoelectric tracking device. The servo control module is communicatively connected to the configuration panel. The servo control module is used to collect the motor current value, motor bus voltage, and encoder status information of the photoelectric tracking device in real time. The configuration panel is used to obtain and display the motor current value, the motor bus voltage, and the encoder status information in real time. It is used to determine in real time whether the photoelectric tracking device is faulty based on at least one of the motor current value, motor bus voltage, and encoder status information. When a fault is detected in the photoelectric tracking device, it sends a signal to the device in real time to trigger its alarm mechanism. It is also used to input a constant-amplitude frequency-converted current with a chirp function waveform into the open-loop current loop of the q-axis of the servo control module. This is used to provide a zero value to the open-loop current loop d-axis of the servo control module. Indicates time; The servo control module is also used for real-time data acquisition. The corresponding A-phase current value of the motor in the photoelectric tracking device, The corresponding B-phase current value of the motor in the photoelectric tracking device, The corresponding encoder value is used to calculate the FOC control algorithm in real time. Corresponding feedback current value The encoder is used to measure the rotation angle of the photoelectric tracking device; The configuration panel is also used to obtain real-time data. , used according to and The amplitude-frequency characteristics of the photoelectric tracking device are calculated in real time, and the first-order inertial model of the photoelectric tracking device is calculated in real time using the recursive least squares algorithm.

10. A photoelectric tracking system capable of integrated intelligent detection and automatic identification of photoelectric tracking devices according to claim 9, characterized in that, The configuration screen is also used to fit the amplitude-frequency characteristic curve, display the amplitude-frequency characteristic curve using the historical curve control API interface function, and display the first-order inertial model of the photoelectric tracking device.

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