Intelligent Control Device Signal Acquisition Method, Device, Equipment and Storage Medium

Abstract

The present invention relates to the technical field of intelligent control of devices, and discloses an intelligent control device signal acquisition method, device, equipment and storage medium. The present invention performs digital simulation on each intelligent control device to obtain a scenario collaborative intelligent control model, analyzes and forms a scenario live simulation framework and a perception interference analysis framework based on this model, collects real-time intelligent control features and substitutes them into the framework to obtain a scenario live information matrix and a perception interference information matrix, and obtains the perception collaborative countermeasures of each device according to the matrix analysis, and drives the device to work and perform scenario perception according to the collaborative countermeasures, so as to realize signal acquisition and interference removal. This method can eliminate the perception interference between intelligent control devices through accurate modeling and real-time feedback, realize the collaborative work between devices, provide more accurate control and perception capabilities for the devices, and solve the problem that the work between multiple intelligent control devices in the prior art affects the perception capabilities of the devices.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of devices, and particularly to a method, device, equipment and storage medium for signal acquisition of intelligent control devices. Background Art

[0002] With the continuous development of intelligent control systems, many application scenarios rely on the collaborative work of multiple intelligent control devices to improve the overall performance and response speed of the system. In complex working scenarios, the collaborative work of these intelligent devices is particularly important. They need to efficiently collect signals, remove perceived interference, and ensure the stability and accuracy of the system. Due to the complex relationships among the working parameters, sensing parameters, working environments, and sensing objects of multiple intelligent devices, most current intelligent control systems only optimize the working or sensing effects of devices within certain local ranges, lacking attention to the collaborative optimization of the entire system.

[0003] Especially, the smart home system realizes automation and remote control by connecting various intelligent devices (such as smart lights, smart air conditioners, smart door locks, smart speakers, etc.). During the working process of these intelligent devices, they may interfere with each other, thus affecting the operation efficiency and experience of the entire system. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device, equipment and storage medium for signal acquisition of intelligent control devices, aiming to solve the problem in the prior art that the work among multiple intelligent control devices affects the sensing ability of the devices.

[0005] The present invention is implemented as follows. In the first aspect, the present invention provides a method for signal acquisition of intelligent control devices, including:

[0006] Obtain the device performance correlation information of each intelligent control device in a specified working scenario, and perform digital simulation on each intelligent control device in the specified working scenario according to the device performance correlation information to obtain a scenario collaborative intelligent control model;

[0007] Based on the scenario collaborative intelligent control model, perform an interactive analysis of the device working effect and the device sensing object for each intelligent control device to obtain a scenario live simulation framework and a sensing interference analysis framework of the scenario collaborative intelligent control model;

[0008] Perform real-time acquisition of the working parameters of each intelligent control device to obtain the real-time intelligent control features of the specified working scenario;

[0009] Substitute the real-time intelligent control features into the scenario live simulation framework and the sensing interference analysis framework to obtain a scenario live information matrix and a sensing interference information matrix of the specified working scenario;

[0010] Performing collaborative countermeasure analysis on the perception interference removal of each intelligent control device in the specified working scenario according to the scenario live information matrix and the perception interference information matrix to obtain the perception collaborative countermeasures of each intelligent control device in the specified working scenario;

[0011] Driving each of the intelligent control devices to perform device work and scenario perception according to the perception collaborative countermeasures to realize the acquisition of perception interference removal signals of the intelligent control devices for the specified working scenario.

[0012] In a second aspect, the present invention provides an intelligent control device signal acquisition device for implementing an intelligent control device signal acquisition method according to any one of the first aspects.

[0013] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements an intelligent control device signal acquisition method according to any one of the first aspects.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the processor is caused to execute an intelligent control device signal acquisition method according to any one of the first aspects.

[0015] The present invention provides an intelligent control device signal acquisition method, which has the following beneficial effects:

[0016] The present invention performs digital simulation on each intelligent control device to obtain a scenario collaborative intelligent control model, analyzes and forms a scenario live simulation framework and a perception interference analysis framework based on this model, collects real-time intelligent control features and substitutes them into the framework to obtain a scenario live information matrix and a perception interference information matrix, obtains the perception collaborative countermeasures of each device according to the matrix analysis, and drives the device to perform work and scenario perception according to the collaborative countermeasures to realize signal acquisition and interference removal. This method can eliminate the perception interference between intelligent control devices through precise modeling and real-time feedback, realize the collaborative work between devices, provide more precise control and perception capabilities for the devices, and solve the problem that the work between multiple intelligent control devices in the prior art affects the perception capabilities of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a step schematic diagram of an intelligent control device signal acquisition method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] Refer to Figure 1 As shown, a preferred embodiment is provided by the present invention.

[0021] In a first aspect, the present invention provides a method for signal acquisition of an intelligent control device, including:

[0022] S1: Obtain the device performance correlation information of each intelligent control device in a specified working scenario, and perform digital simulation on each intelligent control device in the specified working scenario according to the device performance correlation information to obtain a scenario collaborative intelligent control model;

[0023] S2: Based on the scenario collaborative intelligent control model, perform an interactive analysis of the device working effect and the device perception object for each intelligent control device to obtain a scenario live simulation framework and a perception interference analysis framework of the scenario collaborative intelligent control model;

[0024] S3: Perform real-time acquisition of the working parameters of each intelligent control device to obtain the real-time intelligent control characteristics of the specified working scenario;

[0025] S4: Substitute the real-time intelligent control characteristics into the scenario live simulation framework and the perception interference analysis framework to obtain a scenario live information matrix and a perception interference information matrix of the specified working scenario;

[0026] S5: Perform a collaborative countermeasure analysis for removing perception interference on each intelligent control device in the specified working scenario according to the scenario live information matrix and the perception interference information matrix to obtain a perception collaborative countermeasure for each intelligent control device in the specified working scenario;

[0027] S6: Drive each intelligent control device to perform device work and scenario perception according to the perception collaborative countermeasure to realize signal acquisition for removing perception interference of the intelligent control device on the specified working scenario.

[0028] Specifically, in step S1 of the embodiment provided by the present invention, the system collects the performance information of each intelligent control device through sensors, historical data, or the monitoring function built into the device. Such performance information generally includes the device's working state, response time, power consumption, load capacity, sensing accuracy, etc. For example, information such as the brightness adjustment range, color temperature adjustment ability, and response time of intelligent lighting devices is obtained; information such as the temperature control range, wind speed setting, energy efficiency rating, and noise level of intelligent air conditioning devices is obtained; information such as the volume adjustment range, sound quality ability, and response time of intelligent audio devices is obtained. By analyzing the correlations between the performances of different devices, potential mutual influences between devices are obtained. For example, the temperature control of the air conditioner may affect the readings of the temperature and humidity sensors, and the brightness of the lights may affect the automatic adjustment of the curtains, etc.

[0029] More specifically, based on the collected device performance information, a mathematical model of each intelligent control device is established to reflect the device's performance under specific working conditions. For example, the digital model of the air conditioner can calculate its power demand based on the difference between the set temperature and the actual temperature in the room, and predict its impact on the air flow and temperature changes in the room. The digital model of the lamp can simulate the changes in user perception caused by its brightness and color temperature adjustments.

[0030] More specifically, based on the above device models, the operating states and performance indicators of each device are combined to construct a comprehensive scenario model. In this model, the behaviors of each device interact with each other to simulate the comprehensive effect in the home environment. For example, the interaction simulation of intelligent air conditioners, intelligent lights, intelligent audio, etc. can take into account the impacts of different devices on the room environment (such as temperature, brightness, noise, etc.). At this time, the collaborative working effect of the devices is integrated into a scenario collaborative intelligent control model.

[0031] More specifically, by comparing the actually collected data with the simulation results, the accuracy and effectiveness of the model are verified. If there are errors, the parameters of the device model are adjusted to optimize the simulation results. For example, if there is a deviation between the air conditioner simulation result and the actual temperature change, the heat exchange efficiency of the air conditioner model can be adjusted, or more environmental factors (such as the opening and closing of windows) can be considered. On the basis of verification, the actual effect of the scenario model is optimized. For example, there may be unnecessary interference between some devices (such as the air flow of the air conditioner affecting the accuracy of the temperature and humidity sensors), and the model is optimized to reduce such interference, thereby improving the overall collaborative efficiency of the system.

[0032] More specifically, the digital simulation results of all devices and their interaction relationships are integrated into a unified scenario collaborative intelligent control model. This model can describe the collaborative working effect of devices in the smart home scenario. The scenario collaborative intelligent control model can provide the capabilities of device collaborative scheduling, optimizing scenario effects, and dynamically adjusting devices. For example, under certain environmental changes (such as a person entering the room and the indoor temperature changing), the model can automatically adjust the working states of devices such as air conditioner temperature and light brightness to ensure environmental comfort and energy-saving effects.

[0033] It can be understood that by establishing a digital simulation model, it is possible to accurately predict the working states and interactions of devices without actually operating the devices. Optimize the working efficiency of devices and the overall effect of the scenario. For example, in the living room scenario, through model analysis, automatically adjust the working modes of the air conditioner, lights, and stereo to achieve the best comfort. Through performance correlation analysis, identify potential interference sources between devices (such as the air conditioner air flow interfering with the operation of the temperature and humidity sensor), and reduce the occurrence of interference by optimizing the model. For example, the model can automatically adjust the air flow direction of the air conditioner to avoid the temperature and humidity sensor being affected by the air flow, thereby improving the accuracy of the sensor.

[0034] More specifically, the scenario collaborative intelligent control model can dynamically adjust the working states of devices according to real-time data and environmental changes. For example, when the temperature or brightness in the room changes, the model will adjust the air conditioner temperature, light brightness, etc. according to the current state of the intelligent control devices to ensure that the environmental conditions always remain within the ideal range. The model can not only optimize the operation efficiency of devices but also automatically adjust the behavior of devices according to user needs and environmental changes. For example, when the user is at home, the model can automatically adjust the air conditioner temperature and light brightness, and when the user leaves home, automatically turn off the devices or adjust them to the energy-saving mode.

[0035] More specifically, through the intelligent scheduling of the collaborative intelligent control model, the system can automatically adjust the device operation according to the activities of family members and environmental changes, reduce unnecessary energy consumption, and at the same time improve the comfort of the overall environment. For example, the collaborative work of the air conditioner and the temperature and humidity sensor ensures that the temperature is always within the comfortable range of the user, while avoiding resource waste between devices. The system can gradually optimize the scenario collaborative intelligent control model through learning historical data and user behavior, making the collaborative work between devices more and more efficient and in line with the personal needs and habits of users.

[0036] Specifically, in step S2 of the embodiment provided by the present invention, according to the scenario collaborative intelligent control model, analyze the working effects of each intelligent control device. The working effect of the device refers to the impact it has on the perceived objects (such as people, temperature, humidity, etc. in the room) under specific environmental conditions. For example, the temperature control of the intelligent air conditioner on the temperature change in the room, the brightness adjustment of the intelligent lamp on the environmental brightness, etc.

[0037] More specifically, the perceptual objects that interact with the device are analyzed, including environmental factors (such as temperature, humidity, light, air quality, etc.) and user-related factors (such as user activities, location, preferences, etc.). These perceptual objects are the reference basis for the device to dynamically adjust according to its working effect. For example, the temperature adjustment of the air conditioner will affect the feedback of the indoor temperature perceptual object, and the change in the brightness of the light will affect the perception of the human eye. By analyzing the interaction relationship between the device and the perceptual object, the impact of the device on the perceptual object is identified. For example, the environmental temperature and humidity data sensed by the intelligent temperature and humidity sensor are the basis for the air conditioner to adjust the temperature. At the same time, the operation of the air conditioner will also affect the accuracy of the temperature and humidity sensor, forming a closed loop of interaction.

[0038] More specifically, based on the scenario collaborative intelligent control model, the system needs to collect the working state data of each device and the state data of the perceptual object in real time. These data include the current working parameters of each device (such as the temperature setting of the air conditioner, the brightness level of the lamp, etc.), and the state of the perceptual object interacting with the device (such as indoor temperature, light intensity, etc.). These real-time data are input into the scenario live simulation framework to simulate the working effect of each device in the current environment. For example, simulate the comprehensive performance of devices such as the air conditioner adjusting the temperature, the lamp adjusting the brightness, and the speaker adjusting the volume in the current scenario, considering the mutual influence between devices. Based on the simulation results, evaluate the matching degree between the device effect and the perceptual object in the current scenario. For example, whether there is a situation where the temperature and humidity sensor is interfered by the air flow of the air conditioner, or whether the light adjustment meets the user's needs.

[0039] More specifically, the possible interference sources between different devices are analyzed. For example, the air flow of the air conditioner may affect the sensing accuracy of the temperature and humidity sensor, and the volume of the audio device may affect the working effect of other sensors in the environment (such as the noise sensor). Through the scenario collaborative intelligent control model, these potential interference sources are identified, the degree of influence of these interference sources on the device perceptual object is analyzed, and it is determined which interference between devices is significant. For example, the air flow of the air conditioner may cause a large error in the reading of the temperature and humidity sensor, while the change in the brightness of the lamp may not affect the temperature and humidity sensor. In this way, the interference effect between devices is identified. Based on the interference analysis, a strategy for removing or mitigating interference is formulated. For example, by adjusting the air direction or speed of the air conditioner, the interference on the temperature and humidity sensor is reduced, or by adjusting the working mode of the device (such as automatic adjustment of the lamp brightness) to avoid perceptual interference.

[0040] More specifically, the results of the scenario live simulation framework and the perception interference analysis framework are integrated to form a comprehensive feedback mechanism. This mechanism can evaluate the effectiveness of the device during operation in real time and analyze the perception interference between devices. By monitoring the interaction between the device and the perception object in real time, the system can adjust the working state of the device in real time according to the changes in the scenario. For example, when it is recognized that the air flow of the air conditioner interferes with the temperature and humidity sensor, the system will automatically adjust the air direction of the air conditioner to reduce the interference.

[0041] It can be understood that through the analysis of the device working effect and the interactivity with the perception object, the impact of each device on the environment and users can be accurately predicted. For example, it can be predicted whether the temperature control of the air conditioner can reach the comfortable environmental temperature expected by the user within the set time, and whether the brightness adjustment of the lamp meets the user's needs. Through the scenario live simulation framework, the system can dynamically adjust the working mode of the device according to the actual working state and environmental conditions of the device, ensuring that the collaborative work between devices reaches the optimal state. For example, optimizing the interaction between the air conditioner and the temperature and humidity sensor to ensure the stability and comfort of the environment.

[0042] More specifically, through the perception interference analysis framework, the system can identify potential interference sources between devices and optimize the device settings to reduce the performance degradation caused by interference between devices. For example, adjusting the working mode of the device to avoid measurement errors caused by the interaction between devices. Through real-time simulation feedback and optimization strategies, the automation control ability of the smart home system can be improved. The system can automatically adjust the working mode of the device according to environmental changes, improve the comfort of family members, and ensure the efficient cooperation of devices. The system can monitor the interaction between the device and the perception object in real time and adaptively adjust the device according to the changing environmental conditions. Through this mechanism, the smart home system can provide personalized and real-time services to ensure a comfortable experience and efficient operation in different scenarios.

[0043] Specifically, in steps S3 and S4 of the embodiment provided by the present invention, for each intelligent control device in the scenario (such as intelligent air conditioner, intelligent lighting, smart home audio, etc.), its working state is obtained in real time through sensors or communication protocols. The working state parameters include: for the air conditioner, temperature setting value, wind speed, operation mode (cooling / heating), indoor temperature and humidity, etc.; for the lighting device, brightness setting, switch state, color temperature (such as RGB adjustment), working time, etc.; for other devices (such as curtains, audio, humidifier, etc.), their working parameters (such as switch state, volume, humidity adjustment, etc.) should also be collected in real time.

[0044] More specifically, in addition to the device status, the status data of the perceived objects (such as ambient temperature and humidity, air quality, light intensity, noise, etc.) also need to be collected in real time. These perceived data provide key basis for subsequent scene simulation and interference analysis. The working parameters of each device are collected in real time through the embedded sensors of the device, intelligent gateways, and Internet of Things protocols (such as Zigbee, Wi-Fi, Bluetooth, etc.). The data acquisition system should have the ability to collect at high frequency and high precision to ensure that it can capture the dynamic changes of the devices and perceived objects.

[0045] More specifically, substitute the real-time collected device working parameters and the status data of the perceived objects into the intelligent control model to calculate the intelligent control characteristics of the current scene. The intelligent control characteristics reflect the interaction between the device and the environment (including the perceived objects) under the current working state. For example: the working efficiency of the air conditioner, calculate the working effect of the air conditioner based on the current indoor and outdoor temperature difference, the set temperature of the air conditioner, the wind speed, etc.; the adjustment effect of the lighting, calculate the impact of the brightness change of the current lighting system on the indoor light environment; the comprehensive comfort index, combine various perceived data such as temperature and humidity, light, and noise to calculate a comprehensive comfort index.

[0046] More specifically, substitute the real-time intelligent control characteristics into the scene live simulation framework. This framework uses the working parameters of the intelligent control devices and the real-time data of the perceived objects to simulate the actual effects of the devices in the current working scene. For example, simulate the impact of the air conditioner set temperature on the room temperature, simulate the impact of lighting adjustment on the indoor brightness change, and even the impact on other perceived data such as noise and air quality. Substitute the real-time intelligent control characteristics into the perceived interference analysis framework to analyze the mutual interference between devices or between devices and perceived objects. For example, the air flow of the air conditioner may affect the sensing accuracy of the temperature and humidity sensor, and the operation of the lighting device may interfere with the readings of the light sensor.

[0047] More specifically, the scene live information matrix contains various impacts of the devices on the environment. For example: temperature change: the result of the combined action of the air conditioner and the ambient temperature; lighting brightness change: the result of the combined action of light adjustment and ambient light intensity; air quality change: the combination of the adjustment of devices such as humidifiers and ambient perception; the perceived interference information matrix summarizes the degree of mutual interference between devices and the degree of interference on the perceived objects. For example, the impact of the air flow of the air conditioner on the temperature and humidity sensor, the impact of the lighting device on the light sensor, etc. These two matrices are usually stored in the form of a two-dimensional matrix. The elements of the matrix represent the mutual relationships or interference intensities between different devices or perceived objects. Through matrix analysis, the device scheduling can be further optimized to reduce interference and improve the intelligent control effect of the scene.

[0048] More specifically, the scenario live information matrix and the perceived interference information matrix are combined to form a comprehensive feedback mechanism. Based on real-time simulation and interference analysis results, the system automatically adjusts the working state of the device to optimize the overall environmental performance. According to the scenario live and interference information feedback, the system can make adaptive adjustments. For example, when it is recognized that the impact of a certain device on the environment does not meet expectations, the system can adjust the parameters of the device, or adjust the working mode of the device in the case of large perceived interference to ensure the maximization of comfort and effect in the scenario.

[0049] It can be understood that by collecting the working parameters of the device and the perceived object data in real time, the current environmental state can be accurately grasped, and the real-time intelligent control characteristics can be calculated, and then precise environmental regulation can be carried out. By substituting into the scenario live simulation framework and the perceived interference analysis framework, the collaborative effects and mutual interferences of different devices can be comprehensively simulated and analyzed, and the working methods of the devices can be optimized to achieve efficient collaboration. Through the perceived interference analysis framework, the system can identify the interference relationships between devices or between devices and perceived objects in real time, adjust the device parameters in a timely manner to avoid interference from affecting the environmental quality, and improve the user experience. According to the scenario live information matrix and the perceived interference information matrix, the system can automatically adjust the working state of the device according to real-time feedback, so that the devices and the environment in the scenario always maintain the best coordination state. Through real-time data collection and intelligent analysis, users can enjoy a more intelligent and personalized home environment. The system can automatically adjust according to user needs and environmental changes to provide a more comfortable and energy-saving living experience.

[0050] Specifically, in steps S5 and S6 of the embodiment provided by the present invention, the matrix includes the operating states of all devices in the specified working scenario and the interaction between the devices and the environment. It provides data support for the collaborative effects and mutual interferences between devices, and the matrix records the interference degrees between devices and between devices and perceived objects. By analyzing this interference information, it is possible to identify which devices may generate interference and affect the environmental perception results.

[0051] More specifically, based on the scenario ground truth information matrix and the perception interference information matrix, the system first identifies the parts of each device that may interfere with the perception signals of other devices in the current working scenario. The interference signals may come from the electromagnetic radiation, temperature changes, noise, etc. of the devices, which affect the accurate perception of the environmental state by the sensors. Based on the interference information, the system calculates and proposes a collaborative adjustment strategy between the devices to achieve the effect of removing or reducing interference. For example, if the temperature sensor is affected by the air conditioner wind speed, the system can adjust the air conditioner wind speed or the position of the sensor to reduce interference. If the light of the lighting device affects the reading of the ambient light sensor, the system can adjust the lighting brightness or change the angle of the light source. Through multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), the interference between devices is minimized, and the collaborative control strategy is optimized to ensure the accuracy of scenario perception and the efficient operation of the devices.

[0052] More specifically, finally, through the above collaborative countermeasure analysis, the system obtains the specific perception collaborative countermeasures for each device. The collaborative countermeasures for each device not only include how to adjust its own working parameters, but also include how to coordinate with other devices to reduce the possible perception interference. For example, for the air conditioner, adjust the wind speed or change the temperature setting to avoid affecting the temperature and humidity sensor; for the lighting, adjust the brightness or angle to avoid interfering with the ambient light sensor; for the sensor, adjust the sampling frequency or position to reduce the impact of environmental noise or air flow on the perception data.

[0053] More specifically, according to the analysis results, a perception collaborative countermeasure matrix for each device is formed. Each element in the matrix represents a perception adjustment strategy of a device in a specific scenario or an action to collaborate with other devices. Through matrix visualization, the system can better understand the collaborative relationship between devices and their respective adjustment goals.

[0054] More specifically, according to the generated perception collaborative countermeasures, the system will drive each device to make adjustments according to the collaborative countermeasures. For example, adjust the working mode of the air conditioner, control the brightness of the lighting device, or adjust the position of the sensor, etc. During the device adjustment process, the system continuously monitors the perception data to ensure the dynamic feedback between the device working state and the collaborative countermeasures. For example, when the signal received by the temperature and humidity sensor is interfered by the air conditioner air flow, the system eliminates the interference by collaboratively adjusting the air conditioner wind speed or temperature and optimizes the sensor data.

[0055] More specifically, after the adjustment of the collaborative countermeasure, the device continues to collect perception data. At this time, the data collected by the sensor should no longer be affected by interference, or the interference has been minimized. The feedback signal of the device will more accurately reflect the true state of the environment. The system monitors the feedback data of each device and sensor in real time to determine whether the interference has been removed. If there is still perception interference in some cases, the system will continue to adjust the working parameters of the device according to the real-time feedback to ensure the effect of removing the perception interference.

[0056] More specifically, after the implementation of the perception collaborative countermeasure, the system continuously collects the device operation data and perception data, and analyzes the effectiveness of the current collaborative countermeasure. If a new interference source is found or the collaborative effect is not good, the system will automatically adjust the collaborative strategy to optimize the working state of the device, forming a closed-loop optimization mechanism. The system can also further optimize the collaborative countermeasure based on historical data and real-time feedback through machine learning algorithms to improve the adaptability and perception effect of the system in different working scenarios.

[0057] It can be understood that through the analysis and implementation of the collaborative countermeasure, the perception interference between devices can be accurately identified and removed, avoiding unnecessary mutual influence during the operation of the devices, ensuring the accuracy of the environmental perception data. Through the collaborative countermeasure, the devices can perform intelligent coordination, enabling each device to make dynamic adjustments according to the states of other devices, reducing interference, optimizing the working efficiency, and achieving high-level coordination of the intelligent control system. After the perception interference is effectively removed, the system can obtain accurate environmental state data in real time and provide high-quality perception results. This provides more accurate support for applications such as smart home and automated environmental control.

[0058] More specifically, the system has the ability of real-time adjustment and can perform dynamic optimization according to the changing working environment and device states to ensure that the collaborative effect and perception results of the devices are continuously maintained in the best state. By reducing the interference between devices and improving the accuracy of the perception data, users will be able to enjoy a more intelligent, comfortable, and energy-saving working and living environment. Through the collaborative optimization between devices, waste of resources is avoided. For example, it reduces the excessive operation or misoperation of devices caused by interference and improves the overall energy efficiency.

[0059] The present invention provides a method for collecting signals of intelligent control devices, having the following beneficial effects:

[0060] The present invention performs digital simulation on each intelligent control device to obtain a scene collaborative intelligent control model. Based on this model, a scene live simulation framework and a perception interference analysis framework are analyzed and formed. Real-time intelligent control features are collected and substituted into the frameworks to obtain a scene live information matrix and a perception interference information matrix. According to the matrix analysis, the perception collaborative countermeasures of each device are obtained, and the device is driven to work and sense the scene according to the collaborative countermeasures, so as to realize signal collection and interference removal. This method can eliminate the perception interference between intelligent control devices through accurate modeling and real-time feedback, realize the collaborative work between devices, provide more accurate control and perception capabilities for the devices, and solve the problem that the work between multiple intelligent control devices in the prior art affects the perception capabilities of the devices.

[0061] Preferably, the steps of obtaining the device performance association information of each intelligent control device in a specified working scene and performing digital simulation on each intelligent control device in the specified working scene according to the device performance association information to obtain a scene collaborative intelligent control model include:

[0062] S11: Collect multi-dimensional parameters of the device working performance, device perception performance, and device setting position of each intelligent control device pre-set in the specified working scene to obtain the device working performance data, device perception performance data, and device setting position data of each intelligent control device. The device working performance data, device perception performance data, and device setting position data of the intelligent control device together constitute the device performance association information of the intelligent control device;

[0063] S12: Perform performance simulation and performance combination of work and perception on each intelligent control device according to the device working performance data and device perception performance data of each intelligent control device to obtain a basic simulation unit corresponding to each intelligent control device;

[0064] S13: Perform overall combination processing on each basic simulation unit according to the device setting position data of each intelligent control device, and perform range adjustment processing on the working performance and perception performance of each basic simulation unit after overall combination according to the device setting position data of each intelligent control device to obtain a scene collaborative intelligent control model.

[0065] Specifically, in order to comprehensively understand the performance of each intelligent control device in a specified working scenario, it is first necessary to collect data from multiple dimensions of the device. These dimensions include: device working performance, collecting the operating status data of the device (such as the load, operating speed, power consumption, etc. of the device), and these data reflect the working ability of the device in the scenario; device sensing performance, collecting the sensing data of the device (such as the accuracy, sensitivity, response time, etc. of the sensor), and these data represent the sensing ability of the device to environmental changes; device setting location, recording the location of each device in the scenario and its relative position relationship (such as the coordinates, installation angle, working range, etc. of the device).

[0066] More specifically, integrate the above three types of data (device working performance data, device sensing performance data, device setting location data) together to form the device performance association information of each intelligent control device. This information provides multi-dimensional performance parameters of the device, enabling subsequent simulation analysis to reflect the comprehensive performance of the device in actual work.

[0067] More specifically, based on the obtained device working performance data and device sensing performance data, conduct the following simulations: working performance simulation, based on the working performance data of the device, establish a working state model of the device. By simulating the operation of the device in a specified working scenario, predict the possible operating states, load distributions, and performance changes of the device; sensing performance simulation, based on the sensing performance data of the device, establish a response model of the device to environmental changes. This kind of simulation can help predict the response ability of the device to environmental changes in different scenarios, including sensing accuracy, latency, noise interference, etc. Combine the working performance and sensing performance, and through a mathematical model or algorithm (such as linear regression, neural network, etc.), obtain the basic simulation unit of each device. Each basic simulation unit represents the comprehensive performance of the device in the specified scenario (including the performance in both the working and sensing aspects).

[0068] More specifically, the actual position of each device in the scenario affects its performance. For example, the distance of the device from the target area, the position angle, the occluder, etc. may all affect its working and sensing capabilities. Therefore, it is necessary to integrally combine the basic simulation units according to the set position data of the devices, and adjust the performance of each basic simulation unit according to the set position data of the devices. For example: adjustment of the working performance range. If the set position of the device is in a complex environment or near a source of interference, it may cause a decline in the working performance of the device. At this time, performance correction is carried out according to the specific position of the device; adjustment of the sensing performance range. The sensing ability of the device is also affected by its position. For example, the detection range of the sensor may change due to the change of the position angle, or the device is affected by environmental light, noise, etc., and the sensing accuracy may be different. By combining the position data of all devices and the correlation relationship between them, the comprehensive working and sensing capabilities of each device at its position are obtained. At this time, the working performance and sensing performance of the device have been adjusted and optimized according to its position.

[0069] More specifically, after completing the combination and position-dependent adjustment of the basic simulation units of each device, the simulation units of all devices are integrally combined to obtain a collaborative intelligent control model based on the scenario. This model describes the comprehensive behavior of each intelligent control device in the specified working scenario, including the interaction between devices, the collaborative working mode, the sensing results and their optimization and adjustment. In the model, by calculating the collaborative effects between devices (such as interference between devices, benefits of collaborative work, etc.), the intelligent control of the entire scenario is optimized, and through optimization algorithms (such as optimized scheduling, optimal path planning, etc.), the working efficiency and sensing accuracy of the overall system are further improved. The obtained scenario collaborative intelligent control model can not only provide a decision-making basis for the collaborative scheduling between devices, but also realize real-time adjustment and optimization to adapt to various environmental changes in the scenario, thereby improving the working and sensing effects of the devices in practical applications.

[0070] It can be understood that through the multi-dimensional data collection and analysis of the working performance, sensing performance and set position of the devices, the system can accurately simulate the performance of each device in the specified working scenario, providing a reliable basis for subsequent intelligent control decisions. By combining the working and sensing performances of the devices for simulation and adjusting according to the position data, the comprehensive performance of each device can be comprehensively improved, enabling it to exhibit the best working and sensing capabilities in the actual working scenario. Based on the generated collaborative intelligent control model, the system can dynamically adjust the cooperation mode of each device, optimize the overall working efficiency and sensing effect, and improve the collaborative efficiency between devices.

[0071] More specifically, the final scenario-based collaborative intelligent control model can provide real-time decision support for the automation system, ensuring that devices can work together in different environments and conditions, avoiding mutual interference between devices, and improving the overall intelligence level of the system. Through real-time monitoring and simulation, the system can dynamically adjust according to environmental changes to keep device performance in the optimal state. Whether it is environmental changes or equipment failures, it can respond and adjust strategies quickly to ensure the long-term stable operation of the system. Since the intelligent control system can sense and adjust the equipment status in real time, the end user will experience a more efficient, intelligent, and comfortable operating environment, reduce human intervention, and improve the level of automation.

[0072] Preferably, the steps of performing an interactive analysis of the working effect of each intelligent control device and the device perception object based on the scene collaborative intelligent control model to obtain a scene real-time simulation framework and a perception interference analysis framework of the scene collaborative intelligent control model include:

[0073] S21: performing traversal setting of device executable working parameters for each of the basic simulation units in the scene collaborative intelligent control model, so as to obtain a preparatory working parameter sequence of each of the basic simulation units;

[0074] S22: performing independent equipment working effect simulation on each basic simulation unit in the scene collaborative intelligent control model according to the preparatory working parameter sequence of each basic simulation unit, so as to obtain an independent working effect characteristic curve of each basic simulation unit corresponding to the preparatory working parameter sequence;

[0075] S23: performing interactive superposition processing of effect characteristics of multi-device collaborative work on each of the independent work effect characteristic curves based on the scene collaborative intelligent control model to obtain a collaborative work effect characteristic map of the scene collaborative intelligent control model;

[0076] S24: mapping working parameters of each of the basic simulation units according to the collaborative work effect characteristic map of the scene collaborative intelligent control model to obtain a mapping relationship between each of the basic simulation units and the collaborative work effect characteristic map, and reversely analyzing and integrating the collaborative effect generation factors of the collaborative work effect characteristic map based on the mapping relationship to obtain a scene real-time simulation framework of the scene collaborative intelligent control model;

[0077] S25: performing a perception interference analysis on the device perception objects of each of the basic simulation units based on the collaborative work effect characteristic map of the scene collaborative intelligent control model, so as to obtain a perception interference status curve of the device perception objects of each of the basic simulation units corresponding to the collaborative work effect characteristic map;

[0078] S26: According to the mapping relationship between each of the basic simulation units and the collaborative working effect characteristic map, map the working parameters of the perception interference status curves of each of the basic simulation units to obtain the mapping relationship between the perception interference status curves corresponding to each of the basic simulation units and the working parameters of each of the basic simulation units;

[0079] S27: Based on the mapping relationship, perform reverse analysis and correlation integration of the factors causing perception interference for each of the perception interference status curves to obtain the perception interference analysis framework of the scenario collaborative intelligent control model.

[0080] Specifically, for each basic simulation unit (i.e., each intelligent control device) in the scenario collaborative intelligent control model, perform traversal settings of executable working parameters. This step refers to the system setting and adjusting the working parameters of each device in multiple dimensions, covering various possible states of device operation, including workload, operating speed, sensor sensitivity, etc., to generate a preliminary working parameter sequence for each basic simulation unit, that is, all possible combinations of working parameters.

[0081] More specifically, based on the generated preliminary working parameter sequence, perform independent device working effect simulations for each basic simulation unit respectively. The simulation process should consider the working state, response ability of the device, and its performance under different parameter settings, to generate independent working effect characteristic curves for each basic simulation unit under different preliminary working parameters. This curve reflects the performance of the device under various working conditions, such as efficiency, power consumption, response time, etc.

[0082] More specifically, based on the scenario collaborative intelligent control model, perform interactive superposition processing of the independent working effect characteristic curves of each basic simulation unit for the collaborative working effect characteristics of multiple devices, that is, during the simulation process, consider the interaction between devices and perform superposition of the collaborative working effects to display the comprehensive performance of the devices in the integrated environment, to obtain a complete collaborative working effect characteristic map. This map reflects the overall effectiveness of different devices working together, including collaborative gains, collaborative losses, and influencing factors between devices.

[0083] More specifically, according to the obtained collaborative working effect characteristic map, map the working parameters of each basic simulation unit to determine the relationship between each device's working parameters and the collaborative working effect. Through these mapping relationships, further perform reverse analysis and correlation integration of the factors causing the collaborative effect, which means analyzing which factors (such as device location, performance differences, interference, etc.) have a positive or negative impact on the collaborative effect based on the performance of the collaborative working effect, to obtain a complete scenario live simulation framework, which not only describes the independent performance of each device, but also reflects the collaborative effect between devices and the impact of their interaction on the overall effect.

[0084] More specifically, based on the collaborative work effect characteristic map in the scene collaborative intelligent control model, the perception interference analysis is performed on the device perception object of each basic simulation unit. The perception interference here refers to the interference of the device perception object (such as sensor or external environment) by the working efficiency of other devices, resulting in reduced perception accuracy. The perception interference status curve corresponding to each basic simulation unit in different working states is obtained, which describes the interference situation of the device perception object in the collaborative work environment.

[0085] More specifically, based on the collaborative work effect characteristic map and the perceived interference status curve of each device, the working parameter mapping of perceived interference is performed. That is, through the mapping relationship, the relationship between the perceived interference status and the device working parameters is analyzed, and the cause of the interference (such as device location, interference between devices, etc.) is further inferred. The mapping relationship between the perceived interference status curve and the working parameters is obtained, and the key parameters affecting the perceived interference are clarified.

[0086] More specifically, based on the mapping relationship of the perception interference state curve, the factors that cause the perception interference are reversely analyzed and correlated. This means that the interference factors are deeply analyzed to determine their impact on the perception performance, and the relationship between the factors is integrated to obtain a complete perception interference analysis framework. This framework can accurately reflect the perception interference source, interference intensity, interference path, etc., to help adjust the device configuration and optimize the perception effect in complex scenarios.

[0087] It can be understood that by traversing and setting the working parameters of each basic simulation unit and independently simulating them, the effect of the equipment under different working conditions can be accurately predicted, which provides a basis for subsequent collaborative work analysis. By superimposing the effects of multiple devices working together, the system can identify the collaborative gains and losses between devices, help optimize the equipment collaborative work strategy, and achieve the best collaborative effect, which can effectively improve the overall work efficiency and accuracy of the system.

[0088] More specifically, by mapping and reverse analyzing the characteristic maps of collaborative work effects, the system can build an accurate scene-based real-time simulation framework to provide a theoretical basis for the adaptation and adjustment of equipment in complex working environments. Through the perception interference analysis framework, the system can analyze the sources and impacts of perception interference in detail, help design more accurate sensor configurations and adjustment strategies, and reduce the negative impact of interference on perception accuracy.

[0089] More specifically, through the comprehensive analysis of working parameters and perceived interference, a targeted optimization plan can be provided for the device to ensure that when working in collaboration, the device can not only maintain the best working performance but also minimize the perceived interference, improve the overall intelligent level of the system. The obtained scenario collaborative intelligent control model has the ability of adaptive adjustment and can respond in real time to environmental changes and device state changes. By optimizing the working parameters and perception configuration, it ensures that the system can exhibit optimal performance in various scenarios.

[0090] Preferably, the step of obtaining the real-time intelligent control features of the specified working scenario by collecting the working parameters of each of the intelligent control devices in real time includes:

[0091] S31: Deploy network data interfaces for each of the intelligent control devices so that each of the intelligent control devices is in a data connection state with a specified control center unit through the deployed network data interfaces;

[0092] S32: Collect the working parameters of each of the intelligent control devices in real time through the control center unit that is in a data connection state with each of the intelligent control devices to obtain the real-time working parameters of each of the intelligent control devices;

[0093] S33: Summarize and process the real-time working parameters of each of the intelligent control devices to obtain the real-time intelligent control features of the specified working scenario.

[0094] Specifically, deploy network data interfaces for the intelligent control devices. Deploy network data interfaces on each intelligent control device to ensure that these devices can transmit and interact data with the control center unit through the network. The selection of network data interfaces may include wireless or wired network interfaces, depending on the requirements of the device and the environment, to ensure that each intelligent control device can establish a data connection with the control center unit to achieve remote monitoring and data exchange.

[0095] More specifically, through the data connection state established between the control center unit and each intelligent control device, collect the real-time working parameters. The control center unit will obtain the real-time working data of each device through the network interface, including but not limited to the load, running state, power consumption, sensor data, etc. of the device, and obtain the real-time working parameters of each intelligent control device. These parameters will provide basic data for the subsequent analysis of real-time intelligent control features.

[0096] More specifically, the working parameters of each intelligent control device collected in real time by the control center unit will be summarized and processed. The summary can be carried out in various ways, such as data merging, statistical analysis, time series analysis, etc., to ensure that the equipment operation status of the specified working scene can be fully and accurately reflected. Through summary processing, the comprehensive real-time working status information of each intelligent control device in the specified working scene can be obtained, providing a data basis for further feature extraction.

[0097] More specifically, based on the summarized working parameters, the real-time working status of each device is further analyzed to extract the real-time intelligent control features of the specified working scenario. These features may include indicators such as work efficiency, collaborative working status, mutual influence between devices, energy efficiency, response time, etc., to obtain the real-time intelligent control features of the working scenario, which reflects the comprehensive performance and status of each device when working together under specific working conditions.

[0098] It can be understood that the deployment of network data interfaces enables each intelligent control device to achieve seamless data connectivity with the control center unit, ensuring that the working status of the equipment can be monitored and collected in real time. In this way, the control system can quickly respond to equipment changes and ensure the continuity and stability of system operation. By collecting the working parameters of each device in real time, the system can fully understand the operating status of each device, which not only provides the working data of individual devices, but also provides basic information for the collaborative work analysis of multiple devices.

[0099] More specifically, by summarizing and extracting features of the collected real-time working parameters, the system can quickly generate real-time intelligent control features under specified working scenarios. These features not only reflect the individual working status of the equipment, but also display key performance indicators such as the synergy effect and energy efficiency between devices. Based on the real-time intelligent control features, the system can perform intelligent analysis of the current working scenario, identify system bottlenecks, resource utilization, equipment coordination status, etc., and provide data support and decision-making basis for further optimizing equipment operation and coordination strategies.

[0100] More specifically, due to the use of a network data interface for data transmission, the system has high flexibility and scalability. It can easily add new intelligent control devices or modify the working parameters of existing devices to adapt to different work scenario requirements. This step can quickly process and feedback working parameters. Through real-time monitoring and data analysis, it supports the system to quickly respond to changes such as equipment failures, performance bottlenecks, etc., to ensure the efficiency of scene management and the reliability of system operation.

[0101] Preferably, the step of substituting the real-time intelligent control feature into the scene real-time simulation framework and the perception interference analysis framework to obtain the scene real-time information matrix and the perception interference information matrix of the specified working scene includes:

[0102] S41: Substitute the real-time intelligent control features into the scenario live simulation framework, and let the scenario live simulation framework perform factor mapping on the real-time intelligent control features for the collaborative work effect, so as to obtain the collaborative work effect features of the specified work scenario;

[0103] S42: According to the collaborative work effect features, perform effect analysis and vector representation on each specific location of the specified work scenario to obtain the scenario live vectors of each specific location of the specified work scenario, and combine the scenario live vectors of each specific location of the specified work scenario to obtain the scenario live information matrix of the specified work scenario;

[0104] S43: Substitute the real-time intelligent control features into the perception interference analysis framework, and let the perception interference analysis framework perform factor mapping on the real-time intelligent control features for the perception interference situation, so as to obtain the perception interference situation features of the specified work scenario;

[0105] S44: According to the perception interference situation features, perform perception interference analysis and vector representation on each specific location of the specified work scenario to obtain the perception interference vectors of each specific location of the specified work scenario, and combine the perception interference vectors of each specific location of the specified work scenario to obtain the perception interference information matrix of the specified work scenario.

[0106] Specifically, substitute the intelligent control features collected in real time into the scenario live simulation framework, and this framework will perform mapping on these features, paying attention to factors such as the interaction between devices, resource allocation, work efficiency, etc. Through this process, the simulation framework generates the collaborative work effect features under the specified work scenario according to the real-time intelligent control features, understands and quantifies the collaborative work effect of each device or system in a specific working environment. For example, how multiple intelligent devices coordinate and cooperate to achieve the best performance.

[0107] More specifically, based on the collaborative work effect features, perform effect analysis on each specific location of the specified work scenario, and convert the analysis results into vector representation. These vectors represent the working states of different positions or regions, reflect the working efficiency or collaborative efficiency of each position in the scenario, generate scenario live vectors containing the working states of each position, and provide a data basis for subsequent scenario analysis.

[0108] More specifically, combine the scenario live vectors of all specific locations to form a comprehensive scenario live information matrix. This matrix can express the comprehensive working states of each location in the specified work scenario, including information such as location, device state, and collaborative work efficiency, and obtain a complete scenario live information matrix, which can comprehensively present the real-time dynamic state of the work scenario.

[0109] More specifically, substitute the real-time intelligent control features into the perception interference analysis framework. The perception interference analysis framework will map based on these features and focus on possible interference factors such as communication signal interference, equipment failures, external environmental interference, etc. This framework evaluates how these interference factors affect the perception performance of the entire working scenario, quantifies the impact of perception interference on the specified working scenario, and generates relevant perception interference status features.

[0110] More specifically, based on the perception interference status features, perform interference analysis on each location in the specified working scenario and convert these analysis results into perception interference vectors. Each perception interference vector represents the intensity and nature of the interference at a specific location or area, obtaining a detailed analysis of the perception interference and presenting these interferences in vector form for subsequent analysis and processing.

[0111] More specifically, combine the perception interference vectors of all locations to form a perception interference information matrix. This matrix reflects the distribution of perception interference in different working locations or areas, can provide detailed data support for subsequent optimization and interference management, complete the generation of the perception interference information matrix, and present the interference status of each location in the specified working scenario.

[0112] It can be understood that through the mapping of the intelligent control features by the scenario live simulation framework, the collaborative working effect in the specified working scenario can be comprehensively simulated and analyzed. This can not only evaluate the working status of each device at different locations but also reveal the collaborative effect between devices, optimize resource allocation. The vector representation and effect analysis refine the working effect of the scenario to each specific location or area. This refined representation provides a more accurate data basis for optimizing the working process and adjusting strategies. Through the interference mapping of the perception interference analysis framework, the impact of various interferences can be quantified in real time, and through the generation of perception interference vectors and information matrices, the interference risks at different working locations can be effectively managed and identified to ensure the stable operation of the system.

[0113] More specifically, the generated perception interference information matrix provides the ability to dynamically monitor the global and local interference status. This matrix can help identify potential interference problems and provide decision-making support for the location and optimization of interference sources. After obtaining the scenario live information matrix and the perception interference information matrix, the system can perform real-time feedback and intelligent optimization based on these information, adjust the working status of the devices, collaborative strategies or interference management strategies to improve the overall system performance. Precise interference analysis and the mapping of collaborative working effects contribute to improving the stability and robustness of the system in a changing environment, being able to predict and respond to various interference situations and keep the system running efficiently.

[0114] Preferably, the steps of performing collaborative countermeasure analysis for removing perception interference on each intelligent control device in the specified working scenario according to the scenario actual situation information matrix and the perception interference information matrix to obtain the perception collaborative countermeasures of each intelligent control device in the specified working scenario include:

[0115] S51: Perform vector clustering operations on the scenario actual situation information matrix and the perception interference information matrix to obtain a set of scenario actual situation vector clusters corresponding to the scenario actual situation information matrix and a set of perception interference vector clusters corresponding to the perception interference information matrix;

[0116] S52: Based on the set of perception interference vector clusters, perform vector cluster mapping on the perception interference status of each intelligent control device in the specified working scenario to obtain the perception interference index of each intelligent control device in the specified working scenario corresponding to the set of perception interference vector clusters;

[0117] S53: Based on the set of scenario actual situation vector clusters, perform an overall scenario effect evaluation on the specified working scenario to obtain the overall scenario effect index of the specified working scenario;

[0118] S54: Perform a descending process on the perception interference index of each intelligent control device, and perform reverse feedback on the set of perception interference vector clusters according to the perception interference index of each intelligent control device after the descending process to obtain several forms of perception interference vector cluster adjustments corresponding to the perception interference index after the descending process of the set of perception interference vector clusters;

[0119] S55: Based on various forms of perception interference vector cluster adjustments, analyze the requirements for adjusting the working parameters of intelligent control devices in the scenario collaborative intelligent control model to obtain the device working parameter adjustment characteristics corresponding to various forms of perception interference vector cluster adjustments;

[0120] S56: According to the device working parameter adjustment characteristics corresponding to various forms of perception interference vector cluster adjustments, perform corresponding correction analysis on the overall scenario effect index to obtain the predicted correction effect index corresponding to various forms of perception interference vector cluster adjustments;

[0121] S57: Using the predicted correction effect index as a supervision condition, evaluate the executability of removing perception interference for various forms of perception interference vector cluster adjustments, and determine the form of perception interference vector cluster adjustment for actual application according to the evaluation results;

[0122] S58: According to the form of perception interference vector cluster adjustment determined for actual application, analyze and combine the working parameters and perception parameters of each intelligent control device to obtain the perception collaborative countermeasures of each intelligent control device.

[0123] Specifically, a vector clustering operation is performed on the scene live information matrix and the sensing interference information matrix to generate a set of scene live vector clusters and a set of sensing interference vector clusters. This operation clusters multiple data points, grouping similar scene live conditions and interference situations together, facilitating subsequent analysis and processing. It simplifies the complex scene live and sensing interference information into a smaller set of clusters, making it easier to effectively analyze and process this information.

[0124] More specifically, based on the set of sensing interference vector clusters, the sensing interference situation of each intelligent control device is mapped to the corresponding set of sensing interference vector clusters, thereby obtaining the sensing interference index of each device. This step helps to quantify the sensing performance of each device under different interference situations, evaluate the impact of sensing interference on each intelligent control device, and obtain its sensing interference index for further processing and optimization.

[0125] More specifically, based on the set of scene live vector clusters, an overall scene effect evaluation is carried out to calculate the overall scene effect index of the entire working scene. This index comprehensively reflects the effect of the entire working scene, including the performance in aspects such as device collaboration and resource allocation, obtaining the overall scene effect evaluation result of the working scene, providing guidance for subsequent optimization.

[0126] More specifically, a reduction process is performed on the sensing interference index of each intelligent control device. This means that by optimizing the working state, adjusting the control strategy, etc., the negative impact of interference on device sensing is reduced, the sensing interference index of the device is lowered, and the sensing ability and working efficiency of the device are improved.

[0127] More specifically, according to the reduced sensing interference index, a reverse feedback is performed on the set of sensing interference vector clusters to adjust the manifestation form of the sensing interference vector clusters. This means that during the optimization process, the sensing interference situation is re-evaluated, forming several new adjustment forms of the sensing interference vector clusters, optimizing the manifestation of the sensing interference vector clusters, enabling them to better meet the needs of intelligent control devices, and thus further improving the performance of the devices.

[0128] More specifically, based on the adjusted form of the sensing interference vector clusters, an analysis of the working parameter adjustment requirements of intelligent control devices is carried out. This step aims to analyze and calculate the requirements of different adjustment forms for device working parameters, find the most suitable adjustment plan, determine the device working parameters that need to be adjusted, and ensure that the device can better adapt to the working state after sensing interference removal.

[0129] More specifically, by adjusting the features according to the device operating parameters and performing a correction analysis on the overall scene effect index, it means that after adjusting the device operating parameters, it is necessary to evaluate whether the overall effect of the scene has been optimized, calculate the predicted correction effect index, and through the correction analysis, quantify the impact of different adjustments on the overall scene effect to ensure the effectiveness of the adjustment plan.

[0130] More specifically, taking the predicted correction effect index as the supervision condition, evaluate the executability of various forms of adjustment of the perception interference vector clusters. This evaluation helps to determine which adjustment plans can effectively remove interference, provides feasible plans for practical applications, determines the most effective perception interference removal plan, and ensures the feasibility of interference removal.

[0131] More specifically, based on the determined form of adjustment of the perception interference vector clusters for practical applications, analyze and combine the operating parameters and perception parameters of each intelligent control device. This is to ensure that the device can maintain the best perception ability and working state during engineering implementation. By combining the operating parameters and perception parameters, specific perception cooperation countermeasures are formed to ensure that the device can effectively reduce perception interference in practical applications.

[0132] More specifically, based on the comprehensive analysis results of the engineering and perception parameters of each device, formulate specific perception cooperation countermeasures. These countermeasures can help the devices cooperate more effectively in a complex working environment, reduce perception interference, and optimize the overall working effect, ensuring that each device can work in coordination with other devices, maximizing the overall performance of the system, while eliminating perception interference and improving the stability and efficiency of the system.

[0133] It can be understood that through vector clustering operations and perception interference analysis, different working scenarios and interference states can be accurately divided, which makes the analysis of the working environment more detailed and provides a clear goal for subsequent optimization. Through the reduction processing and reverse feedback of the perception interference index, the negative impact of perception interference on device performance can be significantly reduced. The optimized perception interference removal plan can improve the perception ability and working efficiency of the device.

[0134] More specifically, the correction analysis of the overall scene effect index and the evaluation of the executability of perception interference removal provide a dynamic feedback mechanism for optimization, which can adjust the optimization plan in a timely manner according to the actual effect to ensure the continuous improvement of the system performance. Through the combined analysis of the operating parameters and perception parameters, the formulated perception cooperation countermeasures can ensure the efficient cooperation between devices in a complex scenario. This improves the stability and robustness of the system, ensures efficient operation in an uncertain environment, and the finally determined form of adjustment of the perception interference vector clusters not only removes interference but also optimizes the adjustment of the device operating parameters to ensure that the system can achieve the best performance under the condition of minimizing interference.

[0135] Preferably, the step of driving each of the intelligent control devices to perform device operation and scenario perception according to the perception cooperation countermeasure to achieve the acquisition of the perception interference removal signal of the specified working scenario by the intelligent control device includes:

[0136] S61: Adjust the working parameters of each of the intelligent control devices according to the perception cooperation countermeasure, so that each of the intelligent control devices exerts a corresponding influence on the specified working scenario according to the adjusted working parameters, so as to achieve the removal of the perception interference of the specified working scenario;

[0137] S62: Perform perception driving on the perception parameters of each of the intelligent control devices according to the perception cooperation countermeasure, so that each of the intelligent control devices performs signal acquisition and processing on the specified working scenario in the state of perception interference removal, so as to achieve the acquisition of the perception interference removal signal of the specified working scenario by the intelligent control device.

[0138] Specifically, adjust the working parameters of the intelligent control device. According to the perception cooperation countermeasure, adjust the working parameters of each intelligent control device. This adjustment aims to make the device exert an appropriate influence in the given working scenario to achieve the goal of perception interference removal. For example, adjust the control strategy, power, output signal strength, etc. of the device. By optimizing the working parameters of the device, it can more effectively exert an influence on the working scenario, thereby reducing or removing the perception interference.

[0139] More specifically, after the working parameters are adjusted, each intelligent control device exerts a corresponding influence on the working scenario according to the adjusted working parameters. The device interacts with the scenario through the adjusted working mode, thereby reducing or removing the perception interference, ensuring that the perception interference in the working scenario is removed, and enabling the intelligent control device to perform signal acquisition and processing more precisely.

[0140] More specifically, drive the perception parameters of each intelligent control device according to the perception cooperation countermeasure to ensure that the device has appropriate perception capabilities to collect the scenario signals after interference removal. This may involve adjusting the sensor sensitivity of the device, optimizing the perception algorithm, etc., improving the perception capabilities of the device in the scenario after perception interference removal, and ensuring that they can accurately collect the scenario signals to provide reliable data for subsequent analysis and processing.

[0141] More specifically, each intelligent control device performs signal acquisition and processing in the state of perception interference removal. At this time, the perception parameters of the device have been adjusted, and it can effectively collect and process the signals in the working scenario to achieve the removal of the perception interference, ensuring that the device can efficiently collect and process the signals after interference removal, and further optimizing the perception capabilities and performance of the entire system.

[0142] More specifically, through signal acquisition and processing, the device provides signals after removing perceived interference. These signals can be used for system optimization, environmental monitoring, device adjustment, etc., thereby enhancing the effect of removing perceived interference of the intelligent control device in the specified working scenario, achieving the removal of perceived interference of the intelligent control device in the working scenario, and ensuring the best signal acquisition ability of the device under the condition of minimizing interference.

[0143] More specifically, by adjusting the working parameters of the intelligent control device, the adaptability of the device in a complex environment can be significantly improved. By optimizing the working parameters, the device can control more effectively and reduce interference when interference exists, thereby enhancing the overall performance of the device. By driving the sensing parameters, the sensing ability of the device is enhanced. The adjusted sensing parameters enable the device to more accurately identify and process the signals after interference removal, ensuring higher signal quality collected, and further improving the sensing efficiency of the system.

[0144] More specifically, through fine adjustment of the working parameters and sensing parameters, the device can effectively remove perceived interference and reduce the impact of external noise on device sensing. This enables the intelligent control device to work in a clearer and more reliable environment, enhancing the stability and accuracy of the system. The optimization of the device signal acquisition and processing ensures that the signals after interference removal can be accurately processed, avoiding signal distortion or misinterpretation. This can provide higher-quality data support for the system and help with subsequent decision-making and operations.

[0145] More specifically, through coordinated adjustment of the sensing and working parameters of each device, the intelligent control devices can work more efficiently in cooperation. The working and sensing capabilities of each device are optimized synchronously, thereby enhancing the cooperation efficiency and overall performance of the entire system in a complex environment.

[0146] In a second aspect, the present invention provides a signal acquisition device for an intelligent control device, which is used to implement a signal acquisition method for an intelligent control device according to any one of the first aspects.

[0147] In a third aspect, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a signal acquisition method for an intelligent control device according to any one of the first aspects.

[0148] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by the processor, it causes the processor to execute a signal acquisition method for an intelligent control device according to any one of the first aspects.

[0149] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for collecting signals of an intelligent control device, characterized in that: include: Acquire device performance association information of each intelligent control device of the specified working scene, and digitally simulate each intelligent control device of the specified working scene according to the device performance association information to obtain a scene collaborative intelligent control model; Based on the scenario collaborative intelligent control model, the interactive analysis of the working effect of each intelligent control device and the device perception object is performed to obtain the scenario real-time simulation framework and the perception interference analysis framework of the scenario collaborative intelligent control model; Collect working parameters of each intelligent control device in real time to obtain real-time intelligent control features of the specified working scene; Substituting the real-time intelligent control feature into the scene real-time simulation framework and the perception interference analysis framework to obtain the scene real-time information matrix and the perception interference information matrix of the specified working scene; Performing a collaborative countermeasure analysis of perceptual interference removal for each intelligent control device of the specified working scene according to the scene real-time information matrix and the perceptual interference information matrix, so as to obtain a perceptual collaborative countermeasure for each intelligent control device of the specified working scene; According to the perception coordination strategy, each of the intelligent control devices is driven to perform device operation and scene perception, so as to realize the collection of perception interference removal signals of the intelligent control device for the designated working scene; The steps of obtaining device performance association information of each intelligent control device of a specified working scene, and digitally simulating each intelligent control device of the specified working scene according to the device performance association information to obtain a scene collaborative intelligent control model include: Collect multi-dimensional parameters of device working performance, device perception performance, and device setting location of each intelligent control device pre-set in a specified working scene to obtain device working performance data, device perception performance data, and device setting location data of each intelligent control device, wherein the device working performance data, device perception performance data, and device setting location data together constitute device performance association information of the intelligent control device; According to the device working performance data and device perception performance data of each of the intelligent control devices, the working and perception performance of each of the intelligent control devices are simulated and combined to obtain a basic simulation unit corresponding to each of the intelligent control devices; According to the device setting position data of each of the intelligent control devices, each of the basic simulation units is integrated, and according to the device setting position data of each of the intelligent control devices, the working performance and perception performance range of each of the basic simulation units after the integration are adjusted to obtain a scene collaborative intelligent control model; The steps of analyzing the interactivity between the working effect of each intelligent control device and the device perception object based on the scene collaborative intelligent control model to obtain the scene real-time simulation framework and the perception interference analysis framework of the scene collaborative intelligent control model include: Performing traversal settings of device executable working parameters for each of the basic simulation units in the scene collaborative intelligent control model respectively, so as to obtain a sequence of preliminary working parameters for each of the basic simulation units; According to the preparatory working parameter sequence of each basic simulation unit, each basic simulation unit in the scene collaborative intelligent control model is independently simulated for the equipment working effect, so as to obtain the independent working effect characteristic curve of each basic simulation unit corresponding to the preparatory working parameter sequence; Based on the scenario collaborative intelligent control model, interactively superimpose the effect characteristics of multi-device collaborative work on each of the independent work effect characteristic curves to obtain a collaborative work effect characteristic map of the scenario collaborative intelligent control model; According to the collaborative work effect characteristic map of the scene collaborative intelligent control model, the working parameters of each of the basic simulation units are mapped to obtain a mapping relationship between each of the basic simulation units and the collaborative work effect characteristic map, and based on the mapping relationship, the collaborative work effect characteristic map is reversely analyzed and correlated to obtain a scene real-time simulation framework of the scene collaborative intelligent control model; Based on the collaborative work effect characteristic map of the scene collaborative intelligent control model, a perception interference analysis is performed on the device perception objects of each of the basic simulation units to obtain a perception interference status curve of the device perception objects of each of the basic simulation units corresponding to the collaborative work effect characteristic map; According to the mapping relationship between each of the basic simulation units and the collaborative work effect characteristic map, the working parameters of the perceived interference status curve of each of the basic simulation units are mapped to obtain a mapping relationship between the perceived interference status curve of each of the basic simulation units and the working parameters of each of the basic simulation units; Based on the mapping relationship, reverse analysis and correlation integration of the factors causing the perception interference are performed on each of the perception interference state curves to obtain a perception interference analysis framework of the scene collaborative intelligent control model; Substituting the real-time intelligent control feature into the scene real-time simulation framework and the perception interference analysis framework to obtain the scene real-time information matrix and the perception interference information matrix of the specified working scene includes: Substituting the real-time intelligent control feature into the scene real-time simulation framework, and allowing the scene real-time simulation framework to map the factors of the collaborative work effect on the real-time intelligent control feature, so as to obtain the collaborative work effect feature of the specified work scene; According to the collaborative work effect characteristics, the effect of each specific position of the designated work scene is analyzed and represented by a vector to obtain the scene real-time vector of each specific position of the designated work scene, and the scene real-time vector of each specific position of the designated work scene is combined to obtain the scene real-time information matrix of the designated work scene; Substituting the real-time intelligent control feature into the perception interference analysis framework, and allowing the perception interference analysis framework to map the factors of the perception interference condition to the real-time intelligent control feature, so as to obtain the perception interference condition feature of the specified working scene; The perceptual interference analysis and vector representation of each specific location of the designated work scene are performed according to the perceptual interference condition characteristics to obtain the perceptual interference vector of each specific location of the designated work scene, and the perceptual interference vectors of each specific location of the designated work scene are combined to obtain the perceptual interference information matrix of the designated work scene.

2. The intelligent control device signal acquisition method according to claim 1, characterized in that: The step of collecting working parameters of each of the intelligent control devices in real time to obtain real-time intelligent control features of the specified working scene includes: Deploy a network data interface for each of the intelligent control devices so that each of the intelligent control devices is in data communication with a designated control center unit through the deployed network data interface; The control center unit in data communication with each of the intelligent control devices collects the working parameters of each of the intelligent control devices in real time to obtain the real-time working parameters of each of the intelligent control devices; The real-time working parameters of each of the intelligent control devices are aggregated and processed to obtain the real-time intelligent control characteristics of the specified working scene.

3. The intelligent control device signal acquisition method according to claim 1, characterized in that: The step of performing a collaborative countermeasure analysis of perceptual interference removal for each intelligent control device of the specified working scene according to the scene real-time information matrix and the perceptual interference information matrix to obtain a perceptual collaborative countermeasure for each intelligent control device of the specified working scene includes: Performing a vector clustering operation on the scene live information matrix and the perception interference information matrix to obtain a scene live vector cluster set corresponding to the scene live information matrix and a perception interference vector cluster set corresponding to the perception interference information matrix; Based on the perceived interference vector cluster set, vector cluster mapping of the perceived interference status is performed on each intelligent control device of the specified working scene to obtain a perceived interference index of each intelligent control device of the specified working scene corresponding to the perceived interference vector cluster set; Based on the scene actual vector cluster set, the designated work scene is evaluated for the overall scene effect, so as to obtain the overall scene effect index of the designated work scene; Performing a reduction process on the perceived interference index of each of the intelligent control devices, and performing reverse feedback on the perceived interference vector cluster set according to the reduced perceived interference index of each of the intelligent control devices, so as to obtain several perceived interference vector cluster adjustment forms of the perceived interference vector cluster set corresponding to the reduced perceived interference index; Based on various perception interference vector cluster adjustment forms, the scenario collaborative intelligent control model is used to analyze the working parameter adjustment requirements of the intelligent control device to obtain the device working parameter adjustment characteristics corresponding to the various perception interference vector cluster adjustment forms; Performing corresponding correction analysis on the overall effect index of the scene according to the device operating parameter adjustment characteristics corresponding to various perception interference vector cluster adjustment forms, so as to obtain the predicted correction effect index corresponding to various perception interference vector cluster adjustment forms; Using the prediction correction effect index as a supervision condition, evaluating the feasibility of perceptual interference removal for various perceptual interference vector cluster adjustment forms, so as to determine the perceptual interference vector cluster adjustment form for actual application according to the evaluation result; According to the perception interference vector cluster adjustment form determined for actual application, the working parameters and perception parameters of each of the intelligent control devices are analyzed and combined to obtain the perception coordination countermeasures of each of the intelligent control devices.

4. The intelligent control device signal acquisition method according to claim 1, characterized in that: The steps of driving each of the intelligent control devices to perform device operation and scene perception according to the perception coordination strategy to realize the collection of the perception interference removal signal of the intelligent control device for the designated working scene include: According to the perception coordination strategy, the working parameters of each of the intelligent control devices are adjusted accordingly, so that each of the intelligent control devices exerts a corresponding influence on the designated working scene according to the adjusted working parameters, so as to achieve the removal of the perception interference of the designated working scene; According to the perception coordination strategy, the perception parameters of each of the intelligent control devices are perception-driven, so that each of the intelligent control devices performs signal collection and processing on the designated working scene in the perception interference removal state, so as to realize the perception interference removal signal collection of the intelligent control device for the designated working scene.

5. A signal acquisition device for intelligent control equipment, characterized in that: Used to implement a signal acquisition method for an intelligent control device as described in any one of claims 1-4.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the method for collecting signals of an intelligent control device according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute a signal acquisition method for an intelligent control device as described in any one of claims 1 to 4.

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