A control method, device, system and storage medium for a self-contained device

Abstract

The present invention discloses a control method, device, system and storage medium of a self-moving device, which relates to the field of automatic control, including starting the self-moving device, sequentially detecting and activating the device's sensor, execution unit, beacon device transmitting component and communication device, and entering a standby state; when the working state is adjusted, the self-moving device is interconnected with the base station through the communication device, and the location information, state information and environmental data are exchanged in real time, and transmitted to the central processing unit in real time; the central processing unit combines the link budget data of the beacon device, performs multi-dimensional data fusion, and generates an environmental model; based on the environmental model, the self-moving device uses an intelligent algorithm to generate action decisions, transmits control commands to the motion control unit, and adjusts the motion state of the device. The present invention uses an intelligent algorithm to dynamically adjust the motion state of the device, thereby enhancing the device's adaptive ability and obstacle avoidance ability in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of automatic control, and in particular to a control method, device, system and storage medium for a self-moving device. Background Art

[0002] With the continuous advancement of artificial intelligence and automation technology, autonomous devices are increasingly used in many fields such as industrial automation, smart homes, logistics, etc. These devices usually rely on sensors, communication equipment and control algorithms to complete complex operations such as autonomous navigation, path planning, and task execution. However, a core problem faced by existing autonomous devices in practical applications is that the perception, decision-making and execution capabilities of the devices are limited in complex and dynamically changing environments. Specifically, the sensor systems of traditional devices can usually only function in relatively ideal or static environments, and it is difficult to cope with real-time changing external environments, such as the dynamic appearance of obstacles, sudden changes in paths, etc. In addition, the existing communication systems and control methods have a slow response speed, making it difficult to achieve efficient interconnection and collaboration between devices and base stations or other devices, resulting in delayed decision-making of devices in a changing environment, which in turn affects the efficiency and accuracy of task execution.

[0003] For example, traditional self-moving devices usually navigate through preset path planning and fixed sensor data fusion algorithms, but these algorithms have obvious defects when facing complex scenarios: they lack the ability to perceive and respond to changing objects or obstacles in the environment in a timely manner, and cannot adapt to real-time updated data, which makes the device prone to route deviation or even collision. More importantly, the existing communication mechanism is easily affected by noise, interference, etc. during data transmission. Especially in multi-device collaboration scenarios, the delay in information transmission will significantly reduce the collaborative efficiency between devices. These problems greatly limit the application of self-moving devices in scenarios that require high-precision and high-real-time operations, resulting in the inability of self-moving devices to perform tasks autonomously and efficiently in dynamic and complex environments. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method, device, system and storage medium for a self-moving device to solve the problem that the existing self-moving devices have insufficient perception and decision-making capabilities in complex environments.

[0006] In order to solve the above method problems, the present invention provides the following method solutions:

[0007] In a first aspect, an embodiment of the present invention provides a control method for a self-moving device, which includes starting the self-moving device, sequentially detecting and activating a sensor, an execution unit, a beacon device transmitting component, and a communication device of the device, and entering a standby state;

[0008] In the standby state, the external signal is received through the beacon device transmitting component, and the received intermediate frequency signal is pre-processed;

[0009] The pre-processed intermediate frequency signal is converted into a radio frequency signal through an up-conversion circuit of a mobile device;

[0010] The radio frequency signal is adjusted within the frequency range and then transmitted to the communication device, which is analyzed and the corresponding operation mode is selected to adjust the working state of the communication device;

[0011] When the working state is adjusted, the mobile device is interconnected with the base station through the communication device to exchange location information, status information and environmental data in real time, and transmit them to the central processing unit in real time;

[0012] The central processing unit combines the link budget data of the beacon device to perform multi-dimensional data fusion and generate an environmental model;

[0013] Based on the environmental model, the mobile device uses intelligent algorithms to generate action decisions, transmit control commands to the motion control unit, and adjust the motion state of the device.

[0014] As a preferred solution of the control method of the self-moving device of the present invention, wherein: the sequential detection and activation of the device's sensor, execution unit, beacon device transmitting component and communication device, the specific steps are:

[0015] Use a multimeter to measure whether the power input voltage of the sensor is within the rated range. If it is within the rated range, the voltage is normal;

[0016] If it is not within the rated range, the power supply is abnormal, the operation stops and an alarm sounds;

[0017] When the power input voltage of the sensor is normal, the controller is used to send an activation command through the communication interface of the sensor to activate the acquisition function of the sensor;

[0018] After the acquisition function is activated, the execution unit, the beacon device transmitting component and the communication device are activated in sequence;

[0019] Start the built-in self-check function of the execution unit, the controller sends a self-check instruction, and the execution unit performs a self-check of the circuit and control logic;

[0020] The position sensor in the execution unit returns the current position information. By reading the feedback signal, it is confirmed whether the position sensor is working properly;

[0021] Using a spectrum analyzer, measuring the intermediate frequency input signal of the transmitting component and the spectrum analyzer measuring the radio frequency output signal of the transmitting component within a predetermined range;

[0022] Test the communication interface and data transmission function, send test data packets, and continuously detect the output signal of the transmitting component through the status register returned by the communication device to confirm that the data transmission link is normal and uninterrupted.

[0023] As a preferred solution of the control method of the self-moving device of the present invention, wherein: the pre-processed intermediate frequency signal is converted into a radio frequency signal by an up-conversion circuit of the self-moving device, and the specific steps are as follows:

[0024] The controller sends a standby command through the communication interface, and the beacon device transmitting component enters the standby state;

[0025] Use an antenna connected to the IF input terminal of the beacon device to receive an external IF signal, and monitor the frequency and power of the received IF signal using a spectrum analyzer;

[0026] The intermediate frequency signal passes through a bandpass filter to eliminate out-of-band noise and interference signals, and a digitally controlled attenuator is used to adjust the signal gain;

[0027] Start the up-converter, the controller sends a frequency conversion command, and the up-converter selects the corresponding frequency from the local oscillator;

[0028] The controller sets the frequency of the local oscillator by sending a command to start the up-converter, detects the frequency of the RF signal using a spectrum analyzer, and adjusts the frequency of the local oscillator;

[0029] The controller sends a command to set the local oscillator frequency, adjusts the RF output power, uses a spectrum analyzer to detect the RF output power, and adjusts the RF output power;

[0030] Use a spectrum analyzer to detect in-band spurious signals. If the spurious signals exceed the standard, recalibrate the upconverter.

[0031] As a preferred solution of the control method of the self-moving device of the present invention, wherein: the analysis and selection of the corresponding operation mode to adjust the working state of the communication device are specifically performed as follows:

[0032] In the up-converter, after adjusting and confirming that the frequency and power of the RF signal are within the target range, the RF signal is transmitted to the receiving end of the communication device through the signal link;

[0033] The receiving end of the communication device introduces an intelligent adaptive filter, which dynamically adjusts the center frequency and bandwidth of the filter by real-time analysis of the received signal spectrum;

[0034] Analyze the key features of RF signals through pre-trained machine learning models;

[0035] The pattern recognition algorithm automatically identifies the operating mode of the current signal by comparing the real-time signal characteristics with the reference patterns in the model library;

[0036] If a change in the modulation mode of the signal is identified, the communication device adjusts its demodulation module;

[0037] The communication device automatically switches to the appropriate demodulation mode based on the output of the machine learning model;

[0038] After adjusting the working state, the communication device transmits feedback information back to the controller to verify the validity of the current state;

[0039] If the signal quality does not meet expectations, the communication device will re-analyze the signal and adjust parameters until the system enters the optimal working state.

[0040] As a preferred solution of the control method of the self-moving device of the present invention, the real-time transmission to the central processing unit comprises the following specific steps:

[0041] When the working state is adjusted, multiple distributed antennas are enabled from the mobile device, and the transmission phase of each antenna is adjusted through the phase compensation factor pre-calculated by phase synchronization;

[0042] The self-mobile device sends an initial handshake signal to the base station through coordinated communication with the adjusted transmission phase. The base station receives the signal and feeds back the signal quality index to adjust the communication parameters.

[0043] Use sensors in mobile devices to collect location information, status information and environmental data;

[0044] Use median filtering and Kalman filtering to preprocess the collected data to eliminate noise and outliers;

[0045] Missing data were filled by linear interpolation;

[0046] Use adaptive compression algorithm to compress the collected location information, status information and environmental data;

[0047] For highly redundant data, use a high compression ratio;

[0048] For data with high real-time requirements, a low compression ratio and adaptive compression algorithm are used. The expression is:

[0049] ;

[0050] in, For the The amount of change in class data, For the The transmission delay of class data, is the total number of data categories, Index variable representing the data category;

[0051] The compressed data is transmitted to the base station through a distributed phase cooperative communication method and forwarded to the central processing unit through the base station.

[0052] As a preferred solution of the control method of the self-moving device of the present invention, wherein: the central processing unit combines the link budget data of the beacon device to perform multi-dimensional data fusion to generate an environmental model, and the specific steps are:

[0053] The multi-scale decomposition method is used to preliminarily stratify the link budget data and generate link budget components at different time scales.

[0054] Based on multi-scale decomposition, the link budget data, sensor data and base station feedback data are unified into a probabilistic framework through nonlinear multi-level Bayesian inference to infer the beacon device status of the environmental model;

[0055] Use extended Kalman filtering to estimate the state of beacon devices and update the motion state in real time;

[0056] After the state estimation is completed, the link budget data, sensor data, base station feedback data and the device state output by the extended Kalman filter are integrated to form a high-dimensional data set;

[0057] For high-dimensional data sets, high-order integral transforms are used to represent nonlinear changes in complex environments and generate environmental models. , the expression is:

[0058] ;

[0059] in, Indicates Time series of class data, is the weight of the data, is the second-order derivative of the data.

[0060] As a preferred solution of the control method of the self-moving device of the present invention, wherein: the control command is transmitted to the motion control unit to adjust the motion state of the device, and the specific steps are:

[0061] The key features in the environment model are extracted by the central processing unit and normalized;

[0062] Based on the normalized data, the shortest path algorithm is used to calculate the path from the device to the target point;

[0063] Combined with the path of the target point, the dynamic window method is used for real-time obstacle avoidance;

[0064] Through deep reinforcement learning algorithms, combined with the path of the target point and real-time obstacle avoidance, the overall motion strategy of the device is optimized;

[0065] According to the path planning and obstacle avoidance results, the linear velocity and angular velocity of the device are calculated, and corresponding control instructions are generated;

[0066] Use PID controller to adjust the motion state of the device in real time;

[0067] The generated control instructions are sent to the motion control unit through the communication bus, and the driving device performs the corresponding motion operation;

[0068] The real-time motion state of the device is obtained through sensors, compared with the expected state, the error is calculated, and real-time adjustments are made through the PID controller;

[0069] The motion control unit dynamically adjusts the device's speed and direction based on feedback information and monitors the device's stability through gyroscopes and accelerometers.

[0070] In a second aspect, the present invention provides a control system for a self-mobile device, including a device initialization module, a beacon signal processing module, a signal up-conversion module, a signal processing module, a communication management module, an environment perception module and a motion planning module;

[0071] The device initialization module is used for the device initialization module to start the self-mobile device, detect and activate the device's sensor, execution unit, beacon device transmission component and communication device in sequence, and enter the standby state;

[0072] The beacon signal processing module is used to receive external signals through the beacon device transmitting component in the standby state and pre-process the received intermediate frequency signal;

[0073] The signal up-conversion module is used to convert the pre-processed intermediate frequency signal into a radio frequency signal through an up-conversion circuit of the mobile device;

[0074] The signal processing module is used to adjust the radio frequency signal within the frequency range and transmit it to the communication device, analyze it and select the corresponding operation mode to adjust the working state of the communication device;

[0075] The communication management module is used to interconnect the mobile device with the base station through the communication device after the working state is adjusted, exchange location information, state information and environmental data in real time, and transmit them to the central processing unit in real time;

[0076] The environment perception module is used for the central processing unit to combine the link budget data of the beacon device to perform multi-dimensional data fusion and generate an environment model;

[0077] The motion planning module is used to generate action decisions based on the environment model from the mobile device using an intelligent algorithm, transmit control commands to the motion control unit, and adjust the motion state of the device.

[0078] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for controlling a self-mobile device as described in the first aspect of the present invention is implemented.

[0079] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for controlling a self-mobile device as described in the first aspect of the present invention is implemented.

[0080] The beneficial effects of the present invention are as follows: the present invention realizes precise control of each key module of the device by activating the sensor, the execution unit, the beacon device transmitting component and the communication device in sequence, thereby ensuring the stability and reliability of the system startup process. Secondly, by converting the received intermediate frequency signal into a radio frequency signal after preprocessing, and transmitting it to the communication device after adjusting it within the frequency range, the problem that traditional equipment is susceptible to environmental interference during data transmission is solved, and the stability and efficiency of communication are significantly improved. Based on multi-dimensional data fusion technology, the present invention combines the link budget data of the beacon device in the central processing unit to generate a real-time environmental model, and uses an intelligent algorithm to dynamically adjust the motion state of the device, thereby enhancing the device's adaptive ability and obstacle avoidance ability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the method scheme of the embodiment of the present invention, the drawings required for use in the description of the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary method personnel in this field, other drawings can be obtained based on these drawings without paying creative work.

[0082] Figure 1 This is a flow chart of the control method of the self-moving device in Example 1.

[0083] Figure 2 This is a module diagram of the control method of the self-moving device in Example 1. DETAILED DESCRIPTION

[0084] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0085] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and method personnel in this field may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0086] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0087] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for controlling a self-moving device, comprising the following steps:

[0088] S1. Start the self-mobile device, detect and activate the device's sensor, execution unit, beacon device transmitting component and communication device in sequence, and enter the standby state.

[0089] Furthermore, start the self-moving device and use a multimeter to measure whether the power input voltage of the sensor is within the rated range (such as ±5% error). If it is within the rated range, the voltage is normal;

[0090] If it is not within the rated range, the power supply is abnormal, the operation stops and an alarm sounds;

[0091] When the power input voltage of the sensor is normal, use a controller (such as MCU or PLC) to send an activation command through the sensor's communication interface (such as I²C, SPI, UART, etc.) to activate the sensor's acquisition function;

[0092] After the acquisition function is activated, the execution unit, the beacon device transmitting component and the communication device are activated in sequence;

[0093] The activation command is sent through the respective communication interface (such as RS-232, RS-485, CAN, etc.) to ensure that each module receives and executes the activation instruction.

[0094] Start the built-in self-check function of the execution unit, the controller sends a self-check instruction, and the execution unit performs a self-check of the circuit and control logic;

[0095] The position sensor (such as encoder or potentiometer) in the actuator returns the current position information. By reading the feedback signal, it is confirmed whether the position sensor is working properly;

[0096] It should be noted that when reading the feedback signal of the position sensor, the position sensor usually returns a coding value or a position value, which represents the current actual position information, and the information is usually transmitted through a digital or analog signal.

[0097] Digital signal: For example, a digital encoder might return a digital position information via a communication protocol such as SPI, I²C, or RS-485.

[0098] Analog signal: For example, a potentiometer might reflect its current position via a voltage signal (such as 0-5V).

[0099] A known movement command or position setting command is sent to the execution unit by the controller, for example, to move to a predetermined position (such as "move to 0 degrees" or "move to the initial position").

[0100] Read the feedback signal of the position sensor to see whether the actual returned position information is consistent with the control instruction.

[0101] Digital encoder: The position information returned should represent the current angle or distance in digital form. For example, if you send a "move to 0 degrees" command, the feedback signal should return a value of 0 degrees or close to 0 degrees.

[0102] Analog sensor: If it is a potentiometer, the feedback voltage value should correspond to the expected angle or position. For example, 0 degrees may correspond to 0V, and 90 degrees may correspond to 2.5V. If the feedback voltage does not match the expected, it may indicate a problem with the sensor.

[0103] During the execution process, the feedback signal of the position sensor is monitored in real time to confirm that it changes with the movement of the execution unit. The specific steps are as follows:

[0104] Send different movement instructions: For example, send instructions for different angles such as "move to 30 degrees" and "move to 60 degrees".

[0105] Read feedback signals in real time: During the movement of the actuator, read the feedback signal of the position sensor to ensure that it can continuously and smoothly reflect the current position of the actuator.

[0106] Check the change curve of the feedback signal: The feedback signal should be consistent with the movement trajectory of the actuator, without jumps or abnormal fluctuations. Record the change curve of the feedback signal through the controller to observe whether it is linear and in line with expectations.

[0107] Compare the feedback value of the position sensor with the actual position of the actuator. Error analysis can be performed in the following ways:

[0108] Actual position measurement: If there are other external measurement methods (such as laser rangefinder, mechanical ruler, etc.), the actual position of the actuator can be measured.

[0109] Error calculation: Compare the feedback position signal with the actual measured position and calculate the error. The error should be within the design allowable range (such as ±0.1 degrees or ±1 mm). If the error is too large or unstable, it may indicate that the position sensor is not working properly.

[0110] Read the feedback signal over a period of time to check for noise interference or instability, especially for analog signals. The output signal of a digital encoder should be stable. If there is a jump or mutation, it may be an encoder failure. For analog sensors, use an oscilloscope or AD converter to monitor the signal to ensure that there is no obvious noise fluctuation in the feedback signal. If the noise is too large, it may affect the accuracy of the sensor.

[0111] Many modern position sensors (especially digital encoders) have self-diagnostics that report the health of the sensor via a status register.

[0112] The status registers return error codes if the sensor detects an internal fault (such as a broken magnetic ring, optical failure, etc.) Reading these registers can help determine if the sensor is operating properly.

[0113] Using a spectrum analyzer, measuring the intermediate frequency input signal of the transmitting component and the spectrum analyzer measuring the radio frequency output signal of the transmitting component within a predetermined range;

[0114] For example, the intermediate frequency input signal of the transmitting component needs to ensure that its frequency and power are within the predetermined range (e.g., 1.4 to 2.4 GHz, power -25 ± 2 dBm);

[0115] The RF output signal of the transmitting component needs to ensure that the frequency, power and in-band spurious signals of the RF output meet the design requirements (such as 5-6GHz, power 3±2dBm, spurious suppression ≥45dBc);

[0116] Test the communication interface and data transmission function, send test data packets, and continuously detect the output signal of the transmitting component through the status register returned by the communication device to confirm that the data transmission link is normal and uninterrupted.

[0117] S2. In the standby state, the beacon device transmitting component receives an external signal, pre-processes the received intermediate frequency signal, and converts the pre-processed intermediate frequency signal into a radio frequency signal through an up-conversion circuit of the mobile device.

[0118] Furthermore, the controller sends a standby command through a communication interface (such as SPI, I²C or UART), and the beacon device transmitting component enters the standby state;

[0119] The format of the standby command is: STANDBY_MODE_ENABLE, which is used for feedback confirmation, and the beacon device returns a confirmation signal of the standby state.

[0120] Use an antenna connected to the IF input terminal of the beacon device to receive an external IF signal, and monitor the frequency and power of the received IF signal using a spectrum analyzer;

[0121] It should be noted that the receiving antenna is used to capture external intermediate frequency signals, and the signal frequency range is usually The specific measurement operations are as follows: connect the spectrum analyzer to the intermediate frequency input terminal; monitor the signal frequency to ensure that it is within the design range (1.4~2.4GHz); measure the signal power to ensure that it is within the range of -25±2dBm.

[0122] The intermediate frequency signal passes through a bandpass filter to eliminate out-of-band noise and unnecessary interference signals, and a digitally controlled attenuator is used to adjust the signal gain;

[0123] The center frequency of the filter should be set within the operating frequency range of the intermediate frequency signal, usually (1.4~2.4GHz), to eliminate out-of-band noise and interference signals and ensure the quality of the intermediate frequency signal.

[0124] The digitally controlled attenuator is used to adjust the gain of the intermediate frequency signal and control the gain flatness within the range of ±2dB;

[0125] The controller sends a command in the format of ATTEN_CTRL_SET(atten_value), where the attenuation range is 80dB and the resolution is 1dB.

[0126] When the ATTEN_CTRL_SET(atten_value) command is executed, the digital controlled attenuator returns a confirmation message to ensure that the attenuation value is set successfully.

[0127] Start the up-converter, the controller sends a frequency conversion command, the up-converter starts working, and the up-converter selects a suitable frequency (for example: 3GHz) from the local oscillator (LO) to up-convert the intermediate frequency signal into a radio frequency signal;

[0128] Select the appropriate value for the local oscillator frequency (for example, 3 GHz) to ensure that the up-converted RF signal falls within the designed frequency band (such as 5-6 GHz, 9-10 GHz, or 11.8-17 GHz).

[0129] After the controller sends the "start upconverter" command, confirm the feedback signal of the upconverter to ensure that it has successfully entered the working state. If the upconverter fails to start successfully, record the fault log and conduct troubleshooting.

[0130] The controller sets the frequency of the local oscillator by sending a command to start the up-converter, detects the frequency of the RF signal using a spectrum analyzer, and adjusts the frequency of the local oscillator;

[0131] The frequency calculation expression is:

[0132] ;

[0133] in, Indicates the RF output frequency, represents the intermediate frequency input frequency, Indicates the local oscillator frequency;

[0134] The selection of the local oscillator frequency should be determined according to the target frequency band of the output RF, and the local oscillator frequency should be adjusted to ensure that the RF signal falls within the designed frequency band.

[0135] Use a spectrum analyzer to detect the RF output frequency to ensure that it is within the designed frequency band. If the RF frequency is offset, adjust the local oscillator frequency.

[0136] For example, fine-tune again through a digitally controlled attenuator until the power stabilizes within the designed range (such as 3±2dBm).

[0137] The controller sends a command to set the local oscillator frequency (frequency value), adjusts the RF output power, uses a spectrum analyzer to detect the RF output power, and adjusts the RF output power;

[0138] It should be noted that the adjustment of the local oscillator frequency is specifically to adjust the local oscillator frequency through the controller to ensure that the frequency of the RF output signal is within the target design frequency band; the adjustment of the output power is specifically to adjust the signal gain through the digital attenuator or power control module to ensure that the RF output power is stable within the design range (such as 3±2dBm). If the power is detected to be too high or too low, use the digital attenuator to fine-tune until the power meets the requirements;

[0139] Use a spectrum analyzer to detect in-band spurious signals. If the spurious signals exceed the standard, recalibrate the upconverter.

[0140] Among them, in-band spurious suppression specifically refers to using a spectrum analyzer to detect the in-band spurious of the RF signal to ensure that the spurious signal suppression is ≥45dBc. If the spurious signal exceeds the standard, recalibrate the filter and gain control module of the upconverter; the calibration operation specifically refers to adjusting the signal amplifier and filter of the upconverter to ensure that the spurious suppression of the signal meets the design standard, and test the frequency, power and spurious of the RF output signal again until all parameters meet the requirements.

[0141] S3. The radio frequency signal is adjusted within the frequency range and then transmitted to the communication device, which is analyzed and a corresponding operation mode is selected to adjust the working state of the communication device.

[0142] Furthermore, in the up-converter, after adjusting and confirming that the frequency and power of the RF signal are within the target range, the RF signal is transmitted to the receiving end of the communication device through the signal link;

[0143] It should be noted that when transmitting to the receiving end of the communication device, it is necessary to ensure that there is no significant loss in the signal transmission link (such as coaxial cable, waveguide), and use low-loss cables or appropriate amplifiers to compensate for transmission losses when necessary. The transmission loss expression is:

[0144] ;

[0145] in, is the signal power after transmission, is the input signal power, is the loss on the transmission path;

[0146] The receiving end of the communication device introduces an intelligent adaptive filter, which analyzes the received signal spectrum in real time, dynamically adjusts the center frequency and bandwidth of the filter, eliminates external interference signals, and improves the signal-to-noise ratio (SNR) of the received signal;

[0147] The core parameter control expression of the adaptive filter is:

[0148] ;

[0149] in, is the center frequency of the filter, is the frequency of the RF signal, Depends on the spectrum analysis results, usually Hz or kHz;

[0150] The pre-trained machine learning model is used to analyze the key features of the RF signal (such as frequency, amplitude, and modulation mode). The model uses feature vectors ,in: is the signal frequency, is the signal power, Modulation method (such as QAM, PSK, etc.);

[0151] The model compares the real-time signal characteristics with the reference pattern and automatically identifies the signal operation mode;

[0152] After parsing the signal and adjusting the working state, the communication device transmits feedback information (such as signal quality, bit error rate, power, etc.) to the controller. If the feedback shows that the signal quality does not meet the standard, the controller will trigger the readjustment of gain, bandwidth or modulation mode;

[0153] The pattern recognition algorithm automatically identifies the operating mode of the current signal by comparing the real-time signal characteristics with the reference patterns in the model library;

[0154] If a change in the modulation mode of the signal is detected (e.g., switching from QPSK to QAM), the communication device adjusts its demodulation module to adapt to the new operating mode;

[0155] The gain adjustment expression is:

[0156] ;

[0157] in, is the new gain value, represents the historical gain value, is the target power, is the currently measured signal power;

[0158] The bandwidth adjustment expression is:

[0159] ;

[0160] in, is the new bandwidth adjustment value, is the center frequency of the identified signal, It is the bandwidth adjustment amount automatically calculated by the system according to the signal characteristics;

[0161] The communication device automatically switches to the appropriate demodulation mode based on the output of the machine learning model;

[0162] After adjusting the working state, the communication device transmits feedback information back to the controller to verify the validity of the current state;

[0163] If the signal quality does not meet expectations, the communication device will re-analyze the signal and adjust parameters until the system enters the optimal working state.

[0164] S4. When the working state is adjusted, the mobile device is interconnected with the base station through the communication device, and the location information, state information and environmental data are exchanged in real time and transmitted to the central processing unit in real time.

[0165] Furthermore, when the working state is adjusted, multiple distributed antennas are enabled from the mobile device, and the phase compensation factor pre-calculated by phase synchronization is Adjust the transmission phase of each antenna so that the signals between the antennas work together to enhance the communication effect. The expression is:

[0166] ;

[0167] in, is the final output signal, For the The signal amplitude of each antenna is For the The carrier frequency of each antenna, Indicates The phase compensation factor of each antenna is Indicates the number of antennas, An index variable indicating the number of antennas;

[0168] The device sends the initial handshake signal through coordinated communication with the adjusted transmission phase. After receiving the signal, the base station feeds back the signal quality indicators (such as signal-to-noise ratio, RSSI).

[0169] The mobile device dynamically adjusts the communication parameters based on the signal quality indicators fed back by the base station to ensure the stability of the link. The adjustments include transmission power, antenna direction and communication frequency to optimize signal transmission and reception quality.

[0170] Various sensors in mobile devices (such as GPS, IMU, environmental sensors) begin to collect location information, status information and environmental data;

[0171] Among them, these data include the device's real-time coordinates, speed, acceleration, attitude angle, as well as the environment's temperature and humidity, light intensity, etc.

[0172] Use median filtering and Kalman filtering to preprocess the collected data to eliminate noise and outliers;

[0173] For missing data, linear interpolation is used to fill in the missing data. The prediction step expression is:

[0174] ;

[0175] in, is the state vector of the device, is the state transfer matrix, is the control gain matrix, is the control input, is the process noise;

[0176] The update step expression is:

[0177] ;

[0178] in, is the actual observed value, is the observation matrix, To measure noise;

[0179] Use adaptive compression algorithm to compress the collected location information, status information and environmental data, and select different compression ratios according to the data change rate and redundancy;

[0180] For highly redundant data (such as historical location data), a high compression ratio is used; for data with high real-time requirements (such as state change information), a low compression ratio and adaptive compression algorithm are used. The expression is:

[0181] ;

[0182] in, For the The amount of change in class data, For the The transmission delay of class data, is the total number of data categories, Index variable representing the data category;

[0183] The compressed data is transmitted to the base station through a distributed phase cooperative communication method, and forwarded to the central processing unit through the base station;

[0184] After receiving the data, the base station decompresses it and restores the original data. The communication link between the device and the base station is continuously optimized through a real-time feedback mechanism.

[0185] S5. The central processing unit combines the link budget data of the beacon device to perform multi-dimensional data fusion and generate an environmental model.

[0186] Furthermore, a multi-scale decomposition method is used to preliminarily stratify the link budget data and generate link budget components at different time scales;

[0187] It should be noted that in self-mobile devices, beacon devices will continuously collect link budget data (such as signal strength, channel gain, signal-to-noise ratio, link packet loss rate). In order to better process these data and adapt to different time change scales, these data need to be decomposed into multiple scales first.

[0188] The discrete wavelet transform (DWT) is used to perform multi-scale decomposition of the time series, and the expression is:

[0189] ;

[0190] in, represents the signal components at different scales, Represents the original link data, is the wavelet function, represents the sampling point, To decompose the scale;

[0191] Based on multi-scale decomposition, the link budget data, sensor data and base station feedback data are unified into a probabilistic framework through nonlinear multi-level Bayesian inference to infer the beacon device status of the environmental model;

[0192] The Bayesian inference expression is:

[0193] ;

[0194] in, is the posterior probability, indicating that given the data Case parameters The probability distribution of is the likelihood function, which means that given the model parameters Get data The probability of is the prior probability, indicating the parameter The prior distribution of is the normalization constant;

[0195] Based on the above Bayesian inference, the extended Kalman filter (EKF) is used to estimate the state of the beacon device and update the motion state of the device in real time;

[0196] The state transition equation expression is:

[0197] ;

[0198] in, For the moment The state vector of is a nonlinear state transfer function, which means that Towards the moment Status updates, is the process noise, which is usually assumed to be zero-mean Gaussian noise;

[0199] After the state estimation is completed, the link budget data, sensor data, base station feedback data, and the device state output by the extended Kalman filter are integrated to form a high-dimensional data set;

[0200] For high-dimensional data sets, high-order integral transforms are used to represent nonlinear changes in complex environments and generate environmental models. , reflects the comprehensive impact of different data sources on the environment, and the expression is:

[0201] ;

[0202] in, Indicates Time series of data such as link budget, sensor data, etc. is the weight of the data, is the second-order derivative of the data, indicating its nonlinear dynamic changes.

[0203] S6. Based on the environmental model, the mobile device uses an intelligent algorithm to generate action decisions, transmit control commands to the motion control unit, and adjust the motion state of the device.

[0204] The central processing unit extracts key features in the environment model, such as obstacles, path planning information, dynamic changes, etc., and performs normalization processing to ensure the uniformity of different data dimensions for subsequent calculations;

[0205] Based on the normalized data, use the shortest path algorithm (such as Dijkstra algorithm) to calculate the path from the device to the target point;

[0206] Among them, the path planning results provide the basic route basis for subsequent action decisions;

[0207] Combined with the path of the target point, the dynamic window algorithm (DWA) is used for real-time obstacle avoidance.

[0208] Preferably, DWA generates a local optimal motion trajectory according to the speed of the device and the distance to the obstacle, and provides dynamic feedback for the motion adjustment of the device.

[0209] Through the deep reinforcement learning (DRL) algorithm, the overall motion strategy of the device is optimized by combining the path of the target point and real-time obstacle avoidance. The output of the algorithm is the optimal motion decision of the device.

[0210] According to the path planning and obstacle avoidance results, the linear velocity and angular velocity of the device are calculated, and corresponding control instructions are generated;

[0211] Use PID controller to adjust the motion state of the equipment in real time to reduce motion errors and ensure that the equipment runs along the predetermined path;

[0212] The generated control instructions are sent to the motion control unit through a communication bus (such as CAN bus), and the driving device performs the corresponding motion operation;

[0213] Obtain the real-time motion state of the device through sensors (such as position sensors and IMUs), compare it with the expected state, calculate the error, and make real-time adjustments through PID controllers;

[0214] It should be noted that the "expected state" refers to the state that the device should theoretically reach under given environmental models, path planning, obstacle avoidance strategies and other conditions, including the device's position, speed, direction and other motion parameters.

[0215] Specifically, the expected state usually consists of the following parts:

[0216] Position: The target position of the device in space (based on the results of path planning calculations).

[0217] Speed: The device's theoretical linear and angular speeds (calculated by the path planning and obstacle avoidance algorithms).

[0218] Direction: The direction in which the device should move (the direction of the trajectory calculated by combining path planning and obstacle avoidance).

[0219] These expected states are the ideal motion states calculated by the intelligent algorithm after the device has gone through path planning and dynamic obstacle avoidance. For example, where the device should go at a certain moment, at what speed, and in what direction, all of these belong to the category of expected states, such as:

[0220] Path planning step: The various target points on the path calculated based on the shortest path algorithm constitute the expected location of the device.

[0221] Dynamic obstacle avoidance step: Combining path planning and real-time obstacle avoidance, the generated local optimal trajectory includes how the device should adjust its speed and direction, which are all part of the expected state.

[0222] Control instruction generation steps: Based on the path planning and obstacle avoidance results, the generated linear velocity and angular velocity are the expected state of the device at a certain moment.

[0223] The motion control unit dynamically adjusts the speed and direction of the device based on feedback information to ensure smooth movement and avoid obstacles;

[0224] The stability of the device is monitored through the gyroscope and accelerometer to ensure that the device remains balanced and safe during exercise.

[0225] The present embodiment also provides a control system for a self-moving device, including: a device initialization module, a beacon signal processing module, a signal up-conversion module, a signal processing module, a communication management module, an environment perception module and a motion planning module; the device initialization module is used for starting the self-moving device, sequentially detecting and activating the device's sensor, execution unit, beacon device transmitting component and communication device, and entering a standby state; the beacon signal processing module is used for receiving an external signal through the beacon device transmitting component in the standby state, and pre-processing the received intermediate frequency signal; the signal up-conversion module is used for converting the pre-processed intermediate frequency signal into a radio frequency signal through the up-conversion circuit of the self-moving device ; A signal processing module is used to adjust the radio frequency signal within the frequency range and transmit it to the communication device, analyze it and select the corresponding operation mode to adjust the working state of the communication device; a communication management module is used when the working state is adjusted, the self-mobile device is interconnected with the base station through the communication device, and the location information, status information and environmental data are exchanged in real time, and transmitted to the central processing unit in real time; an environmental perception module is used for the central processing unit to combine the link budget data of the beacon device, perform multi-dimensional data fusion, and generate an environmental model; a motion planning module is used to generate action decisions based on the environmental model from the mobile device using an intelligent algorithm, transmit control commands to the motion control unit, and adjust the motion state of the device.

[0226] This embodiment also provides a computer device, which is suitable for the control method of the self-moving device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the control method of the self-moving device proposed in the above embodiment.

[0227] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other methods. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0228] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the control method for implementing a self-moving device as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0229] In summary, the present invention realizes precise control of each key module of the device by activating sensors, execution units, beacon device transmitting components and communication devices in sequence, thereby ensuring the stability and reliability of the system startup process. Secondly, by converting the received intermediate frequency signal into a radio frequency signal after preprocessing, and transmitting it to the communication device after adjusting it within the frequency range, the problem that traditional equipment is susceptible to environmental interference during data transmission is solved, and the stability and efficiency of communication are significantly improved. Based on multi-dimensional data fusion technology, the present invention combines the link budget data of the beacon device in the central processing unit to generate a real-time environmental model, and uses an intelligent algorithm to dynamically adjust the motion state of the device, thereby enhancing the device's adaptive ability and obstacle avoidance ability in complex environments.

[0230] It should be noted that the above embodiments are only used to illustrate the method scheme of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary method personnel in the field should understand that the method scheme of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the method scheme of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A control method for a self-moving device, characterized in that: include, Start the self-mobile device, detect and activate the device's sensor, execution unit, beacon device transmitting component and communication device in sequence, and enter the standby state; In the standby state, the external signal is received through the beacon device transmitting component, and the received intermediate frequency signal is pre-processed; The pre-processed intermediate frequency signal is converted into a radio frequency signal through an up-conversion circuit of a mobile device; The radio frequency signal is adjusted within the frequency range and then transmitted to the communication device, which is analyzed and the corresponding operation mode is selected to adjust the working state of the communication device; When the working state is adjusted, the mobile device is interconnected with the base station through the communication device to exchange location information, status information and environmental data in real time, and transmit them to the central processing unit in real time; The central processing unit combines the link budget data of the beacon device transmitting component, performs multi-dimensional data fusion on the link budget data, sensor data, base station feedback data, and device status output by the extended Kalman filter to generate an environmental model; Based on the environmental model, the mobile device uses intelligent algorithms to generate action decisions, transmit control commands to the motion control unit, and adjust the motion state of the device.

2. The control method of the self-moving device according to claim 1, characterized in that: The specific steps of sequentially detecting and activating the device's sensor, execution unit, beacon device transmitting component and communication device are as follows: Use a multimeter to measure whether the power input voltage of the sensor is within the rated range. If it is within the rated range, the voltage is normal; If it is not within the rated range, the power supply is abnormal, the operation stops and an alarm sounds; When the power input voltage of the sensor is normal, the controller is used to send an activation command through the communication interface of the sensor to activate the acquisition function of the sensor; After the acquisition function is activated, the execution unit, the beacon device transmitting component and the communication device are activated in sequence; Start the built-in self-check function of the execution unit, the controller sends a self-check instruction, and the execution unit performs a self-check of the circuit and control logic; The position sensor in the execution unit returns the current position information. By reading the feedback signal, it is confirmed whether the position sensor is working properly; Using a spectrum analyzer, measuring the intermediate frequency input signal of the transmitting component and the spectrum analyzer measuring the radio frequency output signal of the transmitting component within a predetermined range; Test the communication interface and data transmission function, send a test data packet, and continuously detect the output signal of the transmitting component through the status register returned by the communication device to confirm that the data transmission link is normal and uninterrupted.

3. The control method of the self-moving device according to claim 2, characterized in that: The pre-processed intermediate frequency signal is converted into a radio frequency signal through an up-conversion circuit of a mobile device, and the specific steps are: The controller sends a standby command through the communication interface, and the beacon device transmitting component enters the standby state; Using an antenna connected to the intermediate frequency input terminal of the beacon device transmitting component to receive an external intermediate frequency signal, and monitoring the frequency and power of the received intermediate frequency signal by using a spectrum analyzer; The intermediate frequency signal passes through a bandpass filter to eliminate out-of-band noise and interference signals, and a digitally controlled attenuator is used to adjust the signal gain; Start the up-converter, the controller sends a frequency conversion command, and the up-converter selects the corresponding frequency from the local oscillator; The controller sets the frequency of the local oscillator by sending a command to start the up-converter, detects the frequency of the RF signal using a spectrum analyzer, and adjusts the frequency of the local oscillator; The controller sends a command to set the local oscillator frequency, adjusts the RF output power, uses a spectrum analyzer to detect the RF output power, and adjusts the RF output power; Use a spectrum analyzer to detect in-band spurious signals. If the spurious signals exceed the standard, recalibrate the upconverter.

4. The control method of the self-moving device according to claim 3, characterized in that: The specific steps of analyzing and selecting the corresponding operation mode to adjust the working state of the communication device are: In the up-converter, after adjusting and confirming that the frequency and power of the RF signal are within the target range, the RF signal is transmitted to the receiving end of the communication device through the signal link; The receiving end of the communication device introduces an intelligent adaptive filter, which dynamically adjusts the center frequency and bandwidth of the filter by real-time analysis of the received signal spectrum; Analyze the key features of RF signals through pre-trained machine learning models; The pattern recognition algorithm automatically identifies the operating mode of the current signal by comparing the real-time signal characteristics with the reference patterns in the model library; If a change in the modulation mode of the signal is identified, the communication device adjusts its demodulation module; The communication device automatically switches to the appropriate demodulation mode based on the output of the machine learning model; After adjusting the working state, the communication device transmits feedback information back to the controller to verify the validity of the current state; If the signal quality does not meet expectations, the communication device will re-analyze the signal and adjust parameters until the system enters the optimal working state.

5. The control method of the self-moving device according to claim 4, characterized in that: The real-time transmission to the central processing unit comprises the following specific steps: When the working state is adjusted, multiple distributed antennas are enabled from the mobile device, and the transmission phase of each antenna is adjusted through the phase compensation factor pre-calculated by phase synchronization; The self-mobile device sends an initial handshake signal to the base station through coordinated communication with the adjusted transmission phase. The base station receives the signal and feeds back the signal quality index to adjust the communication parameters. Use sensors in mobile devices to collect location information, status information and environmental data; Use median filtering and Kalman filtering to preprocess the collected data to eliminate noise and outliers; Missing data were filled by linear interpolation; Use adaptive compression algorithm to compress the collected location information, status information and environmental data; For highly redundant data, use a high compression ratio; For data with high real-time requirements, a low compression ratio and adaptive compression algorithm are used. The expression is: ； in, For the The amount of change in class data, For the The transmission delay of class data, is the total number of data categories, Index variable representing the data category; The compressed data is transmitted to the base station through a distributed phase cooperative communication method and forwarded to the central processing unit through the base station.

6. The control method of the self-moving device according to claim 5, characterized in that: The multi-dimensional data fusion is performed to generate an environment model, and the specific steps are as follows: The multi-scale decomposition method is used to preliminarily stratify the link budget data and generate link budget components at different time scales. Based on multi-scale decomposition, the link budget data, sensor data and base station feedback data are unified into a probabilistic framework through nonlinear multi-level Bayesian inference to infer the state of the beacon device transmission component of the environmental model; Use extended Kalman filtering to estimate the state of the beacon device's transmitting component and update the motion state in real time; After the state estimation is completed, the link budget data, sensor data, base station feedback data and the device state output by the extended Kalman filter are integrated to form a high-dimensional data set; For high-dimensional data sets, high-order integral transforms are used to represent nonlinear changes in complex environments and generate environmental models. , the expression is: ； in, Indicates Time series of class data, is the weight of the data, is the second-order derivative of the data.

7. The control method of the self-moving device according to claim 6, characterized in that: The specific steps of transmitting the control command to the motion control unit to adjust the motion state of the device are as follows: The key features in the environment model are extracted by the central processing unit and normalized; Based on the normalized data, the shortest path algorithm is used to calculate the path from the device to the target point; Combined with the path of the target point, the dynamic window method is used for real-time obstacle avoidance; Through deep reinforcement learning algorithms, combined with the path of the target point and real-time obstacle avoidance, the overall motion strategy of the device is optimized; According to the path planning and obstacle avoidance results, the linear velocity and angular velocity of the device are calculated, and corresponding control instructions are generated; Use PID controller to adjust the motion state of the device in real time; The generated control instructions are sent to the motion control unit through the communication bus, and the driving device performs the corresponding motion operation; The real-time motion state of the device is obtained through sensors, compared with the expected state, the error is calculated, and real-time adjustments are made through the PID controller; The motion control unit dynamically adjusts the device's speed and direction based on feedback information and monitors the device's stability through gyroscopes and accelerometers.

8. A control system for a self-moving device, based on the control method for a self-moving device according to any one of claims 1 to 7, characterized in that: Including, device initialization module, beacon signal processing module, signal up-conversion module, signal processing module, communication management module, environment perception module and motion planning module; The device initialization module is used to start the self-mobile device, detect and activate the device's sensor, execution unit, beacon device transmitting component and communication device in sequence, and enter the standby state; The beacon signal processing module is used to receive external signals through the beacon device transmitting component in the standby state and pre-process the received intermediate frequency signal; The signal up-conversion module is used to convert the pre-processed intermediate frequency signal into a radio frequency signal through an up-conversion circuit of the mobile device; The signal processing module is used to adjust the radio frequency signal within the frequency range and transmit it to the communication device, analyze it and select the corresponding operation mode to adjust the working state of the communication device; The communication management module is used to interconnect the mobile device with the base station through the communication device after the working state is adjusted, exchange location information, state information and environmental data in real time, and transmit them to the central processing unit in real time; The environment perception module is used for the central processing unit to combine the link budget data of the beacon device transmitting component to perform multi-dimensional data fusion and generate an environment model; The motion planning module is used to generate action decisions based on the environment model from the mobile device using an intelligent algorithm, transmit control commands to the motion control unit, and adjust the motion state of the device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method of the self-moving device described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of the self-moving device described in any one of claims 1 to 7 are implemented.

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