Control method of robot and related device

Abstract

The application is suitable for the technical field of robot control, and provides a control method of a robot, which comprises the following steps: after the robot is started, multi-dimension data from multiple data sensors is acquired according to a preset sampling frequency; the multi-dimension data is filtered and standardized to obtain processed multi-dimension data; the processed multi-dimension data is packaged according to a preset frame format to obtain a packaged multi-dimension data frame; and the multi-dimension data frame is sent to a receiver according to a preset period, so that the receiver analyzes the multi-dimension data frame to obtain a data analysis result. The application can solve the problems of the existing control method, such as single data acquisition dimension, separation of control and acquisition, poor data transmission mode adaptability, lack of unified main control adaptability, and inability to comprehensively monitor and manage in a complex high-altitude operation environment.

Description

Technical Field

[0001] This application belongs to the field of robot control technology, and in particular relates to a robot control method and related equipment. Background Technology

[0002] Curtain wall cleaning robots are key equipment for replacing manual labor in high-altitude glass curtain wall cleaning operations. Currently, the control methods for curtain wall cleaning robots only focus on core safety parameters such as obstacle distance detection, lacking synchronous data collection of crucial parameters such as internal robot temperature (detecting overheating), power supply voltage / current (reflecting battery life and load status), and ambient wind speed (affecting high-altitude operation stability). This results in an inability to comprehensively assess the robot's operational status. Furthermore, the distributed control architecture, where data acquisition modules and main control units operate independently without unified integrated control logic, leads to data transmission delays, poor parameter coordination, and affects the robot's response speed to complex working conditions. Wireless transmission methods such as Bluetooth and WiFi are prone to data loss in high-altitude environments with strong electromagnetic interference. Wired transmission solutions, on the other hand, are not optimized for the synchronous transmission of multi-dimensional data, resulting in data congestion and low transmission efficiency. Finally, the main control chips are mostly dedicated chips with poor scalability. When additional parameter acquisition functions are needed (such as adding current monitoring later), the control logic must be redesigned, leading to high development costs and long development cycles. Therefore, there is an urgent need for a robot control method that can achieve comprehensive state perception, highly reliable communication, and support flexible configuration, in order to solve the problems of existing control methods, such as single data acquisition dimension, separation of control and acquisition, poor adaptability of data transmission methods, lack of unified master control adaptability, and inability to fully monitor and manage in complex high-altitude working environments. Summary of the Invention

[0003] This application provides a robot control method that addresses the problems of existing control methods, such as single data acquisition dimensions, separation of control and acquisition, poor adaptability of data transmission methods, lack of unified master control adaptability, and inability to comprehensively monitor and manage in complex high-altitude working environments. After the robot starts, it acquires multi-dimensional data from multiple data sensors at a preset sampling frequency. The multi-dimensional data is then filtered and standardized to obtain processed multi-dimensional data. This processed multi-dimensional data is then packaged according to a preset frame format to obtain packaged multi-dimensional data frames. These multi-dimensional data frames are sent to a receiver at a preset period, and the receiver is controlled to analyze the data to obtain the analysis results. This method solves the problems of existing control methods, such as single data acquisition dimensions, separation of control and acquisition, poor adaptability of data transmission methods, lack of unified master control adaptability, and inability to comprehensively monitor and manage in complex high-altitude working environments.

[0004] In a first aspect, embodiments of this application provide a robot control method, the method comprising the following steps:

[0005] After the robot is started, it acquires multi-dimensional data from multiple data sensors at a preset sampling frequency;

[0006] The multi-dimensional data is filtered and standardized to obtain the processed multi-dimensional data;

[0007] The processed multidimensional data is packaged according to a preset frame format to obtain a packaged multidimensional data frame.

[0008] The multidimensional data frame is sent to the receiver according to a preset period, and the receiver is controlled to perform data analysis on the multidimensional data frame to obtain the data analysis results.

[0009] Optionally, before acquiring multi-dimensional data from the data sensor at a preset sampling frequency, the method further includes:

[0010] Initialize peripheral configuration;

[0011] Simultaneously, self-test commands are sent to multiple data sensors to enable the multiple data sensors to complete self-tests.

[0012] Optionally, after the plurality of data sensors have completed self-testing, the method further includes:

[0013] The state of each data sensor is determined based on the self-test feedback data corresponding to the data sensor.

[0014] Based on the status of the data sensors, the target faulty data sensor is identified, and a fault alarm signal is sent to the receiver.

[0015] Optionally, the filtering and standardization of the multi-dimensional data to obtain processed multi-dimensional data includes:

[0016] The multi-dimensional data is preprocessed to obtain preprocessed multi-dimensional data;

[0017] The preprocessed multidimensional data is then filtered and standardized to obtain processed multidimensional data.

[0018] Optionally, the multi-dimensional data includes wind speed data, distance data, temperature data, voltage data, and current data. The filtering and standardization of the preprocessed multi-dimensional data to obtain processed multi-dimensional data includes:

[0019] The preprocessed wind speed data and distance data are filtered to obtain filtered wind speed data and distance data.

[0020] The preprocessed temperature data, voltage data, and current data are standardized to obtain standardized temperature data, voltage data, and current data.

[0021] Optionally, after controlling the receiver to perform data analysis on the multidimensional data frame and obtain the data analysis results, the method further includes:

[0022] Real-time monitoring of parameter adjustment commands from the receiver;

[0023] The operating parameters are dynamically updated based on the parameter adjustment instructions.

[0024] Optionally, after transmitting the multidimensional data frame to the receiver according to a preset period, the method further includes:

[0025] If the loss of the multidimensional data frame is detected during transmission, a retransmission mechanism is automatically triggered.

[0026] Secondly, embodiments of this application provide a robot control device, the robot control device comprising:

[0027] The acquisition module is used to acquire multi-dimensional data from multiple data sensors at a preset sampling frequency after the robot is started.

[0028] The first processing module is used to filter and standardize the multi-dimensional data to obtain processed multi-dimensional data.

[0029] The second processing module is used to package the processed multidimensional data according to a preset frame format to obtain a packaged multidimensional data frame.

[0030] The control module is used to send the multidimensional data frame to the receiver according to a preset period, and control the receiver to perform data analysis on the multidimensional data frame to obtain data analysis results.

[0031] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the robot control method provided in embodiments of the present invention.

[0032] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the robot control method provided in the embodiments of the present invention.

[0033] The above-described solution of this application has the following beneficial effects: After the robot starts, it acquires multi-dimensional data from multiple data sensors according to a preset sampling frequency; the multi-dimensional data is filtered and standardized to obtain processed multi-dimensional data; the processed multi-dimensional data is packaged according to a preset frame format to obtain packaged multi-dimensional data frames; the multi-dimensional data frames are sent to the receiver according to a preset period, and the receiver is controlled to perform data analysis on the multi-dimensional data frames to obtain data analysis results. This invention solves the problems of existing control methods, such as single data acquisition dimension, separation of control and acquisition, poor adaptability of data transmission methods, lack of unified master control adaptability, and inability to comprehensively monitor and manage in complex high-altitude working environments.

[0034] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a robot control method provided in one embodiment of this application;

[0037] Figure 2 A structural diagram of a robot provided in one embodiment of this application;

[0038] Figure 3 This is a schematic diagram of the structure of a robot control device provided in one embodiment of this application;

[0039] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0041] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0042] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0044] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0046] like Figure 1 As shown, Figure 1 This is a flowchart of a robot control method provided in an embodiment of the present invention. The robot control method includes the following steps:

[0047] 101. After the robot is started, it acquires multi-dimensional data from multiple data sensors according to the preset sampling frequency.

[0048] In this embodiment of the invention, the robot control method described above can be applied to the robot's main control chip, which can be an STM32 main control chip. The robot described above can be a curtain wall cleaning robot, etc. A curtain wall cleaning robot is an intelligent device used for automatically cleaning curtain walls of high-rise buildings made of materials such as glass, metal, or stone. Figure 2 As shown, Figure 2 This is a structural diagram of a robot provided in an embodiment of the present invention. Specifically, the robot can be based on an STM32 main control chip, integrating sensor modules, a serial port module, and a receiver. The STM32 main control chip can be an STM32F103 series chip, or it can be adjusted according to actual needs. The STM32 main control chip is responsible for receiving data collected by various sensors, performing data preprocessing, and controlling the serial port module to transmit the collected data to the receiver. It is understood that the STM32 has rich peripheral features; temperature sensors, wind speed sensors, distance sensors, and voltage / current sensors can be connected to different peripheral pins (without affecting each other's operation). The aforementioned sensor module consists of multiple sensors responsible for collecting robot operation-related parameters. The sensor module includes temperature sensors, wind speed sensors, distance sensors, voltage / current sensors, etc. The aforementioned temperature sensor can be a DS18B20 digital temperature sensor, etc. The temperature sensor's acquisition range can be -55℃ to +125℃, with an accuracy of ±0.5℃, and it communicates with the STM32 main control chip via a single bus. The aforementioned distance sensors can be thermal anemometers, with a sampling range of 0–60 m / s, outputting analog signals which are converted into digital signals by the STM32 main control chip's built-in ADC to obtain real-time ambient wind speed. Alternatively, the aforementioned distance sensors can be ultrasonic sensors, such as the HC-SR04 model, with a sampling range of 2 cm–400 cm and an accuracy of ±3 mm. They communicate with the STM32 via I / O ports to detect the distance between the robot and the curtain wall and surrounding obstacles. The aforementioned voltage / current sensors can collect the robot's power supply voltage and operating current. Both voltage and current sensors communicate with the STM32 via an I2C bus or are sampled via an ADC for real-time monitoring of the power supply status. The aforementioned serial port transmission module can be based on the STM32 main control chip's built-in UART interface, configured with a baud rate of 9600 bps (adjustable as needed), responsible for packaging and transmitting pre-processed multi-dimensional data to the receiver. The aforementioned receiver can be an industrial-grade serial port receiver that receives packaged data from the STM32 main control chip. It can be connected to a local monitoring terminal or cloud platform to realize data storage, display, and subsequent analysis.

[0049] The aforementioned preset sampling frequency can be a pre-set sampling frequency used to periodically trigger the number of times per unit time for multi-dimensional data acquisition actions, such as 100ms / time.

[0050] The aforementioned data sensors can be temperature sensors, wind speed sensors, distance sensors, voltage / current sensors, etc.

[0051] The aforementioned multi-dimensional data can be a collection of data obtained from different data sensors, including internal temperature data, ambient wind speed data, obstacle distance data, etc.

[0052] The robot can be started after it has completed the preparatory processes such as power-on, hardware initialization, and self-test, and has entered a stable and working main loop state.

[0053] It should be noted that the collected multi-dimensional data can be used for robot operation status analysis, such as optimizing operation path planning through GPS positioning, assessing battery life through voltage / current data, and adjusting operation speed through wind speed data, providing data support for refined operation management and improving overall operation efficiency.

[0054] 102. Filter and standardize the multi-dimensional data to obtain the processed multi-dimensional data.

[0055] In this embodiment of the invention, the filtering and standardization processing described above can be filtering and standardization processing. The filtering processing can suppress or remove noise and interference components in the signal while preserving useful signal characteristics. Noise can be random fluctuations or interference of a specific frequency from the environment, equipment, or transmission process. The filtering processing can employ algorithms such as moving average filtering. Moving average filtering is a commonly used digital signal processing technique, mainly used to smooth data and suppress noise. It reduces the impact of random fluctuations by calculating the average value of data points within a fixed window to replace the current data point. The core principle of the moving average filtering algorithm is to maintain a fixed-length sliding window. Whenever new data arrives, the oldest data is removed from the window, the new data is added, and the arithmetic mean of all data within the window is recalculated as the output result. It can be understood that the sensor continuously collects data, grouping the collected multi-dimensional data into a set. Each time a new data point is collected, the oldest data in the set is deleted, and the average value of all data in the set is calculated. The final output is the smoothed value. The standardization processing described above can be a process of converting multi-dimensional data into a unified decimal data format.

[0056] The processed multidimensional data mentioned above can be obtained by filtering and standardizing multidimensional data.

[0057] 103. Pack the processed multidimensional data according to the preset frame format to obtain a packaged multidimensional data frame.

[0058] In this embodiment of the invention, the above-mentioned preset frame format can be a pre-set frame format, such as a frame format of: 1 byte frame header + 4 bytes of data bits + 1 byte bit + 1 byte frame tail, etc.

[0059] The above-mentioned packaging process can be a process of organizing and encapsulating the processed multi-dimensional data into a continuous and complete binary data block according to a pre-set frame format.

[0060] The aforementioned packaged multidimensional data frame can be a data transmission unit with complete semantics and verifiability obtained by packaging the processed multidimensional data according to a preset frame format.

[0061] 104. Send multidimensional data frames to the receiver according to the preset period, control the receiver to perform data analysis on the multidimensional data frames, and obtain the data analysis results.

[0062] In this embodiment of the invention, the preset period can be a pre-set data transmission period, such as 200ms / frame, 500ms / frame, etc.

[0063] The aforementioned receiver can be an industrial-grade serial port receiver that receives packaged data from the STM32 main control chip. It can be connected to a local monitoring terminal or cloud platform to realize data storage, display, and subsequent analysis.

[0064] The aforementioned data analysis can be a process where, after receiving a multidimensional data frame, the receiver calculates, judges, and mines the multidimensional state information contained within the frame, and generates data analysis results. Data analysis can involve verifying data integrity based on checksums in the multidimensional data frame, parsing the frame to obtain standard data for each dimension, comparing the data with preset safety thresholds to generate equipment status reports or warning information, or associating and storing data with corresponding timestamps and equipment IDs for historical queries and operational efficiency analysis. By analyzing multidimensional data frames, real-time and accurate monitoring of the robot's operating status, fault warnings, and operational optimization decisions can be achieved.

[0065] The above data analysis results can be obtained after analyzing multi-dimensional data frames. It is understood that the data analysis results not only include real-time status displays of parameters in each dimension, but may also include tiered early warning information for risks such as overheating, overload, strong winds, and low battery levels.

[0066] It should be noted that the present invention can display the robot's data analysis results in real time through local monitoring software. The data analysis results include the robot's internal temperature, ambient wind speed, obstacle distance, power supply status, and real-time location. The present invention also supports data storage (such as writing to a local database) for subsequent fault analysis and operation optimization.

[0067] In this embodiment of the invention, multiple data sensors, including those for temperature, wind speed, distance, and voltage / current, are integrated. After the robot starts, multi-dimensional data from these sensors is collected comprehensively at a preset sampling frequency. The multi-dimensional data is then filtered and standardized to obtain processed multi-dimensional data. This processed multi-dimensional data is packaged according to a preset frame format to obtain packaged multi-dimensional data frames. These frames are then sent to a receiver at a preset cycle, and the receiver is controlled to analyze the data to obtain the analysis results. This enables comprehensive monitoring of the robot's own status, working environment, power supply, and location information. It can provide early warnings of risks such as overheating of electronic components, abnormal power supply, excessive wind speed, and obstacle collisions, solving the problem of single monitoring in traditional solutions and significantly improving the safety of high-altitude operations.

[0068] In this embodiment of the invention, after the robot is started, multi-dimensional data from multiple data sensors is acquired at a preset sampling frequency; the multi-dimensional data is filtered and standardized to obtain processed multi-dimensional data; the processed multi-dimensional data is packaged according to a preset frame format to obtain packaged multi-dimensional data frames; the multi-dimensional data frames are sent to a receiver at a preset period, and the receiver is controlled to perform data analysis on the multi-dimensional data frames to obtain data analysis results. This invention solves the problems of existing control methods, such as single data acquisition dimension, separation of control and acquisition, poor adaptability of data transmission methods, lack of unified master control adaptability, and inability to comprehensively monitor and manage in complex high-altitude working environments.

[0069] It is understood that in the specific implementation of this application, multi-dimensional data, frame data, frequency data and other related data are involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required. Moreover, the collection, use and processing of related data, as well as the training, deployment and invocation of algorithm models, must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0070] Optionally, before acquiring multi-dimensional data from the data sensors at a preset sampling frequency, the peripheral configuration can be initialized; at the same time, self-test commands can be sent to multiple data sensors to enable them to complete self-tests.

[0071] In this embodiment of the invention, the initialization described above may be a series of hardware and software configuration actions performed by the STM32 main control chip after the robot is powered on and before it begins to perform the main monitoring tasks, with the aim of enabling the robot to enter a stable, controllable, and ready working state.

[0072] The aforementioned peripheral configurations can include I / O ports, ADCs, UARTs, I2C, and other peripheral configurations. I / O port initialization can involve setting the pins used for triggering and echoing ultrasonic sensors to output / input mode and establishing initial levels; ADC initialization can involve setting the sampling channel, accuracy, and reference voltage for the analog-to-digital converter used to read analog signals from the wind speed sensor; I2C initialization can involve setting the clock speed and addressing mode for voltage / current sensors; and UART initialization is used for communication with the GPS module and ultimately with the receiver, setting the baud rate, data bits, and parity bits.

[0073] The aforementioned data sensors can be temperature sensors, wind speed sensors, distance sensors, voltage / current sensors, etc.

[0074] The aforementioned self-test command can be a digital command sent during the initialization phase to the connected data sensor via a pre-configured communication interface (such as I2C, single bus, or UART) to query the data sensor's status or trigger the data sensor's internal self-test process. The purpose of the self-test command is to trigger the data sensor to execute its internal diagnostic program to verify whether its hardware and communication functions are normal.

[0075] The aforementioned self-test can be performed by the data sensor after receiving a self-test command, executing a series of diagnostic procedures to comprehensively check its hardware, communication, and core functions, and generating self-test feedback data containing detailed status information.

[0076] It should be noted that after the robot is started, the peripheral configuration can be initialized. At the same time, self-test commands can be sent to multiple data sensors to enable them to complete self-tests. This allows for proactive checks on the functionality of all critical sensing components before engaging in high-risk high-altitude operations.

[0077] Optionally, after the steps of enabling multiple data sensors to complete self-tests, the status of each data sensor can be determined based on the self-test feedback data corresponding to the data sensors; based on the status of the data sensors, the target faulty data sensor can be identified, and a fault alarm signal can be sent to the receiver.

[0078] In this embodiment of the invention, the self-test feedback data may be a status code or detailed diagnostic information generated by the data sensor after performing a self-test.

[0079] The aforementioned data sensor status can be a judgment made on the current health status of the corresponding data sensor based on self-test feedback data.

[0080] The aforementioned target fault data sensor can be a data sensor that is in a faulty state.

[0081] The aforementioned fault alarm signals can be warning messages used to indicate the abnormal state of target fault data sensors. The core purpose of fault alarm signals is to promptly convey the severity of the problem and the handling requirements when a fault occurs, so as to ensure safety and prevent further damage.

[0082] It should be noted that the status of the data sensor can be determined as normal, warning, or faulty based on the status code and diagnostic information in the self-test feedback data corresponding to the data sensor. When a faulty data sensor is determined, the sensor is marked as the target faulty data sensor, and a fault alarm signal containing the data sensor identifier, fault type, timestamp, and GPS location information is generated and sent to the receiver through the serial communication module. This allows for quick location of the time, location, and cause of robot faults, reducing maintenance and troubleshooting time and lowering maintenance costs.

[0083] Optionally, in the step of filtering and standardizing multi-dimensional data to obtain processed multi-dimensional data, the multi-dimensional data can be preprocessed to obtain preprocessed multi-dimensional data; and the preprocessed multi-dimensional data can be filtered and standardized to obtain processed multi-dimensional data.

[0084] In this embodiment of the invention, the aforementioned multi-dimensional data can be a collection of data obtained from different data sensors, including internal temperature data, ambient wind speed data, obstacle distance data, etc.

[0085] The data preprocessing mentioned above can be preprocessing operations such as data cleaning of multi-dimensional data, with the aim of transforming multi-dimensional data into high-quality multi-dimensional data.

[0086] The preprocessed multidimensional data mentioned above can be multidimensional data obtained after data preprocessing of multidimensional data.

[0087] The filtering and standardization processes described above can be categorized as filtering and standardization. The filtering process aims to suppress or remove noise and interference components from a signal while preserving its useful characteristics. Noise can be random fluctuations or interference at specific frequencies originating from the environment, equipment, or transmission process. The standardization process involves converting multi-dimensional data into a unified decimal data format.

[0088] The processed multidimensional data mentioned above can be obtained by filtering and standardizing the preprocessed multidimensional data.

[0089] It should be noted that the moving average filtering algorithm can be used to filter multi-dimensional data. The moving average filtering algorithm is a commonly used digital signal processing technique, mainly used for smoothing data and suppressing noise. It reduces the impact of random fluctuations by calculating the average of data points within a fixed window and replacing the current data point with the average. The core principle of the moving average filtering algorithm is to maintain a fixed-length sliding window. Whenever new data arrives, the oldest data is removed from the window, the new data is added, and then the arithmetic mean of all data within the window is recalculated as the output.

[0090] Optionally, the multi-dimensional data includes wind speed data, distance data, temperature data, voltage data, and current data. In the step of filtering and standardizing the pre-processed multi-dimensional data to obtain processed multi-dimensional data, the pre-processed wind speed data and distance data can be filtered to obtain filtered wind speed data and distance data; the pre-processed temperature data, voltage data, and current data can be standardized to obtain standardized temperature data, voltage data, and current data.

[0091] In this embodiment of the invention, the aforementioned wind speed data can be wind speed data collected from the environment via a wind speed sensor, which is susceptible to instantaneous gusts and turbulence, and contains a large amount of high-frequency random noise. The aforementioned distance data can be distance data between the robot and the curtain wall and surrounding obstacles collected via a distance sensor. The aforementioned temperature data can be temperature data inside the robot collected via a temperature sensor. The aforementioned voltage data can be robot power supply voltage data collected via a voltage sensor. The aforementioned current data can be robot operating current data collected via a current sensor.

[0092] The aforementioned filtering process can be described as a process of suppressing or removing noise and interference components from the signal while preserving the useful signal characteristics in the pre-processed wind speed and distance data. It is understandable that filtering wind speed data can smooth out instantaneous spikes caused by gusts and turbulence, enabling the robot to accurately identify persistent strong wind trends rather than overreacting to a gust; filtering distance data can eliminate accidental ranging jumps caused by complex ultrasonic reflection conditions or electromagnetic pulses.

[0093] The filtered wind speed and distance data mentioned above can be obtained by filtering the pre-processed wind speed and distance data.

[0094] The above standardization process can be a process of converting pre-processed temperature data, voltage data, and current data into a unified decimal numerical format.

[0095] The standardized temperature, voltage, and current data mentioned above can be obtained by standardizing the pre-processed temperature, voltage, and current data.

[0096] It should be noted that wind speed and distance data are susceptible to transient environmental interference, so filtering can be performed to obtain smooth, stable, and effective data. For temperature, voltage, and current data, which change relatively continuously and require high unit consistency, standardization can be performed to convert them into a unified decimal numerical format.

[0097] Optionally, after controlling the receiver to perform data analysis on the multidimensional data frame and obtain the data analysis results, the receiver's parameter adjustment instructions can be monitored in real time; and the operating parameters can be dynamically updated according to the parameter adjustment instructions.

[0098] In this embodiment of the invention, the aforementioned real-time monitoring can be used to monitor and respond to parameter adjustment commands from the receiver in real time. Specifically, it can be used to continuously monitor the receiver while the robot is performing periodic tasks (collecting, processing, and sending data), and promptly detect and read parameter adjustment commands actively issued by the remote monitoring terminal. For example, these could be adjustment commands such as modifying the sampling frequency or adjusting the distance alarm threshold.

[0099] The aforementioned parameter adjustment commands can be control commands used to dynamically modify the values ​​of operating parameters such as robot sampling frequency and distance alarm threshold.

[0100] The aforementioned dynamic update can be a process that updates and modifies functions, data, or resources during robot operation without requiring a complete restart or reinstallation.

[0101] The aforementioned operating parameters can be a series of configurable numerical thresholds, coefficients, or mode identifiers used to determine the robot's data acquisition strategy, safety monitoring rules, signal processing algorithms, and operation control behavior. For example, they can be preset sampling frequencies, preset periods, etc. The preset sampling frequency can be a pre-set sampling frequency used to periodically trigger the number of times per unit time for multi-dimensional data acquisition actions, such as 100ms / time; the preset period can be a pre-set packaged data transmission period, such as 200ms / frame, 500ms / frame, etc.

[0102] It should be noted that when adding new acquisition parameters, there is no need to refactor the core control logic; only the data sensors need to be expanded and the communication protocol adapted, significantly reducing development costs and time. New acquisition parameters can be added later, such as humidity, vibration detection, or positioning systems.

[0103] Optionally, after sending the multidimensional data frame to the receiver according to a preset period, a retransmission mechanism can be automatically triggered if the loss of the multidimensional data frame is detected during transmission.

[0104] In this embodiment of the invention, the aforementioned multidimensional data frame can be a data transmission unit with complete semantics and verifiability obtained by packaging the processed multidimensional data according to a preset frame format.

[0105] Specifically, during transmission, the check bit can be used to determine the data loss. The check bit is the result of adding the valid data and retaining the lower 8 bits. If the check bits are inconsistent, it indicates that the multidimensional data frame has been lost, and the multidimensional data frame retransmission mechanism will be automatically triggered.

[0106] It should be noted that if a multidimensional data frame is detected to be lost during transmission, a retransmission mechanism is automatically triggered, which can ensure the stable and efficient transmission of multidimensional data frames and solve the problems of easy loss in wireless transmission and congestion in wired transmission.

[0107] like Figure 3 As shown, an embodiment of the present invention provides a robot control device, which includes:

[0108] The acquisition module 301 is used to acquire multi-dimensional data from multiple data sensors at a preset sampling frequency after the robot is started.

[0109] The first processing module 302 is used to filter and standardize the multi-dimensional data to obtain processed multi-dimensional data.

[0110] The second processing module 303 is used to package the processed multidimensional data according to a preset frame format to obtain a packaged multidimensional data frame.

[0111] The control module 304 is used to send the multidimensional data frame to the receiver according to a preset period, and control the receiver to perform data analysis on the multidimensional data frame to obtain data analysis results.

[0112] Optionally, the device is also used to initialize peripheral configuration; at the same time, it sends self-test commands to multiple data sensors to enable the multiple data sensors to complete self-tests.

[0113] Optionally, the device is further configured to determine the state of each data sensor based on the self-test feedback data corresponding to the data sensor; determine the target faulty data sensor based on the data sensor state, and send a fault alarm signal to the receiver.

[0114] Optionally, the first processing module 201 is further configured to perform data preprocessing on the multi-dimensional data to obtain preprocessed multi-dimensional data; and to perform filtering and standardization on the preprocessed multi-dimensional data to obtain processed multi-dimensional data.

[0115] Optionally, the first processing module 201 is further configured to filter the preprocessed wind speed data and distance data to obtain filtered wind speed data and distance data; and to standardize the preprocessed temperature data, voltage data and current data to obtain standardized temperature data, voltage data and current data.

[0116] Optionally, the device is also used to monitor the parameter adjustment instructions of the receiver in real time; and to dynamically update the operating parameters according to the parameter adjustment instructions.

[0117] Optionally, the device is also configured to automatically trigger a retransmission mechanism if the loss of the multidimensional data frame is detected during transmission.

[0118] like Figure 4 As shown, this embodiment of the invention also provides an electronic device, including a processor, which can execute any of the robot control methods described above.

[0119] Specifically, it includes a processor 401 and a memory 402, as well as a computer program stored in the memory 402 and capable of running on the processor 401 to execute the robot's control method, wherein:

[0120] The processor 401 executes the calculator program for the robot control method stored in the memory 402, performing the following steps:

[0121] After the robot is started, it acquires multi-dimensional data from multiple data sensors at a preset sampling frequency;

[0122] The multi-dimensional data is filtered and standardized to obtain the processed multi-dimensional data;

[0123] The processed multidimensional data is packaged according to a preset frame format to obtain a packaged multidimensional data frame.

[0124] The multidimensional data frame is sent to the receiver according to a preset period, and the receiver is controlled to perform data analysis on the multidimensional data frame to obtain the data analysis results.

[0125] Optionally, before acquiring multi-dimensional data from the data sensor at a preset sampling frequency, the method executed by the processor 401 further includes:

[0126] Initialize peripheral configuration;

[0127] Simultaneously, self-test commands are sent to multiple data sensors to enable the multiple data sensors to complete self-tests.

[0128] Optionally, after the plurality of data sensors have completed their self-tests, the method executed by the processor 401 further includes:

[0129] The state of each data sensor is determined based on the self-test feedback data corresponding to the data sensor.

[0130] Based on the status of the data sensors, the target faulty data sensor is identified, and a fault alarm signal is sent to the receiver.

[0131] Optionally, the filtering and standardization processing performed by the processor 401 to obtain processed multi-dimensional data includes:

[0132] The multi-dimensional data is preprocessed to obtain preprocessed multi-dimensional data;

[0133] The preprocessed multidimensional data is then filtered and standardized to obtain processed multidimensional data.

[0134] Optionally, the multi-dimensional data includes wind speed data, distance data, temperature data, voltage data, and current data. The processor 401 performs filtering and standardization processing on the preprocessed multi-dimensional data to obtain processed multi-dimensional data, including:

[0135] The preprocessed wind speed data and distance data are filtered to obtain filtered wind speed data and distance data.

[0136] The preprocessed temperature data, voltage data, and current data are standardized to obtain standardized temperature data, voltage data, and current data.

[0137] Optionally, after controlling the receiver to perform data analysis on the multidimensional data frame and obtain the data analysis results, the method executed by the processor 401 further includes:

[0138] Real-time monitoring of parameter adjustment commands from the receiver;

[0139] The operating parameters are dynamically updated based on the parameter adjustment instructions.

[0140] Optionally, after the multidimensional data frame is sent to the receiver according to a preset period, the method executed by the processor 401 further includes:

[0141] If the loss of the multidimensional data frame is detected during transmission, a retransmission mechanism is automatically triggered.

[0142] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the robot control method provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0143] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling a robot, characterized in that, Includes the following steps: After the robot is started, it acquires multi-dimensional data from multiple data sensors at a preset sampling frequency; The multi-dimensional data is filtered and standardized to obtain the processed multi-dimensional data; The processed multidimensional data is packaged according to a preset frame format to obtain a packaged multidimensional data frame. The multidimensional data frame is sent to the receiver according to a preset period, and the receiver is controlled to perform data analysis on the multidimensional data frame to obtain the data analysis results.

2. The robot control method according to claim 1, characterized in that, Before acquiring multi-dimensional data from the data sensor at a preset sampling frequency, the method further includes: Initialize peripheral configuration; Simultaneously, self-test commands are sent to multiple data sensors to enable the multiple data sensors to complete self-tests.

3. The robot control method according to claim 2, characterized in that, After the plurality of data sensors have completed self-testing, the method further includes: The state of each data sensor is determined based on the self-test feedback data corresponding to the data sensor. Based on the status of the data sensors, the target faulty data sensor is identified, and a fault alarm signal is sent to the receiver.

4. The robot control method according to claim 1, characterized in that, The filtering and standardization of the multi-dimensional data to obtain processed multi-dimensional data includes: The multi-dimensional data is preprocessed to obtain preprocessed multi-dimensional data; The preprocessed multidimensional data is then filtered and standardized to obtain processed multidimensional data.

5. The robot control method according to claim 4, characterized in that, The multi-dimensional data includes wind speed data, distance data, temperature data, voltage data, and current data. The preprocessed multi-dimensional data is then filtered and standardized to obtain processed multi-dimensional data, including: The preprocessed wind speed data and distance data are filtered to obtain filtered wind speed data and distance data. The preprocessed temperature data, voltage data, and current data are standardized to obtain standardized temperature data, voltage data, and current data.

6. The robot control method according to claim 1, characterized in that, After controlling the receiver to perform data analysis on the multidimensional data frame and obtain the data analysis results, the method further includes: Real-time monitoring of parameter adjustment commands from the receiver; The operating parameters are dynamically updated based on the parameter adjustment instructions.

7. The robot control method according to claim 1, characterized in that, After transmitting the multidimensional data frame to the receiver according to a preset period, the method further includes: If the loss of the multidimensional data frame is detected during transmission, a retransmission mechanism is automatically triggered.

8. A control device for a robot, characterized in that, The robot's control device includes: The acquisition module is used to acquire multi-dimensional data from multiple data sensors at a preset sampling frequency after the robot is started. The first processing module is used to filter and standardize the multi-dimensional data to obtain processed multi-dimensional data. The second processing module is used to package the processed multidimensional data according to a preset frame format to obtain a packaged multidimensional data frame. The control module is used to send the multidimensional data frame to the receiver according to a preset period, and control the receiver to perform data analysis on the multidimensional data frame to obtain data analysis results.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the robot control method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the robot control method as described in any one of claims 1 to 7.

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